June 13-17, 2014; San Francisco, CA Full Report - Draft

Executive Highlights

In this final report, we provide our full coverage of the 74^th Scientific Sessions of the American Diabetes Association (ADA), held at the Moscone Center in our “city by the bay” home base of San Francisco, CA from June 13-17, 2014. The conference drew 17,300 attendees, slightly fewer than last year’s 17,737 attendees and the 17,890 attendees in 2012. In our conversation with ADA organizers, they noted that ADA 2014 included 14,443 medical professionals, a slight increase over last year’s 14,000 HCP attendees. A striking 60% of registered attendees were international, with over 120 countries represented (up from 117 in 2013). Despite “American” in the name of the conference, this statistic truly symbolizes the global proportions of the diabetes epidemic. Notably, the largest contingents hailed from India (693 attendees), followed closely by China (645), Japan (606), United Kingdom (500), and Germany (466).

With 771 expert speakers this year (an increase from last year’s 730 speakers), the five-day meeting spanned 96 symposia, 50 oral abstract sessions, 10 interest group discussions, 18 meet-the-expert sessions, and 10 special lectures, and more than 2,000 poster presentations – a phenomenal array of brainpower, depth of discussion, and breadth of topics. We’ve sorted through the learnings in this final report and include our detailed commentary on 300 talks (a bit of a tighter focus than last year’s 321 talks) – drawn from symposia, lectures, oral presentations, corporate symposia, and meet-the-expert sessions – as well as our coverage of 32 posters and 31 exhibits, compared to 31 posters and 25 exhibits last year.

Below, we have organized our writing into 14 specialized reports: (1) Big picture conference themes; (2) Diabetes technology; (3) Incretin-based therapies; (4) SGLT-2 inhibitors, SGLT-1 inhibitors, and other non-incretin oral antidiabetic agents; (5) Insulin therapies; (6) Novel drugs, drug development, and basic science; (7) Cardiovascular disease and other complications; (8) Treatment algorithms and strategies; (9) Healthcare delivery, economics, and epidemiology (10) Obesity; (11) Type 1 diabetes therapies (cure related); (12) our Exhibit hall report; (13) Analyst events; and (14) Additional topics. 

Our full report includes over 200 talks not published in our daily ADA highlight reports, and the titles of these talks are highlighted in blue. We’ve also highlighted in yellow any presentations and comments that we found particularly notable. For your viewing pleasure, ADA has also made selected webcast presentations available here for free viewing; impressively, the entirety of the Scientific Sessions webcasts is available by subscription purchase for nine months, post-meeting. After nine months, all content is available for free online.

Themes

Diabetes Technology

• The profile of automated insulin delivery (also known as the artificial pancreas as well as the bionic pancreas) continues to rise at each ADA, and this year’s conference truly exemplified that theme. That said, ADA did not bring any major breakthrough data; partly this is timing and partly this reflects the fact that ATTD is increasingly becoming the go-to hub for new diabetes technology data. Visibility aside, we do point out there is a huge spectrum of approaches to automating insulin delivery, with very different demands on the person with diabetes as well as different levels of perceived risk. There is also more disagreement in the field; though reimbursement has long been a source of uncertainty, there are also more commercial questions that have come to the fore that weren’t as visible even a year ago.
• A major positive headline in automated insulin delivery came during Dr. Steven Russell’s (MGH, Boston, MA) presentation on the bionic pancreas – results from the Beacon Hill and Summer Camp Studies were published in the NEJM (topline results were originally shared at ATTD 2014). The paper received a veritable media blitz and quickly became the most popular article of the last month on NEJM.org by a very wide margin (195,000+ views vs. 42,000 views for the #2 article). As we understand it, the impressive popularity will put it among NEJM's most viewed articles of 2014. This publication itself – and the resulting wave of attention – was a clear reminder of just how far the field has come in less than a decade, moving from a bedside research project at just a couple of institutions to making major national and international headlines.
  
  • The single oral session on the artificial pancreas was headlined by the Cambridge team, who presented some of the longest, largest, and most real-world studies we’ve seen to date: Dr. Hood Thabit shared data from a four-week overnight home study, while Dr. Lalantha Leelarathna highlighted results from a seven-day home study testing unsupervised 24-hour closed-loop control. Both studies showed that closed-loop outperformed open-loop therapy as measured by time-in-target range (hypoglycemia was very low at baseline and did not improve significantly). The major area for improvement was also clear in both studies: connectivity. The seven-day trial has paved the way for the AP@home04 study, which will include a striking three months of day+night closed-loop control in up to 42 adults. ClinicalTrials.gov currently indicates an anticipated primary completion date in June 2015, which of course also underscores the runway needed to enroll and complete these longer studies.
    • Detractors of this approach note that what the system is doing is modulating basal insulin; patients still count carbohydrates and deliver full meal boluses. While this is still a dream to many patients (we’d have our basals modulated any day!), the “worst case” scenario (which is a much bigger focus today than a year ago) may be that the constraints on the algorithm are such that it could not handle a meal if the bolus were not delivered. To what extent this reduced the burden of managing diabetes will vary from patient to patient, and family to family, of course. We would imagine that near-term, most patients would be more than happy with this, but it is true that approaches vary as does how “automating” is defined. System vulnerability seems to be a big issue of late; e.g., whether systems could over-bolusing insulin, under bolus other hormones (e.g., glucagon) used by some systems, etc. How systems optimize patients’ regimens is also a question; in studies, reducing the risk of inappropriate boluses may be easier than in real life.  
  • Challenges aside (which all first generation products surely have), the excitement is palpable – and the pressure is on to sort out commercialization. “We’re right on the cusp. People are wearing closed loop at home, and these systems are safer than what we’re doing right now. We’ve got to drive towards commercialization.” – JDRF’s Dr. Aaron Kowalski at the JDRF/NIH Closed-Loop Meeting at ADA (this is invite only but we go into detail in this report on the meeting). This quote from Aaron served as an excellent reminder of where the field stands right now and where it must clearly go in the next couple of years. Now that closed-loop control has demonstrated safety, efficacy, and feasibility in inpatient and transitional studies of increasing rigor, the big unanswered questions surround commercialization – What will pivotal studies look like (size, length, comparator group)? What will commercial products look like (i.e., level of automation, user interface, etc.)? Who will commercialize automated insulin delivery (industry, academic investigators, partnered companies/institutions, etc.)? Indeed, the JDRF/NIH Closed-loop evening shared industry perspectives from Medtronic, Animas, and Dose Safety (an artificial pancreas software startup) in an aptly named session, “The Last Mile – Bridging From Academia to Industry.” The tone of the industry presentations and subsequent panel discussion was somewhat negative, centering on the challenges around safety, robustness, and everything that can go wrong. This contrasted markedly with the second panel, which featured off-the-charts enthusiasm from patients and closed-loop researchers. All were highly enthusiastic about the systems they’ve experienced first-hand – said patient Ms. Willa Spalter, “Nothing is perfect, but I definitely would sign up [right now]. It’s much better than what everybody else is wearing now.” There is clearly a hard balance to strike between patients’ interests in systems to come now, and industry’s concerns over liability and investment and who will own what.
• This was a lighter year for new CGM data and systems, perhaps a testament to the field’s laser focus on automating insulin delivery – indeed, we thought it was notable that ADA 2013 had an entire oral session devoted to CGM, while this year’s conference focused all new CGM data into poster presentations. One could argue this was an in-between year for Dexcom and Medtronic, as both have recently commercialized their next-gen sensors (G4 Platinum and Enlite, respectively) and are working diligently on future add-on products or new sensors (Dexcom Share, Gen 5, Gen 6; Medtronic’s Enlite 2, 3, redundant sensors, MiniMed 640G).
  
  • However, notable new CGM data did emerge in posters from Dexcom, Roche, and Senseonics – accuracy of these systems hit or exceeded the ~10% MARD threshold. Dexcom had some of the biggest CGM news of the meeting in late-breaking poster #75, which shared clinical data (n=51) on a version of the G4 Platinum with an improved algorithm ( “G4AP”). Overall G4AP MARD vs. YSI was an impressive 9.0%, compared to the Contour USB meter’s MARD of 5.6% vs. YSI. This poster, along with several accompanying posters, made a case that the G4’s accuracy is approaching that of fingersticks – a smart move from Dexcom as it seeks to eventually obtain an insulin-dosing claim for CGM. Meanwhile, in poster #846, Roche shared data comparing its prototype CGM to the Dexcom G4 Platinum – the mean seven-day MARD was 10.9% for the G4 and 8.6% for the Roche prototype. Senseonics also shared new data on its implantable CGM – over 90 days of use, overall MARD vs. YSI was 11%, ranging from a low of 7.7% to a high of 17.7%. The Senseonics and Roche posters did not divulge a lot of study design details, so it’s hard to know how real world the accuracy is. Still, we are encouraged to see new CGMs in development from established players, along with new entrants.
  • Aside from the recent EU launch of the MiniMed Duo combination insulin infusion-CGM device, Medtronic was notably stealth on the next-gen sensor front. It was nice to see the MiniMed Duo in the international section of Medtronic’s ADA exhibit hall booth, though we continue to believe commercialization will be challenging for this product (see our report for more details). In terms of the company’s CGM pipeline, Enlite 2 was launched in Europe in conjunction with ATTD 2014, though no accuracy data has ever been shared on this product. Meanwhile, the US study of the MiniMed 640G predictive low glucose suspend device will use the Enlite 3 sensor – according to Medtronic’s June 5, 2014 Analyst Day, this product will include “intelligent diagnostics” and “improved accuracy and comfort.” The company also has an orthogonally redundant CGM (data last shared at ATTD 2014) and a redundant glucose oxidase sensor system (last discussed at DTM 2013).
• ADA 2014 included three notable partnership developments in diabetes technology: Medtronic/Sanofi, Dexcom/Insulet, and Joslin/Glooko. We see all three partnerships as valuable news for patients, since there is so much room to expand device penetration, to get more patients to goal, and to make providers’ lives easier.
  
  • Medtronic and Sanofi jointly announced a “strategic alliance” focused on improving the management of type 2 diabetes. The partnership will pair Sanofi's insulin and GLP-1 portfolio and drug development expertise with Medtronic’s background in insulin pumps and CGM – a particular priority is new drug-device combinations, including new form factors that are affordable, convenient and easy to-use. We could imagine multiple products that could come out of the alliance, especially prefilled patch pen-like wearable devices (like Valeritas or CeQur) or simplified prefilled insulin pumps; too, we think the information from CGM will be hugely valuable for Sanofi as it expands and begins to serve patients across a broader spectrum of diabetes.
  • Dexcom announced that its upcoming Gen 5 mobile app will pull data from Insulet’s next-gen PDM via Bluetooth. This was major and fairly unexpected news following the dissolution of their PDM-CGM integration partnership in 4Q12 (a move that was ironically motivated by Dexcom’s desire to move to the smartphone in the first place).
  • Joslin and Glooko debuted their HypoMap software to identify and improve hypoglycemia unawareness, which we covered in detail just prior to ADA 2014.
• Though still in the “early adopter” phase, ADA 2014 reminded us of the increasing demand for more connected diabetes devices. The desire for better connectivity has long been a theme of many device presentations (particularly those related to the artificial pancreas), though the DiabetesMine D-Data Exchange event  showed us how some smart and driven patients are taking matters into their own hands (#WeAreNotWaiting on Twitter). Most notably, we got a look at Nightscout/CGM in the Cloud, which has rocketed in popularity on Facebook – the project allows anyone to download instructions to “hack” their Dexcom CGM and send the data to the cloud and then to any device. The patient enthusiasm for this approach underscored just how much demand there is for greater connectivity, and correspondingly, we continue to be encouraged by industry’s progress: Dexcom Share was shown under glass in the exhibit hall (still awaiting FDA approval); Glooko attracted a number of interested attendees in the exhibit hall with its MeterSync cable, HypoMap software, and in-development Bluetooth product; LifeScan’s OneTouch VerioSync, Sanofi’s iBGStar, and Telcare’s BGM were all being demoed for eager attendees; Tidepool’s Blip (web-based universal data platform) started a clinical trial at UCSF (Adam and Kelly are both dying to get into it) and drew lots of interest at the D-Data Exchange; and smartphone BGMs from Dario and iHealth are now out in the marketplace. In our view, the major question is whether patients and providers will embrace these systems, particularly given the poor reimbursement for non-face-to-face patient/physician interactions. In addition, we’d note that these new systems are at the stage of simplifying data upload (a very laudable goal, given historical challenges on this front!) – we think the major inflection point will come when software takes the uploaded data and delivers actionable recommendations to patients and providers. Baby steps first, but the future looks bright in our view. 
• Insulin pumping for type 2 diabetes was not a major focus of ADA 2014, though two trials were encouraging, headlined by data from Medtronic's long-awaited OpT2mise trial – the randomized, six-month study compared insulin pump therapy (n=168) to MDI (n=163) in type 2 patients in poor control. From a baseline of 9.0%, A1c declined by 1.1% in those in the pump group vs. a 0.4% decline in the MDI group (p<0.001) after 27 weeks; 55% of the pump group achieved an A1c <8% vs. 28% of the MDI group. Given the high starting A1c of 9.0%, the magnitude of reduction (-1.1%) was perhaps not quite as high as some would have expected – we wonder if insulin titration could have been better, if a simpler device with on-body bolusing (e.g., Valeritas’ V-Go or CeQur’s PaQ) could have helped drive patients even lower, or if this simply underscores what a challenging population this is to manage. Certainly 8% is impressive and some patients were likely well below 8%; virtually all patients were also taking less insulin, a major reduction in cost and personal burden. We were also intrigued by a study from Dr. Anand Velusamy (King's College Hospital NHS Foundation Trust, London, UK), which examined the impact of pumping U500 insulin in very poorly controlled, highly insulin resistant type 2s. A1c declined by 1.9% at six months (baseline: 10.4%), 2.3% at 12 months, and was maintained out to 36 months. At the same time, total daily insulin requirements declined by ~20%. Perhaps most notable were the cost implications – using U500 in the pump vs. U100 insulin was estimated to save ~2,200 British pounds per patient per year (~$4,000 USD). Though the study was uncontrolled and patients did receive nursing support, we thought these were very strong clinical results in a highly challenging population. This encouraging clinical data lines up well with efforts from pump companies that are now actively pursuing type 2 focused products (Insulet’s U500 OmniPod with Lilly; Tandem’s 480-unit reservoir t:slim; Medtronic’s new type 2 business unit). The need is substantial in the severely insulin resistant population, and it’s terrific to see movement from industry that corresponds to encouraging clinical data that continues to accrue on this front.
• ADA 2014 left us with many outstanding questions in automated insulin delivery and CGM: Following predictive low glucose suspend, what will be the next commercial product – overnight-only closed-loop, daytime treat-to-range, 24/7 hybrid closed loop? What level of automation will be meaningful enough to get a sizeable number of patients on automated insulin delivery? Will academic groups pursue commercialization of closed-loop devices? How will industry balance patient demand to close the loop with liability concerns and R&D constraints? How large and long will pivotal studies be? What’s an appropriate comparator group – sensor-augmented pump therapy or whatever therapies patients are currently using? What data on automated insulin delivery will it take to support broad reimbursement and access? What level of CGM accuracy and reliability is needed for safe and effective overnight treat-to-target/daytime treat-to-range control? How will chronic use of glucagon be defined and what will trial requirements be? Will Xeris’ stable glucagon be ready for the Bionic Pancreas pivotal study?
• This year’s ADA highlighted several key issues in self-monitoring of blood glucose (SMBG), most notably the FDA’s recent draft standards on BGM accuracy and DTS’ new post-marketing error surveillance grid. Dr. David Sacks (NIH, Bethesda, MD) discussed the recent FDA draft guidance, which he described as “really narrowing the range for error” in comparison to the 2013 ISO and CLSI standards. He described the particularly stringent guidelines for point-of-care meters as “obviously not feasible in most circumstances” (in line with his comments from the EASD Diabetes Technology Conference in February), pointing out that even some central lab methods cannot meet the accuracy bar. The concern that many in-hospital meters won’t meet the draft accuracy standards is not a new – we have heard similar concerns from Dr. David Klonoff (Mills Peninsula Medical Center, Burlingame, CA), who addressed another SMBG hot topic – the new DTS Surveillance Error Grid (SEG) for post-marketing surveillance. Compared to the older Parkes and Clarke Error Grids, the SEG now accounts for DCCT trial results, analog insulins, new information about hypoglycemia, and raised BGM accuracy standards. Dr. Klonoff noted that the FDA has already begun using the SEG as a model to assess other measuring devices, and the hope is that the FDA will use the SEG as a post-market surveillance tool for BGM (definitely not for pre-market use). Currently, the FDA doesn’t conduct post-market surveillance, but assessing and enforcing meter accuracy remains a concern for both patients and providers. As of a June 12 email we received, DTS announced that a Steering Committee had been assembled for its post-market Surveillance Program for cleared BGMs. The first committee meeting will take place in July in Washington, DC. This follows the May announcement that DTS had kicked off the surveillance program planning process with funding from Abbott.
  
  • Since ADA 2013 largely ignored last year’s issues of competitive bidding and the increasingly challenging environment of blood glucose monitoring, we were pleased to see renewed interest in SMBG. We counted a total of 17 abstracts, three orals, and four late-breaking posters this year related to BGM, a solid increase from 2013, which featured 13 abstracts, one oral, and no posters by our count. However, similar to last year, the Big Four BGM companies did not make a strong showing at the exhibit hall; of the Four (Bayer, Roche, Abbott, and J&J), only J&J was present. The trend worsened from last year, where both J&J and Roche were present. 
• The value of SMBG for non-insulin users remains controversial, but speakers did provide evidence for its use, assuming it is bundled with education. Dr. Richard Grant (Kaiser Permanente Northern California, Oakland, CA) noted that in a review of 12 RCTs of patients with type 2 diabetes, SMBG reduced mean A1c by a marginal 0.26%. However, he emphasized that the “mixed bag” of results for RCTs speaks to the necessity of prescribing SMBG for patients with type 2 diabetes in the context of a larger educational effort and as a tool to effect change in self-care or medication. An oral presentation by Dr. Yi Sun Yang (Chung Shan Medical University, Taichung, Taiwan) on SMBG models in type 2 diabetes supported Dr. Grant’s emphasis. All three SMBG models (six-pair, three-pair, and seven-point testing) all demonstrated substantial reductions in A1c (-1.7%, -1.8%, and -1.1%, respectively) in combination with thorough education and protocols for translating results into treatment changes.
  
  • These presentations were encouraging, particularly in light of the recent legislative effort in Oregon to restrict test strips for patients with type 2 diabetes not on insulin. Audience members during Dr. Grant’s presentation were particularly interested in this issue, bringing up during Q&A that the Oregon legislation used Dr. Grant’s DISTANCE study to support test strip restrictions. For background, Dr. Grant’s study suggested that 15% of patients reported that their SMBG results were not used by anyone to make adjustments to diet, exercise or medicine. Notably, Dr. Grant was quick to clarify that “he would never have come to the conclusion that test strips should be restricted for all patients with type 2 diabetes not on insulin.” Rather, he would focus on individualizing care and on prescribing SMBG to patients who will benefit from it. With regard to the Oregon legislation, Dr. Grant commented, “Using population-based prescriptions to restrict strips doesn’t make any sense... I do not agree with it at all.”
  • There was no data on Abbott’s Flash Glucose Monitoring at this year’s ADA, though there is certainly clear momentum behind it, as we could tell from multiple hallway conversations; we expect to see a major presence at EASD 2014. The last data on Flash Glucose Monitoring was shared in a symposium at ATTD 2014 in February. As a reminder, Abbott’s Flash Glucose Monitoring system is intended to overcome some of the limitations of both BGM (pain, inconsistent and hard to interpret data) and CGM (alarm fatigue and cost) – the factory calibrated two-week subcutaneous sensor is expected to have an insulin dosing claim, and for the most part, patients will not need to use any test strips – if it works as advertised, it could the change the paradigm of glucose monitoring. The device is still pending a CE Mark, and management expects the technology to launch in late summer, according to Abbott’s 1Q14 financial update. The system’s sensor patch, which will be the size of a €2 coin, could be particularly attractive for type 2 patients desiring a low-hassle and discreet option for measuring blood glucose. There is still no US timeline on this device, but we are optimistic.

Diabetes Drugs

• There were plenty of updates and new data on GLP-1 agonists, with a central focus on improving drug delivery – a better patient experience will be key to growing the class. Combination use with basal insulin is another key area. A potentially transformative development in GLP-1 agonist delivery is Intarcia’s ITCA-650, an implantable exenatide mini-pump. At the exhibit hall we were treated to a private demo of the insertion and removal process for the ITCA-650, and we were quite impressed with the simplicity (just a few minutes) and patient experience (~4 mm incision with no sutures needed). We also saw demos of (and data on) the injection devices for some once-weekly GLP-1 agonists – device design and administration protocol stand to be key differentiating factors between AZ’s Bydureon (exenatide), Lilly’s dulaglutide, and GSK’s Tanzeum/Eperzan (albiglutide). Dulaglutide is the only one of the three that comes as a ready-to-use suspension that does not require reconstitution, and Lilly appears to be capitalizing on this advantage through a very patient-friendly single-use pen. The pen (previously referred to as an auto-injector) has a hidden pre-attached 29-gauge needle. With one button, the device extends the needle into the skin, administers the injection, and withdraws the needle, meaning that the patient never has to see the needle. A poster (122-LB) presented on the usability of the single-use pen showed that it was generally easy to use and reduced patients’ fear of injection. We got our hands on the new pen for Tanzeum at the GSK medical booth at the exhibit hall. The device bears some similarities to the new Bydureon dual-chambered pen in that it simplifies an otherwise very complicated reconstitution process, but the protocol for both devices takes upwards of 15 minutes. We also saw clinical data on TransTech’s oral GLP-1 agonist program, which distinguishes itself from other oral GLP-1 agonists in development by using small molecule agonists rather than encapsulated polypeptides. 
  
  • There was some notable new data on the exciting GLP-1 agonist/basal insulin combination class. We saw results from a phase 2b study of Sanofi’s LixiLan (332-OR), a fixed-ratio combination of the short-acting GLP-1 agonist Lyxumia (lixisenatide) and the market-leading basal insulin Lantus (insulin glargine). The results showed significant postprandial improvements on LixiLan compared to Lantus (LixiLan reduced excursions by 70 mg/dl compared to 12 mg/dl on Lantus; p<0.001), but similar A1c reductions (1.8% on LixiLan and 1.6% on Lantus from an 8% baseline in both arms, with a stronger-than-expected showing from the Lantus arm). Sanofi’s presentation was only from a phase 2b proof-of-concept trial (IDegLira’s striking DUAL trials were phase 3 registrational trials), and the smaller number of patients makes it harder to interpret too much from the efficacy data. On IDegLira, we saw one-year follow-up data from the DUAL I trial presented by Dr. Stephen Gough (University of Oxford, UK), which showed that the substantial A1c reductions, weight benefit (vs. insulin), and hypoglycemia benefit (vs. insulin) seen after 26 weeks were preserved through to 52 weeks (A1c reductions after 52 weeks were 1.8% on IDegLira, 1.4% on insulin degludec and 1.2% on liraglutide). It is hard to overstate the promise presented by GLP-1 agonist/basal insulin combinations for easing insulin initiation by mitigating insulin’s adverse side effects – conversely, it is disappointing that both IDegLira and LixiLan’s timelines will be pushed back by regulatory delays (the Tresiba CRL for IDegLira, and Sanofi’s Lyxumia NDA withdrawal for Lixi/Lan).
  • We saw full presentations on several phase 3 trials on Lilly’s dulaglutide; interestingly, two of these trials compared the once-weekly GLP-1 agonist against basal insulin. GLP-1 agonists are typically cited for their effect on postprandial glucose lowering (due to their mechanism of stimulating insulin secretion in a glucose-dependent manner), and are usually positioned for use in combination with basal insulin rather than in place of it. Therefore, it was quite unique and notable that Lilly sought to compare dulaglutide against basal insulin (known, in contrast, for its fasting glucose lowering effects) to such a great extent in phase 3. The two trials compared dulaglutide to Sanofi’s Lantus (insulin glargine) on the background of mealtime insulin (AWARD-4) or metformin + sulfonylurea (AWARD-2), and in both trials dulaglutide emerged as the winner in terms of A1c, weight, and hypoglycemia. As expected, nausea was much more of a problem with dulaglutide (which we think is a commercial issue), but reductions in weight, hypoglycemia, and frequency of administration lead to an overall very patient-friendly profile. Lilly management has stated on multiple occasions that it views dulaglutide as a means to grow the GLP-1 agonist class, and the results of these trials show that it might be an easier initiator injectable for many patients.
• The most exciting data on SGLT-2 inhibition this year was on SGLT-2 inhibitor/DPP-4 inhibitor fixed-dose combination (FDCs). Leading up to ADA, we had heard a great deal of optimism about this combination of insulin-dependent and insulin-dependent mechanisms, which (theoretically) should yield additive or even synergistic efficacy. Recent findings that SGLT-2 inhibitors’ efficacy is blunted by a compensatory increase in glucagon would also suggest that incretin therapies are a good mechanistic pair for SGLT-2 inhibitors. The results presented by AZ and Lilly/BI (on Empa/Lina [empagliflozin/linagliptin] and Saxa/Dapa [saxagliptin/dapagliflozin], respectively) were certainly very positive: saxa/dapa demonstrated a nearly 1.5% A1c reduction from baseline (albeit from a relatively high mean baseline of >9%) and Empa/Lina yielded solidly >1% A1c reductions from baseline. However, the results fell short of the truly additive efficacy many were hoping for, and in most cases it appeared that the FDCs’ efficacy was largely driven by the SGLT-2 inhibitor component. We would imagine, however, that these FDCs will best distinguish themselves in real-world situations, when the advantages of consolidated dosing and co-pays will have a greater positive impact on adherence. There is definitely still a great deal of excitement about SGLT-2/DPP-4 inhibitor combinations, especially given the delays in getting GLP-1 agonist/basal insulin combinations through development and the regulatory process. Even when that injectable combination class gets into patients’ hands, it is an open question how its strong efficacy would compare against oral FDCs’ convenience advantage.
  
  • While the flow of phase 3 data on J&J’s Invokana (canagliflozin) and AZ’s Forxiga/Farxiga (dapagliflozin) has largely run its course, we did see solid long-term phase 3 data on Lilly/BI’s Jardiance (empagliflozin). The data we had seen previously raised speculative questions about how empagliflozin’s efficacy would compare against the rest of the pack, but the results presented at ADA were quite positive. The EMPA-REG H2H-SU found that empagliflozin provided statistically superior A1c reductions after a full two years than the SFU glimepiride (-0.66% vs. -0.55% from a baseline of 7.9%). Although the A1c benefit was modest, the weight and hypoglycemia benefits were far more pronounced. Of course, comparison against SFUs is generally an easy comparison to win for most of the newer diabetes drug classes, but we also saw empagliflozin come out on top of Merck’s DPP-4 inhibitor Januvia (sitagliptin) in a 52-week extension study of the 24-week EMPA-REG MONO trial. Although empagliflozin and sitagliptin provided comparable A1c reductions at the week 24 primary endpoint, at 76 weeks empagliflozin yielded a significantly greater placebo-adjusted reduction from baseline than sitagliptin. In both trials, empagliflozin held a weight advantage against the comparator. We learned shortly after ADA that Lilly/BI have resubmitted the NDA for empagliflozin in the US (read our report).
  • We saw impressive phase 2 data on one of the newest kids on the SGLT-2 inhibitor block, Islet Science’s remogliflozin etabonate. The 12-week dose ranging study found a mean placebo-adjusted A1c reduction of 1.1% from baseline, quite striking for a 12-week study, although small sample size and the lack of a head-to-head comparison with other SGLT-2 inhibitors make it hard to draw too many conclusions at this stage. The current formulation requires twice-daily dosing, which is a major disadvantage, but the company has developed an intriguing biphasic formulation that should allow for once-daily dosing and additionally is theorized (by the company) to preserve efficacy while reducing side effects.
• It was quite a basal-insulin heavy year at ADA, with a great deal of new phase 3 data for next-generation basal insulins that offer incremental benefits over first-generation basal analogs. We saw new phase 3 data for Sanofi’s Toujeo (U300 insulin glargine), BI/Lilly’s new insulin glargine formulation (LY2963016), and Novo Nordisk’s Tresiba (insulin degludec) as well as a look into the mechanism of action for Lilly’s novel basal insulin peglispro (LY265541). Each of the new basals has some incremental advantage over the current Lantus gold standard (e.g., less hypoglycemia with Toujeo, Tresiba, and peglispro; presumably lower cost with BI/Lilly’s new glargine), and we’ll be watching to see how the basal market segments once new options become available.
  
  • ADA 2014 was the first time data for BI/Lilly’s investigational insulin glargine LY2963016 (LY) had ever been released, and it appears to act very similarly to Lantus in both type 1 and type 2 diabetes – in the 24-week ELEMENT-1 trial (type 1 diabetes; 69-OR) LY achieved a mean 0.4% A1c reduction vs. 0.5% on Lantus, and 35% of LY users reached the target A1c of 7% vs. 32% of Lantus users. In the 24-week ELEMENT 2 (type 2 diabetes; 64-OR) patients achieved identical A1c reductions of 1.3% whether they were given LY or Lantus, and 49% of patients treated with LY reached the target compared to 53% of patients treated with Lantus. Speakers concluded that there were no clinically significant differences between the two products; though some presume Lilly intends to sell it at a price advantage should it be approved, the company has said specific nothing on this front.
  • Turning toward Sanofi’s next-generation basal insulin Toujeo, all data from the global phase 3 program that had not already been presented in 2013 were released at ADA. In EDITION III (68-OR) and EDITION IV (80-LB) Toujeo conferred non-inferior A1c reductions as Lantus (as was the case in EDITION I and EDITION II), but Toujeo did not significantly reduce rates of hypoglycemia based on the pre-specified endpoints (in contrast to EDITION I and II). Sanofi held an investor call to discuss these data where management emphasized that the consistency of the trend in the right direction was reassuring. Also notably on Toujeo, one poster (919-P) presented results of a sub-analysis of patients taking Toujeo at fixed dosing intervals (every 24 hours) or flexible dosing intervals (24 ± 3 hours) and found the change in A1cs to be comparable. While this is not quite as flexible as the alternating 8/40-hour dosing intervals afforded by Novo Nordisk’s Tresiba, it is still clinically significant.
  • On the topic of Tresiba, Novo Nordisk presented a poster (402-P) in which it re-analyzed Tresiba’s nocturnal hypoglycemia data using several different definitions of nocturnal and hypoglycemia. It demonstrated that the company’s original claim that Tresiba has a nocturnal hypo benefit over Lantus, (which has been criticized by the FDA) was fairly consistent across different definitions. We would love for FDA to consider more the heterogeneity of patients and to realize that although the overall “average” reduction may not look so impressive, there is a range and some patients will have far more need and some less.
  • Another poster of interest (886-P) described the hepato-preferential mode of action for BI/Lilly’s peglispro from a small, open-label euglycemic clamp study. The clinical significance will be made clearer once detailed phase 3 results are released in 2015 (topline phase 3 results were released in May).
• An entire session devoted to prandial insulin therapy shared data on promising ultra-rapid-acting candidates from Novo Nordisk (FIAsp), MannKind (Afrezza), and Biodel (BIOD-123). Dr. Tim Heise (Profil Institute for Clinical Research, Chula Vista, CA) presented pretty compelling PK/PD data showing that Novo Nordisk’s faster-acting insulin aspart (FIAsp, aka NN1218) has a clinically significant faster onset of action than its predecessor, Novolog (insulin aspart). The time to 50% maximum concentration for FIAsp was 27 minutes vs. 35 minutes for insulin aspart, which translated to a 22 mg/dl lower postprandial glucose excursions after one hour (26 mg/dl lower after two hours). Other orals in this session from Biodel and MannKind shared full results that augmented data we had previously seen – faster onsets of action, improved postprandial glucose excursions, less risk of hypoglycemia, and advantage (or decrement) in A1c. Based on the data, we have little doubt that these new insulins are indeed faster and offer some improvement over current prandial offerings – the bigger question is whether the incremental gains (in the absence of improved A1c) will be enough to support approval, reimbursement from payers, and sustainable pricing for companies and patients. We’ll find out soon with the recently approved Afrezza (see our report here).
  
  • A valuable talk from Dr. David Klonoff (UCSF, San Francisco, CA) reminded us that ultra-rapid-acting insulins may not necessarily demonstrate lower A1c levels, but they do have meaningful clinical benefits: less postprandial hypoglycemia, less postprandial hyperglycemia, and less time spent out of glycemic range. However, he reminded attendees that the FDA’s 2008 Guidance for Industry on Diabetes Drug Development is quite black-and-white: “For the purposes of drug approval and labeling, the final demonstration of efficacy should be based on reduction in A1c, which will support an indication of glycemic control.” The aforementioned data on FIAsp, BIOD-123, and Afrezza supports Dr. Klonoff’s view, and we wonder how companies can think strategically about designing trials to show enough efficacy that approval and reimbursement are more certain (using CGM is a no-brainer in our view). Certainly, all eyes were on the FDA as it decided whether to approve Afrezza by the July 15 PDUFA date; in fact, two weeks early, it came forward with a positive decision. As a reminder, the PDUFA had been extended by three months following the original April 15 PDUFA date, which was of course a long shot given the proximity to the April 1 FDA Advisory Committee.
• Last year’s ADA saw a boon in early-stage research on type 2 therapies for type 1 patients, and there was not as much shown at ADA on this front in 2014. We suspect that they we are simply in a data-lull while larger trials are going on over the next year or two (e.g., Novo Nordisk’s liraglutide for type 1 entered phase 3 in 4Q13). One poster of interest on this topic this year (1051-P) was on a small, open label study of Lilly/BI’s SGLT-2 inhibitor empagliflozin in type 1 diabetes showing a trend in improvement in CGM parameters such as glucose exposure, glucose variability, glucose stability, and time in zone. We imagine that with a larger trial, those trends could reach significance. Certainly there is significant advocate interest on this front …
• Perhaps unsurprisingly, there continued to be relatively few presentations on DPP-4 inhibitors. The flow of data from sub-analyses of the SAVOR and EXAMINE cardiovascular outcomes trials (for AZ’s Onglyza [saxagliptin] and Takeda’s Nesina [alogliptin], respectively) is largely complete by now, and the most exciting data was on the combination of DPP-4 inhibitors with SGLT-2 inhibitors. That being said, there was some clinical data presented on the small new batch of once-weekly DPP-4 inhibitors, including Merck’s omarigliptin and Takeda’s trelagliptin, which we learned recently will likely not be developed further in the US and EU due to the exceedingly high costs of phase 3. Merck featured a set of posters comparing its market-leading Januvia (sitagliptin) against sulfonylureas in terms of progression to insulin or intensification of therapy, showing that treatment with Januvia generally required less modification of therapy. This sort of data, we imagine, could have appeal for payers in particular, especially given the high costs of progressing a patient to insulin, at least traditional insulin. Finally, a study presented by Dr. Stuart Ross (University of Calgary, Alberta, Canada) showed that linagliptin + metformin was able to achieve a mean A1c reduction of 3.4% in newly diagnosed type 2 diabetes patients with high baseline A1c (mean baseline = 10.5%), suggesting that insulin initiation is not the only option for newly diagnosed patients that are very poorly controlled. 
• Questions about incretin safety, namely the potential association of incretins and pancreatitis and DPP-4 inhibitors and heart failure, were on the forefront of many people’s minds. In a compelling Meet the Expert Session with Dr. John Buse (University of North Carolina, Durham, NC), many audience members seemed concerned over how “real” the heart failure signal is for DPP-4 inhibitors, and Dr. Buse suggested that it is probably a spurious signal and that if any real-life risk of heart failure exists, it would likely be less than the 27% relative increased risk seen in SAVOR (which was already pretty modest, he said) since the SAVOR trial was enriched for people with high CV risk whereas in the “real world”, DPP-4 inhibitors are generally used in patients much earlier in the disease progression. With regards to incretins and pancreatitis. Dr. Jacqueline Koehler (Samuel Lunenfeld Research Institute, Toronto, Canada – she works with Dr. Dan Drucker) cogently reviewed current preclinical and clinical data surrounding the risk of incretins and pancreatitis/pancreatic cancer in front of an overflowing audience. As Dr. Koehler remarked, much of the debate over incretins and pancreatic safety has been fueled by a few publications of preclinical and clinical data that have not been reproducible, and the majority of clinical data has come from retrospective database and case-controlled observational studies, which are subject to numerous biases and limitations. Reassuringly, in the presentation of full results for Novo Nordisk’s SCALE Diabetes trial (liraglutide 3.0 mg for the treatment of obesity in people with diabetes), we learned there were zero cases of pancreatitis in this trial. Notably, in Dr. Daniel Drucker’s (Samuel Lunenfeld Research Institute, Toronto, Canada) Banting Medal Lecture, he gave reasons not to sound the alarm over GLP-1’s consistently demonstrated effect on increasing pancreas mass: he has evidence from mice that increased pancreas mass is not due to edema, inflammation, or increased cellular proliferation (which would be cause for concern about cancer). In fact, he has shown that it is due to increased protein synthesis possibly reflecting communication between the gut and the pancreas to increase pancreatic protein synthesis following meal ingestion. The mechanism here is not well understood, but overall it seems that concerns about incretin safety have been mellowing.
• Encouraging news emerged from the hunt for a type 1 diabetes biological cure; as usual, questions continue (and are perhaps heightening) on what is an appropriate level of risk for a type 1 diabetes cure. Dr. Francesca D'Addio (Harvard Medical School, Boston, MA; and San Raffaele Hospital, Milan, Italy) presented a series of studies on the controversial potential of autologous hematopoietic stem cell transplantation (AHSCT), also known as the Brazil Cocktail, to cure newly-diagnosed type 1 diabetes patients (124-OR). The therapy yielded some striking benefits: insulin independence was achieved in all patients (n=65) at least once during the trial, and persisted for an average of 18 months. However, such benefit did not come without cost:  the frequency of severe side effects was fairly high (34 out of 65 patients experienced an AE), and the list included one death due to sepsis that may have been related to the therapy, as it is immunocompromising. Thus, while the results of this trial suggest that type 1 diabetes, indeed, can be cured, its unclear if the risks are reasonable to take on. At the 2013 Rachmiel Levine Symposium, the highly respected Dr. David Harlan (University of Massachusetts School of Medicine, Worcester, MA) stressed that new therapies must be quite safe in order to have a positive benefit:risk profile, since the excess mortality associated with type 1 diabetes is down to ~2% over 20 years. This may become even more true over time if automating insulin delivery moves ahead successfully (and is accessible, etc.)
  
  • In the hunt for an efficacious but safer “Brazil Lite” cocktail, Dr. Michael Haller (University of Florida, Gainesville, FL) presented positive phase 2 results for a combination of antithymocyte globulin (ATG) and granulocyte colony stimulating factor (GCSF), two of the four components of the Brazil Cocktail, in people with recent onset type 1 diabetes (173-OR). In the single blinded trial, the treatment arm’s (n=17) two-hour C-peptide level was preserved over the 12 months pst-treatment at ~2 ng/ml (baseline was 2.14 ng/ml). In contrast, the placebo group experienced a significant C-peptide decline to ~1 ng/ml (p-value for the difference between the two groups = 0.05). On the safety side, while the therapy seemed to be safer than the Brazil cocktail, Dr. Haller still seemed disappointed in the rates of adverse events (14 participants in the experimental arm experienced cytokine release syndrome). This safety profile raises the question of whether Brazil Lite is lite enough to offer a compromise on the high efficacy and serious risks of the Brazil Cocktail – we should get a better sense after the readout of results from a phase 2 study in people with new onset type 1 diabetes, which is set to begin this summer.
  • Offering an alternative approach, Dr. Stephen Gitelman (UCSF, San Francisco, CA) presented optimistic phase 1 results for a T-regulatory cell expansion therapy (174-OR). Full two-year data was only available for six patients who received the smallest cellular doses. In these groups, C-peptide levels and A1c remained fairly stable over the entire time period, with perhaps a slight upward trend in exogenous insulin usage. The incomplete data from patients who received larger doses of Tregs was more variable, but it was too early to tell, and the study is not sufficiently powered to examine efficacy.  Notably, NovoStem has licensed the Treg technology from UCSF, and is partnering with UCSF and TrialNet for phase 2 development.
  • Shortly after ADA, at ENDO, we also learned of a phase 2 study (n=66) of imatinib (Novartis’ Gleevec) for recent onset type 1 diabetes. The study has an estimated completion date of January 2018. This agent, a tyrosine kinase inhibitor, may have effects not only on the immune system, but also on beta cells and insulin sensitivity.
• No “next big thing” emerged at this year’s ADA in the field of novel type 2 diabetes therapies, perhaps as a sign of rising regulatory and reimbursement pressures as well as the ever-higher bar for ideal therapies. Data from Pfizer on its phase 2 FGF21 analog failed to show a significant effect on glucose, matching what was seen with Lilly’s since-discontinued FGF21 analog. Of course, many novel therapeutic candidates are discontinued at some point, and it takes more and more to succeed each year. What continues to be disconcerting is that many academics and for-profit organizations seem fatigued by even the thought of trying, given all the constraints in diabetes that are different in other therapeutic areas. While we do look forward to watching how current therapies become part of combinations – those seem “new” to us even though they may not be novel – we don’t think the field is so successful that it can afford for innovation to slow. Yet, that is certainly happening, largely due to the 2008 FDA CV Guidance that has placed a significant additional artificial barrier to drug development. Each month, we see more and more explicit evidence of the burden that the CV Guidance is placing on diabetes drug development – Takeda announced during its recent F4Q13 update, for example, that it would not pursue its once-weekly DPP-4 inhibitor trelagliptin in the US due to the high cost of phase 3 and CVOTs. Barring a much-needed change in policy, we wonder whether we can even expect to see another big development in novel therapies any time soon – we cannot forget that not too long ago, SGLT-2 inhibitors were in the novel drugs category.
• Despite the lack of big-picture progress in novel therapies, there were some areas that give us reason to be optimistic:
  
  • There was encouraging data and commentary on better glucagon formulations and glucagon-based therapies this year, including a talk by the highly respected Dr. Ken Ward (OHSU, Portland, OR) and a poster each from Xeris and Zealand Pharma. Xeris’ approach uses a non-aqueous solvent to stabilize glucagon, while Zealand’s ZP-GA-1 uses a glucagon receptor analog that is stable in aqueous solution. We learned during a Zealand analyst breakfast that ZP-GA-1 could enter clinical development this year, and (taking advantage of an expedited combined phase 2/3 testing for bioequivalence) could be filed as early as 2017. Better glucagon tools are critical for the development of more user-friendly hypoglycemia rescue kits and could be very helpful for the development of closed loop systems. We also see a great deal of interest in glucagon “mini-dosing” and we’ll be very interested to learn more about the regulatory advice companies receive.
  • Isis presented promising phase 2 data on its glucagon receptor antagonist ISIS-GCGRRx, showing impressive A1c reductions 1.2%-1.4% from a baseline of ~8.5%-9.0% after 13 weeks. Previous studies have indicated that the agent does reach peak A1c lowering until closer to 26 weeks of treatment, meaning that long-term efficacy could be greater than what was seen in this trial. Interestingly, the drug also appears to increase GLP-1 levels and promote insulin secretion. Isis plans to target this agent toward patients with more advanced and poorly controlled type 2 diabetes. We’re eager to see more data on this class as a whole – Lilly management has previously suggested that its glucagon receptor antagonist is one of its most exciting pipeline candidates. Adding to the excitement, glucagon receptor antagonism has potential for the treatment of type 1 diabetes as well as type 2 diabetes (as we learned from a Lilly presentation at last year’s ADA).
  • New potential therapeutic targets continue to emerge from the exciting field of brown adipose tissue. ADA featured an entire symposium on the topic, where we saw promising data on three adipocyte-related targets (PRDM16, BMP9b, and LR11) for obesity and/or diabetes from labs at Cambridge in the UK as well as Harvard. The wide-reaching metabolic effects of brown adipose tissue make it a compelling target for a range of metabolic disorders, although researchers still must find specific inducers to act on the brown-fat-related targets that have been identified, meaning that this field is likely some years away from the clinic.
• For the first time, we learned about Zealand’s GLP-1/GLP-2 dual agonist program, which is partially predicated on activity of the gut and gut microbiome. Researchers have theorized that the infiltration of bacterial cell wall components across the gut is the cause of some of the inflammation that is associated with diabetes. Preclinical data presented by Zealand at ADA suggested that adding GLP-2 agonism to GLP-1 agonism improves gut integrity. Interest in gut microbiota is skyrocketing, and this therapy represents one of the more concrete therapies being investigated that in some way address the gut microbiome. We continue to be very impressed with this 100-person company and all it is achieving.
• Some of the data we saw this year reinforced long-standing questions about the role of SFUs in clinical trials. The AWARD-2 trial compared Lilly’s once-weekly GLP-1 agonist dulaglutide against Sanofi’s basal insulin Lantus (insulin glargine) on a background of metformin and sulfonylureas, and found benefits in terms of A1c, weight, and hypoglycemia favoring dulaglutide. However, during Q&A, the point was raised that having patients on a sulfonylurea could have kept providers from aggressively titrating the insulin due to fears of hypoglycemia (presenter Dr. Francesco Giorgino [University of Bari Aldo Moro, Bari, Italy] acknowledged the likelihood of that effect). One major phase 3 trial for Lilly/BI’s SGLT-2 inhibitor empagliflozin compared the agent against the SFU glimepiride, and showed strong hypoglycemia and weight benefits (as expected) in addition to a modest A1c advantage. Although SFUs are still used widely in many geographies due to their affordability, the real-world relevance of comparing new drugs to SFUs is diminishing in the US and Europe. We have heard suggestions that behind the scenes (and seen at FDA Ad Com meetings), the FDA is suggesting that SFUs be avoided as comparators for clinical trials; while we would agree that there are probably better options that could provide more meaningful comparisons, given that so many patients are taking SFUs, we think the novel therapies should be compared against them. If FDA is saying that it’s too dangerous to have patients taking SFUs in trials, we would ask why they are still on the market. . 
• The data and discussions at ADA 2014 raised multiple questions on diabetes drugs, particularly combination therapy and new basal insulins. For example, what will be the impact of new insulin glargines in the marketplace and on patients and access? How will new glargines be priced? Practically speaking, how should healthcare providers determine the proper dosage and titration scheme for initiating GLP-1/basal insulin combination therapy? With Lyxumia and LixiLan’s postprandial benefit over Victoza and Lantus, respectively, now established, what will the impact be on prescribing? How easy will basal/GLP-1 combinations be to take? To what degree will LixiLan show more robust A1c reductions compared to Lantus in a phase 3 trial and how much earlier will patients go on basal/GLP-1 combinations vs. basal only? How will the combos be priced vs. monotherapy? Why did the early SGLT-2/DPP-4 inhibitor fixed dose combination products (both saxa/dapa and empa/lina) have a less-than-additive effect on glucose lowering? Will these FDCs do better in a real-world setting where the advantages of consolidating dosing and co-pays will have a positive impact on adherence? What will the next transformative wave be on the type 2 diabetes drug front?

Obesity

• This ADA was quieter on the obesity pharmacotherapy front (in contrast to ADA 2013, which was the first ADA after the launch of Vivus’ Qsymia [phentermine/topiramate ER] and Arena/Eisai’s Belviq [lorcaserin]). For example, there were no abstracts on Qsymia this year at all – we would have loved to have seen more prevention data. More broadly, the new data that was presented on anti-obesity pharmacotherapies seemed to center around their ability to treat prediabetes and prevent type 2 diabetes – this is clearly a smart way to go given that payers would be more likely to care about diabetes prevention than cosmetic weight loss. Data to this point was presented at ADA for Belviq (99-OR) and Orexigen/Takeda’s Contrave (buprioprion/naltrexone; 1046-P), and Novo Nordisk had announced such data for its liraglutide 3.0 mg a few days later at ENDO. We think this focus on the prevention of obesity comorbidities could be more commercially successful, than prior messaging centered on weight loss more broadly, since it could gain more coverage by payers and side-step a general culture in which HCPs don’t view obesity as a medical condition requiring pharmaceutical treatment. Indeed, Orexigen has indicated that it/Takeda’s launch plans for Contrave especially target people with prediabetes and type 2 diabetes (including potentially developing a DPP-4/Contrave fixed-dose combination). If Contrave and Novo Nordisk’s liraglutide 3.0 mg are approved by the FDA this year (Contrave PDUFA date and liraglutide Advisory Committee are both September 11), we think the obesity pharmacotherapy area will be particularly interesting at the next ADA. The meeting will offer some hints on how invested Takeda and Novo Nordisk are in these agents (how much floor space in the exhibit hall will each dedicate to their respective drug?) and if they can make larger strides in this underdeveloped market than Vivus and Eisai have mustered so far.

Exhibit Hall

• The ADA 2014 exhibit hall seemed to have less fanfare, attendance, and excitement than we’ve seen in the past. We took particular note of who was absent from the hall –Bayer, Roche, and Abbott, for a start – which clearly reflects the challenging SMBG environment in the US, and perhaps a different perception on the return on investment from exhibit hall booths. In the modern era of digital and social media marketing, an argument could be made that the ROI of exhibit hall booths is a bit hard to measure. Our coverage includes the following companies and organizations: America’s Diabetes Challenge, Amgen, AstraZeneca (new Bydureon pen still deemphasized), Becton Dickinson, Boston Therapeutics (our first time covering this company), BI/Lilly, Dexcom (G4 Platinum Professional vs. Medtronic iPro2), Eisai, Freedom MediTech, Glooko (Bluetooth sync and Joslin HypoMap), GSK (new Tanzeum pen), iHealth (new smartphone BGM), Insulet, J&J Janssen, J&J LifeScan/Animas, Lilly, Medtronic (MiniMed Duo on display, next-gen pump platform displayed), Merck, Nipro Diagnostics, Novartis, Novo Nordisk (pre-launch of the FlexTouch pen), NeuroMetrix, Practice Fusion, San Meditech (Chinese CGM company with a Telcare partnership), Sanofi, Takeda, Tandem, Telcare, Type 1 Diabetes TrialNet, Valeritas, and Vivus.

Public Policy

• Political issues surrounding diabetes care and research (e.g., research funding, food policies, workforce shortages) were frequently mentioned during ADA 2014, and we hope to see a strengthening of advocacy work as a result. During our joint TCOYD/the diaTribe Foundation event, Dr. Rury Holman (University of Oxford, UK) remarked that the biggest surprise to him at ADA was learning of the disconnect between diabetes prevalence and NIH funding – this is certainly increasingly worrisome. Dr. Bernie Zinman (University of Toronto, Canada) echoed this stressing, “We need to get YOUR congress to stop bickering about trivial things and address the big issues.” Dr. John Anderson (The First Clinic, Nashville, TN) explained that when the disproportionately small funding for diabetes in the US is pointed out “it falls on deaf ears.” Additionally, this ADA featured (in one of the larger lecture halls) an excellent symposium on how the ACA’s implementation is impacting diabetes care. In this session, Dr. Darius Lakdawalla (University of Southern California, Los Angeles, CA) indicated that one in every two Medicare dollars is spent on a person with diabetes – clearly a substantial increase from one in three dollars in 2004. Additionally, Dr. Sandra Decker (CDC, Atlanta, GA) emphasized the criticality of Medicaid reimbursement rates increasing to that of Medicare, if people with diabetes newly insured under the Medicaid expansion are going to actually have access to care (currently only ~50% of PCPs and endocrinologists are accepting new patients on Medicaid, as compared to ~80-90% of privately insured patients). Unfortunately, this valuable symposium was sparsely attended. While we are encouraged by the increasing awareness of these issues, we are eager for the conversation to take the next steps in focusing on what can be done to impact these policies, and who will take the lead in driving advocacy efforts.

Artificial Pancreas

Oral Presentations: Constructing an Artificial Pancreas

Multiday Outpatient Glycemic Control in Adolescents with Type 1 Diabetes Using a Bihormonal Bionic Pancreas: The Barton Center Summer Camp Study (237-OR)

Steven Russell, MD, PhD (Massachusetts General Hospital, Boston, MA)

Dr. Steven Russell shared topline results from the bionic pancreas Summer Camp and Beacon Hill studies, which were simultaneously published online in the New England Journal of Medicine (“Outpatient Glycemic Control with a Bionic Pancreas in Type 1 Diabetes”) – this publication received significant press (New York Times, Wall Street Journal, Washington Post, Boston Globe, Time, Bloomberg, USA Today, Huffington Post, NPR, CBS, Popular Science, US News & World Report, Fortune, and more listed here) and quickly rose to become the most popular NEJM paper of the past month. Though Dr. Russell’s topline results presentation was similar to those given at ATTD 2014, it was terrific to see the excitement in the room among fellow researchers – said Dr. Roman Hovorka, “Congrats on the publication; it’s brilliant for the field and for you as well.” In his talk, Dr. Russell emphasized the impressive average level of glucose control during closed loop (133 vs. 159 mg/dl in adults; 138 vs. 157 mg/dl in adolescents), which was simultaneously achieved with no increase/significant reduction in hypoglycemia (4% vs. 7% in adults; 6% vs. 8% in adolescents). Dr. Russell also noted the challenging circumstances of these studies (strong usual care control in the camp environment; 45% of the Beacon Hill adults wore their own CGM during usual care) and the robustness of the control algorithm (adaptable over time; initializes only based on weight). The team’s first home use study began on June 16 – these randomized, crossover experiments in adults with type 1 diabetes will compare 11 days with the Bionic Pancreas to 11 days of usual care. The multicenter study will take place at MGH, UNC, Stanford, and UMass, with 12 subjects expected per site. Patients must either work or go to school at the institutions, and their home must be within 30 minutes of the center. Notably, they are allowed to travel as far as 60 minutes driving time away (including driving their own car!). Remote monitoring will be quite minimal. This study is certainly one of the more ambitious and real-world outpatient studies to date, and we cannot wait to see it get off the ground.

• The results from the bionic pancreas Summer Camp and Beacon Hill studies were published online in the New England Journal of Medicine (“Outpatient Glycemic Control with a Bionic Pancreas in Type 1 Diabetes”) on June 15. The paper impressively combines both studies into a single manuscript. The publication has rich detail on the performance of the bionic pancreas and provides lots of illustrative data and statistics.
• The randomized, crossover Summer Camp study compared five days on the bionic pancreas to five days of supervised camp care. The study took place at Camp Joslin (n=16 boys) and Clara Barton (n=16 girls) in 2013. Point of care capillary blood glucose checks occurred during the day and night (no venous glucose monitoring!). The same mobile platform was used as in Beacon Hill – two Tandem t:slim pumps (insulin and glucagon), a Dexcom G4 Platinum sensor and transmitter, and an iPhone 4S controller. Study staff and camp staff provided 24-hour, round-the-clock telemetry to monitor glycemia. A total of 160 days on the bionic pancreas were accumulated. For more information and interviews with trial participants, please see our detailed Closer Look write-up after we visited the study site this past summer.
  
  • Relative to usual care, the bionic pancreas improved mean blood glucose (158 to 142 mg/dl) and simultaneously reduced hypoglycemia (2.2% to 1.3% of time <60 mg/dl). Dr. Russell showed a plot charting individual patients’ mean glucose (days 2-5) in the control condition vs. with the Bionic Pancreas. Notably, thirty-one out of the 32 patients had a mean glucose <168 mg/dl (the ADA goal of <7.5%) on the bionic pancreas. Most patients experienced a striking decline in mean glucose, with some going from over 215 mg/dl to <145 mg/dl. In the handful of patients who did see a rise in mean glucose after wearing the bionic pancreas (5/32), the system reduced high baseline hypoglycemia and still brought patients to goal (<168 mg/dl).
  • One patient did have a mean glucose exceeding 168 mg/dl on the bionic pancreas, a finding attributed to the adaptive algorithm. In most patients, it takes the algorithm ~18 hours to adapt to patients (initialization only requires weight) – this is why the team focuses on study data for days 2-5 (i.e., day one is not representative of how the system would perform ad infinitum). In the case of this single camper, it took the algorithm two days to adapt to the patient (instead of the typical 18 hours). Indeed, the patient’s average on days 3-5 was a solid 142 mg/dl, well below goal. The team has since modified the algorithm to allow the system to begin adapting immediately once it first comes online.

Summer Camp Study Results, Days 2-5 (n=32 adolescents, 160 bionic pancreas days)
	Bionic Pancreas		Supervised Camp Care		Baseline
	CGM	% CGM <60 mg/dl	CGM	% CGM <60 mg/dl	CGM
Mean	142 mg/dl	1.3%	158 mg/dl	2.2%	189 mg/dl
Projected A1c	6.6%		7.1%		8.2%

• The randomized, crossover Beacon Hill study compared five days on the bionic pancreas to five days of “usual care” (what a patient would normally do, though with the addition of blinded CGM). The study included 20 adult type 1 patients >21 years. The bionic pancreas mobile platform consisted of two Tandem t:slim pumps (insulin and glucagon), a Dexcom G4 Platinum sensor and transmitter, and an iPhone 4S controller. Patients had free run of a three-square mile area of the Boston peninsula. Point of care capillary blood glucose checks occurred during the day via 1:1 nursing. At night, patients slept in a hotel with venous blood glucose monitoring and 1:2 nursing. A total of 100 days on the bionic pancreas were accumulated. For more information, read our diaTribe test drive on the Beacon Hill study.
  
  • Similar to the summer camp study, the bionic pancreas improved mean blood glucose (159 to 133 mg/dl) and substantially reduced hypoglycemia (3.7% to 1.5% of time <60 mg/dl). Dr. Russell emphasized that the comparison to “usual care” was quite challenging, since 45% of patients wore their own CGM in addition to blinded CGM. In addition, 100% of patients were on insulin pumps in the control condition. This contrasts significantly with “real world” care of type 1 diabetes, where it is estimated that ~30% of patients are on pumps and ~10% are on CGM in the US.
  • All 20 patients in Beacon Hill had a mean glucose <154 mg/dl (the ADA goal of <7%) on the bionic pancreas. Most patients experienced a striking decline in mean glucose, with one patient going from an average of 215 mg/dl to <120 mg/dl on the bionic pancreas. Only one patient saw a rise in mean glucose after wearing the bionic pancreas (approximately +10 mg/dl), and in that case, the system reduced a high level of baseline hypoglycemia. Under usual care, there was also a wide dispersion in mean glucose between patients, with some having a mean under usual care of >210 mg/dl and others at <120 mg/dl. After wearing the bionic pancreas, mean glucose levels converged to 115-153 mg/dl.

Beacon Hill Study Results, Days 2-5 (n=20 adults, 100 bionic pancreas days)
	Bionic Pancreas		Usual Care
	CGM	% CGM <60 mg/dl	CGM	% CGM <60 mg/dl
Mean	133 mg/dl	1.5%	159 mg/dl	3.7%
Projected A1c	6.2%		7.1%

• Dr. Russell emphasized the robustness of the bionic pancreas algorithm, which only requires weight for initialization, adapts over time, and does not mandate pre-meal priming boluses. Patients can optionally announce meals to the system, but they only enter qualitative information using a slider – is this “more,” “about the same,” or “less” than the amount of carbs that you typically eat? At ATTD 2014, Dr. Damiano called this, “Diabetes without numbers.” In Beacon Hill, patients could use the meal announcement feature if they desired, but were not reminded about it if they forgot. Only about 70% of the meals were actually announced to the system. As a reminder, the algorithm consists of three insulin controllers (basal, bolus, and meal priming) and one glucagon controller. The algorithm’s adaptive capabilities are described in El-Khatib et al., J Clin Endocrinol Metab 2014.

Questions and Answers

Dr. Roman Hovorka (University of Cambridge, UK): Wonderful stuff. Congrats on the publication – it’s brilliant for the field and for you as well. In the camp study, you compared closed-loop to standard treatment. Did standard treatment have real-time CGM?

A: In both studies, the bionic pancreas was compared to usual care for that patient. In the adult study, 45% of the time subjects were using their own unblinded CGM. All patients wore blinded CGM across the board. But if they used CGM, they were allowed to wear it during the usual care period of the study. Usual care in the camp study was much better than at home. But only about 9% of them used CGM during the camp usual care arm.

Dr. Hovorka: So some of the benefit is due to closed-loop, but also some could be due to adding CGM…?

A: It’s very important to highlight, “What are we aiming to find out?” You take a population of people with type 1 diabetes and provide them with this technology. I think this study underestimates that effect size. We know only about 9% of the US population uses CGM. It is possible that some of the achieved benefits are from using real-time CGM. On other hand, that would underestimate the effect, because CGM is not widely used.

Dr. Hovorka: You could look at it two ways. One should use the best possible treatment vs. the standard of care. Another is to use what people are using. The argument NICE would take is that you must go against the best possible treatment.

A: Granted, but it’s unlikely we’re going to convert everyone to CGM.

Dr. Bruce Buckingham (Stanford University, Stanford, CA): If you look at the adults, there was no remote monitoring in the control arm. There was a significant benefit in hypoglycemia. In the camp study, you remotely monitored, and the camp control group was very closely watched. You really set yourself up to a disadvantage in showing a benefit...

A: That’s right. And we did see a benefit. In the next study, monitoring will really be reduced – alerts will only trigger for hypoglycemia that persists for more than 15 minutes.

Dr. Irl Hirsch (University of Washington, Seattle, WA): You’ve been doing subcutaneous glucagon infusion. How often did you change the sites, and did you see any problems with skin reactions?

A: Glucagon was changed every day. As has been well pointed out in this session, current formulations of glucagon are not very stable. Many companies are working to develop stable glucagon, and some are quite far. We’re doing a clamp study with the Xeris glucagon right now. Preliminary data suggests it is equivalent in PK/PD in microdoses. There was no difference in the rate of skin reactions at infusion sites.

Four Weeks’ Home Use of Overnight Closed-Loop Insulin Delivery in Adults with Type 1 Diabetes: A Multicentre, Randomised, Crossover Study (233-OR)

Hood Thabit, MD (University of Cambridge, Cambridge, United Kingdom)

Dr. Hood Thabit shared notable results from a 28-day, crossover, home-use study of overnight closed-loop control in adults with type 1 diabetes (n=24). At baseline patients had mean age 43 years, A1c 8.1%, duration of diabetes 29 years, duration of pump use of six years, and BMI 26 kg/m². Compared to the period with open-loop CGM/pump therapy, the overnight closed-loop period – including all data whether closed loop was turned on or not – included statistically significantly more overnight time in target 70-144 mg/dl (53% vs. 39%), lower overnight mean glucose (148 mg/dl vs. 162 mg/dl), less overnight time >144 mg/dl (44% vs. 57%), lower mean glucose at 7 am (130 vs. 158 mg/dl), a lower 24-hour mean glucose (157 mg/dl vs. 167 mg/dl), and more 24-hour time in target (66% vs. 59%). Dr. Thabit explained that the improved glycemic control was due to overnight insulin delivery that was significantly higher-dose (6.4 vs. 4.9 U/night) and more variable (SD 0.6 vs. 0.1 U); however, thanks to better glycemic control at the start of the day, total insulin dose was not significantly higher (34.5 vs. 35.4 U/day). Rates of nocturnal hypoglycemia <70 mg/dl were low (1.8% vs. 2.1%) and not significantly different between groups, due to optimization of open-loop therapy. Closed-loop control was interrupted for technical reasons on roughly 20% of nights; the main problem was a loss of connectivity with the pump (81%). Two severe hypoglycemic episodes occurred, both overnight during interruptions of closed-loop connectivity; both patients recovered fully. Shortly after this presentation, trial results were published (Thabit et al., Lancet Diabetes Endocrinol 2014).

• This trial of overnight closed-loop control included adults (≥18 years) with type 1 diabetes who use insulin pumps (n=24). Patients were enrolled at three sites: Cambridge, Sheffield, and London. Mean baseline data were as follows: A1c 8.1%, age 43 years, diabetes duration 29 years, pump use duration 6.3 years, total insulin dose 0.5 U/kg/day, BMI 26 kg/m². After a two-to-four week run-in period in which patients wore blinded CGM, sensor glucose data were downloaded to evaluate compliance and to optimize pump therapy. Patients then participated in a 28-day period of either 24-hour sensor-augmented pump use, or overnight closed-loop control with daytime sensor-augmented pump use. They then went through a four-week washout period and crossed over to the alternate condition. The first night of the overnight closed-loop experiment was spent at the clinical research center, for training and competency assessment with the closed-loop system. While at home patients had unsupervised access to a 24/7 support line. 
• Closed-loop control was performed with the FlorenceD2 prototype system, which includes an Abbott FreeStyle Navigator 2 transmitter, Abbott FreeStyle Navigator 2 receiver, Dana Diabecare pump, and an ultraportable PC that hosts a model-predictive control (MPC) algorithm and communicates with the pump wirelessly.
• During the period between midnight and 7 a.m., sensor glucose values fell in the target range (70-144 mg/dl) significantly more often with overnight closed-loop control than sensor-augmented pumping (53% vs. 39%, p<0.001).  Rates of overnight hyperglycemia >144 mg/dl were also significantly reduced with closed-loop control. However, overnight hypoglycemia was quite rare in both groups (mean <10 minutes per night), and the rates were not significantly different between groups. By way of partial explanation, Dr. Thabit said that each patient in the open-loop group had their overnight basal rate was “optimized” after the run-in period. Basal rate was then adjusted as needed in the weekly conversations with study clinicians. 

OVERNIGHT (00:00 – 07:00)	Overnight Closed-Loop	Sensor-Augmented Pump	P-value
% Time 70-144 mg/dl	53%	39%	<0.001
% Time 70-180 mg/dl	73%	61%	<0.001
% Time >144 mg/dl	44%	57%	<0.05
% Time <70 mg/dl	1.8%	2.1%	NS
% Time <50 mg/dl	0.2%	0.2%	NS

• Overnight closed-loop led to lower mean sensor glucose in the overnight period (148 vs. 162 mg/dl, p<0.05), lower mean glucose at 7 a.m. (130 vs. 158 mg/dl), and a trend toward less between-night coefficient of variation (CV). Dr. Thabit noted that within-night CV was actually higher with closed-loop control, because patients who began the night hyperglycemic were more consistently brought to a lower range by the closed-loop system. Sensor glucose fell below 63 mg/dl on 36 nights in the closed-loop period and 58 nights in the open-loop period; this difference did not reach statistical significance (p=0.18). 

OVERNIGHT (00:00 – 07:00)	Overnight Closed-Loop	Sensor-Augmented Pump	P-value
Mean glucose (mg/dl)	148	162	<0.05
SD glucose (mg/dl)	36	34	NS
Within-night CV glucose (%)	24	21	<0.05
Between-night CV glucose (%)	26	29	NS
AUC <63 mg/dl (mmol/l*min)	4.0	5.3	NS
Glucose at 7 am (mg/dl)	130	158	<0.05

SD = Standard Deviation; CV = Coefficient of Variation; AUC = area under the curve

• The benefits of overnight closed-loop control extended beyond the night, such that 24-mean glucose and 24-hour time in target were improved relative to sensor-augmented pumping. Dr. Thabit presented a modal-day chart showing the median and inter-quartile range of sensor glucose for each treatment condition. Median sensor glucose was lower with overnight closed-loop control through the morning and into the mid-afternoon, even though these patients used open-loop control during the day. We assume that the closed-loop group was able to achieve better daytime control because they were more likely to wake up in their target range.   

24-HOUR	Overnight Closed-Loop	Sensor-Augmented Pump	P-value
% Time 70-180 mg/dl	66%	59%	<0.005
Mean glucose (mg/dl)	157	167	<0.005
Change in A1c from baseline (7.9%)	-0.2%	No change	P<0.05

• The closed-loop controller delivered overnight insulin doses that were higher and more variable than with sensor-augmented pumping. Dr. Hood observed that the system increased insulin variability in order to reduce glycemic variability – an equation often described in talks on overnight closed-loop control. 

	Overnight Closed-Loop	Sensor-Augmented Pump	P-value
Mean insulin dose, overnight (U)	6.4	4.9	<0.001
SD insulin dose, overnight	0.6	0.1	NS
Mean insulin dose, 24-hour (U)	34.5	35.4	NS

• Closed-loop control was used on 555 nights (86% of the full intervention period) for a median of 8.3 hours per night. The control system was turned on and off at median times of 10:52 pm and 7:23 am, respectively.
• The total number of interruptions to closed-loop control was 112 (an average of once every five nights). These interruptions were due mainly to lack of connectivity between the control device and the insulin pump (61%). Other causes of interrupted closed-loop control were inability to start the closed-loop cycle within 30 minutes (19%), changes in pump settings by the user (10%), unavailability of sensor data (6%), operating system malfunction (3%), and error in the control device’s software system (1%). 
• Two episodes of severe hypoglycemia occurred during the study, both at night and both when closed-loop control was interrupted. Both patients had a history of hypoglycemia unawareness. One of the episodes was attributed to a suspected overbolus of insulin while the pump was being primed. The cause of the other episode was not clear, but risk for hypoglycemia had been raised by increased physical activity during the day. Both patients recovered fully. Both patients also reduced their participation in the closed-loop experiment to only 14 days, with advice from the Steering Committee. (The other 14 days were not counted toward the total number of closed-loop days. Thus the total number of closed-loop days in the intent-to-treat analysis was 2*14 + 22*28 = 644). No episodes of hyperglycemia with ketosis were observed during the study.

Questions and Answers

Dr. Yogish Kudva (Mayo Clinic, Rochester, MN): There were 112 interruptions of closed-loop control over 555 days. What was the duration of interruptions?

A: I don’t have those data, sorry.

Q: What did it mean to optimize the non-closed-loop pump group’s overnight basal rate before starting the study?

A: Following the run-in period, the CGM was downloaded. We tried to optimize control as much as possible. Participants were also in conversation with clinicians once a week during the study. Both sides tried to optimize as much as possible, within limits.

Q: These results looked better than optimized, there was so little hypoglycemia with sensor-augmented pumping.

A: This was one reason we couldn’t see a difference. The baseline rate of hypo was very low, as I said – less than 10 minutes per night. To see a difference from this baseline, one would either need to recruit more hypoglycemia-prone patients or get a higher number of patients.

Dr. Nancy Bohannon (San Francisco, CA): The two severe episodes you had were when closed loop was turned off. Did these occur during the day, or during the night?

A: Both occurred at night. The closed-loop system wasn’t functioning; it wasn’t because the patient turned it off. There was a loss of connectivity, so the system reverted to the patient’s own basal pump setting. We wanted patients to use closed-loop control every night it was available, but sometimes they wouldn’t. If CGM was available, they used it close to 92% of the time; only 8% of the time they didn’t want to use it.

Day and Night Home Closed-Loop Insulin Delivery in Adults with Type 1 Diabetes: Three-Centre, Randomised, Crossover Study (235-OR)

Lalantha Leelarathna, MD (University of Cambridge, Cambridge, UK)

Dr. Leelarathna shared exciting results from a feasibility study of a closed loop system under free-living home conditions for seven days and nights in adults with type 1 diabetes (n=17). Patients were randomized to receive either the FlorenceD2 closed loop system or sensor augmented pump therapy. After seven days of treatment, patients in the closed loop arm spent significantly more time in the target range (defined as 70-180 mg/dl) compared to patients on sensor augment pump therapy (75% vs. 62%). The closed loop system also outperformed sensor augmented pump therapy on multiple secondary outcomes, including mean glucose (146 mg/dl vs. 158 mg/dl) and standard deviation of glucose (52 mg/dl vs. 59 mg/dl), without any significant difference in time spent in hypoglycemia. Operationally, however, Dr. Leelarathna emphasized that the portability and connectivity of the closed loop system needs to be improved going forward. Nevertheless, this study validated the feasibility of day and night closed loop and supported the initiation of the AP@home04 study, which includes three months of day and night in 30 adults (clinicaltrials.gov currently indicates an anticipated completion date in the second half of 2015).

• The objective of this study was to evaluate the feasibility of day and night closed loop system under free-living home conditions for seven days in adults with type 1 diabetes. The trial was conducted under the AP@home consortium and recruited 17 patients from Germany, UK, and Austria. Type 1 diabetes patients were eligible for the study if they were on insulin pump therapy, had an A1c <10%, and did not have any significant comorbidities or hypoglycemia unawareness. Patients were randomized either to the FlorenceD2 closed loop system or an open loop treatment (consisting of insulin pump therapy combined with real-time CGM). The FlorenceD2 system contains three components: the Dana R insulin pump, the Navigator II receiver, and the control algorithm device. Each treatment phase included 23 hours in a clinical research facility followed by seven days at home. After a 1-3 week washout period, patients were crossed-over to receive the other treatment. During the research facility phase of the study, patients were trained on using the closed loop system; however, after the 23-hour inpatient stay, participants went home and used the system without any supervision – they could consume any meals of their own choice. Patients were encouraged to engage in moderate physical activity, but were advised to avoid strenuous activity or driving.
• The study recruited 17 adult patients with type 1 diabetes. The participants included 10 males and 7 females, with an average age of 34 years and an average duration of diabetes of 19 years. At baseline, patients were reasonably well controlled, with a mean A1c of 7.6%.
• Patients on closed loop treatment experienced significantly greater time in target range (defined as 70-180 mg/dl [3.9-10 mmol/l])] during the seven day home phase, compared to the open loop arm (75% vs. 62%). When using YSI reference glucose values, the target zone remained consistent (74% vs. 61%). The total daily dose of insulin infusion was lower in the closed loop group, but did not reach statistical significance (total basal insulin was slightly higher with closed loop, but boluses were significantly lower).
• There were a total of 194 operational interruptions, translating to an interruption event every 12 hours (out of 2,333 total hours of closed loop operation in this study). The two most common reasons for these events were lack of pump connectivity and CGM unavailability. One severe hypoglycemia episode occurred in the closed loop arm because the sensor stopped working and the patient administered two manual boluses. Dr. Leelarathna highlighted this as an opportunity for improvement and mentioned that the next study (AP@home04) has moved to a mobile home platform with wireless communication between devices.

Questions and Answers

Dr. Irl Hirsch (University of Washington, Seattle, WA): Regarding the differences in bolus insulin on closed loop, what can I take away from that? Does that mean we are we doing too much bolus insulin or were patient eating differently on closed loop?

A: One of the reasons was that glucose was more in target on closed loop. Therefore, the correction bolus was lower in patients on closed loop.

Dr. Hirsch: When you say bolus, you’re including the correction as a bolus?

A: Correct.

Dr. Hirsch: I would suggest for reporting in the future, we need to separate bolus from correction from basal.

Ms. Arlene Pinkos (FDA, Silver Spring, MD): Can you confirm if the subjects kept diaries during the study? If so, were you able to trace the glucose excursion on closed loop to specific activities?

A: We did give patients diaries, but unfortunately very few patients followed our advice. So, unfortunately I can’t answer that. Anecdotally, I would say the excursions were due to a miscalculation of carbohydrate bolus or exercise.

Hypoglycemia Reduction Capability and Insulin Dosing Behavior of a Predictive Controller (232-OR)

Daniel Finan, PhD (Animas Corporation, Westchester, PA)

Dr. Daniel Finan presented an update on Animas’ efforts to automate insulin delivery – the company has not been able to move quickly to larger studies or a commercial product, which is perhaps understandable given the challenges in the LifeScan/Animas business. This non-randomized, in-clinic, uncontrolled feasibility study examined 24-hour overnight use of a predictive low glucose suspend algorithm running on a laptop computer with the Dexcom G4 Platinum CGM, OneTouch Ping pump, Sansum APS system. The study was not powered to assess glucose outcomes and merely tested the algorithm’s insulin dosing decisions based on three different “aggressiveness factors” in 12 patients – conservative (n=4), medium (n=4), and aggressive (n=4). [We’d note that Animas reported on a slightly more ambitious hypoglycemia-hyperglycemia minimizer at ADA 2013.] The conservative predictive suspend approach activated only 7% of the time, and on average only reduced basal insulin by 4%. By contrast, the medium and aggressive approaches activated 21% and 23% of the time, on average reducing basal insulin dose by 19% and 21%, respectively. Animas was “encouraged” by the CGM results in this short and small study, as a median 0% of the time was spent <70 mg/dl and 72% of the 24-hour period was spent in the range 70-180 mg/dl. Further work will include appropriately powered clinical studies, developing this “study tool into a target product,” mapping out use cases, and validating human factors. There is clearly a long road ahead for this to turn into a commercial product, though it’s encouraging to see that Animas is still pursuing it.

Questions and Answers

Dr. Yogish Kudva (Mayo Clinic, Rochester, MN): What was time horizon you looked at for prediction? And was there an IOB setting you used, or did you just use that which the patient came into study with? Why did you choose to show just one standard deviation?

A: We look pretty far into the future – eight hours. That allows us to get an idea of all the dynamics that will take place in the near future. We want to map out our model’s best guess on what glucose will do. Regarding insulin on board, it’s a special IOB calculation that is done within this predictive model. I could have showed two standard deviations, and that would certainly show that some patients did go below 70. But this was a 12-patient study. I know that the data is so variable in this disease, I thought I would keep simple.

