DTM 2018 (Diabetes Technology Meeting)

November 8-10, 2018; Rockville, MD; Full Report – Draft

Executive Highlights

  • In the fan-favorite annual CGM updates session, we (finally!) saw Dexcom G6’s accuracy stacked up against the specific iCGM special controls (lower 95% CI bound included), first G6 accuracy data for 14-day wear and 1-hour warmup, longevity data on a new and improved adhesive, and plans for diminished lag time. Abbott shared day-by-day 14-day wear accuracy and discussed new manufacturing protocol through which each individual sensor is factory calibrated. Medtronic detailed electrochemical impedance spectroscopy as an algorithmic means to boost accuracy while lowering user calibrations (“self-calibrations”). Lastly, WaveForm (AgaMatrix) shared new photos and details on its 14-day wear, one cal/day Cascade CGM, now slated for a 3Q19 EU launch.

  • Also in CGM, adhesive juggernaut 3M (of Post-it notes and medical tape) discussed the “elusive” feasibility of >14 day wear, and we enjoyed a round-up of the implantable CGM landscape (including Glysens, Theranova, Biorasis, and Physiologic Devices).

  • In automated insulin delivery, we have fascinating MiniMed 670G user experience data from Mayo Clinic/ASU, showing highly varied experiences and satisfaction with the hybrid closed loop (including high Manual Mode use in some users). In addition, 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. Lastly, we saw data from Protocol 1 of the IDCL (a three-month RCT) and heard from Dr. Roman Hovorka on fully closed loop in the hospital (+ new data!)

  • In smart pens and decision support, 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. The following day, Prof. Moshe Phillip shared that the DreaMed Advisor Pro has received an updated indication for adjusting pump settings based on SMBG data alone, expanding on the initial indication for CGM. Dr. Boris Kovatchev explained how an in silico digital twin could drive care and described ongoing efforts to bring Dexcom summary data into the UVA EMR.

  • We heard infusion set updates from Convatec (Unomedical) on the Lantern catheter (soon-to-start Stanford study testing 7-day wear); Capillary Biomedical on potential for 7+ day wear (much lower inflammation in pig studies); and Pacific Diabetes Technologies single-port CGM/insulin catheter (2Q19 feasibility study).

  • 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!

Our team attended the 18th annual Diabetes Technology Meeting in Rockville, Maryland just a bit over a month ago. For your reading convenience, we’ve categorized all 42 of our daily highlights into the following categories: (i) CGM; (ii) Automated Insulin Delivery; (iii) Smart Pens, Decision Support, Digital Health, and BGM; (iv) Insulin and Insulin Delivery; (v) Outcomes Beyond A1c; and (vi) Regulatory and Big Picture. Key highlights include Dexcom G6’s accuracy stacked up against the specific iCGM special controls, a first look at Insulet’s preliminary Horizon hybrid closed loop in a preschool-age patient, and infusion set updates from Convatec (Unomedical), Capillary Biomedical, and Pacific Diabetes Technologies. We were also pleased to see sustained interest in beyond A1c, with Dr. Roy Beck presenting another retrospective analysis of DCCT data (biochemical hypoglycemia predicted subsequent severe hypos), and then asserting that there is now a “compelling case” for regulators to accept CGM-measured time-in-range as an endpoint in trials. For all this and much, much more – 36 additional highlights, to be exact – read on.


Table of Contents 

CGM Highlights

Revealed: Dexcom G6 Accuracy vs. iCGM Special Controls (high bar indeed); G6 Enhancements for 14-Day Wear, 1-hr Warmup, New Adhesive, Less Lag; Vagueness on Verily; TypeZero Next-Gen Algorithm Work

Dexcom VP Mr. Peter Simpson shared a number of never-before-seen slides, including G6’s accuracy stacked up against the iCGM special controls and data on next-gen enhancements to G6: 14-day wear and 1-hour warmup (still sub-10% MARD, >90% of points within 20/20), a new adhesive that propels ~90% of sensors lasting 14 days (up from ~75% now), and reduced time lag down to 2.5 minutes vs. YSI (currently ~4 minutes). Vagueness on the fully disposable Verily sensor continues, with just a side profile picture shown this time around without any scale comparator – less detail than we got two years ago at DTM, and it remains unclear if two Verily generations will launch. We also heard about new TypeZero/Dexcom work on a next-gen closed-loop algorithm, which will enable simplified/no meal announcement, a more aggressive and personalized algorithm to optimize time-in-range, and a simplified system startup. Dexcom and TypeZero are also joining forces on MDI decision support, including a trend-based bolus calculator and more dosing guidance. Many slides and details are enclosed below!  For the latest pipeline and financial updates from Dexcom, see our report on the recent Investor Day.

