DTM 2018 (Diabetes Technology Meeting)

November 8-10, 2018; Rockville, MD; Day #2 Highlights - Draft

Executive Highlights

  • In Beyond A1c, Dr. Roy Beck presented another retrospective analysis of DCCT data, this time showing that hypoglycemia –readings below 70 mg/dl and 54 mg/dl in 7-point testing – were strongly predictive of ≥1 severe hypoglycemia events over the subsequent three months. Referencing his ADA presentation showing that 7-point time-in-range in DCCT correlated with microvascular complications (published in Diabetes Care), Dr. Beck said there is now a “compelling case” for regulators to accept CGM-measured time-in-range as an endpoint in trials, but there may be more work needed to validate biochemical hypoglycemia. Shortly after, FDA’s Dr. Courtney Lias spoke about surrogate outcomes like time-in-range and hypoglycemia, suggesting that the field focus on better defining outcomes and specifying the context in which they want them to be implemented. We’d love to see devices/therapies indicated for improving time-in-range!

  • In automated insulin delivery, Insulet’s Dr. Trang Ly provided an excellent overview of Horizon hybrid closed loop trials to date, presented the first ‘n of 1’ sample of preschool age data, and showed an image of the smartphone interface – the system will now launch in 2H20 with smartphone control. UVA’s Dr. Stacey Anderson presented IDCL Protocol 1 data from a three-month RCT testing a Roche pump, Dexcom CGM, and TypeZero algorithm on a phone; there was benefit over SAP (especially overnight) in time <70 mg/dl and >180 mg/dl, despite only ~60% of time spent in closed loop (connectivity issues). UVA’s Dr. Marc Breton provided a glimpse of promising hypo data from the ongoing RCT testing CGM-based insulin dosing decision support in MDIs with Dexcom’s G5, TypeZero algorithms, and Novo Nordisk smart pens. An interesting panel riffed on whether there is a market for automated insulin delivery in type 2 diabetes – see thoughts below from Bigfoot, Insulet, Medtronic, Roche, and Dr. Roman Hovorka. Finally, the NIH is funding a new, exciting series of artificial pancreas studies led by Harvard’s Dr. Eyal Dassau to develop and evaluate a pregnancy-specific AID system for women with type 1.

  • In CGM, FDA’s Dr. Alain Silk covered the iCGM regulatory pathway, sharing the clear  efficiency advantages, especially for pump-integrated CGMs. We also saw a poster from PercuSense, a sensing startup founded in 2016, headed by former Medtronic employees Dr. Rajiv Shah and Mr. Brian Kannard, and targeting 14-day wear and factory-calibration – in-human trials are ambitiously expected to start in 2Q19. A rep told us the company is also working on a grant for integrated continuous glucose and ketone sensing.

    Greetings from Rockville, Maryland, where we present to you our top highlights from Day #2 of DTM!

    On Day #1, we saw fascinating MiniMed 670G user experience data from Mayo Clinic/ASU, heard a deep dive on CGM adhesives and implantable CGM/insulin delivery, and more.

    We’ll be back shortly with coverage of Day #3 highlights, headlined by an excellent CGM session with updates from Dexcom, Abbott, Medtronic, and Waveform.

    Table of Contents 

    Outcomes Beyond A1c Highlights

    1. Dr. Roy Beck Presents Second (Unpublished) Analysis of DCCT Showing Biochem Hypo to Predict Subsequent Severe Hypos; “Compelling Case” for Regulators to Accept CGM-Measured TIR as Endpoint in Trials

    Jaeb’s Dr. Roy Beck presented for the first time a soon-to-be-published analysis showing a correlation between biochemical hypoglycemia (as measured by 7-point testing) and subsequent severe hypoglycemia in the DCCT. In the DCCT, participants performed 7-point glucose measurements every three months; Dr. Beck et al. calculated the frequency of readings <70 mg/dl and <54 mg/dl, and looked at the frequency of severe hypoglycemia over the next three months prior to the next 7-point test. In this trial, severe hypoglycemia was defined as requiring assistance and associated with a measured blood glucose <50 mg/dl or prompt recovery after rescue carbs/glucagon/IV glucose. In total, 30,586 profiles were analyzed. The relationship was strongly statistically significant (p<0.001), as only 4% of individuals without a single reading <70 mg/dl during the 7-point test had a severe hypo in the subsequent three months, while 14% of those who had ≥4 readings <70 mg/dl proceeded to experience ≥1 severe hypo. A similar relationship also held for number of readings <54 mg/dl. As seen in the figure below, a single reading <54 mg/dl puts the user at risk of severe hypoglycemia almost to the same extent as ≥4 readings <54 mg/dl; similarly, having two readings <70 mg/dl appears to carry the same severe hypo risk as having ≥4. That is, any amount of biochemical hypoglycemia elevates risk; though we do wonder if factors such as time of biochemical hypoglycemia (i.e., day vs. night) interacted with subsequent hypoglycemia risk. In a separate analysis, Dr. Beck pooled all data from the DCCT and found that a host of hypoglycemia metrics significantly correlated with number of severe hypoglycemia events. See the table below. For example, those with zero, one, and more than one severe hypos had time <70 mg/dl throughout DCCT of 5%, 8%, and 12%, respectively – similar relationships held for time <54 mg/dl, area over curve 70 mg/dl, and LBGI. These data very nicely add to the time-in-range paper that appeared last month in Diabetes Care.

    • In a preceding talk, UVA’s Dr. Boris Kovatchev showed that those in the DCCT with LBGI <1.1 were 17x less likely to experience a severe hypo than those with LBGI >5.0! Dr. Beck had a few other relevant data points from previous studies: (i) in the JDRF CGM trial, CGM-measured hypoglycemia on one day was strongly associated with a severe hypo event on the subsequent day (risk 10x higher when glucose <70 mg/dl for >30% of time vs. <5% of time on prior day; ≥30 minutes with values <54 mg/dl on prior day more than doubled risk); (ii) Kovatchev et al. previously showed that hypoglycemia measured by BGM over one month associated with severe hypo in the next six months and that BGM hypoglycemia was more frequent in the 24 hours prior to a severe hypo than on other days; and (iii) a HypoDE sub-analysis from EASD showed that baseline CGM-measured hypoglycemia over four weeks was associated with severe hypo risk in the subsequent six months.