Dr. Roman Hovorka (University of Cambridge, UK): It’s good to see this project moving on. I take issue with the study design – the number of subjects, non-randomized, non-controlled. There are problems taking conclusions from the study design. Hypoglycemia might just happen by chance. I’m sure your team thought about it...

A: Point well taken. It’s tricky in these phase of feasibility studies. You don’t want to invest too much of anything into the development effort, so you have to do these small studies. It was not statistically powered. It’s a tricky thing. We thought this was our best way to do the diligence.

Dr. Hovorka: You mentioned a 20% insulin reduction. Over how long of a period was that?

A: We collected all the data from the four patients at one of the aggressiveness factor values. It’s across the whole study, all in the same bucket.

Effect of Glucose Concentrations on Hepatic Glucagon Sensitivity and Glucagon Clearance in Type 1 Diabetes (234-OR)

Ling Hinshaw, MD, PhD (Mayo Clinic, Rochester, MN)

Dr. Ling Hinshaw shared results from a physiology study demonstrating that hepatic glucagon sensitivity does not vary with the prevailing glucose concentration in patients with type 1 diabetes. The rationale for the study stems from bi-hormonal artificial pancreas work – does a glucagon controller need to account for increased endogenous glucagon production (EGP) when patients are hypoglycemic? In this study, 27 people with type 1 diabetes were randomized to receive either a euglycemic or a hypoglycemic clamp overnight on three separate occasions, and their response to low, medium, and high doses of glucagon (0.65, 1.5, and 3.0 ng/kg/min, respectively) was measured. There was no statistical difference between the euglycemic and hypoglycemic clamp groups in the level of endogenous glucose produced in response to the three doses of infused glucagon, suggesting that the hypoglycemic subjects were not more sensitive as originally hypothesized. The researchers also measured glucagon clearance, which increased linearly with the dose of glucagon in both groups. This means that although the glucagon controller in an artificial pancreas would not need to vary its dose based on the prevailing glucose concentration, it would need to account for increased glucagon clearance at higher doses.

• The goal of this study was to determine the dose response of endogenous glucagon production (EGP) to glucagon in patients with type 1 diabetes under hypoglycemic and euglycemic conditions, and to measure the rate of glucagon clearance at different concentrations. The relationship between hepatic glucagon sensitivity and prevailing blood glucose concentrations was previously unknown and would have important implications for the design of bi-hormonal artificial pancreas control algorithms. The authors of this study hypothesized that hepatic glucagon sensitivity would be increased in hypoglycemic subjects compared to euglycemic subjects based on evidence from animal studies.
• The study involved 27 subjects with type 1 diabetes who were randomly assigned to either a hypoglycemic or a euglycemic clamp overnight for a total of three nights. The average duration of diabetes for all participants was approximately 20 years. The hypoglycemic clamp group (n=14) had an average age of 45 years, an average BMI of 27 kg/m², and an average A1c of 7.5%, and their plasma glucose was maintained at 59 mg/dl. The euglycemic clamp group (n=13) had an average age of 38 years, an average BMI of 28 kg/m², and an average A1c of 7.4%, and their plasma glucose was maintained at 92 mg/dl.
• EGP was measured in all subjects in response to infusions of low (0.65 ng/kg/min), medium (1.5 ng/kg/min), and high doses (3.0 ng/kg/min) of glucagon, and both groups displayed similar dose responses – in other words, hepatic glucagon sensitivity was statistically comparable during hypoglycemia and euglycemia. The infusions were given on separate days in a random order, and plasma insulin levels were held constant with a low-dose insulin infusion. All patients received an infusion of [3-3H] glucose to measure EGP. There was no significant difference in the dose response curve to glucagon between the hypoglycemic and euglycemic groups. In the hypoglycemic group, EGP was 0.5 mmol/kg/min with the low glucagon dose, 12.5 mmol/kg/min with the medium dose, and 14.4 mmol/kg/min with the high dose. In the euglycemic group, EGP was 0.6 mmol/kg/min with the low dose, 11.9 mmol/kg/min with the medium dose, and 14.6 mmol/kg/min with the high dose.
• Glucagon clearance was also similar between the two groups, though it increased in a linear fashion as the dose of infused glucagon increased. This implies that a glucagon controller in a future artificial pancreas would need to account for the increased clearance of glucagon at higher doses. Dr. Hinshaw mentioned in passing that the researchers also measured plasma epinephrine concentrations, which were higher and more variable in the hypoglycemic group.

Questions and Answers

Q: Congratulations! I was well aware of the paper in dogs showing a bigger response from the liver to glucagon in hypoglycemia and I always assumed it would happen in people too. We did a similar study in which we varied the insulin level, not glucose. Everything was done at euglycemia and we saw a similar dose response curve. At high insulin levels, there was a decreased response to a higher glucagon dose. How high was the insulin level here?

A: The insulin level in this study was 200 pM, which is a middle to high physiological concentration in someone with type 1 diabetes. To measure glucagon response, we gave three doses, achieved three glucagon concentrations, and the dose response was not different between hypoglycemia and euglycemia.

Q: Very beautiful study. That epinephrine response is common with long-duration type 1 diabetes, but I got the impression it was different at different doses. Is that correct?

A: Many studies have observed a variable and higher epinephrine concentration in patients with type 1 diabetes, which was one reason we hypothesized the glucagon response might be higher at hypoglycemia, but the results show no glucagon sensitivity difference between hypoglycemia and euglycemia, and also we think the primary regulator of EGP is glucagon, not epinephrine.

Q: Right, but my question was whether different glucagon doses affected epinephrine.

A: We didn’t look at the data for that.

Q: Your data on glucagon clearance showed that it increased with different glucagon doses, which is very unusual. Can you speculate why?

A: It was reported in the 1980s that glucagon clearance differed between hypoglycemia and euglycemia. Our study showed different results because of the method of the glucagon assay and a different insulin/glucagon ratio. Our study was similar to a recent report saying that the rate of clearance did not differ between euglycemia and hypoglycemia.

Q: But it differed at different glucagon levels. Isn’t that unusual?

A: I don’t have an answer for that now. We can look at the data and try to answer it later.

Hypoglycemia Reduction and Changes in A1c in the ASPIRE In-Home Study (231-OR)

Timothy Bailey, MD (AMCR Institute, Escondido, CA)

Dr. Timothy Bailey presented a subgroup analysis of the ASPIRE in-home study, a randomized controlled trial of the Medtronic MiniMed 530G pump with vs. without its threshold suspend feature enabled (n=121 vs. 126). (For our primary coverage of the ASPIRE in-home study, see our ADA 2013 coverage or the NEJM paper.) In this analysis Dr. Bailey and colleagues stratified both groups according to whether A1c decreased by >0.3% (n=25 with threshold suspend, 28 without), remained within 0.3% of baseline (n=65, 77), or increased by >0.3% (n=26, 19). Mean end-of-study A1c values in the “decreased,” “stable,” and “increased” subgroups were 7.1%, 7.1%, and 7.7%, respectively. The researchers also looked at nocturnal hypoglycemia, defined as ≥20 minutes of sensor glucose ≤65 mg/dl with no evidence of user-pump interaction, between 10 p.m. and 8 a.m. Patients with threshold-suspend enabled had 1.5 nocturnal hypoglycemic events per week regardless of A1c-change subgroup; this was lower than the corresponding rates in the “decreased” or “stable” A1c subgroups without threshold suspend. In all three threshold-suspend subgroups, patients experienced several benefits in nocturnal-hypoglycemia statistics: higher nadir sensor-glucose value, shorter event duration, and smaller area under the curve below 65 mg/dl. Also of note, sensor-glucose coefficient of variation (a measure of glycemic variability) decreased by a statistically significant margin in patients whose A1c decreased or was stable.

Questions and Answers

Dr. Irl Hirsch (University of Washington, Seattle, WA): Given what we’ve learned about hypoglycemia in patients older than 50 and 60 years old in the T1D Exchange, did you look at hypoglycemia and coefficient of variation in these groups?

A: We should look at that, but I think the numbers will be small. We didn’t have that many older folks in study.

Q: In the group with increased A1c, could you identify them up front? Are there any characteristics to define this group that would not benefit from threshold suspend?

A: In some patients, A1c went up in trial. Obviously this happened in both groups; it wasn’t a feature of threshold suspend. We haven’t looked yet at what would predict failure.

Dr. Hirsch: You didn’t mention how much A1c went up in the increased-A1c group or down in the decreased-A1c group. What was the baseline? What was the mean of the increase or decrease?

A: Baseline A1c was 7.3% in the threshold suspend group and 7.2% in the control group. I don’t have the mean values of change for the groups that increased or decreased by more than 0.3%.

Q: Could you extrapolate certain types of patients in whom to use this technology, or do you think there was no relationship between the technology and A1c?

A: I wasn’t sure I would see significant effect in all the A1c groups. I was surprised to see benefits. I was also surprised to see how much hypoglycemia is going on in our patients. A lot goes undetected, especially at night. The technology is potentially a benefit to all patients; the question is how much benefit for how much money. Coverage for this device represents an important step to increase access to a device that helps protect patients from hypoglycemia.

The Quest for a Pumpable, Liquid Glucagon: A Novel Use for Ferulic Acid (236-OR)

Parkash Bakhtiani, MD (Oregon Health and Science University, Portland, OR)

Dr. Parkash Bakhtiani presented a series of promising preclinical experiments with a new formulation of glucagon, which uses ferulic acid as a stabilizing agent. (Ferulic acid is a highly stable phenolic compound found naturally in many foods.) Dr. Bakhtiani reviewed that the current formulation of synthetic human glucagon quickly degrades and forms insoluble fibrils, especially at high temperatures. However, when a ferulic acid formulation of glucagon (FAFG) was aged for seven days at body temperature (37°), no fibrillation was seen, bioreactivity was maintained, and degradation was low. (These attributes were assessed by transmission electron microscopy, a glucagon receptor / protein kinase A assay, and reverse-phase high-performance liquid chromatography, respectively.) Dr. Bakhtiani also described a pharmacodynamics experiment in Yorkshire swine treated with octreotide (to suppress their native glucagon secretion). Aged FAFG was compared to fresh FAFG and fresh regular human glucagon (n=8 for each condition); the glycemic rises over baseline were 91±12 mg/dl, 93±23 mg/dl, and 84±30, respectively – not statistically significantly different from each other. Dr. Bakhtiani looked forward to research on this new formulation’s pharmacokinetics, shelf-life studies at various temperatures, leachability/extractability, toxicity, dose-response curves, and clinical safety and effectiveness.

• Dr. Bakhtiani briefly reviewed the challenges associated with glucagon in the setting of bihormonal closed-loop control. He reminded the audience that liquid glucagon is chemically unstable; it quickly degrades and also forms insoluble aggregates. Dr. Bakhtiani noted that even when a liquid-stable formulation becomes available, glucagon rescue therapy could fail if the CGM overestimates a patient’s glucose levels or if insulin levels are so high that hepatic glucose output is suppressed.   

Questions and Answers

Dr. Yogish Kudva (Mayo Clinic, Rochester, MN): Ferulic acid has got a phenol group. Is there any concern that it might cause a false signal on CGM, as acetaminophen does? Or is the concentration so low that this is not a concern?

A: It’s possible. I think that’s a great point. We should look at that.

Q: Is ferulic acid used as a stabilizer for human-use products already?

A: It is used as a therapy for some conditions, and it is present in various foods and beverages.

ADA Diabetes Care Symposium – New Drug Therapies, Innovative Management Strategies, and Novel Drug Targets

Safety and Efficacy of Outpatient Closed-Loop Control – Results from Randomized Crossover Trials of a Wearable Artificial Pancreas

Boris Kovatchev, PhD (University of Virginia, Charlottesville, VA)

Chosen to speak for the second year in a row at the prestigious ADA Diabetes Care Symposium, Dr. Boris Kovatchev discussed recent and upcoming closed-loop studies involving the University of Virginia’s Diabetes Assistant (DiAs) platform. His main focus was a 40-hour crossover study (n=20) in which partially closed-loop control reduced low blood glucose index (effect size of 0.64) and time spent below 70 mg/dl (1.25% in open loop vs. 0.64% in closed loop). The downsides of this hypoglycemia reduction were slightly higher mean glucose (152 mg/dl in open loop vs. 161 mg/dl in closed loop) and less time in target (70.7% vs. 66.1%), though these results were not statistically significant. At a higher-level view, Dr. Kovatchev emphasized that “AP systems are mobile medical networks,” and he explained how the DiAs system’s “network” is distributed among the CGM, the pump, and a smartphone, so that if any component fails, the patient can remain safe. To the question of “are we there yet?” with the artificial pancreas, Dr. Kovatchev said that the control algorithm “is probably there yet” and that “there are several viable algorithms around the world.” He said that today the main limiting factors for the artificial pancreas are hardware and clinical research – especially the latter. Looking to the next year or so, Dr. Kovatchev whetted our appetites for a month-long study of round-the-clock closed-loop control at home and a weeklong study of round-the-clock closed-loop control at a diabetes camp.

• Emphasizing that “AP Systems ARE mobile medical networks,” Dr. Kovatchev explained that his group uses a distributed-computing model designed for redundancy in case one or more components fail. The insulin pump contains safety algorithms, and the smartphone “hub” contains the Diabetes Assistant (DiAs) graphical user interface and the main control algorithm; the pump and hub communicate with each other (and the CGM) on a Medical Android network. Also, a cloud-based component can run various functions such as GPS location, remote data transmission, and alert calls.
• The main subject of Dr. Kovatchev’s talk was a 40-hour crossover trial of closed-loop control in a semi-outpatient setting, on which Dr. Kovatchev had shared background and top-line results at ADA 2013’s ADA Diabetes Care Symposium. The multi-site trial enrolled five patients at each of four sites: University of Virginia, Sansum Diabetes Research Institute, University of Padova, and University of Montpellier (n=20 total). Each session included a 45-minute walk through town and mandatory restaurant dinner; meal size was not restricted as long as patients estimated carbohydrate count and announced the meal to the closed-loop system. Alcohol consumption was allowed. Patients performed fingerstick blood glucose tests before and after meals and at bedtime, but not overnight. 
  
  • The trial’s primary outcome was reduction in hypoglycemia as measured by low blood glucose index; this goal was met with a statistically significant effect size of 0.64. Compared to open-loop control, closed-loop control led to lower percentage of time with glucose below 70 mg/dl (1.25% vs. 0.7%), lower number of hypoglycemic episodes requiring treatment (2.39 vs. 1.22 episodes per person per session), and lower amount of carbohydrates required for treatment (39.7 vs. 17.6 g per person per session). All of these differences were statistically significant.
  • The chief downsides of closed-loop control were that, compared to open-loop control, patients spent a lower percentage of time in target (70.7% vs. 66.1%; p>0.1) and had a higher mean glucose (152 vs. 161 mg/dl). The p-value for the mean glucose difference was 0.04, but the result was not considered statistically significant because it did not meet the threshold of p=0.01 (which was used to correct for multiple comparisons, because conducting multiple comparisons increases the chances of differences arising by chance alone).
• Dr. Kovatchev mentioned three other closed-loop trials using DiAs that have been published in the past year, including two being presented at ADA 2014 (late-breaking abstracts 104 and 106).
  
  • First he reviewed a study of overnight closed-loop control at diabetes camp (n = 54 nights of closed-loop control vs. 52 nights of open-lop sensor-augmented pumping). Dr. Kovatchev described that overnight closed-loop control “virtually eliminated” nocturnal hypoglycemia while also decreasing time spent above 180 mg/dl or above 250 mg/dl (Buckingham et al., Diabetes Care 2014). 
  • Dr. Kovatchev then showed results from an eight-hour, crossover trial of daytime closed-loop control in teenagers (n=16), to see how the system might handle a missed meal bolus. The adolescents ate a 30-g snack at 9 a.m. with no bolus, and then they consumed an “underbolused” lunch. Patients in the closed-loop condition had statistically significantly lower postprandial excursions compared to open-loop control. This research was presented at ADA 2014 as 106-LB.
  • The other recent trial Dr. Kovatchev mentioned was a five-day study of overnight closed-loop control (50 vs. 50 patient-nights). He said that the overnight controller improved mean nighttime glucose to 139 mg/dl (vs. 168 mg/dl with open loop), increased nighttime time in target to 85% (vs. 60%), and reduced nocturnal hypoglycemia to 0.6% (vs. 2.1%). The system even improved glucose control on the following day, Dr. Kovatchev noted. This research was presented at ADA 2014 as 104-LB.
  • Dr. Kovatchev briefly previewed four closed-loop trials scheduled for this year. A one-month multi-site trial of round-the-clock control at home funded by JDRF; a week-long summer-camp study of round-the-clock control funded by the Helmsley Charitable Trust; a five-day multi-site trial of bedside closed-loop control funded by the NIH; and a study using closed-loop control to reduce hypoglycemia unawareness funded by the NIH.

Best of Diabetes Care 2013-2014 – Artificial Pancreas Developments

William Tamborlane, MD (Yale University, New Haven, CT)

Dr. William Tamborlane provided a concise and organized overview of the past year in artificial pancreas research, focusing on the safety of low glucose/predictive suspend and overnight closed-loop. Regarding the latter, he noted that “the holy grail of unsupervised full closed-loop control may be just around the corner, given the results from Dr. Hovorka’s recent study. Dr. Tamborlane’s presentation covered four main studies: Beck et al., Diabetes Care 2014 and Sherr et al., Diabetes Care 2014 (both demonstrating the safety of low glucose/predictive suspend); Hovorka et al., Diabetes Care 2014 (demonstrating the safety of unsupervised overnight closed loop); and Buckingham et al., Diabetes Care 2013 (no impact of short-term hybrid closed loop immediately following diagnosis). Though closed-loop studies have demonstrated feasibility in the inpatient setting, Dr. Tamborlane explained that there are still some key obstacles to broad outpatient use: making the device user interfaces easy to use and establishing redundant safeguards to minimize the risk of excessive insulin administration.

• Why do we need an artificial pancreas? First, too many type 1 diabetes patients fail to achieve A1c goal – Dr. Tamborlane highlighted the latest data from T1D Exchange, which suggests that adolescents have an average A1c of 9.0%! Second, he noted that severe hypoglycemia remains an ever-present danger, and based on T1D Exchange data, patients with a high A1c are not at reduced risk. Third, the burden of care is “extremely high” and has increased with the translation of new diabetes technologies into clinical practice.
• Dr. Tamborlane described the iterative progression to an artificial pancreas, starting with low glucose suspend and predictive low glucose suspend, followed by nighttime closed-loop + daytime open-loop – the latter has emerged as a debate in the field, as some believe the regulatory path is easier for a 24-hour automated system (daytime treat-to-range + nighttime treat-to-target) vs. an overnight-only system. Said Dr. Tamborlane, “If you told a parent that their child could go to bed and reliably wake up at 120 mg/dl with no hypoglycemia, parents would take this is a moment.”
• Dr. Tamborlane covered two papers relating to the safety of low glucose/predictive low glucose suspend:
  
  • Beck et al., Diabetes Care 2014 – Frequency of Morning Ketosis After Overnight Insulin Suspension Using an Automated Nocturnal Predictive Low Glucose Suspend System.  The study concluded that two-hour pump suspends are safe and won’t result in DKA or excessively dangerous ketone levels.
  • Sherr et al., Diabetes Care 2014 – Safety of Nighttime 2-Hour Suspension of Basal Insulin in Pump-Treated Type 1 Diabetes Even in the Absence of Low Glucose. Dr. Tamborlane noted that one of the FDA’s main concerns with the Veo/MiniMed 530G was the safety of two-hour suspends when the CGM was reading inaccurately low. This clever study had patients program a two-hour zero basal rate on random nights, regardless of the current glucose level. The study concluded that systems that suspend basal insulin for two hours are safe and do not lead to clinically significant ketonemia even if the blood glucose level is elevated at the time of the suspension.
• Hovorka et al., Diabetes Care 2014 examined overnight closed-loop under free-living conditions in 16 young people with type 1 diabetes. Dr. Tamborlane highlighted that the study was done in the “real world” and demonstrated the efficacy and safety of nighttime closed loop + daytime open loop. Indeed, overnight closed loop increased time in zone by a median 15% and reduced mean overnight glucose by a mean of 14 mg/dl. Said Dr. Tamborlane, “The holy grail of unsupervised fully closed-loop control (i.e., overnight) may be just around the corner.”
• Buckingham et al., Diabetes Care 2013 – Effectiveness of early intensive therapy on beta-cell preservation in type 1 diabetes. This ambitious study examined whether three to five days of inpatient hybrid closed loop at the time of diagnosis could preserve C-peptide one year later. There was “absolutely no difference” in A1c, CGM, or the rate of C-peptide decline between the intervention and control groups. This was a major disappointment when these results were first shared, as many had high hopes for this study. Dr. Tamborlane commented that in new onset type 1 diabetes “it appears we’ve already achieved about as much as we can achieve in slowing the loss of residual beta cell function by maintaining strict glycemic control,” regardless of using closed loop or open loop.  

Symposium: Closed-Loop Insulin Delivery — One Step at a Time (Sponsored by The Helmsley Charitable Trust)

Proof-of-Concept Trials

Stuart Weinzimer, MD (Yale University School of Medicine, New Haven, CT)

Dr. Stuart Weinzimer provided an in-depth review of recent advances in artificial pancreas research over the past two years, focusing on predictive low glucose suspend, hybrid closed loop, and full closed loop. While he spent most of his time reviewing early feasibility data, he emphasized that these trials will only provide preliminary safety and effectiveness information, but will lay the groundwork for more rigorous transitional (and eventually pivotal) clinical trials. Given the pace of research over the past few years, Dr. Weinzimer was encouraged and optimistic that the “future will show great promise” for closed loop.

• Dr. Weinzimer began by reviewing the types of closed loop studies and the caveats of comparing data across trials. In general, closed loop studies fall into three categories, starting with small feasibility studies (to demonstrate preliminary safety and effectiveness), followed by transitional studies (with a greater number of patients and/or a greater duration), followed by pivotal studies, which will be designed for regulatory approval. Given the variation in trial design, Dr. Weinzimer cautioned against comparisons across trials without taking into account key factors, such as the presence/absence of controls, age of patients, hybrid vs. full closed loop use, size of meals, glycemic targets, and the treatment/definition of hypoglycemia.
• There has been considerable progress in predictive low glucose suspend. Dr. Weinzimer focused on two major studies published in 2014: Danne et al., Diab Technol Ther 2014 and Maahs et al., Diab Care 2014. The Danne et al., study induced hypoglycemia with an exercise regimen in type 1 diabetes patients (n=16). The system shuts off insulin delivery when hypoglycemia is predicted to occur, a feature that averted actual hypoglycemia in 13 patients. Dr. Weinzimer noted that larger studies of predictive low glucose suspend are currently being planned and conducted. In the second study (Maahs et al., Diab Care 2014), the predictive low glucose suspend was shown to decrease both the percentage of nights with hypoglycemia (across all definitions) as well as the duration of hypoglycemia.
• Dr. Weinzimer then reviewed overnight closed loop trials, which represent the next level of automation, after the predictive low glucose suspend. The longest duration studies of the closed loop come from the Cambridge group (Hovorka Diab Care 2014), which published last month the results of a three week study in 16 adolescents – this study showed a significant reduction in glucose variability overnight and a significant improvement in glucose levels within target range (70% of glucose levels within range). Dr. Weinzimer also reviewed the results of the DREAM project collaboration, which studied an overnight closed loop in 56 children at a diabetes camp. While he characterized this study as a feasibility study due its short one-night duration, he did note the “impressive” number of patients included in the study. This consortium is moving to in-home studies; recently published data demonstrate improvements in time in target, glucose variability, and exposure to hypoglycemia in the home environment with four consecutive nights (Nimri et al., Pediatr Diab 2014).
• Finally, Dr. Weinzimer briefly touched on a few recently published studies of fully closed loop systems. In one 24-hour trial (Harvey et al., Diab Technol Ther 2014), a fully closed loop system was studied without manual meal boluses and no announcements to the patient (n=12). Dr. Weinzimer showed patient glucose profiles to demonstrate the slight increase in glucose excursions that would be expected without announcements. He also shared a study from his own group (Weinzimer et al., Diab Care 2012), in which a fully closed loop was tested in eight patients. After 48 hours of use, 71% of glucose levels were in target zone with no incidences of hypoglycemia.

Special Meeting: JDRF/NIH Artificial Pancreas Evening

A who’s who of closed-loop researchers, industry, non-profit organizations, and patient advocates gathered at the annual JDRF/NIH Closed-Loop Research Meeting on Sunday night of ADA 2014. This engaging evening featured a presentation from JDRF’s Dr. Aaron Kowalski on the past year of closed-loop research, followed by three industry perspectives (Medtronic, Animas, Dose Safety), and a panel that included researchers Drs. Stacey Anderson, Bruce Buckingham, Roman Hovorka, Moshe Phillip, and type 1/experienced closed-loop patients Ms. Kelly Close, Tia Geri, and Willa Spalter.

Brief Overview of AP Highlights from the Past Year

Aaron Kowalski, PhD (JDRF, New York, NY

“We still need better tools to treat patients with diabetes...someone on my airplane had a full-blown hypoglycemic seizure on my flight out here,” said Dr. Aaron Kowalski in his opening remarks to the JDRF/NIH closed-loop night. He provided a quick review of the past year of artificial pancreas (AP) research, noting that there are currently outpatient trials running in every bucket of the six-step JDRF roadmap. Notably, since ADA 2013, there were more than 14 approvals of new or significantly modified studies by FDA, MHRA, and other regulatory bodies. In addition, the past year saw over 34 peer-reviewed manuscripts and abstracts related to closing the loop. Dr. Kowalski rapidly highlighted recent work at more than 13 artificial pancreas research institutions around the world (see below). Similar to his comments at ATTD 2014, he shared that “predictive low glucose suspend is right around the corner” and “is going to be a huge step for this field.” He concluded his talk with lots of optimism: “We’re right on the cusp. People are wearing closed loop at home, and they are safer than what we’re doing right now. We’ve got to drive towards commercialization. JDRF is working with industry, working with the FDA, and already working with payers, to drive closed loop systems into commercial embodiments.”

• UVA – JDRF-2 (outpatient closed-loop control), NIH 1 (five-day bedside closed-loop), Helmsley 2 (overnight summer camp studies), UVA Launchpad (adolescent missed bolus during the day), JDRF VCU (heart-rate monitoring). Said Dr. Kowalski, “This is just remarkable stuff, and it’s consistent – the benefit of waking up in the morning at 110 or 120 without hypoglycemia.”
• Stanford – Overnight closed-loop camp studies (see our ATTD 2014 coverage; upcoming camp studies will test UVA’s DiAs and Medtronic’s system); full closed-loop at camp using UVA’s DiAs; in-home predictive low glucose suspend; predictive nocturnal hypo and hyper minimizer; a hotel-based study of Medtronic’s Android-based Hybrid Closed Loop during the day and full closed loop at night; drug eluting insulin infusion sets; the bionic pancreas multi-center study; and studies to detect sensor and infusion set failures.
• Yale – Safety of nighttime suspension, reducing hypoglycemia following exercise, ultra-fast insulin (InsuPatch warming device). Current/upcoming inpatient closed-loop studies will examine injected liraglutide, hyaluronidase, and pre-exercise snacking. Upcoming outpatient studies will examine an ambulatory closed-loop device (collaboration with Stanford & Barbara Davis, initiating this summer), a Medtronic ambulatory closed-loop device (initiating this summer), and Medtronic’s predictive low glucose suspend (initiating this summer).
• AP@home – Two transition trials completed (2-7 days); final trials started (2-3 month home-studies testing both overnight-only and 24/7 control).
• MD-Logic – Ongoing bolus calculator study (n=20), ongoing weekend 60-hour home study (n=24).
• UCSB – Closed-loop with Afrezza (“looks like it will be approved in July as a very, very rapid-acting insulin”), control-to-range, outpatient clinical trials with zone MPC, predictive pump suspension, exercise detection.
• RPI – Advanced closed-loop algorithms that have supported clinical sites in Colorado and at Stanford.
• Cambridge – Over 1,200 home study nights and 100 day/nights of unsupervised, free-living use. Said Dr. Kowalski, “Cambridge has blazed the trail in terms of home studies.”
• Dose Safety – Algorithm work to challenge closed-loop systems (e.g., high fat meals, exercise).
• Joslin/Boston Children’s – PID control, skipped meals, bolusing for meals with high fat content.
• IIT – Algorithm research on fault detection; control systems for AP use during and after exercise.
• Dual hormone –Drs. Ed Damiano and Steven Russell were published in NEJM just a couple hours prior to Dr. Kowalski’s presentation; the latest bi-hormonal closed-loop research from Montreal shows that the addition of carb counting does not add much benefit to closed-loop control.
• Australia (Dr. Tim Jones) – At-home and overnight closed loop studies using Medtronic’s Android-based research system. Studies that are underway include: ambulatory all day home studies (Medtronic); at home overnight studies (Medtronic); and a six-month multicenter predictive low glucose management randomized controlled trial.

The Last Mile – Bridging from Academia to Industry – Medtronic

Lane Desborough, MSc (Chief Engineer, Insulin Delivery, Medtronic Diabetes, Northridge, CA)

The insightful Mr. Lane Desborough addressed what he sees as the biggest issue in artificial pancreas development – how do we take closed loop from a narrow use case (CRC, transitional studies) to an extremely broad use case (running on anyone with diabetes)? His presentation was very similar to that given at ATTD 2014, though it was valuable to hear his perspective once again. He had a compelling slide summarizing the magnitude/scale changes on the path to commercialization (see below) – “the last mile between a sponsored study and a commercial product is vast by just about any measure.” To get there, Medtronic is learning from other fields (e.g., aerospace, automotive, nuclear power, oil refining, petrochemical) and thinking strategically about human-centered automation, information flow/display, and the components in closed-loop “systems.” Mr. Desborough concluded that the flow of information and the flow of insulin are both key to ensuring that we have a deep understanding of the way closed loop will operate before it is released as a commercial product.

Study Type	Size	Length	Cost
Sponsored Studies	N=15	3 days	$1,500/hour
Supervised Studies	N=30	7 days	$150/hour
Home Studies	N=150	14 days	$15/hour
Commercial Product	N=150,000	4 years	$0.15/hour

• “By my estimate there are more control loops then there are people on the planet - billions.” Mr. Desborough highlighted that closing the loop is not a new concept, and some industries like aviation have been using feedback control for over fifty years. “Why start with a blank sheet of paper,” he asked, “when we can instead leverage the hard-learned lessons of successfully closing the loop in cockpits, control rooms, and driver’s seats?” A number of engineers from these industries work at Medtronic and are helping the company think about closing the loop.
• Closed-loop systems are comprised of far more than just a CGM, algorithm, and insulin pump. They include the BGM (strips, calibration), insulin, infusion set, batteries, the person wearing the system (health, activities, competency, training, the surrounding environment), rescue countermeasures (carbs, glucagon), the person(s) adjusting the system, and other stakeholders (regulators, payers, insurers, care partners). Mr. Desborough highlighted in a big bold red circle, “Safety, efficacy, and burden are properties of an entire system” – the individual components and how they interact determine the outcomes.
• Mr. Desborough emphasized the importance of human centered automation – the human and the automation must cooperate to succeed. He emphasized that the human must be at the center of the automation, rather than on the sidelines. The key is to avoid “de-skilling,” where the human doesn’t know what to do when the system breaks down.
• The flow of information from the system to the human must allow completion of three tasks: maintenance, context, and supervision.
  
  • Provide maintenance to the system: calibrate CGM, change insulin/reservoir/infusion site; replace CGM sensor; recharge/replace batteries; adjust therapy settings.
  • Provide input to the system about significant events: meals, exercise, illness, manual injections of insulin.
  • Resume direct management when desired and/or necessary: maintain situational awareness, prevent mode confusion, enable safe experimentation, avoid de-skilling.

The Last Mile – Bridging from Academia to Industry – Animas

Krishna Venugopalan, PhD (Head of R&D, Animas, Westchester, PA)

In a rather corporate strategy-like presentation, Dr. Krishna Venugopalan described Animas’ approach to commercializing the artificial pancreas. He noted, “academia has the skill and inclination to develop the technological/scientific kernel, and industry has the resources and system to produce a product.” Dr. Venugopalan emphasized the importance of designing systems for “robustification” – sensor changes, start-up time, CGM communication errors, infusion set changes, insulin refills, battery changes, stopping closed-loop control, etc. Like Mr. Desborough, he also addressed the critical importance of component integrations and human factors. The latter must “minimize user errors” he said, pointing to Google’s driverless car – the vehicle only includes start and stop buttons (i.e., no brakes or gas). The rationale is that “an abrupt shift to driver-controlled piloting would be unpredictable and potentially dangerous.” [While an instructive analogy, the direct relevance to closed-loop may be a bit of stretch, since a shift to human control will be necessary in cases of system component failure.] He concluded that as we drive to close the loop, “communication and collaboration are critical” between HCPs, regulators, and payers. “We need investment in healtheconomics data,” said Dr. Venugopalan.

The Last Mile – Bridging from Academia to Industry – Dose Safety

Bob Kircher (VP Engineering and Regulatory Affairs, Dose Safety)

Mr. Bob Kircher, a former engineer at Boeing, highlighted the perspective of Dose Safety, a Seattle-based company developing artificial pancreas control software. We appreciated the company’s mission: “A holiday from diabetes.” Like the preceding presentations from Medtronic and Animas, Mr. Kircher’s remarks focused on appropriate human-centered design that takes automation learnings from other fields into account.

• “People naturally resist change. You must work from the known and trusted to the new.” From a human factors perspective, early closed-loop users will likely be current users of sensor-augmented pumps. As a result, artificial pancreas systems should provide situational awareness mechanisms and tactile user interfaces that are consistent with users’ past experience.
• Automation should default to hide the complexity from the user. We thought this was an important point, since there is always a temptation to show users everything that is going on. Ideally, we believe systems would hide the complexity, but also allow users to look “under the hood” and customize if they desire.
• Automotive cruise control offers human factors learnings about user adoption. At first, cruise control was a discrete feature that could be added to a car’s feature set (i.e., much like automated insulin delivery will be an available feature on pumps). Mr. Kircher noted that cruise control “sometimes worked terrifically – when no cars are on the road” (i.e., automated insulin delivery at night). But for maximum benefit in more challenging cases, cruise control requires user interaction. Similarly, closed-loop control will require user interaction for optimal glycemic control in more challenging circumstances (meals, exercise). Like cruise control, closed loop should have an on-screen indicator to reflect the status of automated vs. manual insulin dosing. Mr. Kircher proposed that an AP’s software goal could be to maximize time in auto-dosing.
• To see rapid market uptake and improvement of AP systems, Mr. Kircher believes the following are needed: adequate AP reimbursement by payers; CGM and pump data input/output data standards; and regulatory approval for AP-ready pump and control algorithms (i.e., any AP-ready control algorithm could run on any AP-ready pump).

Panel Discussion: The Last Mile – Bridging from Academia to Industry

Dr. Aaron Kowalski (JDRF, New York, NY): When I think about this, there is one key component – it’s an evolutionary process. We will get to better and better systems. But we must get to a first system. As you think about the risk-benefit from an industry perspective, how do you judge when you’re ready to pull the trigger? We need to appreciate that there is a need to learn about the first commercial products. In the fields you talked about, where control loops matured and gained steam, where was the risk-benefit before a launch happened?

Mr. Lane Desborough (Medtronic Diabetes, Northridge, CA): I was speaking with a doctor about a month ago, and he said, “My hat goes off to Medtronic to take on the responsibility for the closing loop.” Right now, all mistakes are made by the patient or doctor. Now, we’re taking on that responsibility – it’s kind of like Uncle Ben from Spider Man – with great power comes great responsibility. Industry is taking on the tasks that have traditionally been assumed by the human. We need to be confident in our ability to take on that responsibility.

Mr. Bob Kircher (Dose Safety, Seattle, WA): People with diabetes use sensor-augmented pump therapy today to control diabetes. Many do really, really well. The challenge from a software standpoint is to do what they do. Dr. Venugopalan used the term, “Robustification.” He showed a week in the life of a person with diabetes with stop signs (calibrate CGM, change reservoir, etc.). Those are opportunities for implementing robustness where the system turns off auto-dosing when it encounters something.

Dr. Kowalski: How do you judge success – weighting hypoglycemia vs. hyperglycemia risk?

Mr. Desborough: When automation is applied, there is usually a failsafe button you can press – you can bring the oil refinery down to a safe place. But there is no fail safe in diabetes. You are managing between two extremes constantly and trying to understand the tradeoff between those. So you come up with what control engineers call an objective or cost function – a careful consideration between the balance of hyperglycemia and hypoglycemia. At the end of the day, the control loop is transferring variation from where it hurts to somewhere it doesn’t – in this case, variation in glucose to variation in insulin dosing. The application of these objective functions, which balance these risks, is a very important input to the design of controllers.

Mr. Brandon Arbiter (Tidepool, Palo Alto, CA): I love the analogy of cruise control. Sometimes you turn it on, sometimes you don’t. Dr. Venugopalan mentioned how often things are complicated. As a first step, I don’t want to wake up in the morning above 150 mg/dl. Sometimes the system is on, sometimes it’s off. But what are practical applications of that? Can it really mitigate enough risk?

Mr. Kircher: I think it can. It requires the user to interact with the system when the system isn’t controlling glucose well enough. Even if the blood glucose is 250 mg/dl, if the system is green (auto dosing), the user could say, “I will leave it alone and see what happens.” If the glucose doesn’t come down, you could take a manual bolus. Cruise control is a good analogy.

Dr. Venugopalan: For hyperglycemia and hypoglycemia management, it’s about risk and responsibility and your level of confidence. Systems are still single point of failure devices – that is the quandary. I don’t have an exact answer. There is a lot lower bar in terms of responsibility for hypoglycemia minimization as opposed to hyperglycemia minimization. We need more real world experience on how the system will function.

Dr. Stu Weinzimer (Yale University, New Haven, CT): We heard from all of you a recurring theme: the first devices aren’t going to be perfect. That’s okay when a lot of technology is being developed in academia. Once you start upscaling to a major manufacturing level, you have huge corporate investments in a product. Isn’t there a disincentive to make rapid improvements to a system? You must almost re-envision how you roll out pipelines.

Mr. Desborough: Right – you need the governance and structure to successfully manage change over very wide scales.

Mr. Kircher: The example of Boeing’s 777 is a phenomenal one. Three years went into the deployment of the aircraft. The goal was to let the aircraft land itself. It turned out that the length of life of the tires was so much better when the computer landed the aircraft

Mr. Desborough: There are a variety of ways to build trust in automation. I don’t know anybody who cedes life critical tasks the next day to an automated device. There is a gradual process of trust building.

Mr. Kircher: In a cockpit, there is a notion of piloted command – there is always at least one other pilot if that person wants to give control to someone else.

Mr. Desborough: We do this every night as parents. Who has the first shift, who has the second shift? Now you’re adding another agent into the team to perform tasks.

Dr. Hovorka: Mr. Kircher, you mentioned that your company’s mission is a “holiday from diabetes.” And then Mr. Desborough, you mentioned “de-skilling” – these two things refer to same thing. You can see one as a good thing, and another as a bad thing. What are we going to call it, and how are we going to manage it?

Mr. Kircher: They may be automatically dosed, but they must still own that responsibility.

Dr. Kowalski: There has been progress, but are we being too conservative? Someone has a full tilt seizure on my airplane this week. We have teens with an average A1c over 9% in the US. And we have airplanes that are landing themselves. Lane and I went on a ride in a new Ford car. It has lane recognition and it can parallel park itself. If a car can auto-brake at 70 mph, are we being too conservative here with automated insulin delivery?

Mr. Kircher: CGMs and pumps work well enough. People mainly control their own blood glucose. The finish line is in sight.

Mr. Desborough: It’s very difficult to answer that. If I put on my parental hat, I want this desperately. I want it yesterday. I want to get some sleep, I want my family to get some sleep, and I want my child to be safe. On the other hand, I understand the incredible responsibility that this places on device manufacturers. The question is how to resolve that? The urgency is there – there are unsafe things happening every day in diabetes. We have to be extremely careful about how we take on that responsibility.

Q: What lessons can we take from the automation of automobiles and planes – how do those industries manage the liability question? Clearly there are big liability issues when those systems fail...

Mr. Desborough: One is playing out in the news right now – Toyotas with unintended acceleration. These examples have many common factors with what we’re trying o do. We need to internalize and understand what these industries are facing.

Dr. Kowalski: Even implanting defibrillators in people, those systems are running control loops...

Dr. David Kerr (Sansum Diabetes Research Institute, Santa Barbara, CA): In terms of commercialization from a payer and clinical perspective, where will this lie in the hierarchy? Is your vision that the numbers will stack up, and a large volume of people with type 1 diabetes will use this product? Or do we need to bring this to the marketplace at an earlier stage for specific groups: hypoglycemia unaware, those with recurrent severe hypoglycemia, and those with early retinopathy. From a commercialization point of view, where do you see this going?

Dr. Venugopalan: When you start looking at trying to advance these technologies, there is a very diverse set of users – in terms of both skill level and interest level. That’s one of the reasons why we struggle. Some people are more or less tech savvy, and that may or may not be the person that most needs the technology.

Ms. Tamar Sofer-Geri (CarbDM, Mountain View, CA): I am a mother of a daughter with type 1 diabetes. I’m wondering about how to get through the last mile in the regulatory process. We now have FDA guidance on the artificial pancreas, and we made huge progress in getting there. But technologies are still approved in Europe way before the US. To me, that’s the biggest barrier to get to a closed loop.

Mr. Desborough: My perspective is that this is a new frontier for our regulatory partners. There was no department of the artificial pancreas at the FDA because there hasn’t been an AP. How do we work with our regulatory partners to build the knowledge that systems are safe and effective and less burdensome? We should also reach out to the FAA, who has decades of experience in similar ventures. We must bridge gaps to understand what is safe and effective.

Dr. Kowalski: Having spent a lot of time with Dr. Stayce Beck and her team at the FDA, the pathway is pretty reasonable right now. The trials laid out in the guidance are reasonable. We need to get at them. There are things to solve on human factors and commercial embodiments. One thing to end on, which you said Lane – and I’ll take that back to the team at the FDA – is this idea of iterating. How do we iterate and not get hung up in PMAs that take a year at a time, especially when we can add on things that are better? We need to leverage other fields and examples and do it safely. This is going to be an area where we’ll learn quickly and improve. But we need a pathway to improve.

Panel Discussion – The Real World Challenges and Successes of AP Clinical Studies

Mr. Adam Brown (Close Concerns, San Francisco, CA): When designing closed-loop systems for younger patients, what are the most important device design aspects to keep in mind?

Dr. Bruce Buckingham (Stanford University, Stanford, CA): It’s got to be small – some of these patients don’t have a lot of on-body real estate to work with. The research systems right now still require carrying around a lot of stuff. We also need more research into infusion sets, since they fail so frequently.  

Mr. Brown: In addition to your work on infusion sets, you've done lots of incredible work on alarms, showing that patients often don’t wake up at night to them. When we think about designing alarms for real-world AP systems, what can we learn from your experience? How do we tradeoff more aggressive alarms with annoying users?

Dr. Buckingham: One alarm a day is too much for me. So we want to minimize alarms. I’m not too worried about highs, even for a few hours – it’s the lows that I am worried about.

Mr. Brown: The development of the UVA DiAs system included a lot of focus group work with patients, as I understand it. What was learned from this work that your team applied to the design of the interface?

Dr. Stacey Anderson (University of Virginia, Charlottesville, VA): Yes, we conducted many interviews with patients to understand how to design the user interface. We went through several iterations before arriving at the traffic-light user interface you are familiar with. For instance, we learned that patients wanted to see the glucose number and trend, even though the system was automating insulin delivery. We also made sure to keep the buttons on the home screen simple. Clear buttons to start and stop closed loop, a button to enter food, a button to enter exercise, a button to enter a fingerstick glucose value, and indicators on the top for connectivity to the CGM and pump.

Mr. Brown: I understand you recently changed the DiAs system based on recent closed-loop studies. Can you talk about those changes?

Dr. Anderson: Yes, we made some changes for prolonged home use. Portability is always an issue. Nobody wants to wear a fanny pack with three phones. There have been some advances thanks to Bryan Mazlish, and we are minimizing the things we need patients to carry. On the pump, patients have asked for temporary basals. There was some alarm fatigue, so our alarm now turns off for 15 minutes if the patient has given carb treatment and then re-checks. Our system now fail-safes in an intelligent way to the pre-programmed basal on the Roche pump. For example if the light was red, then it fails to a temp basal of zero for sixty minutes. If it was a yellow light, it’s zero for thirty minutes. This is going to be safer for the user at home.

Mr. Brown: Dr. Phillip, you've had tremendous experience doing home studies in Europe. What was the biggest challenge or biggest learning in taking MD-Logic outpatient, first to diabetes camps and then into patients’ homes?

Dr. Phillip: In 2011, when we did the diabetes camps, we thought we understood how patients react at night. But home is a totally different game. Patients do what they want, despite what we say. They don’t care about your instructions – they exercise, they eat whatever, and they disconnect when it’s convenient for them. You have to address this all the time. Now that we use algorithms instead of basal/bolus, we have to explain a whole new way of thinking to the patient. And nobody talked about the parents. We don’t have a parent who sleeps a full night if their child has diabetes. That’s why we call MD-Logic the Glucositter – like a babysitter. You build confidence with time. You start with surveillance and then get rid of it gradually. Parents like automatic control – in six weeks at home at night we lose patients in the control group, but the intervention group keeps using MD-Logic. Those were the lessons we learned. We need to bring this to them as soon as possible because it seems like it is safer and we get them closer to goal.

Mr. Brown: We’ve mentioned designing for patients quite a bit, but how do we design for parents?

Dr. Phillip: The same as for the children  - keep it simple. Don’t make them go into engineering school. Make the device small. Try to make one device, not ten. Make it strong. They might throw it into the corner of the shower.

Mr. Brown: Just to reiterate the point about simple – the metric I always like to use is whether I need an instruction manual to use the product. I shouldn’t need an instruction manual to use the device! Turning to you, Dr. Hovorka, your recent paper in BMJ (Barnard et al., 2014) does an amazing job of capturing the patient experience on closed-loop technology, both positive and negative. You cited four main thematic areas that were negative: Calibration difficulties/frustration when equipment ‘fails’; size and alarms; accuracy/trust; and discomfort/painful. As you think about the move to commercially available closed loop systems, which of these areas is most important for industry to keep in mind? If you were in industry and had to pick one, where would you put your resources?

Dr. Roman Hovorka (University of Cambridge, UK): This work was done by Dr. Katharine Barnard at Southampton. I think the psychosocial factors are under-researched. If you give closed loop to people, they see it as a single system, not individual components. What is new to us is the concept of “building trust.” If I was a manufacturer, I would invest in two things: connectivity should be good, and size matters to most people.

Mr. Brown: One other commercialization question for you, Dr. Hovorka. I remember a great exchange between you and Dr. Aaron Kowalski at the EASD Diabetes Technology Conference earlier this year. It revolved around the regulatory path for an overnight-only system vs. a full 24-hour system? Can you share your perspective on that?

Dr. Hovorka: The technology can handle 24/7, but the manufacturers want to play it safe.

Dr. Phillip: The enemy of the good is the best. We can answer an unmet need. The night is the most dangerous time. This is when parents and patients are afraid. If we can solve this, then it’s a huge value for patients. But we’re also studying 24-hour control to show that it is safe as well.

Mr. Brown: Kelly, you've had a chance to interact with two very different closed-loop systems - the Bionic Pancreas in the Beacon Hill study and the UVA Diabetes Assistant. What aspects of the device design and user interface did you like about each? If you were to start using either tomorrow, what would you want to see improved on each, if anything?

Ms. Kelly Close (The diaTribe Foundation, San Francisco, CA): I tend to be enthusiastic about any progress towards insulin automation, so I was so thankful to have an opportunity to be in both of these studies. In Beacon Hill, I didn’t actually care that the system was clunky. I was my best self – I didn’t have to think about diabetes and all the stress it brings every day. And it wasn’t until that was removed that I realized how much mindshare diabetes consumers. For me, having glucagon felt like something safe.

When I went to the overnight system, I didn’t think it was going to be as good, but I underestimated the power of having a really great night and waking up in the right place for the day. It also seems that this might be easier from a regulatory perspective. For me, whatever can get us there the fastest, obviously in a safe way, would be fantastic. I like that there are many different kinds of systems being created – patients have one thing in common, a diagnosis. But the spectrum of patients out there should be reflected in the variety of systems that come to market.

Mr. Brown: You and I have often discussed the need for incremental steps in automated insulin delivery, which means early products will improve with subsequent products. The major challenge, of course, is that early adopters are more likely to be intensively managed patients, who are doing pretty well already. With that in mind, how do we set patient expectations appropriately? What should industry keep in mind as they bring these systems to market?

Ms. Close: We are so lucky to be in this environment. We can ask for all these bells and whistles, but we must bear in mind that it is going to get better and better over time. We just need to get to a first product and get experience. Patients are getting more demanding all the time, and that’s just reality. But perfect should not be the enemy of the good.

Mr. Brown: Willa and Tia, You’ve been in so many closed-loop/artificial pancreas studies! What did you most like about the design of these systems? What do you think is the most important thing to improve on?

Ms. Willa Spalter: Probably a year ago, I was in an overnight study in a hospital, and I didn’t go over 128 mg/dl the whole night, which was great. Sometimes some of the sensors aren’t accurate and they shut off when you are not low, and then you go super high. It’s annoying when you are high.

Ms. Tia Geri: I did a study testing predictive low glucose suspend at night. Right now I wear the MiniMed 530G – it’s only half a closed loop. When I went low in the predictive study, I didn’t have to eat as much. It’s kind of like a backup safety net, and I like that about it.

Mr. Brown: If I said that you could have that system to wear tomorrow, what would you expect out of it?

Ms. Geri: It would shutoff before you I go low, and prevent me from going high. Insulin isn’t extremely fast right now, so you have to take it in advance. And I would expect the sensor to be accurate and reliable.

Ms. Willa Spalter: I would want it to shut off before you are going low. It’s really important to have an accurate sensor – it’s not good when it’s wrong.

Mr. Brown: Did you feel and act differently when you were on these systems? Were you scared?

Ms. Spalter: When I did my first study, it was literally a week after I got diagnosed. I was kind of scared because I didn’t know anything. It’s cool when you don’t have to treat in the middle of the night – usually if I eat, it’s hard for me to go back to sleep, because I have all this sugar in me.

Ms. Geri: I was excited to see how it would all work. So I almost wanted to test the system and make myself go low to see what would happen. [Laughter]

Ms. Tamar Sofer-Geri (CarbDM, Mountain View, CA): We already have an accurate device. Most people on CGM treat without checking their blood sugar. I say go for it. The predictive low glucose suspend is a no-brainer. I understand the risks. It’s never going to be a true vacation.

Ms. Close: It is amazing to be on closed loop. It’s like taking a vacation. It’s so special. But you must still interact with the system – no question about it.

Ms. Spalter: It’s really cool, but most of my studies are overnight. So it’s benefitting me, but it’s also for my Mom and my Dad who come and test me in the night. They wake me up if I am low, and it’s good if I don’t wake up in the middle of the night.

Ms. Geri: After I had finished one study, I was waiting for the pump to shutoff when I was going low. But it didn’t, because I was no longer in the study. I had to eat, and it was strange because I had grown so used to it doing its job.

Brandon Arbiter (Tidepool, Palo Alto, CA): Based on your most recent trial experience and what actually went wrong, would you be comfortable commercializing the closed-loop device you used?

Dr. Moshe Phillip: I think we are ready to commercialize the night. No question about it. It prevents hypoglycemia, provides tighter control, and influences the entire day. The night is ready. Waiting to solve the challenges of the day before seeking approval is wrong.

Dr. Buckingham: We would be ready if the connectivity was ready. If it was, I would go for 24-hour closed-loop control. We aren’t aiming for perfection during the day, but a treat-to-range system would be good.

Dr. Hovorka: We are ready now. It doesn’t mean it will happen soon, because there are other considerations.

Ms. Close: We have made amazing progress, but people ask about ‘edge cases’ – well, have conditional approval or make people sign something!

Ms. Spalter: Nothing is perfect, but I definitely would sign up. It’s much better than what everybody else is wearing now.

Ms. Geri: Things go wrong with what we use every day. So why not give people the best things that we have?

Dr. Kowalski: This year will be a tipping point in this field, don’t you think?

CGM

Posters

CGM Is Not a Limiting Factor in Artificial Pancreas Systems (75-LB)

T Bailey, K Nakamura, A Chang, M Christiansen, D Price, A Balo

This exciting poster shared in-clinic data from 51 patients that wore a version of the G4 Platinum with an improved algorithm (called “G4AP” in previous Dexcom presentations). The device’s accuracy was compared to YSI and fingerstick values (Bayer Contour USB) on days one, four, and seven. The poster also compared the accuracy of Bayer Contour USB values to YSI – a clear move from Dexcom to demonstrate that its next-gen CGM accuracy is approaching fingersticks. Overall G4AP MARD vs. YSI was an impressive 9.0%, compared to a fingerstick MARD of 5.6% vs. YSI. Notably, G4AP and fingersticks had a similar mean absolute difference (MAD) in hypoglycemia vs. YSI: 6.4 mg/dl and 4.2 mg/dl, respectively. In addition, the Clarke Error Grid data vs. YSI suggested G4 AP is really approaching the clinical accuracy of fingersticks– A+B Zone data was nearly identical (99.5% with G4AP vs. 99.6% with the Contour USB) and A-Zone accuracy was quite similar (92% vs. 99%). Overall, we thought the data were very, very strong and showed highly impressive accuracy using Dexcom’s existing G4 Platinum sensor and an improved algorithm – this hits the “holy grail” bar of a sub-10% MARD for CGM, a level of accuracy that some have called for to safely run tight closed loop control. This poster also underscored how much inherent inaccuracy there is in SMBG, and it makes us even more encouraged about the possibility of an insulin-dosing claim and factory calibration. A presentation later in the day noted that the “Share AP receiver” with the G4AP algorithm will be available for artificial pancreas research use in December 2014 (US) and 1Q15 (EU). We’re not sure if this would be rolled out to consumers, but are optimistic.

• The poster concludes, “The clinical performance of this CGM is approaching that of current SMBG systems, particularly after the first day of use and in hypoglycemia ranges. The system could be adequate for use in diabetes management decisions without the need for SMBG tests, in particular for reducing hypoglycemia. Accordingly, the CGM accuracy should not limit AP development.” Given how many patients already use their existing G4 Platinum CGMs to dose insulin (technically “off label”), we agree and believe that G4AP surpasses the bar for independent diabetes management decisions.
• This clinical trial enrolled 51 patients at three US centers. Patients inserted and wore one sensor for seven days and participated in three 12-hour clinic sessions (days one, four, and seven) with YSI every 15 minutes and SMBG capillary tests every 30 minutes. Glucose was manipulated to provide sufficient data in low and high glucose ranges during the clinic session. The CGM was removed at the end of the seven-day wear. The closest matched data point between CGM, SMBG, and YSI were used to assess CGM performance. The fingerstick meter used was a Bayer Contour USB. The CGM calibration scheme was twice daily fingersticks, prospectively calibrated.
• The science behind the G4AP algorithm was described by Garcia et al., JDST 2013. The G4AP employs the same sensor and transmitter as the G4 Platinum, but contains updated denoising and calibration algorithms for improved accuracy and reliability. The JDST study used a retrospective G4AP algorithm application to the G4 Platinum pivotal study data. This poster reports on the prospective, clinical use of the G4AP algorithm – as we understand it, the G4AP clinical data (overall MARD: 9.0%) is even better than the retrospective data (overall MARD: 11.7%) because the study execution was better.

	G4AP vs. YSI	SMBG vs. YSI	G4AP vs. SMBG
Matched pairs	2,263	994	2,992
Overall MARD   On Day 1   On Day 4   On Day 7	9.0%   10.7%   8.0%   8.5%	5.6%   5.3%   4.9%   6.6%	11.2%   12.7%   10.9%   9.9%
MAD in Hypoglycemia(<70 mg/dl)	6.4 mg/dl	4.2 mg/dl	7.8 mg/dl
Overall Clarke Error Grid	A+B Zones: 99.5% A Zone: 92.4%	A+B Zones: 99.6% A Zone: 98.5%	A+B Zones: 99.6% A Zone: 98.5%
% within 20%/20 mg/dl	93%	99%	87%

Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Excursions (846-P)

S Pleus, C Haug, M Link, E Zschornack, G Freckmann, K Obermaier, M Schoemaker

The authors compared the accuracy of two CGM systems: the Dexcom G4 and a prototype CGM system developed by Roche. This Roche-funded study enrolled 10 patients with type 1 diabetes who each spent about a week wearing four sensors simultaneously (two G4, two prototype). In an interesting wrinkle, the authors compared the performance of the sensors during two induced glucose excursions, which occurred roughly 40 hours and 70 hours after sensor placement. Measurements were compared to reference blood glucose readings drawn every 15 minutes during the excursions. (According to the poster these blood glucose measurements were also used for calibration; we are not sure exactly what this means or how it affected the results.) Notably, the G4 had numerically higher MARD than the prototype in every category of glycemic rate of change assessed, suggesting that the Roche sensor could be more clinically useful while glucose levels are rising or falling. The mean seven-day MARD was 10.9% for the G4 and 8.6% for the prototype. More than 80% of the prototype sensors had overall MARD below 10%, as compared to 20% of the G4 sensors.

	Dexcom G4			Roche Prototype
Rate of Change (mg/dl/min)	MARD (%)	SD (%)	n	MARD (%)	SD (%)	n
< -3	24.9	15.6	46	10.6	8.4	44
≥ -3 to < -2	19.2	13.0	75	10.9	9.4	73
≥ -2 to < -1	17.1	12.5	151	9.8	8.4	144
≥ -1 to < 0	12.6	10.1	227	8.2	6.3	217
≥ 0 to < 1	11.3	9.1	88	10.0	10.6	83
≥ 1 to < 2	19.5	12.2	44	10.2	9.7	39
≥ 2 to <3	21.1	14.0	28	12.2	7.4	28
≥ 3	29.6	11.9	44	16.3	12.4	44

Clinical Benefit in Glycemic Control Using a Long-term, Implantable, Continuous Glucose Monitoring System in a 90-Day Feasibility Study (837-P)

C Mdingi, R Rastogi, A Dehennis

In this poster, Senseonics presented 90-day data (45 days blinded + 45 days unblinded) on its implantable CGM system (fluorescence-based sensor, body-worn transmitter with Bluetooth connectivity, and a mobile smartphone app). Twelve patients took part in the three-month study, and sensor accuracy was compared to YSI at in-clinic visits every ~14 days. Overall MARD vs. YSI was a strong 11%, ranging from a low of 7.7% to a high of 17.7%. The Clarke Error Grid showed 85% of points in Zone A and 14% in Zone B (n=1,890 paired CGM-YSI points). However, the poster did not divulge the calibration scheme, in-clinic glucose ranges, or specific study design details/protocols, so it’s hard to know how real-world this accuracy is. [From the Clarke Error Grid, the vast majority of points appeared to fall in the 70-180 range.] There was also no mention of the percentage of sensors lasting 90 days or any details on explantation. Indeed, the poster was really focused on comparing the 45-day blinded period of sensor wear to the 45-day unblinded (i.e., real-time) sensor wear – average glucose significantly improved from 175 mg/dl (blinded) to 156 mg/dl (unblinded), which included a 7% reduction in hyperglycemia, a 1% reduction in hypoglycemia, and an 8% improvement in time in range (75-180 mg/dl). Overall, these feasibility results are encouraging, but we would like to see a longer, larger, and more real-world study, along with more details on the sensor’s calibration scheme.

Symposium: Closed-Loop Insulin Delivery — One Step at a Time (Sponsored by The Helmsley Charitable Trust)

Present State of Sensor Technology

Jessica Castle, MD (Oregon Health and Science University, Portland, OR)

Dr. Jessica Castle provided a thorough overview of CGM sensors and their use in artificial pancreas (AP) systems, including a summary of current CGM accuracy/performance, interfering substances, telemetry, and connectivity. She compared Medtronic’s Enlite, Dexcom’s G4 Platinum, and Abbott’s FreeStyle Navigator by referencing the Damiano et al., head-to-head-to-head study recently published in JDST – Dr. Castle emphasized that the G4 Platinum had the best accuracy (MARD: 10.8%) vs. the Navigator (12.3%) and Enlite sensors (17.9%). Similarly, the G4 Platinum had the lowest egregious error rate (MARD >50%) – 0.5% vs. 4.3% with the Enlite and 1.4% for the Navigator. She emphasized that CGM performance is significantly less accurate on the first day compared to subsequent days, providing a key area for future sensor improvements. Dr. Castle also highlighted the critical importance of calibration accuracy, as MARD is significantly higher when a sub-optimal meter is used for calibration – we would note this presents both a challenge (poor SMBG technique and suboptimal meters negatively influence sensor accuracy) and an opportunity (factory calibration!). Towards the end of her talk, she reviewed the ASPIRE in-home study testing Medtronic’s MiniMed 530G, noting its ability to reduce hypoglycemia (we would note that the current label does not include a hypoglycemia claim, since the ASPIRE data was not incorporated into the approved label). She concluded by looking forward to the future, highlighting Roche’s prototype sensor (MARD <10%) and Dexcom future G4AP algorithm – see elsewhere in this report for updates on both products.  

• Dr. Castle highlighted the importance of accurate CGM calibration, noting that meter can have a MARD almost double that of YSI (16% vs. 8.5%). She pointed out how powerful it is to wash hands prior to calibrating and to make sure no substances from the outside environment are interfering. We think this is an underappreciated piece of real-world CGM accuracy.
• Dr. Castle highlighted the issue of telemetry, in which sensors momentarily stops transmitting data to the receiver. The Dexcom G4 Platinum made a particular advance on this front – the G4 Platinum captures 97% of all data on average compared to the Seven Plus’ 90%. We’d note that the improvements in communication reliability and transmission range were widely lauded by patients when the G4 Platinum came out.
• Accuracy was shown to improve with the new algorithm G4AP compared to G4 Platinum. G4AP had a MARD >20% only 7% of the time compared to the G4 Platinum’s 20%.
• Based on OHSU’s bi-hormonal AP work using the G4 Platinum, using two sensors does not seem to significantly boost accuracy more than using one single sensor. In the past, OHSU had seen a benefit to using two Dexcom Seven Plus sensors.

Questions and Answers

Q: Regarding calibration, I’d like to highlight the timing of calibrations. When is the best time to do this?

A: This is definitely an important concept when you’re calibrating a device. Do it when glucose is relatively stable so you can get around the delay issue.

Q: You focused a lot on commercially available sensors, but what are your thoughts on long-term implantable sensors?

A: I know great research is being done in that arena. I think some of the concerns are the invasiveness of having implanted sensors as well as the long-term complications of that. I don’t foresee long-term implantable sensors as the perfect solution for the AP system.

Q: Can you comment on how the number of times you calibrate the meter affects the accuracy of the sensor?

A: It depends on the device. Data from Medtronic showed improved accuracy when you calibrate three to four times compared to less frequently. But really it also depends on change in device’s sensitivity over time. Medtronic’s a little more sensitive to calibration, so that improves accuracy. It also depends on the situation, if the patient is having more drift or not. If the sensor is drifting over time, more calibration is going to improve accuracy.

Symposium: New Frontiers in Inpatient Diabetes Management

Is the Hospital Ready for Continuous Glucose Monitoring?

Michael Agus, MD (Boston Children’s Hospital, Boston, MA)

Dr. Michael Agus wittily prefaced his presentation as an entirely “off-label discussion,” since using CGM in the hospital has not been approved by the FDA. Despite the “near insurmountable hurdle” of FDA approval, Dr. Agus stressed that CGM in hospitals can offer an “enormous addition” to the data available at the bedside, allowing healthcare teams to capture useful trending data and extreme glucose variability that results in earlier blood glucose checks and subsequent clinical decisions to prevent adverse events. He called an MARD of <10% “outstanding” and safe enough for closing the loop, 10-14% “pretty good,” and 14-18% “mediocre” (though still good enough to get “terrific” avoidance of hypoglycemia). Indeed, in reviewing the ongoing HALF PINT study in pediatric ICU patients (n=1,900), Dr. Agus highlighted that the CGM protocol detected 45% (18/40) of hypoglycemic events (<60 mg/dl) before nurses/glucometers, thus triggering blood glucose checks and subsequent insulin infusion – this was with a MARD of 17.6% with an unspecified sensor (we assume Medtronic Guardian or Dexcom Seven Plus). Dr. Agus urged CGM manufacturers to “make the move” to producing hospital-based CGMs, and equally important, for the FDA to provide guidance for hospital CGMs rather than “stonewalling.”

• Dr. Agus also shared his view in Q&A if companies get engaged in closed loop in the hospital, it will “be a standardized part of ICU care in the future.” Medtronic is certainly the most logical company to take this on – it already has a hospital-based CGM in the EU (Sentrino), and the ability to pair that with insulin dosing algorithms in-house.
• While the “single greatest benefit for patients” of CGM use in hospitals is managing and preventing hypoglycemia, Dr. Agus added that hospital-based CGMs also help manage hyperglycemia, prevent diabetic ketoacidosis, evaluate the safety of insulin or anti-hypoglycemia medication, and save nursing time.

Questions and Answers

Ms. Arleen Pinkos (FDA, Silver Spring, MD): We know that alarm fatigue has been problematic. Do you have any recommendations for hospitals on where to set these alarms and schedules? Second, as these CGMs get better, what kind of performance criteria do you think will be necessary to use devices in hospital settings for dosing?

A: I don’t have specific recommendations for alarm fatigue, though we do worry about it. However in my world of pediatric endocrinology, nurses are more worried when there is no alarm. Adding alarms and finger sticks help them feel much more confident in the care they deliver to our young patients. Most of the fatigue in our department comes from arrhythmia monitors that are completely irrelevant. Secondly, to pick a number for dosing based of CGM, I believe we need MARD <10%. MARDs in the range of 10-14% add an enormous amount of value as a safety value and trigger for obtaining value. The blood measure is accurate but invasive and an enormous number of patients could benefit from CGM-based dosing.

Q: Do you have any experience in non-ICU pediatric patients with use of CGM? Also, if you could have a crystal ball and look into the future, when do you think we’ll be looking at closed loop?

A: We don’t have much hyperglycemia in pediatrics outside of diabetics and the ICU, but we have a fair amount of hypoglycemia in NICU. We are starting a trial in NICU this summer. If we can get companies engaged in closed loop, my crystal ball says that this will be a standardized part of ICU care in the future.

Q: What is the cost for training the nursing staff to react to CGM alarms?

A: There is some degree of training but it’s absolutely trivial. This is a decrease in nursing burden and nurses appreciate knowing what’s happening in between drawn blood values.

Q: I appreciated your enthusiasm and description of learning, but I want to add that there are still patients for whom subcutaneous samples aren’t terribly accurate. My advice: get an arterial sample please! Also, based on CGM use in outpatients, accuracy of hypoglycemia detection is at 50%. Can model prediction based on trends make this much, much better?

A: As we bring it in-house, there is absolutely modification in the algorithm that we can do to improve accuracy. Secondly, I absolutely share your bias that in sicker patients, CGM wouldn’t perform as well. However, that’s not what we found. Yet I agree that we have to use arterial samples in those sicker patients.

Q: With the increase in IV acetaminophen and the spike it causes in CGM monitoring, how do you train your team to ignore that glucose result?

A: In the ICU trials, it turns out that acetaminophen isn’t used that much. Through luck we’ve avoided it in our big trials. Since we’re bringing patients in, we tell them to avoid acetaminophen. Dexcom is trying to address this currently, and Medtronic did address this in Europe. The real answer to your question is that companies must address this issue.

Q: How often do you address blood glucose with CGM; for day-of calibration, is there a different protocol?

A: In the first 24 hours, we know that performance isn’t as good, but we haven’t adapted our algorithm to that. We don’t make any clinical treatment decisions unless we have a blood value. We used to do an early blood glucose check if the CGM was telling us that something was awry. We set up a schedule as if there was no CGM; we haven’t had the confidence to reduce blood drawing yet.

Corporate Symposium: Clinical Application of Real-Time CGM: Professional Use, Pediatrics, and the Pathway to the Bionic Pancreas (Supported by an unrestricted educational grant from Dexcom)

Pathway to the bionic Pancreas

Steven Russell, MD, PhD (Harvard Massachusetts General HospitalUniversity, Boston, MA)

Dr. Steven Russell updated the audience on his group’s development of a bionic pancreas that delivers both insulin and glucagon using input from the Dexcom Gen 4 CGM. Dr. Russell announced that they would begin a two-week home-use study of the system in just two days. Monitoring in the study is minimal, and freedom is high: patients are even allowed to drive while under closed-loop control! For details on this latest trial, see our coverage above of Dr. Russell’s oral presentation on the Summer Camp and Beacon Hill Studies (237-OR). Dr. Russell and his team are also conducting clinical PK/PD research on Xeris’ glucagon, which is liquid-stable for up to two years at room temperature. The hope is to use this stable glucagon in a pivotal trial (more than three months long) in ~2015, with FDA review “as early as the end of 2016” and possible approval in 2017. Dr. Russell was positive on his group’s relationship with the FDA, noting that all of their investigational device exemptions (IDEs) have been approved within the initial 30-day window: “We sometimes joke that our regulatory consultant has been the FDA itself.”

CGM Use in Pediatrics

Bruce Buckingham, MD (Stanford University, Packard Children’s Hospital, Stanford, CA)

Dr. Bruce Buckingham emphasized that age, BMI, and subcutaneous sensor location do not seem to affect CGM accuracy, but the size of the sensor and transmitter do matter to pediatric populations (particularly adolescents who face competing priorities of peer acceptance and body image concerns). Citing two different studies, one using the Medtronic Enlite sensor and the other using the Dexcom G4 Platinum sensor, Dr. Buckingham highlighted that sensor accuracy (MARD) remained similar across varying age groups. In his opinion, “CGM accuracy is dependent on the quality of the meter glucose reading,” (i.e., no dirty fingers allowed – a bad calibration results in bad data). Additionally, in a short plug for his poster presentation on Sunday, Dr. Buckingham also mentioned his current study assessing the effect of lipohypertrophy on CGM accuracy; interim data showed that median ARD for lipohypertrophy sites was actually better than median ARD for normal sites (10.0% vs. 12.2%). Looking ahead at innovations that could widen pediatric adoption of CGM, Dr. Buckingham was very enthusiastic about the peace of mind benefits that Dexcom Share will provide to parents and spouses of people with diabetes. However, he provided no timeline updates for the Share other than, “FDA approval is pending… with a pregnant pause.” As of Dexcom’s 1Q14 call, approval was characterized as “in the final stages of review.”

• Dr. Buckingham emphasized that CGM use in adolescents is challenging due to the competing priorities of social acceptance, body image concerns, sports schedules, etc. To much laughter in the audience, he added, “Adolescence is a stage of temporary insanity… I tried to teach a high school class once and it was the hardest thing I’ve ever done. The number one priority at that age is friends. Priority two is friends, priority three is friends, and priority four is… sports. Sadly, you won’t find diabetes in that top priorities list.”
• Dr. Buckingham highlighted the results from a recent multicenter study using the Medtronic Enlite sensor – accuracy was not affected by age or BMI. Overall MARD was 15%: the youngest of the five different age groups assessed (3-7 years old) exhibited a MARD of 16%, while the oldest age group (25-46 years old) saw a MARD of 15%. Similarly, MARD based on BMI hovered around the median of 15% for each of the age groups.
• Similarly, accuracy data on the Dexcom G4 Platinum also showed that patient age does not significantly affect sensor performance. In patients ages 2-5 years, average MARD was 17%; in patients ages 6-12 years, average MARD was 16%; and in patients ages 13-17 years, average MARD was 15%. See our detailed coverage of the data from ATTD 2013. As a reminder, the FDA approved the Dexcom G4 Platinum in February for use in patients as young as two years old. Previously, marketing was only allowed to patients 18 years and older.
  
  • In a comparison to adult G4 Platinum performance, accuracy in pediatrics appears worse. In camp studies (n=740), MARD was 17.5% and median ARD was 13.5%. In comparison, inpatient studies on adults (n=201) suggested an MARD of 10.4% and median ARD of 7.7%. Dr. Buckingham hypothesized that the discrepancy in accuracy between pediatric and adult populations could be explained by incorrect meter calibration values used at camp.
    • Put simply, a real challenge for pediatric patients is clean fingers when testing. Indeed, testing himself, Dr. Buckingham noted that a clean finger resulted in glucose levels around 94 mg/dl, blood + sugar water = 94 mg/dl, blood + milk= 310 mg/dl, blood + jam= 361 mg/dl, and finally blood + pancake syrup= 526 mg/dl.
• Dr. Buckingham also mentioned his current study looking at the effect of lipohypertrophy on CGM accuracy. The preliminary data from eight subjects (though total study n=30) was presented at a poster session during ADA 2014. Sensors’ median ARD at lipohypertrophy sites was 10.0% vs. a median ARD at normal sites of 12.2% (precision ARD= 9.5%). Dr. Buckingham optimistically stated, “Patients do have a place for sensor insertion in the ‘wasteland’ of lipohypertrophy.”
• Dr. Buckingham was very optimistic about Dexcom Share, which will use a cradle to send G4 Platinum receiver data to the cloud and up to five “followers.” Using the Dexcom Follow App, followers can then view the receiver data and receive notifications on an iOS device. He emphasized the peace of mind that this device will bring for families with diabetes. In Dr. Buckingham’s examples, the Dexcom share would make sleepovers, business trips, and travel much less stressful. No updates were provided on FDA approval; the PMA supplement was filed in July 2013, and as of Dexcom’s 1Q14 call, approval was characterized as “in the final stages of review.” In Dr. Buckingham’s words, “FDA approval is pending… with a pregnant pause.”
• Dr. Buckingham noted that Medtronic’s mySentry remote monitoring is “really loud,” but it is “several thousand dollars” and “very few people can afford it.” The FDA approved this system in January 2012 – and it was certainly a big advance to get it through the FDA, as we understand it. Since that time, the mySentry has not taken off to our knowledge, and it certainly has not been mentioned on any Medtronic earnings calls. We assume Medtronic has not prioritized obtaining reimbursement for the device, given plans to commercialize the Connected Care device/smarter transmitters, which will send pump and CGM data to the cloud and mobile phones.

Panel Discussion

Q: If accuracy increases with sensor use, why do you need to change it every week?

Dr. Bruce Buckingham (Stanford University, Stanford, CA): You don’t. (Laughing) I’m sorry. 

Dr. Jay Skyler (Miami University, FL): If you follow the label you do!

Dr. Buckingham: You should absolutely follow the label and change it every week.

Q: Given the tendency of your current prototype to rely on a wireless system, do you have concern about patient going into areas with heavy radio frequency?

Dr. Steven Russell (Massachusetts General Hospital, Boston, MA): I should mention that the current version of the device is dependent on Bluetooth for communication with pumps. We monitor how many dose commands didn’t get to the pump at the initial try and this was 4-7% of time, depending on the study. The good news is that the Bluetooth almost always spontaneously reconnects. We think the system could do better if it could deliver the dose command immediately. We’re looking forward to a fully integrated version, which won’t rely on wireless connectivity to the pumps.

Q: I’ve heard a rumor about tachyphylaxis with glucagon. Is that the case?

Dr. Russell: I’ve hard rumors of that too, but I’ve never seen evidence of that. It may be, in part, because we’re giving microdoses of glucagon. We’re not giving the huge doses that strip the liver of glucagon. In fact, serum levels of glucagon appear to be in the normal range most of the time despite our dosing.

Q: How much glucagon is typically infused in 24 hours by the bionic pancreas? How does this compare to the non-diabetic pancreas?

Dr. Russell: Our total daily dose of glucagon is typically 750-800 micrograms. The FDA-approved rescue dose is one milligram.

Q: If glucagon must be reconstituted and mixed, how does that complicate matters?

Dr. Russell: The stability concerns us a lot – it is probably not commercially viable to reconstitute every day and the current glucagon formulation probably would not be approved for even one day. Fortunately quite a few small companies, and some of the big ones, are interested in glucagon. A company from Austin, TX called Xeris is a bit further ahead of the rest. Xeris’ formulation is stable for more than two years at room temperature. Their primary interest is an EpiPen-like device. However, their glucagon could be used in a pump. We are now doing a clamp study comparing freshly reconstituted Lilly glucagon to Xeris glucagon at low doses (50 micrograms). We have seen no difference in PK or PD in preliminary use. We hope to substitute Xeris glucagon for the pivotal trial.

Q: For Ms. Kruger, what is the effect of acetaminophen on accuracy of the Dexcom G4? If you are taking Tylenol, how long should a patient wait to calibrate the CGM?

Ms. Davida Kruger (Henry Ford Health System, Detroit, MI): In our clinical experience, when we use the G4, we don’t see as much interference with Tylenol (acetaminophen) but we do tell our patients to be aware of this interference and to expect two to four hours of hyperglycemia readings, but to ignore this because it will correct itself afterwards.

Q: Have you considered incorporating a threshold-suspend feature into your bionic pancreas?

Dr. Russell: If the blood glucose is falling fast enough, that will actually happen. That’s just part of the control algorithm. If blood glucose is falling fast enough, it will stop giving insulin and give some glucagon.