    • Dr. Beck concluded that we can “probably” make a compelling case for an interaction between measured biochemical hypoglycemia and severe hypoglycemia, though more data is needed to make a strong case for that validation – he proposed critically evaluating more of the available data (obviously, there can’t be an RCT exposing groups to different levels of hypoglycemia) and determining if more data are needed. While the story may not be clear cut, he noted that there is a lot of supporting data and “a lot of common sense” for taking biochemical hypoglycemia into consideration, given that it has been shown to be tied to cognitive impairment, cardiac arrhythmias (mortality), an increase in car accidents, adverse effect on quality of life (including sleep), and reduced productivity. From a different angle, while severe hypoglycemia has been accepted by FDA as an outcome measure for RCTs, modern advances in therapies and technologies have mitigated severe hypo risk to the point that it is simply not financially or temporally feasible to consistently power studies to show differences in event rates (unless eligibility is restricted to individuals at very high risk). To illustrate this point, Dr. Beck calculated that if the severe hypoglycemia rate in a control group was 15 per 100 person-years, for a 50% treatment effect in a three-month trial, the trial would have to enroll 3,480 people! For a control group with a rate of 5 events per 100 person-years, the trial would have to enroll 10,480! In our view, Dr. Beck’s post-hoc analyses of DCCT other studies, combined with the other demonstrated health and quality of life detriments of hypoglycemia and the critical importance of moving to biochemical hypoglycemia in clinical trials for feasibility’s sake, makes for a compelling case for biochemical hypoglycemia. We wonder how much new data would move the needle, or whether it is simply a matter of overcoming inertia by lifting patient, clinician, and researcher voices.

    • After reviewing the recently-published Diabetes Care paper showing a strong correlation between 7-point profile time-in-range and microvascular complications in the DCCT – stronger, in fact, than the correlation between A1c and complication burden – Dr. Beck asserted that a “compelling case” can be made for regulators to accept CGM-measured time-in-range as meaningful endpoint for clinical trials. Further, he believes it’s reasonable to surmise that with CGM data now available, even stronger associations may surface. [He did note, citing a DirecNet study comparing 8-point testing with CGM in 161 type 1 children, that time-in-ranges calculated by SMBG and CGM track extremely well.]

      • Dr. Kovatchev offered his own, less measured, conclusion for this paper: “It may be time to put HbA1c on the shelf of history and use CGM data to reveal the true nature of glucose fluctuations in diabetes.” The paper’s actual conclusion was a bit more measured: “Although hemoglobin A1c remains a valuable outcome metric in clinical trials, TIR and other glycemic metrics, especially when measured with continuous glucose monitoring, add value as outcome measures in many studies.”

    • In his review of other potential approaches to validating CGM-based metrics, Dr. Beck pointed to the PERL RCT of allopurinol to reduce the progression of kidney disease, which uses CGM and is expected to complete next year. In a discussion later in the day, Dr. Rich Bergenstal suggested figuring out a way to encourage CGM in all CVOTs. FDA’s Dr. Lias responded it’d take a pretty strong incentive for drug companies to incorporate CGM in long, large trials, but perhaps NIDDK would be willing to fund such an undertaking.  

    2. FDA’s Dr. Lias Asks for Specificity In TIR/Hypo Surrogate Outcome Discussions: For Clinical, Guidelines, or Regulatory? For Devices or Drugs? For Safety or Safety + Effectiveness? Which Tools? How Analyze Data?

    FDA’s Dr. Lias offered an in-depth look at the Agency’s perspectives on CGM-based endpoints to “replace or supplement A1c,” emphasizing that measures such as time-in-range need context of use and more definition before they can be qualified endpoints. She also came back time and again to specificity: “One thing I hear frequently discussed is using endpoints in clinical management, general discussions and guidelines, and regulatory guidelines. Those contexts are not the same, and I recommend the community get together and decide what’s most important in each setting. [In regulatory,] for devices it’s probably not as important to get new endpoints. You can get a device on the market without a new endpoint – you can show the device is safe without it. It could be valuable if other drug therapies are not available to patients because A1c is not an appropriate endpoint, then maybe focus there. Make sure you’re definitely talking about one thing – really decide what is most valuable and go from there.” We thought this statement made a lot of sense; MiniMed 670G, for example, is on the market and time-in-range data is included in the user guide. However, the same is not true of therapies, and this could be especially important for SGLT-2 inhibitors in type 1 diabetes. If companies could get a therapy indicated for improving time-in-range, it could change the narrative beyond A1c alone. It’s a testament to progress and FDA’s receptivity that we’re now hearing more nuanced conversations about how, where, and when new metrics should be applied, rather than what they are or whether they have any value. Indeed, Dr. Lias “can’t emphasize how much [FDA] wants to have new endpoints and new therapies on the market.” As always, she urged attendees to come talk to her and her team, be it through pre-submission discussions, conferences and consensus meetings, or the branches’ dedicated programs for this purpose (CDER’s Biomarker Qualification Program or CDRH Medical Device Development Tools). She did emphasize that just because CDRH qualifies an endpoint, that doesn’t mean it translates over to CDER – a frustrating state of affairs. We think CDER can learn a lot from CDRH and hope to see much more of that happen in the coming years.