Q: With relying on the sensor to pick up a meal-related rise, I’m surprised you don’t have more hyperglycemia. Even if patients pre-bolus based on accurate carb counts, they get hyperglycemia.

Dr. Russell: We do have a postprandial rise. It can be diminished with meal announcement, when the system gives 75% of the calculated dose. We have good control in the fasting period and very good control at night; that contributes to the average blood glucose. But there’s no way to avoid some postprandial rise without the pancreas dumping insulin into portal vein. Even the pancreas doesn’t prevent postprandial rises. If I eat a bowl of ice cream I’ll come up to 150 mg/dl. Now, I’ll come back down spontaneously. But I don’t think even the normally functioning pancreas is capable of clamping blood glucose without any excursions.

Q: In non-pump patients who want to bolus for post-prandial glucose levels, what guidelines do you have for bolusing safely?

Ms. Kruger: It’s common for them to be taking insulin without a pump. We will give them a very similar algorithm with a correction factor, treatment goal, and carb ratio. It’d be very similar to what a pump does but they have to do this in their head.

Dr. Buckingham: With insulin you have to prevent stacking. When you’re on MDI, we need smart pens to incorporate that feature. This could be solved pretty quickly. Also, in our practice, we tell patients not to do correction until two hours after a meal.

Dr. Russell: I just had a meeting with a company that is making this smart pen. It is on its way.

Q: How much insulin are you putting in these bionic pumps? How long does it retain effectiveness?

Dr. Russell: We use a Tandem t:slim pump, which has a capacity of 300 units of insulin that we fill up entirely. Typically, we switch out the insulin every two days just to make sure that loss of insulin efficacy isn’t affecting our results. However, the insulin is going to be OK longer than that in most cases. The question I thought you were going to ask is how much insulin we use. People who already have their blood glucose under target don’t use any more insulin on the bionic pancreas relative to usual care. However, adults who have an average blood glucose above target did use higher amounts of insulin on bionic pancreas than usual care in the Beacon Hill trial – they may just not have been getting enough insulin during usual care. Interestingly, that wasn’t the case for kids with high averages in the Summer Camp study – there was no difference in insulin usage between the bionic pancreas and control arms.

Q: What criteria of patients using professional CGM predict future continued success of personal CGM?

Ms. Kruger: I think after a week the patient understands what CGM is and isn’t. They understand that it will give alarms, what it’s like to sleep and shower with it. As long as patients come back with a positive: “I really want to own this.” Often people want the system for another week, or not to give it back. These are positive signs. If patients opt to own it, they usually do very well.

Dr. Skyler: The numbers you showed were that of 402 people, 205 went on personal CGM – that’s about half, right? That’s a good predictor.

Ms. Kruger: Four-to-five years ago people said there was no value to professional CGM, because we wouldn’t know if people would be good candidates to own systems. Based on our experience we didn’t agree with that. Patients have no concept of what CGM is unless they’ve used it. We don’t want to spend $1,000 in insurance money for the system to sit on the shelf.

Dr. Skyler: That makes good sense.

Q: Dr. Russell, have you tried using faster acting insulins? Do you think you would have to change algorithms? Would you see better results?

Dr. Russell: We use lispro in all our studies, and haven’t tried an ultra rapid-acting insulin. We think we would get significantly improved results with faster acting insulins. I should emphasize, though, that we clearly don’t need faster acting insulin to get good glucose control with the bionic pancreas. One thing that was surprising to us was just how variable the absorption of insulin could be. Patients absorbed lispro at Tmax values ranging from 30 to 180 minutes. There was also significant variability within a person, but despite that variation those who were slow absorbers tended to absorb at a slower than average rate each time we looked and fast-absorbers tended to absorb faster than the overall average. We had to make an adjustment to the PK assumptions in our algorithm to account for this variability. Right now the algorithm is set conservatively to handle slow absorbers. We could use rapid acting insulin right now, and it wouldn’t harm anyone due to our conservative algorithm, but we wouldn’t take full advantage of the more rapid absorption without an adjustment to the PK assumptions. The nice thing is that we would just have to turn one knob in the algorithm to adjust for more rapid absorption if it were available.

Q: It would for sure be more physiological though, right?

Dr. Russell: Of course. The delay in insulin absorption is a big challenge and this would make it a lot easier.

Dr. Skyler: You do so well already though.

Dr. Russell: I think if insulin were absorbed faster with a Tmax at 30 min, we could have had an overall mean blood glucose in our Beacon Hill study cohort of 120 mg/dl instead of the 133 mg/dl we actually saw.

Q: Studies have used GLP-1 therapy to reduce glucose variability. What does that say about the etiology of glucose variability? Would you ever go and use three hormones, adding a GLP-1 agonist, in a bionic pancreas?

Dr. Buckingham: We’re not supposed to use GLP-1 agonist in pediatrics. But I think that’s a great idea.

Dr. Russell: If you’re going to have a second hormone, I think the question is whether you want to use glucagon or pramlintide. There might be some benefit in using a once-a-day or once-a-week GLP-1 analog to slow gastric emptying and suppress endogenous glucagon secretion. In fact, there is some paradoxical endogenous glucagon secretion occurring in response to meals, and we would need less insulin if we could suppress that. However, given the performance we have observed with the bionic pancreas, we don’t need to do that.

Q: Would the device that keeps track of insulin dosing have to be on a smart pen, or could we maybe even have it on CGM – to tell it that you’ve had your insulin dose?

Dr. Russell: The nice thing about the smart pen is that then the patient doesn’t have to remember to enter the dose into anything. What you have with the pump is that it knows every dose it gave. But if you are using a pen, you have to put that information into an app. If you build a pen that automatically transmits, you get that functionality. That is probably the main benefit of pumps, honestly. There are some tricks you can do with different patterns of dosing, but I think the big benefits [with pumps] come from the built-in bolus calculator, insulin on board, and never forgetting which doses are given when.

Dr. Skyler: What proportion of your patients are on pumps vs. MDI?

Ms. Kruger: My patients include a pool of type 1 and type 2 patients, so less than half are on pumps.

Dr. Skyler: Certainly CGM works in MDI as well?

Dr. Kruger: Though we’d like everyone to use bolus wizards and we explain how to use it and even set it up for them, sometimes they miss the insulin on board. That’s a big risk to the patient. A lot of patients can do very well on CGM without moving to pumps.

Dr. Skyler: The nice thing about CGM compared to pumps is that you don’t have to take it off when you’re swimming.

Dr. Kruger: Well there are pumps that you don’t have to take off in water either. But sometimes patients have to think about what they can afford and what their insurance will reimburse and cover. If a patient is faced with the choice of only one, while we are strong proponents of pumps, CGM may be the better choice.

Q: Do you worry about the battery of the smartphone draining out? Do you have a backup system in place? Regarding safety mechanisms and malfunctions in general, what do you do if there are erroneous glucose readings? How do you ensure that everything works in the bionic system?

Dr. Russell: For this first device, we’re cobbling it together from parts, so it did have battery issues. We had to plug it in at night and once during the day. After all, it was on all the time, and an iPhone can’t last all day. However, we think that a couple of AAA batteries in the final device will let it run for weeks, once we optimize the system. In terms of what the bionic pancreas would do if it got erroneous glucose information, it would give the wrong dose of hormones. However, you can find ways to deal with that. Let’s say the device has been mis-calibrated. If the CGM is running high relative to blood glucose and that leads to the patient being low, they would feel that and be motivated to recalibrate.

What about people with hypoglycemia unawareness? What might not be obvious is that this device keeps people out of hypoglycemia almost all day, so even people who have lost hypoglycemic awareness are going regain it. So they’ll know if they are low. If the device was recalibrated low relative to blood glucose and that leads to running high, you’ll tolerate that for 12 hours until the next calibration, so not too much to worry about. And finally, what if the infusion set pulls out? It’s going to have two adhesive patches and two steel cannulas that are connected so if one pulls out, the other will as well. So you’ll be unlikely to lose just one hormone. Finally, the system now has an option to move the target glucose up by as much as 30 mg/dl, so the user can opt for a larger margin of safety and accept a somewhat higher average blood glucose.

SMBG

Symposium: Joint ADA/AACC Symposium – Self-Monitoring of Blood Glucose – 21^st-Century Issues

Update on Blood Glucose Meter Accuracy

David Sacks, MB, ChB (NIH, Bethesda, Maryland)

Dr. David Sacks provided an overview of BGM accuracy, focused on recent guidelines from ISO, CLSI, and FDA. He focused on the FDA’s January release of draft guidance for home and point-of-care glucose monitoring, which has since been met with significant pushback from the scientific and industry communities. Dr. Sacks was slightly more conservative in his opinions on the FDA draft guidance compared to the last time we heard his thoughts in February at the EASD Diabetes Technology Conference – at the time, he was quite frank, “There will be no glucose meters approved in the future.” However, he remained consistent that the FDA’s guidance for point-of-care meters is “much tighter than anyone has proposed before,” and proving this level of accuracy is not feasible given the inaccuracy in lab methods.

• Dr. Sacks emphasized that the point-of-care FDA draft guidance leaves impossibly little room for error. In order for 99% of results to be within ±10% of the reference method, the coefficient of variation must be 3.8% and bias must be 0. For context, the plasma glucose measurements in central labs very rarely provide coefficients of variation below 3%. Furthermore, the imprecision of the reference method must also be zero, and according to Dr. Sacks, “no reference method is at zero.” To give weight to the statistical improbability of the guidance, Dr. Sacks consulted a statistician on the point-of-care guidance, who argued that to prove 99.9% accuracy, one would need 30,000 device measurements – a requirement that Dr. Sacks described as “obviously not feasible in most circumstances.”
• Dr. Sacks reminded attendees of January’s FDA draft guidance, commenting that the guidance for SMBG meters for home use is “really narrowing the range for error” and the guidance for point-of-care meters is “much tighter than anyone has proposed before.” For more detail and comparison to the ISO and CLSI standards, see our report on the January guidance.
  
  • Regarding SMBG devices (home use), 95% of measured values must be within ±15% of reference (across the entire glucose range) and 99% of SMBG values must be within ±20% of reference (across the entire glucose range).
    • The home standards are much tighter than the updated 2013 ISO standards, which Dr. Sacks illustrated with an example of a patient with a true glucose value of 45 mg/dl. ISO would accept a 30 - 60 mg/dl range, which “is clearly not reliable to detect hypoglycemia.” However, with FDA proposed criteria, acceptable results are 38 - 52 mg/dl, providing a much tighter, and range.
  • Regarding point-of-care devices (healthcare facility use), 99% of measured values must be within ±10% of reference for >70 mg/dl and within ±7 mg/dl for <70 mg/dl. Additionally, no individual result should exceed ±20% of the reference method for samples >70 mg/dl or ±15 mg/dl for <70 mg/dl.
    • These new standards are much tighter than the updated 2013 CLSI standards, and have received “a lot of feedback, probably from many in this audience.” Due to the guidance’s extremely stringent accuracy requirements, Dr. Sacks anticipated that “it will probably be some time” before the official FDA guidelines are released.
• For additional expert opinion on this controversial topic, see Dr. Irl Hirsch’s recent comments at AACE 2014 and commentary from the Diabetes Technology Society’s Hospital Diabetes Meeting.

Questions and Answers

Comment: I think its important to emphasize that what we see is only a partial picture because it only refers to error that comes from devices itself and does not take into consideration the overall system of errors. For instance, the human factor is a bigger contributor than even the reference method. I think I would emphasize that this picture you presented is even more difficult in reality.

A: Yes, that is a good point. These studies were done in optimum circumstances, and very few guidelines actually have account for patients doing these measurements.

Comment: I find it interesting this idea that hospital meters should be much more accurate than home meters. Inpatient glucose control is actually less stringent than for home control. In-hospital patients are surrounded by HCPs, while at home they can be driving cars, etc. Do you want to comment on this idea that it’s okay to have different standards at home and in the hospital?

A: Yes this is an interesting idea. Some people think that they should be different and each care environment should have different meters. Some people think that all meters should be equally accurate. The FDA, I think – I don’t know for sure – but I think their draft guidelines were driven in part by the meters used in ICUs. That is off-label and should not be done, but people still do them.

The New Error Grid – Rationale, Development, and End Product

David Klonoff, MD (Mills Peninsula Health Services, San Mateo, CA)

We had trouble finding seats in the packed lecture hall where Dr. David Klonoff unveiled the new Surveillance Error Grid (SEG), a new alternative to the Clarke Error Grid (CEG) and Parkes Error Grid (PEG). The SEG was published today in the Journal of Diabetes Science and Technology. We’ve been looking forward to this presentation since April when Dr. Klonoff hinted at the error grid during the 2014 Clinical Diabetes Technology Meeting. To start, the SEG looks very different from the CEG and PEG, with a tie-dyed look, fading from combinations of green to orange to yellow to red based on averaged risk across survey takers (see our Diabetes Technology Meeting 2013 Day #1 report for more details). Dr. Klonoff explained the rationale behind developing a new error grid for surveillance, namely that the treatment of diabetes has changed, accuracy standards for BGM have become tighter, and our understanding of hypoglycemia has increased making it necessary to develop a new grid that will incorporate the new treatment and clinical treatment of diabetes care. To support this, Dr. Klonoff noted that, when comparing the attributed risk results between graphs, there was a 0.58 correlation between the CEG and PEG, but only a 0.36 correlation between the CEG and SEG results and a 0.31 correlation between PEG and SEG. Dr. Klonoff concluded from this that the SEG is similar enough to the CEG and PEG to conclude that a similar metric was being used but dissimilar enough to show that the SEG is useful in measuring something different – Dr. Klonoff called this the “sweet spot.” Looking at how this translates to surveillance, Dr. Klonoff noted that BGM standards of accuracy in SEG increased from CEG and PEG. Dr. Klonoff concluded by commenting that the new software accompanying the SEG can be downloaded for free at www.diabetestechnology.org/SEGsoftware, allowing people can take their own reference BGM data points and find out what zone their data lie in.

• According to the “Computing the Surveillance Error Grid Analysis” that developed the software mentioned above (and was also published today in the Journal of Diabetes Science and Technology), having more than 3.2% of data points in the at-risk zone (outside of the green), corresponds to more than 5% of data points outside the ISO 2013 standards. While this should not be taken not as hard evidence that a meter is not as accurate as it should be, we appreciate Dr. Klonoff’s efforts to put more responsibility into patient hands.
• Dr. Klonoff explained that the SEG allows for clinician flexibility in how risk is assessed. After HCPs ranked the risk factor for reading vs. reference value on a nine-point, whole number scale, Dr. Klonoff and his team created a finer gradation through implementation of 0.5 risk units, providing 15 points of division (e.g., splitting the “none” risk zone into “slight risk for hypoglycemia,” “lower,” and “none”). Although the FDA advocates for use of this 15 division system (and the paper states, “We expect that for regulatory processes the 15-zone distribution will be used.”), Dr. Klonoff highlighted that the 15-zone distribution could be condensed into eight zones by disregarding directionality of risk, and the original nine zones could be condensed to five zones by similar methods. Additionally, Dr. Klonoff commented that a “pass-fail” model could also be used. For example, a certain distribution percentage points of data need to be above or below a cutoff score.
• Dr. Klonoff outlined several advantages of SEG over PEG and CEG, including that the latter grids didn’t account for DCCT trial results that came out in 1993, analog insulins that emerged in 1996, new information about hypoglycemia, and raised accuracy standards for meters. For example, the CEG has a 20% error separating the A and B blocks. Additionally, it is unclear who the clinicians were that were surveyed in the development of the PEG; PEG is based off of 100 people surveyed at ADA 1994. He also reviewed the development of CEG and PEG, remarking that while CEG focused only on treatments and PEG only focused on outcomes, SEG blends both treatment and outcomes.
• Dr. Klonoff briefly touched on the fact that this study supports BGM use in patients with type 2 diabetes not on insulin, since HCPs ranked risks across blood glucose levels the same in this population as in other groups. This is significant given the recent move in some areas (such as Oregon) to restrict strip access to patients on Medicare and Medicaid. We hope that such clinical data continues to be circulated among HCPs, payers, and CMS to demonstrate the HCPs do see the risk in not testing patients with type 2 diabetes.
• “The Surveillance Error Grid” was published on the Journal of Diabetes Science and Technology today. Along with individual authors, Dr. Klonoff also called attention to organizations pivotal in the development of the error grid, including the Diabetes Technology Society, the FDA, the ADA, the Endocrine Society, and the American Association for Advancement of Medical Instrumentation. Additionally, he acknowledged the hard work of error grid panel members, including 25 people in academia and industry such as Medtronic, Dexcom, LifeScan, Abbott, Bayer, Roche, and Sanofi – a strong circle of the leaders in Diabetes Care technology.
• Notably, Dr. Klonoff added that the FDA has already begun using the SEG as a model to assess other measuring devices. We think that this bodes well for the FDA actually beginning to use the SEG as a post-market surveillance tool for BGM. Currently, the FDA does not conduct post-market surveillance, but assessing and enforcing meter accuracy remains a concern for both patients and providers, particularly in ICU settings where BGM use is still off-label.
• The SEG was developed by surveying 206 clinicians and 28 non-clinicians asking what actions would be taken for each blood glucose value between 0 mg/dl and 600 mg/dl. The survey takers were then asked to assign risk (from “none” to “extreme”) if a reference value is misread either high or low. From there, all responses were averaged, allowing the SEG developers to impart more granularity on the grid by taking 0.5 risk units (Dr. Klonoff explained it similar to grades – although a student may only be able to get a 3 or 4 in a given class, if their grades are averaged across all classes, then they would be able to have a GPA of 3.5). See our coverage of methodology of developing the SEG in Day #1 of the Diabetes Technology Meeting in October 2013.

Questions and Answers

Q: Could you speak to how looking at the consensus between people responding to the survey translates into figuring out what level of granularity is appropriate for drawing boundaries between risk levels?

A: We had a large number of respondents, so we with mean; each person was his or her own control. We had an extreme idea of what the extreme scenarios would be. Additionally, often people would call blood glucose levels clinical significant when they were not in the “green” zone.

Comment: If you have a mean of 3.5; are most people saying 3 and 4 rather than 2 and 5?

A: Each value had a specific definition associated with it, and participants had to accept those definitions. Once defined, it is easier to chop values into gradations – it is like chopping up grades. It is very mathematically oriented.

Self-Monitoring of Blood Glucose in Non-Insulin Users – What is the Evidence?

Richard Grant, MD, MPH (Kaiser Permanente Northern California, Oakland, CA)

Dr. Richard Grant brought a primary care physician’s perspective to the discussion of self-monitoring of blood glucose. He argued that SMBG in non-insulin-using type 2 diabetes patients improves glycemic control only when prescribed in the context of a larger educational effort and as a tool to effect change in self-care or medication. In a review of 12 randomized controlled trials of patients with type 2 diabetes for at least one year, SMBG reduced mean A1c by just 0.26% compared to control treatment (lifestyle and oral), with mixed A1c results in individual studies. Additionally, Dr. Grant provided sobering results from the DISTANCE survey, in which 15% of patients reported that their SMBG results were not used by anyone to make adjustments to diet, exercise or medicine. Notably in Q&A, a PCP from Oregon criticized the DISTANCE study, commenting that the data were cited by the state of Oregon to restrict test strips for people with diabetes not on insulin. Dr. Grant was quick to clarify that he “would never have come to the conclusion that test strips should be restricted for all patients with type 2 diabetes not on insulin.” Rather, he would focus on individualizing care and on prescribing SMBG to patients who will benefit from it. With regard to the Oregon legislation, Dr. Grant even commented, “Using population-based prescriptions to restrict strips doesn’t make any sense... I do not agree with it at all.”

• Dr. Grant also cited the well-known STeP study by Dr. William Polonsky et al. that highlighted the benefit a structured testing protocol: 1.2% A1c reduction compared to 0.9% reduction in the control group (Diabetes Care 2011). Patients with type 2 diabetes (n=256) were assigned to a 7-point testing schedule to be completed on the three consecutive days prior to study visit. The seven points included fasting, pre-prandial/2 hr postprandial at each meal, and bedtime tests. Unsurprisingly, this structured SMBG protocol required extensive education and diabetes care team support. The control group received quarterly clinic visits that focused specifically on diabetes-management and were given free blood glucose meters and strips as well as access to an office point-of-care A1c capability (n=227). Dr. Grant noted that though the control patients received good diabetes care, the structured testing still showed benefit.
• In the DISTANCE Survey of the Kaiser Permanente Northern California diabetes registry, among patients who said that they used SMBG, 15% reported that their SMBG data were not used by anyone to make adjustments to diet, exercise, or medicine. Breaking down results into components, 37% of patients reported that both they and their and provider used data to change care, 34% reported that only they themselves used the data, and 14% reported that only their provider used the data. For providers not to use SMBG data is a “worst-case scenario” in Dr. Grant’s opinion. We found these data demoralizing, especially since they were used by the state of Oregon to justify restrictions on test strips in patients who are not treated with insulin.
• Dr. Grant noted that most patients with type 2 diabetes not on insulin are being treated in primary care settings where PCPs have an endless list of competing priorities for the 15-minute visits. Primary care physicians have a typical patient panel of 1,500-2,000 patients, and type 2 diabetes prevalence makes up 10-25% of these patients (~200 patients with type 2 diabetes). Given the urgency of behavioral interventions on diet, exercise, smoking, medication adherence, etc., interpreting SMBG may rank at the bottom of PCPs’ priorities. Another challenge is that 80% of patients with type 2 diabetes have concurrent chronic conditions like COPD, heart failure, and obesity. However, Dr. Grant also noted that SMBG data could be used to leverage lifestyle counseling and optimize medication management.
• Dr. Grant recommended that SMBG prescriptions should be made in the context of a shared-decision making framework to individualize care and ensure SMBG is the most time and cost-effective strategy.

Questions and Answers

Q: I was surprised that you didn’t consider in your review the PRISMA study in Diabetes Care in 2013 that is the largest comparison structure as SMBG in type 2 diabetes patients with more than 1,000 patients and up to one year follow up. The results showed significant reduction of A1c. This was thanks to a higher frequency in changes of medication exactly as you noted. I would emphasize that I wouldn’t consider SMBG useless in these categories of patients. Maybe we could discuss if it is cost-effective, but clinical usefulness in my opinion is clearly demonstrated.

A: I only included those in the original Cochrane comparison. As you said, A1c went down because of medication. I would argue that SMBG isn’t necessary to change medication. Also, I wouldn’t argue that SMBG is useless, but that if it is used, it should be used correctly. You can have excellent A1c control without SMBG.

Comment: I also disagree with your statement because if you have a patient on a sulfonylurea, hypoglycemia is a real danger and SMBG can help prevent this danger as well as improve quality of life.

A: I agree that hypoglycemia is important. There are a great number of patients not at risk for hypoglycemia, though. In these patients SMBG may not be as useful. Part of this discussion is not that it’s a bad thing, but that in larger context there are patients who don’t need it.

Q: I’ve also seen this particular research being used against us. In Oregon, the legislature cited this [DISTANCE study] to restrict strips for Medicare and Medicaid patients not on insulin. Have you looked at broad orals and tiered out non-hypoglycemia agents? I agree in part, but not in whole. We still see sulfonylureas as the number two prescribed medication in Oregon. Your data is currently being leveraged against us, but the data is not one group of people and I think you’d agree. Yes as SMBG may not be effective in primary care but this legislation is restricting SMBG and instead pushing the use of agents like sulfonylureas.

A: In the study I presented, we were really trying to predict the worst-case scenario. It doesn’t matter what they’re on. If you prescribe SMBG, someone should be looking at that data. Back to Oregon, I wouldn’t conclude that we should restrict test strips. We should use strips for certain patients. Using population-based prescriptions to restrict strips doesn’t make any sense. I do not agree with it at all. Some patients would be tremendously motivated. Equally, some patients wouldn’t benefit at all. That is the whole theme of the ADA/EASD recommendation; we need to individualize care to move levers.

Posters

Hypoglycemia Prediction Using SMBG Data and Patient Medication Information (397-P)

B Sudharsan, M Shomali

This poster presented the latest update to WellDoc’s exciting type 2 diabetes hypoglycemia prediction model, which was first unveiled at DTM 2013. The original model accurately predicted hypoglycemia risk (90% of the time) on the following day based on seven prior days of infrequent SMBG data (e.g., ~1 test per day) – this poster explored the additional benefit of adding patient medication information (drug dosing and class: short-acting insulin, long-acting insulin, pre-mix insulin, orals). Notably, the enhanced model was also constructed to predict the hour of the occurrence of hypoglycemia on the following day, a big step over the previous model’s aim to predict whether hypoglycemia would occur in the next 24 hours. Adding medication information significantly boosted the model’s specificity for accurately predicting hypoglycemia – 92% in the enhanced model vs. 70% in the previous SMBG-only model. The model’s sensitivity for predicting hypoglycemia remained high at 89%, comparable to the prior model’s 92% sensitivity. The study concluded that real-world SMBG frequency (~1 test per day) and medication information can provide adequate data to predict hypoglycemia in type 2 diabetes. The plan is to eventually incorporate this prediction module into BlueStar, WellDoc’s FDA-approved mobile prescription therapy for type 2 diabetes We continue to be impressed by the company’s approach, which centers on using data, algorithms, and real-time feedback to help patients better manage diabetes with minimal provider burden.

• As we understand it, the WellDoc clinical and behavioral R&D team intends to optimize the patient education and coaching around predicted hypoglycemia, and then incorporate the hypoglycemia prediction model into BlueStar. Once incorporated, BlueStar’s automated, real-time coaching will educate patients about how to best manage and avoid hypoglycemia. From a patient perspective, this system would be an incredible asset to managing diabetes, particularly in those who don’t test very often or are at high risk of severe hypoglycemia.
• The researchers used de-identified self-monitored blood glucose (SMBG) data and medication information from a randomized controlled trial (Quinn et al., 2011) to train a probabilistic model. For each data sample, 11 SMBG data points were used in the seven days prior to a hypoglycemic event (defined as SMBG <70 mg/dl). Control samples used for training contained no hypoglycemia on the eighth day. The model was constructed to predict the hour of the occurrence of hypoglycemia. In order to validate the model after training, 2,099 samples not used for training the model were presented to the model without the SMBG data from the eighth day. Sensitivity and specificity for predicting the hour of hypoglycemia or no hypoglycemia on day eight were then calculated. Further validation was performed with another distinct data set of 524 samples.
• The model is grounded in a key assumption: most type 2s are not CGM users or high frequency testers. As a result, this model was designed to work based on a very real-world testing frequency observed in type 2 patients. Indeed, we think a model based on one test per day is pretty magical from a clinical and commercial relevancy standpoint. The hypoglycemia prediction is especially relevant in type 2s, where there are more patients on hypoglycemia-causing agents than there are type 1s in total.
• We’d note that WellDoc has been pretty quiet following January’s $20 million Series A round of financing (led by Merck’s prestigious Global Health Innovation Fund) – the investment was expected to fund a dedicated sales force to regionally rollout BlueStar.

Oral Presentations: Diabetes Self-Management Education – Making a Difference

Comparison of Different Models of Structured Self-Monitoring of Blood Glucose in Type 2 Diabetes (14-OR)

Yi Sun Yang, MD (Chung Shan Medical University, Taichung, Taiwan)

Dr. Yi Sun Yang presented data on a comparison study (n=96) of three different models of structured self-monitoring of blood glucose in non-insulin using patients with type 2 diabetes: i) six-paired tests/week (48 tests/month), ii) three-pair tests/week (24 tests/month), and iii) seven-point profiles/week (28 tests/month) – described below. All three SMBG testing models met the primary endpoint, reduction in A1c, at three and six months, but the six-pair and seven-point testing models provided slightly greater reductions in A1c after six months (-1.7% and -1.8%; baselines of 8.8% and 8.9%, respectively) compared to the three-pair testing model (-1.1% from a lower baseline of 8.5%). The secondary endpoints of hypoglycemia occurrence and treatment change were similar among the three groups, but based on the PDSMS questionnaire, patients in the six-pair group reported more negative attitudes about their diabetes self-care – not surprising considering the higher volume of tests. The results are strong at face value, but are hard to interpret since there was no control group and all patients received diabetes education. In addition, 25% of patients did not complete structured testing, and it’s not clear how these patients were counted in the results. Still, the data are encouraging and in line with other studies (e.g., Polonsky et al., Diabetes Care 2011) supporting the value of structured SMBG in patients not on insulin.

• 106 patients with type 2 diabetes not on insulin were randomized to one of three structured SMBG models: six-pair testing/week (n=37), three-pair testing/week (n=36), or seven-point testing per week (n=33). Diabetes education and self-care goal and regimen suggestions were also provided. The primary endpoint was change in A1c from baseline to 24 weeks and secondary endpoints were change of treatment, lifestyle modification (defined as any change in diet, exercise, or other self-care behavior), and questionnaires on patient-reported outcomes [WHO-5 general well-being scale, Perceived Diabetes Self-Management Scale (PDSMS), Short form - Problem Areas in Diabetes - Chinese version (S-PAID-C), and Center for Epidemiological Scale – Depression (CES-D)].
  
  • Model 1 (n=31): Six-pair testing per week for a total of 48 tests per month. For example, a patient on this structured model would test pre- and post-breakfast on Monday and Tuesday, then test pre- and post-lunch on Wednesday and Thursday, and lastly test pre- and post-supper on Friday and Saturday.
  • Model 2 (n=31): Three-pair testing per week for a total of 24 times per month. For example, a patient on this structured model might include pre- and post-breakfast tests for Monday, Tuesday, and Wednesday. Dr. Yang noted that this model provided three pairs of consecutive data to encourage immediate change diet and activity if necessary.
  • Model 3 (n=34): Seven-point testing per week for a total of 28 times per month. For example, a patient on this structured model would on one given day test pre- and post-meals for a total of three pairs and once at bedtime.
• After six months, A1c declined in all three groups: -1.7% in the six-pair testing group (baseline 8.8%), -1.8% in the seven-point testing group (baseline: 8.9%), and -1.1% in the three-pair testing group (baseline: 8.5%). Additionally, fasting plasma glucose and postprandial glucose levels were significantly reduced in each of the three models. The proportion that did not complete structured SMBG were similar (25%). No severe hypoglycemic events were reported.
  
  • The secondary endpoints of hypoglycemia occurrence and treatment change were similar among the three groups, but patients in the 6-pair group reported more negative attitudes about their diabetes self-care based on the PDSMS questionnaire. As this model required approximately double the number of tests (48 per months) compared to the other two models (24 and 28 per month), the attitudes of increased burden are not surprising. 
• The study had some important limitations: the sample size in each group was relatively small, and 25% of participants did not complete structured SMBG. We also note that there was no control group and 10 of the initially allocated patients were excluded from analysis either due to discontinued intervention or dropout during the follow-up period.
• Baseline characteristics were similar across the three intervention groups.

	Six-pair glucose testing	Three-pair glucose testing	7-point glucose testing
Age (years)	58	59	59
Duration of diabetes (years)	9	9	10
A1c (%)	8.76	8.46	8.94
BMI (kg/m2)	25	26	25
Fasting Plasma Glucose (mg/dl)	198	174	181
Postprandial Blood Glucose (mg/dl)	254	210	224

Questions and Answers

Q: How often were medical therapy adjustments made during the course of six months?

A: Patients visited at 12 and 24 weeks, so we had adjustments at the 12-week checkpoint.

Q: I noticed that you presented the data but not by intention-to-treat. If you did intent-to-treat analysis, what did you find?

A: We didn’t analyze that statistic yet, but we may calculate that later.

Insulin Delivery

Posters

Efficacy and Safety of Insulin Pump Therapy in Type 2 Diabetes: The Opt2mise Study (102-LB)

Y Reznik, O Cohen, I Conget, R Aronson, S Runzis, J Castaneda, S De Portu, SW Lee, Opt2mise Study Group

This poster presented the long-awaited results from the randomized, six-month Opt2mise trial, comparing insulin pump therapy (n=168) to MDI (n=163) in type 2 patients in poor control (mean A1c: 9.0%). Following a run-in phase, patients were 1:1 randomized to either use a pump or MDI. From a baseline of 9.0%, A1c declined by 1.1% in those on an insulin pump compared to 0.4% in the MDI group (p<0.001) after 27 weeks; 55% of the pump group achieved an A1c <8% vs. 28% of the MDI group. CGM data (baseline vs. six months) revealed no significant increase in hypoglycemia. Meanwhile, the group on pumps used 20% less insulin than those on MDI (p<0.001). HDL cholesterol improved by 8% in the pump group and declined by 7% in the MDI group (p=0.01). One episode of severe hypoglycemia occurred in the MDI group, while none occurred in the pump group. It was valuable to see this positive data from a randomized, controlled, multi-center study of pumps in type 2 diabetes – most importantly, we like that the investigators enrolled a population that could most use easier and more convenient approaches to insulin delivery. Given the high starting A1c of 9.0%, the magnitude of reduction (-1.1%) was perhaps not quite as high as some would have expected although patients may have been very hard to manage. We wonder if insulin titration could have been better, if a simpler device with on-body bolusing (e.g., Valeritas’ V-Go or CeQur’s PaQ) could have helped drive patients even lower, or if this simply underscores what a challenging population this is to manage.

• Following a three-visit run-in phase to optimize MDI therapy, 331 patients were randomized to six months of either pump therapy (n=168) or MDI (n=163). The objective of the run-in phase was to optimize MDI therapy. All oral medications were replaced by metformin, and insulin therapy was intensified to >0.7 units/kg/day. During the study phase, the pump group initially used the same total daily insulin dose as before; patients randomized to MDI continued titration to target range. After six months, the MDI arm crossed over and switched to the pump. Both groups then spent months six through 12 on the pump during the study’s continuation phase. 
• Patients had a mean age of 56 years, a mean 15 year duration of diabetes, a mean A1c of 9.0%, a mean BMI of 33 kg/m², a mean total daily dose of ~109 units per day. The study had a high completion rate – 90% in the pump group vs. 96% in the MDI group.
• From a baseline of 9.0%, A1c declined by 1.1% in those on an insulin pump compared to 0.4% in the MDI group (p<0.001) after 27 weeks; 55% of the pump group achieved an A1c <8% vs. 28% of the MDI group. As would be expected, patients in the highest tertile of baseline A1c realized the largest improvement in A1c after six months of pump use.

Baseline A1c Tertile	8-8.5%	8.6-9.2%	9.3-11.9%
Difference in A1c Change (MDI-Pump)	-0.3%	-0.5% (p=0.01)	-1.1% (p<0.001)

• Despite the improved A1c, the group on pumps used 20% less insulin vs. those on MDI (p<0.001) at the end of six months. The MDI group saw total daily insulin dose steadily increase from 106 units per day to ~120 units per day. Meanwhile, the pump group saw total daily insulin dose decline from 112 units to ~100 units per day.
• CGM data (baseline vs. six months) revealed a significant improvement in 24-hour mean glucose, a significant reduction in hyperglycemia, and no significant increase in hypoglycemia. CGM data was collected over six days at baseline and at the end of the study. We assume the iPro2 was used, though it was not specified.

	Pump	MDI
Change in 24-hour Mean Glucose	-23 mg/dl*	-6 mg/dl*
Change in time spent >180 mg/dl	-226 minutes per day**	-57 minutes per day
Change in time spent <70 mg/dl	+9 minutes per day	+ 5 minutes per day

*p<0.01; **p<0.001

• One episode of severe hypoglycemia occurred in the MDI group, while none occurred in the pump group. There no episodes of DKA in either group. Four device-related serious adverse events occurred in the MDI group: two hyperglycemic hospitalizations (not DKA), one episode cellulitis, and one abscess.

Oral Presentations – Prandial Insulin Therapy

Effective Use of U-500 Insulin via Insulin Pump in Severely Insulin Resistant Patients

Anand Velusamy, MRCP (King's College Hospital NHS Foundation Trust, London, UK)

Dr. Anand Velusamy presented an uncontrolled study examining the impact of U500 insulin delivered via pump in very poorly controlled (baseline A1c: 10.4%), highly insulin resistant patients (mean daily dose: 306 units) with type 1 (n=3) and type 2 diabetes (n=11). A1c declined an impressive 1.9% at six months (n=14), 2.3% at 12 months (n=11), and was maintained out to 36 months (n=6). At the same time, total daily insulin requirements declined from 306 units at baseline to 244 units at 12 months and 250 units at three years. Weight did not change significantly, with patients gaining an average of 2 kg at 12 months and three years (baseline: 108 kg). Perhaps most notable were the cost implications – using U500 in the pump vs. U100 insulin was estimated to save ~2,200 pounds per patient per year (~$4,000). Though the study was uncontrolled and patients did receive nursing support, we thought these were very strong clinical results in a highly challenging population. Of course, this gels with efforts from companies like Insulet, Tandem, and Medtronic, who are all now actively pursuing type 2 focused products (Insulet’s U500 OmniPod with Lilly; Tandem’s 480-unit reservoir t:slim; Medtronic’s new type 2 business unit). The need is incredibly great in the severely insulin resistant population, and it’s great to see movement from industry that corresponds to the positive clinical data that continues to accrue on this front.

• Patients had a mean age of 56 years, a mean of 16 years of diabetes, a mean A1c of 10.4%, a mean total daily dose of 306 units, and a mean weight of 108 kgs. Prior to the study, eight patients were on basal/bolus insulin regimens, one was on a mixed regimen, three were on U500, and two were on U100 in pumps (i.e., they changed their reservoirs every day).
• The protocol for U500 initiation included a 30% reduction in total daily dose. Half of this reduction was used as flat basal replacement (we assume this means that 15% of the baseline total daily dose was pre-programmed as the initial basal). Patients used fixed dose boluses to start, though learned carb counting over time. A “bolus advisor” (we assume the pump’s bolus calculator) was used to provide corrective doses. Patients had downloads and telephone support to titrate insulin doses – this was likely a contributing factor to the robust reductions in A1c. We wish the study had been controlled to compare. The model of pump was not specified.
• Most patients were either lost to death or bariatric surgery over the three-year study period. From a baseline of 14 patients, there were 11 patients at one year (one had bariatric surgery, two were “intolerant”), and six patients left at three years (two patients died, one had bariatric surgery).

Questions and Answers

Q: This is a difficult group of patients. Some were already on U500, some were on a pump. What about use of GLP-1 therapy in these patients?

A: That’s a very valid question. These patients were on very high doses of insulin. They had long-duration diabetes. Three patients had type 1 diabetes and two had renal impairment, which precluded use of GLP-1 analogs. A couple patients were working towards bariatric surgery, and we were trying to optimize control quickly. GLP-1 could be used in this scenario.

Q: I live in the diabetes/obesity belt in America. We use lots of U500. Regarding insulin dosing with meals, they were on a fixed dose with meal? They were not carb counting?

A: Totally daily dose was reduced by 30%. They were on a fixed basal rate. As a starting dose, they did fixed boluses, but had help from bolus advisors. They did not carb count initially, but eventually they did.

Q: How much time before the meal was the bolus given? Was there hypoglycemia data?

A: There was no increase in hypoglycemia in terms of rates. They did have a couple of hypos, but none needing any third party assistance compared to the previous regimens.

Symposium: Closed-Loop Insulin Delivery – One Step at a Time (Supported by a grant from The Leona M. and Harry B. Helmsley Charitable Trust)

Present State of Insulin Delivery

Bruce A. Buckingham, MD (Stanford University, Stanford, CA)

The renowned Dr. Buckingham presented a compelling and thorough overview of the history, present status, and future of insulin delivery systems. With respect to pens, Dr. Buckingham emphasized that the future of the market rides on the integration of insulin delivery with glucose information, noting that it doesn’t take much to transfer the technology given to pump users – bolus calculators and correction factors with insulin on board – to a display on a pen. Notably, Dr. Buckingham also argued that insulin sets are presently the “weak link” in insulin delivery, as sets remain relatively faulty and become occluded or result in unexplained hyperglycemia in many users. Dr. Buckingham noted that scarring and hyperpigmentation remain big problems at infusion sites as well. Finally, Dr. Buckingham focused on pump development, quickly reviewing the history of the market (even discussing the early DANA and APS systems!) before reviewing the Debiotech Jewel and Medtronic MiniMed 640G pumps, among others, that represent the future of the industry. He concluded by suggesting that the development of pumps with Bluetooth connectivity, integrated continuous glucose monitoring systems, and web connectivity for data sharing is a must-have as the industry looks to move toward the development of practical closed loop systems.

• Referring to the development of pens, Dr. Buckingham noted that it “doesn’t take much” to integrate insulin delivery with more glucose information. He would like to see the technology given to pump users – bolus calculators and correction factors with insulin on board – displayed on a small screen on a pen. He referenced one product, approved in Europe, that is moving in this direction.
• Infusion sets are the “weak link” in insulin delivery. Dr. Buckingham noted that we have developed decent technology for insulin delivery itself, but the issue is that patients are not necessarily getting the insulin. In particular, he cited data that 66% of patients using infusion sets suffer from occlusions or unexplained hyperglycemia. Given the disparity in approved duration of use for infusion sets (two to three days) and continuous glucose sensors (six to seven days), he also noted the limited period of an infusion set undermines the use of combination sets or patch pumps and sensors on a single platform.
• Scarring and hyperpigmentation remain big problems at infusion sites. Dr. Buckingham reported that young patients often resort to using a sensor until failure, rather than obeying approval guidelines, in order to avoid the hassle of removing and replacing infusion sets.
  
  • In a small study performed by Dr. Buckingham examining infusion site failure, 64% of sites failed before seven days of wear and 30% of subjects suffered from hyperglycemia. This trial enrolled 20 subjects, ages 13-47, who all used Silhouette infusion sets filled with either Humalog or Novolog. They were asked to use the sets continuously for one week or until failure (identified via hyperglycemia). Based on these data, Dr. Buckingham emphasized the need for infusion sets approved for longer-term wear and noted that treatment of infusion site areas with hyaluronidase might offer a solution to improve insulin uptake.
• Of all the pumps he reviewed, Dr. Buckingham spent the most time discussing the Medtronic MiniMed 640G. This system features a new user interface, pump design, transmitter, an and the Enlite 3 sensor. Dr. Buckingham was impressed by the system’s predictive low glucose management algorithm, which anticipates blood glucose 30 minutes into the future and can suspend the delivery based upon that forecast. As of Medtronic’s last update, launch in the EU was expected by April 2015, while a US trial was just getting started (ClinicalTrials.gov Identifier: NCT02130284).
• Dr. Buckingham drew attention to the integrated use of CGM, Bluetooth connectivity, and web connectivity as three important elements that must be incorporated into closed loop technology going forward. He also mentioned that it would be helpful, for the consumer and researcher, if common communication standards were established between devices.

Questions and Answers

Q: I was wondering whether you could speak to the using of plastic vs. glass reservoirs?

A: I don’t think that’s a big issue. We tested insulin coming out for a week, and we didn’t see any increased fibrillation. I don’t see any glass tubing coming out of a body in the future. That seems fragile to me. Alternatively, Ed Damiano was trying to use DMSO is his bi-hormonal system, but that’s toxic to a lot of chemicals in reservoirs.

Q: Could you speak about any studies on site failure? On subcutaneous failure and scaring related to that?

A: I’ve got an ongoing study regarding this issue, so I can’t speak to the results.

Comment: I have trouble with patients not inputting all their lows into their system. It would be helpful if we had pumps that mandate that data goes into the system.

A: The logic here escapes me. The FDA does not want us to have devices that can wirelessly transmit all the data to the cloud. You’re right that numbers sometimes get transposed. Patients don’t do it immediately. There are a lot of issues. Electronic meter transmission is important and should be done.

Q: Regarding site failure, it sounds like body fluids are coming back up into the catheter. Could we get a one-way valve?

A: You’re always on the innovative side.

Q: Are there any other ideas for improving infusion sets beside hyaluronidase?

A: You could co-infuse an anti-inflammatory. You could coat the infusion set with an anti-inflammatory. There are other ways to reduce inflammation, too. And also, the question is: How much of the inflammation is the material? We saw no difference in using a steel versus a Teflon catheter in our testing. And the inflammation may not be the insulin itself. It may be stabilizing agents that are in insulin. So I’m not sure where it’s coming from.

Dr. Irl Hirsch (University of Washington, Seattle, WA): Clearly, we have not done well with infusion sets. However, in some adults, we don’t see inflammation, even after 20-25 years of pump therapy, but we do see that absorption is all over the map. In these cases, we’ve taken pump holidays, taking patients off pump therapy for three to four months. So we’re getting these people who are getting such poor absorption, but not because of occlusions, but because of scarring. It really goes to show that we don’t know anything about what’s happening under the skin. And what’s concerning is that now we’re putting children on pump. If they develop scarring, they’re not going to be able to continue on the pump in 30 years, or even worse, begin closed loop therapy.

A: I hope you’re wrong.

Q: Are implanted pumps going away?

A: The person who could best answer that question is Eric Renard. Eric?

Dr. Eric Renard (Montpellier University, Montpellier, France): There is nothing happening on this front in the US. But we have programs in France that should expand in Europe, especially to Germany. It is a clear answer for the problem for inflammation with infusion sites. In this case, using the peritoneal route is very effective.

Q: Is insulin precipitation due to the loss of cresol?

A: I don’t know.

Meet-the-Expert Sessions

Insulin Pump Therapy

Irl Hirsch, MD (University of Washington, Seattle, WA)

Dr. Irl Hirsch provided a concise overview of insulin pump therapy, covering bolus calculators, pump download best practices, and several patient case studies. His talk emphasized the importance of proper bolus calculator settings (his clinic defaults to five hours), focusing on the nighttime basal first, relying on certain statistics (standard deviation times two or three should be less than the average blood glucose), and coaching patients on optimal insulin dose timing. He walked attendees through his approach to several typical cases, showing all Medtronic downloads (“over 70% pumps used in this country are Medtronic”). His presentation did a good job of tying overwhelming pump download data to specific patient behaviors, which translated very clearly into clinical recommendations. Said Dr. Hirsch, “I know this is a session only on pumps. But it’s getting harder and harder to separate pumps from sensors.” As a testament to the forward-thinking, tech-savvy ADA attendee base, ~75% of audience members had patients on CGM.

Data, Digital Health, and Connected Devices

Special Meeting: DiabetesMine D-Data Exchange

App Demos

• NightScout/CGM in the Cloud is a remote monitoring platform for people wearing CGM. The system consists of a Dexcom G4 Platinum CGM receiver wired via USB cable to an Android phone. That links up with a database, a cloud server, and an app running on a glanceable display. The goal is safety and peace of mind. See the online instructions here for putting the system together. The entire project was crowdsourced and used open source software development. About two months ago, a tipping point was hit and a Facebook group was created. This “caused an avalanche” and was cited as a “shining example of #WeAreNotWaiting.” The Facebook group has 1,270 users and gets 50-100 new people every day.
• Tidepool showed off the latest version of Blip, which is now in a clinical trial at UCSF. As a reminder, this diabetes data platform is intended to display device data together in a very sleek and highly usable web interface – Mr. Look told us that one endocrinologist was ecstatic when he learned that Blip can seamlessly integrate Medtronic pump and CGM data. Mr. Look ran us through several fascinating examples where he communicated with his daughter’s endocrinologist and other members of the Tidepool team to troubleshoot out of range blood sugar numbers. We cannot wait until this rolls out.
• We got a look at the latest innovation in the sleek mySugr Diabetes Companion app. This app has a beautiful design and encourages consumers to “tame their diabetes monster” by logging glucose values, insulin, exercise, mood, etc. The newest innovation uses the smartphone camera and image recognition to scan glucose meter values into the app – it’s cable free and very cool! As we understand, this was a massive coding undertaking and required rewriting much of the standard optical recognition technology.
• Do-It-Yourself-Pancreas (#DIYPS) is an impressive cloud-based CGM alarm system/remote monitor with predictive analytics developed by Dana Lewis and Scott Leibrand.
• Joslin HypoMap powered by Glooko – read our report from earlier this week on this hypoglycemia unawareness survey and web-based module, spearheaded by the very smart Dr. Howard Wolpert.
• LabStyle Innovations’ Dario is an all-in-one smartphone BGM that plugs into the headphone jack of smartphones. The meter has been soft-launched in Europe and is under FDA review in the US. Read our previous report on LabStyle Innovations.
• Galileo Cosmos, a project of Anna McCollister-Slipp, is focused on data visualization.
• Ben West’s “Let’s chat with an insulin pump” (hacking a Medtronic pump).

“Unconference” Interactive Group Discussions

This session featured breakout groups with discussion centered around several topics. We’ve detailed the group leader summaries below.

• Engaging with Regulatory Bodies – Type 1 dad Mr. Lane Desborough (Medtronic Diabetes, Northridge, CA) summarized what sounded like an excellent small group discussion that included the FDA’s Dr. Stayce Beck.  He noted that the FDA really does want to engage and better understand what #WeAreNotWaiting is. He explained that the FDA’s job was significantly easier 15 years ago, as only a handful of companies could actually make a medical device. Now, one particularly motivated person can create a medical device. We thought this was an incredibly astute point, especially considering the agency’s limited resources. That said, the #WeAreNotWaiting movement is about individuals taking things into their own hands, which creates a situation with “multiple shades of gray” on what constitutes a medical device. Certainly, an artificial pancreas is clearly a medical device, and a Fitbit is clearly not a medical device – but what about a secondary display of CGM data on a smartphone or tablet? And in the case of Nightscout/CGM in the Cloud, does putting open source code on a website count as “distributing” a medical device? (In speaking with the FDA’s Dr. Stayce Beck after the session, she told us that Nightscout/CGM in the Cloud is indeed a regulated medical device, though the regulation is “tricky.” Technically, the software developer is responsible for pursuing regulatory approval, though in the case of open source development, it’s less clear. Still, Dr. Beck seemed excited and encouraged by the development, and noted that CGM in the Cloud is exactly the kind of device that the FDA wants to see in the marketplace.) Mr. Desborough emphasized that all of these shades of gray can be resolved by following the FDA’s pre-submission process – it’s free, easy, and there are timelines by law with how fast the FDA must respond. This process is as simple as giving the FDA a two-page document and a list of unanswered questions.
  
  • A questioner wondered about regulation linked to personal development of a secondary CGM display, and when such a product crosses the line and becomes regulated. The audience seemed to conclude that building such a device for personal use is okay, but once it is shared with one person, it drifts into regulation. Still, it was noted that the terms “share” and “distribute” fall squarely into the shades of gray area.
• Barriers to Device and App Adoption – On the device side, cited barriers included: cost/insurance; “it reminds me of my diabetes (e.g., extra alarms that you don’t have control over); cost-benefit analysis/short-term psychological focus (i.e., benefits of better control accrue over long term); something on my body; devices only give negative feedback and fail to give positive feedback; people get into established routines over time and are resistant to change from what works; cool, consumer-friendly design; prescribers not prescribing these devices, in part due to data management; and basic awareness that these devices are even available. On the app side, cited barriers to adoption included usefulness of data and manual vs. passive data collection. Attendees pointed to the need to take consumer-friendly design into account, the importance of collecting data mindlessly (vs. manually logging), including a social component (kudos, commenting, comparisons vs. other people), allowing goal setting, integrating challenges, and tracking personal bests (i.e., providing positive feedback and feelings of success). The idea of comparing one’s diabetes data to others was pointed to as both a motivating or demotivating factor, depending on the patient.
• Data visualization – Audience members agreed that there are different ways of representing data, including use of log scales, acute vs. long-term care, and systems like Tidepool. Some pointed to the use of heat maps to really understand long-term care processes. All agreed that the bar is incredibly low right now, since any visualization is better than a logbook, and rates of downloading are so incredibly low. One group member pointed out that simply liberating the data is the first big challenge before optimally visualizing it.
• Meeting Device Makers – This discussion centered on the challenge of adopting universal standards for diabetes devices. Group members expressed frustration that standards are not a priority amongst CEOs, VPs, people in charge of operations, and those in marketing (“It has to get into their top three. It’s not even in their top 10”). Some argued that transferring to universal standards does have a return on investment – products can potentially get to market faster and might even have an expedited regulatory review (though this remains to be seen with the FDA). The automobile and World Wide Web were two examples of the consumer market driving standards. A representative from PCHA/Continua highlighted that universal diabetes device standards are not that far away – the CGM standards document “is technically sound and stable and ready” to be in the Continua guidelines within a year. Universal standards on insulin pump data read outs are expected in 2015, and insulin pump control and command standards are expected in 2016.
• Open source development and device hacking – This group allowed the makers of  Nightscout, Tidepool, and the Do-It-Yourself Pancreas to demo their devices.

Call to Action: #WeAreNotWaiting Pledge & Goals for Fall

Howard Look (President/CEO, Tidepool, Palo Alto, CA)

Tidepool’s Mr. Howard Look wrapped up the day with an inspiring talk and call to action. He shared unanswered questions surrounding data and made a case for device makers to open their thinking, data, and device protocols.

• “#WeAreNotWaiting – this brilliant hash-tag means so many things to so many people” – For peace of mind that our children with type 1 diabetes are safe. To allow people with diabetes to have a choice in how they see their own diabetes data. To bring together the best and brightest minds from around the world to help make things better for people with diabetes.
• “We’ve been talking to device makers a lot. We’re making a slow progression here, and it’s a stepwise forward progression in helping them become comfortable.” Mr. Look described the progression of data as a ladder – lower tiers entail the release of less controversial data, while higher tiers are often more challenging for companies to part with:
  
  • Does the patient own his or her own health data?
  • Can patients donate and repurpose their data? Mr. Look argued that these first two are an obvious pass/fail test – “We must all agree on this.”
  • Cloud services – machine accessible APIs? Devices – documented protocols?
  • Devices – allow identification?
  • Data format/protocols complete and unambiguous?
  • Provide safety/efficacy diagnostic data.
  • Source code available for inspection?
• Mr. Look wondered, “What if there were an open and transparent scorecard?” He explained that Tidepool has not done this yet, but device makers must decide where on the seven-step ladder they feel comfortable. “You don’t have to have them all,” he said, “but what do you want to be able to say about your device?”
• Who owns the data? At first, it’s an easy answer – “It’s my disease, it’s my data.” However, Mr. Look’s deeper dive revealed how much more complicated it is – personal health data, contextual data, device identification data, diagnostics/safety /efficacy/proprietary data. The balance between patients’ owning this data and device makers owning this data is an important and critical question confronting the field.
  
  • Personal health data: blood glucose data, basal rate settings, IOB, ICR, ISF, basal rate change events, boluses. It’s fairly uncontroversial that patients own this data.
  • Contextual data – location, activity tracker, meal information, calendar events.
  • Device identification data – “where it gets interesting.” Device identification, brand, model, and revision.
  • Diagnostics/safety/efficacy/proprietary data – ISIG values, pump occlusion pressure, internal temperature, battery recharge cycles, internal error logs. “As a device maker, you might not want your competitor to know this.”
• “I’ve removed the name of the manufacturer, but here is an example of a troubling end user license agreement (EULA)” – “Any data submitted through [the service] shall be property of [the service] and you hereby waive all right, title, and interest to the submitted data.”
  
  • While Tidepool has not decided on its EULA, Mr. Look hopes it looks something like this – “Any data submitted by you through our service is owned by you. We are stewards of your data.” “If you like to make your data accessible to someone else, or to different software, just let us know.” “If you like to donate your data to anonymous research, just let us know.”

New Clinical Collaborations for D-Data Innovations

David Kerr, MD (Director of Diabetes Research and Innovation, Sansum Diabetes Research Institute, Santa Barbara, CA)

Dr. David Kerr (newly transitioned to Sansum from the UK) shared his view on creating a “Smart Diabetes Society” – one with devices that are open (interoperable); based on a cloud architecture; adaptable (to physiology and through learning); are social (big data), effectiveness-based (evidence, trust), incentives focused (stickiness), and use data semantics that both machines and humans can understand. He argued for adaptive diabetes systems that save patients and clinicians time, not simply software that collects and spits data back out. Notably, Sansum is focusing efforts on diabetes and exercise through a big data collection project hosted at ExCarbs.com.

• Dr. Kerr noted some key areas where diabetes technology can really improve.
  
  • Unattractive – “You would not choose to wear some of those devices. They are plain ugly.” We need more consumer electronic-like devices.
  • Impersonal technology – “make it mine”
  • Inaccessible technology – visual, functional, cognitive
  • One-size-fits-all technology – reservoirs, tubing, strips
  • Unconnected technology – it should sync with phone, records, and be social
  • Unintelligent technology – we need education and learning
• “Clinicians want to do less; not more. Doctors are tearing their hair out and saying, ‘I don’t want all this data.’” Dr. Kerr believes it is more important that the individual has the data and the machine/algorithms support the learning. He emphasized the need to create “adaptive diabetes systems.”
• According to the 2014 Diabetes App Market Report, mobile diabetes apps are currently used by only 1.2% of the target group. The analysis also revealed that 14 diabetes app publishers have 65% market share of the app market. 

Continua’s New Personal Health Alliance – Applications to Diabetes Care

Horst Merkle (Vice Chair, Personal Connected Health Alliance [PCHA]; Director, Diabetes Management Solutions, Roche Diagnostics)

Mr. Horst Merkle discussed the work of Continua/PCHA to drive towards interoperability and universal device standards for diabetes devices. He noted that the FDA acknowledges IEEE 11073 interoperability standards, and Continua developed the device profiles of the seven mentioned devices, including the glucose meter profile (10417). Continua/PCHA are looking to expand the guidelines for diabetes soon – insulin pump data read out (expected in 2015), insulin pump control and command (expected in 2016), and CGM (the slide said “2016,” though a latter comment suggested this could come in 2015). Mr. Merkle emphasized that these standards are incredibly important steps as the field moves towards the artificial pancreas.

Incretin-Based Therapies

Banting Medal for Scientific Achievement Award Lecture

Deciphering Metabolic Messages from the Gut Drives Therapeutic Innovation

Daniel Drucker, MD (Lunenfeld Tanenbaum Research Institute, University of Toronto, Canada)

Dr. Dan Drucker, recipient of this year’s Banting Medal, is a truly unparalleled mind in the realm of gut hormone-related therapeutic innovations. Dr. Drucker opened by noting he was originally supposed to be a thyroid hormone specialist, but serendipitously ended up being assigned to work on the glucagon gene instead. He’s worked on proglucagon-derived peptides from the very beginning, since the first cDNAs and genes had just been cloned and no one knew what any of those peptides did. In the early days, Dr. Drucker and colleagues discovered that one fragment of the GLP-1 peptide was a potent regulator of insulin gene expression, cAMP production and beta cell secretion – it seemed trivial to him at the time, but his mentor Joel Habener filed a patent in 1986, which is the patent that has laid the foundation for GLP-1 as a therapy. Since then, Dr. Drucker and his team have extensively characterized the pharmacologic and physiologic actions of GLP-1, including characterizing GLP-1’s potential beta cell preservation and proliferation effect in mice, GLP-1’s numerous actions outside of glucose regulation, and delineating DPP-4 inhibitors’ mechanism of action. He also touched on the hot topics of pancreatic safety and potential cardioprotective effects. Notably, Dr. Drucker gave reasons not to sound the alarm over GLP-1’s consistently demonstrated effect on increasing pancreas mass – he has evidence from mice that increased pancreas mass is not due to edema, inflammation, or increased cellular proliferation. In fact, he has shown that it is due to increased protein synthesis, possibly reflecting the communication between the gut and the pancreas to increase pancreas protein synthesis following meal ingestion, however the mechanism is not fully understood. The thousands of audience members gave Dr. Drucker a thunderous standing ovation at the conclusion of his presentation.

• Dr. Drucker described his early work in the 1980s first characterizing peptides derived from the proglucagon gene, which led to the discovery and patenting of GLP-1 as a glucose regulator. From a self-described “unimaginative” experiment that involved “dumping” several glucagon-like peptides on “as many cell cultures as we could,” Dr. Drucker and his colleagues observed that the 7-37 fragment of GLP-1 was a potent regulator of insulin gene expression, cAMP production, and beta cell secretion. At the time Dr. Drucker did not think much of the finding, but his mentor, Joel Habener, filed a patent for the molecule on May 5, 1986, which is the patent that has laid the foundation for GLP-1 agonist therapies. It was at this point, said Dr. Drucker, that he was introduced to the concept of science forming the foundation for new therapeutics, which he has since gone on to do in spades. He noted at the end of his presentation that the proglucagon gene has given rise to more drugs for the treatment of human disease than any other gene in the human genome.
  
  • Dr. Drucker noted that we’re still interested in new clinical ways to activate the GLP-1 receptor signaling pathway. The well known methods currently used are (i) inhibiting the inhibitor of GLP-1 by using DPP-4 inhibitors, and (ii) pharmacologically activating the GLP-1 receptor using analogs. Ongoing work remains in potentially developing GLP-1 secretagogues or neutraceuticals/functional foods that activate GLP-1 secretion from the gut.
• In addition to the pharmacological effects of GLP-1, Dr. Drucker has also worked extensively on characterizing the role of endogenous GLP-1 using GLP-1 receptor knockout mice (Glp1r ^-/-).
  
  • GLP-1 is not only secreted after meals, but throughout the entire day: Unexpectedly,  Glp1r ^-/- mice had fasting hyperglycemia in addition to glucose intolerance, which led to the finding that there is not only postprandial secretion of GLP-1, but also some basal level of GLP-1 activation that helps control fasting glucose.
  • Endogenous actions other than stimulating insulin secretion include enhancing beta cell preservation and proliferation: Dr. Drucker and colleagues were also among the first to characterize GLP-1’s potential effect on beta cell preservation and proliferation. In an experiment where mice were treated with exendin-4 (exenatide’s precursor) for seven days, and given STZ to injure their beta cells, then exendin-4 treatment was stopped, three weeks later the animals that had received exendin-4 treatment had higher plasma insulin levels than those who had not and lower blood glucose levels. Dr. Drucker and colleagues were able to demonstrate that GLP-1 receptor activation was inhibiting beta cell apoptosis in mice as a direct effect of GLP-1 receptor activation on the beta cells. Furthermore, as demonstration that this was also an effect of endogenous GLP-1, not just pharmacologic GLP-1, Dr. Drucker has shown that mice with no GLP-1 receptor activity are more sensitive to STZ-induced apoptosis compared to wildtype mice. With regards to proliferation, Dr. Drucker’s lab also demonstrated that incretin receptors are necessary to for increased insulin production in response to high-fat diet-induced insulin resistance.
• Dr. Drucker remarked that the evidence thus far presented a seeming paradox: GLP-1 could both increase insulin secretion while also preserving beta cell function (whereas other insulin secretagogues seemed to wear down beta cell function). Normally, increased protein production (in this case insulin) would upregulate endoplasmic reticulum (ER) protein synthesis, and at a certain point start causing ER stress. Dr. Drucker’s colleagues reconciled this conundrum by finding that incretin receptors directly engage several arms of the ER stress response, allowing the upregulation of insulin biosynthesis while simultaneously preserving cell survival. Dr. Drucker noted that “incretin secretion is uniquely positioned to enhance insulin secretion and prolong beta cell survival,” in contrast to other insulin secretagogues – of course the positive effect on beta cells has only been convincingly shown in mice, and human data has been sparse to date.
• Dr. Drucker has also characterized where GLP-1 acts outside of the beta cell, including the brain. Native GLP-1 (which is a relatively small peptide) has direct central nervous system effects, and years ago it was not known whether synthetic high-molecular weight GLP-1 agonists would also be able to interact with the brain (since they would be too large to penetrate the blood-brain barrier). Dr. Drucker’s group compared exendin-4 (small peptide) to albiglutide (large peptide) and found that they produced about the same extent of central nervous system (CNS) activation, and had the same ability to inhibit food intake and reduce gastric emptying. Thus, they learned that GLP-1 does not have to directly penetrate the CNS to activate the classical actions of GLP-1 receptor signaling in the brain – Dr. Drucker noted that this finding informed the decision to move forward with development of high-molecular weight, longer-acting GLP-1 receptor agonists (e.g., Lilly’s dulaglutide and GSK’s albiglutide).
• Dr. Drucker’s work on glucagon-derived hormones also led to a better characterization of DPP-4 inhibitors’ mechanism of action. With the DPP-4 enzyme’s dozens of substrates, it at first was not clear which ones were truly important for its effect on glucose metabolism. Dr. Drucker’s group showed that both GLP-1 and GIP receptor activation was necessary for DPP-4 inhibitors to exert their effect.
• In working with GLP-1’s sister peptide, GLP-2, Dr. Drucker also discovered that GLP-2 stimulates small bowel growth, which led to its development as a treatment for small bowel syndrome.
• Finally, Dr. Drucker explored the clinical relevance of some of the non-glycemic actions of GLP-1 in the intestine, pancreas, and cardiovascular system, highlighting that we still have much to learn.
  
  • Intestinal safety: Dr. Drucker noted that many have questioned why GLP-1 is made in the distal gut if its role is to stimulate insulin secretion after we eat (nutrients don’t reach the distal gut for several minutes or even hours after food being ingested). However, GLP-1, like GLP-2, is a potent bowel growth factor, and Dr. Drucker’s group has found that GLP-1 activation increases growth of intestinal polyps and tumors when activated in the distal bowel of a rodent cancer model. When GLP-1 receptors are removed in this model, tumor size and number significantly decrease. Dr. Drucker implied that we may need to more closely monitor the potential for inappropriate intestinal growths in treating patients with GLP-1 agonists.
  • Pancreatic safety: GLP-1 has been consistently found to increase the mass of the mouse pancreas by about 10%, but Dr. Drucker gave reasons not to sound the alarm over potential tumor formation – he noted that “what we have failed to address, as a community, is why the pancreas is bigger.” Dr. Drucker’s team has shown that the increased pancreas size is not due to increased inflammation, edema (water and edema actually decreases in mice given exendin-4), or cellular proliferation. The reason he has pinpointed is that it GLP-1 stimulates the pancreas to make more protein in response to meal intake. We believe we heard Dr. Drucker say that his team has identified which proteins are selectively modulated by GLP-1 signaling in the mouse pancreas, but he did not disclose what those were. The takeaway here is increased pancreatic mass on its own is no reason to jump to conclusions about potential for pancreatic cancer – however, he noted that we do not completely understand the mechanisms through which GLP-1 receptor signaling modifies pancreatic protein synthesis.
  • Cardiovascular effects: GLP-1’s cardioprotective effects on mice have been well established, and Dr. Drucker noted that for years we assumed it was due to direct activation of GLP-1 receptors on ventricular cardiomyocytes (which are the cells most directly involved in CV events like heart failure or myocardial infarction). However, Dr. Drucker upended this thinking by showing that the receptor is not actually expressed in the ventricular cardiomyoctyes. Instead, it has been found in the atrium. Dr. Drucker, furthermore, has shown that direct GLP-1 receptor activation in the heart is not even necessary for GLP-1 to exert its cardioprotective effects: a mouse model with no GLP-1 receptor expression in the heart was no different than a wildtype mouse with full GLP-1 receptor expression when responding to ischemia (blood supply shortage). Therefore, Dr. Drucker concluded that GLP-1 does not act directly on the heart to exert its potential cardioprotective effects, but likely acts through some other unknown indirect mechanism. So the obvious next question is why there are GLP-1 receptors in the atria in the first place – Dr. Drucker demonstrated that these receptors were actually important for heart rate regulation rather than playing a role in cardioprotection. Dr. Ducker gave a presentation on this topic at the Keystone Symposium on Diabetes Complications earlier this year.
• Looking to the future, Dr. Drucker outlined a slew of potential new roles for GLP-1 agonists: as potential treatments for obesity, prediabetes, type 1 diabetes, children with type 2 or type 1 diabetes, neuroprotection and neurodegeneration, anti-inflammation, fatty liver disease, cardiovascular indications, and microvascular disease.

Oral Presentations: GLP-1 Agonists

Benefits of a Fixed-Ratio Formulation of Once-Daily Insulin Glargine/Lixisenatide (LixiLan) vs. Glargine in Type 2 DM Inadequately Controlled on Metformin Monotherapy (332-OR)

Julio Rosenstock, MD (Dallas Diabetes and Endocrine Center, Dallas, TX)

Dr. Julio Rosenstock presented results of a 24-week proof of concept study demonstrating that the combination of the lixisenatide and Lantus (Sanofi’s LixiLan GLP-1 agonist/basal insulin fixed-ratio combination product) has advantages over Lantus alone. This study randomized 323 people with type 2 diabetes on background metformin (relatively early-stage type 2 diabetes), to LixiLan or Lantus. LixiLan dose was adjusted according only to Lantus basal insulin requirements. Both arms achieved similar and striking A1c reductions (1.8% reduction on LixiLan compared to 1.6% on Lantus from 8% baseline) to a final A1c of 6.3% with LixiLan that was statistically superior to the 6.5% achieved with Lantus as LixiLan blunted postprandial glucose excursions substantially more so than Lantus (LixiLan reduced excursions by 70 mg/dl compared to 12 mg/dl on Lantus; p<0.001). A striking 84% of patients on LixiLan achieved an A1c goal of <7% (although a surprisingly high 78% of Lantus patients also achieved this goal); extraordinarily, 72% on LixiLan reached an A1c goal of ≤6.5% compared to 65% of Lantus patients (again, still pretty high for Lantus, probably indicating the investigators’ skill at titrating dose). Moreover, LixiLan produced a -1.2 kg [-2.6 lb] weight change over the 24 weeks compared to a 0.4 kg weight gain [0.9 lb] on Lantus. Documented symptomatic hypoglycemia (≤70 mg/dl) was similar between the two groups at 22-23%. The study also examined several composite efficacy endpoints of interest as outlined in the table below in which LixiLan was consistently better than Lantus. Frequency of nausea and vomiting with LixiLan (7.5% and 2.5%, respectively) was less in this trial compared to what has been reported for other GLP-1 agonists likely due to the slow titration determined by the insulin adjustments so that for each increment of two units of insulin glargine there was a lixisenatide increase of only 1 μg. In all, the data are very impressive – yet another testament to the strength of the GLP-1 agonist/basal insulin combination that we think bodes very well to future therapeutic approaches and combinations. In the introduction to his talk, Dr. Rosenstock positioned LixiLan as a better alternative for basal insulin intensification compared to adding on prandial insulin.

Composite Endpoint	LixiLan (n=161)	Lantus (n=162)
A1c ≤7% and No Weight Gain	56%	37%
A1c≤7% and No Symptomatic Hypoglycemia	64%	57%
A1c≤7% and No Weight Gain and No Symptomatic Hypoglycemia	46%	29%

Efficacy and Safety of Once-Weekly Dulaglutide vs. Insulin Glargine in Combination with Metformin and Glimepiride in Type 2 Diabetes Patients (AWARD-2) (330-OR)

Francesco Giorgino, MD, PhD (University of Bari Aldo Moro, Bari, Italy)

Dr. Francesco Giorgino presented the results of the AWARD-2 trial, which compared Lilly’s once-weekly GLP-1 agonist dulaglutide against Sanofi’s basal insulin Lantus (insulin glargine) added on to maximally tolerated doses of metformin and a sulfonylurea in type 2 diabetes patients for 78 weeks. At week 78, dulaglutide 1.5 mg (the highest of the two doses tested) led to a superior reduction in A1c from baseline (-0.90%) than insulin glargine (-0.59%), while dulaglutide 0.75 mg provided a non-inferior mean A1c reduction (-0.62%) relative to insulin glargine. Dulaglutide 1.5 mg led to weight loss of 2 kg (~5 lbs) at week 78, while the insulin glargine group gained over 1 kg (~3 lbs) on average. The incidence of total hypoglycemia was ~60% higher with insulin glargine than dulaglutide, and the incidence of nocturnal hypoglycemia was more than double the incidence with dulaglutide. As expected, the incidence of nausea was higher in the dulaglutide arms (15% and 8% for dulaglutide 1.5 mg and 0.75 mg, respectively) than the insulin glargine arm (2%). These results serve as evidence that GLP-1 agonism can be a more beginner-friendly initiator injectable for patients not at goal on oral medications, especially those that cause hypoglycemia. However, the fact that investigators may have held back in up-titrating insulin due to fears about hypoglycemia should be considered when discussing the relative clinical efficacy of dulaglutide and insulin glargine.

Harmony 3 Year 3 Results: Albiglutide vs. Sitagliptin and Glimepiride in Patients with T2DM on Metformin (329-OR)

Molly Carr, MD (GSK, King of Prussia, PA)

GSK’s Dr. Molly Carr presented the results of the phase 3 Harmony 3 study for the once-weekly GLP-1 agonist Tanzeum/Eperzan (albiglutide). As with other studies in the Harmony program, Harmony 3 ran for three years, quite a long duration for a phase 3 program – we think it is fantastic when studies can run (and be funded) this long. Dr. Carr’s team analyzed the trial’s results in two ways: the first analysis excluded data from patients who needed hyperglycemia rescue from the point the rescue occurred, consistent with the way that phase 3 efficacy data is generally presented. In this analysis, at the two year primary endpoint, albiglutide demonstrated a -0.91% placebo-adjusted reduction in A1c from a baseline of 8.1%, which was 0.35% greater than the reduction seen with sitagliptin and 0.27% greater than the reduction seen with glimepiride (2 – 4 mg). The differences in A1c were still visible but less pronounced at year three. Notably, in Harmony 3, patients who required hyperglycemia rescue continued to receive their randomized therapy. Taking advantage of this design element, Dr. Carr also presented an analysis of efficacy in an intent-to-treat population, including those who required rescue. In this analysis, albiglutide still demonstrate significantly greater placebo-adjusted A1c lowering from baseline at year three (-0.68%) than sitagliptin (-0.27%) or glimepiride (-0.41%). Notably, albiglutide did not provide better weight loss than placebo or sitagliptin in the long term, but was also associated with a relatively low rate of GI side effects compared to other GLP-1 agonists. Seeing evidence of sustained efficacy out to three years was reassuring, but it remains to be seen how the seemingly modest weight loss but also a low incidence of GI side effects will factor into providers’ and patients’ perspectives on this agent.

• Study design: The randomized, double-blind, placebo and active-controlled, parallel-group phase 3 study randomized 1012 type 2 diabetes patients in a 3:3:3:1 fashion to albiglutide 30 mg -> 50 mg, sitagliptin 100 mg, glimepiride 2 mg -> 4 mg, or placebo. A notable aspect of the study design, which Dr. Carr repeatedly emphasized, was that patients that underwent hyperglycemia rescue (as per FDA requirements) continued receiving study medication in a blinded fashion after rescue. Although this protocol added some extra complexity to the results, we also believe it added real-world relevance, given that diabetes is a progressive disease that for most patients requires occasional intensification of treatment. At baseline, patients’ mean age was ~55 years, mean BMI was 33 kg/m², and mean A1c was ~8.1%.
• Dr. Carr first presented the primary endpoint analysis of A1c change through week 104. Albiglutide provided a statistically superior reduction in A1c from baseline relative to sitagliptin, glimepiride, and placebo. Placebo-adjusted reductions in A1c for each group were -0.91% for albiglutide, -0.64% for glimepiride, and -0.56% for sitagliptin (mean baseline A1c = 8.1%). The curves for albiglutide and glimepiride were comparable for the first six months, and subsequently the effect of glimepiride began to wear off while albiglutide’s efficacy largely remained. At week 104, 39% of patients in the albiglutide group achieved an A1c below 7% compared to 31% in the glimepiride group, 32% in the sitagliptin group, and 16% in the placebo group. Given that less than half of patients in the albiglutide group had not reached a sub-seven A1c, we were surprised that only 53% of patients in the albiglutide group had been uptitrated to the higher 50 mg dose by week 104.
• Dr. Carr next presented three-year data in the population of patients that did not require hyperglycemia rescue. Albiglutide still provided the greatest reduction in A1c, although the difference was more modest at year three: albiglutide provided a -0.42% placebo-adjusted reduction in A1c from baseline, compared to -0.13% with glimepiride and -0.10% with sitagliptin. However, it also bears mentioning that the number of patients who were still in the study at year three and did not require hyperglycemia rescue was very small – only 16 of the 94 patients on placebo made it from study initiation made it to week 156, and perhaps as a result, the placebo group saw a -0.46% A1c reduction from baseline at week 156. Albiglutide also led to a modest reduction in fasting plasma glucose at week 156, on the order of -22 mg/dl (the slide did not indicate whether the difference was statistically significant).
• Because patients that required hyperglycemia rescue continued taking their randomized therapy, Dr. Carr was able to present the results of an intent-to-treat analysis. Significantly fewer patients randomized to albiglutide required hyperglycemia rescue (29%) by year three, relative to 38% with sitagliptin, 34% with glimepiride, and 49% with placebo. In this patient population (which included more patients than the per-protocol 156-week pool), albiglutide led to a placebo-adjusted reduction in A1c of -0.68%, compared to -0.41% with glimepiride and -0.27% with sitagliptin.
• Unusually for a GLP-1 agonist, albiglutide was not associated with a long-term weight benefit relative to placebo or sitagliptin. While there might have been a slight advantage for the first half year, from then onwards out to year three, the three curves tracked roughly in line with one another. Interestingly, the three groups held steady at ~1 kg (~2 lbs) weight loss for the entire study period, rather than the upward drift we would have expected with placebo and perhaps sitagliptin for such a long time period. The lack of weight loss with albiglutide was brought up during Q&A, where Dr. Carr acknowledged the likelihood that albiglutide’s large molecular size prevents it from crossing the blood-brain barrier and interacting with satiety-regulating hypothalamic nuclei. However, she also noted that this phenomenon could explain the low rates of nausea seen with the drug (see below).
• Generally, relatively low weight loss would be negative, but it appears that the mechanism behind that also leads to very low nausea rates. Indeed, in in this trial, albiglutide was associated with a relatively low incidence of nausea and vomiting. The respective incidences of nausea/vomiting were 12%/7% with albiglutide relative to 7%/5% with sitagliptin, 8%/4% with glimepiride, and 13%/1% with placebo. The three-year duration of the study explains the slightly elevated incidence of GI side effects across the board. Given that nausea is one of the most challenging tolerability issues that patients experience with GLP-1 agonists, this profile might portray albiglutide as a more beginner-friendly option.
  