    • Dr. Lias first asked for surrogate measures to be defined. As an example, she proposed a scenario: In study A, a treatment lowers time <70 mg/dl from 3.3% to 0.3%, and in study B, the treatment lowers time <70 mg/dl from 3.3% to 3.0%. However, this doesn’t take into consideration the magnitude of the hypoglycemia – it turns out treatment B was less effective at reducing time <70 mg/dl, but study B’s control arm had very, very low blood glucoses (e.g., <54 mg/dl) that the intervention elevated to just below 70 mg/dl. Dr. Lias suggested that both actual time and magnitude (how low) matter, and there is a lot of focus on the former. Dr. Roy Beck pushed back on this idea in Q&A, saying that a paper will soon be published in JDST showing that “time <70 mg/dl, time <54 mg/dl, LBGI, and area above the curve (magnitude) all show a high correlation around 0.95. You rarely see anyone who’s just hovering right below threshold” (the paper shows the same phenomenon for hyperglycemia metrics). Dr. Beck makes a great point, and we’d note CGM collects both time and magnitude, meaning a composite of area under curve plus time <70 mg/dl would also be easy to report. Dr. Lias similarly noted that a time-in-range of 85% is worse than a time-in-range of 80% if the time-out of range is concentrated in extreme glucose ranges of <50 mg/dl and >250 mg/dl. A point championed by Drs. Rich Bergenstal and Thomas Danne to report time 70-180 mg/dl and time <70 mg/dl together could be useful – in that case, time >180 mg/dl could be deduced, and Dr. Beck’s soon-to-be-published JDST paper would suggest that severity and hypoglycemia and hyperglycemia would follow from time in those ranges. (Again, we’d note that CGM collects all this data, so reporting it is just a matter of putting it in the paper/submission/label.) Other questions from Dr. Lias: What range(s) are meaningful? (We believe the field has settled on an answer here.) What margins of time difference between arms is clinically meaningful – 5%? 10%? How do you measure it? This last point is definitely a next frontier for the field – what delta in time-in-range matters for outcomes, and what is the point of diminishing marginal returns?

      • As a whimsical example of why defining an endpoint is important, Dr. Lias gave an example: “Number of live bats in a cave as a surrogate for how good a habitat would be.” Is it that lots of live bats (the animal) make for a great habitat? Does one live bat (Batman) make for an OK habitat? Do no live bats (but one baseball bat) make for a terrible habitat? “How you define your endpoint matters, so be very specific…don’t let bats vs. Batman be the thing that sinks your indication.”

    • Once the measure is defined, FDA has a further set of questions. A full list of questions the Agency may have about surrogate endpoints can be found here. For example:

      • How does this endpoint relate to patient health? [Ideally, researchers can continue to generate convincing evidence that high time-in-range correlates with low complication burden and that biochemical hypoglycemia correlates with severe hypoglycemia.]

      • What is the appropriate context of use for this endpoint? Will it be used for both safety and effectiveness? For effectiveness only? Will it be used in drug trials? For all drugs, or only for drugs with certain mechanism? FDA currently does allow time-in-range on labels, just not as a surrogate endpoint like A1c. “For a full safety and effectiveness indication, you need a whole lot of information – if the context of use is much narrower, a lot less information is needed to qualify the tool.”

      • Are there appropriate tools to measure it? “Not all CGMs may be created equally in the range of 54-70 mg/dl, for example. Some have warnings about overestimation of hypoglycemia [Editor’s Note: FreeStyle Libre Pro] – maybe that CGM would fall outside appropriate use for that endpoint. Or maybe no CGM is very good below 54 mg/dl, how much precision do you need? It doesn’t need to be perfect, maybe the current technology is ok, but it’s good to think ahead about how much precision you need.” As Dr. Beck mentioned in the discussion, this issue may be largely eliminated if both the control group and the treatment group use the same sensor, since bias would be equal in both groups.

      • How do you analyze the endpoint? Do you study at a population-level? Averages can make a difference and may not always tell the full story.

    3. www.GlucoseProfile.com to Rank AGP Profiles; Drs. Bergenstal, Kovatchev Discuss GMI (Formerly eA1c), now published in Diabetes Care

    A very cool poster at DTM introduced www.glucoseprofile.com, a Novo Nordisk/DTS collaboration to crowdsource the determination of what constitutes an ideal AGP profile. Users anonymously tick a box next to the description of their level of experience and are given nine AGP profiles to drag into rank order from “best” to “worst” – see the pictures below. This is a fantastic idea! We encourage readers to do this, as it reveals how incredibly nuanced this tasks is – it’s very difficult to tradeoff the various metrics. (The next step would be to debate how to treat each profile!) This reminds us of the clinician survey method used to generate the Clarke Error Grid and then again for the DTS BGM surveillance error grid.

    • IDC’s Dr. Rich Bergenstal told the story of how GMI (Glucose Management Indicator) came to be: estimated A1c (“eA1c”) used to be on reports, but then FDA and others received countless complaints that eA1c ≠ lab A1c and asked CGM companies to remove it. eA1c has now been rebranded as “GMI,” which should be back on CGM reports soon. See the Diabetes Care paper, just published this month (Bergenstal et al.). Jaeb and IDC now both have GMI calculators, leveraging modern CGM studies rather than the old ADAG study. As expected, GMI still doesn’t always agree with lab A1c, something the Diabetes Care paper makes clear with a discussion guide. Dr. Bergenstal cited recent data showing that CGM-measured GMI is within 0.1% of lab A1c 19% of the time, but it is >0.4% different from lab A1c 40% of the time, and >0.5% different 28% of the time. The important part of the metric, he said, is using it to inform interpretation of A1c. “If your A1c in lab is 7% but your GMI is always saying 6.5% and derived from a reliable 14 days or more of CGM, then you probably need to think about your targets of A1c to be safe. Maybe you need to run a little higher because for your personal glucose values, you’ll be running a little lower than others with the same A1c.” Dr. Kovatchev proposed that the eA1c nomenclature could stay if it was based on a model reflecting hemoglobin glycation and clearance and calibrated with the individual’s actual A1c value, which he noted wouldn’t be difficult to do. If not calibrated, then the metric should be called GMI.

    Automated Insulin Delivery Highlights

    1. First Peek at Insulet Omnipod Horizon Preschool Performance; Horizon Study Summary and User Interface on Samsung Galaxy Smartphone

    Insulet’s Dr. Trang Ly offered a peek at promising four-day data from a single 2.6-year-old child, showed a picture of the Horizon closed loop interface on what appeared to be a Samsung phone, and presented concise summary slides of Horizon closed loop trials to date (through IDE 3). She also answered some questions in Q&A about next-gen systems and whether or not different closed loop algorithms would be needed for children and adults.

    • Insulet is not sharing all of its Horizon study data from the 2-6 year old (preschool) group yet – eyes on ATTD or ADA? – but Dr. Ly did present data from the youngest child to ever be on the system to date, one of Dr. Bruce Buckingham’s 2.6-year-old male patients. The glucose trace from day three on the system is posted below. Over the course of four days, the child’s mean glucose was 127 mg/dl and time 70-180 mg/dl was 86%. Remarkably, total daily dose of insulin on closed loop was half that at baseline (13.7 -> 6.8 u/day), suggesting more efficient use and probably lower risk of hypoglycemia. eA1c in the study was 6.1% after a baseline lab A1c read out as 7.0%. It would’ve been great to see a baseline trace for comparison, but at first glance the trace looks solid and we cannot wait to see the full data set. Dr. Ly noted that young children are a very challenging age group because they have a lot of glycemic variability – “different physiology, different glycogen stores, so stakes are higher in terms of hypoglycemia and the response. The test of any algorithm is how it responds in this clinical case.” She added that the minimum bolus increment of 0.05 units appears to work well in the low total daily dose setting. To that end, during Q&A, UCSF’s Dr. Saleh Adi asked if there is a case to be made for developing different algorithms for different age groups; the gist of responses from both Drs. Eric Renard and Ly was that it shouldn’t be necessary, so long as the pump is accurate and the system can deliver low-enough doses, which it seems to have done in Insulet’s ‘n of 1’ case study.  

    • Dr. Ly opened her talk by showing the Horizon user interface on what looks like a Samsung smartphone. As a reminder, Insulet announced on its 3Q18 call last week that Horizon would launch in 2H20 with direct smartphone control, and a few days later a partnership with Samsung that would enable certain Galaxy smartphones to directly control the Omnipod. We love the look of the interface, which doesn’t appear to stray far from the previous design of the Dash PDM Horizon display. We’re not sure about Insulet’s plans for direct iPhone control.

    • The table below is a wonderful summary of trials Insulet has performed with Omnipod Horizon to date. Dr. Ly highlighted that studies have consistently delivered time-in-range of 69%-79% – up to 85% overnight – and <2% hypoglycemia. The studies shown have enrolled people ages 6+ (now in ages 2-6), former MDIs, and challenged the system with exercise and meals. Dr. Ly said that in the next 12 months, the company plans to do further studies to gain additional confidence in the algorithm before moving to clinical studies on its own commercial system (Omnipod + Dexcom G6 + phone control). Per the 3Q18 call, a fourth IDE study (pre-pivotal) of Horizon will soon test real-world use in 20-30 people, putting a pivotal study firmly into 2019.

    • When asked about future enablers for better closed loop control in Q&A, Dr. Ly responded: “Beyond Horizon with gen 2, gen 3, we need faster insulins, and potentially biosensors that can increase signaling and make algorithms smarter. It’s about reducing burden for patients – reduce the need to announce meals, they don’t want to do that, they just want to live their lives. I’m excited to be at this conference and learning about new technologies and what we can do in future generations.”

    2. CGM-based Decision Support with NN Smart Pens/Dexcom G5/TypeZero: Study Ongoing, 35% Reduction in Hypoglycemia from Baseline

    UVA’s Dr. Marc Breton provided a glimpse of promising hypoglycemia data from the ongoing RCT testing CGM-based insulin dosing decision support in MDI users with Dexcom’s G5, TypeZero algorithms, and Novo Nordisk smart pens (NFC-enabled NovoPen5Plus and NovoPen Echo Plus). Seventy patients across the three sites – UVA, Stanford, Mt. Sinai – have completed the study and n=13 are still in progress. Dr. Breton couldn’t share much as the study is ongoing, but did provide a glimpse of glycemic control for the first time. The decision support system has provided “strong hypoglycemia protection,” taking time <70 mg/dl from 4% at baseline to 2.6% while on treatment – a meaningful 35% reduction, though a comparison to the control group is not available yet. (This reduction is actually on par with Tandem’s Basal-IQ.) Notably, study participants were already well managed at baseline, with an average time-in-range of 59%, but with significant variability across the population – one person had 2% time-in-range at baseline, while another had 98% (time-in-range). Dr. Breton noted that “system is designed to reduce glucose variability and preliminary results indicate possible improvements” – presumably the population’s mean time-in-range will improve along with range of variability. The study is expected to complete by the end of December, according to ClinicalTrials.gov. Perhaps we’ll see data at ATTD or ADA 2019…

    • Dr. Breton did a great job of covering the system’s features (see below) and the two smart pen posters on priming and bolus habits shared at ADA. He emphasized the headline stat from the latter, noting 28% of meals have had either a late or missed bolus – a clear reminder of how much valuable data smart pens + CGM are going to add.

    • The study is randomizing MDI users to 12 weeks of decision support system (DSS) use with CGM vs. CGM only. The DSS group is running the software on a smartphone, which downloads the pens via NFC (presumably every day or multiple times per day) and talks to the Dexcom G5. It has a very impressive list of features – bolus and basal dosing advice, exercise advice, forward-looking hypoglycemia prediction, and even before-bed eating advice to avoid lows. Wow is this better than what MDI users have now!