  • There were two cases of definite or probable pancreatitis in the trial, both in the albiglutide group. The incidence of injection site reactions was somewhat elevated in the albiglutide group (18%). The numbers of adverse events that led to withdrawal of active treatment were perhaps slightly higher in the albiglutide group (8%) than with sitagliptin (4%) or glimepiride (6%), but the overall number of events was relatively small for a three-year trial.

Questions and Answers

Q: It was puzzling that there was no weight loss with this GLP-1 agonist – it appeared that sitagliptin caused just as much weight loss. Dr. Michael Nauck theorized two years ago that albiglutide might not show weight loss because it does not cross the blood-brain barrier. Did you examine that?

A: I would agree with Dr. Nauck’s hypothesis – albiglutide is a large 73 kilo-Dalton peptide, which is unlikely to have much passage through the blood-brain barrier to gain access to the hypothalamic nuclei. Those nuclei that regulate satiety and food intake are close to the centers that regulate nausea, which could be the reason that we saw low rates of nausea and vomiting. But we do not have data yet specifically on blood-brain barrier penetration.

Efficacy and Safety of Liraglutide vs. Placebo When Added to Basal Insulin Analogs in Patients with Type 2 Diabetes (LIRA-ADD2BASAL) (331-OR)

Andrew Ahmann (Oregon Health and Science University, Portland, OR)

Dr. Andrew Ahmann described a 26-week randomized controlled trial of liraglutide 1.8 mg/day vs. placebo in patients with poorly controlled type 2 diabetes using basal insulin, with or without metformin (n=550). Mean baseline data in the liraglutide and placebo groups were as follows for A1c (8.2% vs. 8.3%), BMI (32.3 vs. 32.2 kg/m²), diabetes duration (12.1 vs. 12.1 years), and stable dose of insulin analog dose (48.3 vs. 45.9 U/day). Insulin dosage could not be increased following randomization; during Q&A Dr. Ahmann said that this rule was made to clearly show liraglutide’s effect on A1c. At 26 weeks, liraglutide was statistically significantly superior to placebo with respect to A1c decrease (1.30% vs. 0.11%), FPG decrease (1.44 vs. 0.16 mmol/l [25.9 vs. 2.9 mg/dl]), weight loss (3.54 kg vs. 0.42 kg [7.9 lbs vs. 0.9 lbs]), percentage of patients achieving A1c <7.0% (59% vs. 14%), and percentage of patients achieving A1c <7.0% without weight gain or hypoglycemia (42% vs. 9%). Liraglutide-treated patients also used a significantly lower ratio of their initial basal insulin dose (0.87 vs. 0.98). The liraglutide group had more documented symptomatic hypoglycemia (30.7% vs. 20.4%) and more asymptomatic hypoglycemia (31.6% vs. 27.6%). Adverse events occurring in at least 5% of patients were more common with liraglutide (69% vs. 58%), due largely to gastrointestinal side effects such as nausea (22% vs. 3%), vomiting (9% vs. 1%), and decreased appetite (10% vs. 2%). No known pancreatitis occurred in either group.

Questions and Answer

Dr. Richard Bergenstal (International Diabetes Center, Minneapolis, MN): Were there any seven-point glucose profiles? I’m trying to get a sense of fasting and postprandial.

A: Seven-point profiles were done three times during the trial. Fasting glucose started at roughly 145 mg/dl. In the liraglutide group fasting glucose decreased by 26 mg/dl. Postprandial glucose was significantly different; the delta was about 18 mg/dl. The mean values for the seven-point profiles were consistent with the A1c changes.

Q: If the control group could have kept adjusting their basal insulin, they probably would have had better A1c and maybe more hypoglycemia. How did you decide to design the trial to prevent up-titration of insulin?

A: The intention was to focus on A1c in part because of regulatory guidance trying to make that the point.

Q: Were the decreases in systolic blood pressure seen in all patients, or only those patients starting with higher systolic blood pressure?

A: I don’t know exactly; the blood pressure was not exceptionally high as a whole. In this particular trial we have not looked at that yet. In other trials, there appears to be more effect with higher baseline SBP.

Q: The cynic in me thinks that if you make someone feel sick, whatever drug you give them, they tend to get better. Was A1c different in the patients with nausea?

A: In other trials, significant benefits on A1c and weight were seen even without nausea. It was not separated out in this study.

Dr. Bergenstal: Thank you for using composite indexes. I think these are valuable and we need to decide which to use.

Liraglutide and the Preservation of Beta-Cell Function in Early Type 2 Diabetes: A Randomized, Controlled Trial (328-OR)

Ravi Retnakaran, MD (University of Toronto, Canada)

Dr. Ravi Retnakaran of Bernard Zinman’s group presented results suggesting that beta cell function is preserved if not enhanced in people on liraglutide who did not have glucotoxicity at baseline. Dr. Retnakaran suggested that this finding warrants further investigation into the long-term clinical effects of early liraglutide use on the progression of type 2 diabetes. During Q&A, however, several attendees objected to the use of a placebo instead of an active comparator. Due to this decision it is difficult to determine if the difference in beta cell function between the two groups at the end of the study was because of the glucotoxicity the control group likely experienced. Had an active comparator (e.g., IIT) been used it might be easier to conclude the direct impact liraglutide had on beta cell preservation. That said, comparing the liraglutide group’s beta cell function at the end of the treatment period against that at baseline, does suggest the therapy preserved or enhanced beta cell function. 

• Dr. Retnakaran critiqued other studies of anti-diabetes drugs’ effects on beta cell function for not addressing the confounding effect of glucotoxicity. Studies evaluating the effects of anti-diabetes therapies on beta cell function are typically confounded by glucotoxicity at baseline and its alleviation by the therapy under study. As a result, improvements in beta cell function attributed to a drug could partly reflect the removal of glucotoxicity as opposed to a direct effect of the therapy on the beta cells. Thus, elimination of glucotoxicity is needed for the objective assessment of the capacity of a therapy to directly preserve or improve beta cell function.
• To address this confounding variable, the researchers placed participants (n=63) on four weeks of intensive insulin therapy (IIT) to eliminate glucotoxicity before randomizing them to either liraglutide or placebo. To determine if glucotoxicity had been removed the investigators tested if a person’s fasting venous glucose level was less than 126 mg/dl a day after ending the IIT – in this way they saw if a person’s endogenous beta cell function could keep his/her FPG in the euglycemic range. People were only randomized to treatment if they met this threshold. Of the 61 people initially enrolled, 51 were randomized to either liraglutide or placebo. These participants were then followed for 48 weeks.
• At the time of randomization, 60% of participants were male, 70% were white, and they had an average age of 58 years. The average diabetes duration was slightly different between the liraglutide (three years) and control (1.5 years) groups. The average A1c was similar between the two groups at 6.4% in the liraglutide arm and 6.2% in the placebo arm. The measure of beta cell function utilized was ISSI-2; at randomization the liraglutide group’s was 193 and the control’s was 220 (for context, an ISSI-2 associated with NGT is >800). Dr. Retnakaran did not seem concerned about the difference in ISSI-2 between the two groups.
• The liraglutide arm’s ISSI-2 was enhanced by more than 50% (from 193 to ~350) in the first 12-weeks post-randomization and was preserved thereafter. In contrast, the placebo arm’s ISSI-2 stayed flat at roughly 250. Similarly, the researchers identified significant improvements in OGTT insulin, glucose, and C-peptide response curves in the liraglutide arm relative to the control. This conferred better glycemic control in the liraglutide arm, with significantly more people in the treatment group than in the control achieving an A1c <6% at each point in time. No significant difference in insulin sensitivity was observed, though the liraglutide group did lose weight.
• The improvement in ISSI-2 did not persist after a washout period. The liraglutide group’s ISSI-2 dropped to 192 and the control’s continued at 238.

Questions and Answers

Dr. Julio Rosenstock (University of Texas Southwestern Medical School, Dallas, TX): Beautiful study. That is a great concept to assume that in the hyper-milieu you probably wont see the effects of beta cell preservation. I would have kept the basal insulin so that way you would have had an active control. 

A: Absolutely, thank you for that point. We have eliminated glucotoxicity at the outset; however, the two meds do have differing glucose lowering effects subsequently. It could be that with an active comparator we would address that part of the design.

Q: At the end of these 48 weeks glucose control was worse in the placebo arm. How can you exclude that the difference in these effects were due to the difference in glucose control? You might have seen the effects of glucotoxicity.

A: It needs to be recognized that ISSI-2 is a measure of beta cell function, not glucose control. Your concern is – how do we know that this is the direct effect of liraglutide on beta cell function at the end of the study given the lack of glucose control in the placebo. That is right. What we can say is that glucotoxicity was not a confounder at the outset. At the end there could be some effect of glucotoxicity.

Comment: Then it is difficult to conclude that liraglutide has this protective effect after 48 weeks, because this might be due to the difference in glucose control over the 48 weeks.

A: The justification of the statement of preservation is that at the outset they were the same and then when we gave this drug we enhanced beta cell function and maintained that.

Q: I love the concept of intervening to reverse glucose toxicity, but I cannot tell from this study how much of that occurred. At the end you had 250 when normal was 800; so you only had 30% of beta cell function.

A: The reason is that we are still in the non-normal range is that these people still have diabetes. We have only addressed the glucotoxicity.

Comment: You did not improve ISSI-2 so you probably did not have to do the IIT at the start.

A: We would not know that the ISSI-2 would not improve until we have done it.

Comparison of Glycemic Control and Beta-Cell Function in Newly Diagnosed Type 2 Diabetes Patients Treated with Exenatide, Insulin, or Pioglitazone: A Multicentre, Randomized, Parallel-Group Trial (CONFIDENCE) (327-OR)

Wen Xu, MD (Yat-sen University, Guangzhou, China)

Dr. Wen Xu shared 48-week data from a randomized comparison of AstraZeneca’s Byetta (exenatide), pre-mix insulin, and Takeda’s TZD Actos (pioglitazone) – three therapies that have been associated with improved beta-cell function. The Lilly-funded trial enrolled 416 Chinese adults with newly diagnosed type 2 diabetes and mean baseline A1c ~8.0%, age ~50, and weight ~70 kg (~155 lbs). In the trial’s primary endpoint, exenatide was shown non-inferior to pre-mix insulin and pioglitazone in 48-week A1c decline (1.8% vs. 1.74% vs. 1.47%); the difference between exenatide and pioglitazone was statistically significant. Exenatide caused weight loss (3.25 kg [7.15 lbs]), pre-mix insulin caused weight gain (1.0 kg [2.2 lbs]), and pioglitazone was weight-neutral.  As for beta-cell function, all three treatments significantly improved acute insulin response, insulin/proinsulin ratio, and disposition index, though none improved HOMA-B. The improvement in disposition index was numerically largest with exenatide (statistics not given). Dr. Xu did not comment about how much of exenatide’s effects on disposition index could be directly attributed to weight loss.  

• The CONFIDENCE trial enrolled patients with newly diagnosed type 2 diabetes, no previous exposure to antihyperglycemic drugs, A1c between 7.0% and 10.0%, and BMI between 20 and 35 kg/m². The study was conducted at 25 national-university-affiliated hospitals in China. Patients were randomized to receive exenatide (n=142), premix insulin (n=138), or pioglitazone (n=136) for 48o weeks; the number of completers in each group was 110, 114, and 118, respectively. In the premix insulin group, dosage was titrated in order to maintain blood glucose targets; the average daily dose 26 units.  
• An A1c below 7.0% was achieved by more than three-quarters of the patients in each treatment group: 84% with exenatide, 78% with pre-mix insulin, and 76% with pioglitazone. The percentage of patients achieving A1c below 6.5% was statistically significantly highest in the exenatide group.
• All three treatments had beneficial effects on one or more markers of cardiovascular risk, but these changes seemed to be most promising in the exenatide group. Relative to baseline, the exenatide group had lower systolic blood pressure (4 mm Hg), lower diastolic blood pressure (3 mm Hg), and better lipid profile.
• Rates of symptomatic hypoglycemia <70 mg/dl with exenatide, premix insulin, and pioglitazone were 9.2%, 13.0%, and 3.8%, respectively. Dr. Xu noted that six exenatide-treated patients reduced their dose due to frequent confirmed hypoglycemia; excluding these patients, the rate of hypoglycemia with exenatide was 4.9%. The most common side effects with exenatide were gastrointestinal; with pioglitazone the most common side effect was edema.

Questions and Answers

Dr. Richard Bergenstal (International Diabetes Center, Minneapolis, MN): What do you think is more important for beta-cell function: lowering glucose, or something about a particular agent?

A: We are sure that glucose toxicity is one of the things that makes beta cell function decline. All three drugs are non-inferior in beta cell function. Still, we see different impact on beta-cell function. Insulin/proinsulin ratio and acute insulin response improved similarly; disposition index improved most in exenatide.

Q: Can you test patients again in a study extension?

A: We have an extension phase. Hopefully we can check them again in five years.

Q: Was insulin titrated in the study to a certain target?

A: Yes, we had specific recommendations. We did not have a specific glycemic target, but we did ask investigators to increase doses if fasting or after-dinner glucose rose above specific thresholds.

Q: Was control similar in all three groups?

A: Yes.

Q: What type of insulin was used? What was the average insulin dose used?

A: All the insulin was pre-mixed. The average dose was roughly 26 U/day.

Q: Did you find patients early enough that no metformin had been introduced, or is it not necessarily given to everyone early like it might be in US?

A: No, no one had used metformin. That was one of the inclusion criteria.

Q: How soon after diagnosis were these patients?

A: The patients had been just diagnosed.

Exenatide Affects the Distribution of Cerebral Postprandial GLucose Uptake: A Double-Blind, Randomized Clinical Trial (325-OR)

Guiseppe Daniele, MD, PhD (University of Texas, Health Science Center, San Antonio, TX)

Dr. Guiseppe Daniele presented new data on the effects of the GLP-1 agonist exenatide on cerebral glucose uptake. Though exenatide’s effects on the pancreas and the GI tract have been studied relatively extensively, little is known about its potential impact on glucose metabolism in the brain. This double-blind study involved 15 male subjects with impaired glucose tolerance (IGT) or new-onset type 2 diabetes (with an average A1c of 5.7%) who were randomly assigned to receive an injection of either exenatide or placebo prior to undergoing an oral glucose tolerance test (OGTT). The results demonstrated that the rate of glucose uptake in several brain regions implicated in food intake and/or glucose regulation was significantly higher with exenatide. Interestingly, the rate of glucose uptake was actually lower in the hypothalamus. Other results confirmed previously known metabolic effects of GLP-1 agonists, including lower plasma glucose and reduced gastric emptying. The researchers found that in the relevant brain regions, the increased glucose uptake was strongly correlated with the reduction in gastric emptying but was not correlated with suppression of endogenous glucose production (EGP). Based on these results, Dr. Daniele concluded that exenatide has a postprandial effect on cerebral glucose disposal and maintains glucose homeostasis in part by regulating the rate of gastric emptying and thus the rate of entry of ingested glucose into the bloodstream.

• The goal of this study was to evaluate exenatide’s postprandial effects on cerebral and peripheral glucose metabolism. GLP-1 agonists like exenatide are increasingly becoming part of the treatment paradigm for type 2 diabetes and their effects on appetite suppression and insulin and glucagon secretion are widely known, but little was previously known about whether they have an effect on glucose metabolism in the brain.
• The double-blind study enrolled 15 male subjects with IGT (n=12) or new-onset type 2 diabetes (n=3). The average BMI of the subjects was 29 kg/m², their average A1c was 5.7%, and their average age was 56.
• Subjects were randomly assigned to receive a subcutaneous injection of exenatide (5 mcg) or placebo 30 minutes before undergoing an OGTT. PET scans were used to measure brain glucose uptake. All subjects underwent the procedure under both the exenatide and placebo conditions 2-3 weeks apart.
• Exenatide increased glucose uptake in a variety of brain regions involved in food intake and glucose regulation but decreased glucose uptake in the hypothalamus. Treatment with exenatide led to a significant increase in glucose uptake in total gray matter and total cortex, as well as in the frontal, temporal, and occipital lobes, though not in the parietal lobe. Among regions that have been shown to be involved in glucose regulation, uptake was significantly increased in the insula and the putamen with exenatide; rates were comparable between the groups in the nucleus tractus solitarius (NTS), brainstem, caudate, and amygdala, and surprisingly, there was a significant decrease in uptake in the hypothalamus. Among regions known to be involved in regulation of food intake, there was a significant increase in glucose uptake in the orbitofrontal cortex, thalamus, posterior cingulate, and putamen with exenatide, but rates were comparable between the groups in the anterior cingulate, caudate, and amygdala.
• Other results from this study confirm the previously studied effects of GLP-1 agonists on glucose metabolism. The area under the curve for plasma glucose was significantly lower (1231 mg/dl) with exenatide than with placebo (1594 mg/dl) for the first hour after the beginning of the OGTT. Somewhat unexpectedly, the area under the curve for plasma insulin was also significantly lower (45 mU/l) with exenatide than with placebo (63 mU/l) during the same time period – we would speculate that this might have been due to a substantial slowdown in gastric emptying. Glucagon levels and endogenous glucose production (EGP) were lower with exenatide, as was the rate of appearance of oral glucose, an indication of reduced gastric emptying. Glucose clearance was comparable between the two groups.
• An analysis of this data found that increased cerebral glucose uptake was correlated with reduced gastric emptying and was not correlated with EGP suppression. In the brain regions implicated in glucose control that displayed increased glucose uptake with exenatide, there was no correlation between the rate of uptake and the rate of EGP, but there was an inverse correlation between the rate of uptake and the rate of appearance of oral glucose.

Questions and Answers

Q: This is fascinating and amazing data; it’s really exciting. You saw a remarkable increase in uptake despite lower plasma glucose with exenatide. If you just gave exenatide and measured glucose uptake without the glucose load, would you get the same effect?

A: We haven’t done that experiment. Another group did a study using fMRI with subjects in a fasting condition and found that exenatide increased connectivity between the hypothalamus and the rest of the brain, but no one has measured glucose uptake.

Comment: That would be interesting. I would wonder whether hepatic glucose production compensates and glucose is redistributed from the brain.

Q: Did you ask the participants about GI side effects, e.g. nausea? If so, you would need a nausea control group.

A: That’s an important question. We asked subjects to evaluate their level of GI effects but we didn’t observe any GI effects, so we couldn’t have a nausea control group.

Q: I was struck by the marked decrease in uptake in the hypothalamus. Are there any implications of that?

A: I can only speculate since we can’t study that directly, but I can speculate that the reduction is related to the decrease in appetite and regulation of food intake. This is something that’s been shown with GLP-1, where researchers infused GLP-1 in healthy subjects with no OGTT and observed the same thing. But that was native GLP-1, so it could be different.

Oral Presentations: Basal Insulin Therapy

One-Year Efficacy and Safety of IDegLira in Patients with Type 2 Diabetes (65-OR)

Stephen Gough, MD (University of Oxford, Oxford, UK)

Dr. Stephen Gough presented findings from a 26-week extension of the original 26-week DUAL I trial on Novo Nordisk’s IDegLira, the fixed-ratio combination of Novo Nordisk’s ultra-long-acting basal insulin Tresiba (insulin degludec) and GLP-1 agonist Victoza (liraglutide).  The 52-week results were remarkably consistent with the results of the original 26-week study, which were first presented at last year’s ADA (read our coverage). From a baseline of ~8.3%, patients in the IDegLira arm achieved a mean final A1c at 52 weeks of 6.5% (reduction of 1.8% from baseline), a statistically significant improvement over insulin degludec’s final A1c of 6.9% (reduction of 1.4%) and liraglutide’s 7.1% (-1.2%). The theme of conserved results also applied to fasting plasma glucose (IDegLira was significantly better than liraglutide but not insulin degludec), hypoglycemia (IDegLira was associated with 37% less confirmed hypoglycemia than insulin degludec monotherapy), and body weight (IDegLira was fairly weight neutral and fell squarely between the weight gain of insulin degludec and weight loss of liraglutide). The constancy of efficacy and safety results from the original 26-week results was reassuring for a few reasons – it reduces the likelihood that the 26-week findings were due to chance, and also demonstrates that the combination product’s efficacy is not only strong, but also fairly durable (patients’ mean IDegLira dose was flat from week 26 to week 52).   

• Following the 26-week trial DUAL I study, the investigators began an additional 26-week long extension study. Attrition rates were approximately equal among the three arms. Out of the 1,442 patients that completed the 26-week DUAL I study, 84% completed the extension phase. Notably, the mean daily dose of IDegLira remained steady during the full 26-week follow-up, while the average dose in the insulin degludec group rose slightly.
• Results from the extension study showed that IDegLira’s A1c-lowering efficacy was stable through 52 weeks – overall many of the efficacy values were remarkably similar from the 26-week results. IDegLira yielded a final absolute A1c reduction of -1.8%, versus insulin degludec (-1.4%) and liraglutide (-1.2%) from an average baseline of 8.3% (p<0.0001 for both comparisons). Percentages of patients who met A1c targets also remained stable: IDegLira users continued to achieve A1c targets of 7% and 6.5% (78.2% and 66.9% of patients, respectively) significantly more than insulin degludec users (62.5% and 49.2%) and liraglutide users (56.5% and 38.2%).
• Compared to insulin degludec, IDegLira still yielded significantly fewer episodes of confirmed hypoglycemia, showed decreased glycemic variability, and resulted in no weight gain. At 52 weeks, IDegLira users experienced only 63% of the hypoglycemia rate of insulin degludec users, a significant difference (p<0.0001). Hypoglycemia rate ratios remained relatively stable from 26 weeks to 52 weeks. Post-prandial glucose excursions were significantly smaller for IDegLira users compared to insulin degludec users for every meal. IDegLira also resulted in slight weight loss (0.4 kg, ~1 lb), compared to insulin degludec’s 3 kg (~5 lb) weight gain and liraglutide’s 3 kg (~7 lb) weight loss, all of which were relatively stable after the first 26 weeks.
• Compared to liraglutide, IDegLira showed significantly greater reductions in fasting plasma glucose as well as fewer GI adverse events. After 52 weeks, fasting plasma glucose in the IDegLira arm (103 mg/dl) was comparable to that in the insulin degludec arm (108 mg/dl), but still significantly lower than that in liraglutide users (132 mg/dl) (p<0.0001 for the IDegLira – liraglutide difference). Percentage of subjects with nausea remained stable after the initial 26 weeks, with IDegLira users reporting more nausea than insulin degludec but less than liraglutide (overall incidence was very low compared to the first few weeks of the trial). Adverse GI events associated with liraglutide usage were also mitigated in IDegLira. Events included diarrhea (10.2% of IDegLira users, compared to 6.8% for insulin degludec users and 16.3% for liraglutide users), the common cold (13.9%, 12.6%, 13.3%), headache (12.8%, 10.9%, 14.6%), nausea (10.3%, 3.9%, 22.3%), increased lipase (5.8%, 4.4%, 8.5%), vomiting (5%, 2.4, 9.2%), and decreased appetite (2.7%, 0.5%, 7.3%).
  • In terms of serious adverse events, four major adverse cardiovascular events occurred in the IDegLira group, and one each occurred in the insulin degludec and liraglutide groups (note that the IDegLira arm was randomized 2:1:1). Total adverse events still did not differ significantly after 52 weeks: 71.2% IDegLira users experienced adverse events, compared to 70.6% insulin degludec users and 77.2% liraglutide users. Rates of serious adverse events remained low across the board. After 52 weeks, 4.6% IDegLira users, 5.3% insulin degludec, and 5.8% liraglutide users experienced serious adverse events.

Questions and Answers

Q: I wonder about the ratio between insulin and liraglutide. The average dose of liraglutide in IDegLira was 1.4 mg. What about patients who used 20 to 30 units of insulin in the combination product, because they would have received liraglutide less than 0.6 mg, and far less than 1.2 mg, the effective dose of liraglutide. Can you speak about the distribution of doses, and how subgroups compared with high doses versus low doses for each of the drugs?

A: This is a fixed ratio of degludec and liraglutide. The question is: what ratio should you have to suit the most people? We know that 40% of the patients did get the max dose and 70% achieved the target A1c. There are patients who could benefit from a little more insulin or a little more liraglutide, but on the whole a great portion of patients responded and a large portion reached the target of 7%. Obviously some may benefit from free mixing but this combination seemed to work pretty well.

Comment: I was wondering if patients using less than 30 units of insulin did better on the combination, or would there be no difference between IDegLira and degludec?

A: I don’t have those analyses with me.

Q: The hypoglycemia data is very encouraging, but I was wondering whether it was reduced calorie intake or decreased gastric emptying.

A: I’m not sure we know the exact answer to that, but we know that liraglutide has very little, if any, effect in terms of gastric emptying in the long term, at least relative to the shorter-acting GLP-1 receptor agonists.

Impact of BMI on HbA1c Reduction, Hypoglycemia Rates, and Insulin Requirements in Response to IDegLira in Patients with Type 2 Diabetes (T2D) (66-OR)

John Buse, MD, PhD (University of North Carolina, Chapel Hill, NC)

In this packed room, over a thousand attendees watched the legendary Dr. John Buse take us through a post-hoc analysis of Novo Nordisk’s DUAL I and DUAL II studies. These studies were an investigation of the effectiveness of IDegLira (Xultophy) – a combination of the GLP-1 agonist Victoza (liraglutide) and ultra-long-acting basal insulin Tresiba (insulin degludec) – compared to each its two components (insulin degludec and liraglutide). Dr. Buse showed that regardless of baseline BMI, IDegLira lowered A1c more or less by the same amount, and there was a reduction in hypoglycemia with increasing BMI in the DUAL I study. By and large, IDegLira demonstrated larger A1c reductions than insulin degludec alone, with less hypoglycemia. Meanwhile, insulin use increased with BMI, as might be expected.

• Dr. Buse presented the results of a post-hoc analysis of the DUAL data, stratifying A1c reduction and hypoglycemia by baseline BMI, and demonstrated that there is no significant difference in IDegLira’s efficacy with increasing BMI and only a slight decrease in hypoglycemia with increasing BMI. In DUAL I, the A1c reduction with IDegLira was around 1.9% from baseline, which was constant across four BMI cohorts and superior to insulin degludec or liraglutide alone. In DUAL II there was a bit more variation with BMI, but it was probably because it was a smaller trial. Turning to severe hypoglycemia, the DUAL I data suggested that hypoglycemia is consistently lower with IDegLira than insulin degludec, regardless of BMI, and that hypoglycemia incidence halved going from BMI <25 kg/m² to BMI >35 kg/m².where it was in the range of one event per patient year. Again the DUAL II data was less clear, this time because of outliers.
• Finally, in DUAL I, insulin requirements increased with increasing BMI across the four cohorts, (as might be expected) and the IDegLira group used consistently less insulin than the insulin degludec group, regardless of BMI. DUAL II was less clear because there was a cap on insulin dose, which most patients reached.
• Last year’s presentation of the original DUAL I results (by none other than Dr. Buse) was one of the most exciting happenings of ADA 2013 – read our coverage.

Questions and Answers

Q: Did any of the patients have impaired renal function, and how do you use this drug in this patient group?

A: If I remember correctly, theses studies did not include patients with impaired renal function. The drug is only available in trials, but we know that liraglutide is metabolized in circulation. However, there is a higher risk of hypoglycemia with these patients, so I would apply it cautiously.

Q: The studies started with quite a high IDegLira dose. What was the incidence of nausea?

A: In DUAL II, we started with the equivalent of the 0.6mg dose of liraglutide. But rates of nausea were quite low; 2-3% at maximum at any point. But remember that the trial was blinded and patients had not been told to expect nausea. So starting with a lower dose creates less nausea, not telling patients lessens nausea, and so does slow titration.

IDegLira Is Efficacious Across the Range of Disease Progression in Type 2 Diabetes (T2D) (67-OR)

Helena Rodbard MD, FACE, MACE (Rockville, MD)

Dr. Helena Rodbard continued the analysis of Novo Nordisk’s DUAL I and DUAL II trials by demonstrating that regardless of baseline A1c, duration of diabetes or prior diabetes medications, IDegLira (Xultophy, Novo Nordisk’s combination of the GLP-1 agonist Victoza [liraglutide] and the ultra-long-acting basal insulin Tresiba [insulin degludec]) lowers A1c better than liraglutide or insulin degludec alone, with lower rates of hypoglycemia than insulin degludec. As might have been expected, A1c reduction was proportional to A1c at baseline. None of Dr. Rodbard’s post-hoc analyses gave us reason to suggest that IDegLira might be meaningfully less effective in any major patient subgroups.

• IDegLira is a fixed-ratio combination of liraglutide and insulin degludec that delivers one unit of insulin degludec for every 0.036 mg of liraglutide.
• IDegLira was compared to its individual components in the DUAL I trial, which was one of the most striking presentations of last year’s ADA (read our coverage). DUAL II compared IDegLira to insulin degludec only. DUAL I was a 52-week open label study of over 1,600 insulin naïve patients who were failing oral therapy. The three arms were IDegLira, insulin degludec alone and liraglutide alone. At baseline, participants were middle aged with BMI around 31 kg/m², duration of diabetes about 7 years and A1c around 8.5%. DUAL II was designed for a specific regulatory purpose – to study the impact of the liraglutide component compared to insulin degludec alone. DUAL II was a 26-week study of nearly 400 patients, who were uncontrolled on basal insulin and oral drugs. At baseline, the participants were taking 20-40 units of basal insulin daily, had BMI of 34 kg/m², duration of diabetes 10-11 years, and baseline A1c of 8.7%.
• Dr. Rodbard showed that as expected, the A1c reduction obtained with IDegLira increases with increasing baseline A1c; however regardless of baseline A1c, iDegLira was more effective than either insulin degludec or liraglutide alone. In DUAL I, the A1c reduction for IDegLira ranged from 2.5% at A1c > 9% down to 1.2% at A1c <7.5%. The results of the DUAL II analysis were broadly similar, although reductions were not quite as large because of the trial design.
• Turning to hypoglycemia, in DUAL I, IDegLira demonstrated consistently less hypoglycemia than insulin degludec alone, regardless of baseline A1c. Hypoglycemia for IDegLira declined slightly with increasing baseline A1c, in the region of two events per patient-year. In DUAL II there was no real difference between iDegLira and insulin degludec in the incidence of hypoglycemia, (except in the 8.5%-9.0% group which was driven by two outlier patients).
• A1c reduction was independent of diabetes duration and more or less the same regardless of prior diabetes medications. Scatter plots showed no significant relationship between duration of diabetes and A1c reduction for IDegLira, insulin degludec or liraglutide even out to patients with >35 years duration. In DUAL I, participants were formerly taking metformin or metformin and pioglitazone. In both cases, iDegLira delivered a strong A1c reduction, with only small (but statistically significant) differences (slightly better reduction in the metformin/pioglitazone group). In DUAL II, the reduction was the same regardless of pre-trial insulin dose and similar for the different pre-trial oral therapies (metformin or metformin/sulfonylurea/glinide).

Questions and Answers

Q: The average hypoglycemia event rate is not so meaningful since the distribution is highly skewed, and it has affected both yours and John Buse’s presentations. Shouldn’t we use the percentage of participants affected instead? Do you have this data?

A: I don’t think that this was done, but thanks for the suggestion.

Oral Presentations: Oral Incretin-Based Therapies

TTP273, an Orally-Available Glucagon-like Peptide-1 (GLP-1) Agonist, Notably Reduces Glycemia in Subjects with Type 2 Diabetes Mellitus (155-OR)

Stephanie Gustavson, PhD (Trans Tech Pharma, High Point, NC)

Dr. Gustavson presented data from a dose-ranging study of TTP273, Trans Tech’s second-generation oral GLP-1 agonist, in patients with type 2 diabetes (n=112). The objective of this trial was to evaluate the safety, tolerability, and PK/PD profile of TTP273. Dr. Gustavson commented that this second-generation oral GLP-1 agonist demonstrated increased potency compared to Trans Tech’s first-generation oral GLP-1 agonist, TTP054. In terms of efficacy, dose-responsive decreases in fasting plasma glucose and mean daily glucose were observed (39 mg/dl and 42 mg/dl, respectively, at the 75 mg BID dose). In addition, there were very few GI-related adverse events, with only four patients in the entire study exhibiting any nausea. However, given that this was a dose-ranging study and many patients received low doses of TTP273, we look forward to longer-term trials with therapeutic doses of TTP273 to better understand the adverse event profile and GI-related side effects.

• Trans Tech conducted a 14-day inpatient clinical trial of TTP273 in type 2 diabetes patients on stable doses of metformin. Patients were checked into a clinical research facility for three weeks – arriving five days prior to the first dose and remaining until day 16. The purpose of the inpatient design was to ensure adherence and standardize diets, in order to isolate the glycemic effects of TTP273. The study included a 23-point mean daily glucose assessment and a mixed meal tolerance test (MMTT) on days -1 and 14. The trial investigated a broad range of dosing options, with 10 dose cohorts (25-450 mg QD, 25-150 mg BID, and an alternative dosing of 75 mg QPM).
• Treatment with TTP273 was associated with dose-responsive decreases in mean daily glucose and fasting glucose. At baseline, the mean A1c was 8.1% (8.5% in placebo and 8.0% in active). TTP273 was quickly absorbed, with a t_max of two hours and a half-life of six hours. The 24-hour glucose profile showed maximal reduction in glycemic control at the 150 mg BID and 450 mg QD doses; however, the BID and QPM regimens resulted in greater glucose lowering compared to the QD regimens. For mean daily glucose, TTP273 resulted in a 42 mg/dl reduction (75 mg BID), which was significantly greater than the 11 mg/dl reduction observed in the placebo group. TTP273 also significantly reduced fasting glucose, with patients in the 75 mg BID arm experiencing a 39 mg/dl reduction in FPG compared to 11 mg/dl in the placebo group.
• All adverse events were characterized as mild, with diarrhea being the most common GI-related adverse event. There were no serious adverse events or incidences of hypoglycemia. While diarrhea was the most frequent GI adverse event, Dr. Gustavson noted that there was no clear dose response relationship for this adverse event and that it often occurred on meal test days when patients were required to consume meals in a certain period of time.
• While this study was not designed to assess changes in secondary parameters, there were favorable trends in metabolic and cardiovascular markers. While specific data on secondary markers were not disclosed, Dr. Gustavson commented that there was a trend toward reduced body weight (up to 2 kg) and improvements in triglycerides, systolic blood pressure, and diastolic blood pressure.

Questions and Answers

Q: Could you say more about the chemical that is being administered and how it mimics and acts on GLP-1 receptor?

A: This compound is an allosteric agonist of the receptor. We’ve shown that exendin (9-37) competes with functional activity and we don't activate glucagon or GIP. What’s really interesting is that it may be more physiological in terms of cell signaling and receptor bias activation. Our compound does not activate ERK pathway, unlike current GLP-1 agonists; it just activates the cAMP pathway, so there may be a lower risk of cell proliferation.

Q: So it is acting on a different site on the receptor?

A: Yes.

TTP054, a Novel, Orally-Available Glucagon-like Peptide-1 Agonist, Lowers HbA1c in Subjects with Type 2 Diabetes Mellitus (156-OR)

Stephanie Gustavson, PhD (Trans Tech Pharma, High Point, NC)

In back-to-back presentations, Dr. Gustavson provided results from a 12-week trial of Trans Tech Pharma’s first-generation oral GLP-1 agonist, TTP054. In the trial, subjects with type 2 diabetes on stable metformin were randomized to receive 200 mg QD (n=19), 400 mg QD (n=28), or 800 mg QD (n=35) TTP054 or placebo (n=31) for 12 weeks. Results indicated placebo-corrected A1c declines of 0.9% (baseline 9.1%), 1.0% (baseline 9.0%), and 0.6% (baseline 8.8%) with the 200, 400, and 800 mg dose groups, respectively – similar to the values estimated from phase 1 data at ADA 2013. Weight loss was non-significant across the groups, at 0.1 kg (0.2 lbs), 0.4 kg (0.9 lbs), and 0.7 kg (1.5 lbs) in the 200, 400, and 800 mg dose groups, respectively, though Dr. Gustavson noted weight loss became significant when patients who discontinued sulfonylureas at baseline were excluded from the analysis; she also noted that weights at baseline were relatively low for diabetes trials (82-87 kg; 181-192 lbs). Rates of GI adverse events were 4%, 4%, and 16% in the 200, 400, and 800 mg dose groups, respectively, and 10% in the placebo group. Dr. Gustavson suggested the second-generation agonist, TTP273, has shown increased potency in early studies, though we will be interested to see how this plays out in clinical trials. As a reminder, there has increasing interest in oral formulations of GLP-1 as of late, with numerous candidates in clinical trials, including Novo Nordisk’s NN9924 (phase 2), NN9928, NN9926, and NN9927, Oramed’s ORMD-0901 (phase 2), and Zydus’s ZYOG1 (phase 1).

• In this double-blind trial, subjects with type 2 diabetes on stable metformin were randomized to receive 200 mg QD (n=19), 400 mg QD (n=28), or 800 mg QD (n=35) TTP054 or placebo (n=31) for 12 weeks. Patients on sulfonylureas at baseline stopped use with a wash out period prior to initiation in the trial. Baseline weights were 83 kg (182 lbs), 82 kg (181 lbs), and 87 kg (192 lbs) in the 200, 400, and 800 mg dose groups, respectively, and 84 kg (185 lbs) in the placebo group.
• Results indicated placebo-corrected A1c declines of 0.9% (baseline 9.1%), 1.0% (baseline 9.0%), and 0.6% (baseline 8.8%) with the 200, 400, and 800 mg dose groups, respectively, with no clear dose response (p<0.01). Weight loss, however, increased with increased dose, a 0.1 kg (0.2 lbs), 0.4 kg (0.9 lbs), and 0.7 kg (1.5 lbs) in the 200, 400, and 800 mg dose groups, respectively, though results were not significant. Dr. Gustavson noted that when patients who discontinued sulfonylureas at the beginning of the trial were excluded from the analysis, weight loss became significant across the groups. She posited this may gave been due to shifting weights at baseline in this population.
• Rates of GI adverse events were 4%, 4%, and 16% in the 200, 400, and 800 mg dose groups, respectively, and 10% in the placebo group. There were no episodes of hypoglycemia during the study. Dr. Gustavson indicated that two patients in the 800 mg dose group experienced serious adverse events with regards to elevated liver function test levels, though when scrutinized both occurred with contributing factors and resolved. She suggested this was not a major concern for the company given there was no increase in median liver function tests over the other dose groups and elevated levels were never seen in other clinical studies of the drug.

Questions and Answers

Dr. Zachary Bloomgarden (Mt. Sinai, New York, NY): Is the newer agent more potent for fasting blood glucose when corrected for baseline?

A: We compared earlier studies with this compound. Our second-generation compound does seem to lower more than our leading compound.

Q: Does the drug reach the brain?

A: They do not – we feel it is neural signaling in the gut that acts in the brain.

Q: Did you look at the population that did not respond at all?

A: We did. We were unable to identify why that could be. But I think it’s similar to what we see with the peptides with response rates.

Q: What about heart rate?

A: This wasn’t designed to pick that up. We did see minor fluctuations but didn’t see any major increases in heart rate. I think we need long-range studies to definitively answer that.

Incretin Therapy and Risk of Acute Pancreatitis: A Nationwide Population-based Case-Control Study (154-OR)

Reimar Thomsen, MD, PhD (Aarhus University, Aarhus, Denmark)

Dr. Thomsen presented the results of a population-based case-control study investigating the association between incretin use and the risk of acute pancreatitis. Using the Danish national patient registry, 12,868 patients with first-time hospitalization of acute pancreatitis were identified and matched with 128,680 control patients. Nationwide prescription data was then used to identify ever incretin use of 0.69% in pancreatitis patients versus 0.53% in controls. When adjusted for common risk factors, there was no significant increase in the risk of pancreatitis amongst incretin users (RR 0.95; 95% CI 0.75-1.21), in line with meta-analyses from clinical trials and FDA/EMA’s recent conclusions. Intriguingly, Dr. Julio Rosenstock (Dallas Diabetes and Endocrine Center, Dallas, TX) stepped out in the Q&A to critique the use of population-based studies to assess the risk of pancreatitis associated with incretin therapies – due to the lack of prospective design and the need for risk factor adjustment, he suggested “the answer will only come from randomized controlled trials.”

• Using the Danish national patient registry, 12,868 patients with first-time hospitalization of acute pancreatitis were identified and matched for age, gender, index date, and residence with 128,680 control patients from 2005-2012. Nationwide prescription data was then used to identify 8.5% ever use of glucose lowering drugs in the pancreatitis patients versus 6.1% use in the controls. Overall, ever incretin use was 0.69% in pancreatitis patients versus 0.53% in controls. Other known risk factors for pancreatitis identified included gallstone disease (16.8% vs. 4.0%), obesity (7.4% vs. 3.1%), and heavy alcohol use (15.4% vs. 4.4%).
• When adjusted for common risk factors (gallstones, alcohol use, obesity, inflammatory bowel disease, cancer), there was no significant increase in the risk of pancreatitis amongst incretin users (RR 0.95; 95% CI 0.75-1.21). This remained non-significant when specified for current GLP-1 use (RR 0.84; 95% CI 0.53-1.34) and current DPP-4 use (RR 0.78; 95% CI 0.53-1.16).
• Dr. Thomsen concluded by noting the relative strengths and weaknesses of the study design. The population-based design, he suggested, reduced the risk of selection and referral biases. However, he acknowledged that these studies are dependent on the choice of confounders used for adjustments as well as other potential unknown confounders affecting the data.

Questions and Answers

Dr. Zachary Bloomgarden (Mt. Sinai, New York, NY): This was interesting – I think most studies like this use diabetes diagnosis to identify patients – you didn’t use diabetes itself as a diagnosis but rather glucose-lowering drug use as a proxy. It seems in other studies, diabetes itself is a strong predictor of pancreatitis risk. Do you have any comment?

A: Yes, I think it worked out. We tried our best to match our cases and our controls.

Q: There are other known common drugs associated with pancreatitis. Have you been able to put these in the model?

A: That is a good recommendation – we could.

Dr. Julio Rosenstock (Dallas Diabetes and Endocrine Center, Dallas, TX): Very nice presentation, with very cleanly presented data and in transparent fashion noting the limitations. One of the problems these days with all these reports from population databases is that none of them are prospectively designed to look at pharmacovigilance. The problem with these data is that you precisely do not need to do the adjustments that are required – it is these populations that may be at the increased risk. In reality, those are the patients exposed – those that drink, that are obese, that have gallstones. It’s a very nice exercise; I applaud you for that, but the answer will only come from randomized controlled trials.

A: Thank you for the comment. We hope to improve this model.

DPP-4 Is Expressed in Human Pancreatic Islets and Its Inhibition Improves the Function and Survival of Type 2 Diabetic Beta Cells (149-OR)

Marco Bugliani PhD, PharmD (University of Pisa, Pisa, Italy)

Dr. Marco Bugliani’s talk focused on a novel inter-islet regulatory pathway in the pancreas. Since it is understood that there is an active GLP-1 response system in the islet cell, Dr. Bugliani set out to investigate the presence and role of DPP-4 in human islets. He tested alpha and beta cells, and found that DPP-4 was indeed present in islets, mostly in alpha cells, in both non-diabetic individuals and (to a lesser extent) in type 2 diabetes patients. Exposing cultured islets to a DPP-4 inhibitor protected the beta cells from both glucotoxicity and lipotoxicity, and also increased GLP-1 levels in the culture medium. Given the reduction in DPP-4 expression in type 2 diabetes patients, these protective effects would likely be hard to elucidate in the real world – no major clinical trials have provided conclusive evidence of a protective effect. If the protective action seen in this preclinical trial is indeed real, the long-term data from the longer DPP-4 inhibitor cardiovascular outcomes trials stand the best chance of providing corroborating evidence. We await the results …

• This work was designed to explore the presence and role of DPP-4 in human islet cells, since it is known that GLP-1 acts in the islet. Alpha and beta cells were isolated from 17 non-diabetic individuals and type 2 diabetes patients. Isolated islets were cultured for 24 hours in the presence of a DPP-4 inhibitor (MK-0626) and then observed in gluco- and lipotoxic conditions (using glucose and palmitate respectively). 
• DPP-4 could indeed be observed in pancreatic tissue and islets, and it was localized primarily to a subset of alpha cells. DPP-4 expression was reduced by roughly two thirds in the type 2 beta cells compared to non-diabetic cells.
• In cultured islets, the DPP-4 inhibitor MK-0626 improved insulin secretion, accompanied by a partial restoration of the volume density of insulin granules. DPP-4 inhibition increased insulin secretion more in the non-diabetic islets compared to the islets from type 2 diabetes patients, perhaps due to the reduced islet DPP-4 expression in type 2 diabetes. MK-0626 was also able to partially prevent beta cell apoptosis via the exposure to palmitate. In the presence of DPP-4, higher concentrations of GLP-1 were observed in the culture medium.

Questions and Answers

Q: Other peptides can be degraded or activated by DPP-4, so how do you know that these effects on the beta cell are mediated via GLP-1?

A: This is a very good point. We are testing other compounds that might be implicated, although we are relatively confident in our results.

Q: This work suggests that the effect on the beta cells is indirectly coming via a subset of alpha cells. What do you know about these cells?

A: We know that from a morphological point of view, DPP-4 is present in a subset of alpha cells, but we don’t currently know anything about the characteristics of these alpha cells versus the others.

Oral Glucose Lowering with Linagliptin Plus Metformin Is a Viable Initial Treatment Strategy in Patients with Newly Diagnosed Type 2 Diabetes and Marked Hyperglycemia (150-OR)

Stuart Ross, MD (University of Calgary, Alberta, Canada)

Dr. Ross presented the results of a pre-specified subgroup analysis from a 24-week clinical trial comparing linagliptin+metformin to linagliptin alone in newly diagnosed type 2 diabetes patients with marked hyperglycemia (mean baseline A1c of 9.8%). This sub-analysis examined A1c reductions in various subgroups, including patients with extreme hyperglycemia (≥9.5% A1c at baseline). In this subgroup, patients on the linagliptin+metformin combination experienced robust A1c reductions of 3.4% after 24 weeks, compared to a 2.5% reduction in the linagliptin alone group (from a high baseline A1c of 10.5%). In the overall study population, 61% of patients achieved a target A1c of <7% in the linagliptin+metformin group, and 40% of patients achieved target A1c in the linagliptin alone group. Dr. Ross acknowledged he was not expecting such a robust A1c reduction with linagliptin alone or in combination. As a result, he challenged the commonly held belief that insulin initiation is required to achieve target A1c levels in newly diagnosed patients with marked hyperglycemia. During Q&A, Dr. Zachary Bloomgarden hypothesized that newly diagnosed patients may be uniquely responsive to glycemic control interventions, citing data from the UKPDS study that showed similarly impressive A1c reductions during a three-month diet-based run-in period. Dr. Ross noted that diet and exercise did not appear to have played a major role in the A1c reductions achieved by linagliptin with or without metformin.

• The 24-week randomized trial compared linagliptin+metformin vs. linagliptin in drug-naïve type 2 diabetes patients within 12 months of diagnosis who had marked hyperglycemia. In this study, the linagliptin+metformin combination was associated with a 2.8% reduction in A1c, compared to 2.0% reduction in the linagliptin arm (from a mean baseline A1c of 9.7%). In the full-analysis set, patients on linagliptin+metformin experienced an A1c reduction of 2.7% compared to 1.7% for patients on linagliptin alone (from a mean baseline A1c of 9.8%).
• Notably, the majority of patients achieved an A1c <7% after 24 weeks of treatment with linagliptin+metformin. For patients with a baseline A1c <9.5%, the linagliptin+metformin group experienced an A1c reduction of 2.0%, which was significantly greater than the 1.4% reduction in the linagliptin alone group (mean baseline A1c of 8.7%). For the cohort of patients with a baseline A1c ≥9.5%, the linagliptin+metformin combination was associated with an impressive 3.4% reduction in A1c, compared to a 2.5% reduction in the linagliptin alone group (mean baseline A1c of 10.5%). These reductions in A1c were consistent across all major subgroups, including age, BMI, renal function, race, and ethnicity.

Questions and Answers

Dr. Zachary Bloomgarden, MD (Mount Sinai Hospital, New York, NY): Congratulations on getting 300 treatment-naïve patients recruited into your study. That is quite an accomplishment. This is reminiscent of the UKPDS cohort, where patients entered the trial with an A1c of 9%, had a three-month diet run-in and on average went to 7% with diet alone, suggesting this may indeed be a group of people who are uniquely able to respond to all sorts of interventions. So it’s fascinating that linagliptin alone was not bad.

A: It was a little bit of a surprise to us. We raised the question ourselves of diet and exercise. In the linagliptin alone group, there was no weight loss and in the linagliptin+metformin combination group, there was 1 kg weight loss. It’s interesting how we’ve always been led to insulin being the right way to bring A1c down in this patient population.

Q: Did response rates differ by race or ethnicity?

A: We wondered about that because there has been a lot of interest in this, especially with international clinical trials. There appears to be differences in some other treatments, so we did look for bias. We didn't have a large range of ethnicities (Hispanic, Asian, and Caucasian); however, we saw no difference at all among these racial subgroups. That does not mean there is no difference, we just did not find any evidence of a difference.

Q: Did you exclude ketonuria?

A: There were no patients with ketonuria. We were worried we’d be seeing type 1 diabetes instead of type 2 diabetes, so ketones were assessed, but nothing suggested they had ketonuria.

Posters

Efficacy and Tolerability of ITCA 650 (Continuous Subcutaneous Exenatide in Poorly Controlled Type 2 Diabetes with Baseline A1c >10% (114-LB)

RR Henry, J Rosenstock, and MA Baron

Dr. Robert Henry and colleagues report six-month data from an open-label trial of Intarcia Therapeutics’ ITCA 650 (continuous subcutaneous exenatide infusion) in 60 type 2 patients with baseline A1c >10% (FREEDOM-1HBL). The participants first received the three-month, low dose (20 mcg/day) ITCA 650 mini-pump for 13 weeks followed by the six-month high dose (60 mcg/day) mini-pump for 26 weeks. Background anti-diabetic medications were maintained for the treatment period. This initial interim analysis included data from the patients who had completed treatment up to 13 weeks (n=50), 19 weeks (n=39), or 26 weeks (25). Increasing reductions in A1c were observed at each time point: -2.5% at 13 weeks, -2.9% at 19 weeks, and -3.2% at 26 weeks. Furthermore, an impressive proportion of these patients achieved A1c reductions of ≥2% (78%), ≥3% (50%), ≥4% (22%), and ≥5% (10%). Of the cohort, 30% achieved the A1c target of ≤7% and only two patients were classified as non-responders (i.e., A1c reduction <0.05% at the time of the interim analysis). Lastly, a mean weight loss of 2.4 lbs (1.1. kg) was observed at 26 weeks. Based on these data, the authors conclude that ITCA 650 has the potential to markedly improve glycemic control in patents with severe hyperglycemia and longstanding diabetes.

• This study enrolled 60 type 2 patients whose high A1c level (>10%) made them ineligible to participate in the main double-blind placebo-controlled trial (FREEDOM 1). These patients met all of the other inclusion criteria for FREEDOM 1. At baseline, the participants had a mean age of 52 years, BMI of 32 kg/m², A1c of 10.7%, fasting plasma glucose of 248 mg/dl, and duration of diabetes 9 years. Sixty-nine percent of the cohort also used oral anti-diabetic medications and 33% were male.
• The figure below gives mean baseline A1c and mean A1c reduction for participants completing treatment periods of 13, 19, and 26 weeks:

	13 weeks     (n=50)	19 weeks    (n=39)	26 weeks   (n=25)
Mean baseline A1c	10.8%	10.7%	10.9%
Mean A1c at time point	8.3%	7.8%	7.7%
Mean change in A1c	-2.5%	-2.9%	-3.2%

• The authors note that adverse events were consistent with previous trials with ITCA 650 (data not provided).

Efficacy and Safety of Once Weekly Dulaglutide vs. Once Daily Liraglutide in Type 2 Diabetes (AWARD-6) (110-LB)

KM Dungan, ST Povedano, T Forst, JGG González, C Atisso, W Sealls, JL Fahrbach

This poster presented the results of the long-awaited AWARD-6 trial, which found that Lilly’s once-weekly GLP-1 agonist dulaglutide provided non-inferior A1c lowering relative to Novo Nordisk’s once-daily Victoza (liraglutide). The open-label study randomized 599 type 2 diabetes patients on metformin. Head-to-head studies can sometimes use fairly wide non-inferiority margins (we’ve seen as large as 0.4%), so even though the topline results announced that dulaglutide achieved non-inferiority, there were plenty of potential surprises in the full data. However, the two drugs had similar glycemic effects – dulaglutide led to a mean A1c reduction of 1.42%, while liraglutide 1.8 mg led to a mean reduction of 1.36% (p < 0.001, baseline A1c = 8.1%). Approximately 68% of both groups achieved a final A1c of less than 7%, and seven-point SMBG profiles were effectively superimposable. The slight differences between groups emerged in the weight and hypoglycemia categories. Patients in the liraglutide arm lost an average of 3.6 kg (~8 lbs), while patients in the dulaglutide arm lost an average of 2.9 kg (~6 lbs) (p = 0.01) – we wonder if a difference that small will be perceived as clinically meaningful by HCPs. The incidence of hypoglycemia was slightly higher in the liraglutide arm (0.52 events/patient/year) than the dulaglutide arm (0.34 events/patient/year), but the number of events was so small that the difference is likely not very clinically meaningful. On the whole, the results of the study demonstrate that dulaglutide has a comparable clinical profile to liraglutide, and sets the stage for the two to compete on other points like pricing, device design, and patient preferences on does frequency.  

• The incidence of hypoglycemia in both groups was very low, but appeared very slightly higher in the liraglutide arm. The incidence of hypoglycemia (defined as blood sugar at or below 70 mg/dl with or without symptoms) was 0.34 events/patient/year in the dulaglutide arm and 0.52 events/patient/year in the liraglutide arm. The incidence in both groups was so low that the difference may not be clinically meaningful, whether or not it is statistically significant (which was not mentioned). 
• Liraglutide came out ahead with regards to weight loss, but only slightly. From a mean baseline of 94 kg (~210 lbs), patients in the dulaglutide arm lost 2.9 kg (~6 lbs), while patients in the liraglutide arm lost 3.6 kg (~8 lbs). The difference was statistically significant (p = 0.01), but given that both groups achieved weight loss of ~3kg, the difference may not be highly clinically significant. Novo Nordisk management has speculated that Victoza would come out ahead on weight during previous quarterly updates, primarily because liraglutide is a smaller molecule that is believed to cross the blood-brain barrier to a greater extent and act at neural appetite regulation centers.
• The incidence of nausea was similar between arms – we have heard in the past that longer-acting GLP-1 agonists have less of an effect on GI motility and, as a result, generally cause less nausea. We might have therefore expected a slight advantage for dulaglutide, but of course other characteristics of the molecule beyond PK/PD could impact GI tolerability. Approximately 19% of patients in both arms experienced nausea, while ~7-8% experienced vomiting.
• The results of AWARD-6 make dulaglutide the only GLP-1 agonist to achieve non-inferiority to liraglutide in a phase 3 trial.

 

Better Glycemic Control and Less Weight Gain with Once Weekly Dulaglutide versus Once Daily Insulin Glargine, Both Combined with Pre-Meal Insulin Lispro in Type 2 Diabetes Patients (AWARD-4) (962-P)

J Jendle, J Rosenstock, L Blonde, V Woo, J Gross, H Jiang, Z Milicevic

This phase 3, open-label, 52-week study (n=884 with type 2 diabetes) compared Lilly’s once-weekly GLP-1 agonist dulaglutide to the basal insulin Lantus (once-daily insulin glargine), both in combination with the mealtime insulin lispro (Lilly’s Humalog). The idea that a GLP-1 agonist could actually replace a basal insulin is a pretty novel concept, and the authors of the poster note that AWARD-4 is the first study exploring use of a GLP-1 agonist with mealtime insulin. Impressively, results of this trial show that (as in AWARD-2) both the 0.75 mg and 1.5 mg doses of dulaglutide showed statistically greater A1c reductions at 26 and 52 weeks compared to insulin glargine (see table below). At 52 weeks, statistically more patients on dulaglutide 1.5 mg achieved an A1c goal of <7% (59%) compared to glargine (49%). The difference between dulaglutide 0.75 mg (56%) and glargine was not significant. At 52 weeks, there was no statistically significant difference between arms for achieving A1c ≤6.5%. Consistent with the other phase 3 results for dulaglutide presented at ADA, dulaglutide showed very modest weight changes (but still better than insulin glargine; see table below). Total hypoglycemia in events/patient/year was lower on dulaglutide 1.5 mg compared to glargine (44 vs. 63), but similar between dulaglutide 0.75 mg and glargine (53 vs. 63). Percentage of patients modest weight and hypoglycemia benefit of dulaglutide vs. glargine in this trial is likely explained by the background insulin lispro therapy. Patients at baseline were 59 years old, had mean A1c of 8.5%, mean BMI of 32.5 kg/m², mean insulin dose of 56 U/day, and mean diabetes duration of 12-13 years.

	Dulaglutide 1.5 mg (n=295)	Dulaglutide 0.75 mg (n=293)	Insulin glargine (n=296)
Primary Endpoint: 26 weeks
A1c change	-1.64%	-1.59%	-1.41%
Patients with A1c <7%	68%	69%	57%
Weight change	-0.9 kg (-1.9 lb)	-0.2 kg (-0.4 lb)	2.3 kg (5.1 lb)
Final Endpoint: 52 weeks
A1c change	-1.48%	-1.42%	-1.23%
Patients with A1c <7%	59%	56%	49%
Weight change	-0.4 kg (-0.8 lb)	0.9 kg (1.9 lb)	2.9 kg (6.4 lb)

Safe and Effective Use of the Single-Use Pen for Injection of Once Weekly Dulaglutide in Injection-Naïve Patients with Type 2 Diabetes (122-LB)

G Matfin, A Zimmermann, K Van Brunt, R Threlkeld, D Ignaut

This poster presented results from an open-label, four-week outpatient study investigating the usability of the single-use pen (SUP) designed to administer 0.5 ml of Eli Lilly’s dulaglutide in injection-naïve individuals with type 2 diabetes, as assessed by the injection success rate during the final of four weekly injections of placebo using the SUP. Study participants (n=211) were on average 61 years old, with diabetes duration of 7.7 years, and BMI of 31.7 kg/m² at baseline (36% of participants only had a high school education or less). All but two of the 211 participants successfully injected placebo using the SUP during their final (fourth) injection, for a success rate of over 99%. The injection success rate for the initial injection was 97.2%, suggesting ease of use without much practice. Participants reported experiencing very little injection pain, rating the pain an average across injections of 1.0 on a 0-10 scale. In addition, participants reported a significant reduction in fear of self-injecting, as assessed by the change in their average modified D-FISQ Fear of Injecting Subscale Score. The vast majority of participants found the pen easy to use, and said they would be willing to use the pen if it were available.

Effect of Saxagliptin on Renal Outcomes (544-P)

O Mosenzon, D Bhatt, L Litwak, M Shestakova, G Liebowitz, B Hirshberg, A Parker, N Iqbal, B Scirica, R Ma, I Raz

This poster presented renal outcomes results from SAVOR-TIMI 53, the cardiovascular outcomes study for BMS/AZ’s saxagliptin (Onglyza). As a reminder, SAVOR randomized 16,452 patients with type 2 diabetes and established cardiovascular disease or multiple risk factors to saxagliptin or placebo for a median follow-up of 2.1 years (presented initially at ESC 2013; stratification of results by baseline renal function presented at ACC 2014). As noted in the primary analysis, patients treated with saxagliptin demonstrated more improvement in albumin to creatinine ratio (ACR; 11% vs. 9%; p<0.01) and less worsening in ACR (13% vs. 16%; p <0.01) versus placebo-treated patients, with greatest benefit observed in patients with known baseline microalbuminuria (31.3% returned to normal albuminuria vs. 25.7%; p<0.0001). Interestingly, this effect appeared independent of glucose control, with improvement in ACR similar in patients with A1c decline of >0.5% at one year versus those without. However, this benefit to ACR did not translate to significant differences in predetermined renal outcomes with saxagliptin, including doubling of serum creatinine (HR 1.04; 95% CI 0.83-1.30), initiation of chronic dialysis, renal transplant, or serum creatinine >6.0 mg/dl (HR 0.90; 95% DI 0.61-1.32), or composite end point of death and all of the above (HR 1.08; 95% CI 0.96-1.22).

Medication Compliance Rates of Weekly Albiglutide vs. Daily Oral Comparators in Phase III Trials (994-P)

LA Leiter, RA Scott, J Ye, MC Carr

This study compared the rates of compliance between GSK’s once-weekly GLP-1 agonist albiglutide (now Tanzeum following its recent US regulatory approval) and three once-daily oral comparators (glimepiride, pioglitazone, and sitagliptin) in HARMONY trials 3, 5, and 8. Overall compliance was consistently higher in both the albiglutide and albiglutide matching placebo groups than in any of the oral comparator groups. Low compliance was defined as ≤ 80% compliance – many providers would probably love to get long-term adherence of 80%. Low adherence was more frequent in the oral comparator groups (7.5% to 14.9%) than in the albiglutide groups (1.6% to 2.3%). Compliance was measured at each visit using pen and/or pill counts. While the results bode well for patient compliance on albiglutide, it will be important to conduct follow-up studies in real-world clinical settings. Device design has a major impact on adherence for GLP-1 agonists – see our GSK exhibit hall coverage (from today as well) for an overview of how the administration process works.

Effect of Lixisenatide vs. Liraglutide on Glycemic Control, Gastric Emptying, and Safety Parameters in Optimized Insulin Glargine T2DM ± Metformin (1017-P)

JJ Meier, J Rosenstock, A Hincelin-Mery, C Roy-Duval, A Delfolie, HV Coester, T Forst, C Kapitza

This study compared the effect of Sanofi’s Lyxumia (lixisenatide) and Novo Nordisk’s Victoza (liraglutide) on postprandial glucose in patients with type 2 diabetes ± metformin after optimal insulin glargine titration. In an 8-week, open-label trial, patients were randomized to three treatment arms: lixisenatide 20 µg (n=46), low-dose Victoza (liraglutide 1.2 mg; n=44), and high-dose Victoza (liraglutide 1.8 mg; n=46). Lixisenatide showed a benefit over both liraglutide doses in lowering postprandial glucose and delaying gastric emptying. All arms benefited from decreased A1c and body weight, with liraglutide 1.8 mg arm seeing the greatest decrease in body weight. Symptomatic hypoglycemia was slightly more frequent in the lixisenatide arm (14 events vs. 9 and 10 events in the liraglutide arms), whereas more GI side effects were noted in the liraglutide treatment arms (17 in lixisenatide vs. 21 and 22 events in the liraglutide arms). One case of severe symptomatic hypoglycemia was noted in the lixisenatide arm. Overall, both lixisenatide and liraglutide provided improved glycemic control, with lixisenatide offering a greater effect on postprandial glucose and gastric emptying and liraglutide providing a slightly better safety profile. This finding is not surprising given our understanding of short-acting

AUC PPG (h)(mg)/dl	lixisenatide 20µg	liraglutide 1.2 mg	liraglutide 1.8mg
Baseline mean ± SD	282.2 ± 120.9	280.1 ± 99.9	307.0 ± 103.2
Week 8 mean ± SD	63.6 ± 117.9	171.7 ± 95.2	156.7 ± 62.2
LS mean change from baseline ± SD	-240.2 ± 20.0	-131.8 ± 20.2	-157.1 ± 21.0

Relationship between Changes in Postprandial Glucagon, Patient Characteristics, and Response to Lixisenatide as Add-On to Oral Antidiabetics (971-P)

M Nauck, S Azar, L Blonde, D Dicker, MP Domingo, FG Eliaschewitz, E Nikonova, r Roussel, K Sakaguchi, L Sauque-Reyna, C Bailey

This poster presented an analysis of the predictors and consequences of glucagon changes in response to treatment with lixisenatide, Sanofi’s once-daily “short-acting” GLP-1 agonist, as an add-on to oral therapy for type 2 diabetes. A total of 423 patients drawn from the GetGoal-M and –S trials were divided into two groups (“Greater Change” and “Smaller Change”) based on the magnitude of their change in two-hour postprandial glucagon levels over the course of the 24-week study. The authors found that patients who experienced larger reductions in postprandial glucagon also displayed significantly greater improvements in a variety of efficacy and safety parameters (see table below), suggesting that lixisenatide’s effects on glucagon suppression are an essential part of its overall therapeutic impact. The authors also determined that patients with newer-onset diabetes who had spent less time on their baseline oral medication regimen were more likely to end up in the Greater Change group, suggesting that lixisenatide treatment is most effective when begun early in the progression of type 2 diabetes.

Table: Differences in glycemic control and other parameters based on change in postprandial glucagon

	Greater glucagon change	Smaller glucagon change
A1c reduction (average)	1.1%	0.67%
Fasting plasma glucose reduction (average)	25.2 mg/dl	9.3 mg/dl
Postprandial glucose reduction (average)	129.4 mg/dl	78.22 mg/dl
Weight loss (average)	2.3 kg	1.2 kg
Percent achieving A1c<7%	46.5%	32.4%
Preprandial glucose reduction	31.9 mg/dl	16 mg/dl
Fasting glucagon reduction	17 mg/dl	5.6 mg/dl
Percent experiencing symptomatic hypoglycemia	6.1%	12.9%

*All differences listed in the table were statistically significant

• The goals of this analysis were to determine: (i) which baseline characteristics best predicted the magnitude of postprandial glucagon reduction with lixisenatide; and (ii) whether patients who experienced greater postprandial glucagon reduction displayed differences in other efficacy and safety outcomes. The outcomes assessed included (i) changes in A1c, fasting plasma glucose (FPG), postprandial glucose (PPG), and body weight; (ii) the percentage of patients who achieved glycemic targets; (iii) indicators of beta-cell function; and (iv) frequency of hypoglycemia.
• The analysis involved 423 patients with type 2 diabetes on oral medications who were randomly assigned to receive once-daily injections of either lixisenatide or placebo. The patients analyzed were participants in the GetGoal-M and GetGoal-S studies evaluating the effects of lixisenatide added to oral therapy in the treatment of type 2 diabetes. The patients in the GetGoal-M study were receiving metformin monotherapy and the patients in the GetGoal-S study were receiving either sulfonylurea monotherapy or sulfonylurea and metformin combination therapy. All patients had been diagnosed with type 2 diabetes for at least 1 year and had a baseline A1c of 7-10%.
• Patients receiving lixisenatide were divided into two cohorts based on their change in 2-hour postprandial glucagon levels over the course of the 24-week study. The Greater Change cohort (n=213) consisted of patients with a median change of >23.57 ng/l and the Smaller Change cohort (n=210) consisted of patients with a median change of £23.57 ng/l.
• Short duration of diagnosed diabetes and less time on oral medications were associated with greater reductions in postprandial glucagon with lixisenatide. Patients in the Greater Change cohort had an average diabetes duration of 7.3 years, compared to 9 years in the Smaller Change cohort. Patients in the Greater Change cohort had been treated with oral medications for an average of 4.5 years, compared to 5.7 years in the Smaller Change cohort. Multivariate regression analysis found that older patients and males were more likely to see a greater change in glucagon. In our minds, it was curious and somewhat contradictory that older age and shorter diabetes duration were both correlated with a greater change in glucagon.
• Greater reductions in postprandial glucagon were associated with greater improvements in efficacy parameters over the course of the study.
  
  • The average A1c reduction was 1.10% for the Greater Change cohort compared to 0.67% for the Smaller Change cohort. The average reduction in FPG was 25.2 mg/dl for the Greater Change cohort compared to 9.3 mg/dl for the Smaller Change cohort. The average PPG reduction was 129.4 mg/dl for the Greater Change cohort compared to 78.22 mg/dl for the Smaller Change cohort. All else being equal, it is not surprising that greater improvements in glucagon levels led to improved glycemic control.
  • Patients in the Greater Change group lost an average of 2.3 kg (~5 lbs), while patients in the Smaller Change group lost an average of 1.2 kg (~3 lbs).
  • Approximately 47% of patients in the Greater Change cohort achieved an A1c <7% by the end of the trial compared to 32% of patients in the Smaller Change cohort.
  • Beta-cell function: Patients in the Greater Change cohort showed greater improvements in beta-cell function as demonstrated by a greater reduction in preprandial glucose (31.9 mg/dl compared to 16 mg/dl) and fasting glucagon levels (17 mg/dl compared to 5.6 mg/dl). The Greater Change cohort also experienced a significant increase in the HOMA-b index, while the Smaller Change cohort did not.
• Greater reductions in postprandial glucagon were also associated with lower rates of hypoglycemia. No patients in either group experienced severe hypoglycemia, but only 6.1% of the patients in the Greater Change cohort experienced symptomatic hypoglycemia, while 12.9% of the patients in the Smaller Change group did. This was a somewhat counterintuitive finding, as a greater change in glucagon (i.e.: less glucagon secretion) could be seen as a predictor of more hypoglycemia, not less. This phenomenon suggests that the differences in the changes in glucagon might be a manifestation of more sweeping changes in metabolic health occurring as a result of lixisenatide treatment.

Meet-the-Expert Session: Incretin-Based Drugs

Incretin-Based Drugs

John Buse, MD, PhD (University of North Carolina, Durham, NC)

Incretin expert Dr. John Buse led an engaging discussion in front of hundreds of attendees during this meet-the-expert session on incretin-based therapies (DPP-4 inhibitors and GLP-1 agonists). Several recurrent themes emerged in the discussion:

• A major question on many people’s minds is how “real” the heart failure signal is with DPP-4 inhibitors. As a reminder, Onglyza’s cardiovascular outcomes trial, SAVOR, unexpectedly found a 27% relative increased risk of heart failure compared to placebo. Dr. Buse has not significantly changed his prescribing patterns, and his opinion is that if any real risk exists, it would be quite moderate in the real-world DPP-4 population; SAVOR was heavily enriched for people at high risk of CVD, whereas most people prescribed DPP-4 inhibitors in the real world are much earlier in the disease progression. Said Dr. Buse, “Yes, there is a finger being pointed, and there’s always a finger being pointed, but I’d say nine times out of 10 the relationship is spurious. My guess is at the end of the day, 20 years from now, there will not be a major issue with DPP-4 inhibitors being agents that cause heart failure in the routine management of type 2 diabetes.”
• With regard to the potential for incretins to show cardioprotection Dr. Buse is not terribly optimistic: “I think it’s almost asking too much in these relatively short-term trials […]. I think it’s a bit much to expect [CV risk reduction] out of a diabetes drug. From day one, and I don’t know if Galen felt this way or the Ancient Egyptians that first described honeyed urine, but from day one glucose management has been focused primarily on microvascular complications.” He noted that the EXSCEL trial, Bydureon’s cardiovascular outcomes trial, is the only one he is aware of that was primarily designed as a superiority study (the others were designed with the primary endpoint of CV safety to satisfy the FDA’s CV safety requirements).
• Dr. Buse was also fairly lukewarm about the prospect of incretins for other indications outside of type 2 diabetes, such as type 1 diabetes or obesity: Dr. Buse remarked, “My personal experience with my patients who have type 1 diabetes has not been overwhelmingly positive.” With regards to obesity, he noted that the data for liraglutide 3.0 mg has yet to be fully disclosed, so it would be premature to use GLP-1 agonists to treat people with obesity and not diabetes. He pointed to SGLT-2 inhibitors as additional promising agents for both type 1 diabetes and obesity.
• Other notable quotes from the session included:
  
  • “With regard to whether there are specific ethnicities that might respond better [to GLP-1 agonists], I don’t believe we have strong data in that regard. But I  can’t tell you with absolute certainty. There is a report, for instance, and I just read the title this morning, that metformin after 50 years on the marketplace is more effective in African Americans than Caucasian Americans. It’s stunning to me that this would just come out now in 2014. So certainly to answer a question like the ones you’ve raised requires a very specific kind of study, and I’m not aware of that having been done.”
  • “I think SFUs are certainly a legitimate choice for managing diabetes. From my perspective, what I personal avoid doing is using high doses. Particularly, a little tiny dose of SFU can provide a lot of efficacy and relatively low risk for hypoglycemia […]. When you start increasing the dose of an SFU because A1c is inadequate, that’s when you have risk of trouble. If you find a patient on maximum doses of SFU, that person should be on another drug and on a half-maximal or lower dose of SFU.”

“I personally think the data [for beta cell regeneration with incretin-based drugs] is pretty weak. The strongest data come from animal models, especially young animals. My personal belief is if the appropriate long-term study is done, if there is an effect, it will be quite modest.”

Symposium: Cardiovascular Outcomes in Recent Diabetes Trials

Saxagliptin (SAVOR-TIMI 53) Trial

Benjamin Scirica, MD, MPH (Harvard Medical School, Boston, MA)

Dr. Benjamin Scirica reviewed the results of SAVOR-TIMI 53, the cardiovascular outcomes study for BMS/AZ’s saxagliptin (Onglyza). As a reminder, SAVOR’s primary results (presented initially at ESC 2013 Day #1) demonstrated non-inferiority on cardiovascular safety with saxagliptin compared to placebo in 16,452 patients with type 2 diabetes at high risk for cardiovascular disease, though with a slightly greater risk of hospitalization due to heart failure in the saxagliptin group. Subgroup analyses of these patients at AHA 2013 failed to identify a subpopulation for which the relative risk of heart failure was particularly high or low with saxagliptin versus placebo; further subgroup analyses at ACC 2014 indicated similar outcomes with saxagliptin when patients were stratified by baseline renal function. In addition to these results, Dr. Scirica reviewed a recently published analysis (Raz et al., Diabetes Care 2014) that reiterated similar risk for pancreatitis between the treatment arms (HR 1.09; 95% CI 0.86-1.79), which held true following adjudication of events for any pancreatitis (0.29% in the saxagliptin group vs. 0.26% with placebo; HR 1.13; 95% CI 0.63-2.06), definite acute pancreatitis (0.2% vs. 0.1%; HR 1.88; 95% CI 0.86-4.41), definite plus possible pancreatitis (HR 1.36; 95% CI 0.72-2.64), and chronic pancreatitis (HR 0.33; 95% CI 0.05-1.44). Dr. Scirica noted that the distribution of definite and possible acute pancreatitis and chronic pancreatitis events was similar between the groups, suggesting that events were spontaneous rather than drug-induced. He closed by reinforcing that few agents in diabetes have now been studied as extensively as saxagliptin, emphasizing the benefit to microvascular outcomes without adverse macrovascular outcomes.

Questions and Answers

Dr. Vivian Fonseca (Tulane University, New Orleans, LA): I believe dose adjustment was required for the renal patients. How well was that adhered to?

A: It was very well adhered to. The patients were getting yearly central labs as well as local labs. Instructions for dose adjustments were complied with.

Q: Do you think the dose should be reduced further?

A: I again see consistency in results regardless of eGFR. I think given the neutrality with CV outcomes and effects on hyper- and hypoglycemia that dose reduction was appropriate.

Q: Did you check your hospitalization from heart failure in patients with prior coronary disease?

A: Patients with prior MI did have a higher risk. It was a moderate correlate behind history of heart failure, chronic renal insufficiency, and microalbuminuria. It wasn’t a strong predictor, but it was certainly.

Q: Can you tell us a bit more about what investigators were instructed to do with regard to improvement in hyperglycemia?

A: We gave instructions that providers should follow their local practice guidelines for the management of glucose control. We did not mandate any treatment algorithm or control. The only thing that was prohibited was open-label incretin therapy. There were very small drop-ins of incretin therapies and of TZDs.

Alogliptin (EXAMINE) Trial

William White, MD (University of Connecticut, Farmington, CT)

Dr. William White reviewed results of EXAMINE, the cardiovascular outcomes trial for Takeda’s DPP-4 inhibitor Nesina (alogliptin). For coverage of these results when they were first presented, see our reports from ESC 2013 Congress at and ACC 2014 Scientific Sessions. During Q&A, Dr. White noted that blood levels of NT-proBNP (a marker of heart failure) decreased from baseline in both groups. However, he said the decrease was larger in the alogliptin group. These data were from a subanalysis of roughly 1,000 patients that has not yet been formally presented. Dr. White added that the researchers were continuing to explore this finding. Other questioners asked about the increased rate of heart failure seen in SAVOR-TIMI 53, the CVOT for BMS/AZ’s DPP-4 inhibitor Onglyza (saxagliptin), which was not seen in EXAMINE. Dr. White said that it is “too early” to say whether heart-failure risk is a class effect, and that he thinks the question will remain unresolved even if a slight risk signal is seen in TECOS, the ongoing CVOT for Merck’s Januvia (sitagliptin).