    • As a reminder, Dexcom now owns TypeZero, meaning this trial (or a future one) could pave the way for approval of a Dexcom MDI insulin dose decision support app. On its 3Q18 call this week, EVP Steve Pacelli said Dexcom is running some “smaller pilots” with TypeZero and Novo Nordisk and “certainly by 2020” investors can expect a broader commercial rollout – we expect this product is the foundation of that! Medtronic is developing MDI advice on its own (see its Analyst Meeting), and of course Abbott has its Bigfoot partnership to offer MDI advice with smart pens via Inject (see FFL 2018).

    3. IDCL Protocol 1 (Mobile CL vs. SAP) Meets Both Primary Outcomes of Superiority in Time <70 mg/dl and Non-Inferiority in Time >180 mg/dl Despite Connectivity Issues – Only 60% of Time in Closed Loop!

    UVA’s Dr. Stacey Anderson presented results from Protocol 1 of the IDCL trial, a multi-site, three-month RCT (n=125) that randomized participants 1:1 on closed loop (Roche Accu-Chek Spirit Combo pump + Dexcom G4/G5 + inControl AP algorithm on phone + Ascensia Contour Next One meter for calibrations) and sensor-augmented pump therapy. Both primary outcomes – superiority in time <70 mg/dl and non-inferiority in time >180 mg/dl – were met, despite connectivity issues that led to the closed loop group only automating insulin delivery a mean ~60% of the time. Regarding hypoglycemia: The closed loop group saw time <70 mg/dl halved from 5.0% at baseline to 2.5% post-randomization, while the SAP group’s time <70 mg/dl dropped mildly from 4.7% to 4.0%. This translates to a relative reduction of 24 mins/day (-1.7%) <70 mg/dl. For hyperglycemia: The closed loop group saw time >180 mg/dl drop from 40% at baseline to 34% post-randomization, while the SAP group dropped from 43% to 39%. This amounts to a relative reduction of 43 mins/day (-3.0%) >180 mg/dl in favor of closed loop. As seen in the figure below, most of these benefits were derived almost exclusively in the overnight period; while there was a significant reduction in hypoglycemia for the closed loop group during the day, time-in-range and time >180 mg/dl were similar to the SAP group. In the overnight period, the closed loop group had a 2.3%-point advantage on time <70 mg/dl (-8 min/night, assuming period is 12-6 am) and a 6.9%-point advantage in time >180 mg/dl (-25 min/night, under the same assumption). From a safety standpoint, there was one episode of severe hypoglycemia, but it wasn’t related to the study device (there was no DKA). Sub-analyses are planned to look at differences between those with baseline A1c <7.5% vs. ≥7.5%; age <25 years vs. ≥25 years; and percent time in closed loop (<80% vs. ≥80%). We’re especially curious about the last one analysis, as, on average, members of the closed loop group were still using manual, open-loop 10 hours/day! Obviously these numbers aren’t showing a slam dunk for closed loop, but given the connectivity issues, a commercial product would perform much better. Dr. Anderson also noted that baseline CGM use was above 70% in both groups, so we’d be interested to see an analysis of time in closed loop and time-in-ranges broken down by prior CGM experience.

    4. Dr. Eric Renard Shares Positive Pediatric AID Three-Day Hotel Study (n=24) Results; Freelife Kid AP Study (Dexcom G6/Tandem t:slim X2/Control-IQ) Kicks Off in France

    Montpellier University Hospital’s Dr. Eric Renard shared results from a three-day, randomized cross-over hotel study (n=24) in prepubertal children with type 1 diabetes (ages 7-12) comparing closed loop (Dexcom G4/Tandem t:slim/DiAs) vs. threshold low glucose suspend. Results were published in Diabetes, Obesity Metabolism in July. While there was no observed significant difference in the primary hypoglycemia outcome (time <70 mg/dl), time between 70-180 mg/dl and 70-140 mg/dl, as well as mean sensor glucose were significantly improved with closed loop (p<0.001), indicating glycemic benefits were achieved without increased risk of hypoglycemia – see the slides below, which show an ~2-hour/day time-in-range advantage for closed loop (~60% vs. ~49% in 70-180). Dr. Renard highlighted a significant increase in participants’ artificial pancreas acceptance score following the study. As Dr. Renard acknowledged, there are several barriers to use of closed loop systems in children in this age group, including reduced insulin needs, limited body surface area, skin reactions, the potential need for specific algorithms, and safety concerns. We’re very encouraged by these promising results and look forward to larger and longer pediatric closed loop studies (see immediately below).

    • Dr. Renard announced that the Freelife Kid AP study kicked off in France just two days ago. The Freelife Kid AP study, PI’d by Dr. Renard, compares time-in-range (70-180 mg/dl) between nocturnal and 24-hour use of an AID system (Dexcom G6 CGM/Tandem’s t:slim X2 pump/ Control-IQ algorithm) in 120 prepubertal children (6+ years, as we understand it). According to Dr. Renard, Freelife Kid AP will be the largest real-life AP study ever conducted in children. Following a 14-day run-in phase, which includes pump and CGM training, participants will be randomized to undergo either dinner and nocturnal use or 24-hour use of the AID system for 18 weeks. An 18-week extension phase (we assume of 24-hour use) completes the study.

    5. AID in Type 2: Is There a Market? Panel with Bigfoot, Insulet, Medtronic, Roche Shows Basic Questions Are Unanswered, Wide Spectrum of Views

    A mostly industry panel discussion tackled an exciting new question for the field: is there a market for automated insulin delivery in type 2 diabetes? Bigfoot CEO Jeffrey Brewer, Insulet CCO Brett Christensen, Medtronic VP Alejandro Gallindo, Roche Global Head of Diabetes Care Marcel Gmuender, Cambridge’s Dr. Roman Hovorka, and a representative from EOFlow (we didn’t catch his name) took turns answering questions from Sansum’s Dr. David Kerr (moderator) and the audience. The big takeaway here was there wasn’t really one –this market has not been thought through in a nuanced way, there is no data beyond Dr. Hovorka’s hospital data (which is quite stunning), and basic questions about AID in type 2 remain unanswered. Who would get a closed loop system in type 2, either using pump or injections? How big is the potential market? When would someone get a closed loop – e.g., at diagnosis to induce remission? What’s the competition, given where type 2 diabetes therapy is now and is going in the future? How much different does the AID algorithm have to be for type 2? How different is the user interface and system design? Is there a viable business for AID in type 2 – could it save the system costs or reduce use of expensive therapies? Would a CVOT be required for a closed loop in type 2? See some themes and key questions below, followed by quotable quotes.

    • Segmentation – AID in who and when? The biggest point of discussion concerned segmenting the type 2 market for AID. Bigfoot’s Jeffrey Brewer and Insulet’s Brett Christensen noted the obvious target: people with type 2 currently using insulin, especially those on MDI. Both suggested that market alone could be as big or perhaps even bigger than the current type 1 market. By contrast, Roche’s Marcel Gmuender argued that “the segment is much smaller” – potentially even smaller than the type 1 market – since AID in type 2 is likely to be used as a “last cause.” Funnily enough, there were arguments on the other end of the spectrum, too: Dr Hovorka mentioned the potential to use closed loop at diagnosis in type 2, normalizing glucose quickly, giving the beta cells a rest, and potentially putting individuals into remission. This was indeed a hotter topic in the pump field a few years ago, when some data out of Korea (Choi et al.) suggested it was possible to induce type 2 diabetes remission following aggressive short-term pump therapy; clearly, more study is warranted here! Dr. Hovorka also noted the obvious potential to transform treatment in type 2 hospitalized patients, summarizing the compelling data he shared on Day #1.

    • The pathophysiology of type 2 diabetes – when would AID make sense (e.g., low C-peptide)? One of the more interesting points of discussion concerned the development of type 2 and hyperglycemia – hyperinsulinemia vs. insulin deficiency. In Q&A, one speaker pointed out that “pumping more insulin into someone with type 2 will just worsen the condition,” taking a hyperinsulinemia view of type 2 diabetes – i.e., will someone on 150 units of insulin per day benefit from AID? Some commented that AID segmentation should focus on using C-peptide as a biomarker of who might benefit – those with type 2 and no insulin production should be targeted for AID, while those with lots of insulin production may not be good candidates. Said one questioner, “Type 2 diabetes is not one disease. People with type 2 may make no insulin or may make tons of insulin. There is so much diversity. and targeting people with insulin deficiency (for AID) is the first thing to do. I take care of patients with cystic fibrosis and pancreatectomies, and they have insulin deficiency. I actually think Medicare has done something good with their criteria for having technology – low c-peptide. That’s a reasonable thing to do and a good jumping off point into research.” Adding another wrinkle, Dr. Roman Hovorka commented that in their hospital-based closed loop studies in type 2, improving glucose levels actually reduces insulin resistance. “You get a big reduction in insulin once you get good glucose control at night.”

    • What is the competition for AID in type 2 – better oral therapies? Mr. Brewer wondered if type 2s on expensive therapies – GLP-1s and SGLT-2s – could actually be put on AID and get better long-term outcomes. Dr. David Harlan stepped up in Q&A and noted that with the positive CVOT data for both classes, this might be a hard sell. “In patients referred to me with poor type 2 diabetes control and on 150 units of insulin, within 6 months, we can get them off insulin with normal BGs with these classes. Defining the population for a pump with type 2 is really challenging.” This is indeed a big question – how will AID compete on outcomes with the less burdensome GLP-1 and SGLT-2s classes?

    • What is the AID product in type 2? How simple should it be? Mr. Brewer echoed his presentation at Friends for Life, noting that Bigfoot is building an AID portfolio from basal-only type 2s (using a smart pen and BGM) to MDI users on smart pens + CGM to full AID with a pump and CGM – “Obviously those have different costs, but when you step back and focus on insulin and how to deliver it safely, type 2 is the biggest part of the AID market.” Insulet’s Brett Christensen did not mention closed loop plans for type 2, though he did reiterate the hope to launch Omnipod U500 in “late 2019,” with U200 “some time after that. This is something we’ve wanted to do at Insulet for a long time – we have a 200-unit reservoir, and concentrated insulin is a natural fit for the pod.”

    • How will AID impact total cost of treatment in type 2? Roche’s Marcel Gmuender noted that “this treatment will cost more money,” a concern given advancement with oral drugs. Conversely, Dr. Barry Ginsberg pointed out that in developing medical products for ~30 years, “Every bad decision I’ve made was because we thought about cost too much upfront. I think as we think about products, electronic things are going to shrink in size and substantially shrink in price, making them much more available to patients.”

    Quotable Quotes

    • “Our perspective on segmentation, is that all the existing approaches to delivering insulin with technology are focused on a highly engaged segment of population – whether someone has type 1 or type 2 diabetes, and is on pump and/or CGM. There is a big opportunity for people who are not capable of using CGM data to titrate their insulin, and who are not interested in wearing pumps. We’re all about ease of use, minimizing the data burden, taking steps out, and doing things like a single prescription that is automatically filled. The market is very narrow today, our belief is that pushing a simple, easy, and consolidated product will open up a big opportunity to help a lot more folks.” – Jeffrey Brewer (Bigfoot)

    • “Complexity is the enemy with adoption of pump therapy, and many patients think pump therapy is complex. But the second challenge outside of complexity is awareness. We hear from type 2s that they are not interested in pumps, but then we show them Omnipod and they are interested. Complexity and awareness are the keys to this. But it’s not a market that is hundreds of millions of people.” – Brett Christensen (Insulet)

    • “We’ve published in NEJM, and what we’ve shown is that in the hospital, we can actually achieve tremendous improvements in glycemic control in type 2 because it is so poorly controlled. Hospitals are very dangerous places to be, especially for those on dialysis and on parenteral nutrition. We need longer-term data, not just showing improved time-in-range, but reduced comorbidities and reduced length of stay.”