Questions and Answers

Q: Did BNP change?

A: These data have not been formally presented. The change was not in BNP but in NT-proBNP, which fell significantly in a group of roughly 1,000 patients evaluated at six months. Although levels fell in both groups, there was a greater fall with alogliptin and placebo. We are furthering research in this area at this time.

Q: Can you comment on the difference in heart failure between EXAMINE and SAVOR-TIMI 53?

A: The differences relate to CV morbidity. SAVOR had fairly high-risk patients, with a 7% event rate. But the proportion of patients with baseline disease was higher in EXAMINE. It’s reassuring that alogliptin did not increase cardiovascular events, including heart failure, in this population. I cannot speculate why there was a difference in heart failure alone between the studies. It is important to see if there was confounding by death; we did have a nominal reduction in death in EXAMINE, and I do not believe this was observed in SAVOR.

Q: A couple of these DPP-4 inhibitors have been shown to have more tendency to develop CHF in primary coronary artery disease. Am I to conclude that this is a class effect?

A: Everyone in EXAMINE had coronary artery disease, and they did not develop a greater risk of heart failure when treated with alogliptin. It is too early to say that this is a class effect; I would not make that statement. Even if TECOS sees a small signal, I still don’t know if I’d call that an increased risk. Patients with heart failure had more renal disease; these patients were sicker. Patients on insulin might have higher risk, as might those with other comorbidities or longer duration of diabetes. We must be careful, especially because there was no clear-cut mechanistic reason for why DPP-4 inhibitors would increase the risk of heart failure.

Symposium: Initial Treatment of Type 2 Diabetes – New and Not-So-New Ideas

GLP-1 Receptor Agonists

Richard Pratley, MD (Florida Hospital Diabetes Institute, Orlando, FL)

Dr. Richard Pratley provided an overview of the efficacy and safety of GLP-1 agonists, focusing on their potential use as a first line treatment of early stage type 2 diabetes. He began by discussing the pathophysiology of type 2 diabetes, drawing on Ralph DeFronzo’s “ominous octet” to highlight the many organ systems affected by hyperglycemia and arguing that the primary defects of insulin resistance and beta cell dysfunction are apparent even before diabetes is diagnosed. He described his ideal anti-hyperglycemic agent as one that is effective, well-tolerated, and addresses multiple metabolic abnormalities, among other more wishful criteria. He claimed that out of the many treatment options now available for type 2 diabetes, incretin-based therapies like GLP-1 agonists come closest to meeting those criteria. He presented evidence from several studies demonstrating GLP-1 agonists’ high glucose-lowering efficacy, low risk of hypoglycemia, and weight loss, which he said his patients were very enthusiastic about. Ultimately, Dr. Pratley presented a compelling case for GLP-1 agonists as an appealing treatment option for certain patients with early stage type 2 diabetes, though he did not address the issue of affordability in depth or make substantive comparisons with many of the other available drug classes. The development of once-weekly GLP-1 agonists and better administration devices should help grow the class and make it more “beginner-friendly.”

• In Dr. Pratley’s opinion, the appearance of multiple metabolic abnormalities early in the progression of type 2 diabetes supports the use of incretin therapy as a first line treatment. He was careful to note as a disclaimer that GLP-1 agonists are not recommended as a first-line therapy by current guidelines, but said that “we can still have a scientific discussion” about the possibility of first-line GLP-1 agonist therapy. He discussed the advantages of selecting treatment options like GLP-1 agonists that more proactively deal with multiple elements of type 2 diabetes pathophysiology rather than beginning with metformin and inevitably adding more medications later.
• Dr. Pratley presented an overview of data on the efficacy and safety of currently available GLP-1 agonists and concluded that they meet many of his criteria for an ideal type 2 diabetes therapy. Clinical studies of already-approved GLP-1 agonists have shown A1c reductions of 0.7% to 1.5% that in many cases were superior to other diabetes drug classes, and GLP-1 agonist therapy generally also led to at least a small amount of weight loss. Importantly, the risk of hypoglycemia is very low with this drug class, as its effects on both insulin and glucagon are diminished when blood glucose levels are low.
• Dr. Pratley admitted that a slight risk of pancreatitis has been reported and that more information is needed about cardiovascular risk, but he claimed that neither of those issues presents a serious concern at this time. The main downsides to this drug class in his mind are the GI side effects and the fact that there is no oral formulation currently available.
• Because of their high glucose-lowering efficacy, acceptable safety profile, and beneficial effects on body weight, Dr. Pratley believes GLP-1 agonists could be a good first line treatment for certain patients with early type 2 diabetes. The ideal candidates for initial GLP-1 therapy would be people who can’t tolerate metformin, are overweight or at high risk of hypoglycemia, and don’t mind injections. The additional issue of affordability came up during Q&A: Dr. Pratley humorously added “well-insured and independently wealthy” as one of his criteria for the ideal patient for initial GLP-1 agonist therapy.

Questions and Answers

Q: Is there a difference in gastric emptying between long-acting and short-acting GLP-1 agonists? What about targeting fasting plasma glucose vs. postprandial?

A: It looks like the longer-acting GLP-1 agonists have less of an effect on gastric emptying long-term, although they do at first. Possibly as a consequence, they also have less of an effect on postprandial glucose. The best options for controlling postprandial glucose are exenatide and lixisenatide.

Q: What do you think about DPP-4 inhibitors as a first line therapy in early type 2 diabetes?

A: I didn’t have time to address that class today, but many of the same reasons would apply. We have monotherapy studies on DPP-4 inhibitors showing that they’re well-tolerated, safe, and effective, and they would work well for the same type of patients that would do well on GLP-1 agonists.

Q: I really enjoyed your talk, but affordability can be a stumbling block for this class because these drugs are so expensive.

A: Yes, I should have said that the ideal patient is also well-insured and independently wealthy.

Symposium: Can We Limit the Long-term Decline of Beta Cells in Type 2 Diabetes?

Can Gastrointestinal Hormones Limit the Long-Term Decline of Beta Cells in Type 2 Diabetes?

Patricia Brubaker, PhD (University of Toronto, Toronto, Canada)

Dr. Patricia Brubaker reviewed that GLP-1 receptor agonist therapy increases beta-cell mass and/or function in type 2 diabetes. She emphasized that scientists still do not understand this process clearly, because GLP-1’s effects seem to be age-and-species dependent, and because “we know extremely little about human islets, unfortunately.” Incretin therapy stimulates beta-cell proliferation in some rodent models of diabetes. However, Dr. Brubaker does not think that current evidence strongly supports this theory in humans (especially adults, in whom beta-cell replication seems minimal). The more validated mechanism in humans is prevention of apoptosis, as seen in some experiments with isolated human islet cells (Farilla et al., Endocrinology 2003; Buteau et al., Diabetologia 2004). In clinical studies the benefits of GLP-1 receptor agonists on beta cells appear durable for at least three years, but these benefits seem to disappear or diminish shortly after treatment is stopped (Bunck et al., Diabetes Care 2011), and researchers do not know whether the effects are on beta-cell mass, beta-cell function, or both. In the last several minutes of her talk Dr. Brubaker discussed other gastrointestinal hormones, which could potentially be combined with each other or GLP-1 to benefit beta cells.

• In rats with diabetes, treatment with GLP-1 enhances beta-cell growth and decreases apoptosis (Xu et al., Diabetes 1999; Farilla et al., Endocrinology 2002; Perfetti et al., Endocrinology 2000). Similar effects have been seen in young mice, but old mice do not respond as well, indicating that GLP-1’s effects on the beta cell are species- and age-dependent (Tschen et al., Diabetes 2009; Rankin and Kushner, Diabetes 2009). Dr. Brubaker said that she continues to think that rodent beta cells are an appropriate model for people. However, she hopes that we learn much more about inter-species similarities and differences. She suggested that someday scientists might use such knowledge to create a humanized rodent-cell model.  
• After spending most of her presentation on GLP-1, Dr. Brubaker briefly reviewed several other gastrointestinal hormones with possible therapeutic effects on beta cells. Therapy with gastrin “doesn’t seem to do much by itself,” Dr.
  Brubaker said, but it does increase beta-cell mass in mice when given with either TGF-alpha or EGF. In vitro data with human cells suggests that gastrin-plus-EGF increases beta-cell number (Suarez-Pinzon et al., J Clin Endocrinol Metab 2005), and Zealand Pharma has performed promising pre-clinical experiments with the gastrin/GLP-1 dual agonist ZP3022 (Fosgerau et al., Diabetes Obes Metab 2013). Ghrelin therapy seems beneficial in rats, but human data are not available. Therapy with gastric inhibitory peptide (GIP) seems to help in streptozotocin-treated rats, but not streptozotocin-treated mice; data are not yet available in humans. Therapy with cholecystokinin (CCK) seems not to help in mice or humans. 

Questions and Answers

Q: So many people a few years ago thought that absolute mass matters. But in humans it looks like we see little improvement in mass but a big improvement in function. Is this your understanding?

A: We don’t yet know if it’s increasing function, mass, or both in humans. Evidence shows that GLP-1 therapy does improve ability to lower blood glucose, whether by beta-cell function or mass.

Comment: Given what you said about how beta-cell replication seems to slow down in adults, maybe we’ll learn that these drugs have special effects in teenagers with diabetes.

Corporate Symposium: Update on GLP-1 based Therapies – Effects on Exocrine and Endocrine Pancreas (Supported by an Unrestricted Educational Grant from Merck)

Incretin Effects on Human and Rodent Beta Cells

Colin Leech, PhD (SUNY Upstate Medical University, Syracuse, NY)

Dr. Colin Leech presented data comparing the effects of incretins (GLP-1 and GIP) on pancreatic islets in rodents and humans. Both rodents and humans have GLP-1 receptor signaling pathways, notably involving the “new player” SAD-A kinase (which represents a nexus point between glucose signaling and GLP-1 signaling). However, one notable difference is that insulin secretion from human islets is dependent on protein kinase A (PKA) activity; this is not required for rodent insulin secretion. The second half of the presentation explored the effects of pancreatic beta cell GLP-1 receptors and neuronal GLP-1 receptors on regulation of insulin secretion. Dr. Leech made the point that a rat can lose either the pancreatic GLP-1 receptor OR the neuronal GLP-1 receptor and still maintain most insulin secretion, although losing both types of GLP-1 receptors impairs secretion greatly. Additionally, administering a GLP-1 receptor agonist to the portal vein disrupted glucose homeostasis, but no effect when it was the jugular vein, suggesting that the portal vein has a critical neuronal GLP-1 receptor. This seems physiologically very possible, as endogenous GLP-1 from the gut is first transported through the portal circulation. Overall, Dr. Leech stressed that incretin hormone pathways are highly complicated and involve GLP-1 receptors in the brain, liver, stomach, and of course the pancreas.

Inconvenient Truths – Clinical Data Informing the Safety of Incretin-Based Therapies

Jacqueline Koehler, PhD (Samuel Lunenfeld Research Institute, Toronto, Canada)

In front of an overflowing audience, Dr. Jacqueline Koehler reviewed the current preclinical and clinical data surrounding the risk of incretins and pancreatitis/pancreatic cancer. While there was no new data in the presentation, she synthesized the current state of affairs very cogently: much of the debate over incretins and pancreatic safety has been fueled by a few publications of preclinical and clinical data that have not been reproducible. The majority of the available clinical data has come from retrospective database and case-controlled observational studies, which are subject to numerous biases and limitations. In particular, she explored the possibility for over-diagnosis based on elevated pancreatic enzyme levels (a marker of pancreatitis that is not, in itself, sufficient for diagnosis). Dr. Koehler suggested that the highest quality clinical data on this issue will come from the large cardiovascular outcomes trials (CVOTs) ongoing for incretin-based therapies – she found the neutral pancreatic findings from SAVOR and EXAMINE “promising” and remarked that they demonstrated that if any risk exists, it is so low that it was not even detected with a 16,000+ person trial (SAVOR). She noted that we will have to wait for more data from the outcomes studies to draw better conclusions on the benefit/risk profiles for incretin-based drugs.

• Retrospective, observational studies are plagued by a number of limitations and biases, including the potential for over-diagnosis of pancreatitis based solely on elevated lipase levels. About 10-20% of people with type 2 diabetes exhibit elevated baseline lipase levels with no other symptoms for pancreatitis, independent of incretin treatment (Steinberg et al., Gastroenterology 2012). GLP-1 agonist treatment can increase median lipase activity ~5-10 U/L (remaining within the normal range, but persistent until treatment discontinuation), as shown in phase 3 trials of Novo Nordisk’s Victoza (liraglutide 1.8 mg), liraglutide 3 mg for obesity, and Lilly’s dulaglutide. The majority were unaccompanied by pancreatitis events, but the overall elevations of lipase levels in this population makes an accurate diagnosis of acute pancreatitis more challenging – clinicians who are not as thorough as to use imaging to confirm the diagnosis may over-diagnose based on lipase levels alone. Dr. Koehler, thus, emphasized the need for formalized procedures to assess these adverse events (e.g., in randomized-controlled studies).
• Dr. Koehler highlighted all of the CVOTs for incretin-based therapies that are ongoing, or recently completed, saying that the neutral pancreatitis findings in SAVOR and EXAMINE were “promising” and suggest that any risk of pancreatitis is very low (0.3-0.4% chance over median follow ups of 1.5-2 years in these two trials).
• Finally, Dr. Koehler reviewed the FDA and EMA’s independent assessment (published in NEJM February 2014 – see our report here). She characterized this undertaking as extremely thorough, noting that the agencies evaluated all preclinical and clinical data, including more than 250 toxicology studies in over 15,000 rodents and almost 2,500 non-rodents, 120 pancreatic histopathology slides, their own commissioned pancreatic toxicology studies with exenatide, all clinical safety databases to date including more than 200 trials, and results from the recent SAVOR and EXAMINE outcomes trials.

Corporate Symposium: Getting Straight to the Point – A Theatrical Play Exposing the Misconceptions Around Injections and Tackling the Barriers They Create (sponsored by Novo Nordisk)

Steven Edelman (UCSD, San Diego, CA); Stephen Brunton, MD (University of North Carolina, Chapel Hill, NC); Melissa Magwire, RN, CDE (Shawnee Mission, KS)

Introduction

Moderator Ms. Melissa Magwire opened the session by explaining its innovative and unconventional format. A trio of short plays would explore the particular anxieties and challenges facing patients as they considered moving to injectable therapies. Each act would be followed by a short panel discussion. Novo Nordisk previously staged a version of this show – written and directed by English playwright Mr. Tim Gomersall – at last year’s IDF in Melbourne, but this iteration benefited immensely from the presence of Dr. Stephen Brunton and Dr. Steve Edelman (University of San Diego, San Diego, CA). The two panelists were able to speak both as healthcare providers and as patients; Dr. Edelman has type 1 diabetes and is the founder of Taking Control of Your Diabetes (TCOYD), while Dr. Brunton has type 2 diabetes and is the father of a child with type 1 diabetes. This session was packed to the brim, and we feel that HCPs walked away with some valuable learning.

Act One: “The Injection Barrier”

The first act of the play spotlighted how doctors and patients could come away from the same consultation equally frustrated, albeit for very different reasons. A doctor and a nurse discussed their difficulties; the former complains of a “whirlwind” patient who assumed that her internet research was comparable to his carefully considered recommendation that she begin injectable therapy, while the nurse explained her patient clammed up at the first mention of injectables, probably out of fear of needles. On the other side of a dividing wall, those same patients gave their side of the story. Fiery Isabel explained that she sought out other options because she considered injectables a sign of personal failure, while timid Paul said he simply couldn’t understand what the nurse was trying to tell him about his treatment! Eventually, the wall between them was removed, giving each pair another chance to communicate more effectively. What follows are some particularly incisive lines from the scene:

• Paul: “I’m not good with my change. I have my routines. It was hard enough adding all those pills into my life, now they want to add an injection into my life.”
• Isabel: “It’s more a matter of a pride. It was like he was saying I was at the end of the road, and that wasn’t fair to me.”
• Paul: “I was pretty sure you told me that I needed more medicine, but I was so busy trying to understand that much that I didn’t have time to understand why.”
• Doctor: “It’s not necessarily your fault if you can’t manage your diabetes through orals and a healthy lifestyle alone… Diabetes is a progressive disease.”

 

Act One Panel Discussion

To begin the discussion, Ms. Magwire asked the audience, “What do you think is the biggest reason that patients are hesitant to switch from orals to injectables?” Fourteen percent attributed this to “feelings of guilt, shame, or failure,” 41% to “lack of understanding about disease progression,” 6% to “concerns about possible side effects,” and 39% to “fear of needles/pain associated with injection.” During their discussion, the panel referred to several findings from Novo Nordisk’s DAWN2 trial.

Ms. Magwire: What you do you think about the answers to the audience response questions?

Dr. Brunton: I think all of those things are correct, and the answer will vary between different patients. We as providers make a lot of the issue of fear with needles and injections, but needles are so thin now, and people don’t have the same kind of fear they used to have.

Dr. Edelman: The option I picked was “the lack of understanding around disease progression.” I’m a big fan of patient education. The more you educate people about the natural history of diabetes and the pros and cons of different medicines and show what it's going to do for them, then the better the buy-in and adherence is. Education combats a lot of the fear and the anxiety that patients deal with. This act of the play brought up of those issues, and the patient and their understanding is where the rubber meets the road, so to speak.

[Screens displayed Novo Nordisk data (on file) indicating that 80% of patients are “open to” or “comfortable with” the idea of self-injection.]

Ms. Magwire: Studies have shown that the majority of patients are willing to at least consider injectable therapy.

Dr. Edelman: I don't think there's a fear among patients, many physicians, caregivers, nurse practioners, whatever – they’re afraid of starting insulin because of the time commitment, they’re hesitant to approach the patient, just the hassles involved. I think all of us in general, no matter what we do as a profession, we tend to take the path of least resistance.

[Screens displayed data (Nakar et al., J Diabetes Complications 2007) showing that 12% of patients feared the pain of injections, but 48% of providers thought their patients feared the pain of injections.]

Ms. Magwire: As we can see here, providers often overestimate patients’ fear of injectables.

Dr. Edelman: Yeah, absolutely, I’m not surprised by this at all. I’ll just provide one 15-second story: Many years ago, I was recruiting a patient for a study, I believe it was GLP-1, and I went through this long, 10-minute monologue about injections and how it really is fairly painless, and, at the end, they just said: “Okay.” And I said, “What do you mean, ‘Okay’?” They said, “I have no problem with it.” I said, “Why didn’t you stop me from this whole 10-minute explanation, could have saved me 10 minutes!” And I’ll always remember that because it really shocked me that, hey, this patient wasn’t against injections when you go through the explanation of why it’s going to help them and how it’s going to help them.

Dr. Brunton: The real concern many patients have is that initiating injectable therapies means that they have failed. We need to have open-ended discussion with our patients to see what the issue is. It may not be what we think it is. We think it might be a fear of pain, when really it's a fear of failure.

Dr. Edelman: From now until the end of time, starting a patient on an injectable is going to be a challenge.

[Screens displayed data from the Diabetes Attitudes Wishes and Needs 2 Trial (DAWN2), indicating that many healthcare professionals wanted to receive more training on diabetes care.]

Dr. Brunton: One universal thing we do not receive in our education is background on motivational interviewing. If we can remove the wall between us and our patients, and understand not only what our patients are thinking but also how to motivate them better, it will drive us towards better treatment.

Dr. Edelman: The DAWN2 study was excellent because it brought in the family factor. If you don’t address the education and motivation of patients’ family support networks, you’re missing a big part of the diabetes care puzzle.  

Dr. Brunton: There’s nothing better for learning about a disease than having the disease yourself. It really makes you understand the process! It's interesting for me, having the role both as a patient and a practitioner, recognizing how other practitioners speak to me. And it's such a crucial idea that even though there's this overall disease, diabetes, it's really one disease that one person has at one time. Part of the communication is finding out what diabetes means to the individual patient. What's your understanding of it and what troubles you the most about your diabetes? That way we can develop a very individualized approach.

Dr. Edelman: The individualization of care can feel new with the ADA's recent move in that direction, but we've needed to individualize care since the beginning of medicine! It's not a novel concept at all. Every patient is different, and we have to approach them in terms of their education, their motivation. I think our role is to motivate patients to put diabetes higher on their priority list.

Act Two: “The Trial of Mrs. Annabel Jenkins”

This high-concept scene literalized the judgment that patients often experience in the form of an actual trial, as Annabel Jenkins, a middle-aged woman whose attempts to improve her diet and lifestyle continuously failed, was forced to defend her resistance to initiating injectable therapy. The prosecuting attorney was merciless, calling a series of witnesses who spoke to Mrs. Jenkins’ inability to keep her promises to improve her lifestyle. Ultimately, Mrs. Jenkins and her doctor were able to reach more of an understanding, as both confronted the fact that her need to switch to injectables was about more than any personal failings in treatment. Some of our favorite quotes from the scene include:

• The presiding judge: “This trial, investigating Annabelle Jenkins’ poor diabetes management, is hereby convened.”
• Mrs. Annabel Jenkins: “My reluctance to start on injectables is not a matter of self-neglect – it’s a matter of self-preservation! I’ve read how insulin can cause side effects like weight gain, which can’t be good for me. I’m sure I can control my blood sugar with diet and exercise alone.
• The personification of the Internet: “I’ve found some great blogs for you, on how insulin makes you gain weight, gives you regular hypoglycemia, and even a story about a grandmother on insulin who lost her leg. Scientific evidence? Where’s the fun in that?”

Act Two Panel Discussion

To begin the discussion, Ms. Magwire polled the audience: “Which area do you feel needs the most improvement when treating people with diabetes?” Twenty-nine percent said “ensure a greater understanding of disease progression,” 20% said “improve communication skills,” 13% said “better understanding of the injection barrier,” and 39% said “provide resources and education for patients and caregivers.”

Ms. Magwire: That act may have seemed a little bit extreme, but maybe not. We do find there is a lot of guilt and a lot of judgment, unfortunately, involved sometimes. 

Dr. Edelman: The play wasn’t so far off reality in many situations. We label our patients, and I think we're all guilty to some degree, as "non-compliant." You get someone who has type 2 who is older and heavier, they have to change their lifestyle. They've suddenly got 100 things to do in terms of lifestyle: pills, injectable, seeing the doctor, seeing the eye doctor, seeing the foot doctor, washing the feet every night - there is a lot of things that we ask our patients to do. Many of those things we probably wouldn't be so great at doing ourselves! And then they come back to the clinic and they haven't done them, and you just think, "That person doesn't care at all about their own health." It's really an attitude issue, and I think we're all guilty to some degree. It gets hard to see patients one after the other in the clinic, who don’t bring their logbook, don’t achieve weight loss, and that can be frustrating as a caregiver. And then that attitude starts to build up.

Dr. Brunton: What I appreciated about the play - obviously it was very dramatically done - but that idea of criminalizing the patient. I think we do that a lot. We ask patients to do so much, in all diseases but particularly in diabetes, which is perceived by both patients and providers as a lifestyle disease. So therefore, it's your fault: You're overweight, you don't exercise, etc. We ask patients to make a lot of changes when it's difficult for us to make even one change. I did an experiment with some residents in which they chose one lifestyle and tried to change it for a month. We did this for ten years, and one of the things the residents picked was that they weren't going to have cookies for a month. At the end, out of 70 residents, only two people were able to make the changes! Back to the play, you look at the fear that Annabel had about taking injections. Once again, there are people telling her that she's at fault. The problem is that a lot of providers think the same thing. As we've seen, diabetes is a progressive disease; it's going to eventually happen. That's why I talk to patients very early on and tell them that they will probably eventually have to have an injectable, and that it's a natural kind of therapy, replacing what is naturally wrong. And then it's not a guilt thing. It's just part of the disease itself.

Dr. Edelman: You probably have to say that more than one, so that it sinks in! We have great tools, including orals and injectables, but we have to go beyond that. We’re limited in the time we have with our patients, both in the US and around the world.

Dr. Brunton: Education is power. One of the things about having diabetes for many patients is a loss of control. So you provide resources for the patient's education, particularly with regards to the disease progression or even what the disease is, then the patient is able to take more control and feel better about themselves.

Dr. Edelman: The other thing that I try to do with newly diagnosed type 2 diabetes is to re-frame the issue. The fact is that if you are diagnosed with type 2 diabetes, you stand to live a longer and healthier life, because now you’ll pay more attention to your health, start looking at cholesterol, blood pressure, and all the things you may have ignored before you got the diagnosis. I try and turn it around and make it a positive, because I’m a glass-half-full kind of guy.

Dr. Magwire: Patients and providers also come out of consultations with very different memories of how the consultation went, and what was discussed.

Dr. Brunton: We need to go beyond asking, “How are you doing?” That is just a social question; a patient’s leg may be falling off, and they would say that they are doing well in response to that question. We have to really ask deeper questions.

Dr. Edelman: The discrepancy stems from a lack of proper communication, and a lack of understanding of where patients and providers are each coming from.

Ms. Magwire: Do you think that education is the biggest barrier to initiation on injectables?

Dr. Edelman: I think it is key. You have to bring out and show them the new needles. I show my patients the 32-gauge 4 mm needles, and insert it in their arm with their eyes closed. Most of the time they don’t say they feel it until I tell them I actually did it!

Act Three: “It’s Complicated”

The final act of the play focused on Michael, an overworked businessman who has been on injectable therapy for some time. His heartless boss sends him on open-ended business trips to Japan on a moment’s notice, his wife worries and accuses him of not taking good enough care of his diabetes, and his doctor insists that he needs to get more serious about his therapy. As he tries to juggle all the demands on his life, he soon realizes just how much he has lost control. Some key lines follow:

• Michael: “I’m just struggling to stay on top, and failing.”
• Michael’s wife: “I’ve lost count of the number of times I’ve come home to find you asleep, but your insulin pen is just as full as the night before.”
• Michael: “If I have more medicine, aren’t there going to be complications like weight gain and hypogly-whatchamacallit?”

Act Three Panel Discussion

Ms. Magwire once again polled the audience, asking “Which do you feel is the biggest area of concern for your patients living with diabetes?” Nine percent said “lack of caregiver involvement/support,” 44% said “day-to-day stress/feeling overwhelmed,” 13% said “feeling discriminated because of their condition,” and 35% said “daily diabetes management at home. Ms. Magwire closed the panel by asking one final audience question: “As a result of my participation in this program, I intend to _____.” Thirty-two percent said “engage in more effective communication with my patients, 25% said better address the psychosocial impact diabetes can have in my patients, 24% said “more effectively incorporate family members into the diabetes management plan,” and 19% said “make no changes as this program validated my current practice.”

Dr. Brunton: The issue is that Michael cannot communicate his problems, due to a mix of embarrassment and shame about his diabetes. It's not just a personal or even a family disease; it involves everyone from his boss to his physician.

Dr. Edelman: The other point that I'd like to make is the "diabetic police wife." It all comes from a place of love, but the way that she communicates with him puts him on the defensive. Family members have to be educated to communicate in a constructive way that doesn't turn off their partner.

Dr. Brunton: Part of the reason why the wife is the police is because she has fears of her own. She's frightened that he will die or lose a foot. She becomes overly protective and makes his life even more miserable than ever before.

Dr. Edelman: Diabetes is a 24/7 commitment, and that brings up issues of self-management, because you can't follow your patients around 24/7. That's where education becomes so important, and the more information they have, the less stress they'll have. That's true for patients and for caregivers as well.

Ms. Magwire: One of the issues that came up in the play was that the patient was hesitant to tell his boss what was going on.

Dr. Edelman: I think there's real discrimination out there. At least in the United States, it's become less and less prevalent out there, because so many people have diabetes now. I think the whole thing is communication and telling the people that you have tell, and then more people who know will understand. Certainly that includes family, although I've seen patients hide things from their family members. If you work, you've got to tell some people at work. It comes to just education and information and just not being fearful of what might happen.

Dr. Brunton: There is such a lack of understanding within the public in terms diabetes is. If someone sees you taking insulin, god help you, it must be a very serious disease. There's this issue of communicating with the public. Too often, your disease can come to define you.

Dr. Edelman: There are a lot of people who wrongly think that every person with type 2 diabetes ate themselves into their disease.

Ms. Magwire: In your type 2 diabetes practice, how do you impress upon patients the importance of self-management?

Dr. Brunton: I'm a family physician, so we manage many different kinds of diseases. Really, the patient owns the disease. I serve as consultant to help them do that. But I think part of the issue is that we're asking people to do a lot. The behavior change is challenging anyway. So the key is choosing one behavior that they think that they can manage, and get some success with that before they have to change everything.

Dr. Edelman: One thing I do that helps is to give patients a 30-day challenge. Patients will try basal insulin for 30 days, and at the end of that time, if the injections are too painful, or if they don’t see improvements, or if they simply want to stop for any reason, they can stop. They look at me in disbelief when I tell them this idea. I never saw a patient who improved on basal insulin in the 30-day period who decided to go off it.

Dr. Brunton: When you've had high blood sugar for a very long time, that's just the way you feel. So when patients start on an injectable, one great thing that happens is that their blood sugars go back to normal. They feel better. It's like a new lease on life.

Ms. Magwire: For a busy practitioner, what is the key factor to address first?

Dr. Brunton: We've talked generically about the issue of education. For me, it's about addressing it very early and not talking about it as a punishment. You just say that an injectable is simply about the natural progression of the disease. I think for a clinician who doesn't already have his or her team in the office, it's about developing a team. Have someone in the office who can handle the education aspects for you. It's about reinforcement for the patients, staying in touch with them and addressing their issues.

Ms. Magwire: What is your pearl of wisdom for overcoming the injection barrier?

Dr. Edelman: You need to ask truly open-ended questions, to prompt patients to tell you what their biggest concern is, and you need to listen closely. If you ask good open-ended questions, you will pick up a lot of what is stopping the patient from reaching their glycemic goals, and then you can address the problem.

Question and Answer Session

Q: How do you deal with patients’ travel?

Dr. Edelman: I have worked on publications showing that, depending on the magnitude of the time zone change, there are ways to advance basal insulin administration from nighttime in California to nighttime in France, for example. It is a complicated answer.

In terms of individualizing diabetes education, what is your goal to creating a successful plan for each patient?

Dr. Brunton: What we're starting to understand is that patients have different levels of health literacy. It's really about trying to understand where they're coming from so that I know where to take them. I start by asking them, "What is your understanding about diabetes?" The answer to that can develop an entire curriculum. Most people know that it's something about blood sugar. It depends on their necessity, their urgency, and what are their biggest fears. If I can deal with all that up front, I think the rest of it follows. I work closely with a diabetes educator so that that person can spend a lot more time with patients and is very skilled in handling all that. It's also important to be open at any time to that education, and just to understand where the patient is coming from.

Dr. Edelman: Use your diabetes educators. They have the luxury of spending an hour with your patients when you only have 15 minutes, during which you have to deal with all the other things you need to discuss with them.

Q: Do you approach different injectable therapies differently?

Dr. Edelman: Starting a basal insulin is different than starting a GLP-1 agonist – each has its own pros and cons. I think overcoming the fear of injections is an overlap, but there are differences in terms of what each drug class will do to patients.

Dr. Brunton: As a family physician, it is easier for me to initiate a GLP-1 agonist, as there are fewer concerns about hypoglycemia. Patients also seem to have a particular bias against insulin, and GLP-1 agonists don’t seem to have the same stigma. We’ve seen tremendous adoption of both injectable classes.

Q: How can we better support patients who start injectables?

Dr. Edelman: There is nothing like peer influence – it can do more than a provider who only gets 15 minutes with a patient. You get a whole group of patients who have succeeded at taking injections. I think that's way more powerful than a doctor trying to talk someone into it in 15 minutes. And we see that at shared medical appointments that we do at the university. It's quite helpful.

Dr. Brunton: And actually I was going to say the same thing. There's a big movement in the US now to be very efficient. Bring six or eight patients together, and they talk amongst themselves, so they can share best practices and give them support that they don't feel right asking for, and then you as a practitioner can take an individual patient into a room and deal with what needs to be done. But that group support thing is a way of alleviating distress. We saw that in the first play. In dealing with some of the concerns that the patients had, other patients had more credibility than doctors.

Q: Some final pearls of wisdom: What are some best practices when initiating discussion of injectable therapy?

Dr. Brunton: I think that you have to overcome what's traditionally being done in many places, which is saying back in the day, when the patients were children, people would say, "Behave, or the Doctor's going to give you a shot!" So, now there's this thought, if you don't behave i.e. not speak to your doctor or not take care of your diabetes, you're going to have to get a shot. You're going to have to get insulin. So my big thing to patients is that, at a certain point in time, you're going to have injectable therapy: What are your thoughts? It's very open-ended. And that way I can really address their big concerns.

Dr. Edelman: Yeah, I agree, Steve. Even though we talk about it, it's hard to get someone who is relatively early in their natural history to talk about injections, maybe even give them a sample. Tell them, "You don't need it now, you may or may not need it in the future, but you most likely will need an injectable." So, you don't have to come back to it for years, but at least that fear is out of their minds. And the other pearl is that 30-day challenge. That really does work. If they don't get better, if the insulin or GLP-1 injections are too painful, or they just don't want to keep taking it, we'll stop it. They like that fact that it's not the rest of their life. It's always going to be a challenge. I don't care what you're taking, it's always going to be harder to take an injection than to swallow a pill. That's for sure.

Product Theaters

The Critical Role GLP-1 Receptor Agonists Play in Addressing the Treatment of Type 2 Diabetes (Sponsored by AstraZeneca)

Zachary Bloomgarden, MD (Mount Sinai Medical Center, New York, NY), Susan LaRue, CDE (Amylin Pharmaceuticals, San Diego, CA)

In this presentation, Dr. Zachary Bloomgarden reviewed some of the basic science behind GLP-1 receptor agonists and described the efficacy and side effects of AstraZeneca’s once-weekly Bydureon (exenatide). Bydureon achieves steady and continuous release in the bloodstream via degradation of microspheres, which are made from the same polymers as surgical sutures and orthopedic implants. Dr. Bloomgarden next presented data on Bydureon’s efficacy. Results from the phase 3 DURATION-5 study, which compared Bydureon with AZ’s Byetta (once-daily exenatide), demonstrated Bydureon’s superiority in both reducing A1c (1.5% reduction versus 0.9% with Byetta) and fostering weight loss (5 pounds vs. 3 pounds with Byetta). With regards to adverse events, Dr. Bloomgarden stressed several times that hypoglycemia was only a problem when Bydureon was taken concurrently with sulfonylureas. GI effects were quite prevalent, although Dr. Bloomgarden noted that they were “part of the baggage” for GLP-1 agonists. He also mentioned that patients who have or may have pancreatitis or renal impairments should not use Bydureon. Afterwards, he invited Ms. Susan LaRue, a renowned certified diabetes educator, to speak about AZ’s efforts in patient education. The presentation concluded with a demonstration of Bydureon’s single dose tray – although AZ recently received approval for a new dual-chambered pen that greatly improves the reconstitution and administration process from a patient perspective, the pen will only be launched later this year, which is why the demonstration used the single-dose tray. 

• Dr. Zachary Bloomgarden presented data from the DURATION-5 study, which compared efficacy and safety of once-weekly Bydureon against once-daily Byetta; results showed that Bydureon was generally superior. For more details, read our coverage of the topline DURATION-5 data from 2009.
• There was a strong focus on patient education – certified diabetes educator Ms. Susan LaRue talked about AZ’s education programs and demonstrated how to mix and inject Bydureon. Ms. LaRue described AZ’s SteadySTART Educator Network as the largest diabetes educator network in the US. SteadySTART’s team of 84 full-time clinical educators and 400 on-demand educators train office staff, retail pharmacists, and patients themselves on how to use Bydureon. Ms. LaRue also talked briefly about another program that helped patients develop healthy eating and exercising habits. For the finale, she stood in front of the audience and demonstrated, step-by-step, how a patient should administer Bydureon (using the old single-dose tray, because the recently-approved dual-chambered pen will likely not be launched until later this year). According to Ms. LaRue, 88% of a group of type 2 diabetes patients (n=102, 78% insulin-naïve) were able to use the single dose tray successfully on the first try without hands-on help. Regardless, we think the new Bydureon pen will be much easier.

Questions and Answers

Q: I’m concerned about the 15 to 20% nausea and problems with diarrhea in this prep. Also, shouldn’t 2 mg of exenatide at once cause more GI effects compared to the smaller doses of Byetta?

Dr. Bloomgarden: When Bydureon first became available, that was exactly my thought. I thought we could start everyone on Byetta, get them to accept that, and then use Bydureon. That turns out to be incorrect, probably because of fluctuations in the blood level after immediate release. The likelihood of developing GI side effects initially seems to be higher, maybe not with diarrhea, which is interesting and suggests a slightly different mechanism. With nausea, though, you don’t reach a steady state level until six weeks have gone by. This is actually a gentler way of initiating treatment, compared with Byetta. Starting Bydureon at 2 mg is actually better tolerated than Byetta.

Q: A patient of mine was on basal bolus with Levemir and NovoRapid, and I suggested she lose some weight with Bydureon. It was her first time taking a GLP-1 agonist. She was fine for six months, but then she had a severe hypertensive crisis where she had to go to the emergency room. We thought it was an isolated event. Then there was another hypertensive crisis. I don’t know what to do now. Should I take her off Bydureon? Is it related to the crises?

Dr. Bloomgarden: At the present time, Bydureon is not recommended for use in conjunction with insulin. I’m acting on behalf of AstraZeneca, so we can only say that that’s off-label use. It may or may not be related to the hypertensive process.

Q: What if she were just on Bydureon, with no insulin?

Dr. Bloomgarden: There’s no reason to think any GLP-1 agonist should increase blood pressure. I would evaluate her as with anyone having a hypertensive crisis.

Q: There was not much explanation given about the microsphere technology. Would that information be available online?

Dr. Bloomgarden: I’m going to ask you to direct that question to the AstraZeneca people here to get full details on the composition of the polymer. I don’t actually personally know that.

Q: One more quick question. If we calculate 10 micrograms exenatide twice per day, which is the Byetta dosage, that’s 140 micrograms, or 1.4 mg, per week. Bydureon is 2 mg per week. Why the difference?

Dr. Bloomgarden: There’s a tremendous difference in 24-hour exposure comparing quick release Byetta and slow release Bydureon. There’s a very rapid increase and decrease in concentration of exenatide after the injection of Bydureon. Almost certainly there is some degree of degradation brought about by its microsphere formulation. After one develops an agent, we must find out what’s the most effective dose. The 2 mg dose seems to be a good balance between efficacy and side effects with the once-weekly drug, whereas the once-daily drug works well with 140 micrograms per week.

SGLT-2 Inhibitors and SGLT-1 Inhibitors

Oral Presentations: SGLT-2 Inhibitors

Empagliflozin (EMPA) Monotherapy for ≥76 Weeks in Drug-Naïve Patients with Type 2 Diabetes (T2DM) (264-OR)

Michael Roden, MD (Leibniz Center for Diabetes Research, Dusseldorf, Germany)

Dr. Michael Roden presented the results of a 52-week extension study of a 24-week registrational trial (EMPA-REG MONO) comparing Lilly/BI’s SGLT-2 inhibitor Jardiance (empagliflozin) at both 25 mg and 10 mg doses against Merck’s Januvia (sitagliptin 100 mg) and placebo in drug-naïve type 2 diabetes patients. The extension study enrolled 68% of patients enrolled in the initial study. The 24-week data demonstrated that empagliflozin provided comparable A1c reductions to sitagliptin, but the curves diverged slightly during the extension study. At week 76, empagliflozin 25 mg led to a significantly greater placebo-adjusted drop in A1c (-0.89%) relative to sitagliptin (-0.66%) (p = 0.005). The empagliflozin 10 mg arm showed a non-significant trend towards increased efficacy, with a placebo-adjusted reduction of -0.78%. As at 24 weeks, both empagliflozin doses led to significantly greater weight loss (~2.5 kg, ~6 lbs) than sitagliptin, which was weight-neutral. There were slightly more drug-related adverse events with empagliflozin than sitagliptin, which were (unsurprisingly) driven by a ~5% increase in genital infections. The sustained efficacy over 76 weeks and superior A1c reduction over sitagliptin should provide Lilly/BI with some good talking points as it begins empagliflozin’s launch in Europe and in new discussions with the US FDA after the recent re-submission.

• Results from the original 24-week study (EMPA-REG MONO) were published in the journal Lancet in September of last year.
  
  • J&J’s Invokana (canagliflozin) previously demonstrated superiority to sitagliptin in a 52-week trial in patients on metformin +/- sulfonylurea – we covered the results at ADA 2012 (read our report). AZ’s Forxiga/Farxiga (dapagliflozin) has been studied as an add-on to sitagliptin, but not versus sitagliptin in phase 3.
• Of the 899 patients treated in the initial 24-week study, 68% patients continued in the extension study. Characteristics of the patients that continued into the extension study were comparable to those of patients that did not continue. There were slightly more male patients in the study, with a median age of approximately 55 years. Notably, nearly two-thirds of the patients in the study were Asian, with 50% of that group from China, 30% from Japan, and the remaining 20% from India. It is unclear how this might impact the interpretation of the results – some very early studies have suggested that DPP-4 inhibitors have greater efficacy in East Asian populations – but Dr. Roden noted during Q&A that no interaction was found between efficacy and ethnicity (Asian vs. Caucasian). Diabetes duration was on the low side, with most patients having a disease duration of 1 – 5 years. The four arms of the study were placebo, sitagliptin 100 mg, empagliflozin 10 mg, and empagliflozin 25 mg. The placebo group seemed to lose patients to follow-up at a more rapid rate than the other study arms during both the initial study and the extension study, and at week 76 only 65 patients remained in the placebo arm (relative to 132 in each of the empagliflozin arms). Perhaps as a result, the A1c results were displayed as absolute reductions and not placebo-adjusted reductions, although we calculated placebo-adjusted values below.
• After 76 weeks from the beginning of the initial study, the empagliflozin 25 mg arm achieved a significantly greater placebo-adjusted A1c reduction (-0.89%) than the sitagliptin arm (-0.66%). The difference was statistically significant (p = 0.005); baseline A1c was ~7.9%. The empagliflozin 10 mg arm achieved a numerically greater placebo-adjusted A1c reduction than sitagliptin (-0.78% vs -0.66%), but the difference was not statistically significant (p = 0.131). For background, neither empagliflozin dose achieved a statistically greater A1c reduction than empagliflozin at 24 weeks; the difference emerged gradually during the extension study, as the sitagliptin arm’s A1c crept slowly upwards while the empagliflozin groups’ A1cs held slightly more level.
• Empagliflozin led to significantly weight loss and reductions in systolic blood pressure than sitagliptin. Both empagliflozin groups achieved weight loss of approximately 2.5 kg (~6 lbs), while sitagliptin and placebo were weight neutral (p<0.001 for both empagliflozin groups vs sitagliptin). Systolic blood pressure fell by approximately 4 mmHg in the empagliflozin groups, and here to there was no appreciable change in the placebo or sitagliptin groups (p=0.001). 
• As expected, there were more genital infections in the empagliflozin arms (6%) than the sitagliptin (1%) or placebo (2%) arms. This difference drove a difference in overall treatment-related adverse events between empagliflozin and sitagliptin. Other than this difference, there were no imbalances in safety or tolerability between empagliflozin and sitagliptin. Hypoglycemia incidence was identical between the four arms.

Questions and Answers

Q: You said that the enrollment criterion for “drug-naïve” was no diabetes drug use for 12 weeks prior to the study. Were there patients in the trial who had previously been on diabetes drugs and were taken off? If so, do you know the proportion of patients who were previously treated versus those who were truly drug naïve?

A: Subjects could have been on previous metformin treatment, as long as it was discontinued more than 12 weeks before the trial. We don’t have data on the distribution, but there was no imbalance in that proportion of previously-treated patients between the groups.

Q: You mentioned that around two-thirds of the study population was Asian. Could this have introduced any genetic discrimination?

A: Yes, a specific feature of this study is that it included a high percentage of people from Asia. Out of that group, around 50% was from China, 30% from Japan, and 20% from India. There have been subgroup analyses to compare the efficacy between Asians and Caucasians, and there was no real effect – the P-value for the interaction was higher than 0.1.

Q: Could you provide more detail on the temporality of the 4 mmHg reduction in systolic blood pressure you saw at week 76? Was the difference more dramatic at the beginning of the trial, and was subsequently attenuated?

A: If you look at the time curves, the reduction was more or less maintained throughout the study. There were no marked changes at the very end, or at points in between.

Empagliflozin (EMPA) Compared with Glimepiride (GLIM) as Add-on to Metformin (MET) for 2 Years in Patients with Type 2 Diabetes (T2DM) (266-OR)

Martin Ridderstråle, MD, PhD (Steno Diabetes Center, Gentofte, Denmark)

Dr. Martin Ridderstråle presented the results of the two-year EMPA-REG H2H-SU trial, which compared Lilly/BI’s Jardiance (empagliflozin 25 mg) to the sulfonylurea (SFU) glimepiride (1-4 mg), both in addition to metformin. At week 104, from a baseline of 7.9%, empagliflozin yielded a greater reduction in A1c (-0.66%) than glimepiride (-0.55%); the difference, although modest, was enough to achieve the statistical margins for both non-inferiority and superiority. Notably, in the glimepiride arm, the average daily dose was only 2.7 mg per day – Dr. Ridderstråle suggested during Q&A that the glimepiride dosing does impact the interpretation of the efficacy results, but that the more notable findings were in areas beyond A1c. Empagliflozin was associated with a far lower incidence of hypoglycemia (2.5%) than glimepiride (24.2%). This high rate of hypoglycemia suggests to us that pushing the dose of glimepiride further would likely have been unsafe. Empagliflozin came out ahead with regards to body weight (4.5 kg [~10 lbs] difference) and blood pressure, but slightly increased cholesterol and the incidence of genital infections. These results show that empagliflozin is strongly differentiated from SFUs when it comes to factors such as hypoglycemia and weight that are very important for patients, and that the drug might even be able to deliver better A1c-lowering efficacy in the long-term. 

• This study enrolled 1545 type 2 diabetes patients who were on stable metformin therapy. Approximately one-third of enrolled patients were Asian, and approximately 17% had diabetes duration of over 10 years. The study randomized patients 1:1 to empagliflozin 25 mg or glimepiride, with a dose range of 1-4 mg.
• Empagliflozin led to a statistically superior reduction in A1c from baseline (-0.66%) at week 102 compared to the -0.55% reduction seen with glimepiride (p = 0.015). Dr. Ridderstråle noted that the mean maximum glimepiride dose in the glimepiride arm was 2.7 mg, with only 40% reaching the maximum dose. When asked about that fact during Q&A, Dr. Ridderstråle suggested that the efficacy should be viewed as more or less comparable, with the more meaningful differentiation coming in the form of benefits beyond A1c. The time-course of A1c reductions was in line with what we have seen in previous trials: glimepiride caused a sharp initial decline that began to creep upward, while empagliflozin led to a more gradual initial reduction that held fairly steady. The curves crossed at around week 40.
• There was markedly less hypoglycemia with empagliflozin than glimepiride. Approximately 24% of patients on glimepiride experienced a confirmed hypoglycemic adverse event, while the incidence was under 3% in the empagliflozin arm (p<0.001). The only events requiring assistance (n = 2) were in the glimepiride group. Beyond providing reassurance on empagliflozin, these results indicate that the glimepiride dose was probably titrated well, as raising the dose would likely have caused an unacceptable level of hypoglycemia.
• The empagliflozin group lost 3 kg (~7 lbs) on average, while the glimepiride group lost 1kg (~3 lbs) on average. The empagliflozin group saw a slight 3 mmHg reduction in systolic blood pressure, while the glimepiride group saw a rise of 3 mmHg (p<0.001).
• There were more drug-related adverse events in the glimepiride group, driven by the increased hypoglycemia. There were more serious adverse events, however in the empagliflozin arm, although Dr. Ridderstråle noted that there was no specific signal or pattern among individual serious adverse events that drove this imbalance. Genital infections were higher with empagliflozin (~12%) than glimepiride (~2%).
• As has been seen in other SGLT-2 inhibitor trials, empagliflozin led to slight increases in total cholesterol, LDL-C, and HDL-C.

Questions and Answers

Q: Given that the mean daily dose of glimepiride in the study was only 2.7 mg, which is a ways away from the maximum licensed dose, is the superiority result questionable?

A: I do not think that it is questionable, but I do think that you have to view the data in terms of that difference in dose. I’m inclined not to emphasize the superiority finding, as I do not think that is the important finding. A better way to look at the efficacy is that the results were more or less comparable, and then interpret the other effects in that light.

Q: Why was there an elevation in genital infections but not urinary tract infections?

A: I’m not an expert on infectious disease, but from the data I have seen, apparently bacteria in the urinary tract like glucose but do not need it, while mycotic agents absolutely love glucose.

Q: Did you measure if there was a correlation between infections, volume depletion, and baseline blood sugar levels?

A: There was no difference in the level of volume depletion or infections with respect to blood sugar levels. They are seen in all levels of glycemia.

Q: I noticed that LDL cholesterol and triglycerides went up despite a reduction in weight loss and hyperglycemia. Did you measure ApoB?

A: The lipid findings were interesting, but we haven’t evaluated those details yet. Concerning triglycerides, we actually saw a decrease at 52 weeks, then a slight increase. Regarding LDL, this slight increase appears to be consistent but small, and we don’t know if it is clinically significant. There are some hypotheses about why you see that increase in LDL. One is that there is a slight increase in hematocrit, and that if you adjust for that, you see less of an increase. Another interesting theory is that if you lose sugar through urine, you need to compensate with increased energy intake and your dietary intake and composition may change. Supporting that theory is the fact that you see increases in both LDL and HDL.

Improvement in Glycemic Control and Reduction in Body Weight over 52 Weeks with Dapagliflozin as Add-on Therapy to Metformin plus Sulfonylurea (267-OR)

Stephan Matthaei, MD (Diabetes-Center Quackenbruck, Quackenbruck, Germany)

Discussing dapagliflozin 10 mg/day as add-on to metformin and sulfonylurea, Dr. Stephan Matthaei presented 52-week data from a randomized controlled trial (24-week phase 3 trial plus follow-on period). The study included adults with type 2 diabetes inadequately controlled on metformin and sulfonylurea (n=216). Baseline mean A1c was 8.1%-8.2%, age 61 years, BMI 32 kg/m², diabetes duration 9.3-9.6 years, and systolic blood pressure 135-136 mm Hg. Patients were randomized 1:1 to receive either placebo or dapagliflozin plus their current medications. At 52 weeks dapagliflozin led to statistically significant reductions in A1c (1.10% vs. 0.39% for placebo) and body weight (-2.66 kg vs. -0.48 kg for placebo [5.9 lbs vs. 1.1 lbs]). Systolic blood pressure was reduced at 24 weeks but then returned to baseline by 52 weeks, albeit remaining lower than placebo (decrease of 0.9 mm Hg vs. increase of 1.3 mm Hg for placebo). Minor hypoglycemic events – blood glucose >63 mg/dl with symptoms, or <63 mg/dl without symptoms – were more common with dapagliflozin than placebo (15.7% vs. 8.3%). Dr. Matthaei attributed the increase to sulfonylurea therapy and recommended that when starting on dapagliflozin, dosage of sulfonylurea (or insulin) should be reduced. Genital tract infections were also more common with dapagliflozin (10.2% vs. 0.9%), especially in women (14.3% vs. 2.0%). Overall adverse events were balanced between dapagliflozin and placebo (70.1% vs. 74.1%), serious adverse events were also balanced (6.5% vs. 7.4%).

Questions and Answers

Q: The blood pressure went down at 24 weeks but then trended back, despite the persistence of weight loss. Do you have any idea why?

A: Based on our four-year comparison to glipizide, when we had stable reduction in weight and blood pressure, this was kind of unexpected. Mean absolute systolic blood pressure at 24 weeks was 129 mm Hg; perhaps doctors reduced other blood pressure meds. We need to look at this.

Q: What was the standard deviation of weight loss?

A: (Shows slide – we estimate the SD at roughly 1 kg [2.2 lbs]). This is good news for us as doctors; it encourages compliance. In Germany, where we have the largest experience, since we’ve had access for 18 months, compliance is very high. These patients have tried to lose with for decades, and suddenly they are able to do it with this new medication

Q: Were dapagliflozin-related adverse events mainly the effect of hypoglycemia due to glycosuria, or were they having other drug-related comorbidities?

A: This was mainly due to more minor episodes of hypoglycemia, which is due to sulfonylurea co-medication. Whenever we try to reach these ambitious glycemic goals with this co-medication, we should adapt the dose or even discontinue sulfonylureas.

Q: Many times in the presentation, themes emerged. The hypoglycemia was striking as you add dapagliflozin, also the increase in genital infections. Should we give any instructions to patients up front?

A: The best instruction is triple combination-therapy: metformin, DPP-4 inhibitor, and SGLT-2 inhibitor. But sulfonylureas are still widely used. The next question is, why not halve the dose of sulfonylurea, and then increase it again only if glycemic goal is not met? It’s the same question with insulin. My recommendation would be to reduce insulin right away by about 20% to have no hypoglycemic episodes.

Dapagliflozin is Safe and Well Tolerated in Older Patients with T2DM (269-OR)

Traci Mansfield, PhD (Bristol-Myers Squibb, Princeton, NJ)

Dr. Traci Mansfield compared the safety and tolerability of Farxiga (AstraZeneca’s dapagliflozin) 10 mg vs. placebo in three age ranges (<65, ≥65, and ≥75 years old), from a pooled analysis of nine phase 3 trials. At baseline, mean A1c was roughly 8.0-8.1% in all groups. Contrasting the younger, older, and oldest patients (n=2707 vs. 1275 vs. 174), the main baseline differences were mean age (55 vs. 70 vs. 77 years old), diabetes duration (8 vs. 13 vs. 15 years), and percentage of patients with moderate renal impairment (8.3% vs. 22% vs. ~25%). Frequency of total adverse events were more common with dapagliflozin than placebo in all three age groups, with adverse events most common in the oldest patients (74.4% vs. 71.5%; 73.1% vs. 70.7%; 80.4% vs. 75.3%). Severe adverse events were balanced between dapagliflozin and placebo (13.7% vs. 14.6%) and were similar in all three age groups. Genital infections and urinary tract infections were generally more common with dapagliflozin than placebo, and rates were similar across age groups. Renal adverse events were more common with dapagliflozin, and they were more common in older patients (3.5% vs. 2.3%; 14.0% vs. 7.9%; 29.9% vs. 20.8%). Dr. Mansfield explained that most renal events involved increased serum creatinine and/or decreased creatinine clearance and that many events occurred in patients with moderate renal impairment, who would be contraindicated today. Volume-related events were more common with dapagliflozin and were seen most in the oldest patients (1.7% vs. 1.2%, 2.3% vs. 1.7%; 3.1% vs. 2.6%); Dr. Mansfield said that the volume-related events were not related to renal function. 

Questions and Answers

Q: I am curious about adding more AstraZeneca products to the mix – what would happen if you added Bydureon and Kombiglyze?

A: My expertise is really in dapagliflozin and SGLT-2 inhibition, and I would probably apply my comments to that.

Questioner: In our clinic we stack often with SGLT-2, Bydureon, metformin of course – I’m not sure about adding a DPP-4 inhibitor in people already taking a GLP-1 receptor agonist – then add basal insulin, and then short-acting insulin for glucose above 180 mg/dl. We get tremendous benefits, including in phenomena of wellness; it takes more than the one bullet of dapagliflozin.

Q: About 20% of older patients had moderate renal impairment, who would be contraindicated for Farxiga. How’d these patients do with renal and volume-related adverse events?

A: These studies were conducted before the label was created. When we exclude those patients, you see a lot fewer renal events. Of the renal events in older subgroup, a lot were driven by patients with eGFR<60. Volume-depletion events were still more common with dapagliflozin than placebo.

Q: Did you perform any subgroup analyses of patients taking loop diuretics or thiazide diuretics?

A: Patients taking loop diuretics were more prone to renal and volume-related events, but we don’t see this with other antihypertensive agents.

Long-term Efficacy and Safety of Canagliflozin (CANA) in Older Patients with Type 2 Diabetes Mellitus (T2DM) Over 104 Weeks (268-OR)

Bruce Bode, MD (Atlanta Diabetes Associates, Atlanta, GA)

Dr. Bruce Bode presented the results of a 78-week extension of a 26-week primary study on the effects of J&J’s Invokana (canagliflozin) in elderly type 2 diabetes patients. The double-blind placebo-controlled study randomized 716 type 2 diabetes patients (age 55 – 80) with inadequately controlled diabetes to placebo, canagliflozin 100 mg, or canagliflozin 300 mg after a two week run-in period; 521 patients completed the follow-up. Canagliflozin 300 mg led to a placebo-adjusted A1c reduction of -0.60%, and canagliflozin 100 mg led to a reduction of -0.49%, from a relatively low baseline of 7.7%. This represented a very slight (and unsurprising) rise in A1c from the week 26 results (-0.70% and -0.57% for canagliflozin 300 mg and 100 mg, respectively). Canagliflozin also caused sustained reductions in fasting plasma glucose, body weight, and blood pressure. The incidence of serious adverse events, which are of particular concern in older patients, were similar for treatment and control groups. Although adverse events such as genitourinary infections were more prevalent in the treatment groups, this was consistent with the effect of canagliflozin on the broader type 2 diabetes patient population.

• Study participants had a mean baseline A1c of 7.7±0.8%, mean fasting plasma glucose (FPG) of 157 mg/dl, and had lived with diabetes for an average of 12 years. Approximately 40% of participants were over 65 years of age; 74% took sulfonylureas or insulin, while 23% were on metformin monotherapy or other agents like DPP-4 inhibitors and GLP-1 analogs that are not associated with hypoglycemia (this balance had implications on the hypoglycemia results). Slightly under 3% were on diet and lifestyle change only at baseline.
• Over 104 weeks, the canagliflozin groups showed reductions in A1c, FPG, body weight, and blood pressure as seen in the primary study. A1c and FPG levels rose slightly from week 26 levels, but weight continued to gradually decline, with a final weight loss of 2.3% and 3.2% for the canagliflozin 100 mg and 300 mg groups, respectively. This was somewhat surprising, as weight loss seen with SGLT-2 inhibitors generally plateaus in the space of six to eight months. During Q&A, Dr. Bode suggested that reduced eating in elderly patients might be the cause.
  
  • In a modified intent-to-treat analysis (last observation carried forward) analysis, canagliflozin 300 mg led to a placebo-adjusted A1c reduction from baseline of -0.60%, and a reduction in FPG of 23.3 mg/dl. Canagliflozin 100 mg led to a placebo-adjusted A1c reduction from baseline of -0.49%, and a reduction in FPG of 21.3 mg/dl. Approximately 42% of patients on canagliflozin 300 mg and 36% of patients on canagliflozin 100 mg achieved an A1c of below 7.0%.
• Consistent with other studies, there were slight rises in both LDL and HDL cholesterol seen with canagliflozin. At the end of the study, patients on canagliflozin 100 mg or 300 mg experienced a 2.5% or 2.8% placebo-adjusted increase in LDL cholesterol. HDL cholesterol rose as well, and remained higher than placebo (3.6% and 4.8% for canagliflozin 100 mg and 300 mg, respectively). Also consistent with expectations, there was a slight reduction in blood pressure seen with canagliflozin.
• The incidence of serious adverse events was similar across groups, although patients on canagliflozin had higher incidences of hypoglycemia, genital infections, and urinary tract infections. Discontinuation rates due to adverse events were similar for canagliflozin 100 and 300 mg compared to placebo (5% and 10% vs. 7%), and severe hypoglycemia only occurred once in the entire trial. Patients on canagliflozin had higher rates of UTIs (15% with canagliflozin 100 mg and 17% with canagliflozin 300 mg vs. 10% with placebo) as well as adverse events related to osmotic dieresis and volume depletion – these did not lead to many discontinuations, but given the risk of falls with elderly patients, providers should be careful to warn patients about this effect. As expected, the rates of genital mycotic infections were elevated with canagliflozin, especially in women.

Questions and Answers

Q: Why were people older than eighty excluded in a study of older people?

A: With people older than eighty, you get too many adverse events that cloud a study, so it is wise to cut the age range off at eighty.

Q: There seems to be an attenuation of the initial A1c improvement as time went on. You said that reflects the progressive nature of diabetes, but the SGLT-2 mechanism has nothing to do with beta cell failure.

A: The difference in A1c from 26 to 104 wks was only 0.1%. Also, the rise in A1c was the same in all groups. In the elderly, beta cell function declines, so you need to improve beta cell function or make up for it with medications that improve insulin resistance or replace insulin secretion.

Q: Regarding weight loss, can you identify patients which patients lost weight and which did not? Are there markers, like higher BMI?

A: I have no specific percentage data, but about 25% of people don’t lose weight. The cause of weight loss is calorie spillage in the urine, and if you compensate by eating, you won’t lose weight. But most people do lose weight.

Q: We saw some extension data in the past where A1c stayed same, and weight loss did not continue. This presentation was different from other presentations, since here A1c increased but weight continued to decrease. Why is that?

A: Beta cell continues to decline with age, which accounts for the increase in A1c, and older people often eat less, which may account for the continued weight loss.

Time Course of Changes in Glycemic Parameters and Body Weight in Patients Receiving Dapagliflozin as Add-on or as Initial Combination Therapy with Metformin (265-OR)

Arie Katz, MD (AstraZeneca, Wilmington, Delaware)

Dr. Arie Katz presented a post-hoc analysis of the time-course of glycemic and body weight changes during the first 24 weeks of treatment with AZ’s Forxiga (dapagliflozin) as an add-on or an initial combination therapy with metformin. Data was drawn from sub-populations of two large phase 3 trials for dapagliflozin. Treatment with dapagliflozin 10 mg as an add-on or initial combination therapy with metformin reduced A1c noticeably even after four weeks of treatment (-0.35% as add-on therapy [baseline = 8%] and -0.39% as initial combination therapy [baseline = 9%]), and the treatment difference continued to expand thereafter. Curves for fasting plasma glucose diverged by week one, and leveled off by week four at a ~15 – 25 mg/dl treatment difference. Surprisingly, body weight curves also appeared to diverge from week one – we imagine the initial loss could be due to diuresis. The curves continued to diverge, and were beginning to stabilize at an ~2.5 kg (~6 lb) treatment difference between dapagliflozin and placebo. Data on the time-course of SGLT-2 inhibitors’ effects will be particularly useful for insulin-treated patients, who walk a fine line between hyperglycemia and hypoglycemia when  starting a new agent. 

• This 24-week post-hoc analysis drew data from two primary trials. The first trial compared dapagliflozin 10 mg vs. placebo as an add-on to metformin, while the second tested initial combination therapy with metformin XR and dapagliflozin 10 mg vs treatment with metformin XR alone. Baseline A1c was ~8% for the add-on trial and ~9% for the initial combination therapy trial.
• There were no instances of severe hypoglycemia events in any of the studies, although genital infections and urinary tract infections were more frequent in patients on dapagliflozin.

Questions and Answers

Q: How many people achieved target A1c levels within 24 weeks?

A: We have no data for this analysis, but consistently more people achieved goals than in comparator groups.

Q: Comparing add-on therapy and the initial combination therapy, do you have any idea whether a similar percentage reached target A1c levels?

A: I’m not sure.

Temporal Changes in Urinary Glucose Excretion (UGE), Urine Volume (UV), and Plasma Volume (PV) in Subjects with Type 2 Diabetes Mellitus (T2DM) Treated with Canagliflozin (CANA) (263-OR)

Sue Sha, MD, PhD (Janssen Research & Development, Raritan, NJ)

Dr. Sue Sha presented results of a randomized, double-blind, placebo-controlled study (n=36 people with type 2 diabetes) characterizing changes in plasma volume and other measures of fluid/electrolyte imbalance on J&J’s Invokana (canagliflozin) after one week and after 12 weeks of treatment with high dose (300 mg) Invokana. As a reminder, a safety concern associated with Invokana is that its diuretic mechanism cause dangerous plasma volume depletion. This study demonstrated that Invokana decreased plasma volume by about 10% after one week of treatment, but that plasma volume returned to baseline after 12 weeks of treatment. However, some markers of plasma volume depletion did not return to baseline after 12 weeks (eGFR, BUN/Cr, and hematocrit), which may suggest that lasting effects of the initial plasma volume decrease may persist longer than the volume depletion itself.

• Invokana lowered A1c (-0.6% placebo-adjusted) and fasting glucose (-29 mg/dl placebo-adjusted) over 12 weeks from a baseline A1c of 7.6-7.7% and baseline FPG of ~150 mg/dl. As the drug is designed to do, Invokana increased urinary glucose excretion by ~90 g/day at 1 week, and this persisted to 12 weeks.
• The small increase in urine volume (~150 ml placebo-adjusted) observed at one week returned to baseline by week 12. Correspondingly, placebo-adjusted mean plasma volume decreased by 9.7% at week one (a 300 ml placebo-adjusted decrease), and essentially returned to baseline by week 12.
• Although the plasma volume effect attenuated at week 12, a small reduction in eGFR, small increase in BUN/Cr, and small increase in hematocrit persisted through week 12, suggesting that some small reduction in plasma volume functionally remained despite the return of plasma volume to baseline.
• Invokana lowered systolic blood pressure pretty significantly in this trial (placebo-adjusted reductions of 10 mmHg sitting and 8 mmHg standing at week one) that were maintained through week 12 (placebo-adjusted reductions or 13 mmHg sitting and 14 mmHg standing). Dr. Sha noted during Q&A that the mechanism for continued blood pressure lowering despite the attenuation of urine volume is not known. The results of this study will be published in Diabetes, Obesity, and Metabolism.

Questions and Answers

Q: You showed quite a dramatic drop in systolic blood pressure, yet diminishing of the urine volume and diuresis at week 12. Any idea how the blood pressure reduction can be sustained despite the reduction of diuresis? Do you have any sodium excretion data?

A: The mechanism is not well understood. It’s very similar to results from other diuretics such as thiazides. The patterns are very similar. There are several mechanisms hypothesized, but still not well understood.

Oral Presentations: ADA Presidents Oral Session

The Glucose Transporter SGLT 2 Is Expressed in Human Pancreatic Alpha Cells and Is Required for Proper Control of Glucagon Secretion in Type 2 Diabetes (386-OR)

Caroline Bonner, PhD (European Genomic Institute for Diabetes, Lille, France)

Dr. Caroline Bonner’s presentation revealed some striking new findings about SGLT-1 and -2 expression and activity in the pancreas. Her group found that SGLT-1/2 mRNA is found to an appreciable extent in pancreatic alpha cells, where it could be serving as a glucose sensor or playing some other role in endocrine signaling. The expression of SGLT-2 mRNA in particular is increased in obese individuals, but paradoxically falls in individuals with type 2 diabetes, apparently due to glucotoxicity. When Dr. Bonner’s team inhibited SGLT2 in human islets using siRNA or dapagliflozin, it led to a substantial stimulation of glucagon expression. Additionally, administering dapagliflozin to an insulin-resistant mouse model caused a significant three-fold increase in plasma glucagon. Until now, the prevailing opinion has been that SGLT-2 is only expressed in the kidney, and these findings (if confirmed in further study) have significant implications on the best way use the drug class. During Q&A, Dr. Bonner suggested (as other KOLs have recently) that GLP-1 agonists might be a complementary partner for SGLT-2 inhibitors due to their glucagon-suppressing activity. An attendee cast the findings in a positive light, suggesting that SGLT-2 inhibitors’ combined reduction in hyperglycemia and increase in glucagon secretion might be a great match for type 1 diabetes.

• Two high-profile studies published earlier this year (Merovci et al. & Ferrannini et al., JCI 2014) demonstrated that SGLT-2 inhibitors led to significant increases in plasma glucagon and hepatic glucose production. We heard Dr. Ralph DeFronzo (University of Texas Health Science Center, San Antonio, TX) discuss this point at CODHy Latin America earlier this year (read our report). During that talk, Dr. DeFronzo suggested that this effect could mean that SGLT-2 inhibitors should be paired with a GLP-1 agonist, which could blunt the increase in glucagon – Dr. Bonner echoed this point during Q&A.
• The current view of SGLT-2 inhibitors largely depends on the view that SGLT-2 transporters are only expressed to an appreciable extent in the kidney. As a result, Dr. Bonner noted, the expression and activity of the transporter elsewhere in the body is poorly understood.
• Through immunofluorescence staining, Dr. Bonner’s group showed that SGLT-1 and SGLT-2 co-localize with glucagon/alpha cells in pancreatic islets. Further studies using cell purification showed that SGLT-1 and SGLT-2 mRNA is enriched in alpha cells in particular – while expression in beta cells is relatively low or nonexistent.
• The expression of SGLT genes is deregulated over the course of the progression into type 2 diabetes. Using cross-sectioned samples from human islets, Dr. Bonner’s group showed that SGLT-1 and SGLT-2 mRNA expression increases as individuals progress into obesity and glucose intolerance. However, interestingly, SGLT-2 mRNA expression drops off dramatically as patients progress from glucose intolerance to diagnosed type 2 diabetes. Glucagon mRNA expression, in contrast, spikes substantially as patients progress from glucose intolerance to type 2 diabetes. The drop-off in SGLT-2 expression is remarkable, as SGLT-2 expression in the kidney is significantly up-regulated as patients progress to type 2 diabetes. When Dr. Bonner’s group exposed human islets in culture to varying levels of glucose, it appeared that the drop-off in SGLT-2 expression could be linked with glucotoxicity.
• Both a knockdown in SGLT-2 using siRNA and the SGLT-2 inhibitor dapagliflozin (AZ’s Farxiga/Forxiga) stimulated glucagon gene expression. Dr. Bonner stated that the two means of blocking SGLT-2 activity also caused a increase in SGLT-1 expression, but in our view it was harder to conclude that from the data. The mechanism by which an SGLT-2 inhibitor might reduce SGLT-2 mRNA is not immediately clear; generally, counterregulatory mechanisms would cause increased expression of a gene if the protein it codes for is being inhibited. During Q&A, Dr. Bonner somewhat cryptically suggested that the action of SGLT-2 inhibition in the pancreas could be at the transcriptional level.
• In a preclinical model of insulin resistance, Dr. Bonner’s team found that dapagliflozin administration led to an increase in plasma glucagon. The mouse model used was a C57blk lineage treated with S961 peptide. The increase in plasma glucagon (ostensibly from pancreatic alpha cells) was roughly three-fold, and was statistically significant.

Questions and Answers

Q: These results raise the questions of whether SGLT-2 inhibitors might be useful in type 1 diabetes, especially brittle type 1 diabetes. Aside from using them to blunt hyperglycemia due to the glycosuric effect, perhaps it could blunt hypoglycemia by increasing glucagon expression.

A: We are doing studies now in STZ models – that’s something we’re looking into.

Q: Regarding therapeutics, if in theory this is a prominent mechanism, you would expect to see more hypoglycemia with SGLT-2 inhibitors, but if anything you see a reduction in glucose. Does that suggest that this intriguing interaction with islets has less therapeutic impact than the class’ effect in the kidneys?

A: We have to wait and see, but I do think that this drug class is very good. Perhaps it might not be used best as a standalone therapy. It might be better used in combination with a GLP-1 analog to combat the oversecretion of glucagon.

Q: Why was it that you saw relatively low expression of SGLT-1 and SGLT-2 in lean individuals, but then the levels were much higher in obese patients, as were levels of glucagon? In healthy individuals there is no connection between SGLT and glucagon expression.

A: In the kidney, SGLTs are very well characterized as glucose transporters. We are not sure what the function is in the pancreas. Perhaps it is a glucose sensor, or is involved in endocrine signaling. We do know that expression is reduced in type 2 diabetes due to an overload of glucose.

Q: Dapagliflozin is meant to be an inhibitor of the SGLT-2 protein, but in vitro you saw reduced SGLT-2 mRNA expression. How can you explain that?

A: There is some evidence in our lab suggesting that SGLT-2 is inhibited at the transcriptional level.

Q: Generally, one of the strongest determinants of glucagon secretion is insulin secretion. The results we saw in the JCI articles were unexpected, as there was an increase in glucagon but no increase in insulin. Did you study the effect of changing insulin levels in the growth media?

A: We haven’t studied the effect of decreasing insulin concentration in the cell media.

Q: Is there anything known about glucagon levels in humans that have loss-of-function mutations for SGLT-2?

A: I do not know that off the top of my head.

Posters

Dual Add-On Therapy in Poorly Controlled Type 2 Diabetes on Metformin: Randomized, Double-Blind Trial of Saxagliptin + Dapagliflozin vs. Saxagliptin and Dapagliflozin Alone (127-LB)

J Rosenstock, L Hansen, P Zee, Y Li, W Cook, B Hirshberg, N Iqbal

This poster featured the first detailed phase 3 results of AZ’s SGLT-2 inhibitor/DPP-4 inhibitor fixed dose combination (FDC), saxagliptin/dapagliflozin (n=534). This 24-week study compared the FDC to each of its individual components: the SGLT-2 inhibitor Forxiga (known as Farxiga in the US; dapagliflozin) and the DPP-4 inhibitor Onglyza (saxagliptin). All patients were on background metformin. AZ released topline results from this trial in May, announcing A1c reductions of 1.5% on the saxa/dapa arm compared to 1.2% in the dapagliflozin arm and 0.9% in the saxagliptin arm (not quite additive, and definitely not synergistic, as had been hoped). From the poster, we learned that the baseline A1c in the trial was quite high: 8.9% in the saxa/dapa and dapagliflozin arms and 9.0% in the saxagliptin arm. See the table below for A1c reductions stratified by baseline A1c – across these subgroups, saxa/dapa consistently had greater A1c reductions than the other two groups, but as would be expected, the A1c-lowering effect was smaller in people with lower starting A1cs. Changes in body weight, as would be expected, seemed driven by the dapagliflozin component. The saxa/dapa group lost 2.1 kg (4.6 lb), the saxagliptin group had zero weight change, and the dapagliflozin group lost 2.4 kg (5.3 lb) from a baseline BMI of 32 kg/m² (baseline weight not specified). No adverse events of interest (including major or minor hypoglycemia, urinary tract infections, or genital infections) were any higher on saxa/dapa compared to dapagliflozin or saxagliptin. Notably, urinary tract infections on saxa/dapa (1%) were actually numerically lower than on either saxagliptin (5%) or dapagliflozin (5%) (p-value not specified), and genital infections in the saxa/dapa arm (0.6%) were the same as the saxagliptin arm (0.6%) and lower than the dapagliflozin arm (6%) (p-value not specified). Overall, it appears that the saxa/dapa combination has an excellent glycemic, weight, and safety profile compared to other oral agents (e.g., metformin, DPP-4 inhibitors, SFUs, TZDs), but is perhaps not quite as different from SGLT-2 inhibitors as had been hoped at this stage – we look forward to seeing longer term data to assess duration, etc.

• As reported in the topline release, both fasting plasma glucose (FPG) and two-hour postprandial glucose (PPG) were significantly better on saxa/dapa vs. saxagliptin, but not significantly different from dapagliflozin. Mean change in FPG was -38 mg/dl on saxa/dapa from a baseline of 180 mg/dl, -14 mg/dl on saxagliptin from a baseline of 192 mg/dl, and -32 mg/dl on dapagliflozin from a baseline of 185 mg/dl. Mean change in two-hour PPG was -80 mg/dl from a baseline of 242 mg/dl on saxa/dapa compared -36 mg/dl from a baseline of 256 mg/dl on saxagliptin and -70 mg/dl from a baseline of 246 mg/dl on dapagliflozin.
• As reported in the topline release, more people on saxa/dapa achieved an A1c goal of <7% compared to saxagliptin or dapagliflozin. On saxa/dapa, 41% of patients got to goal, compared to 18% on saxagliptin and 22% on dapagliflozin. Given that patients had a relatively high starting baseline A1c, this is a pretty strong finding.