    • “In clinical practice (of type 2 diabetes), you rarely go for C-peptide measurement. You look at the glucose and A1c, and make a very random and frightening decision to go with insulin and look at optimization over time. It is very primitive, it is dangerous, and it is ineffective. At least with a smart pen, you have some record of dose and timing; that will make a huge difference.” – Dr. David Kerr (Sansum)

    • “Something worrying me is that the vast majority of individuals with type 2 are managed in primary care. If you produce technology that looks challenging or expensive, what is the expectation that primary care will absorb this?” – Dr. David Kerr (Sansum)

    6. NIH Funded Study Investigates Pregnancy-Specific, Adaptive AID System; NIH/NIDDK and JDRF Funding Opportunities

    NIH’s Dr. Guillermo Arreaza-Rubin, newly minted 2018 winner of DTS’ Artificial Pancreas Award, described a new series of artificial pancreas studies funded by the NIH/NIDDK to develop and evaluate a pregnancy-specific automated insulin delivery system able to adapt to the physiological changes experienced by women with type 1 diabetes during pregnancy. The study will be led by Harvard’s Dr. Eyal Dassau and conducted by a consortium of investigators from Harvard University, Mayo Clinic, Icahn School of Medicine at Mount Sinai, and the Sansum Diabetes Research Institute. Amazing news! The AID algorithm will be finalized through an iterative process involving two supervised 48-hour studies and a one-week home-based study, culminating in a four-week, multisite, single-arm study during the relatively lower-risk 14-28-week pregnancy period, with the option of extension for the duration of pregnancy. Total funding of $813,259 was awarded in September and is intended to last through July 2019. The project is slated to wrap up in July 2021, so it is likely that further funding will be sought. We’re incredibly excited to see a focus on technology in pregnancy –the field is still pretty far behind here and the closed-loop benefits are very clear, as illustrated by Cambridge’s 2016 NEJM publication. Despite the very positive CONCEPTT results, CGM is still not considered standard of care during pregnancy. We very much appreciate the vision and innovation to drive further research into pregnancy; of course, a lot could also be done with current devices and we hope companies can also do those studies with 670G, Control-IQ, Horizon, etc.

    • Dr. Arreaza-Rubin mentioned two other newly NIH/NIDDK funded studies of note: (i) OHSU’s study comparing a robust closed loop system vs. a decision support system in high-risk patients (A1c 8%-10.5%) with type 1 diabetes; and (ii) University of Utah’s study investigating novel insulin molecules discovered from the venom of fish-hunting cone snails.

    • JDRF’s Dr. Daniel Finan described the Foundation’s three main areas of research interest, driving to the ultimate goal of a fully automated closed loop system providing excellent glucose management. He explained that JDRF hopes to: (i) reduce burden (e.g., advanced infusion sets, increased automation, miniaturization); (ii) expand access and innovation (e.g., targeted subpopulations, barriers to adoption, open-protocol AP systems); and (iii) enhance glucose management (e.g., adjunct drugs, more physiologic delivery routes, additional inputs, “genius” personalized and adaptive algorithms).

    • Dr. Arreaza-Rubin highlighted two NIDDK funding opportunities with a second submission date of December 6: (i) RFA-DK-17-023: Clinical, behavioral, and physiological research testing current and novel closed loop systems; and (ii) RFA-DK-17-024: Impact of the use of glucose monitoring and control technologies on health outcomes and quality of life in older adults with type 1 diabetes. JDRF’s Dr. Daniel Finan similarly announced two open RFPs, both due November 28: (i) Identification of AP algorithm enhancements through big data analysis; and (ii) No (type) one left behind: expanding AP adoption and access among targeted populations.

    CGM and Other Regulatory Highlights

    1. FDA on iCGM: From Three PMAs to One 510(k) for a Next-Gen CGM Integrated with Two Pumps; FDA is “open to proposals” for other devices

    FDA’s Dr. Alain Silk reviewed the integrated/interoperable CGM (iCGM) regulatory path paved by Dexcom’s G6, noting the clear innovation and efficiency advantages. The best illustration of this came in the unbranded example noted in the slide below: a standalone CGM that also integrates with two insulin pumps. In the PMA world, updating to the next-gen CGM would require three PMAs – one for the CGM update and one for each pump. In the new iCGM paradigm, a new CGM would require a 510(k) – assuming it meets the special controls – and then labeling updates for the pumps. The pump companies don’t even have to submit to FDA, potentially cutting years off the PMA timeline. Nice! (Though unbranded, this was a real-world example of the Dexcom G4 integrated Tandem/Animas pumps in the PMA era vs. Tandem’s Basal-IQ with iCGM integration – the timing went from well over a year to a few of months.) Dr. Silk reviewed the iCGM special controls at a high-level, which we previously covered in depth in March. He emphasized that iCGMs must have clear communication protocols; there must be a strategy to ensure reliable and secure data transmission to digitally connected devices. Applicants must also describe how complaints/problems will be handled when another system is involved. As Dr Lias has shared in recent talks, Dr. Silk noted that iCGMs do not have any specific calibration scheme, meaning they can be factory calibrated (like G6) or require daily calibration. iCGM clearance also does not mandate connectivity with any other device, though it certainly enables it with interoperability and rapid innovation in mind. However, the path is flexible for companies that want to remain closed. Of course, the tide of the industry is moving to interoperability, and closed innovation strategies will become increasingly untenable and very high-pressure, requiring insular companies to innovate as rapidly as partnered companies. (In the case of Medtronic, this means it has to innovate quickly on all three components of AID, a taller order than what Tandem and Dexcom have to do.)

    • In Q&A, Dr. Silk said FDA is “open to proposals” for granting integrated/interoperable status to other devices, though “the same strategy may or may not be appropriate for specific devices.” The question from Dr. Klonoff was about potential for an iBGM, which has not been discussed previously and could make a lot of sense. For now, Tandem’s t:slim X2 is under FDA review for iPump status (submitted in October), and Tidepool aims to get Loop cleared as an iController. Will we see other companies submit for iCGM and iPump status? For now, Abbott’s FreeStyle Libre falls short of iCGM accuracy and does not send data continuously, Medtronic is slightly short of iCGM accuracy in euglycemia, and Senseonics is first pursuing 180-day wear.

    • A novel CGM can apply right from the start to be an iCGM; it does not have to be PMA approved first. That said, the big challenge for all the companies is achieving the very rigorous iCGM accuracy benchmarks, which Dexcom’s G6 itself only barely meets in some glucose bins.

    2. PercuSense (Founded by Dr. Rajiv Shah) Developing Multi-Analyte Sensing Platform; 14-Day Wear, Factory-Cal, Beginning Human Studies 2Q19; Potential Grant for Combined Glucose/Ketone Sensor

    During the day #2 DTM startup showcase, we chatted with PercuSense, a company developing a multi-analyte sensing platform. The venture was founded in 2016 and is headed by founder Dr. Rajiv Shah (former Medtronic Diabetes VP of Sensor Engineering and Operations) and CEO Mr. Brian Kannard (former Medtronic Director of Sensor Product). PercuSense is focused on diabetes, but beyond glucose: ketones, oxygen (at the infusion catheter to assess infusion set viability), and lactate and oxygen (to detect worsening comorbidities). From a CGM perspective, the company is aiming for a 14-day wear, factory-calibrated device. It intends to begin a 10-person human study in 2Q19 and envisions a potential launch as soon as two years from now (~late 2020 or beyond); that sounds pretty unrealistic to us, given how long CGMs take to develop. The rep told us that PercuSense is currently working on a grant for integrated continuous glucose and ketone sensing with a funding organization (Helmsley?), which has clear implications for SGLT-2s, especially in type 1 and if cost can be driven down far enough. The poster does tout a “low cost sensor approach,” wherein 2000+ transducers per sheet are formed through “high volume influenced microelectronics processes paired with off the shelf industrial materials,” and 10 million sensors could be produced/year. The platform also reportedly supports integration into an infusion set, and preliminary demonstrations show minimal interference in signal from insulin infusion. This is a very early-stage product, but the team’s previous Medtronic Diabetes experience is of note. Still, given the very high – and rising – bar for CGM, 14-day factory calibrated CGM would match product features now – where will CGM be in two years?

    3. FDA’s Quality in 510(k) Review Pilot Program Cuts Review Time by 1/3; Expanded Use of the Abbreviated 510(k) Program to Launch in “Coming Months;” NEST Update

    FDA CDRH Director Dr. Jeffrey Shuren – quite a big-name keynote! – described several of the Agency’s exciting initiatives focused on the least burdensome principle: collecting the minimum amount of information necessary to adequately address a regulatory question or issue through the most efficient manner at the right time. He described the Quality in 510(k) Review Pilot Program as providing a “turbotax style” 510(k) electronic submission template. By standardizing the submission materials, the FDA can be confident that the necessary types of information are included, eliminating the need for a more extensive quality review. This update alone, launched in September, has cut review time by one-third – very impressive. Dr. Shuren also detailed the Expanded Use of the Abbreviated 510(k) Program, a proposal that eliminates the requirement to demonstrate a device is of “substantial equivalence” to a predicate device. In some cases, Dr. Shuren explained, this process can be burdensome and unnecessary. The Abbreviated Program would serve as an optional approach for certain, well-understood device types that relies on guidance documents, special controls, and FDA-recognized consensus standards to facilitate 510(k) review – we assume BGMs, pumps, or perhaps iCGMs might fall in this, though we aren’t positive. The goal of Abbreviated Program, according to Dr. Shuren, would be to drive competition and innovation around safer, more effective devices, as companies could theoretically show that their device is superior to the provided criteria. The FDA plans to finalize and launch the program “in the coming months.”

    • Dr. Shuren reiterated previous timing for version 1.0 of NEST (National Evaluation System for health Technology) to launch by the end of 2019. The ultimate goal of the project is to meet the real-world data needs of medical device ecosystem stakeholders by decreasing real-world data time and cost while increasing its value through a market-driven, collective buying power approach. To do so, NEST will rely on a neural network data model consisting of: (i) an independent Coordinating Center, the MDIC, responsible for standardization of core data elements, data quality, development of advanced analytics, and creation of data use agreements; and (ii) a governing committee comprised of representatives from the medical device ecosystem responsible for establishing policies and procedures, as well as informing direction, priorities, and the Coordinating Center’s investments. Dr. Shuren shared that NEST currently has agreements with 12 data partners, representing over 495 million patient records, 195 hospitals, and 3,942+ outpatient clinics. The most commonly cited areas of expertise include: (i) cardiovascular and cardiac surgery; (ii) women’s health; (iii) neurosurgery; (iv) gastroenterology; and (v) orthopedics.

    4. Dr. Maahs: “No Matter What Your Economic Means, You Will Benefit from CGM …”

    Among the fray of new diabetes technology excitement, Stanford’s Dr. David Maahs made the case for CGM access across all socioeconomic groups. “We are clearly in the CGM era. There has been a clear increase in CGM adoption. But there is still a big gap in looking at CGM use in minority groups, and we need to be aware of that. It’s especially important as we know that A1c is much lower among people who use CGM…I think we have moved into an era where this will be the standard, but we need to keep in mind how we will boost access.” Indeed, with new technologies that can greatly improve glycemic management, the field must not lose sight of driving access as a #1 priority. He referenced amazing data first shown in an ADA poster: After six months, 78% of low-income youth given CGM still used their devices 13 out of 14 days at six months. At six months they had A1c’s of 8.2% (no change from baseline) and 4% time <70 mg/dl. “No matter your economic means, you will benefit from CGM,” concluded Dr. Maahs.


    -- by Brian Levine, Maeve Serino, Adam Brown, and Kelly Close