	Saxa/dapa + met	Saxagliptin + met	Dapagliflozin + met
Baseline A1c <8% subgroup
Mean baseline A1c	7.5%	7.6%	7.4%
N	37	29	37
Adjusted mean change from baseline	-0.8%	-0.7%	-0.5%
Baseline A1c ≥8% to <9% subgroup
Mean baseline A1c	8.4%	8.5%	8.5%
N	56	51	52
Adjusted mean change from baseline	-1.2%	-0.5%	-0.8%
Baseline A1c ≥9% subgroup
Mean baseline A1c	10.0%	9.9%	10.0%
N	65	63	62
Adjusted mean change from baseline	-2.0%	-1.3%	-1.9%

Fixed Dose Combinations of Empagliflozin/Linagliptin for 24 Weeks in Drug-Naïve Patients with Type 2 Diabetes (T2DM) (129-LB)

A Lewin, R DeFronzo, S Patel, D Liu, R Kaste, HJ Woerle, UC Broedl

The first of two phase 3 posters on Lilly/BI’s Jardiance/Trajenta (empagliflozin/linagliptin; “empa/lina”) presented the results of a study in 667 drug-naïve type 2 diabetes patients. There has been a great deal of excitement about the combination of DPP-4 inhibitors and SGLT-2 inhibitors, as the combination of an insulin-dependent and insulin-independent mechanism of action were thought to potentially yield additive or synergistic efficacy. While the efficacy seen with the high-dose FDC (empagliflozin 25 mg/linagliptin 5mg) was certainly strong for an oral compound, it fell well short of additive efficacy – in fact, it did not achieve a statistically significantly greater A1c reduction than dapagliflozin monotherapy. Empa/lina 25 mg/5 mg yielded a mean A1c reduction of 1.08% after 24 weeks, compared to -0.67% with linagliptin 5mg (p<0.001) and -0.95% with empagliflozin 25 mg (p = 0.179), from a baseline of ~8%. From what we could tell, the results may have been dampened by a weaker-than-expected performance from the high-dose FDC group, as the lower-dose FDC (empa 10 mg/lina 5 mg) had a greater mean A1c reduction (-1.24%) and achieved statistically significantly better efficacy than its component monotherapy doses. A sub-analysis in patients with a baseline A1c at or above 8.5% yielded slightly more logical results: although the high-dose FDC once again did not achieve significantly greater A1c reduction than empagliflozin 25 mg monotherapy, it was not less effective than the lower-dose FDC. Although the results were not altogether negative (A1c reductions of over 1% for an oral are impressive), it was somewhat disappointing to not see truly additive efficacy with the combination.

• The phase 3 study randomized 677 drug-naïve type 2 diabetes patients – 667 completed the trial. Patients were randomized to one of five treatments: empagliflozin 25 mg/linagliptin 5 mg, empagliflozin 10 mg/linagliptin 5 mg, empagliflozin 25 mg, empagliflozin 10 mg, and linagliptin 5 mg. The poster presented 24-week data, but the study will go on for a total of 52 weeks.
• Empa/lina demonstrated solid efficacy and non-glycemic effects, but the high-dose combination performance was weaker than we might have expected. The high-dose combination fell short of the low-dose combination across categories, from A1c reduction (1.08% vs. 1.24%, respectively), the number of patients reaching an A1c goal of below 7.0% (55% and 62%, respectively), and weight loss (-2.0 kg [~4 lbs] and -2.7 kg [6 lbs]). While the A1c reductions seen with the low-dose combination were statistically significantly greater than those seen with its component monotherapies, the high-dose combination did not achieve statistical superiority over high-dose empagliflozin monotherapy, although it did over linagliptin monotherapy. The percentage of patients achieving a final A1c below 7.0% provided a more positive framing of the data than did the raw mean A1c reductions. 
• As opposed to the metformin add-on trial (see 130-LB below), empa/lina did not demonstrate significant reductions in fasting plasma glucose relative to empagliflozin monotherapy. The combinations did achieve reductions in FPG over linagliptin monotherapy, on the order of ~23 mg/dl.
• Interestingly, in this study, there was no clear increase in genital infections with empagliflozin, either as monotherapy or in combination with linagliptin. However, the number of overall events was quite small. Other adverse events were more or less balanced between groups.

Fixed-Dose Combinations of Empagliflozin/Linagliptin for 24 Weeks as Add-On to Metformin in Patients with Type 2 Diabetes (T2DM) (130-LB)

R DeFronzo, A Lewin, S Patel, D Liu, R Kaste, HJ Woerle, UC Broedl

The second of two phase 3 posters on Lilly/BI’s Jardiance/Trajenta (empagliflozin/linagliptin; “empa/lina”) presented the results of a study in 674 type 2 diabetes patients on background metformin. As opposed to the other empa/lina poster, the efficacy results here were more logical and consistently statistically significant. From a baseline of ~8%, the high-dose combination (empa 25 mg/lina 5 mg) arm achieved a mean A1c reduction of 1.19%, which beat out the 0.62% reduction with empagliflozin 25 mg and 0.70% reduction with linagliptin 5 mg – we found it interesting that linagliptin had numerically greater efficacy than empagliflozin in this trial. The lower-dose combination (empa 10 mg/lina 5 mg) achieved a mean A1c reduction of 1.08%, while the empagliflozin 10 mg arm achieved a mean reduction of 0.66%. Over 60% of patients on the high-dose combination achieved a final A1c below 7%, while only 33% of empagliflozin 25 mg patients and 36% of linagliptin patients achieved that goal. Weight loss appeared to be tied to the empagliflozin dose – empagliflozin 25 mg (with or without linagliptin) led to a mean weight reduction of ~3 kg (~7 lbs), while empagliflozin 10 mg (with or without linagliptin) led to about a pound less weight loss. Genital infections were more common with empagliflozin, but the relationship did not appear to be dose-dependent. Overall, the combination (and each of the component monotherapies) was well tolerated. Although the A1c reduction for the combination was not additive, the percentage of patients who achieved a goal of < 7.0% was fairly close to additive.

• The study enrolled 686 type 2 diabetes patients on background metformin – 674 patients completed the study. Patients were randomized to one of five treatments: empagliflozin 25 mg/linagliptin 5 mg, empagliflozin 10 mg/linagliptin 5 mg, empagliflozin 25 mg, empagliflozin 10 mg, and linagliptin 5 mg. The poster presented 24-week data, but the study will go on for a total of 52 weeks.
• Both empa/lina arms achieved a mean A1c reduction over 1%. From a mean baseline of ~8%, the empa 25 mg/lina 5 mg arm achieved a mean A1c reduction of 1.19%, which was significantly greater than the 0.62% reduction in the empagliflozin 25 mg arm and 0.70% reduction in the linagliptin arm. We found it interesting that linagliptin beat out empagliflozin – it seemed like the high-dose empagliflozin arm performed worse than might have been expected. The empa 10 mg/lina 5 mg arm achieved a mean A1c reduction of 1.08%, relative to a 0.66% reduction in the empagliflozin 10 mg arm. All comparisons between the combination arms and component monotherapies were highly statistically significant.
  
  • The poster also broke out mean A1c reductions for patients with a baseline A1c at or above 8.5%. From a mean baseline of ~9.1%-9.3%, the high-dose combination group experienced a mean reduction of 1.84%, the low-dose combination group experienced a mean reduction of 1.61%, the high-dose empagliflozin group experienced a mean reduction of 1.22%, the low-dose empagliflozin group experienced a mean reduction of 1.29%, and the linagliptin arm achieved a mean reduction of 0.99%. All comparisons between the combination arms and the component monotherapies were highly statistically significant.
  • Empa/lina helped more patients achieve an A1c goal of less than 7%. Approximately 62% of the high-dose combination group and 58% of the low-dose combination group achieved that goal, compared to 33% of the high-dose empagliflozin group, 28% of the low-dose empagliflozin group, and 36% of the linagliptin group.
  • Both empa/lina combinations achieved significantly greater fasting plasma glucose reductions than the component monotherapies. The difference between the high-dose combination and high-dose empagliflozin was 16 mg/dl, while the difference between the high-dose combination and linagliptin was 22 mg/dl.
• The reduction in weight from baseline appeared to largely be a function of the empagliflozin dose, independent of combination with linagliptin. The high-dose combination arm and high-dose empagliflozin arm lost 3 kg (~7 lbs), while the low-dose combination and low-dose empagliflozin groups lost 2.5 kg (~6 lbs). The linagliptin group lost less than 1 kg (~2 lbs).
• Adverse events were generally balanced between groups.

Sodium Glucose Cotransporter 2 (SGLT2) Inhibition with Emplagliflozin Reduces Microalbuminuria in Patients with Type 2 Diabetes (1125-P)

D Cherney, M von Eynatten, S Lund, S Kaspers, S Crowe, H Woerle, T Hach

This study pooled data from four phase 3 randomized, controlled trials to examine the effect of empagliflozin on urine albumin to creatinine ratio (UACR) in type 2 diabetes patients with microalbuminuria. Of the 2,477 patients who were randomized to placebo or empagliflozin in the 24 week trials, 458 patients on placebo (n=157), empagliflozin 10 mg (n=146), and empagliflozin 25 mg (n=155) started with microalbuminuria (UACR 30-300 mg/g). At week 24, patients on empagliflozin had significantly lower UACR for both the 10 mg (30% reduction; p <0.001) and 25 mg (25% reduction; p=0.004) dose. Finally, the treatment group did not have more adverse events than the control. These findings suggest that SGLT-2 inhibitors like empagliflozin could be renal-protective. Only one SGLT-2 inhibitor is being studied in a renal outcomes trial, J&J’s Invokana in CREDENCE.

• The three groups of patients with microalbuminuria had similar baseline characteristics in terms of mean A1c, blood pressure, BMI, UACR, eGFR, age, and time since diagnosis of diabetes. Mean A1c levels for the placebo, empagliflozin 10 mg, and empagliflozin 25 mg groups were 8.13%, 8.26%, and 8.18%. Average blood pressure was 132.0, 132.8, and 133.6 mmHg, and average UACR was 61.9, 57.5, and 60.1 mg/g, respectively. Mean BMI was ~28-29 kg/m² and mean age was ~55-57 years.
• Patients with microalbuminuria treated with empagliflozin displayed a significant reduction in A1c and blood pressure. At week 24, patients on empagliflozin 10 mg and 25 mg showed significant placebo-adjusted reductions in A1c of 0.56% from a baseline of 8.26% (p <0.001) and 0.62% from a baseline of 8.18% (p <0.001). After accounting for placebo effects, blood pressure for these groups also decreased by 3.6 mmHg (p=0.011) and 3.5 mmHg (p=0.012), respectively.
• Of all the patients in the four clinical trials, patients with microalbuminuria experienced a greater relative reduction in UACR. For the overall pooled population (n=2349), the percentage decrease in geometric mean UACR at week 24 for the empagliflozin 10 mg and 25 mg groups were only 10%, compared to 30% and 25% for the patients with microalbuminuria.

LX4211, a Dual Inhibitor of SGLT1/SGLT2, Reduces Postprandial Glucose in Patients with Type 2 Diabetes Mellitus and Moderate to Severe Renal Impairment (132-LB)

P Lapuerta, A Sands, I Ogbaa, P Strumph, D Powell, P Banks, B Zambrowicz

Dr. Pablo Lapuerta and colleagues present the results of a double-blind randomized, seven-day trial of Lexicon Pharmaceuticals’ LX4211 in 31 type 2 patients with moderate to severe renal impairment (mean baseline eGFR of 43 ml/min/1.73m²; other baseline characteristics detailed below). The participants were randomized to LX4211 400 mg once daily (n=16) or to placebo (n=15) in additional to their insulin therapy or oral anti-diabetic medication, with a treatment period of seven days. A standard breakfast meal was administered on days -1, 1, and 7, and data on glucose and GLP-1 were measured 15 minutes before the breakfast, as well as 1, 2, 2.5, 3, and 4 hours post-breakfast. LX4211 treatment resulted in statistically significant reductions in post-prandial glucose vs. placebo (which were evidence in patients with eGFR <45 ml/min/1.73m²), as well as reductions in fasting plasma glucose (average of -20 mg/dl; p=0.056). Participants on LX4211 also experienced statistically significant increases in post-meal total and active GLP-1 levels vs. those on placebo, which reflected the drug’s inhibition of gastrointestinal SGLT-1. The authors highlight that urinary glucose excretion was only slightly elevated in the LX4211 group (37 g/24 hours) compared a minor decrease in those on placebo (-1.4 g/24 hours; p<0.001). They also note that the PK results support the use of LX4211 400 in renally impaired patients, as there was no increase in LX4211 exposure for patients with eGFR <45 ml/min/1.73m² relative to those with eGFR ≥45 ml/min/1.73m². Based on these results, the authors conclude that LX4211 improves glycemic control in type 2 patients with renal impairment and call for longer-term clinical studies in this patient population.

• At baseline, the participants had a mean age of 66 years, BMI of 34 kg/m², duration of diabetes of 17 years, and eGFR of 43 ml/min/1.73m². Seventeen percent of the patients were male, and 21% were Caucasian. The participants reported recent or concomitant use of insulin (61%), SFU (39%), metformin (29%), TZD (10%), and DPP-4 inhibitors (10%). As expected, the rates of common co-morbidities were high: hypertension (90%), hyperlipidemia (90%), neuropathy (42%), and cardiovascular disease (39%).
• The tables below detail the change in fasting plasma glucose and urinary glucose excretion, stratified by eGFR level:

Table 1: Fasting Plasma Glucose

	LX4211 vs. Placebo	p-value
eGFR 45-59 ml/min/1.73m2	-17	0.29
eGFR <45 ml/min/1.73m2	-27	0.08
Mean for all patients	-20	0.056

Table 2: Urinary Glucose Excretion

	LX4211	Placebo	p-value
eGFR ≥45 ml/min/1.73m2	51.6	-1.9	<0.001
eGFR <45 ml/min/1.73m2	19.4	-1.0	0.032
Mean for all patients	37.3	-1.4	<0.001

• All adverse events were of mild to moderate intensity, and the frequency of adverse events was comparable between the LX4211 and placebo group:

	LX4211         (n=16)	Placebo    (n=15)
Number of patients (%) with ≥1 treatment-emergent adverse event (TEAE)	7 (44%)	5 (33%)
Number of patients (%) with ≥1 drug-related TEAE	1 (6%)	3 (20%)

Efficacy and Safety of Twice-Daily Remogliflozin for the Treatment of Type 2 Diabetes Mellitus (1103-P)

WO Wilkison, AP Sykes, L Kler, J Lorimer, R O’Connor-Semmes, R Dobbins, S Walker

This poster was one of three that Islet Sciences presented on their novel SGLT-2 inhibitor remogliflozin etabonate. This poster featured results from a 12-week dose-ranging phase 2 trial testing twice-daily remogliflozin etabonate (n=336) at five doses (50 mg, 100 mg, 250 mg, 500 mg, and 1000 mg, each twice daily). The top dose tested, 1000 mg twice-daily, achieved an impressive 1.1% placebo-adjusted A1c reduction from baseline (~8.1%) – while it is challenging to compare trials, this is the highest A1c reduction achieved by an SGLT-2 inhibitor in a 12-week phase 2 dose ranging trial, and an Islet press release referred to the results as “best-in-class efficacy”. Weight loss was approximately 3 kg (~7 lbs) for both groups, and there were slight increases in LDL cholesterol and genital mycotic infections (11% in the 1000 mg dose group). Another poster (1102-P) presented efficacy and safety data on a once-daily formulation of remogliflozin etabonate – it found A1c and weight reductions that were statistically significant but less pronounced than those seen with twice-daily dosing, but a relatively low incidence of genital infections and no apparent increase in LDL (perhaps as a result of reduced nocturnal exposure). Based on these phase 2b results, Islet has developed a “biphasic” combination of immediate-release and delayed-release remogliflozin that it theorizes should preserve the efficacy of twice-daily dosing with the safety profile of once-daily dosing. The company also presented results at this ADA from a PK/PD study (1101-P) showing that the biphasic formulation achieved relatively high daytime exposure and low nighttime exposure, but only further testing will show if this characteristic results in the “best-in-class” safety and efficacy profile the company believes is possible.    

• In the phase 2b study investigating twice-daily dosing of the original formulation: weight loss was somewhat dose dependent, with both the 500 mg and 1000 mg doses providing about 3 kg [6.6 lb] weight loss from baseline (baseline weights for these two groups were 87 kg [191 lbs] and 89 kg [196 lbs], respectively).
  
  • As with other SGLT-2 inhibitors, remogliflozin etabonate was associated in this trial with LDL cholesterol increases, although the LDL changes did not appear to have a dose-dependent relationship with the drug (patients experienced up to a mean 13.4 mg/dl increase on the middle [250 mg] dose, but only a 9.4 mg/dl increase on the highest [1000 mg] dose).
  • Rates of genital mycotic infections were somewhat dose dependent, with the highest rate (11%) seen in the highest, 1000 mg, dose.
• Read our report on the topline phase 2b results for thoughts from Islet CEO James Green on the biphasic formulation.

Sodium Glucose Co-Transporter-2 (SGLT2) Inhibitor Empagliflozin (EMPA) in Type 1 Diabetes (T1D): Impact on Diurnal Glycemic Patterns (1051-P)

B Perkins, D Cherney, H Partridge, N Soleymanlou, H Tschirhart, B Zinman, R Mazze, N Fagan, S Kaspers, H Woerle, U Broedl, O Johansen

To further analyze the effects of Lilly/BI’s SGLT-2 inhibitor empagliflozin in type 1 diabetes patients, this research group used CGM to explore diurnal glycemic patterns in 40 type 1 diabetes patients on empagliflozin in a single-arm open-label pilot study lasting eight weeks. It was exciting to see measures like glucose variability as major endpoints in a trial, although the study’s findings on time in zone were surprising and we’ve asked for follow up on this. There were some reductions in overall glucose exposure (AUC) and improvements in glucose variability, but these were not statistically significant in many cases, which was surprising. Moreover, the initiation of empagliflozin therapy caused a reduction in basal insulin dose, but a slight and not statistically significant reduction in bolus insulin dose. We were surprised that the improvements in variability and stability were as modest as they appeared to be in this trial, although the small sample size (n = 40) and lack of a comparator group likely played a role. When empagliflozin therapy was withdrawn, patients saw a rebound in glucose AUC and variability to levels that were actually above baseline, demonstrating proof-of-effect. 

• The study compared AGP profiles at baseline, “mid-treatment,” at the end of treatment, and following cessation of treatment. The AGP profiles combined the two weeks of data into a 24-hour profile.
• For all CGM parameters, the study revealed a general trend of improvement from baseline to mid-treatment and end-of-treatment. However, most of these were not statistically significant. Parameters measured included glucose exposure measured as glucose AUC, glucose variability through blood glucose inter-quartile range, and glucose stability expressed as the mean hourly rate of change of glucose levels. In addition, the parameters also included time spent in hyperglycemia (>180 or >140 mg/dl), normoglycemia (70-140 mg/dl), and hypoglycemia (<70 mg/dl or <60 mg/dl).
• Interestingly, after beginning treatment with empagliflozin, patients saw a statistically significant reduction in basal insulin (25.7 to 19.5 units, p<0.0001) but not bolus insulin (29.0 to 27.0 units, p = 0.19). SGLT-2 inhibitors are known primarily for their effect on postprandial glucose, so one would expect to see a reduction in bolus insulin. It is possible that the modest reductions in glucose variability were enough to allow patients to better titrate their basal insulin.
• The positive trends in glucose control were seen both during the day and during the night. This finding does not support the theory that SGLT-2 inhibition during the day plays a disproportionate role in improving glycemia.
• Demonstrating proof-of-effect, following cessation of treatment, glucose AUC rebounded to levels that were significantly higher than those seen at baseline despite a rebound in daily insulin dose. Glucose stability and glucose variability demonstrated patterns following the cessation of treatment. A rebound would be expected, and is a positive sign that empagliflozin treatment had some effect while it was being administered, but the fact that final glucose exposure was higher than at baseline could indicate the lasting presence of a counterregulatory response (possibly via the glucagon axis), although there is room for it to have been a chance finding.
• This trial suggests avenues for future study. If empagliflozin acts in a more balanced fashion on both fasting and postprandial glucose in type 1 diabetes patients, then it would affect the titration of insulin when patients begin empagliflozin therapy. The relatively small size of the study of course limits the interpretability of its findings, but interest continues to grow in the use of SGLT-2 inhibitors in type 1 diabetes, and we expect to receive data from larger trials in coming years – AZ recently announced plans to begin phase 3 testing of dapagliflozin in type 1 diabetes patients in 2014. 

Symposium: ADA Diabetes Care Symposium – New Drug Therapies, Innovative Management Strategies, and Novel Drug Targets

Improved Glucose Control with Weight Loss, Lower Insulin Doses, and No Increased Hypoglycemia with Empagliflozin Added-On to Titrated Multiple Daily Injections of Insulin in Obese Inadequately Controlled Patients with Type 2 Diabetes

Julio Rosenstock, MD (Dallas Diabetes and Endocrine Center, Dallas, TX)

Dr. Julio Rosenstock presented results of a 52-week trial comparing placebo, Boehringer Ingelheim/Lilly’s empagliflozin 10 mg, and empagliflozin 25 mg in obese patients with inadequately controlled type 2 diabetes on high-dose basal-bolus insulin therapy, with or without metformin (n=566). Compared to placebo, empagliflozin led to lower A1c at 18 weeks (with minimal insulin titration in any group), lower A1c at 52 weeks (after insulin titration was allowed), lower daily insulin dose at 52 weeks, slight weight loss, and equivalent rates of hypoglycemia. The main side effect with empagliflozin was an increased rate of events consistent with genital infection, consistent with the SGLT-2 inhibitor class.

• The EMPA-REG MDI trial enrolled adults with type 2 diabetes on multiple-daily-injection (MDI) insulin therapy with more than 60 units of insulin per day, A1c between 7.5% and 10.0%, BMI between 30 and 45 kg/m², and eGFR at least 60 ml/min/1.73 m² (i.e., no moderate or severe chronic kidney disease). At baseline, mean A1c was 8.3%, mean age was 57 years, mean weight was 96 kg, mean BMI was 35 kg/m², mean insulin dose was 92 U/day, and mean number of mealtime insulin injections was 2.6-2.7/day. Besides insulin, patients were either treated with no oral antihyperglycemic therapy (29% of patients) or with at least 1,500 mg/day of metformin (71%). Patients were treated with placebo (n=188), empagliflozin 10 mg (n=186), or empagliflozin 25 mg (n=189)
• Compared to placebo, empagliflozin 10 mg and 25 mg each led to lower A1c despite lower insulin doses (all comparisons statistically significant). For the first 18 weeks of the trial, clinicians were allowed to adjust insulin doses only by 10% or less, so that empagliflozin’s effect on blood glucose would be apparent. At 18 weeks, A1c decreased by 0.5%, 0.9%, and 1.0% in the placebo, 10 mg, and 25 mg groups, respectively. In weeks 19 through 40, insulin could be freely titrated to meet blood glucose targets of 100 mg/dl before meals and 140 mg/dl after meals. Then from weeks 41 through 52, insulin dose was held within 10% of week-40 levels. At 52 weeks insulin dose had been increased from baseline by 10.2 U/day in the placebo group, increased by only 1.3 U/day in the 10 mg group, and decreased by 1.1 U/day in the 25 mg group. The 52-week A1c decreases in the three groups were 0.8%, 1.2%, and 1.3%.
• Patients using placebo had slight weight gain at one year (0.4 kg [0.88 lbs]), whereas empagliflozin led to slight weight loss (2.0 kg [4.4 lbs] in both the 10 mg and 25 mg groups. These weight changes occurred largely in the first 18 weeks of the trial, when the placebo group gained 0.34 kg (0.75 lbs), the empagliflozin 10 mg group lost 1.0 kg (2.2 lbs), and the empagliflozin 25 mg group lost 1.5 kg (3.3 lbs). The placebo-vs.-empagliflozin differences were statistically significant at both 18 and 52 weeks.
• Rates of systolic blood pressure (SBP) declined slightly in all three groups; the change was statistically non-significantly greater with empagliflozin than placebo. From baseline SBP of 133-134 mm Hg, SBP at 52 weeks was 3-4 mm Hg lower in all three groups. Compared to placebo at 52 weeks, empagliflozin was associated with increased HDL cholesterol (statistically non-significant trend) and increased hematocrit (p-values not provided). Diastolic pressure at 52 weeks was statistically significantly lower with empagliflozin 25 mg than placebo, and triglyceride levels were statistically non-significantly higher with empagliflozin 10 mg than placebo. 
• Patients treated with empagliflozin had higher rates of events consistent with genital infections (in both genders – consistent with the SGLT-2 inhibitor class), events consistent with urinary tract infections (in men), and dizziness; rates of hypoglycemia were similar in all three treatment groups. The percentages of patients who experienced one or more drug-related adverse events in the three groups  – placebo, empagliflozin 10 mg, and empagliflozin 25 mg – were 34%, 30%, and 40%, respectively. The percentages of patients with one or more adverse events of any kind were 90%, 86%, and 85%. The three groups were balanced in their rates of hypoglycemia ≤70 mg/dl and/or requiring assistance (58%, 51%, 58%) as well as hypoglycemia ≤70 mg/dl and requiring assistance (1.6%, 1.6%, 0.5%). Events consistent with genital tract infection were seen more in the empagliflozin groups overall (2%, 4%, 10%), in women (2%, 8%, 11%) and in men (1%, 1%, 8%). Events consistent with urinary tract infections were balanced overall (15%, 16%, 15%) and in women (29%, 24%, 26%). However, in men UTIs were more common with empagliflozin than with placebo (0%, 5%, 4%). Rates of dizziness were 1%, 3%, and 7%. No upper-urinary-tract infections (pyelonephritis) or urosepsis was recorded in the trial.

Questions and Answers

Dr. Robert Ratner (Chief Scientific and Medical Officer, American Diabetes Association): The investigators up-titrated the placebo group’s dose of insulin. What limited the ability to attain an equivalent A1c to the empagliflozin group? What is wrong with high insulin doses?

A: Nothing is wrong with high insulin doses; this population is just hard to treat. In most studies of this population you don’t get to A1c 7.0%. The thing is Dr. Ratner, you haven’t seen patients in quite some time, and that’s why you’re asking this question.

Q: Why do you think triglyceride levels increased in the empagliflozin 10 mg group?

A: I have no explanation for that.

Symposium: The Role of SGLT2 Inhibitors in the Treatment of Type 2 Diabetes

Physiology – The Role of Renal Sodium-Glucose Transport in Diabetes

Edward Chao, DO (UCSD, San Diego, CA)

Dr. Edward Chao presented on SGLT-2 inhibitors’ mechanism of action, and discussed a series of questions that remain unanswered about the new drug class. Some of these questions included: (i) why is the reduction in glucose seen with SGLT-2 inhibition not linearly dose dependent? (i.e.: why are there diminishing returns?); (ii) What role could SGLT-1 inhibition play in the treatment of type 2 diabetes? (iii) Why does SGLT-2 inhibition appear to be correlated with a rise in endogenous glucose production? (iv) What effect might SGLT-2 inhibition have on glucotoxicity? Read on below for Dr. Chao’s answers

• In phase 3 trials, SGLT-2 inhibitors’ glucose-lowering efficacy seems to taper off at high doses. Although SGLT-2 is responsible for 80-90% of renal glucose reuptake, studies estimate that far less than 80-90% of all filtered glucose ends up in the urine in patients on an SGLT-2 inhibitor. To explain this, Dr. Chao suggested that blocking SGLT-2 inhibition could lead to upregulation in the expression of SGLT-1. If true, this phenomenon could increase the relevance of agents with more of an action on SGLT-1 in addition to SGLT-2.
• SGLT-1 inhibition in the gut appears to results in delayed glucose reabsorption, which in turn yields multiple possible metabolic benefits. Inhibition of glucose uptake in the proximal small intestine would increase the delivery of glucose to L cells in the distal gut, where GLP-1 and PYY are produced. Therefore, SGLT-1 inhibition in the gut could act through the incretin axis. One study published last year (Polidori et al., Diabetes Care 2013) demonstrated that J&J’s SGLT-2 inhibitor Invokana (canagliflozin) increased GLP-1 and PYY levels, which Dr. Chao hypothesized was a results of residual SGLT-1 inhibition. 
• Two papers published in JCI earlier this year appeared to show that SGLT-2 inhibition led to an increase in glucagon secretion and endogenous glucose production, which blunted the drugs’ glucose-lowering efficacy. Glucagon appears to drive at least part of this effect. Later at ADA, in the Presidents Oral Presentation Session, we saw preclinical data strongly suggesting that SGLT-2 inhibition did indeed lead to an increase in glucagon secretion. This finding, if true, would suggest that SGLT-2 inhibitors’ efficacy could be greatly increased when paired with an agent like a GLP-1 agonist or glucagon receptor antagonist that could blunt the counterregulatory glucagon response.
• One very positive finding on SGLT-2 inhibitors in the recent literature was that AZ’s Forxiga (dapagliflozin) appears to improve muscle insulin sensitivity. The study (Merovci et al., JCI 2014) found that the reductions in plasma glucose caused by dapagliflozin treatment were associated with an increase in muscle glucose uptake. In Dr. Chao’s view, this finding constitutes early evidence of the glucotoxicity hypothesis, as it shows that a reduction in glucose through glycosuria was able to improve one of the core deficits of type 2 diabetes.
• After discussing answers to the aforementioned unanswered questions, Dr. Chao proposed a set of additional forward-looking questions about SGLT inhibition:
  
  • What is the therapeutic role of SGLT-1/SGLT-2 dual inhibition?
  • Do SGLT-2 inhibitors increase caloric intake?
  • What is the mechanism of the increased endogenous glucose production seen with SGLT-2 inhibitors?
  • Could adding incretin mimetics to SGLT-2 inhibitors have a synergistic effect?

Efficacy – A Review of Current Evidence

Clifford Bailey, PhD (Aston University, Birmingham, United Kingdom)

Dr. Bailey reviewed clinical data with the SGLT-2 inhibitors, providing an overview of trends within the class. Following a review of the history of the class, Dr. Bailey summarized data from phase 2 and phase 3 trials of dapagliflozin (AZ’s Farxiga), canagliflozin (J&J’s Invokana), empagliflozin (Lilly/BI’s Jardiance), and LX-4211 (Lexicon), noting their durable A1c declines, weight loss, and blood pressure reductions. Referencing a plot of A1c decline and weight loss across the trials, he emphasized the tight confidence intervals within the class, suggesting that virtually everyone was responding to the drugs. He reinforced this via FDA Advisory Committee data from canagliflozin, highlighting that amongst subgroup analyses baseline A1c was the only significant associative factor with A1c decline. While he lauded the A1c declines, he suggested “what keeps people particularly interested” in the class is the weight loss, with all candidates demonstrating similar 2-3 kg (4-7 lbs) reductions in body weight. He concluded by noting their compatibility with other diabetes therapies, indicating that provided adequate renal function effectively the drugs could be used anywhere along the natural history of type 2 diabetes.

Questions and Answers

Q: Why does the weight loss bottom out? You’re continuing to lose glucose and everything stables out.

A: I wish I knew the answer to that. It’s probably a combination of improved metabolic efficiency and as your glucose comes down you may be susceptible to feeling hungrier. Although we’ve not been able to get accurate data on this I think folks might be eating more.

Q: Any cases of overdoses in people?

A: I’m not aware of anyone, and I’m not too sure there would be any detriment as there have been studies exploring fairly large doses.

SGLT-2 Inhibitors: Safety and Adverse Effects

Lawrence Leiter, MD (University of Toronto, Toronto, Canada)

Dr. Lawrence Leiter provided a category-by-category overview of the possible safety risks associated with SGLT-2 inhibitor therapy. He ended the presentation by noting that there is not yet enough clinical evidence on the relatively new drug class to fully characterize each of the potential safety risks he outlined, and that the results of the cardiovascular outcomes trials for SGLT-2 inhibitors will be important to see. He pointed out that EMPA-REG OUTCOME (the CVOT for Lilly/BI’s empagliflozin) is progressing faster than expected – earlier this year the company moved the estimated primary outcome date up from early 2018 to early 2015.  

• Hypoglycemia: Dr. Leiter noted that mechanistically, one would not expect SGLT-2 inhibitors to cause a large increase in hypoglycemia. The slight increase in hypoglycemia seen in some clinical trials has generally been due to the concomitant use of sulfonylureas or insulin.
• Genital infections: This is a relatively well-characterized side effect of SGLT-2 inhibitors. Detailed data on AZ’s Forxiga (dapagliflozin) seemed to show that the incidence of genital infections did not increase in a dose-dependent manner, suggesting that it may be more of a threshold effect. Other studies (Johnsson et al., J Diabetes Complications 2013) show that the risk of infections is higher in younger individuals, individuals with a history of recurrent infections, and in obese individuals.
• Urinary tract infections: The signal hear is far less clear than for genital infections. Where increases have been seen, Dr. Leiter noted that there do not appear to be increases in more dangerous upper tract infections.
• Blood pressure and volume depletion: This is another family of effects that is directly linked to SGLT-2 inhibitors’ mechanism of action, and that is generally not a decision-maker (or breaker) for providers or patients. Risk factors for hypotension or volume depletion are concomitant use of loop diuretics and patients with low baseline eGFR.
• Bone health: Most studies investigating bone health with SGLT-2 inhibitors have not shown changes in bone biomarkers beyond what would be expected with slight weight loss. The one slight exception is canagliflozin, which appears to be associated with a slight increase in fractures. Dr. Leiter noted that the mechanism behind this signal is unclear, especially in the absence of changes in biomarkers. He creatively postulated that the cause might be more falls, which (in our view) could be due to postural hypotension, especially since most falls happened relatively soon after initiation of therapy.
• Malignancies: A slight imbalance in bladder cancer was one of the major points of discussion in the most recent FDA Advisory Committee meeting for AZ’s Forxiga (dapagliflozin). Dr. Leiter stated that it is hard to imagine a mechanism that would explain the signal, especially since the majority of the cases were early after the initiation of treatment (most tumors take a long time to develop). He concluded that malignancy is probably not a real safety issue, although data from long-term outcomes studies will be more conclusive.
• Cardiovascular safety: The entire SGLT-2 inhibitor class appears to be associated with a slight increase in LDL, with perhaps a slightly more prominent signal seen with canagliflozin 300 mg. However, Dr. Leiter pointed out that the risk must be interpreted in the context of simultaneous reductions in blood pressure and weight.

Questions and Answers

Q: There is some evidence suggesting that with chronic use of SGLT-2 inhibitors, the substrate balance for energy usage shifts from glucose to lipids. Could an SGLT-2 inhibitor prompt ketosis? Do we need more data on lipid metabolism?

A: I think it is possible that these agents may alter the balance of fuel utilization. We need to understand the mechanism behind the small increases in LDL.

Q: On lipids, could the mechanism behind the increase be an increase in hematocrit?

A: One possibility may be hematocrit, yes. Another mechanism put forth, though not currently supported by much data, is that these agents may cause patients to eat more and alter their pattern of macronutrient consumption. That is a more speculative theory.

SGLT2 Inhibitors in Diabetes – Unanswered Questions

Zachary T. Bloomgarden, MD (Mt. Sinai, New York, NY)

Dr. Zachary Bloomgarden concluded the session by addressing remaining questions surrounding the SGLT-2 inhibitor class, beginning with “why aren’t the SGLT-2 inhibitors more potent?” He noted the possibility of renal glucose uptake via other SGLT/GLUT transporters or a compensatory rise in endogenous glucose production. With glucagon known to play a role in the kidney, Dr. Bloomgarden posited the latter effect could be mediated by an increase in renal gluconeogenesis following SGLT-2 inhibitor use. He then moved to ask, “Are the SGLT-2 inhibitors nephrotoxic or nephroprotective?” highlighting animal studies supporting a reduction in glomerular mesangial expansion and albuminuria after SGLT-2 inhibitor administration. Clinically, he highlighted the recently-initiated CREDENCE study, which is investigating the effects of canagliflozin in patients with stage 2-3 chronic kidney disease – given the level of unmet need in the population of patients with diabetes and renal impairment, evidence of even a modest protective effect could have sweeping effects for the class.

• Dr. Bloomgarden began by asking “Why aren’t the SGLT-2 inhibitors more potent?” While SGLT-2 is responsible for 80-90% of the 160-180 g glucose transported daily, SGLT-2 inhibition only induces a maximum of 50-80 g of glycosuria. Dr. Bloomgarden posited a number of theories to explain this, including incomplete exposure of the drug to the SGLT-2 receptor (due to high binding of the agents to plasma proteins or secretion of the drug distal to the site of reabsorption) and possible compensation of SGLT-1 or other SGLT/GLUT transporters for the increased distal tubular glucose delivery.
• Interestingly, there may be a compensatory response to SGLT-2 inhibition as well, leading to increased endogenous glucose production. Highlighting recent literature (Ferrannini et al., JCI 2014), Dr. Bloomgarden noted increased endogenous glucose production and reduced glucose utilization in type 2 diabetes patients after single dose and four-week administration of empagliflozin, blunting the decline in fasting and prandial glycemia. This was further supported by another recent paper (Merovci et al., JCI 2014), which demonstrated a paradoxical increase in endogenous glucose production in type 2 diabetes patients following two weeks of treatment with dapagliflozin, with an associated increase in glucagon levels. With glucagon known to play a role in the kidney, Dr. Bloomgarden posited this could be mediated by an increase in renal gluconeogenesis following SGLT-2 inhibitor use.
• Dr. Bloomgarden next considered whether SGLT-2 inhibitors are nephrotoxic or nephroprotective. Looking again to the literature (Arakawa et al., Br J Pharmacol 2001), he noted a reduction in glomerular mesangial expansion and albuminuria in mouse models of type 2 diabetes and obesity following administration of the experimental SGLT-2 inhibitor T-1095, suggesting possible nephroprotective effects. He went on to note numerous studies presented at ADA this year supporting possible benefit to renal function, including 532-P, which indicated suppression of glomerular mesangial expansion, interstitial fibrosis, and diabetes-induced oxidative stress in diabetic mouse models following dapagliflozin treatment. Clinically, he highlighted the CREDENCE study, which is investigating the effects of canagliflozin in patients with stage 2-3 chronic kidney disease.
• Dr. Bloomgarden concluded with a number of additional remaining questions he was unable to address in his talk. These included 1) “What is the effect of SGLT-2 inhibitors on LDL cholesterol?” 2) “Do the drugs reduce bone mineral density and through what mechanism?” and 3) “Do the drugs alter glucose sensing in the central nervous system, causing increased appetite?”

Corporate Symposium: The Kidneys in Type 2 Diabetes – Disturbed Glucose Homeostasis to Mechanism-Based Therapy (Supported by an educational grant from AstraZeneca)

Case Presentation 1

Lawrence Blonde, MD (Ochsner Medical Center, New Orleans, Louisiana)

Following an introduction by Dr. Daniel Einhorn (UCSD, La Jolla, CA), session moderator Dr. Lawrence Blonde  presented a case study of a 59-year-old Caucasian patient with a current A1c of 8.7%. Dr. Blonde argued that dual therapy, in addition to lifestyle change, would be more effective for this patient. He then provided a broad overview of the two approved SGLT-2 inhibitors in the US, AZ’s Forxiga (dapagliflozin) and J&J’s Invokana (canagliflozin), noting that both drugs promote reductions in A1c, body weight, and blood pressure. He explained that patient selection for SGLT-2 inhibitors requires consideration of renal function and susceptibility to hypotension to avoid undue risk. Dr. Blonde concluded with the reminder that “diabetes is not just glucose.” He emphasized the importance of targeting multiple metabolic goals in each patient such as blood pressure, lipid levels, weight, and A1c (all of which SGLT-2 inhibitors can help patients achieve). 

Case Presentation 2

Richard Pratley, MD (Florida Hospital Diabetes Institute, Orlando, FL)

“These are really exciting times in diabetes,” Dr. Richard Pratley asserted to begin his case study presentation. His patient was a 70-year-old retired African American woman who lived alone, had a family history of cardiovascular disease, and had been diagnosed with type 2 diabetes for thirteen years. She took metformin and insulin glargine at bedtime, had an A1c of 7.9%, and approached her doctor because she had been waking up in the middle of the night sweating and with a headache, possible signs of nocturnal hypoglycemia. Dr. Pratley suggested that a combination of oral agents may be best for this patient, and presented several studies on such combinations. He noted that fixed-dose combinations of SGLT-2 inhibitors and DPP-4 inhibitors are currently in development. 

Panel Discussion

Dr. Pratley: Can we say anything about relative efficacy and safety among SGLT-2 inhibitors?

Dr. Blonde: We will see head-to-head studies in the future. My prediction would be that the drugs will have pretty similar efficacy and we would expect that from the mechanisms.

Q: We talked about how there is an improvement in weight, but what I didn’t show was the time course of that improvement. As with most therapies associated with weight reduction, weight loss plateaus. Given that the mechanism of SGLT-2 inhibitors is the loss of calories through glucose excretion in the urine, should there be a plateau?

Dr. Richard Pratley (Florida Hospital Diabetes Institute, Orlando, Florida): We don’t know why there is a plateau. While we see weight going down, there isn’t much further weight loss after 24 weeks. There must be some sort of compensation connected to appetite, because it’s unlikely to be that the patient is exercising less.

Dr. Lawrence Blonde (Ochsner Medical Center, New Orleans, Louisiana): Is it possible that there is some change or compensation in terms of energy utilization in the body?

Dr. Pratley: In uncontrolled diabetes, there is increased energy expenditure. When you improve glucose levels, you decrease that metabolic rate, which is one possible explanation for the effect.

Dr. Blonde: I think many people suspect that there is some compensation happening involving greater caloric consumption. Can you elaborate on the increased endogenous glucose production and potential for and increase in glucagon?

Dr. Pratley: As you know, the regulation of glucagon is complicated. It’s related to glucose level and it’s also related to insulin levels through paracrine action. We see lower insulin levels with SGLT-2 inhibitors but we still don’t know what drives the higher glucagon.

Dr. Blonde: If that glucagon effect persists, it would make the combination of a DPP-4 inhibitor and an SGLT-2 inhibitor more attractive.

Dr. Pratley: It would, if you operated according to the pathophysiology. If you add an SGLT-2 inhibitor onto a DPP-4 inhibitor, the reductions in A1c are about what you see in other clinical scenarios. You saw a better reduction with the two drugs than with either drug alone, but it wasn’t additive, which is typical of combination therapies. It’s a good combination but it’s not necessarily better than other combinations.

Dr. Blonde: Another combination that we have less data about is the combination of GLP-1 analogs and SGLT-2 inhibitors, which might cause larger A1c, weight and blood pressure effects. Only small pieces of data have become available.

Dr. Pratley: It is a logical combination that is very attractive, and may work, but we have no clinical data that speaks to its efficacy yet.

Q: What is the potential effect of SGLT-2 inhibitors on chronic kidney disease in type 2 diabetes?

Dr. Pratley: Things could go either way. It’s not unusual to see a small increase in creatinine at the beginning, but it’s transient. The real question is whether the effect is good or bad on the kidneys. There’s some data now about renal protective effects. This is an area we’ll hear more about in the future.

Q: Any idea as to why SGLT-2 inhibitors could be associated with a modest decrease in LDL-C?

Dr. Pratley: I don’t think we know the explanation.

Q: What is the mechanism that connects the data about hypoglycemia and adverse events, particularly the cardiovascular events? The ACCORD researchers couldn’t correlate hypoglycemia with the adverse events that occurred.

Dr. Pratley: There are two ways to look at it. One is that hypoglycemia causes an increased risk of adverse events. The other way to look at it is that these are reciprocations and are affected by the same factor. We don’t know yet.

Q: You pointed out that this was an insulin-independent mechanism and we saw that there was good efficacy across the spectrum of type 2 diabetes patients, from those who took it as monotherapy to those who took it as an add-on to insulin. What about giving an SGLT-2 inhibitor to someone with type 1 diabetes? Do you think it would be beneficial?

Dr. Pratley: A lot of people have wondered about that. There is no reason that it wouldn’t work in the context of type 1 diabetes. Those trials are ongoing. The rationale for that is similar to insulin-treated type 2 patients. It comes down to the ability to get by with lower doses of insulin, produce weight loss in patients who often need it, and achieve better post-prandial control.

Q: You showed some data about improved beta cell function with canagliflozin. Has any other method besides HOMA been used to show improvements in beta cell function with SGLT-2 inhibitors? What about the other effects of SGLT-2 inhibitors, like lowering glucotoxicity and improving insulin sensitivity?

Dr. Pratley: A number of published studies, including those by the Ferrannini and DeFronzo groups, have shown that effects on insulin sensitivity and insulin secretion are consistent across SGLT-2 inhibitors.

Q: Over time, will the excess glucose going through the tubules possibly cause nephropathy?

Dr. Pratley: Remember the rare patients that have a genetic condition with a defect in SGLT-2? They don’t appear to progress to nephropathy.

Q: Are there any medication interactions besides with diuretics?

Dr. Blonde: I’m not aware of any drug-drug interactions, but you raise an important issue about loop diuretics that were associated with more volume-related issues. For those with reasonable renal function, loop diuretics aren’t even a good solution for hypertension. But what about people with heart failure?

Dr. Pratley: That’s a good question. Less hypoglycemia is good for these patients, and some diuresis is probably good. We don’t know if lowering glucose levels in patients with heart failure leads to positive outcomes. However, there are very few patients with heart failure that are not on loop diuretics, so we don’t usually have to worry about it.

Product Theaters

Glucose Removal: An SGLT-2 Inhibitor Treatment Option for Adults with Type 2 Diabetes (Sponsored by AstraZeneca)

Ralph DeFronzo, MD (University of Texas Health Science Center, San Antonio, TX)

Dr. Ralph DeFronzo attracted a standing-room-only crowd for his presentation on the role of SGLT-2 inhibition and specifically on AZ’s Farxiga (dapagliflozin). After a review of SGLT-2 inhibitors’ mechanism of action, Dr. DeFronzo presented data from several clinical trials of dapagliflozin as monotherapy and as an add-on to multiple other drug classes. Dapagliflozin reduced A1c, body weight, and blood pressure in all the trials Dr. DeFronzo referenced, with little incidence of hypoglycemia. He concluded by reviewing the safety warnings associated with the drug, noting increased rates of genital mycotic infections in men and women, UTIs, increases in cholesterol, and the small bladder cancer imbalance seen with dapagliflozin’s pooled phase 3 data.

• Dr. DeFronzo reviewed what he considered to be the three most important clinical studies of dapagliflozin, noting the significant A1c reductions compared to placebo as both monotherapy and add-on therapy. As initial combination therapy, dapagliflozin 5 mg added to metformin XR provided an A1c reduction of 2.1% in 24 weeks from a relatively high baseline of 9.2% (n = 146) with concurrent reductions in body weight of six pounds. As a monotherapy, dapagliflozin demonstrated less extreme but still significant reductions in both A1c among patients with a baseline A1c of roughly 9.1%. As an add-on treatment to metformin, dapagliflozin 5 mg provided a reduction in A1c of 0.7% (n=137; baseline:8.2%) and 0.8% for the 10 mg dose (n=135; baseline: 7.9%) in addition to a reduction in body weight and significant reduction in systolic blood pressure. Finally, Dr. DeFronzo noted that while dapagliflozin plus metformin had comparable reductions in A1c to glipizide plus metformin, the incidence of major and minor hypoglycemia was less for patients treated with dapagliflozin plus metformin compared to patients treated with glipizide plus metformin.
• Dr. DeFronzo addressed the adverse events associated with dapagliflozin, noting increased rates of genital mycotic infections, UTIs, and cases of bladder cancer. Dr. DeFronzo emphasized that there is no dose response effect in genital mycotic infections or UTIs, but that a history of previous infections increases a patient’s chance of experiencing these outcomes. He spent a significant amount of time explaining the prevalence of cases of bladder cancer among the pooled clinical trial results. Ten cases of bladder cancer included nine cases in the dapagliflozin group and one case in the placebo group. If people with preexisting hematuria and risk factors for bladder cancer were excluded, this narrows down the number of cases to four in the dapagliflozin group, which Dr. DeFronzo emphasized was insufficient data to understand the meaning of these numbers. He reiterated that patients with moderate to severe renal impairment (eGFR <60 mL/min/1.73m²) should not use the drug.

Other Oral, Non-Incretin, Oral Anti-Diabetic Agents

Oral Presentations: Diabetes Complications—From Head to Toe

Risk of All-Cause Mortality Varies amongst Sulfonylureas: A Network Meta-analysis (335-OR)

Scot H. Simpson, PharmD (University of Alberta, Edmonton, AB, Canada)

Dr. Scot Simpson presented findings from a network meta-analysis of 16 different studies looking at mortality rates among users of different types of sulfonylureas. The specific sulfonylureas considered were gliclazide, glimepiride, glipizide, and glyburide. Glyburide was the most commonly used sulfonylurea and served as the comparator for the other compounds. Dr. Simpson and his team found that glipizide’s all-cause mortality risk was identical to that of glyburide (0.98 risk ratio), while glimepiride (0.82 risk ratio) and gliclazide (0.65 risk ratio) mortality risks were lower than that of glyburide. Dr. Simpson concluded that glimepiride and gliclazide were significantly different from glyburide and glipizide, but acknowledged the many limitations of this study and the need for a clinical trial to further explore these results. In the meantime, Dr. Simpson suggested that clinicians use these differences when selecting a sulfonylurea.

• Current guidelines in North America and Europe recommend sulfonylureas as second-line therapy for type 2 diabetes patients when metformin is insufficient. While they have side effects of hypoglycemia and weight gain, in the last 40 years, concerns of elevated risk of cardiovascular events and mortality associated with sulfonylureas have come up. Dr. Simpson’s research sought to discover whether different types of sulfonylureas had different mortality risks.
• Dr. Simpson’s team identified 16 directly relevant studies (those containing head-to-head comparisons of sulfonylureas) using Medline and EMBASE. Five of them were randomized controlled trials (n=2,729) and the rest were cohort studies (n=151,011). Only studies that looked at type 2 diabetes patients, used at least two different sulfonylureas, and reported all-cause or cardiovascular mortality (or myocardial infarction rates) were included. Dr. Simpson chose to analyze data comparing the most popular sulfonylureas: gliclazide, glipizide, glyburide (all second generation), and glimepiride (third generation).
• Using a network meta-analysis design, Dr. Simpson was able to estimate pairwise comparisons of all-cause mortality. Glipizide and glyburide had equal risks of mortality, while gliclazide and glimepiride showed significantly lower risks compared to glyburide. All-cause mortality risk ratios used glyburide as a comparator.  Gliclazide had the lowest risk of mortality among the sulfonylureas. Values are given in the table below:

	Pairwise Meta-Analyses Risk Ratio	Network Meta-Analyses Risk Ratio
Glipizide	1.00	0.98
Glimepiride	0.79	0.82
Gliclazide	0.64	0.65

• Dr. Simpson recognized limitations of this study (selection bias, various confounds, observational data, unreported data in potential studies) and the need for a randomized clinical trial to test causation. Nevertheless, he recommended clinicians to “consider these possible risk differences when selecting a sulfonylurea.”

Questions and Answers

Q: I noticed that the relatively newer agents were associated with lower mortality. My concern is that even with RCTs and cohort studies that looked at these agents, there is a whole trend with cardiovascular risk protection, more use of statins, more use of blood control. How much of that is confounded with your study? 

A: There are many uncontrolled confounding factors. We did do a sensitivity analysis where we had some of the adjusted correlational data and associations. But there’s certainly some selection bias.

Symposium: Cardiovascular Outcomes in Recent Diabetes Trials

Aleglitazar (AleCardio) Study

Jean-Claude Tardif, MD (University of Montreal, Montreal, Canada)

Dr. Tardif recapped the results of the AleCardio trial, the phase 3 cardiovascular outcomes trial for Roche/Genentech’s dual PPAR-alpha/gamma agonist, aleglitazar. As a reminder, AleCardio was terminated early when an unplanned futility analysis at the accrual of 55% of total events had a HR of 1.01 with a 1% chance of achieving superiority by trial completion (results initially presented at ACC 2014). Dr. Tardif reviewed results of data prior to the database lock on December 17, 2013 (also published in JAMA), concluding that aleglitazar reduced A1c and improved triglyceride and HDL-C levels but without benefit to cardiovascular outcomes. Noting the difficult track record of the PPAR class, he suggested that AleCardio overall reflected the challenges in the development of PPAR drugs given the unique pattern of gene modulation, complex effects on the metabolic pathways, and unpredictable therapeutic profiles – Dr. A. Michael Lincoff (Cleveland Clinic, Cleveland, OH) sadly had similar sentiments at ACC 2014 in his presentation of the data, suggesting in the Q&A that AleCardio may mean the end of new PPAR agonist trials.

Questions and Answers

Dr. Vivian Fonseca (Tulane University, New Orleans, LA): You had similar side effects in SYNCHRONY and your approach was somewhat unusual picking for cardiovascular versus glycemic effects. Was there some rationale to this decision?

A: With aleglitazar if you look at biomarker profile there was a robust benefit to A1c and HDL and triglyceride levels. Muraglitazar did have a profile in the wrong direction for CV events; tesaglitazar stopped due to renal dysfunction. We believed the renal profile for aleglitazar was more favorable than tesaglitazar. So the decision was largely based on the mentioned robust effects – we thought this drug was likely to help patients with ACS and diabetes.

Q: Do you have information on the blood pressure? Do you have corroborated evidence that the rise in creatinine was a result of eGFR as opposed to increased creatinine production?

A: There was a rise in blood pressure, as well as a demonstrated reversible effect on eGFR.

Symposium: China Medical Tribune Symposium – Progress in Diabetes in China – From Molecular Medicine to Clinical Trials

Search for Genetic and Clinical Predictors of Sulfonylurea Treatment Failure in Type 2 Diabetes

Linong Ji, MD (Peking University People’s Hospital, Beijing, China)

Dr. Linong Ji presented a sub-analysis of a prospective cohort study examining the predictive ability of genetic and clinical markers for sulfonylurea treatment failure in type 2 diabetes patients (n=747). A combination of baseline disposition index (DI) and initial treatment response was found to be a more effective predictor of sulfonylurea treatment failure than genetic markers or any single clinical marker. The combination of DI <6.9 and poor initial treatment response was found to predict treatment failure 82% of the time, compared to possessing multiple genetic factors associated with sulfonylurea failure (46%) or good initial treatment response combined with DI <6.9 (49%). Given that sulfonylureas have several dangerous side effects, using these clinical predictors may allow providers to avoid prescribing sulfonylureas to patients who have a high risk of sulfonylurea treatment failure or frequent hypoglycemia.

• The prospective study followed 747 type 2 diabetes patients from a double-blind, randomized, controlled clinical trial with glibenclamide therapy against traditional Chinese medicine. No difference was found between the two arms of the study, so the subjects were pooled to look at sulfonylurea failure over the course of a year. Treatment failure was defined as a FPG >126 mg/dl measured twice, four weeks after the maximal dose or maximal tolerated dose of glibenclamide was reached.
• The study examined both genetic and clinical variables to search for predictors of sulfonylurea treatment failure. Genotyping was conducted on patients to search for genetic factors that affect type 2 diabetes susceptibility, MODY, beta cell growth and function, obesity and insulin resistance, and sulfonylurea metabolism. Clinical variables included duration of diabetes, demographics, initial FPG response in the first four weeks, and biological factors such as A1c, disposition index, and HOMA. Poor initial response was defined as a FPG reduction <10% from baseline after four weeks of treatment.
• As would be expected, patients with more alleles associated with sulfonylurea failure were more likely to experience sulfonylurea treatment failure. Dr. Ji examined eight genes that have been shown to impact sulfonylureas and divided patients into three groups based on the number of alleles they possessed: low risk (fewer than three), middle risk (three to seven), and high risk (over seven). High risk patients had more treatment failure (p=0.043).
• Of the clinical variables, combining initial response and baseline disposition index (DI) was the best predictor of sulfonylurea success or failure. Dr. Ji did not define how he measured DI during the presentation. Dividing patients into six subgroups based on poor or good initial response as well as baseline DI <6.9, 6.9≤DI<14.0, or DI ≥14.0 revealed that 82% of patients with baseline DI <6.9 and poor initial response experienced sulfonylurea treatment failure, whereas the next best predictor combination was good initial response and baseline DI <6.9 (49%), and the worst predictor of failure was the combination of good initial response and baseline DI ≥14.0 (12%).

Questions and Answers

Q: For patients with good initial response to sulfonylurea treatment, did that increase their chance of hypoglycemia?

A: We did not do this analysis, but it is a good suggestion.

Insulin Therapy

Oral Presentations: Basal Insulin Therapy

New Insulin Glargine 300 U/ml: Glycemic Control and Hypoglycemia in Insulin Naïve People with T2DM (EDITION 3) (68-OR)

Geremia Bolli, MD (University of Perugia, Perugia, Italy)

Dr. Bolli provided the full results of Sanofi’s EDITION III trial of U300 insulin glargine, elaborating on topline results from late last year. As a reminder, EDITION III was a six-month, randomized, open-label trial investigating the effects of the initiation of U300 (n=435) versus standard insulin glargine (n=438) in insulin-naïve type 2 diabetes patients with inadequate control on oral agents alone. Similar to trials EDITION I and EDITION II, results indicated non-inferiority in A1c reduction, with declines of -1.42% with U300 (baseline A1c 8.5%) and -1.46% with standard insulin glargine (baseline A1c 8.6%; p=ns). As noted in topline results, compared to EDITION I and II there was no significant reduction in the percentage of patients with ≥1 severe or confirmed nocturnal hypoglycemic events (≤70 mg/dl) from nine weeks to six months of treatment, at 15.5% with U300 versus 17.4% with insulin glargine (HR 0.89; 95% CI 0.66-1.20). However, this difference became significant in full results when the analysis was extended to include the full six-month study period, with 17.9% of patients experiencing ≤1 event with U300 versus 23.5% with insulin glargine (HR 0.76; 95% CI 0.59-0.99). Though suggestive of benefit, with the improvement in nocturnal hypoglycemia weighted to the initial weeks of treatment, it remains unclear if this will translate to a meaningful difference in the clinical setting – as patients in EDITION III likely had less severe diabetes compared to EDITION I and II patients, the hypoglycemia benefit of U300 may be limited to patients already more prone to hypoglycemia.

• EDITION III aimed to investigate the efficacy and safety of the initiation of U300 versus standard insulin glargine in insulin-naïve type 2 diabetes with inadequate glycemic control on oral antidiabetic medications. Patients were randomized to initiation of U300 (n=435) or standard insulin glargine (n=438) with follow-up of six months. Baseline oral agents were continued with the exception of sulfonylureas due to the potential for confounding hypoglycemia. Patients in both groups demonstrated similar baseline age (58.2 with U300 vs. 57.2 years with standard insulin glargine), BMI (32.8 vs. 33.2 kg/m²), and diabetes duration (10.1 vs. 9.6 years). Use of oral agents was similar between both groups at baseline as well (90.6% vs. 92.0% on metformin, 59.1% vs. 58.6% on sulfonylureas, and 20.7 vs. 22.4% on DPP-4 inhibitors).
• Similar to EDITION I and II, results indicated non-inferiority in A1c reduction, with declines of -1.42% with U300 (baseline A1c 8.5%) and -1.46% with standard insulin glargine (baseline A1c 8.6%) after six months of treatment (p=ns). Likewise, there was no difference in percentage of patients at target A1c <7.0%, at 43% with U300 versus 42% with standard insulin glargine (p=ns). Mean fasting plasma glucose levels were non-significantly different in both groups as well, declining from roughly 180 mg/dl to 120 mg/dl.
• As noted in topline results, compared to EDITION I and II there was no significant reduction in the percentage of patients with ≥1 severe or confirmed nocturnal hypoglycemic events (≤70 mg/dl) from nine weeks to six months of treatment, at 15.5% with U300 versus 17.4% with insulin glargine (HR 0.89; 95% CI 0.66-1.20). However, this difference became significant when the analysis was extended to include the full six-month study period, with 17.9% of patients experiencing ≤1 event with U300 versus 23.5% with insulin glargine (HR 0.76; 95% CI 0.59-0.99). Percentage of patients with ≤1 severe or confirmed hypoglycemic events was additionally significant at any time of day for the full six-month study period  (46.2% vs. 52.5%; HR 0.75; 95% CI 0.57-0.99). By time of day, the difference in hypoglycemic events appeared most concentrated in the early morning with continuation to the early afternoon.
• Interestingly, there was a 17% increase in total insulin dose in patients treated with U300 versus standard insulin glargine at the end of the study, at 0.62 versus 0.52 units/kg/day – this has been consistent across the EDITION studies, though the clinical impact remains unclear. Change in body weight remained similar between both groups, at +0.4 kg (0.9 lbs) with U300 versus +0.7 kg (1.5 lbs) with insulin glargine (p=0.278).

Questions and Answers

Q: Can you clarify the time of day of dosing in both groups?

A: There was no question about the timing of dosing. Everyone in the program received basal insulin from anytime between dinnertime and bedtime – so only an evening injection.

Dr. John Buse (University of North Carolina, Durham, NC): At the end of the study, the A1c was identical but the dose with U300 insulin glargine was 17% higher. From your figure, it appeared most of that 17% was titrated after the eight-week mark. Thus, is it possible that the decline in fasting glucose was faster in the U300 arm?

A: A smart question from a smart man. We have data from self-monitoring of blood glucose that does not support your hypothesis, but it needs to be further analyzed.

Q: Why would the dose requirement be higher with U300?

A: The increased insulin requirement is consistent across studies – it was also shown in EDITION IV. The reason isn’t immediately clear. One hypothesis is that when you increase the concentration you slow down the absorption rate – this results in a more constant and pharmacokinetic release but with some loss of the initial effect. I think we need more studies to understand this effect.

Q: Would it be possible that U300 may be better called U250?

A: The official definition is that one unit of insulin is defined by the molecules of insulin in that volume. This is different than the clinical relevance of comparing units across therapies. So I don’t think it practically means anything to give 10% more or 10% less of insulin in units as long as the clinical outcomes are compared.

Similar Efficacy and Safety with LY2963016 Insulin Glargine Compared with Lantus Insulin Glargine in Patients with T1DM: The ELEMENT 1 study (69-OR)

Thomas Blevins, MD (Texas Diabetes & Endocrinology, Austin, TX)

Dr. Thomas Blevins presented phase 3 data from the ELEMENT 1 study indicating that Lilly’s biosimilar LY2963016 insulin glargine had efficacy and safety profiles comparable to Lantus in patients with type 1 diabetes when combined with insulin lispro. In the study, LY2963016 insulin glargine demonstrated non-inferiority to Lantus in terms of A1c reduction; patients achieved A1c reductions of 0.4% with LY2963016 insulin glargine and 0.5% with Lantus at 24 weeks from a baseline of 7.8%, with 0.3% reduction for both treatments observed at 52 weeks. Thirty-five percent of LY2963016 insulin glargine users and 32% of Lantus users achieved A1c levels of 7.0% after 24 weeks; both user groups experienced a 62% incidence of adverse events at 52 weeks, and the total hypoglycemia rate stood at 77 events per patient per year with LY2963016 insulin glargine and 80 with Lantus. Dr. Blevins concluded that the two products in combination with lispro had similar efficacy and safety profiles with no clinically significant differences.  

• The ELEMENT 1 study was a phase 3, open-label clinical trial involving 535 patients with type 1 diabetes who were randomly assigned to receive basal-bolus therapy with either the investigational glargine (N = 268) or Lantus (N = 267) in addition to insulin lispro. The study lasted 24 weeks, with a follow-up through 52 weeks. The patients had an average BMI of 25 kg/m² and an average A1c of 7.8% at baseline, and all of them had been receiving treatment with insulin glargine, insulin detemir, or NPH prior to the study.
• The results of this study demonstrated that LY2963016 insulin glargine is non-inferior to Lantus in terms of the primary endpoint of A1c reduction. After 24 weeks, the group receiving the investigational glargine had achieved an A1c reduction of 0.35% compared to a 0.46% reduction for the group receiving Lantus. Additionally, 35% of the participants in the investigational glargine group had reached the target A1c of 7% compared to 32% of the Lantus group.
• The investigational glargine also demonstrated non-inferiority on a number of secondary endpoints related to safety and efficacy. There was no significant difference between the groups in fasting plasma glucose reduction, body weight change, or daily insulin dose. The frequency of adverse events was 62% in both groups after 52 weeks, and both groups had similar rates of hypoglycemia (77 events/patient/year with LY2963016 and 80 with Lantus after 24 weeks), allergic events (8% of people receiving LY2963016 and 4% of people receiving Lantus), and antibody responses (9% of the LY2963016 group and 6% of the Lantus group).

Questions and Answers

Q: When looking at safety data like insulin antibodies, I’m not interested in mean levels, I want to know if there are a few people who are severely affected. Your presentation addresses the wrong question. Are there 1-2% who have severe problems?

A: Let me defer to the next presentation where you’ll hear more about how antibody levels and response were measured.

Similar Efficacy and Safety with LY2963016 Insulin Glargine Compared with Lantus Insulin Glargine in Patients with T2DM: The ELEMENT 2 Study (64-OR)

Julio Rosenstock, MD (Dallas Diabetes and Endocrine Center, Dallas, TX)

Dr. Julio Rosenstock presented the phase 3 results of the ELEMENT 2 study, which compared the safety and efficacy profiles of Lilly’s investigational LY2963016 insulin glargine to the currently marketed Lantus insulin glargine. The study demonstrated non-inferiority of LY2963016 compared to Lantus in terms of A1c reduction; patients in the LY2963016 group (N = 376) achieved an average A1c reduction of 1.3% after 24 weeks compared to a 1.4% reduction for the Lantus group (N = 380) from a baseline of 8.3%. 49% of LY2963016 insulin glargine users reached the target A1c of 7%, compared to 53% of Lantus users. The frequency of adverse events was also similar – 52% for LY2963016 insulin glargine and 48% for Lantus – as was the number of hypoglycemic events, with 21 events per patient per year for LY2963016 insulin glargine and 22 for Lantus. Dr. Rosenstock concluded that the two insulin glargine formulations have equivalent efficacy and safety profiles and that there is no clinically significant difference between them.

• The ELEMENT 2 study was a phase 3, double-blind clinical trial involving 759 patients with type 2 diabetes who were randomly assigned to receive either the investigational glargine (N = 376) or Lantus (N = 380) for 24 weeks. The average age of the participants was 59, average weight was 90 kg, and baseline A1c was 8.3% for both groups. All patients had received treatment for diabetes for at least 2 years, 60% with insulin and 40% with oral medications.
• The results of this study demonstrated that LY2963016 insulin glargine is non-inferior to Lantus in terms of the primary endpoint of A1c reduction. Both groups had an average A1c of 8.3% at baseline; the average at the end of the study was 7.0% for the group receiving the investigational glargine compared to 6.9% for the Lantus group. Additionally, 49% of the participants in the investigational glargine group reached the target A1c of £ 7.0% compared to 53% in the Lantus group.
• The investigational glargine also demonstrated non-inferiority on a number of secondary endpoints related to safety and efficacy. There was no significant difference between the groups in fasting plasma glucose reduction, body weight change, or daily insulin dose. The frequency of adverse events was 52% with the investigational glargine compared to 48% with Lantus, and both groups had similar rates of hypoglycemia (21 events/patient/year with LY2963016 and 22 with Lantus) and allergic events (6% of people receiving LY2963016 and 7% of people receiving Lantus).
• Dr. Rosenstock was clear that he considers this product to be a biosimilar insulin glargine, despite the regulatory complexity surrounding this designation. He explained that although it will not be classified as a biosimilar by the FDA, it meets the scientific criteria of  (i) high similarity to the reference product; (ii) minor differences in clinically inactive components; and (iii) no clinically meaningful differences in terms of safety, purity, and potency.

Questions and Answers

Q: Very nice study. I have one question about antibody response: it’s hard to measure antibodies with a high degree of sensitivity, so can you tell us more about the assay you used?

A: I can’t give you specifics on that, but there will be a presentation later that I encourage you to attend.

Q: This was very nice, congratulations. I have one comment about your second slide where you referred to the regulatory designation in the US and “other geographies.” “Other geographies” includes many, many other countries with different rules!

A: Yes, so in my judgment, it is a biosimilar because it has all the specific criteria. I was speaking from a regulatory perspective; here it has to go through a different pathway, so it’s just regulatory terminology. In essence, scientifically, LY is a biosimilar.

Q: Biosimilar guidelines suggest looking for biosimilarity in the most sensitive population – in this case, patients with type 1 diabetes. In this study, you’ve selected the population that would be least likely to show differences, not people who’ve been using insulin for a long time.

A: We had people who’ve been using insulin for 10 years. 60% of participants were insulin naïve, 40% were previously on insulin – we took the whole type 2 spectrum. For a change, you’re wrong!

Evaluation of Immunogenicity of LY2963016 Insulin Glargine Compared with Lantus Insulin Glargine in Patients with T1DM or T2DM (70-OR)

Mark Deeg, MD, PhD (Eli Lilly, Indianapolis, IN)

Dr. Mark Deeg presented specific immunogenicity data from the phase 3 ELEMENT 1 and ELEMENT 2 studies of Lilly/BI’s insulin glargine LY2963016 – immunogenicity is one of the key concerns with so-called insulin biosimilars. Overall, there were no worrying differences in overall immunogenicity. Most importantly (and reassuringly), in both studies, the presence or absence of such an antibody response did not affect the degree of A1c-lowering efficacy or any clinical outcomes. For type 1 diabetes patients in ELEMENT 1, a similar percentage of LY2963016 users had detectable anti-insulin antibodies at both baseline (17%) and at 52 weeks (40%) to patients on Lantus (21% and 39% at baseline and 52 weeks, respectively). ELEMENT 2 found similar parallels: 6% of LY2963016 users at baseline and 15% at week 24 showed detectable insulin antibodies, compared to 4% and 11% for Lantus. Out of all the data presented, only at one time point in one study (week 4 in ELEMENT 2) did LY2963016 demonstrate a significant difference in detectable antibodies relative to Lantus, and there the difference was slight and only barely significant (p = 0.047).

Questions and Answers

Q: Did you look at the antibody patterns in ELEMENT 2 between insulin-naïve patients and previously insulin treated subjects?

A: There was a similar treatment-emergent antibody response between both of those groups.

Q: Did you look at neutralizing antibodies?

A: We did not run specific neutralization antibody assays. Most importantly, there were no clinically meaningful differences in outcomes.

Superior Glycaemic Control Effects with Insulin Degludec (IDeg) to Insulin Glargine (IGlar) in Diabetic Hemodialysis (HD) Patients Assessed by Continuous Glucose Monitoring (63-OR)

Satoshi Funakoshi, MD, PhD (Nagasaki Renal Center, Nagasaki, Japan)

Dr. Satoshi Funakoshi presented the results of a small trial comparing the effects of Novo Nordisk’s Tresiba (insulin degludec) and Sanofi’s Lantus (insulin glargine) using CGM in seven patients with poorly controlled type 2 diabetes (A1c > 8.0%) also on hemodialysis (HD). Hemodialysis has been shown to have an adverse effect on glycemic control in diabetes patients, as glucose levels drop during hemodialysis and have a tendency to rebound afterwards due to counterregulatory hormone action (including glucagon). The study’s results were displayed as overlaid CGM traces. On Lantus, most patients saw the characteristic drop in glucose levels during HD, followed by a sharp rebound immediately afterwards. By comparison, there was no readily apparent drop in glucose during HD with Tresiba, and no visible rebound in most patients. Dr. Funakoshi characterized this flattening of variability as quite surprising for an insulin – we would agree that the difference in the curves was quite striking. Potential causes of this effect include Tresiba’s stability and high binding to albumin (which would minimize loss during HD). From this data alone, it appears that Tresiba may be uniquely suited to use in patients on hemodialysis, at least relative to Lantus. Key study limitations include small size, inter-subject variability, and the sequential exposure to Lantus before Tresiba.

• Insulin is one of the relatively few treatment options available to type 2 diabetes patients with kidney failure. Drug classes such as SGLT-2 inhibitors and (most) DPP-4 inhibitors are contraindicated in this patient population. Additionally, end-stage renal disease is responsible for substantial cost, as well as reduced quality-of-life for patients. According to the CDC’s 2014 Diabetes Statistic Report (which we covered recently), in 2011 a total of 228,924 diabetes patients in the US were on dialysis or received a kidney transplant due to kidney failure.
• Hemodialysis generally leads to temporary reduction in blood glucose, followed by a sharp rise. Glucose, a relatively small molecule, can pass through the dialysis membrane to a greater degree than insulin (a much larger polypeptide). Following the end of HD, blood glucose levels can spike due to a glucagon-driven counterregulatory response. Adding additional variability, insulin can adsorb to the dialysis membrane, and the different binding properties of insulin analogs can affect this binding.
• Methods: Dr. Funakoshi’s study enrolled seven poorly controlled (A1c > 8.0%) type 2 diabetes patients on hemodialysis who were already on treatment with Lantus (baseline doses of 8 – 24 units). After 72 hours of therapy with Lantus (on both on and off-HD days), patients were switched to Tresiba at the same dose. Glycemic control was evaluated using CGM. Concomitant antidiabetic drugs and HD schedules stayed the same throughout the study. 
• Results were presented as CGM trace overlays, and the results were strikingly positive in favor of Tresiba. Dr. Funakoshi first displayed data for days in which patients did not receive HD, and even here, it appeared that Tresiba led to less glycemic variability later in the day than Lantus. On HD days, patients on Lantus saw the characteristic dip in blood glucose of 50 – 150 mg/dl during hemodialysis, followed by a substantial rebound (three of the patients peaked at well above 300 mg/dl). By comparison, those trends were not at all visible in patients on Tresiba – no patients crossed 300 mg/dl following hemodialysis.
• Dr. Funakoshi suggested that the glycemic stability seen with Tresiba could be due to the drug’s PK/PD stability, as well as the fact that it has a high degree of binding to albumin (~99%). Lantus, by comparison, does not have the same binding affinity for albumin. This factor is important, as albumin is a very large protein that is not lost to a great extent during dialysis or adsorbed to the dialysis membrane.
• We would have liked to learn whether CGM was patient-blinded in the study, or whether any of the patients had prior experience with CGM. Those factors could have had a significant impact on the outcome of the study. All patients were exposed to Tresiba later in the trial, following their exposure to Lantus. If patients were able to see the CGM traces, patients learning how to interpret CGM, rather than just the comparative effects of Tresiba could have affected the results of the study. The fact that patients’ doses of insulin and other medications were held constant throughout the study helps alleviate this potential bias to some extent.
  
  • Other potential study limitations include the very small number of subjects, the lack of evaluation of plasma insulin or other hormones, and inter-subject variability.

Questions and Answers

Q: At what time of the day did patients take the insulin?

A: Insulin was taken at breakfast time. I think it might be easier to take ultra-long-acting insulin right after hemodialysis.

Q: We know that insulin degludec has a slower absorption rate, which accounts for much of the longer action. There is also evidence that there may be differences in clearance. Do you have information on whether the action was further prolonged in patients with impaired renal function?

A: I do not think so. The steady state is almost the same compared to patients with normal renal function.

Oral Presentations: Prandial Insulin Therapy

Faster-Acting Insulin Aspart Improves Postprandial Glycemia vs. Insulin Aspart in Patients with Type 1 Diabetes Mellitus (T1DM) (129-OR)

Tim Heise, MD (Profil Institute for Clinical Research, Chula Vista, CA)

Dr. Tim Heise presented pretty compelling PK/PD data showing that Novo Nordisk’s faster-acting insulin aspart (FIAsp, aka NN1218) has a clinically significant faster onset of action than its predecessor, Novolog (insulin aspart). This early-stage study in people with type 1 diabetes (n=36) found that the time to first appearance for FIAsp after co-administration with a standardized meal was just 3.6 minutes compared to 9.8 minutes for insulin aspart (it is quite notable that the insulin was dosed at the same time as the meal, since this is likely when many patients actually take their prandial insulin, rather than the 15-30 minutes prior to eating that is optimal for current rapid-acting analogs). The time to 50% maximum concentration for FIAsp was 27.3 minutes compared to 34.5 minutes for insulin aspart. The earlier onset also translated into greater blood-glucose lowering in the first two hours after insulin administration and meal challenge. After one hour, the postprandial blood glucose excursion was, on average, 22 mg/dl lower on FIAsp than on insulin aspart, and after two hours, it was 26 mg/dl lower. Glucose area under the curve after two hours was 26% lower with FIAsp than with insulin aspart, and area under the curve after six hours was 33% lower. Notably, FIAsp achieved this earlier onset of action with no increase in the rate of hypoglycemia (or hyperglycemia) or any other safety or tolerability concerns.

• FIAsp’s structure and formulation: FIAsp’s molecular structure is identical to the insulin analog aspart (Novo Nordisk’s Novolog), which means that the proline at position B28 in the native insulin peptide has been substituted with aspartic acid. Its excipient formulation is what makes it different from Novolog. FIAsp has two excipients: nicotinamide, a vitamin B3 that is an absorption modifier, and arginine, a naturally occurring amino acid that is a stability enhancer. Dr. Heise highlighted that both of the excipients are on the FDA ingredients list for approved drug products for injection. Thus, with the established safety profile of Novolog, FIAsp should have very few safety concerns.
• The study presented was a randomized, double-blind, cross-over, meal challenge study comparing the PK/PD of FIAsp with insulin aspart (n=36 people with type 1 diabetes). Patients received a single dose of FIAsp or insulin aspart (0.2 U/kg) together with a meal (600 kcal standardized liquid meal consisting of 67% carbohydrate, 17% protein, and 16% fat). Prior to dosing, blood glucose was normalized to 100 mg/dl using IV insulin infusion (stopped no later than five minutes before study drug dosing). After a 3-12 day washout period, patients received another meal test, this time with the opposite drug than they received at the first meal test.
• FIAsp had a faster onset of appearance and greater exposure during the first two hours after dosing. FIAsp’s mean onset of appearance was just 3.6 minutes after dosing compared to 9.8 minutes for insulin aspart (a highly statistically significant difference). The time to 50% maximum concentration for FIAsp was 27.3 minutes compared to 34.5 minutes for insulin aspart.
• Total drug exposure (area under the curve over 10 hours), maximum concentration, and time to maximal concentration were essentially equivalent between FIAsp and insulin aspart. This means that FIAsp’s PK curve was simply shifted to the left of insulin aspart. In other words, it has a faster onset but few other PK differences.
• The earlier onset of appearance translated into greater blood-glucose lowering in the first two hours after administration and meal challenge. After one hour, the postprandial blood glucose excursion was, on average, 22 mg/dl lower on FIAsp than on insulin aspart, and after two hours, it was 26 mg/dl lower (statistically significant). Glucose area under the curve after two hours was 26% lower with FIAsp than with insulin aspart (statistically significant).
  
  • Total glucose area under the curve over six hours was reduced by 33% with FIAsp compared to insulin aspart.
• The incidence of hypo- and hyperglycemia requiring intervention was similar between FIAsp and insulin aspart. Investigators intervened at 50 mg/dl and 306 mg/dl for hypo- and hyperglycemia, respectively. On FIAsp, 20% of patients required hypoglycemic intervention compared to 23% on insulin aspart. On FIAsp, there were zero interventions for hyperglycemia, and there was one for insulin aspart.

Questions and Answers

Q: What was the actual time between the injection and the beginning of the meal?

A: About one minute. Basically, they injected and started to eat.

Q: What are the requirements to get faster-acting aspart approved by FDA?

A: The regulatory requirement is similar to that of other drugs. Because the molecule is the same, there was no necessity for a phase 2 study. So basically after the phase 1 studies, the company directly moved directly into phase 3 and these trials are ongoing. It will be like any other drug.

Q: What is the objective of the phase 3 program?

A: First objective is obviously safety and efficacy under more real-world conditions. So the phase 3 studies will investigate safety data and will look into A1c, hypoglycemia, possibly also quality of life and other things.

Q: What was the time to maximum insulin concentration vs. aspart?

A: If I recall correctly it was about 1 hr for both – the time to maximum was not significantly different.

Safety and Efficacy of Ultra-Rapid-Acting Human Insulin Formulation BIOD-123 in Patients with Type 1 Diabetes

Alan Krasner, MD (Biodel, Danbury, CT)

Biodel’s Dr. Alan Krasner presented full results on the phase 2 study of its ultra-rapid-acting human insulin, BIOD-123 – topline data was shared in September 2013. Our takeaway after seeing these full results was that data interpretation is challenging for many reasons – there were large baseline group imbalances in gender, differences in basal insulin doses, and an open-label design. Overall, BIOD-123 was non-inferior to Humalog as measured by change in A1c. There was a 0.17% treatment difference in favor of Humalog (baseline A1c: 7.3%), though the 95% confidence interval [-0.01, 0.35] just barely met the non-inferiority margin of 0.4%. [Note: this was highly similar to what was observed in the phase 3 trial of MannKind’s Afrezza, which had a 0.19% treatment difference in favor of insulin aspart and a [0.02, 0.36] confidence interval that also just barely met the non-inferiority endpoint.] Ten-point profiles and CGM did not demonstrate consistent postprandial glucose differences between BIOD-123 and Humalog, despite a clear postprandial benefit observed during a liquid meal challenge test (~15 mg/dl improvement with BIOD-123). There was higher injection site discomfort with BIOD-123, a problem that has plagued Biodel since Linjeta – however, these events disproportionately came from certain trial sites, suggesting the possibility of ascertainment bias (the trial was an open-label design). Less basal insulin was used in the BIOD-123 arm, and there was a non-significant trend towards higher overnight glucose levels with BIOD-123 – this could suggest basal insulin was sub-optimally titrated in the BIOD-123 arm. Overall, lots more questions than answers flowed out of this study, and it is perhaps not a surprise that Biodel has not firmed up a partnership on this asset. 

Questions and Answers

Q: There were differences in meal tests in favor of Biodel. But there were no difference in real life. Was the timing of injection before the meal different?

A: They should have been the same. The instructions were to dose the prandial insulin immediately before the meal – in the liquid meal challenge and also at home. We do believe that is practically when most patients take prandial insulin – just before meals.

Q: Together with the previous speaker, ultra-rapid-acting insulins showed lower postprandial glucose excursions. In your study, there not a change in A1c. What level of postprandial reduction is needed to see change in A1c? Perhaps 10-15 mg/dl may not be enough to see a change in A1c. Second, I’m a clinician – how important is this in my clinical day-to-day management?

A: I don’t know the answer to your first question. I don’t know how much postprandial reduction you need for an A1c reduction. Our data is a little complex because of imbalances by chance in dosing. It’s hard to interpret the A1c data. The liquid meal challenge was more controlled circumstances. Glucose was measured by a lab analyzer. It’s possible that some of the home-based readings result from variability and noise in measurements. And differences in dosing that contributed as well.

I don’t know the answer to using it in clinical practice. I would guess these insulins would be used as rapid-acting analogs are used now. We often say take insulin right before meals. We know based on recently published data that may not be the optimal time to take lispro and aspart. Good ultra rapid-acting analogs may be ideal for taking just before meal.

Dr. Melanie Davies (University of Leicester, UK): Thank you very much. There is some promise here, but there is still some work to do.

Reduced Hypoglycemia Risk with an Inhaled Insulin Compared to Injected Prandial Insulin in Type 1 Diabetes

Bruce Bode, MD (Atlanta Diabetes Associates, Atlanta, GA)

Dr. Bruce Bode shared full results from the phase 3 trial comparing mealtime Afrezza (Technosphere Insulin, or “TI”) to insulin aspart in patients with type 1 diabetes – we previously covered this data in detail in our preview of the April 1 FDA Advisory Committee for Afrezza, as well our full report on the Advisory Committee. In type 1 diabetes, Afrezza was non-inferior in A1c reduction vs. insulin aspart (-0.2% vs. -0.4%; baseline: 7.9%), a difference that was maintained over 24 weeks. Afrezza had an advantage over aspart for both moderate and severe hypoglycemia (-30% and -43%, respectively) – the benefit came in the period two to five hours post-meal, suggesting the fast-in/fast-out profile reduced late postprandial hypoglycemia. Dr. Bode characterized Afrezza as “weight neutral” vs. the weight gain observed in the insulin aspart arm (-0.4 kg with Afrezza vs. a +0.9 kg with aspart). Meanwhile, Afrezza was associated with a “manageable adverse event profile” – the most common event was cough, which was “mild and transient.” Dr. Bode’s talk was factual and not full of opinion, but it certainly refreshed many of the outstanding questions around this trial in type 1 diabetes: high dropout rates in the Afrezza arm (attributed to study demands, and patients with longstanding diabetes resistant to change), statistical A1c inferiority to aspart (though Afrezza just barely met the non-inferiority margin of 0.4%), the dosing flexibility of Afrezza in type 1 diabetes, and perhaps most importantly, how patients will use this in the real world. Q&A was particularly illuminating.

• In the primary efficacy results, Afrezza was non-inferior to insulin aspart at the pre-specified A1c margin of 0.4%. A1c declined by 0.2% in the Afrezza group vs. 0.4% in the insulin aspart group, both from a baseline of 7.9%. The treatment difference of 0.19% translated to a p-value of 0.016 and a 95% confidence interval of [0.02-0.36] – just sneaking under the non-inferiority margin of 0.4%.
• Overall, Afrezza led to a 30% reduction in total hypoglycemia (9.8 vs. 14 events per subject-month) and a 43% reduction in severe hypoglycemia (8 vs. 14 events per 100 subject months) vs. insulin aspart [p<0.05 for both]. Over the 24-week study period, Afrezza had a lower total hypoglycemia event rate than insulin aspart for every A1c category (5.5-6.5%, 6.5-7%, 7-7.5%, 7.5-8%, >8%). The difference was greatest in the population with an A1c of 7-7.5%, where Afrezza led to a 40% reduction in hypoglycemia (10.5 vs. 17.4 events per subject-month). The smallest benefit of Afrezza on hypoglycemia came in those with an A1c >8% – a 13% reduction (8.6 vs. 9.9 events per subject-month).
  
  • Afrezza and insulin aspart had nearly identical rates of total hypoglycemia from 0-2 hours post meal; the clear advantage for Afrezza came in the 2-5 hour post-meal window, where it had a much lower rate of hypoglycemia for all 24 weeks of the study. Importantly, during the study’s follow-up period (weeks 25-28) – when Afrezza use was discontinued and all patients reverted to insulin aspart – the 2-5 hour hypoglycemia advantage disappeared. This supports the conclusion that Afrezza’s shorter tail of insulin action reduces the occurrence of delayed post-meal hypoglycemia.

Questions and Answers

Q: What was the dose of basal insulin?

A: The average basal insulin dose was about 30 units with aspart, which went up to about 35 units with inhaled insulin. There was a significant drop in fasting plasma glucose with inhaled insulin.

Dr. Tim Heise (Profil, Neuss, Germany): I have many questions on the data. Most importantly, would you recommend your type 1 patients go on TI despite a significantly worse A1c vs. aspart?

A: A1c was not significantly worse by the non-inferiority criteria. We looked at it in every which way.

Q: But the confidence interval didn’t include zero…

A: But it was less than the non-inferiority margin of 0.4%. There are lots of ways to give prandial insulin, and whatever the patient does best with is what you choose.

Q: Why was there so much withdrawal of consent in the TI vs. the control group?

A: About 9% withdrew because of adverse events – 5%+ were related to cough. These people all had longstanding type 1 diabetes. They didn’t like the idea of using inhaled insulin once they started. They quickly dropped out early – not late. The aspart arm and both group dropped out because of e-diaries and frequent adjustment of insulin.

Efficacy and Safety Evaluation of Technosphere Insulin vs. Inhaled Placebo in Insulin-Naïve Type 2 Diabetes (128-OR)

Julio Rosenstock, MD (Dallas Diabetes and Endocrine Center, Dallas, TX)

Dr. Julio Rosenstock presented the results of one of the two most recent phase 3 trials on MannKind’s inhalable insulin Afrezza (Technosphere Insulin; TI) – these two trials were a direct outcome of the FDA’s last Complete Response Letter for Afrezza. The trial Dr. Rosenstock presented investigated Afrezza in 177 type 2 diabetes patients on oral diabetes medications, compared to a cohort on the Technosphere powder excipient alone (n = 176). We summarized these results in our preview for Afrezza’s most recent FDA Advisory Committee meeting, as the meeting briefing documents covered the data from this trial (see section on Trial 175).  Nothing very new or surprising emerged during the presentation. Highlights of the data included a seemingly modest 0.4% A1c difference between groups (although interestingly the Technosphere powder comparator group saw a 0.42% absolute A1c reduction from baseline that made Afrezza’s relative efficacy seem relatively low) and a significant 130% increase in hypoglycemia. Dr. Rosenstock attributed the hypoglycemia to the high use of SFUs in the trial, and the overall incidence of hypoglycemia was fairly low for a rapid-acting insulin. Cough was the most common side effect (generally dry and transient, and also associated with the Technosphere powder alone), and there was a very slight decrease in FEV1 (a measure of lung function).

• For a complete chronicle of the most recent Afrezza FDA Advisory Committee meeting, read our report. After the meeting, the FDA announced a three-month extension of Afrezza’s PDUFA date to July 15, 2014 (read our report).

Questions and Answers

Q: From what I could see, there was in fact a reduction in FEV1 that was due to the insulin. What is the explanation for that?

A: It is an interesting observation, but you have to remember that we’re looking at a very miniscule change in volume. But yes, it could very well be driven by the insulin.

Q: Can you give us more detail on the background therapy for these patients?

A: The background therapy is important, as it may explain the hypoglycemia findings. Around two-thirds were on metformin and a sulfonylurea, and it is highly possible that if we had fewer patients on sulfonylureas there would have been less hypoglycemia.

Q: Can you comment on patients who may have been smokers?

A: We had so many screen failures because we did not take any patients with a history of smoking or who were active smokers. With studies of previous inhaled products, it looked like smoking in fact increased absorption.

Q: Were there any patients with any type of pulmonary illness?

A: Any people showing a history of obstructive pulmonary disease were excluded.

Q: How is TI impacted by exercise?

A: I am not sure if they have done a study on exercise with TI. I believe that studies were done with Exubera, and that there was increased absorption during exercise, but this is a totally different mechanism of action.

Injection Depth Does Not Affect the Pharmacokinetics or Pharmacodynamics of Insulin Lispro in Healthy Obese or Normal Weight Subjects (131-OR)

Amparo de la Pena, PhD (Eli Lilly, Indianapolis, IN)

Dr. de la Pena presented data confirming no differences between the 5-mm and 8-mm injection depths for insulin lispro for a variety of pharmacokinetic and pharmacodynamics parameters. Of note, the 8-mm – but not the 5-mm – injection depth was performed with a pinch-up. Two separate studies were performed: one in North America studying healthy obese participants and one in East Asia studying normal weight participants. Neither study enrolled people with diabetes. While the sample sizes were small (n=16 for both studies), the studies allowed the research groups to collect more mechanistic data. Both studies showed nearly identical immunoreactive insulin and glucose infusion rate curves with the 5-mm and 8-mm injection depths, and no statistically significant differences were observed with any of the parameters characterizing the curves.

• The data stem from two studies conducted at different times, at different sites, and with different populations, and neither study looked at patients with diabetes. Both studies used a randomized two-period crossover design where each participant received insulin injections at 5-mm and 8-mm injection depths. The first study (n=16) looked at normal weight subjects with a mean age of 31 years and mean BMI of 23 kg/m² in a predominantly East Asian population; the second study (n=16) looked at healthy obese participants with a mean age of 41 years and mean BMI of 34 kg/m² in a predominantly Caucasian population. Of note, the 8-mm – but not the 5-mm – injection depth was performed with a pinch-up.
• In both studies, the pharmacokinetic and pharmacodynamics curves for insulin injection were nearly identical between the 8 mm and 5 mm injection depths. To measure pharmacokinetics, the investigators administered an injection of insulin lispro and collected blood samples from the participant at time points ranging from 10 to 360 minutes to test for immunoreactive insulin lispro-specific activity (IRI). In the normal-weight subject study, the IRI curves for the 8 mm and 5 mm injections were completely superimposed, showing no differences. For the healthy obese subject study, the 8-mm injection depth was delayed by about 10 minutes at early time points; however, this was not statistically significant and the curves overlapped for the later time points. To measure pharmacodynamics, subjects underwent hyperglycemic clamp and glucose infusion rate was measured after insulin lispro injection. The glucose infusion curves for both injection depths were nearly identical, with an approximately five minute delay for the 8-mm depth in healthy obese subjects that did not reach statistical significance. Accordingly, no statistically significant differences were observed between injection depths for any of the parameters derived from the curves.

Questions and Answers

Q: You seem fairly confident that these results will apply to diabetes patients. How sure are you that this will match clinical observations?

A: There is definitely room for more investigation. We hope to compare between normal weight and obese subjects within a single study. I am not aware of any differences in skin thickness between diabetic and non-diabetic individuals.

Q: Identical findings were shown over twenty years ago using radiolabelling and ultrasound just under the skin, but almost nothing has been done since then. It’s nice to see this done with a modern insulin analog. There have been 7 or 8 different studies showing no difference with pens of different lengths. Small question. Where and how did you give the injections? It seems that 8 mm might get close to muscle.

A: We used a pinch up for the 8 mm and no pinch up for the 5 mm, and we alternated injection sites.

Q: There seems to be a much lower C_max and a longer t_max in obese patients.

A: We did not have a direct comparison between the two studies. They are too different: different sites, different times, different populations. I would love to do a study where we have both populations in the same study.

Q: Can you comment on t_Rmax being earlier in both studies?

A: They do look like that at the beginning, but the curves come back together, and the difference is not statistically significant.

Posters

Rate Ratios for Nocturnal Confirmed Hypoglycemia with Insulin Degludec vs. Insulin Glargine Using Different Definitions (402-P)

S Heller, C Mathieu, R Kapur, ML Wolden, B Zinman

This poster presents the results of a post-hoc analysis by Novo Nordisk to determine the robustness of their previous finding that treatment with ultra-long acting insulin degludec (trade name Tresiba) led to significantly lower rates of nocturnal hypoglycemia than treatment with insulin glargine (Sanofi’s Lantus) in patients with type 2 diabetes and numerically lower rates in patients with type 1 diabetes. This analysis was likely fueled by FDA criticism of the methods used in the original meta-analysis in degludec’s registration packet. This new study conducts several analyses using different definitions of nocturnal hypoglycemia including i) only confirmed episodes with symptoms; ii) the ADA definition; and iii) a different time frame for the nocturnal period to show that the original findings remain robust no matter which definition of “nocturnal” or “hypoglycemia” is used. The results of these analyses confirmed the findings from the original meta-analysis under nearly all of these conditions. The one exception was when the nocturnal period was extended to 0:01-7:59, in which case hypoglycemia was reduced only in the population of patients with type 2 diabetes treated with basal-bolus therapy (and not in type 1 diabetes or in insulin-naïve type 2 diabetes). Under all other conditions, treatment with insulin degludec led to significantly lower rates in all patients with type 2 diabetes and to numerically but not significantly lower rates in patients with type 1 diabetes. The table below summarizes the rate ratio and 95% confidence intervals for all conditions (rate ratio of 1 indicates an equal rate of hypoglycemia, <1 indicates a lower rate with insulin degludec, >1 indicates a lower rate with insulin glargine). Overall, the data seems to indicate that treatment with insulin degludec may lead to significantly lower rates of nocturnal hypoglycemia in patients with type 2 diabetes compared to treatment with insulin glargine, though the less impressive results with the time period 0:01-7:59 do provide some reason for cautious skepticism.

Table: Rate ratio for nocturnal confirmed hypoglycemia, insulin degludec/insulin glargine

	Type 2 insulin-naïve IDeg N=1279 IGlar N=631	Type 2 basal-bolus IDeg N=742 IGlar N=248	Type 1 IDeg N=637 IGlar N=316
Nocturnal confirmed hypo, original definition (0:01-5:59)	0.64 [0.48, 0.86]	0.75 [0.58, 0.99]	0.83 [0.69, 1.00]
Nocturnal confirmed symptomatic hypo (0:01-5:59)	0.56 [0.39, 0.80]	0.68 [0.51, 0.91]	0.88 [0.72, 1.08]
Nocturnal ADA documented symptomatic hypo (0:01-5:59)	0.73 [0.56, 0.97]	0.72 [0.55, 0.93]	0.91 [0.74, 1.11]
Nocturnal confirmed hypo, original definition (21:59-5:59)	0.60 [0.45, 0.80]	0.73 [0.59, 0.91]	0.88 [0.76, 1.03]
Nocturnal confirmed hypo, original definition (0:01-7:59)	0.93 [0.75, 1.15]	0.77 [0.60, 0.97]	1.00 [0.86, 1.17]

• This was a post-hoc meta-analysis of six 24- or 52-week randomized, controlled, open-label phase 3a trials involving patients with type 1 and type 2 diabetes and using several definitions of nocturnal hypoglycemia. Definitions included i) confirmed symptomatic episodes; ii) symptomatic episodes with plasma glucose £70 mg/dl (the ADA definition); and iii) the original definition with a different time frame for the nocturnal period (21:59-5:59).
• Insulin-naïve patients with type 2 diabetes treated with basal-only insulin had significantly lower rates of nocturnal hypoglycemia when treated with insulin degludec (N=1279) than with insulin glargine (N=631), using those three definitions. The rate ratios and 95% confidence intervals with the three definitions listed above were i) 0.56 [0.39, 0.80]; ii) 0.73 [0.56, 0.97]; and iii) 0.60 [0.45, 0.80], compared to 0.64 [0.48, 0.86] in the original meta-analysis. A rate ratio of 1 indicates equal rates of hypoglycemia, a ratio <1 indicates a lower rate with insulin degludec, and a ratio >1 indicates a lower rate with insulin glargine.
• Patients with type 2 diabetes on basal-bolus therapy had significantly lower rates of nocturnal hypoglycemia when treated with insulin degludec (N=742) than with insulin glargine (N=248), using those three definitions. The rate ratios and confidence intervals with the three definitions were i) 0.68 [0.51, 0.91]; ii) 0.72 [0.55, 0.93]; and iii) 0.73 [0.59, 0.91], compared to 0.75 [0.58, 0.99] in the original meta-analysis.
• Patients with type 1 diabetes had numerically but not significantly lower rates of nocturnal hypoglycemia when treated with insulin degludec (N=637) than with insulin glargine (N=316), using those three definitions. The rate ratios and confidence intervals with the three definitions were i) 0.88 [0.72, 1.08]; ii) 0.91 [0.74, 1.11]; and iii) 0.88 [0.76, 1.03], compared to 0.83 [0.69, 1.00] in the original meta-analysis.
• Additional analysis using 0:01-7:59 as the nocturnal period demonstrated an advantage of insulin degludec over insulin glargine only for patients with type 2 diabetes on basal-bolus therapy. The rate ratios and confidence intervals were 0.93 [0.75, 1.15] for insulin-naïve patients with type 2 diabetes, 0.77 [0.60, 0.97] for patients with type 2 diabetes on basal-bolus therapy, and 1.00 [0.86, 1.17] for patients with type 1 diabetes.
• Additional analysis of the maintenance period only (after the initial 16-week titration period in each trial) showed that all patients with type 2 diabetes had significantly lower rates of hypoglycemia with insulin degludec than with insulin glargine using all definitions and that patients with type 1 diabetes had significantly lower rates with insulin degludec only with the original definition.

Glycemic Control and Hypoglycemia with New Insulin Glargine 300 U/mL in People with T1DM (EDITION IV) (80-LB)

PD Home, RM Bergenstal, MC Riddle, M Ziemen, M Rojeski, M Espinasse, GB Bolli

This study presented the primary results from the EDITION IV phase 3a trial of Sanofi’s new U300 insulin glargine. In the trial, patients with type 1 diabetes on background basal-bolus therapy (n=549) were randomized 1:1:1:1 to once-daily U300 or standard insulin glargine in either the morning or evening while continuing mealtime insulin. As noted in the topline results, U300 was non-inferior to standard insulin glargine in reducing A1c levels (mean change -0.40% with U300 vs. -0.44% with standard insulin glargine; baseline 8.1%) at the end of six months of treatment. Rates of any time confirmed or severe hypoglycemia (<70 mg/dl) were not different between the two groups, although U300 users had reduced nocturnal hypoglycemia in the first eight weeks of treatment (HR 0.69; 95% CI 0.53-0.91). These results are similar to EDITION III, in which benefit to nocturnal hypoglycemia was weighted to the initial weeks of treatment – while different from EDITION I and II, it remains unclear if this will represent a meaningful benefit overall. We are curious if the inclusion of morning administration was able to reduce the risk of nocturnal hypoglycemia overall. Notably, we do note that rates of nocturnal hypoglycemia from week eight to six months of treatment was not a pre-specified main secondary endpoint for this study.

• This global, multi-center, open-label study, patients with type 1 diabetes (n=549) were randomized 1:1:1:1 to either U300 insulin glargine or standard insulin glargine in either the morning or evening. Average baseline A1c was 8.1%, average BMI was 27.6 kg/m², and average diabetes duration was 21 years for both U300 (n=274) and insulin glargine (n=275) groups. Patients were followed over a six-month period. As noted across the EDITION studies, the total insulin dose at the end of the treatment period was slightly higher for U300 users (+0.19 units/kg/day from baseline) compared to standard glargine users (0.10 units/kg/day from baseline).
• U300 demonstrated non-inferior A1c reductions compared to standard insulin glargine (mean change -0.40% with U300 vs. -0.44% with standard insulin glargine; baseline 8.1%). There were no significant differences between the morning and evening groups.
•  Rates of any time confirmed or severe hypoglycemia (<70 mg/dl) were not different between the two groups, although U300 users had reduced nocturnal hypoglycemia in the first eight weeks of treatment (HR 0.69; 95% CI 0.53-0.91). Rates of hypoglycemia were equal between the morning and evening groups. Overall, severe hypoglycemia was seen in 6.6% of the U300 users versus 9.5% of the standard glargine users.
• Notably, similar to EDITION II, patients taking U300 gained significantly less weight versus standard glargine users (difference -0.56 kg [1.2 lbs]; p=0.037). U300 users gained an average of 0.5 kg (1.1 lbs), while insulin glargine users gained an average of 1.0 kg (2.2 lbs).

New Insulin Glargine 300 U/mL: Efficacy and Safety of Adaptable vs. Fixed Dosing Intervals in People with T2DM (919-P)

MC Riddle, GB Bolli, PD Home, R Bergenstal, M Ziemen, I Muehlen-Bartmer, M Wardecki, L Vinet, H Yki-Jarvinen

Dr. Matthew Riddle and colleagues conducted two sub-studies of Sanofi’s insulin glargine (Lantus) 300 U/ml comparing the effects of fixed dosing (FD) vs. adaptable dosing (AD) in 198 type 2 patients. The sub-studies were part of two larger, six-month open label studies comparing glargine 300 U/ml to glargine 100 U/ml: EDITION 1 (basal insulin plus mealtime insulin; n=53 for FD and n=56 for AD) and EDITION 2 (basal insulin plus oral anti-diabetic medications; n=44 for FD and n=45 for AD). To generate the sub-studies, participants who completed the glargine 300 U/ml on-treatment during the main trials were re-randomized to either FD (injections at 24 hour intervals) or AD (injections at 24 ± 3 hr intervals; participants were asked to use an injection interval of exactly 21 hours or 27 hours at least twice a week).  Endpoint measurements were taken at month nine (three months after the re-randomization) and the intent-to-treat analysis included data from 194 patients. In both sub-studies, the primary endpoint – change in A1c – was comparable between the FD and AD groups. Similarly, the FD and AD groups had comparable rates of adverse events, overall hypoglycemia, and nocturnal hypoglycemia. Based on this data, the authors conclude that type 2 patients who occasionally adapted the timing of their glargine 300 U/ml injections did not compromise the safety or efficacy of the drug. 

• Baseline characteristics were comparable between the FD and AD groups within each sub-study, and between the two sub-studies: average age of 57-61 years, A1c of 7.2-7.5%, and percent male of 43-50%.
• Variability in the timing of injections between the FD and AD groups were measured by recording the time between two consecutive injections during the last seven days before the two endpoint assessments, which occurred one-and-a half and three months following the re-randomization. In the FD group, a low percentage of patients administered injections outside of a 24 ± 1 hr interval (13% for the EDITION 1 sub-study and 11% for the EDITION 2 sub-study). As would be expected, larger percentages were recorded for the AD group (37% and 48%, respectively). Notably, a fraction of patients in the AD group injected their insulin more than three hours above or below the standard 24-hour interval (14% for EDITION 1 and 19% for EDITION 2).
• In both sub-studies, the FD and AD groups experienced similar changes in A1c, fasting plasma glucose, and eight-point SMPG profiles after three months (see table below; note: A1c increased slightly in EDITION 1 sub-study). Furthermore, the participants made only small changes to their mean daily basal insulin dose, and these changes were similar across the FD and AD groups in both sub-studies.

	EDITION 1		EDITION 2
	FD	AD	FD	AD
Change in A1c
Baseline A1c	7.21%	7.17%	7.41%	7.47%
Change	0.21%	0.15%	-0.12%	-0.25%
Mean difference between groups	0.05%		0.13%
Change in Fasting Plasma Glucose
Baseline FPG	132 mg/dl	121 mg/dl	128 mg/dl	129 mg/dl
Change	26 mg/dl	21 mg/dl	-8 mg/dl	-5 mg/dl
Mean difference between groups	4.9 mg/dl		-3.8 mg/dl

• In both sub-studies, the FD and AD groups had similar rates of overall and nocturnal (midnight to 6 am) hypoglycemia (see table below). Only one event of severe hypoglycemia was reported in the sub-studies (in the FD group of Edition 1).

	EDITION 1		EDITION 2
	FD	AD	FD	AD
Percent experiencing any hypoglycemia*	65%	57%	42%	37%
Percent experiencing nocturnal hypoglycemia*	24%	25%	23%	17%

^* numbers estimated from graph

Basal Insulin Peglispro Demonstrates Preferential Hepatic vs. Peripheral Action Relative to Insulin Glargine in Healthy Subjects (886-P)

RR Henry, S Mudaliar, SL Choi, TP Ciaraldi, DA Armstrong, J Pettus, P Garhyan, MP Knadler, SJ Jacober, ECQ Lam, H Linnebjerg, N Porksen, MJ Prince, and VP Sinha

Dr. Robert Henry et al. conducted a single-center, randomized, open-label trial comparing the sites of action of Lilly’s basal insulin peglispro (referred to as LY2605541) vs. Sanofi’s insulin glargine (Lantus) in eight healthy male participants (mean baseline age of 26 years and BMI of 24 kg/m²; all had fasting plasma glucose <108 mg/dl ). The study measured the drugs’ abilities to suppress endogenous glucose production (EGP, which reflects their actions on the liver), as well as their abilities to stimulate the glucose disposal rate (GDR, which reflects their actions outside the liver – i.e., peripheral action). The participants underwent four eight-hour euglycemic clamp studies (maintained at 90 mg/dl): the first three with primed, continuous infusions of LY2605541 (five doses ranging from 5.1 to 74.1 mU/min), and the fourth with insulin glargine (either 20 or 30 mU/m²/min). The investigators used D-[3-³H]-glucose infusion to assess EGP and GDR. Suppression of EGP and stimulation of GDR were observed with increasing concentrations of both insulins. Notably, the LY2605541 dose needed for 100% EGP suppression had little effect on GDR. In contrast, the glargine dose required for a comparable suppression of EGP led to an increase in GDR. These results indicate that in healthy males, LY2605541 exhibits greater hepato-preferential action compared to insulin glargine.

Insulin Therapy: Exploring Provider Perspectives on Needle Phobia and Nonadherence (685-P)

J Krall, K Williams, R Gabbay, L Siminerio

In a BD-supported study, 23 primary care providers and three pharmacists were interviewed to assess their familiarity with and use of smaller and shorter needles as a solution for addressing problems with insulin therapy adherence. A full 70% of the physicians (n=16) reported that needle phobia is the primary challenge to initiating insulin therapy, and a striking 87% of physicians (n=20) stated that the availability of smaller needles would be an important factor in persuading patients to start injections. However, few physicians were familiar with the smallest needle available (BD’s 32-gauge, 4 mm Nano needle) or the fact that shorter needles can be used in any patient regardless of weight. Only 39% (n=9) reported prescribing a specific needle – most physicians instead deferred to default options in prescribing systems or assumed that a pharmacist would choose the best needle. To add insult to injury, the pharmacists in the survey reported referring decisions to PCPs. The authors argue, and we agree, that provider education will need to be revisited in order to increase awareness of these options and ultimately improve patient adherence to insulin therapy.

Symposium: Closed-Loop Insulin Delivery – One Step at a Time (Supported by a grant from The Leona M. and Harry B. Helmsley Charitable Trust)

Do We Need More Rapid Acting Insulins?

David Klonoff, MD (UCSF, San Francisco, CA)

Dr. David Klonoff systematically answered the question posed in his talk title with a resounding “yes,” organizing his thoughts by answering six questions (see below). He showed physiologic, pharmacologic, and clinical data, along with “common sense” to support the benefits of more ultra-rapid-acting insulins: less postprandial hyperglycemia, less late postprandial hypoglycemia, possibly less weight gain, and less glycemic variability. Most importantly, he emphasized that ultra-rapid-acting insulins may not necessarily demonstrate lower A1c levels, something we’ve now seen with MannKind’s Afrezza, Biodel’s BIOD-123, and Halozyme’s Hylenex. We were ecstatic to hear Dr. Klonoff call for study endpoints that look beyond A1c, something that is not currently incorporated into the FDA’s 2008 guidance on developing diabetes drugs.

• Dr. Klonoff on the FDA’s 2008 Guidance for Industry on Diabetes Drug Development: “For the purposes of drug approval and labeling, the final demonstration of efficacy should be based on reduction in A1c. If a drug can improve outcomes other than A1c, then this result would be like trying to fit a square peg in a round hole. Ultra-rapid-acting insulin cause less postprandial hypoglycemia, less postprandial hyperglycemia, and less glycemic variability, but the FDA uses A1c as the only valid endpoint for a diabetes drug.” Dr. Klonoff wondered, “If A1c is the only outcome measure for an insulin, should we avoid a faster acting insulin and stick with a slower acting human regular insulin?” Dr. Klonoff cited Dr. Doug Muchmore’s 2011 JDST article that called for “a composite endpoint that integrates A1c and hypoglycemia risk.” Said Dr. Klonoff, “It’s not just about the A1c… For you pharma companies, if your only endpoint is A1c, you’re going to have a problem. Have some other endpoints.”
• How does rapid-acting insulin affect mean glycemia and glycemic targets? Dr. Klonoff summarized the answer to this nuanced question quite concisely: rapid-acting insulin has little effect on mean glycemia and a small benefit on postprandial hypoglycemia. He cited some negative reviews comparing rapid-acting insulin to regular human insulin – a Cochrane collaboration review and Germany’s IQWiG both didn’t see a meaningful clinical benefit of using rapid-acting insulin. Still, Dr. Klonoff cited an observational study from the University of Colorado, which tracked A1c and severe hypoglycemia prior to and following the DCCT. While A1c improved during the DCCT, the rate of severe hypoglycemia correspondingly increased. Following the introduction of Humalog in 1996, A1c continued to improve, though there was no added increase in severe hypoglycemia.
• How does more rapid-acting insulin affect glucose in closed loop systems? Dr. Klonoff highlighted the 2008 hybrid closed-loop work of Dr. Stu Weinzimer (Diabetes Care), which used pre-meal boluses to cover 25-50% of the meal – pre-meal boluses led to a lower mean glucose, lower postprandial glucose, and no increase in hypoglycemia. Dr. Klonoff also mentioned the closed-loop work with MannKind’s Afrezza at UCSB/Sansum – post-meal time in zone was significantly better with use of Afrezza prior to meals. 
• How can insulin be made to act more rapidly? Dr. Klonoff mentioned the following methods and insulins in development: Novo Nordisk’s FIAsp (currently in phase 3; strong PK/PD data was presented on Day #2 of this meeting); Thermalin’s Fluorolog (ultra-rapid U500); Biodel’s portfolio of candidates (phase 2 BIOD-123 data presented on Day #2); MannKind’s Afrezza; a dissolving microneedle patch (no specific company mentioned); Halozyme’s hyaluronidase; Insuline’s InsuPad (“a nice product” that heats the skin and is being used successfully in Germany); and Roche’s intraperitoneal DiaPort (“not making much progress at this point”).
• How rapidly is insulin released in response to a meal in healthy people? Based on data from as early as 1968 (Curry et al., Endocrinology), healthy people release insulin within a few minutes before/after eating. Dr. Klonoff highlighted the difference between first-phase and second-phase insulin release, noting that even a low dose of early insulin can make a big difference in postprandial glycemia. In people without diabetes, insulin is released when food hits the mouth, far before the glucose hits the blood. Though the cephalic phase of insulin release only represents 1-3% of meal insulin, without it, “control is poor.”

Questions and Answers

Dr. Howard Wolpert (Joslin Diabetes Center, Boston, MA): We should also talk about approval by insurers – they are all fixated on A1c. So we need to get them looking at postprandial glucose as well. Should we suggest some kind of standardized testing protocol?

A: We need new tests and new metrics. The doctors who understand insulin therapy need to work with the FDA to develop additional endpoints. If A1c is the only metric that is considered, then fewer highs and fewer lows is not going to be appreciated.

Symposium: The Need for a Better Basal – What’s on the Horizon

New Basal Insulins in Development

Chantal Mathieu, MD, PhD (KU Leuven, Leuven, Belgium)

Dr. Chantal Mathieu, MD, PhD (KU Leuven, Leuven, Belgium) presented an overview of upcoming basal insulins, focusing on insulin degludec (Tresiba, Novo Nordisk) and Lilly’s peglispro (LY2605541). Dr. Mathieu reviewed data from insulin degludec phase 3 clinical trials and peglispro phase 2 clinical trials. Two themes emerged. First, degludec and peglispro have both demonstrated non-inferiority to insulin glargine (Lantus, Sanofi), in both type 1 and type 2 diabetes patients. Second, both degludec and peglispro tended to reduce nocturnal hypoglycemia compared to glargine in both type 1 and type 2 diabetes patients. Degludec and peglispro also have flatter pharmacodynamic and pharmacokinetic profiles than insulin glargine as a result of longer durations of action. Dr. Mathieu also touched on Sanofi’s U300 triply concentrated insulin glargine, noting that simply increasing the concentration gives a flatter profile and longer duration of action with noninferior A1c reductions. Novel administration procedures and smart insulins (glucose-responsive insulins) were also briefly mentioned.

• According to Dr. Mathieu, current basal insulin therapy has limited flexibility and duration of efficacy. Effects are highly variable between patients and within patients. Current insulins must be administered at the same time every day. They also have problems being mixed with other products and do not always last 24 hours, which she said is the optimal time period from a practical point of view.
• For both type 1 and type 2 diabetes patients, insulin degludec is non-inferior to insulin glargine in reducing A1c, and has a longer half-life, flatter pharmacodynamic and pharmacokinetic profiles, and reduced nocturnal hypoglycemia. In a recent meta-analysis, type 2 diabetes patients had a 32% reduction in nocturnal hypoglycemia risk and type 1 diabetes patients had a 17% reduction in nocturnal hypoglycemia risk when they used insulin degludec, compared to insulin glargine.
  
  • To test dosing flexibility, Dr. Mathieu and Novo Nordisk came up with an “arbitrary design” that alternated dosing times between 40 hours and 8 hours for an entire week. The flexible degludec dosing arm was compared with a fixed dosing degludec arm and insulin glargine arm. The erratic dosing schedule had no deleterious effect: flexible insulin degludec still showed noninferiority versus glargine in terms of efficacy, and still was associated with lower rates of nocturnal hypoglycemia. Similar results were seen in type 1 diabetes patients, with one key difference: flexible dosing was actually associated with 27% lower rates of nocturnal hypoglycemia compared to fixed dosing (p=0.000). Dr. Mathieu addressed this strange finding during the question and answer session (see below).
• Insulin peglispro (LY2605541) phase 2 trials demonstrated more mixed results compared to insulin glargine. Peglispro was noninferior to glargine in terms of A1c change in type 2 diabetes patients (n=176), and was associated with significantly less nocturnal hypoglycemia. Additionally, peglispro users lost weight while insulin glargine users gained weight, likely due to peglispro’s preference for targeting the liver (which has its problems, as acknowledged by Dr. Mathieu). Peglispro actually demonstrated a superior A1c reduction (-0.6%) over insulin glargine (-0.4%) in type 1 diabetes patients (p<0.001), as well as less nocturnal hypoglycemia; however, incidence of overall hypoglycemia was slightly higher with peglispro vs. glargine.
• Basal insulins in the pipeline are being designed with reduced dosing, less frequent administration, and glucose responsiveness in mind.  For example, Sanofi is developing a triply concentrated insulin, degludec holds promise for thrice weekly dosing, Hanmi insulin could potentially be administered once a week, and smart insulins may be able to offer glucose responsive activity.

Questions and Answers

Q: You showed CGM data on peglispro but failed to show data on insulin degludec.

A: The people from Novo Nordisk say that at the time when the studies were done, the technology didn’t allow them to come to clear conclusions and that data were all over the place. That’s what they told me. That’s all I can tell you.

Q: In your study on type 1 patients, I didn’t see any difference between the fixed degludec arm and the glargine arm in terms of nocturnal hypoglycemia. Can you elaborate on that?

A: That study is the only study where there was no advantage of nocturnal hypoglycemia for degludec. What I think happened is that we had a surplus of nocturnal hypoglycemia in the first four weeks because we switched people on twice-a-day detemir, for instance, to dose for dose for degludec. We probably overdosed the degludec in the beginning. The doses for degludec even came down in the first weeks of titration, whereas the glargine doses went up.

Q: Do we really need very long-acting insulins? Many patients have different requirements for insulin during the night and daytime. Is 24 hours really a good time?

A: Yeah, I think 24 hours is a good time. Longer than 24 hours, I have the same doubts.

Q: In the US, pharmacies are changing the basal insulins very frequently. So I think that when people are changing from one insulin to another, there needs to be an established ratio to determine dosing.

A: Substituting one analog for another is something that needs to be undertaken by a healthcare provider.

What Should Be Required for Regulatory Approval of an Insulin?

Eric Brass, MD, PhD (UCLA David Geffen School of Medicine, Los Angeles, CA)

Dr. Eric Brass offered his thoughts on the regulatory process for new basal insulins, reminding the audience that “we have a step between research and reaching patients.” After a brief review of the current FDA guidance requiring new diabetes biologics to meet specific efficacy criteria in high-quality clinical trials, he used the saga of Novo Nordisk’s ultra-long acting insulin degludec (see our report here) to illustrate many of the challenges facing companies that want to develop a new basal insulin product. He explained that although it is fairly straightforward to demonstrate non-inferior glycemic control, the uncertainty about how the FDA will weigh the risk/benefit of effects beyond A1c (hypoglycemia, weight, administration and dosing, etc.) makes the approval process very unpredictable and challenging. He noted that benefits that would be significant for many patients, such as convenient administration and reduction in non-severe hypoglycemia, are either not well captured by typical clinical trials or are not considered important by the FDA. The FDA’s new emphasis on cardiovascular risk has presented a major obstacle for the development of new diabetes drugs; insulin degludec received a complete response letter primarily because the phase 3 data did not sufficiently exclude the possibility of an unacceptable cardiovascular risk. Dr. Brass explained why a typical phase 3 trial is not designed to meet the FDA’s expectations for excluding unacceptable risk, leading to the necessity of conducting specific cardiovascular outcomes trials. He concluded by saying that a new consensus is needed on what constitutes an important clinical benefit, that pragmatic, real-world trials may be a more effective way to demonstrate those benefits than a typical controlled trial, and that the challenges in defining the benefits and safety of new insulin products “may be stifling innovation. I think it already has.” Dr. Brass’ suggestions would surely provide greater clarity to drug developers on regulatory expectations and, thus, improve the efficiency of the development process.

• Dr. Brass explained that meeting the FDA’s primary endpoint for trials of a new basal insulin – effectiveness in lowering A1c – is conceptually straightforward. He hastened to add that “straightforward doesn’t mean easy,” as the FDA requires large, highly controlled clinical trials that involve a full spectrum of patients with type 1 and type 2 diabetes. The phase 3 trials for Novo Nordisk’s Tresiba (insulin degludec), for example, involved over 6,800 patients with type 2 diabetes and over 2,100 with type 1; they included both insulin-naïve and previously insulin treated patients; and they used almost all of the available drug classes as comparators.
• In Dr. Brass’ opinion, it is far more complicated to demonstrate that a new basal insulin has clinical benefits over existing therapies. First of all, it is very difficult to demonstrate superior glycemic control in an intensely monitored clinical trial in which all participants are titrated to goal. So-called “convenience benefits” of dosing and administration, though very important to patients and providers, are also masked in clinical trials, where adherence is strictly enforced. Measuring reduction in hypoglycemia, another extremely important issue for patients, is complicated by various factors, including a lack of clarity from the FDA on what constitutes clinically important hypoglycemia (for example, they did not consider degludec’s nocturnal hypoglycemia reduction claim to be clinically significant) and the fact that most hypoglycemia in type 1 diabetes is caused by the short-acting bolus insulin rather than the basal.
• Dr. Brass discussed the FDA’s new cardiovascular safety requirements for diabetes drugs at length, explaining that a typical phase 3 trial is not sufficiently powered to exclude cardiovascular risk. In the case of insulin degludec, the point estimate of the hazard ratio for cardiovascular events in a meta-analysis of all phase 3 trials was 1.39 (below the FDA’s pre-approval threshold of 1.8), but the upper bound of the confidence interval was 2.565, so the FDA couldn’t exclude the possibility of a high increase in risk. The confidence interval was so wide because there were so few total cardiovascular events (39 with degludec and 15 with the comparator), so Novo Nordisk is now required to perform a cardiovascular outcomes trial that is specifically designed to evaluate these risks.
• Because of the huge amount of uncertainty about how the FDA views the benefits and risks of new basal insulins, Dr. Brass concluded that drug developers may need to develop new clinical trial frameworks. He emphasized the need for consensus on the important clinical benefits of new basal insulins and suggested that in order to most effectively measure them, “maybe we shouldn’t be doing well controlled clinical trials.” As an alternative, he suggested “pragmatic trials” that more closely approximate daily life for patients and providers. He also said that the FDA’s expectations about excluding unacceptable cardiovascular risk will require programs specifically designed to ensure that enough events take place to make meaningful inferences. 

Questions and Answers

Q: What would the definition of a hypoglycemia benefit be for a new basal insulin?

A: The FDA has said nothing about anything other than severe hypoglycemia, and their comments at the Advisory Committee meeting on insulin degludec suggested that the only hypoglycemia they accept as important is severe. That doesn’t mean they don’t have something else in their own thinking, but in terms of rejecting new programs, severe hypoglycemia is the only thing they’ve suggested they’ll accept as clinically meaningful. They didn’t accept confirmed nocturnal hypoglycemia as clinically important with insulin degludec but they would have accepted severe. Where else along that spectrum they would accept, we just don’t know.

Q: What did the FDA ask Novo Nordisk to do to show clinically significant benefit?

A: Nothing publicly. The decision is always benefit vs. risk. In this case, there was no perceived differentiating benefit, and uncertainty about risk, so they said Novo Nordisk needed to remove the uncertainty about risk. If we could establish unique benefits, the uncertainty about risk would become less of a problem, but it doesn’t seem like they accepted any benefits.

Q: I think perhaps it’s time for the FDA to come up to 2014 standards. Using CGM glycemic variability would make much more sense as a measure of glycemic control. What are your thoughts on that?

A: These are complex things. What the FDA and I would insist on is that anything used to establish a benefit must be of unambiguous clinical importance—it can’t just look nice. Other aspects for glycemic control improvement beyond A1c I think have yet to reach unambiguity that translates to clinical meaning. Where I see immediate opportunity is the development of pragmatic trials that translate to how drugs are used to establish superior glycemic control and open-mindedness about what’s important for patients and clinicians.

Biosimilars – Opportunities and Challenges

Marcus Hompesch, MD (Profil Institute for Clinical Research, Chula Vista, CA)

Dr. Marcus Hompesch provided a concise, well-organized overview of the challenges facing companies that hope to enter the biosimilar insulin market in the near future. He anticipates that many companies will want to pursue these products, as the demand for insulin is skyrocketing and several branded insulins are facing a patent cliff. He warned, however, that beginning a biosimilar insulin program requires high “activation energy” – a large initial investment without certainty of approval. He also discussed the challenges of manufacturing such a complex molecule, reminding the audience that making a biosimilar is not a “copy and paste” process and that subtle structural differences can affect the product’s efficacy and safety. Dr. Hompesch also pointed to regulatory uncertainty as one of the major challenges in this area, as guidelines for biosimilar insulins vary from country to country and are nonexistent in some. The “most important rule” in his mind is that it is the applicant’s responsibility to justify the product to regulators at every step of the process. “So,” he warned, “good luck.” Despite these obstacles, Dr. Hompesch ended on a fairly optimistic note, listing the many companies with biosimilar insulin programs and expressing hope that these products could reduce costs for patients and improve efficiency in the health care system.

• Dr. Hompesch believes the biosimilar insulin market offers great opportunities for companies and that this will be a “crowded space” in the coming years. As we are all too aware, the demand for insulin is exploding as the diabetes pandemic grows, and several market leaders are facing the end of their patent protection (Humalog last year, Lantus in 2015). The combination of these forces is likely to lead many pharmaceutical companies to pursue biosimilars in the near future, he said. Dr. Hompesch also expressed hope that this competition will drive innovation and lead to cost savings for consumers, though he said it was too early to draw definitive conclusions.
• Despite this promise, manufacturing a safe and effective biosimilar insulin is a very challenging, complex process. Using the fairly simple manufacturing process for small-molecule generics as a comparison, Dr. Hompesch warned the audience, “sorry, it ain’t that easy.” Insulin is a large, complex molecule, and any subtle difference in its structure could potentially have an impact on its clinical profile. Manufacturing insulin is a multi-step process involving living organisms (usually E. coli or yeast), so there are many opportunities for something to go awry. Additionally, all biological products are potentially immunogenic, so clinical studies are necessary to ensure that there is no excessive risk of a dangerous immune reaction.
• In addition to the manufacturing challenges, the regulatory guidelines for biosimilar insulins are “complex, inconsistent, and incomplete.” Dr. Hompesch used the EMA’s guidelines for biosimilar insulins as a reference but emphasized that there is no clear global standard and that requirements vary from country to country; for its part, the FDA has four guidance documents about biosimilars, though none specific to insulin. His main advice for companies hoping to bring a biosimilar insulin to market was to proceed in a stepwise fashion, as BI/Lilly has with their biosimilar glargine. Flipping to a picture of Presidents Obama and Putin scowling at each other, he stressed that it would be important to better align regulators from different countries in the future, but “I didn’t say it would be easy.”

Building a Better Basal – The Case for Concentrated Insulin

Wendy Lane, MD (Mountain Diabetes and Endocrine Center, Asheville, NC)

In her fast-paced review of concentrated insulins, Dr. Wendy Lane highlighted that these drugs offer several benefits for patients: a flatter action profile, longer duration of action, lower risk of hypoglycemia, smaller injection volume, less discomfort, and a higher dose per injection. Dr. Lane first considered Eli Lilly’s U500, currently the only FDA-approved concentrated insulin in the USA. In comparing it to Sanofi’s U300 insulin glargine (which is not bioequivalent to Sanofi’s U100 glargine [Lantus]), she noted that U300 glargine has a flatter profile, longer duration of action, and less nocturnal hypoglycemia. These advantages allow for the possibility of intensifying therapy with the drug. Dr. Lane also briefly mentioned two other insulins: 1) Novo Nordisk’s U200 insulin degludec (Tresiba), which is bioequivalent to U100 degludec and is more convenient for some patients; and 2) Znsulin (Thermalin), a U500 insulin with a 48 hour action profile.

• Dr. Lane noted that as people become more obese, they require larger insulin doses. She relayed that 2.1 billion people globally are currently overweight or obese, and that the US is a leader in this category. In recent diabetes studies, 35% of participants required ≥60U per day and 21% required ≥80U per day. Illustrating an extreme example, Dr. Lane showed a picture of a patient who requires 500-600 units of insulin per day. She described how current syringes and pens only deliver 100U and 60-80U in a single injection, respectively. Thus, concentrated insulin has the potential to significantly lower the injection burden for patients with high insulin requirements – i.e., obese type 2 patients with severe insulin resistance, as well as patients that are post-op or on glucocorticoid therapy, or patients with systemic infections, genetic defects in insulin action, or rare forms of immune mediated diabetes.
• Concentrated insulins offer several advantages: greater control and predictability, more comfort, increased patient adherence, and prolonged action. The ideal concentrated insulin would have a duration of ≥24 hours, single low volume dosing ideally using a pen, a flat and stable PK profile, minimal risk of hypoglycemia, low variability, and low intrinsic mitogenicity.
• U500 insulin (Eli Lilly) is the only approved concentrated insulin in the US. Its high concentration alters the pharmacokinetics to be similar to that of intermediate acting insulin, and the drug is only available in a vial and syringe. It has a peak action that tapers off in roughly 12 hours, and it works best if given two to three times a day. Patients report improved quality of life compared with U100, primarily because the smaller injection volume results in less pain, less leakage, and fewer injections. While U500 is often used as monotherapy, it can also be used with rapid acting insulin, or off label in a pump, where it performs well (though Dr. Lane noted that it has a long tail and it stacks). She also remarked that U500 has a tendency to confuse pharmacists.
• Sanofi is developing a new U300 insulin glargine, which has a flatter profile compared to U100 insulin glargine (Lantus) and thus is not equivalent in bioavailability. Importantly, the U300 insulin is associated with less nocturnal hypoglycemia. The longer duration and flatter profile gives the potential for intensifying treatment without additional hypoglycemia. The EDITION I trial (an open label 12 month study of U100 vs. U300 insulin glargine) demonstrated an equivalent A1c reduction in both groups, with a 10% higher dose for U300 patients and less nocturnal hypoglycemia associated with U300.
• Novo Nordisk’s U200 insulin degludec (Tresiba) has a smooth, flat action profile that lasts more than 24 hours. U200 insulin degludec is bioequivalent to U100 (i.e., same pharmacokinetics/pharmacodynamics),  and provided the same A1c reduction in clinical trials.
• Znsulin (Thermalin) is a U500 insulin in development. It has an expected 48 hour profile due to the novel placement of Zn ions, which lead to “zinc stapled arrays” of hexamers.

Symposium: Hypoglycemia and Cardiovascular Disease – Lessons from Outcome Studies (Supported by an unrestricted educational grant from Merck)

Evidence from ORIGIN

Lars Ryden, MD, PhD (Karolinska Institute, Stockholm, Sweden)

Dr. Lars Ryden discussed the ORIGIN trial with a focus on the relationship of cardiovascular (CV) disease with hypoglycemia (symptoms confirmed by a glucose reading of ≤54 mg/dl) and severe hypoglycemia (symptoms requiring assistance, plus documented glucose ≤36 mg/dl and/or prompt recovery with oral carbohydrate, intravenous glucose, or glucagon).  Hypoglycemia in ORIGIN was associated with increased risk of all-cause mortality and CV mortality (HR ~1.20, p<0.05 for both), but those relationships disappeared after controlling for known hypoglycemic risk factors (e.g., sulfonylurea treatment). By comparison, severe hypoglycemia was linked with the trial’s MACE composite endpoint, all-cause mortality, CV mortality, and arrhythmic death (HR=1.8-2.1, p<0.001 for each); even after adjusting for known risk factors of hypoglycemia, severe hypoglycemia was linked to roughly 50% higher CV risk. The glargine group had higher prevalence of hypoglycemia (42% vs. 14%) and severe hypoglycemia (6% vs. 2%) compared to the standard-care group. However, in the standard-care group, severe hypoglycemia was associated with roughly double the risk of major CV events, all-cause mortality, CV mortality, and arrhythmic death relative to the glargine group. Dr. Ryden therefore thinks that severe hypoglycemia caused by glargine was unlikely to be a direct “accelerator” of cardiovascular events. Rather, severe hypoglycemia may simply be a marker of risk, as has been suggested by studies of hospitalized patients (e.g., Kosiborod et al., JAMA 2009; Malmberg et al., Eur Heart J 2005).

• As a reminder, ORIGIN’s main finding was that intensive glargine therapy vs. standard care led to similar rates of major adverse CV events [MACE] in people with type 2 diabetes or prediabetes who were at high risk of CV events. See our full coverage of ORIGIN’s primary results at http://www.closeconcerns.com/knowledgebase/r/10be2669.
• The risk of both non-severe and severe hypoglycemia was predicted by use of glargine and/or sulfonylureas. Dr. Ryden noted that sulfonylureas may be especially harmful from a cardiovascular perspective, because they seem to impair myocardial adaptation to ischemia. Predictors of non-severe hypoglycemia included younger age, lower BMI, depression, and baseline diabetes. Predictors of severe hypoglycemia included older age, limited education, hypertension, renal disease, cognitive decline, and lower attained A1c.  

Questions and Answers

Dr. John Yudkin (University College London, London, UK): We are gradually losing focus on what we should be trying to do. The rate of myocardial infarction (MI) in ORIGIN was 0.9 per 100 patient-years, meaning that 4.5% of patients will have an MI over 5 years. It was 0.93 per 100 patient-years in the intensive group. The hypoglycemia rate went up from 0.3 to 1.0 per 100 patient years. So in the standard-treatment group, the ratio of MI to severe hypos was 3:1; with intensive treatment the ratio was 1:1.1, meaning that more people were in the hospital with hypoglycemia than with MI. How do you provide informed consent to a patient when you suggest a treatment that may reduce their CV event rate, but will triple their risk of hospitalization for hypoglycemia?

A: The study that I presented today does not allow us to answer that question. It depends on the specific patient. The risk for CV event seems higher with some therapies than others, but it depends on the character of the patient. You can’t take data from this study to another population. These patients were relatively early in diabetes and with relatively high CV risk.

Dr. David Kendall (Lilly Diabetes): These data affirm that frequent mild-to-moderate hypoglycemia appears not only not-harmful, but may even mitigate the risk of CV events. In ACCORD, deaths tended to be in those patients with less frequent hypoglycemic events. A similar pattern has been seen in other studies. Do you have any thoughts on what may be happening to heart’s electrical system with frequent mild-to-moderate hypoglycemic events?

A: Repeated minor hypoglycemic episodes may make the heart less vulnerable to attack. More-frequent hypoglycemia may train patients’ bodies to respond to severe hypoglycemia. Still, repeated hypoglycemic episodes are not something that you should try to get, because you may unintentionally cause severe hypoglycemia.

Symposium: Can We Limit the Long-term Decline of Beta Cells in Type 2 Diabetes?

Insulin Therapy

Ananda Basu, MD (Mayo Clinic, Rochester, MN)

In this review of insulin therapy’s effects on beta cell function in type 2 diabetes, Dr. Ananda Basu focused on clinical data, from modeling studies by Dr. Claudio Cobelli to the recent multi-center ORIGIN trial. He concluded with a call for more research on the durability of effect with early, tight intensive insulin therapy; non-invasive tests that will allow researchers to correlate beta cell function and mass; and the role of the beta cell in the legacy effect (the delayed, beneficial effect of glucose control on complications risk, which was observed in UKPDS).   

• Dr. Basu explained that the insulin response to a glucose challenge is biphasic whether the glucose is given orally or intravenously, but with oral glucose the response is more reproducible and robust. Both phases of the postprandial insulin response become smaller as people move from prediabetes to type 2 diabetes (i.e., postprandial and basal insulin levels are closer together as diabetes progresses). However, intensive therapy with insulin early in the course of type 2 diabetes may preserve postprandial insulin response and glucose control, as has been suggested in a yearlong, uncontrolled proof-of-concept trial in 16 patients (Ryan et al., Diabetes Care 2004) and a larger, two-year randomized controlled comparison to metformin/sulfonylurea in China (Lancet 2008).
• Dr. Basu also mentioned the ORIGIN trial, in which insulin glargine therapy decreased the risk of progression from prediabetes to diabetes, assessed by oral glucose tolerance test (OGTT) at a median of 100 days after discontinuation of therapy (HR=0.80, p=0.05). Glargine-treated patients were also more likely to have mean A1c below 6.5% for the duration of the trial (up to seven years).

Symposium: Prevention of Hypoglycemia

Insulin Regimens to Reduce the Risk of Hypoglycemia

Niyaz Gosmanov, MD (Oklahoma City Veterans Affairs Medical Center, Oklahoma City, OK)

In this very broad overview, Dr. Niyaz Gosmanov touched on various factors affecting the choice of an insulin regimen. In particular, he stressed the need for insulin therapy to minimize the risk of hypoglycemia, emphasizing that A1c is not the only target. According to Dr. Gosmanov, an insulin regimen is a treatment package that replicates physiological insulin effects as closely as possible. A regimen can range from one shot per day to full replacement of all types of insulin, depending on the endogenous insulin production of the patient (which should not be overlooked as often as it is, in Dr. Gosmanov’s opinion). Insulin can be basal (for elevated fasting glucose levels) or bolus (for elevated post-prandial levels); it can also be human or analog. Critically, issues such as cost and flexibility are just as important for the patient. Unfortunately, the two may have an inverse relationship, as seen with the inflexible but cheaper premixed insulins. Another problem highlighted by Dr. Gosmanov is the complexity posed by insulin regimens, as it can be difficult for patients to keep track of multiple shots per day. He suggested the website www.accurateinsulin.org, an interactive tool that helps determine when and how to adjust insulin doses. Finally, Dr. Gosmanov remarked that the best therapy is the one that the patient is likely to take confidently and his provider prescribes confidently and comfortably.

Questions and Answers

Q: In the 4T study, basically the basal-bolus regimen was found to cause less hypoglycemia than twice daily mixtures. What is your opinion on mixtures versus basal-bolus?

A: Today we put too much on mixed insulins. We’re desperate and using them as a last resort. The evening dose has to be given too early. Patients lose flexibility.

Q: We all agree that standard diabetes care is quite effective and safe. I also feel that it’s quite illogical. For example, using a single number from a finger stick to correct high blood sugar seems illogical and dangerous. I think we should try to prevent large increases in the first place. The pancreas monitors change and prevents an abnormal glucose rise. The reason there’s so much hypoglycemia is that the standard approach in how we teach people to use tools is wrong.

A: I completely agree. A finger stick value comes from just one second out of the day. Type 2 patients have more reliability and predictability compared to type 1, but in an ideal world I would use CGM on everyone. But unfortunately I can’t do that.

Q: I was hoping your talk would be a little more specific on insulin regimens to either avoid or pursue. I want to throw out some errors. First, I see a lot of patients who come from primary care who are over-basalized. They need prandial insulin but they get basal. I think that places them at higher risk for hypoglycemia especially if they sleep in. Another is taking premixed insulin at bedtime, which has to be an error. Third, NPH can be a good basal at bedtime, but at dinner it increases the risk of low blood sugar. Fourth, primary care doctors increase Lantus based solely on A1c levels. Any objections that these are erroneous strategies?

A: Nope. It’s all about education to the patient as well as the provider. Those points were all relevant. I initially had slides related to these, but my colleagues suggested they were too simple. Maybe I’ll include them next time.

Q: I have two questions. First, even though we know that premixed insulin has limits, it is the most prescribed insulin across the world. How can you best use premixed insulin? From the point of diet, how can you best split meals to best suit premixed insulin? Secondly, 10 to 20 years down the lane, type 2 patients behave just like type 1 because all insulin runs out. I think the insulin pump definitely has a role to play. So what type of type 2 patients can benefit from a pump?

A: For the first question, I would refer to the website in the presentation. In the developing world, people try to get away with cheaper versions of insulin. Any time you save on costs, you lose something else, usually flexibility. So the patient needs to eat at least two meals. I would recommend injection post-morning and in the late afternoon. As for how to titrate the insulin, I think the website would give more information. For the second question, we have criteria we use for pump selection in type 1 patients. We can apply some of those criteria to type 2 diabetes before recommending them to proceed with the pump. Certain situations that need good sugar control, like pregnancy, may prompt a pump. There’s evidence that type 2 patients can benefit from it.

Q: Any pearls on how to work with patients with an overly large fear of hypoglycemia, who eat their way to high blood sugars before bedtime?

A: Last year ADA had a nice webcast about this topic. That’s exactly where psychological intervention takes precedence. We’re looking for more psychologists at our diabetes center than nurses or other workers. One episode of severe hypoglycemia may push patients back several steps because they don’t want to repeat that.

Q: A comment on over-basalization: one reason is because tools being put out by drug companies who make the analogs never mention over-basalization. They never talk about it. So physicians just tell the patient, “We’re moving to 7%” and so the patients keep increasing the dosage. Some people never know there’s a max, and next thing they move it up to 90 units before seeing you again.

A: I see some of your concerns, but there are also some counterpoints.

Symposium: Initial Treatment of Type 2 Diabetes – New and Not-So-New Ideas

Short-Term Intensive Insulin Therapy Early in the Course of Type 2 Diabetes

Ravi Retnakaran, MD (Leadership Sinai Centre for Diabetes, Toronto, Ontario)

Dr. Ravi Retnakaran delivered an overview of previous data demonstrating the effectiveness of short-term intensive insulin therapy (IIT) and made a case for treating patients with type 2 diabetes early on with IIT followed by maintenance therapy. In prior studies, IIT was shown to have positive effects on β-cell function even after treatment ended; however, this effect was dependent on administering the treatment soon after the diagnosis of type 2 diabetes. Furthermore, this effect slowly declined over time. Thus, Dr. Retnakaran suggested following the ITT treatment with additional long-term therapy to maintain the gains in β-cell function. Dr. Retnakaran described three clinical trials testing this maintenance drug concept: BEST, LIBRA, and RESET IT. Of these, BEST had largely negative results, RESET IT is ongoing, and LIBRA results were announced the Sunday of ADA. 

• Short-term intensive insulin therapy (IIT) has been shown to improve β-cell function even after treatment cessation. Beta-cell function declines in response to multiple factors in type 2 diabetes, and insulin therapy has been shown to counteract several of these. For example, insulin can reduce glucotoxicity (due to chronic hyperglycemia), lipotoxicity (due to chronically elevated free fatty acids), inflammation, and resistance to incretins. Dr. Retnakaran cited a 2008 Lancet study by Weng et al. which showed that A1c decreased from a baseline of ~9.5% to ~8% following two to five weeks of treatment but further decreased to ~6.5% one year after patients switched from medical treatment to diet and exercise. Remission was more durable in groups initially treated with insulin compared to those initially on oral medications such as metformin.
• The earlier patients start on IIT after diagnosis, the more their beta-cells recover. Dr. Retnakaran described a model by which β-cell dysfunction has both reversible and irreversible components, with the latter increasing over time. Thus, amelioration of dysfunction should be more effective early in the course of the disease. He uses this principle as well as supporting evidence to argue for the use of IIT in early type 2 patients.
• The beta cell improvements seen after IIT decline over time; however, strategies to maintain the improvement are under investigation in several clinical trials. One strategy would be to add a long-term oral medication after IIT cessation. The BEST trial evaluated the DPP-4 inhibitor sitagliptin (Merck’s Januvia) for this purpose but found no difference vs. placebo. The LIBRA trial, whose results were released during ADA 2014, studied the GLP-1 agonist liraglutide (Novo Nordisk’s Victoza). Finally, the ongoing RESET IT trial investigates the effect of administering IIT every 3 months –i.e., using insulin as both the initial and maintenance therapy.

Questions and Answers                            

Q: Do you think combinations of drugs as maintenance therapy may yield even more long-term benefits?

A: Well, the truth is there’s still a ways to go in testing combinations. There may be combinations that provide an even greater benefit.

Q: Can you define how early in diabetes is “early,” and the duration of treatment in IIT?

A: The duration before treatment is initiated is very important; with a longer duration, you see a weaker response. Typically, patients within the first five years of diagnosis are in good shape for seeing a response. The duration of insulin treatment is an interesting question: in LIBRA we used a 4-week duration, and it varies for other studies. However, we see a considerable effect even at 2 weeks, so a shorter duration is possible.

Symposium: New Frontiers in Inpatient Diabetes Management

Are There Alternatives To Insulin?

Roma Gianchandani, MD (University of Michigan, Ann Arbor, MI)

Dr. Roma Gianchandani discussed potential alternatives to insulin therapy as the primary method of treating hyperglycemia in hospitals. Dr. Gianchandani cited conflicting data on insulin’s efficacy for achieving proper glycemic control and reducing hospital complications, noting that the drug has been repeatedly linked with an increased risk of hypoglycemia and is the most error-prone medication in hospitals. Dr. Gianchandani proposed that incretins (GLP-1 agonists and DPP-4 inhibitors) have several desirable characteristics as replacements for insulin: they operate via a mechanism that increases insulin secretion, decreases glucagon secretion, and reduces the risk of hypoglycemia, while remaining relatively easy to use and decrease the need for glucose monitoring. Although gastrointestinal (GI) side effects are a serious concern, and limited data has assessed safety outcomes, she maintained that these drug classes are the most viable alternatives to insulin therapy.

• In a study by Umpierrez et al. (2002), 38% of adults admitted to an Atlanta hospital had hyperglycemia (26% with known history of diabetes; 12% without). Based on these data, Dr. Gianchandani emphasized that achieving better glycemic control remains a challenge in hospital settings.
• The NICE-SUGAR trial demonstrated that tight glycemic control (81-108 mg/dl), achieved via insulin infusion, in critically ill patients is actually associated with higher mortality than patients maintained under conventional glycemic control (< 180 mg/dl). Dr. Gianchandani emphasized that while tight glycemic control is typically thought to improve patient outcomes, this study is one important example that introduces uncertainty into the use of insulin therapy in hospital settings.
  
  • “Insulin is the most error-prone medication used in hospitals.” In addition to being labor intensive and requiring hourly checks and complex calculations, Dr. Gianchandani emphasized the severe risk of hypoglycemia as the primary reason for identifying an alternative to insulin therapy.
• Dr. Gianchandani argued that incretins (GLP-1 agonists and DPP-4 inhibitors) are the most viable options to replace insulin therapy, due to a host of desirable characteristics, including: an action mechanism that increases insulin and decreases glucagon, low risk of hypoglycemia, ease of use, and decreased glycemic variability (and, therefore, decreased need for glucose monitoring).
• In a review of multiple studies, incretins were successful in controlling blood glucose levels, regardless of the method of delivery (continuous infusion, with controlled nutrition therapy, subcutaneous injection, or in conjunction with steroids, which raise blood sugar). Notably, hypoglycemia was not negligible in the majority of studies, but use of incretins did tend to reduce the prevalence of hypoglycemia relative to insulin treatment. Dr. Gianchandani emphasized the high prevalence of GI problems and expressed some concern that concomitant insulin use was still needed in particular cases in which incretins alone failed to reduce blood glucose levels.
• The reduced number of injections, nursing time, and risk of hypoglycemia suggest that incretins could be an attractive and cost-effective alternative to insulin therapy. However, as opposed to insulin therapy for which clinicians have extensive experience and protocols are readily available, Dr. Gianchandani recognized that more data on safety outcomes in hospital use is necessary before use of incretins becomes widely adopted.

Questions and Answers

Q: What kind of patient is a good candidate for incretin therapy?

A: I would suggest a patient who is a surgical candidate.

Q: Isn’t there evidence that DPP-4 inhibitors and GLP-1 agonists may be more effective in patients who have had diabetes less than 10 years or are prediabetic?

A: Many of the studies I reviewed involved patients who were prediabetic. But yes, these drugs do need insulin for effect, so in patients who have progressed in the disease, you may need insulin plus an incretin agent for results. 

Q: Has it been shown that better glycemic control improves patient outcomes?

A: There’s not a lot of data. Anecdotally, people with high blood sugar during surgeries tend to return more often with infections.

Q: What is the cost of using a DPP-4 inhibitor versus insulin in a hospital?

A: We’re looking into that. When you give insulin in a hospital, the actual cost reflects more than just insulin. It includes the nurses, the time required to check blood sugar multiple times. That said, DPP-4 inhibitors are very expensive right now, but I think that if you can increase their use, it will be more cost-effective.

Q: What do you think about subcutaneous GLP-1 agonists?

A: There was a suggestion for using these drugs before coming to the hospital. It’s a very tantalizing therapy. I do know people who are thinking of that in future trials.

Q: Why are you so supportive of incretin therapy given the concerning GI side effects?

A: The limitations of insulin drives us to GLP-1 agonists. That driving force includes nursing time, hypoglycemia, etc. Though if you use a computerized protocol and different targets and are stringent with your application of insulin, then hypoglycemia is not that big a deal.

Computerized Insulin Algorithms

Rattan Juneja, MD (Indiana University School of Medicine, Indianapolis, IN)

Dr. Rattan Juneja presented the results of a pilot study of SUGAR, a computerized insulin-dosing algorithm for managing in-hospital insulin therapy. The algorithm recommends an insulin infusion rate based on the patient’s blood glucose level and a target range set by the healthcare provider with periodic reminders to enter an updated blood glucose value. Results from this pilot study demonstrated that patients achieved blood glucose levels in the target range (80-110 mg/dl or 100-150 mg/dl in different hospitals) in an average of four to six hours, with reduced variability over the course of treatment. The overall rate of hypoglycemia was 3.6%, with only 0.1% of patients reaching a blood glucose level of <40 mg/dl. Overall, this study suggests that computerized insulin algorithms could be useful tools for managing patients in the hospital setting.

• Current procedures for managing hospitalized patients on insulin are limited in their efficacy. Dr. Juneja presented statistics from 2009 showing that of the 700-800 patients treated at Indiana University Health every day, 300-400 of them received insulin, a percentage that he believes is fairly typical for many hospitals. Not all of these patients are diagnosed with diabetes, and many of the professionals caring for them are not well trained in insulin therapy, leading to ineffective treatment and high rates of hypoglycemia. In Dr. Juneja’s opinion, some of the factors contributing to this problem are lack of coordination between nurses giving insulin and staff in charge of food and transportation, inadequate glucose monitoring, indecipherable insulin orders, and a lack of understanding of basal/bolus therapy.
• The SUGAR system uses a computerized insulin dosing algorithm and reminder alarms to help hospital personnel provide more effective insulin therapy. This system, short for “Systematic Utilization of Glucose Assessment and Response,” was tested in eight hospitals in 2006-2007. The central component is an intravenous insulin dosing tool that recommends an infusion rate based on the patient’s current blood glucose and the high and low targets entered by a doctor or nurse. An alarm goes off every hour reminding the nurse to enter a new blood glucose value, and the algorithm adjusts the recommended infusion rate based on the new value and recent trends.
  
  • An additional tool not yet approved by the FDA applies the same principles to basal/bolus therapy. This system provides a standardized form for insulin orders, sets default blood glucose targets and values for carbohydrate ratios and insulin sensitivity based on the patient’s weight, and alerts nurses to enter a new blood glucose value at various times (e.g., before each meal, at bedtime, and at 3 am). Providers can alter the default settings for individual patients, but the goal is to provide some sort of structure for personnel who are unfamiliar with basal/bolus therapy.
• Results from the Indiana pilot study suggest that computerized insulin algorithms can help patients reach their blood glucose targets with a significantly lower risk of hypoglycemia. The average blood glucose level out of the 334,000 measurements taken in the study was 106 mg/dl, with an average time to target of six hours if the goal was 80-110 mg/dl and four hours if the goal was 100-150 mg/dl. The variability of a typical patient’s blood glucose was also drastically lower with the computerized system. In all, 10.1% of patients had a blood glucose measurement of <80 mg/dl at some point, but only 0.1% of them ever dropped below 40 mg/dl. The overall rate of hypoglycemia was 3.6%, much lower than the rates of up to 12.1% seen in other hospital-based studies.

Questions and Answers

Q: Can you comment on the cost of the software and the time on the hospital system side required for education, IT, etc.? And are there safety parameters in place, like a limit on the maximum amount of insulin given?

A: We did not do a cost analysis. Our hospital system decided this was important, so the cost was absorbed into the operating budget. There are safety parameters – we have stops if more than 50 units of insulin are delivered.

Corporate Symposium: Shared Decision-Making in Insulin-Initiation – Live Clinician-Patient Conversation on Overcoming T2DM Treatment Barriers (Sponsored by Sanofi)

The Rationale for Early Insulin Initiation

Om Ganda, MD (Joslin Diabetes Center, Boston, MA)

Since beta cell function is known to begin to decline years before diabetes diagnosis, Dr. Ganda urged symposium attendees to consider insulin sooner rather than later when treating patients with diabetes. Citing results from the ADOPT trial, Dr. Ganda emphasized that monotherapy (sulfonylurea, metformin, or thiazolidinedione) frequently fails to bring patients to blood glucose goals. However, in a study of 133 newly diagnosed patients with A1c ≥9.7%, 51% were able to reduce fasting blood glucose levels to <110 mg/dl on CSII after one year. Forty-five percent of 118 patients on MDI reached remission, whereas only 27% of patients on oral hypoglycemic agents reached remission. Further, patients receiving CSII and MDI had significantly higher acute insulin response through one year following therapy initiation than patients receiving only oral agents. Though the ADA/EASD guidelines emphasize the need for targeted and individualized care, Dr. Ganda finds that guidance beyond initiating metformin is limited and somewhat vague. He hopes that the upcoming NIDDK-funded GRADE study will help providers make better decisions when treating patients early on in the duration of the disease. Not only will the study compare the effects of insulin glargine against liraglutide, sitagliptin, and glimepiride head-to-head in patients who have been diagnosed within the last five years, it will also assess the impact of adding basal insulin and intensifying with rapid-acting insulin on patients who are unable to meet A1c goals.

Panel Discussion

Dr. George E. Dailey, III (Scripps Whittier Diabetes Institute, La Jolla, CA): I certainly use insulin earlier than I used to. I think the ORIGIN trial was particularly useful in demonstrating the safety of basal insulin earlier in the course of the disease.

Dr. Anne Peters (Keck School of Medicine of USC, Beverly Hills, CA): It turns out that, obviously, we have more choices now than ever. And one of the good points is that people are doing better and A1cs are going down. I think it’s because we’re working with our patients better. But we also have all different ways to treat our patients. I think the use of basal insulins and insulin pens has made it easier to initiate insulin. Back then, it was more of a barrier.

Dr. Om P. Ganda (Joslin Diabetes Center, Boston, MA): Well, with all the new drugs that we have, can we afford to postpone the use of insulin? No, that’s not the case. From the numbers we’ve seen, we can still be making some progress. I have a strange feeling that we still have a lot of people who could be using basal insulin who are not doing that yet. This could reflect the barriers we’re talking about.

Dr. Peters: Maybe we should cheer over the numbers from JAMA and the CDC. Over the past 20 years, we have done an amazing job with our patients. We’ve increased he number of patients on statins. We don’t have them all, but we’re doing better. There has been a corresponding 56% reduction over the past 20 years in cardiovascular events. We’re reducing hospital rates from hyperglycemia, but hypoglycemia has been going up. We’re doing better as a group in treating our patients.

Dr. Ganda: We’re doing better with lipid control and blood pressure management. And that’s a nice reduction in cardiovascular outcomes when it comes to microvascular disease. But, I’m afraid we might be seeing more renal disease. But that could be one factor that could be further addressed by better glycemic control.

Dr. Peters: We’re also seeing such an epidemic of diabetes that doesn’t seem to be abating. I like having a combination of options – it makes diabetes care more successful. I feel more and more that I can get patients to target. When I trained a long time ago, all I had was NPH, regular, and sulfonylureas. We’ve come a long way since then.

Barriers to Insulin Therapy: The Clinician, the Patient, and the Insulin Itself

Anne Peters, MD (Keck School of Medicine of USC, Beverly Hills, CA)

Dr. Peters spoke on behalf of Dr. Alan Garber, who was sick and unable to make the symposium. She described various sources of resistance to insulin therapy in type 2 diabetes patients. In a survey of 1,267 patients, it was found that misconceptions about insulin were all too common, especially in those who were unwilling to take insulin. Results from the Translating Research Into Action for Diabetes (TRIAD) study found that insulin-naïve type 2 diabetes patients who did not fulfill their insulin prescriptions often “planned to improve health behaviors instead,” or feared the effects of insulin. Dr. Peters stressed that healthcare providers could be to blame. If a doctor is nervous or hesitant to use insulin, then patients can sense that and also start to feel nervous or hesitant. She encouraged physicians to have confidence in the patient and focus on the patient’s goals over everything else. In shared decision-making, patients need to be informed and have the freedom to choose between options. Dr. Peters presented a three-step model for clinical practice in which the physician and patient talk about choices, options, and decisions. Following an audience response session, Dr. Peters concluded with a discussion with two of her patients. Mike, tall and soft-spoken, expressed an adamant fear of needles. Even though Dr. Peters and Mike had talked several times about insulin injections, he refused and so Dr. Peters respected his request. The second patient, Elaine, described her traumatizing diagnosis by an insensitive, almost brutish doctor, and contrasted that experience with Dr. Peters’s collaborative methods. “When you’re with her [Dr. Peters], no one else seems to matter,” Elaine said. “It’s of utmost importance for a patient to feel that way.”

Panel Discussion

Q: When initiating insulin therapy for type 2 diabetes, I am most likely to:

1. Emphasize the treatment benefits to motivate patients (59%)

2. Ask patients about their fears of insulin (28%)

3. Wait until patients voice negative associations (2%)

4. Reassure them that they’ll soon learn to manage insulin therapy well (12%)

Dr. Ganda: I agree with answer one that emphasizing the benefits is most important. Unless patients are given some background, they’re not going to understand why insulin might be needed.

Dr. Dailey: I think answer two is where we need to focus additional effort. It’s difficult for the patient to express concerns. It is very important to uncover and address them earlier.

Dr. Peters: I sometimes forget how scary it can be to be a patient. For patients who are getting diagnosed, it’s overwhelming to hear the words: “You have diabetes.” Understanding fear and concerns about that is important. Patients will ask about side effects of drugs, but I always talk about it in context with the benefits. One of my patients is Don Rickles, the comedian. He said to me, “Tell me, what’s the worst thing that can happen with me taking statin?” So I said, “It could kill you.” He now calls me Dr. Death, so perhaps I could have chosen a different side effect.

Dr. Ganda: One quick comment. I think answer four is a perfectly reasonable thing, but that’s easier said than done. We have the luxury of nurse educators and nutritionists next to me, but not everyone has that.

Dr. Dailey: A lot of people relate to the worst case scenario. For example, when patients read about the product.

Dr. Peters: The internet is terrible; often I tell people not to look on the internet. I think it’s up to us to help people interpret the package insert and tell them what side effects are realistic.

New Approaches to Insulin Therapy

George Dailey, III, MD (Scripps Whittier Diabetes Institute, La Jolla, CA)

Dr. Dailey provided a high-level overview of strategies for maintaining glycemic control in patients when basal insulin therapy is not sufficient. Dr. Dailey opened his presentation by encouraging a “stepwise” approach to therapy that gradually adds medications in an effort not to overwhelm a patient. He emphasized that ideal glycemic control requires mimicking physiological insulin secretion and that the incorporation of rapid-acting analogs with quick action profiles (two to three hours) into treatment regimens can be a particularly effective option upon failure of basal insulin therapy alone. That said, Dr. Dailey advocated more strongly for GLP-1 agonists, DPP-4 inhibitors, and SGLT-2 inhibitors that he believes offer great potential as insulin alternatives or adjuncts. Citing one study, Dr. Dailey described that treatment with insulin glargine plus twice-daily exenatide resulted in greater A1c reduction and less weight gain relative to glargine treatment alone in type 2 diabetes patients. However, in looking to the future, Dr. Dailey drew attention to the high-concentration and longer-acting analogs under investigation, such as Lilly’s U500 Humulin and Novo Nordisk’s insulin degludec, respectively, arguing that there will be a place for each of these in the market based on pricing and patient preferences. Next, Christine, a patient of Dr. Dailey’s, briefly addressed the issue of patient-provider dialogue and trust. She recalled the trauma she experienced when first diagnosed with diabetes due to providers who did not take the time to fully explain the intricacies of the disease. However, in Dr. Dailey, Christine emphasized that she had found a “partner” who she trusted wholeheartedly and who convinced her to take insulin – which she described as a “nuisance” – by explaining that the benefits outweighed her concerns about weight gain. In this sense, she explained that she was incredibly grateful for a clinician who involved her in the decision-making process and who “really works with me even when I don’t want to do something.”

Panel Discussion

Q: In a patient on basal insulin with normal fasting glucose whose A1c is beginning to rise, I would:

1. Use a “basal bolus” strategy (44%)

2. Add a GLP-1 receptor agonist (56%)

Dr. Ganda: I think you know that basal bolus was certainly a very popular choice until GLP-1 receptor agonists became available. Many patients are concerned with weight gain. Even with basal insulin alone, there can be a case made for adding a GLP-1 agonist, especially for overweight and obese patients. It’s a good choice, provided you can get coverage. We have many agents available, like once-weekly exenatide, so there are more options for the patient.

Dr. Dailey: What about Elaine?

Dr. Peters: First of all, Elaine clearly was at a point where she started to need something else other than basal insulin. I didn’t want her to get hypoglycemic, so adding a GLP-1 agonist was a wonderful option. I was worried about her because she tends to get GI effects from meds, so we increased the dose slowly to get to a point where she could tolerate it. Her A1c is now amazing and I’m able to further reduce insulin. It turned out to be a win-win situation for me. No mealtime insulin, and she can lose weight.

Dr. Dailey: What’s also nice about GLP-1 agonists is that you can see results within a few weeks.  But we know 20% of people simply don’t respond to the drug.

Dr. Ganda: Not all patients are as lucky as Elaine. Some patients have problems with lifestyle, like staying on top of diet and exercise, and so not all patients will be candidates for this combo. We can still get good control in other ways though.

Dr. Peters: I find adding one shot of insulin after the biggest meal is good. It’s not much extra work; it’s not too daunting. It’s also a good time to help patients deal with snacking and eating in the evening, so adding the shot of is another chance to work on lifestyle since we want to make the insulin work better.

Dr. Dailey: Do you have any other tricks to cover “grazing” after dinner?

Dr. Peters: I write prescriptions for dogs frequently. People can walk the dog before sleeping. I also do home makeovers. We found if you take the TV out of the kitchen and move it to the living room, you eat less. Obviously, having healthier foods in the house is good, like having vegetables to snack on instead of unhealthy things. I like to find one healthy snack people like and gradually move toward more healthy snacks.   

Final Panel Discussion

Q: Can you talk about new directions in insulin therapy?

Dr. Peters: We want to give insulin in a physiological way. That’s why these longer-acting insulins are useful to me, especially with my type 1 patients. And I think rapid-acting insulins will be of more use to the artificial pancreas. With my type 2 patients, maybe I need insulins that are more stable, and maybe I don’t want to give them as often. I think, in terms of development, it really depends on the patient population, although I think simplicity and avoiding hypoglycemia are universal key points.

Dr. Ganda: I think insulin analogs have very been helpful since we’ve had them, as they’ve significantly reduced hypoglycemia – especially nocturnal hypoglycemia. The other point I want to make is that the obesity epidemic is here to stay. And when we consider giving higher doses of insulin, we have to weigh that risk against the potential increase in weight. This is a problem. So with higher concentration insulins that are available, we can use less insulin, and we might still get benefits. So in terms of future development, working on higher concentration insulins is a great idea since the U500 is the only more concentrated insulin we have right now, and it does not have a predictable course of action.

Dr. Dailey: The 50% reduction in hypoglycemia we saw between NPH and glargine in the Treat to Target trial will not be seen again. From now onward, we’re going to be talking about lesser reductions. Anything that lowers this barrier will make life easier on us.

Dr. Peters: I think people are beginning to understand that the key is having the time to work with patients. I think it’s about partnership with patients and our teams. We all need to work together. In fact, physicians don’t need to be the most closely allied individual to a patient if other individuals can help and the patient feels more comfortable with him or her. The key is that there is a lot of good we can do collectively.

Q: How do I go about reducing the amount of insulin I’m taking?

Dr. Peters: It takes patients who are capable of self-titration, because most of this has to be done by patients. Patients titrate down gradually, and then we communicate if there’s an issue.

Q: Since ADA guidelines no longer specifically recommend A1c less than 7.0%, and hypoglycemia is a problem with older patients in particular, and since the population is growing older, isn’t having 75% of patients under 8% good?

Dr. Ganda: We need to individualize treatment goals. When I said 7%, that’s an overall acceptable level. It’s not just age, but comorbidities and things like renal failure, kidney disease.  About the older population, the ADA actually said last year that because of comorbidities, we really need to personalize the goal for older patients. In those situations, tight glucose control is not as important.

Dr. Dailey: You’re right. Benefits in reducing microvascular complications require several years to see. If the patient has less than 10 years left, it’d be pushing it to get them under 7%.

Corporate Symposium: Diabetes: Ensayos Clinicos, Tratamientos y Retos (Supported by an unrestricted educational grant from Novo Nordisk and presented only in Spanish)

While we do not have truly fluent Spanish speakers on our core team, we tried to glean as much as we could from this session and bring you some high level takeaways.

“Diabesidad” en el Adolescente (Diabesity in Adolescents)

Ximena Lopez, MD (University of Texas Southwestern Medical Center and Children’s Medical Center, Dallas, TX)

Dr. Ximena Lopez started off her presentation by emphasizing the huge lack of knowledge about the treatment of type 2 diabetes in children. She then presented a study on glycemic control in adolescent type 2 diabetes patients comparing the efficacy of metformin monotherapy, metformin plus lifestyle intervention, and metformin plus thiazolidinediones (TZDs). In addition, she reviewed guidelines for when to start treatment with insulin in children with type 2 diabetes as well as FDA approval for type 2 diabetes drugs for adolescents.

• Dr. Lopez described the results of the TODAY study, which revealed the prevalence of type 2 diabetes in adolescents from Hispanic and African American backgrounds and with lower socioeconomic status. The study examined glycemic control in adolescents ranging from ten to seventeen years old (n=699), who had been diagnosed with diabetes for less than two years. They were treated with metformin, metformin plus rosiglitazone, or metformin plus intense lifestyle intervention. All were overweight, and 65% were female. ~40% were Hispanics, and ~30% were African American. 42% had a family income <$25,000, and only 17% had parents with a university degree. All patients started with A1c <8%.
• Different genders and minorities responded differently to treatment. Overall, there was no difference between metformin monotherapy and lifestyle intervention, and while the TZD group performed better compared to metformin monotherapy (p=0.006). However, boys responded best to metformin and lifestyle intervention (p=0.06). Interestingly, African Americans responded the worst to metformin monotherapy (p=0.003 compared to metformin and TZDs, p=0.008 compared to metformin and lifestyle intervention). On the other hand, Hispanics responded the worst to metformin and lifestyle intervention, although there were no statistically significant differences between the three treatment groups for this subpopulation. A baseline A1c of <6% or >6% was a predictor of treatment failure; subjects with A1c >6% were more likely to fail treatment.
• Dr. Lopez recommended starting children with type 2 diabetes on insulin therapy if they had DKA, an unclear distinction between type 1 diabetes and type 2 diabetes, or A1c >9%.
• Metformin is the only oral drug approved by the FDA for type 2 diabetes patients <18 years. Dr. Lopez suggested intensifying treatment at A1c >7%, and stressed the importance of monitoring glucose levels.

Questions and Answers

Q: It’s interesting that metformin works better in Caucasian children than in African American children, but this is flipped for adults. Why is that?

A: We’re unsure of that – it could be something related to adolescence.

Q: Why not use DPP-4 inhibitors for adolescents?

A: Currently, there are ongoing studies for children, but there are no results yet. It is possible that it could affect the mineral density of children’s bones, or spermatogenesis.

Q: What happens if metformin fails, but the patient hasn’t yet reached the thresholds that you recommended for insulin use?

A: Start the patient on insulin therapy.

Repaso Crítico de la Seguridad en el Uso de las Insulinas Análogas en el embarazo, Niños, y Adolescentes (Safety Review of Insulin Analog Use during Pregnancy, in Children, and in Adolescents)

Israel Hartman, MD (University of Texas Southwestern Medical Center, Dallas, TX)

Dr. Israel Hartman reviewed the safety of insulin analog therapy during pregnancy and for children and adolescents. He highlighted necessity for more studies showing how insulin analogs work in pregnant women and children, since drugs from these classes are the only diabetes treatments approved for pregnancy and for young children. 

• During pregnancy, glycemic control is even more important. Hypoglycemia is incredibly dangerous for both the mother and child, and hyperglycemia can cause abnormalities when A1c rises to over 1% above goal. It’s also important to take insulins that aren’t immunogenic (i.e., don’t cause the body to antibodies against it).
• Dr. Hartman also emphasized the extreme variability in the action profile of insulins and insulin analogs in children. Insulin glargine (Sanofi’s Lantus) is approved for children over six years old, and insulin detemir (Novo Nordisk’s Levemir) is approved for children over two years old. In insulins, there is a lot of variability for children, especially in kids from ages six to eleven years.

Estudios con Nuevas Insulinas Análogas Basales: Últimos Avances (Latest Advances in New Basal Insulin Analogs)

Eduardo Montoya, MD, PhD (University of Barcelona, Barcelona, Spain)

Dr. Eduardo Montoya described the characteristics of an ideal basal insulin: efficacy over a long action period, a flat profile, low variability, and flexibility throughout the day. He reviewed some of the newer insulin analogs that have improved action profiles, such as peglispro. He showed the results of one crossover study in which patients on peglispro had lower incidences of hypoglycemia (p=0.037) and better weight effects (p=0.001) than those on glargine. Moving on to insulin degludec, he highlighted graphs displaying degludec’s flatter action profile.

Administración de Insulina: Lo Que Usted Desconoce que Puede Dañar a su Paciente (Insulin Administration: What You Don’t Know Could Harm Your Patient)

Jaime Davidson, MD (University of Texas Southwestern Medical Center, Dallas, TX)

After covering some statistics on the current state of insulin administration, Dr. Jaime Davidson reviewed some best practices for avoiding lipohypertrophy (an accumulation of fat under the skin that can occur from injecting insulin at the same site too frequently). In most other countries, the majority of patients use pens, but in the US less than 50% of patients use them. In addition, 48% of patients have lipohypertrophy. Dr. Davidson asserted that there is no difference in skin thickness between people of different ages or races, and that it was imperative to educate patients on how to inject themselves correctly, due to the huge variation in effects when injecting into muscle versus the subcutaneous layer. He recommended needles of 4 mm in length for all patients except for those in the third trimester of pregnancy, when skin thickness changes. 

Panel Discussion

In the panel discussion, Dr. Ximena Lopez, Dr. Israel Hartman, and Dr. Eduardo Montoya answered questions from the audience regarding cardiovascular risk in insulin analogs, recommendations for diabetes treatments during pregnancy, and bariatric surgery in adolescents. From what we gleaned, Dr. Lopez answered the question about bariatric surgery, responding that it has not been proven to reduce diabetes in adolescents, although some bypasses are relatively safe for adolescents.

Novel Drugs

Oral Presentations: Novel Therapeutic Agents

TTP399, a Liver-Selective Glucose Kinase Activator (GKA), Lowers Glucose and Does NOT Increase Lipids in Subjects with Type 2 Diabetes Mellitus (T2DM) (122-OR)

Carmen Valcarce, PhD (TransTech Pharma, High Point, NC)

Dr. Carmen Valcarce presented the positive results of a phase 1b/2a study of TransTech’s liver-selective glucokinase activator (GKA) TTP399. The trial studied three doses of TTP399 in 120 type 2 diabetes patients on baseline metformin therapy for 42 days. In that time period, the highest dose (800 mg twice daily) led to an absolute A1c reduction of ~0.9% and a placebo-adjusted reduction of ~0.5% at the end of the study (mean baseline of ~8.1%), with action on both fasting and postprandial glucose (the postprandial effect appeared strongest). A highlight of the results was a sub-analysis showing that the drug’s efficacy was largely preserved in patients that were fairly well controlled at baseline (A1c < 7.5%) – while the placebo arm (n = 8) in this subgroup held steady at around 7.2% A1c, the high dose arm (n = 8) saw an A1c reduction to a mean of 6.4%, making for a greater placebo-adjusted difference (-0.8%) than was seen in the overall cohort. Dr. Valcarce fully acknowledged that the GKA class is burdened by previous failures, which were largely due to increases in lipids or liver enzymes as well as high rates of hypoglycemia; she highlighted that TTP399 was not associated with any increase in lipids, insulin secretion, or hypoglycemia. Those safety data, as well as the drug’s efficacy independent of baseline A1c, might indicate that this compound might be an especially effective tool for intensifying therapy in moderately well controlled type 2 diabetes patients.   

• Before diving into the data, Dr. Valcarce acknowledged that the GKA class has largely fallen from favor in the diabetology community. Roche and Takeda were among the companies that discontinued GKA programs in the past few years, and data on earlier agents in the class showed adverse effects such as increased hypoglycemia, increases in lipids, and elevated liver enzymes. TransTech’s recipe for success is to ensure a high degree of liver selectivity, as action at the pancreas can lead to hypoglycemia through glucose-insensitive induction of insulin secretion. Additionally, TransTech believes that a successful GKA must not interfere with the endogenous glucokinase regulatory protein (GKRP) in order to avoid adverse events. TTP399 meets these two criteria, and Dr. Valcarce presented a summary table of preclinical and early clinical results showing that the compound appears to be succeeding where others failed. In testing so far, TTP399 has not demonstrated increases in hypoglycemia, lipids, or liver toxicity.
• The six-week multiple-dose phase 1b/2a study enrolled 120 type 2 diabetes patients on stable metformin therapy. Drug doses tested were 400 mg BID, 800 mg QD, and 800 mg BID, all versus placebo. Over the 42 days of the study, patients spent three one-week periods in the clinic to conduct meal challenge tests, monitor PK, and monitor blood glucose through three-day CGM periods. Patients had a mean baseline BMI of ~31 kg/m², a mean A1c of ~8.0% – 8.2%, and a mean diabetes duration of 8 - 11 years.
• TTP399 appeared to be safe at all doses, with no imbalances in symptomatic hypoglycemia or liver function tests. There was a slight imbalance in the number of patients with at least one treatment-emergent adverse events between the high dose group (19/30) and placebo (12/30), but this did not seem highly worrying due to the relatively small size of the trial.
• TTP399 800 mg twice daily (the highest dose) led to a placebo-adjusted A1c reduction of ~0.5% at day 42, with action postprandially and (to a lesser extent) on fasting plasma glucose. The A1c-lowering efficacy increased in a dose-dependent manner – the 800 mg once daily dose led to a ~0.4% placebo-adjusted A1c reduction. Factors that made the placebo-adjusted values appear modest include fairly robust placebo group performance (reduction of 0.4%) as well as the short duration of the trial.
• Notably, the A1c reductions seen with TTP399 did not seem to be highly correlated with baseline A1c. In fact, in a subgroup of patients with a baseline A1c at or below 7.5%, patients in the 800 mg twice daily group (n = 8) saw a ~0.8% placebo-adjusted reduction in A1c, and the vast majority achieved an A1c at or below 6.5%. Dr. Valcarce considered this baseline-A1c-independent efficacy one of the highlights of the trial results. 
• Dr. Valcarce ended by suggesting that TTP399’s efficacy profile could make it suitable for intensive glucose control. The company is poised to begin a six-month phase 2b study for TTP399.

Questions and Answers

Q: The numbers in this trial were pretty small, but it did seem like this compound had a good effect in patients that were well controlled.

A: yes, although at the higher end of the baseline A1c spectrum the A1c lowering was increased, there was still a robust effect at the lower end.

Q: This is interesting data, given what we know about this class. The liver has a high degree of autoregulation to keep glycogenolysis and gluconeogenesis constant. Do you plan to do experiments for a longer duration of six to 12 weeks, and perhaps measure hepatic glucose?

A: We’ve done that with animals, and we have not found any signs of tachyphylaxis. In the clinic, that is what we will test next. We will do a six-month phase 2b study.

Q: What is the outcome of the glucose that enters the liver?

A: The glucose is oxidized. It does make sense to know where the glucose that is being taken up by the liver due to TTP399 is going, but you could ask the same thing about metformin, or a DPP-4 inhibitor. But in this case, the literature suggests that it is oxidized.

Moderator comment: It is a little different though, right? Increasing glucose uptake is different than metformin, which reduces hepatic glucose production. You mentioned steatosis, which has been an effect of concern for this drug class. You did not test these patients in terms of liver fat or biopsies, but have you seen it in rodent models?

A: We have not. We did test in ob/ob mice with existing steatosis, and found that we actually improve hepatic steatosis. We also did tests in normal mice, and did not see any detrimental effects.

Metabolic Effects of a Novel FGF21 Analogue Administered for 4 Weeks to Patients with T2DM (126-OR)

Roberto Calle, MD (Pfizer, Groton, CT)

Dr. Roberto Calle shared the results of a double-blind four-week escalating dose study on the company’s twice-weekly phase 2 FGF21 analog PF-05231023. Although the results featured some striking improvements in lipids and body weight, disappointingly there were no statistically significant changes in blood glucose, as measured by A1c and seven-point glucose profile (but not CGM). The trial’s results were largely in line with the clinical data seen on Lilly’s discontinued FGF21 analog: there were robust improvements in lipids (50% reduction in triglycerides, 20% reduction in LDL-C, 25% increase in HDL-C), and a very rapid loss of body weight (5-6% in just four weeks), but the trend towards reduction in glucose levels did not reach statistical significance. This led Dr. Calle to suggest that while the candidate shows promise for dyslipidemia and obesity, its potential for type 2 diabetes is less clear. Excitement in FGF21 as a target for metabolic conditions remains, but work is still needed to develop molecules with a better effect on glucose.

• As of Pfizer’s 1Q14 report, PF-05231023 no longer appeared on the online pipeline. This is a likely indicator that the candidate has been dropped from development.
• The trial was a four-week double-blind multiple dose study. Four cohorts of 12 patients each (10 active, two placebo per cohort) were placed on 5 mg, 25 mg, 100 mg, and 140 mg twice-weekly doses of PF-05231023. Patients were on background metformin monotherapy before the trial. Average patient age was 50 to 60 years, average BMI at baseline was ~30 kg/m², and average A1c at baseline was somewhat variable between groups due to small sample size but generally around 8%.
• The drug led to robust improvements in lipids and body weight. In the two highest dose groups (100 mg and 140 mg), triglycerides fell ~50% from baseline, HDL cholesterol rose ~20% from baseline, and LDL cholesterol fell by ~20% (the only change that was not statistically significant was the LDL decrease in the 100 mg group). Strikingly, patients in the two highest dose cohorts achieved 5-6% weight loss after only four weeks. The small sample size is an important caveat, but we found this result very impressive, especially given that the patients were not starting from extremely high baseline BMIs (the average was ~30 kg/m²).
• Disappointingly, PF-05231023 treatment did not lead to statistically significant reductions in blood glucose, although there was a visible trend towards a decline. The 140 mg cohort law a 12% placebo-adjusted reduction in weighted mean daily glucose (based on seven-point glucose profiles) that trended strongly towards significance (p=0.06). We would have been interested to see if the drug’s effect was primarily on fasting or postprandial glucose, and would have liked to see the full seven-point profile rather than a weighted average of all the points. A 12% reduction in mean glucose in the context of reduced glycemic variability, less hypoglycemia, or some other benefit could be very meaningful for patients, in our view. We would have been glad to see CGM used since seven-point glucose profiles don’t give as much granularity as CGM, particularly next-generation CGM (especially Dexcom’s G4 as well as Medtronic’s next-gen CGM available outside the US) that is now viewed as much more accurate.
• PF-05231023’s safety profile looked fairly agreeable. The most frequent adverse events were gastrointestinal (nausea, loose stools) and were clustered in the highest dose cohorts – these data, we learned during Q&A, may have been impacted by an episode of contaminated water during the trial. There were no drug-related severe adverse events or discontinuations. Two patients in the 140 mg cohort had confirmed anti-PF-05230123 antibodies, but none were neutralizing.
• Dr. Calle concluded by suggesting that PF-05231023 is a potential new therapeutic agent for obesity and dyslipidemia, but only “possibly” for type 2 diabetes. He noted that greater effects on glucose could possible appear over time due to the effects on weight, but from the tone he took in his conclusion, he did not paint an optimistic picture about this particular candidate’s potential in diabetes.

Questions and Answers

Q: It is encouraging to see the repetition of the beneficial effects on lipids reported by Lilly. I wanted to press you on the glucose response. I have always felt that the absence of an effect on glucose with Lilly’s compound was related to its short-acting profile. At least your compound showed a favorable trend. Could you talk more about the assays you used to test how long the compound was in circulation?

A: We had two PK assays, one that measured the intact N-terminus, and one for the intact C-terminus. The assay for the C-terminus was shorter, in the range of seven to ten hours. That is longer than endogenous FGF21 but not as long as we wanted. It is possible that the lack of efficacy on glucose is due to insufficient half-life of the intact C-terminus. Other hypotheses could stem from preclinical data showing that FGF21 might upregulate some of the enzymes involved in gluconeogenesis.

Q: In the abstract, we heard that the placebo group had a number of adverse GI events. Could you explain that?

A: That is related to one of the studies in which there was an episode of contaminated water, and we found that some subjects had GI events that were not clearly related to the drug.

Comment: Well, that’s clinical research for you.

Imeglimin Increases Glucose-dependent Insulin Secretion and Improves Beta-Cell Function in Patients with Type 2 Diabetes (120-OR)

Michael Roden, MD (Leibniz Center for Diabetes Research, Dusseldorf, Germany)

Imeglimin, a member of a new class of oral glucose lowering agents (“glimins”) that act at the mitochondria, has been shown to increase glucose-stimulated insulin secretion in both rodents and humans. Dr. Roden’s group sought to examine specifically how imeglimin affects beta cell function. In a double-blind interventional study of drug-naïve and metformin-treated patients with type 2 diabetes (n=30), Dr. Roden found that seven days of 1,500 mg imeglimin twice-daily treatment more than doubled the insulin response to glucose and improved beta cell glucose sensitivity. Notably, imeglimin appears to affect both beta cell glucose sensitivity and insulin secretion, indicating a potential unique advantage in this intriguing new class of drugs.

• Imeglin is the first in a new tetrahydrotriazine-containing class of oral glucose lowering agents known as “glimins,” and is currently in phase 2b development. Glimins regulate mitochondrial bioenergetics and improve liver and muscle insulin sensitivity. Imeglimin has been found to increase glucose-stimulated insulin secretion in vivo in humans during a three-hour oral glucose tolerance test (OGTT) with a 72% increase in the area under the curve (AUC) insulin/AUC glucose ratio after 4 weeks of treatment, and a 14% increase in AUC insulin/AUC glucose ratio during a six-hour mixed meal test after 8 weeks of treatment.
• Dr. Roden examined the subacute effect of imeglimin on glucose-stimulated insulin secretion in vivo during hyperglycemic glucose clamp tests in 30 type 2 diabetes patients. Individuals who were either drug naïve or on metformin monotherapy were included in the study, and average age, BMI, and baseline A1c (61 years, 30 kg/m², 6.8%, respectively) were similar across the imeglimin and placebo groups. Following a screening period of between one and three weeks and a metformin wash-out period of two weeks, patients were either given a placebo or 1,500 mg of imeglimin twice daily for seven days. Patients were then placed on a hyperglycemic clamp to measure insulin secretion over 90 minutes. Total insulin response was measured using incremental AUC (iAUC) as well as insulin secretion rate, which was calculated from C-peptide deconvolution.
• Though fasting plasma glucose did not significantly decrease with imeglimin, both the insulin response to glucose and the absolute insulin secretion rate increased significantly. The insulin response to glucose increased 112% (p=0.035). In the first 25 minutes following glucose infusion under the hyperglycemic clamp, the absolute insulin secretion rate increased 110% (p=0.034), and by 33% (p=0.031) in the following 15 minutes. Insulin hepatic extraction decreased 13% (p=0.058), and there was no significant effect on glucagon levels.

Questions and Answers

Q: It seems rather unique that this drug appears to affect both insulin sensitivities and secretion. Is there a unifying reason for this, or do you think it is all mitochondrial?

A: It is conceivable that the effect on the beta cell is also related to its effect on mitochondrial function. It’s hard to say, but it seems like a direct effect.

Q: Can you comment on the potency of the agent?

A: Doubling of the insulin secretion rate is a substantial and clinically relevant observation.

Effect of Salsalate on Insulin Action, Secretion, and Clearance in Non-Diabetic, Insulin-Resistant Individuals: A Randomized, Placebo-Controlled Study (121-OR)

Sun Kim, MD (Stanford University, Palo Alto, CA)

This single-blind study by Dr. Kim and colleagues yielded mechanistic insight into the previously reported ability of the anti-inflammatory salicylate salsalate to lower blood glucose levels. The study enrolled 41 individuals with insulin resistance, and randomized them 2:1 to salsalate 3.5 g daily (seven pills per day!) or placebo for four weeks. Salsalate provided a statistically significant decrease in fasting plasma glucose but not postprandial glucose. There was an increase in insulin response, but further analysis revealed that it was due to decreased insulin clearance, not increased insulin secretion or action. Although the implications of this mechanism are not entirely clear, reduction in insulin clearance does not strike us as the most ideal way to increase insulin action, as it might contribute to increased insulin stacking and lead to less predictable insulin action. Seven pills per day is also quite a lot to ask of most patients and we would not be optimistic about adherence to this regimen compared to others now available. .

• The study measured the effects of salsalate on plasma glucose and insulin dynamics in 41 patients with insulin resistance but not diabetes at the time of screening. Salsalate is an NSAID (non-steroidal anti-inflammatory drug) currently used to manage pain and inflammation, and has been previously shown to reduce blood glucose levels, possibly through its anti-inflammatory effects. Participants in this single-blind study were randomized 2:1 to either salsalate 3.5 g (seven pills per day; n = 26 completers) or  placebo (n = 13 completers) for four weeks.  A 75-gram oral glucose tolerance test, graded-glucose infusion test, and insulin suppression test were performed at the start and end of the four-week treatment period. The salsalate and placebo groups had similar baseline characteristics in terms of demographics as well as steady-state plasma glucose and oral glucose tolerance. Dr. Kim chose only to present the before-and-after results for the salsalate group, stating that the placebo group showed no differences. Notably, while the participants had some insulin resistance at baseline and were prediabetic, they had not been diagnosed with diabetes and had no history of cardiovascular disease.
• Salsalate 3.5 g daily led to a 6% mean placebo-adjusted reduction in fasting plasma glucose at week four, as well as a 19% reduction in triglycerides. However, the investigators did not see a significant change in postprandial glucose. Dr. Kim theorized that this lack of postprandial effect was due to action primarily on hepatic glucose production.
• Insulin levels during each test were elevated at four weeks compared to baseline; however, insulin secretion did not increase, suggesting that the higher insulin levels were due to decreased clearance. Insulin levels at week four were approximately 50% higher during the oral glucose tolerance test, 20% higher during the insulin suppression test, and 25% higher during the graded glucose infusion test. Because insulin secretion is inhibited during the insulin suppression test, the elevation implicates decreased insulin clearance as the cause. C-peptide levels confirmed that insulin secretion was not increased at four weeks compared to baseline, again pointing to decreased insulin clearance as the cause for elevated insulin levels.

Questions and Answers

Q: Why don’t you see decreases in post-prandial glucose?

A: I think it’s a predominant effect on hepatic glucose production. In this study, we looked at prediabetes, but there might be more dramatic results in type 2 patients. The predominant effects, though, are still in fasting plasma glucose levels.

Q: It is a bit difficult to interpret the insulin curves in these tests. Have you had the chance to deconvolute C-peptide levels to calculate secretion? It is also hard to interpret the finding that fasting glucose decreased. Did you measure glucagon levels?

A: To answer your second question, we haven’t measured glucagon, but we’re looking into measuring other indices. To address your first question, we did measure C-peptide in two of the tests, and in the graded-glucose infusion test, C-peptide somewhat decreased.

XMetA, a Novel Insulin Receptor Activator, Is Efficacious in Glycemic Control in Rhesus Monkeys with Naturally-Occurring Type 2 Diabetes (123-OR)

Jingsong Zhao, PhD (XOMA Corporation, Berkeley, CA)

Dr. Jingsong Zhao presented preclinical data on XMetA, a first-in-class injectable novel insulin receptor partial agonist believed to be effective in reducing hyperglycemia with low risk of hypoglycemia. XMetA was evaluated at varying dosages in six spontaneously diabetic rhesus monkeys for its effect on fasting and postprandial blood glucose levels. Though significant (31%) reductions of fasting blood glucose levels without hypoglycemia were observed, we await future studies that demonstrate the potential of XMetA in humans over a longer period of time. XOMA is currently conducting longer-term preclinical studies, and believes that once-weekly dosing is a possibility given the preclinical pharmacokinetic data. 

• XMetA is a human antibody that activates the insulin receptor via an allosteric binding site, promoting glucose uptake. In cultured cells, XMetA has been found to signal and promote glucose uptake, and has been shown to be efficacious in multiple rodent models of diabetes.
• Dr. Zhao repeatedly treated six late-stage, insulin-dependent naturally diabetic rhesus monkeys (Macaca mulatta) with 0, 1, 3, 10, and 30 mg/kg doses of XMetA. Each two-day period began with dose administration. After 10 hours, the animals fasted for 18 hours, after which blood was drawn to examine the pharmacokinetic/pharmacodynamic (PK/PD) profile of the drug. Additionally, insulin was not administered during fasting, to isolate the blood glucose lowering effect of the drug.
• At 10 mg/kg, XMetA significantly reduced 18-hour fasting blood glucose values by 31% and modestly reduced post-prandial blood glucose levels, with no hypoglycemia observed. Significant reductions of fasting blood glucose were also observed at 30 mg/kg. The duration of the effect was approximately one week, similar to the elimination half-life as calculated from the PK/PD profile.

Questions and Answers

Q: A decrease in glucose levels was observed in all animals, including placebo. Does the placebo operate as in humans? Is there a placebo effect in the animals?

A: The reason why we observed a decrease in glucose levels under placebo is because the animal was undergoing fasting. With XMetA, there is a substantial reduction of blood glucose levels.

Q: Would a long-acting XMetA work with a short acting insulin? And how can you be confident that there is no risk of hypoglycemia?

A: Without having done the study, I don’t have the results to share with you.

Oral Presentations: Glucose Regulation by Enteric Peptides

The Novel GLP-1-GLP-2 Dual Agonist ZP-GG-72 Increases Intestinal Growth and Improves Insulin Sensitivity in DIO mice (374-OR)

Rasmus Just, PhD (Zealand Pharma, Glostrup, Denmark)

Zealand Pharma researcher Dr. Rasmus Just presented on the company’s preclinical GLP-1/GLP-2 dual agonist program, which we learned about only recently. Zealand scientist Dr. Just began by explaining the rationale of adding GLP-2 agonism to GLP-1 agonism’s well-characterized positive effects on glycemia. A growing body of research suggests that the leakage of bacterial cell wall components such as lipopolysaccharide (LPS) across the intestinal wall can cause “metabolic endotoxemia,” characterized by insulin resistance and low-grade inflammation throughout the body. The key takeaway was that GLP-2 agonism (which Zealand is currently testing independently for GI disorders), by improving integrity of the gut wall and reducing inflammation, could help improve the metabolic cascade that contributes to diabetes and its complications. ZP-GG-72, the company’s first major GLP-1/GLP-2 dual agonist candidate, showed similar glucose-lowering efficacy to liraglutide and strong GLP-2 agonist action in preclinical studies, but had a relatively short half-life. Dr. Just next announced that Zealand has developed a new lead candidate, ZP-GG-23, that also has significant GLP-1-mediated efficacy on glycemic control and significant GLP-2-mediated efficacy on increasing intestinal weight, but with a half life of nearly nine hours that will enable daily dosing. As the importance of inflammation and the gut microbiome on diabetes pathophysiology continues to be elucidated, we imagine interest in Zealand’s program could grow. 

• Rather than linking two separate GLP agonist proteins, ZP-GG-72 (the original lead compound) utilized a GLP-2 backbone with GLP-1 action “dialed-in” by design. ZP-GG-72 had fairly balanced action on GLP-1 and GLP-2 receptors, but was limited by its 0.3-hour plasma half-life.

Posters

The Novel Glucagon Analogue ZP-GA-1 has Superior Physicochemical Properties while Maintaining the Pharmacokinetic and Pharmacodynamic Profile of Native Glucagon (390-P)

P Noerregaard, M Svendgaard, A Valeur, L Giehm, F Macchi, K Fosgerau, D Riber

Dr. Pia Noerregaard et al. provide in vitro and animal data indicating that Zealand Pharma’s glucagon analogue ZP-GA-1 has greater solubility and stability compared to native glucagon while exhibiting a similar PK/PD profile. Specifically, ZP-GA-1 was found to have a solubility of >25 mg/ml at physiologic pH compared to native glucagon (~0.2 mg/ml). Chemical stability studies showed that after seven days at 40°C, ZP-GA-1 (1 mg/ml tested at a pH of 6.5-7) showed a lower rate of degradation (1.8%) compared to native glucagon (51%; 1 mg/ml tested at a pH of 4).  In addition, a 360-day study of ZP-GA-1 at 5°C showed a degradation of 3.3% (no glucagon comparison was performed). Regarding PK/PD parameters, a crossover study in four male beagles showed that ZP-GA-1 and glucagon had similar PD profiles (based on glucose plasma concentrations) and PK profiles (based on Tmax, Cmax, and half-life) when compared as single injections or as IV infusions. The PK results were also confirmed in rats. Similarly, rat models of hypoglycemia indicated that both ZP-GA-1 and native glucagon injections provided dose-dependent increases in blood glucose levels, which were restored to baseline levels or above. In concluding, the authors note that based on these data, ZP-GA-1 is suited as a liquid formulation for the treatment and/or prevention of severe hypoglycemia either as a rescue kit and/or as part of an artificial pancreas.

ISIS-GCGR_RX, an Antisense Glucagon Receptor Antagonist, Caused Rapid, Robust, and Sustained Improvements in Glycemic Control Without Changes in BW, BP, Lipids, or Hypoglycemia in T2DM Patients on Stable Metformin Therapy (109-LB)

E Morgan, A Smith, L Watts, S Xia, W Cheng, R Geary, S Bhanot

Dr. Erin Morgan report the results of a double-blind 13-week trial that randomized type 2 patients on stable metformin therapy to placebo (n=26) or ISIS Pharmaceutical’s ISIS-GCGR_Rx (n=23 for 100 mg dose, n=10 for 200 mg dose with load, n=16 for 200 mg dose without load). As background, ISIS-GCGR_Rx is an antisense drug that targets the mRNA of the glucagon receptor (GCGR). Baseline characteristics were similar across the four groups (mean age of 50-57 years, BMI of 31-38 kg/m², baseline A1c of 8.6-9.1%, and fasting plasma glucose of 168-224 mg/dl). As calculated in the intent-to-treat analysis, ISIS-GCGR_Rx provided statistically significant A1c reductions (ranging from -1.3% to -2.0%) vs. placebo (0.16%). Similar statistically significant improvements were observed for fructosamine and GLP-1 levels (data provided below). Furthermore, a higher percentage of patients in the ISIS-GCGR groups achieved an A1c of ≤ 7% (ranging from 48-75%) vs. those in the placebo group (13%). In addition, ISIS-GCGR_Rx resulted in higher C-peptide levels during a 2-hour oral glucose tolerance test compared to placebo. The authors highlight that ISIS-GCGR_Rx is well tolerated and did not trigger the off-target effects seen with small molecules – i.e., the investigators observed no changes in LDL-cholesterol, triglycerides, blood pressure, or body weight.  The authors conclude that because ISIS-GCGR_Rx directly reduces the production of the glucagon receptor, it may provide greater glycemic control vs. small molecule drugs, with fewer non-specific effects.

• A subset of participants (the 200 mg “with load” group) first received a loading dose of ISIS-GCGR_Rx (four injections over 14 days) followed by the standard once-weekly dosing for 11 weeks. Other participants (the 200 mg “without load” group) received the standard dosing for the entire treatment period.
• ISIS-GCGR_Rx provided statistically significant A1c reductions, as well as a four-fold increase in GLP-1 levels:

	Placebo (n=26)	100 mg (n=23)	200 mg (load; n=10)	200 mg (no load; n=16)
Baseline A1c	8.61%	8.62%	9.13%	8.83%
A1c reduction at week 14*	-0.16%	-1.33%	-1.95%	-1.56%
Baseline GLP-1 level (pmol/l)	5.35	6.83	8.16	4.76
Change in GLP-1 level at week 14* (pmol/l)	-0.31	9.86	16.20	20.01

^* measurements were taken one week after the last drug dose.

• A greater proportion of the ISIS-GCGR_Rx group achieved an A1c ≤7% compared to the placebo group.

	Placebo (n=26)	100 mg (n=23)	200 mg (load; n=10)	200 mg (no load; n=16)
Percentage achieving an A1c ≤7% at week 14*	13%	48%	75%	56%

^* measurements were taken one week after the last drug dose.

• ISIS-GCGR_Rx was well tolerated: the investigators observed no cases of symptomatic hypoglycemia and only infrequent, predominantly mild injection site reactions that resolved rapidly. The authors note that while ISIS-GCGR_Rx did increased liver enzyme levels, these target-related ALT elevations were “consistent with the pharmacology of glucagon receptor inhibition and similar to those observed with small molecule glucagon inhibitors” (mean ALT elevation was 1.6x ULN for the 100 mg group and 2.7x ULN for the 200 mg group). The liver enzyme elevations declined after drug discontinuation, and ISIS-GCGR_Rx had no effect on liver function or bilirubin.

Leucine Amplifies the Effects of Metformin on Insulin Sensitivity and Glycemic Control in Diet-Induced Obese Mice (1108-P)

B Xue, L Fu, F Li, A Bruckbauer, Q Cao, X Cui, R Wu, MB Zemel, H Shi

In this intriguing collaboration between NuSirt Biopharma and Georgia State University, the investigators show synergistic glycemic effects between metformin and the amino acid leucine in mouse studies. Given that metformin is associated with GI side effects that make it intolerable for a substantial proportion of patients (we’ve heard up to 20%), NuSirt hopes that this approach of adding leucine to metformin may enable patients to achieve similar efficacy to full-dose metformin at a lower metformin dose, thereby reducing side effects. In this study, mice fed a high fat diet for six weeks to induce hyperglycemia and hyperinsulinemia were treated with sub-therapeutic levels of metformin (the human equivalent of 150-500 mg/day) combined with the human dose equivalent of leucine 2.2 g/day. While leucine alone had no effect, the Leu+Met combination produced dose-dependent improvements on glucose tolerance within four weeks, insulin tolerance by five weeks, fasting glucose by five weeks, and HOMA-IR.  Specifically, the Leu+Met 500 mg dose (the highest dose tested) produced similar effects compared to full-dose 1,500 mg/day metformin on insulin tolerance area under the curve and fasting glucose. Leu+Met 500 mg produced even statistically significantly greater improvements in area under the glucose tolerance curve and greater improvements in HOMA-IR compared to full-dose 1,500 mg/day metformin. The lower Leu+Met 250 mg dose produced statistically comparable effects to full-dose metformin with regards to all four measures of glucose tolerance, insulin tolerance, fasting glucose, and HOMA-IR. Leu+Met 150 mg only showed an effect similar to full-dose metformin for glucose tolerance, fasting glucose, and HOMA-IR, but not insulin tolerance.

• For background, this group has previously found leucine to activate the sirtuin/AMPK pathway, and metformin is also thought to act on this pathway. NuSirt had previously been focused on resveratrol as a sirtuin/AMPK activator, but found that leucine, sans resveratrol, was sufficient for the effects in this study. Management remarked to us, “Although resveratrol is where we started, it is no longer a part of the NuSirt equation.”
• We would be curious to see data comparing the Leu+Met combinations to metformin 2,000 mg/day, as that is the maximum dose prescribed for many patients. Even 500 mg/day metformin dose can produce GI side effects, so we are also curious to see which Leu+Met dose the company plans to pursue.
• Dr. Zemel is now leading NuSirt; the company’s previous approach was an over the counter one and we are glad to see this directional shift. Joe Cook, the former CEO of Amylin, is a primary investor and we were glad to see the company’s philosophical shift since there is so much “noise” on the OTC front. To be sure, this small company has very impressive leadership and is enormously mission-driven (it understands patient constraints very well) and we look forward to seeing more data.

Symposium: The Alpha Cell Rediscovered

Role of Glucagon in Metabolic Homeostasis

Maureen Charron, PhD (Albert Einstein College of Medicine, Bronx, NY)

Dr. Maureen Charron discussed the effects of knocking out the glucagon receptor in mouse studies on the progression of type 1 diabetes – although the findings were promising in terms of slowing the progression of type 1 diabetes, there were some notable safety signals that emerged in preclinical models. One study determined that glucagon receptor knockout mice are resistant to streptozotocin-induced beta cell loss in comparison to control group mice. The resistance was confirmed in both high and low-fat diets, indicating that dietary adjustments played no significant role in eliciting resistance to beta cell decay. Another study conducted by Dr. Alvin Powers (Vanderbilt University, Nashville, TN) examined transplantation of pancreatic islet cells of wild-type mice into glucagon receptor knockout mice and vice versa. The wild-type islets transplanted into knockout mice had significant alpha cell expansion in comparison to the control group, which had no expansion of the alpha cell population. Dr. Charron stated that these studies support the clinical utility of developing glucagon receptor antagonists capable of eliminating the glucagon-dependent component of type 1 diabetes. However, there seemed to be several potential adverse effects to glucagon receptor antagonist therapy. As the mice in the knockout group aged, they became blind, indicating that overly potent glucagon receptor antagonist therapy may alter visual acuity. Dr. Charron concluded with the fairly conservative sentiment that glucagon receptor antagonism may have several unintended effects that each merit further study.

Development of Glucagon Receptor Antagonists for Treatment of Type 2 Diabetes

Finbarr O’Harte, PhD (University of Ulster, Londonderry, United Kingdom)

Rather than providing a broad overview of the glucagon receptor agonists in clinical development, Dr. O’Harte presented the results of in vitro and in vivo preclinical studies his research group at the University of Ulster has conducted on a series of glucagon receptor antagonists. This family of antagonists was designed by making modifications such as fatty acid acylation or PEGylation to the native glucagon amino acid sequence. Some (but not all) of the variants of desHis1(Glu9)-glucagon alleviated hyperglycemia, prevented a rise in plasma glucagon, and/or prevented the loss of pancreatic insulin content by 35% following exposure to streptozotocin (STZ). However, it was not easy to evaluate effects on glucose regulation from these studies alone, as efficacy was variable between the trial compounds. Dr. O’Harte did mention some of the other glucagon receptor antagonists in development, including Lilly’s phase 2 LY2409021 (an orally-administered compound), Bayer’s BAY27-9955 (we have not seen anything on this candidate in a while), and the small molecule skyrin. Isis also has a glucagon receptor antagonist (ISIS-GCGRRx) in phase 2.

Biodel Luncheon

Glucagon Rescue Delivery Device Demonstration

Michael Crick (Biodel, Danbury, CT) and Molly Miller, PhD (Unilife, York, PA)

We had the privilege to attend a small luncheon hosted by Biodel in which the company previewed its novel glucagon delivery device and sought feedback from diabetes educators and nurse practitioners. The device design has not been altered since we previously reported on the prototype at ADA 2013, as it will still use a dual chamber, automatic reconstitution device with a half-inch, 27-gauge, thin-wall needle – pictures of the device can be seen here. The device contains a lyophilized (freeze dried) cake of glucagon, which can be delivered in three steps: 1) remove a cover and twist, reconstituting the glucagon and unlocking the front needle cover; 2) remove the needle shield; 3) push plunger to give dose (the needle automatically retracts into the barrel following completion of a full dose). The device is expected to have two-year dating and come in 1 mg and 0.5 mg (children) doses. Product development is still on schedule as Biodel’s goal remains an NDA filing by the end of 2015 under the 505(b)(2) regulatory pathway. After seeing the demo and listening to the focus-group conversation, our impression remains that the rescue device offers a significant advantage relative to available options; attendees were particularly impressed by the compact design and ease of use. Management drew attention to the labeling strategy: Biodel would like to include full use instructions on the device’s cover. This approach was seen as a convenient and critical way to reassure the families and friends of patients – the users of the device – who are often deterred from injecting glucagon for fear of hurting a loved one. Overall, the focus group’s feedback was very positive. Members only expressed concern that a small company, like Biodel, might not have the resources to market and promote device so as to drive uptake.

• When the group was asked whether this device would make their practice easier, the answer was a resounding “Yes!” Several educators said that it would be much easier for them to train patients and their loved ones on how to use this device than the current glucagon kit. Attendees specifically praised the easy-to-follow instructions and retractable needle, a great sign indicating there may be quite enthusiastic use. One former pediatric educator mentioned that it might be easier to train school nurses on how to use this device, which would help achieve the goal of increasing access to emergency glucagon kits in schools.
• Management indicated that the novel glucagon rescue product would be priced “comparably” to the current glucagon kits. This statement leaves hope that pricing of the novel device will be reasonable, though does provide Biodel with some wiggle room for a premium. Indeed, in holding this luncheon, Biodel was attempting to gather input that would assist them in catering to patients, so it would be unfortunate if pricing undermined this goal.
• The novel glucagon rescue product features simple instructions and diagrams on the device’s cover in order to make its administration as stress-free as possible. In theory, the instructions are designed as a reminder to a trained individual. From our examination, the instructions appeared clear and concise enough that many novices would be able to understand them and use the product as well. In order to enhance the user-friendly nature of the product, attendees also suggested bright red coloring – the prototype cover is opaque, white plastic – since that is already associated with diabetes. Additionally, attendees recommended using a glow-in-the-dark finish, a clever and functional suggestion, which Biodel management appreciated.
• A theme throughout the discussion was the lack of patient awareness about the need for glucagon. As one attendee put it, “We have a better mousetrap but people don’t know why they need a mousetrap in the first place.” Another attendee was amazed by how many of her patients were unfamiliar with glucagon or who said they had been trained on it once, but didn’t actually know how to use it. The group attributed some of this problem to the current glucagon rescue kits; however, the consensus seemed to be that a serious effort is needed to market this new device to a wider audience.
  
  • One educator repeatedly stressed the need to communicate that glucagon can be used before a patient loses consciousness. She noted that many people are reluctant to use a glucagon kit, which leads to too many patients going “past the point of no return.”
• Attendees proposed giving users a mini dosing option, due to concerns that users will only be able to administer the whole 1 mg dose. “If you have glucagon once, you are not keen on having it again,” said one attendee, referring to the prolonged vomiting that occurs as a result of taking a such a large dose. Biodel acknowledged this idea, but stated that the regulatory path of mini dosing is less clear. As a reminder, Xeris currently has a phase 2 study ongoing at Baylor (ClinicalTrials.gov Identifier: NCT02081014) testing glucagon mini-dosing in up to 18 patients with type 1 diabetes.
• Attendees suggested a mobile app to ease the training of friends and family with the device. Attendees highlighted how an app with a training video and instructions would provide an easy means to instruct friends and families on the kit’s use. Attendees thought that training in the form of DVDs or videos shown on a TV at the doctor’s office are not particularly effective. With a mobile app, caregivers do not miss out on important information if they are unable to accompany patients on office visits.
• At the conclusion of the luncheon, we were encouraged to hear about “Ha!” (or “Hypoglycemia Awareness”) an organization dedicated to educate the public on how to recognize and respond to a hypoglycemic episode. The group’s goal is to train first responders, such as police officers and flight attendants, to appropriately respond to a hypoglycemic episode in order to help and save the lives of those with diabetes. Ha! is hoping to get funding from the Bloomberg Foundation. We applaud these public health efforts and encourage people to learn about how to take action in response to a hypoglycemic event.

Isis Investor Event

The morning after the poster presentation of phase 2 data for Isis’ glucagon receptor antagonist (ISIS-GCGRRx), the company held an investor event next to the Moscone Center in San Francisco to discuss the results and share its vision the agent’s future. Management highlighted that within four-to-five weeks of ISIS-GCGRRx initiation in the 13-week trial (n=75), the average patient, no matter his/her baseline fasting glucose level, reached euglycemia (as measured by FPG). Isis thinks that part of ISIS-GCGRRx’s success lies in it striking a middle ground between DPP-4 inhibitors and GLP-1 agonists in terms of elevating GLP-1 levels: ISIS-GCGRRx increased GLP-1 levels up to four-fold, which is a greater rise than that driven by DPP-4 inhibitors (1.9-2 fold), resulting in better efficacy. On the flip side, the GLP-1 bump is smaller than that conferred by a GLP-1 agonist; as a result, no participants in the ISIS-GCGRRx arm reported GI side effects. Given the striking A1c reduction seen with ISIS-GCGRRx, Isis was quite explicit about its intention to target people with more serious type 2 diabetes (i.e., A1c >9%; failing multiple OADs, or insulin). That was quite interesting to us and we were glad to hear more discussion of how to personalize therapy. Isis is planning to conduct further dose refinement studies, and is seeking to partner the agent before moving it into phase 3 testing. It’s been very interesting to watch Isis of late move to the fore with two drugs in phase 2 – we’re eager to see directionally how far the company can go. This is a classic example, however, of a case in which previously the company may have had very many interested partners but here given the regulatory uncertainty, particularly related to CVOTs and reimbursement, big pharma may opt out. We certainly hope not since those with higher A1cs are certainly a high risk group that is ultimately quite costly to the system; drugs designed for more advanced diabetes could be a very good market, particularly if ultimately they are combined with other compounds and are still easy to take. Isis held an R&D day in late May with details on its GCGRRx – see our coverage here.

• As background, the double-blind, 13-week trial randomized type 2 patients on stable metformin therapy to placebo (n=26) or Isis’s ISIS-GCGRRx (n=23 for 100 mg dose, n=10 for 200 mg dose with load, n=16 for 200 mg dose without load).
• Management highlighted that within four-to-five weeks of ISIS-GCGRRx initiation, the average patient, no matter his/her baseline fasting glucose level, reached euglycemia (as measured by FPG). In the intent-to-treat analysis, ISIS-GCGRRx provided statistically significant A1c reductions (ranging from -1.3% to -2.0%) vs. placebo (0.16%). Indeed, a relatively high percentage (ranging from 48-75%) of patients in the ISIS-GCGR groups achieved an A1c of ≤7% (for comparison, only 13% of the control group reached this goal).
• According to Dr. Robert Henry (University of California, San Diego, CA), during the event, 20% of people with type 2 diabetes have an A1c >9% (and 12% have an A1c >10%). Additionally, he noted than 50% of people on a combination of oral agents are failing treatment, as is 30-40% of people on an OAD and either insulin or a GLP-1, and 10-20% of people on insulin and a GLP-1.

Drug Development

Symposium: Translation of Genetic Findings into Physiological Insight

Genomics and Validation of Drug Targets to Academia from Big Pharma

Brian Hubbard, PhD (Harvard University, Cambridge, MA)

Based on his experience working in industry, Dr. Brian Hubbard laid out his rationale for why drug developers should focus on fewer drugs and use emerging genomics technology to identify the most promising drug targets. He highlighted the comparatively low phase 1 and phase 2 success rate of traditional small molecule drugs, and the comparatively higher success of more targeted approaches validated by genetics. Next, he criticized the tendency in today’s climate to focus on drugs for existing targets, which have a higher probability of success but only offer incremental improvements to patient health. He stressed that many "undruggable" targets simply require more resources and patience than drug developers are currently willing to spend, and that these kinds of drugs will be the big breakthroughs of the future and will be sustainable because of the their higher success rate through clinical trials. Given the relatively high number of diabetes drug classes that exist today, we would agree that the major unmet need is for novel approaches rather than incremental improvements, although we can understand why the funding for those big leaps is slow to materialize – the regulatory and reimbursement hurdles for diabetes drugs have never been higher. As well, it is important to remember that one “incremental” improvement might be combined with another “incremental” improvement and lead to strong results.  

• Dr. Hubbard argued that drug developers need to pursue novel drug targets instead of well-established ones, despite the difficulty of doing so. Due to the financial considerations and uncertainty in today’s market, more and more drug developers are focusing on drugs for well-established targets, which have a higher probability of success but can only offer incremental improvements over existing therapies. Dr. Hubbard offered the explanation that companies are hesitant to tackle “undruggable” targets, which include the vast majority of known proteins, because the chance of success seems low. As a motivating counterexample, he pointed to kinases as a previously “undruggable” class of targets that has since seen tremendous progress. He also advised developers to make the investment to more directly pursue targets rather than targeting the “regulator of a regulator of a regulator.” When pursuing a new target, drug developers should invest in a multi-pronged approach and dedicate significant time with a “can’t fail” attitude, not making tentative small investments and abandoning projects if no short-term progress is made. In the end, even if the new class of drug does not serve the original purpose, it may be co-opted for other purposes.
• Human genetics should be used to select targets with a higher probability of success. Dr. Hubbard conducted his own analysis on the performance of drug candidates in phase 2 trials, which revealed that monogenic protein replacement, which relies on mechanistic human genetics knowledge, had an 88% success rate, compared to a 13% success rate of traditional small molecule drugs. Drugs which he classified as being supported by human genetics had a much higher success rate than those only supported by rodent genetics or cell biology. He thus proposed a strategy to maximize success rate: (i) in a large sample, measure the desired biomarker and sequence each genome; (ii) identify rare extreme clinical phenotypes and the associated rare mutations; and (iii) investigate these genes as targets, possibly including cell and animal studies.
• This approach can be financially feasible because of the higher success rate of genetically-validated drugs in clinical trials. The major driver of the increase in R&D spending is the large number of drug candidates that fail in phase 2 or phase 3 clinical trials, and the consequently large number of drug candidates that companies must pursue simultaneously. By making a larger investment in a smaller number of targets that have actually been validated by human genetics, the overall cost of failed drugs may actually decrease.

Questions and Answers

Q: Do you have any advice for the young scientists in the room?

A: Absolutely. When you’re a young scientist, your education never stops, learning never stops. If you want to get into drug development, dedicate your time and energy into it; don’t compromise and don’t stop pushing for your ideas. It’s not easy and often a little painful.

Q: If you’re suggesting a top-down approach from human genetic studies, and looking or druggable targets, then why not population-based proteomics?

A: I’m not suggesting druggable targets. Go after whatever is real. I’m not against proteomics or peptide-based approaches. I’ve personally found it’s one of the best ways to validate results from genetics, but there’s no reason why it can’t happen at the earlier stages. To me, the gene level approach is the most efficacious path to safety and efficacy, but bringing in multiple disciplines is a good thing.

Q: Human orphan diseases, which are really experiments of nature, can tell us something about the underlying biological processes. What do you think of investigating these as a way of increasing knowledge?

A: I believe that would be excellent. What we’re probably talking about with “orphan diseases” is more that few people represent it. We’ve seen time and time again that when you develop a drug for an orphan disease, it often finds other uses, and coupling orphan diseases is extremely powerful.

Basic Science

Oral Presentations: Regulation of Human Insulin Secretion In Vivo

Beta-Cell Sensitivity to Incretins Is Decreased after Gastric Bypass Surgery (194-OR)

Marzieh Salehi, MD (University of Cincinnati College of Medicine, Cincinnati, OH)

Dr. Marzieh Salehi presented intriguing results that the observed enhanced incretin effect after gastric bypass surgery is not due to the increased insulinotropic effects of GLP-1 and GIP (at least in gastric bypass patients and matched controls that do not have diabetes). Dr. Salehi first provided background that many studies have proven that subjects with gastric bypass surgery have an increased incretin effect and augmented GLP-1 action. This study evaluated the beta-cell sensitivity to incretins by comparing patients who underwent gastric bypass a few years prior with age and BMI-matched non-surgical controls (no subjects had diabetes) under hyperglycemic clamp conditions, using step-wise incremental infusions of GLP-1, GIP, or saline to measure response to incretin signals. Insulin secretion rates in response to escalating doses of GLP-1 and GIP were larger in controls compared to subjects who had undergone gastric bypass surgery. However, after correction for insulin sensitivity, insulin secretion rate between the surgical and non-surgical subjects were not significantly different. The overall findings of Dr. Salehi’s study are somewhat hard to interpret given the likely differences in each group’s progression and medical history – even though the groups were matched at the time of the study, the surgical group likely had a history of much higher BMI than the non-surgical group. If there is a reduction in beta-cell incretin sensitivity following gastric bypass, it may be due to the marked rise in incretin secretion that follows as a direct results of gastric bypass.

• Existing literature has shown that GLP-1 and GIP secretion rises following gastric bypass surgery. Other studies have demonstrated that patients undergoing gastric bypass surgery have an enhanced incretin. Similarly, it has been shown that GLP-1’s effect on beta-cell glucose sensitivity in people with diabetes is increased after gastric bypass surgery.
• The study evaluated beta-cell incretin sensitivity in 10 non-diabetic subjects several years after gastric bypass and nine BMI and age-matched non-surgical controls with normal glucose tolerance. Patients had a mean age of 41 years and a mean BMI of ~32 kg/m². All subjects were non-diabetic and the gastric bypass group had undergone gastric bypass surgery several years prior to the study. All patients were subjected to hyperglycemic clamp conditions and graded infusions of GLP-1 and GIP to measure insulin and C-peptide responses.
• Fasting glucose levels were similar in both of the groups, but insulin sensitivity was higher in the gastric bypass group compared to the controls. The GB group had an insulin sensitivity of 0.270.04 mg/kg/minute and the control’s group had an insulin sensitivity of 0.100.02 mg/kg/minute. This was an interesting finding in our view – presumably the gastric bypass patients had previously had much higher BMIs before the procedures, and were more likely to have had previous insulin resistance.
• Upon controlling for insulin sensitivity, beta-cell function relative to insulin sensitivity did not appear to be different between the two groups. During glucose infusion alone, the average C-peptide response was slightly lower in the GB group. However when incretin-stimulated C-peptide responses were corrected for the glucose-stimulated C-peptide responses, the results showed that the GB subjects in fact had lower beta-cell responses to incretin hormones.
• The overall findings of Dr. Salehi’s study are somewhat hard to interpret given the likely differences in each group’s progression and medical history. Although the two patient pools were matched at the time of the study, the group that underwent bariatric surgery had likely undergone more rapid weight changes (especially the post-surgery decline in weight and possible remission of pre-existing metabolic disorders) in the years before the study than the control group. The fact that none of the patients had diabetes at the time of the study also leads us to question the applicability of these findings to the diabetes patient population. Even if the effects observed in this study prove true, the clinical relevance is not completely clear – a slight decrease in beta cell incretin response following bariatric surgery could be more than compensated for by a massive rise in GLP-1 and GIP secretion in the gut due to the direct effects of bariatric surgery. 

Questions and Answers

Q: Can you elaborate more on this idea that GLP-1 and GIP are not responsible for the incretin effect?

A: Well, that’s not quite entirely true because we know we are bringing in the GLP-1 receptor. There’s no question that increasing the incretin effect has something to do with GLP-1. However, our question was how the sensitivity to GLP-1 and GIP changes, and we show that it appears that you change the sensitivity through surgery.

An Incretin Effect Exists After Amino Acids Administration in Humans: Effects on Incretin Hormones and Insulin After Oral vs. Intravenous Amino Acids (189-OR)

Bo Ahrén, MD, PhD (Lund University, Lund, Sweden)

Though the incretin effect (increased insulin response to oral glucose intake compared to intravenous glucose intake) is well documented, it is not known whether a similar increase in insulin response is elicited when comparing oral vs. intravenous amino acid intake. In a study of 12 healthy males (21-26 years old and BMI 20-25 kg/m²) with normal glucose levels, Dr. Ahrén observed that oral amino acid administration but not intravenous amino acid administration, increased circulating levels of GIP. Notably, neither method of administration affected GLP-1 levels. Oral amino acid administration resulted in a higher increase in insulin and C-peptide levels than intravenous administration (p=0.029 and 0.021, respectively), ostensibly mediated by GIP, although other signals could have played a role. A mixture of 19 different amino acids was used in the study; whether the incretin effect is observed only in response to particular amino acids – or particular amino acids working synergistically - remains unclear.

Oral Presentations: Beta Cell Development and Postnatal Growth

Molecular Profiling of Human Pancreatic Alpha and Beta Cells in Health and Disease (204-OR)

David Blodgett, PhD (University of Massachusetts Medical School, Worcester, MA)

Dr. David Blodgett presented some interesting findings about the patterns of protein expression within alpha and beta cells. As expected, insulin was the most commonly expressed gene in beta cells, with 240 times more insulin mRNA expressed than glucagon mRNA. Alpha cells, as expected, expressed more glucagon mRNA, but the difference was only a factor of 27. In Dr. Blodgett’s studies, 39% of glucagon-positive cells contained insulin mRNA, a finding confirmed by mass spectrometry. Although there was significant insulin mRNA in alpha cells, protein expression was what one would expect – 95.6% of glucagon protein was found in alpha cells, whereas 98.5% of insulin protein was found in beta cells. These findings suggest that alpha cells might have more plasticity than previously realized, and we wonder if therapies for diabetes could involve the reprogramming alpha cells rather than simply suppressing their activity. Dr. Blodgett’s group hopes to further explore the surprisingly high level of alpha-cell insulin mRNA expression and how it might differ in disease states by comparing transciptomes (mRNA) and proteomes in patients with type 1 diabetes as compared to controls.

Activation of GLP-1 and Gastrin Signaling Induces ß-Cell Neogenesis (200-OR)

Shugo Sasaki, MD (Osaka University Graduate School of Medicine, Osaka, Japan)

Because GLP-1 has been shown to positively impact beta cell function by increasing insulin secretion, stimulating beta cell replication, and inhibiting beta cell apoptosis, Dr. Shugo Sasaki’s group investigated the impact of GLP-1 signaling on beta cell neogenesis in mice. The study utilized a transgenic mouse line that overexpressed GLP-1 in ductal and acinar pancreatic cells, but not in islets. Exendin-4 and gastrin co-signaling was found to induce the development of insulin-expressing cells in the acinar area of the pancreas, whereas exendin-4 alone showed no effect. There was no evidence of increased pancreatitis or dysplasia. The findings suggest that gastrin/GLP-1 agonist co-secretion could be used to stimulate the neogenesis of beta cells as a therapy for diabetes.

• A Cre-recombinase/tamoxifen model was used to selectively induce overexpression of GLP-1 receptors in the treatment group, and at 10 weeks both groups were injected with the GLP-1 agonist exendin-4, with or without gastrin. Normally, Sox9-positive progenitor cells differentiate into duct cells and acinar cells in adults, and not endocrine cells. While mice injected with exendin-4 alone did not show increased insulin production and beta cell neogenesis, injecting mice with exendin-4 and gastrin created insulin-producing clusters in control mice and the mice that overexpressed the GLP-1 receptor. However, the number of new beta cells was eight times greater in GLP-1 overexpressing mice compared to the control.
• There were no findings of pancreatic dysplasia and pancreatitis at the age of 12 or 24 weeks. Pancreatitis has been one concern associated with activation of GLP-1 signaling, but it was not seen in this study.

Oral Presentations: Advances in the Diagnosis and Treatment of Type 1 Diabetes

High Prevalence of Insulitis in Adult Patients at the Diagnosis of Human Type 1 Diabetes (T1D) (175-OR)

Lars Krogvold, MD (Oslo University Hospital, Oslo, Norway)

In the Diabetes Virus Detection (DiViD) study, Dr. Lars Krogvold and his team looked at the prevalence of insulitis (the infiltration of white blood cells into islets and resultant inflammation) in newly diagnosed adult type 1 diabetes patients. The six patients that participated in the DiViD study were 23 to 36 years old and had their pancreases laparoscopically biopsied within nine weeks of diagnosis. The islets were grouped into four categories: insulin-deficient islets (IDI) with inflammation, IDI without inflammation, insulin-containing islets (ICI) with inflammation, and ICI without inflammation. The majority of islets were IDI with no inflammation (62%), followed by ICI with no inflammation (27%), ICI with inflammation (9%), and IDI with inflammation (2%). The prevalence of insulitis was higher than has been previously reported in the existing literature, since all six patients met the criteria for diagnosis. Interestingly, Dr. Krogvold found that 36% of all islets that were isolated were ICI, meaning that there were beta cells that still produced insulin at the onset of type 1 diabetes; however 82% of these were inflamed. He speculated that these cells could be preserved if the inflammation could be reversed.

Questions and Answers

Q: Were you surprised that only 11% of the islets had inflammation, or do you think other islets were previously inflamed and went back to normal?

A: I think the islets that were insulin-deficient were inflamed previously, and that normal islets will be inflamed after some time.

Q: Do you see any correlation between insulitis and the number of diagnostic antibodies?

A: No, we did not.

Increased Fecal Beta-Defensin-2 and Altered Microbiota in Prediabetes (177-OR)

Outi Vaarala, MD, PhD (National Institute for Health and Welfare, Helsinki, Finland)

Past studies have demonstrated evidence of altered gut microbiota in type 1 diabetes patients. It is thought that inflammation in the gut could lead to insulitis and trigger beta cell destruction. In this study, Dr. Outi Vaarala et al. investigated gut microbiota and gut inflammation, using fecal beta-defensin-2 levels as a marker, in 26 children with at least one beta cell autoantibody (alongside 26 matched controls without autoantibodies). Dr. Vaarala and her colleagues found significantly higher levels of beta-defensin-2 in the autoantibody-positive group (p=0.012). Concentrations of certain bacteria were also significantly correlated with higher levels of beta-defensin-2, notably Ruminococcus gnavus (p=0.05), Blautia wexlerae (p=0.025), and Porphyromonadaceae (p=0.042). In a timing study, beta-defensin-2 levels were followed in 13 children with high genetic risk for type 1 diabetes and compared to a control group (n=26); no differences were found in the months before development of antibodies in the high-risk group. Dr. Vaarala speculated that perhaps gut inflammation was a consequence of beta cell inflammation, rather than the other way around, and both could contribute to beta cell destruction. The question of what triggers beta cell autoimmunity in the first place remains unanswered.

Questions and Answers

Q: Did all 13 children in the timing study go on to develop type 1 diabetes?

A: These children are between three and four years of age now, so very few have developed type 1 diabetes. But we know that those with autoantibodies are more likely to do so.

Oral Presentations: Adipose Tissue Development and Metabolic Signaling

Beige-Fat-Specific Regulation in Mouse and Human (87-OR)

Jun Wu, PhD (University of Michigan, Ann Arbor, MI)

Dr. Wu noted the recent resurgence in interest in adipose tissue research, and reviewed several properties of beige fat that could impact health – namely, that increasing beige fat in mice increased glucose tolerance and decreased weight gain in response to a high fat diet. In humans, imaging studies have revealed the presence of beige fat “hotspots” in adults, specifically in the supraclavicular region. Animal studies also suggest that in the absence of brown fat, mice may develop cold resistance over time, possibly signaling a role for beige fat in adapting to stresses. Beige fat is distinct from brown fat and white fat in origin and gene expression: while it displays many functional properties of brown fat, it derives from the same precursors as white fat. Dr. Wu and colleague’s new data suggest differential regulation of beige fat and brown fat under certain conditions.

• Beige fat is a less well-established type of adipose tissue that has an origin distinct from brown fat and white fat. The differences between white and brown fat have been understood for years: compared to white fat, brown fat has more mitochondria, expresses the heat-generating protein UCP-1, and requires the protein PRDM16 for its differentiation. Moreover, brown adipose cells share a common lineage with myoblasts. However, some evidence suggests that beige fat cells, despite possessing some brown fat cell features, share a common lineage with white fat cells – a Pdgfr- α + preadipocyte differentiates into a beige fat cell with cold or β -agonists and differentiates into a white fat cell with a high-fat diet. Other evidence suggests that beige fat cells may also derive from smooth muscle cells.
• Beige fat has favorable properties over white fat in promoting health. Compared to wild-type animals, transgenic mice with increased beige fat showed reduced weight gain in response to a high-fat diet and substantially improved glucose tolerance. Beige fat may also help make up for a loss of brown fat activity, as mice lacking brown fat could not maintain body temperature in acute cold conditions, but adapted in chronic cold conditions. In humans, beige fat occurs in hotspots, such as the supraclavicular region, and thus may be relevant in human physiology as well.
• Beige cells have gene expression and regulation profiles distinct from both brown fat and white fat. Dr. Wu described an experiment where her group generated hundreds of cell lines of brown, beige, and white adipose lineages and measured their gene expression profiles. Unsupervised clustering of the profiles showed that beige fat cells clustered together and as a whole, they were more closely related to brown fat cells than to white fat cells. In addition, Dr. Wu found that beige fat, not brown fat, changed gene expression in response to all-trans retinol treatment, demonstrating differential regulation of key genes.

Questions and Answers

Q: Is there evidence suggesting that TZDs like rosiglitazone may activate a beige fat program?

A: I don’t think they do in vivo, per se. Rosiglitazone, through protein stabilization, can lead to PRDM16 activation and increased UCP-1, but we think this program is regulated differently, and the detailed mechanism is still unclear. TZDs probably don’t act directly on fat tissue; at least, I don’t have any evidence that shows that.

Q: Is there a link between exercise and beige fat production?

A: There is some evidence that suggests a link, but exercise is a very complex process where more evidence is needed.

Symposium: Brown Fat, White Fat, Cold Fat, Warm Fat

Ablation of PRDM16 and Beige Fat Causes Metabolic Dysfunction and a Subcutaneous-to-Visceral Adipose Switch

Paul Cohen, MD, PhD (Harvard Medical School, Boston, MA)

Dr. Paul Cohen presented on the thesis of a paper his research group published earlier this year in Cell: that ablation of the adipocyte regulatory protein PRDM16 causes metabolically healthy subcutaneous fat to take on properties of metabolically unhealthy visceral fat, suggesting that increased PRDM16 expression could drive the reverse process. Adipocyte-specific knockout of PRDM16 expression in rodent models causes subcutaneous beige adipocytes (thermogenic and relatively metabolically healthy adipocytes that are closely related to brown fat) to undergo “visceralization,” i.e.: take on the unhealthy metabolic characteristics of visceral fat. These characteristics include less thermogenicity, mild obesity, severe insulin resistance, hepatic steatosis, and macrophage accumulation – interestingly, insulin resistance was especially prominent in the liver. Dr. Cohen’s team also found increased levels of the transcription factor Wt1, which appears to be a reciprocal regulator of PRDM16. Brown fat is a topic that has garnered a great deal of enthusiasm at recent scientific meetings, and as Dr. Cohen suggested in his conclusion, augmenting PRDM16 specifically in beige fat could represent a way to “engineer healthier fat” and treat metabolic disorders.

• For background, there are key metabolic differences between visceral and subcutaneous fat. Subcutaneous fat has a larger concentration of mitochondria-rich thermogenic beige adipocytes, and is relatively metabolically healthy. Visceral fat, by contrast, is composed largely of white fat cells and is associated with a greater degree of metabolic dysfunction, including inflammation and insulin resistance. 
• Interestingly, while the ablation of PRDM16 had a strong measurable impact on beige fat, very little effect was seen on brown fat. Dr. Cohen hypothesized that the timing of the PRDM16 knockout may have been the cause of that lack of effect.  
• Future directions of study for Dr. Cohen’s lab include: (i) examining the effect of beige fat ablation on other aspects of the metabolic syndrome; (ii) examining the effect of beige adipocytes on other metabolic tissues, especially the liver; (iii) clarifying the mechanism of the reciprocal regulation of PRDM16 and Wt1; and (iv) identifying chemical inducers of PRDM16-independent pathways. 

Questions and Answers:

Q: Recent work has shown that Wt1 marks specific subpopulations of adipocytes. Did you see Wt1 expressed evenly throughout subcutaneous adipocytes?

A: We’ve been interested in this question, but have been limited by the lack of high quality antibodies for immunostaining. The approach now is to take GFP mice with reporters knocked-in to Wt1 loci, to help us see if Wt1 localizes to white fat cells.

Q: Might the adipocytes in your mice be making any factors that have an effect on liver or muscle?

A: The data on hepatic insulin resistance suggests that is a possibility, and we are looking into that right now.

Q: A paper from Patrick Seale mentioned that there is sometimes a discordance between PRDM transcription and protein levels.

A: In white fat, it is true that there is some discordance, indicating that there might be significant post-translational modification occurring. In subcutaneous fat the story is very different – there you can detect