  • For the first time since G6 launched, we got to see the sensor’s accuracy stacked up against the iCGM special controls – 95% lower confidence bound finally included for all glucose bins. The G6 label does not actually share the confidence intervals around the point estimates, so this was a long-needed slide. The takeaway remains the same, as Mr. Simpson noted: “FDA has set a very high bar for accuracy.” However, it’s now even clearer just how high that bar is, since G6 barely crosses the thresholds in some areas. (One could more easily argue now that these controls were designed around G6, and like any line drawn in the sand, there is some arbitrariness here – e.g., why “87%” and not “86%” for within 20%?) This slide also reminds us that using the lower bound of the 95% confidence interval raises the bar even higher, since it requires: (i) a point estimate that exceeds the threshold by a meaningful margin; or (ii) a study large enough that the confidence interval is closely aligned to the point accuracy. All in all, it’s clear that Abbott and Medtronic are not likely to obtain iCGM accuracy standards unless they can make leaps forward in sensor accuracy.  Senseonics may meet the bar with Eversense, though it is prioritizing 180-day wear first.

  • Mr. Simpson also shared brand-new G6 platform enhancement data, starting with an n=78 trial of 1-hour startup and 14-day wear. The data look quite good, with MARD still under 10% – including between hour 1-2 – and 91.9%-93.7% of points within 20% of 20 mg/dl. Only data from hours 1-2, day 10, and day 14 were presented, so we’re not sure what the aggregate performance looks like. Adam asked in Q&A about whether this data meets the iCGM standard, and Mr. Simpson clarified that the study was not powered to adequately answer that question; indeed, far more patients/paired points would be needed to get tight enough confidence intervals, especially with the above in mind. Dexcom’s 3Q18 call had not confirmed this trial’s completion, which clearly occurred well within the original plans for “2H18.” CEO Kevin Sayer said Dexcom will first get its new adhesive (see below) ready before moving to 14-day wear, so this enhancement sounds like a mid/late-2019 launch at the earliest – assuming a larger trial confirms 14-day/1-hour G6 still meets the iCGM accuracy benchmarks. For context, G6 currently has an overall MARD of 9.0% and overall 94% of points within 20/20, with a day 1 MARD of 9.3% and 91.1% within 20/20; the below looks right on par.

  • In our view, this is among the most important enhancements to G6, since it would match FreeStyle Libre’s updated wear/warmup time. (Libre 14-day launched in late October in the US following FDA approval in July.) Plus, a 14-day wear G6 would give Dexcom more pricing cushion and better margins, reducing sensors from three down to two per month.

  • Mr. Simpson also showed new data (n=24) from a “high reliability patch” for G6, which prompts an ~20% relative increase in 14-day sensor survival vs. the current adhesive – per the graph below, ~75% of current G6 patches survive to 14 days, which increases to ~90% with the next-gen adhesive. No further details or timing were shared, though this seems like an excellent and necessary upgrade. On the 3Q18 call, CEO Kevin Sayer shared that this new G6 adhesive is coming out in the “not-too-distant future.” We’d guess sometime in 2019 at this stage, though it was not specified. The size of the patch skirt looks unchanged, so presumably this changes the stickiness or materials. We’re not sure if this patch will reduce skin reactions, which can prevent some from using CGM. On Thursday at DTM, 3M noted the hard tradeoffs that come with adhesive, since improving on one dimension (stickiness) often comes at the expense of others (irritation).

  • Dexcom is also working on further reducing G6’s sensor time lag vs. YSI, which is ~4 minutes now and ~2.5 minutes with the next-gen version. The data was marked as unpublished and For context, the G4 platinum had a 6.5 minute lag, G4 with software 505 brought it down to 5.7 minutes, and G5 brought it down to 5 minutes. In Q&A, a comment noted that even if the current G6 lag was zero, it could still be up to five minutes (maximum) since readings only display in five-minute intervals; reducing updates to every minute would help the user experience on the lag front. Of course, that would also demand more battery life to relay Bluetooth reading, so the tradeoff is not quite that simple!

  • Regarding the next-gen disposable CGM with Verily, Dexcom is “not quite ready to show the final design,” but Mr. Simpson did show a profile of the very thin wearable. It looks quite slim, as expected, but there was no size comparator, making it hard to know just how thin it is (e.g., is it the originally planned penny-sized thickness?). This was the only Verily device shown, and it was unclear if this was the first- or second-gen version; we assume the latter, which has the low-cost advantage, the next-gen sensor, and is still under development. As of Dexcom’s 3Q18 call, the company was “considering options” for the gen one Verily device (using current G6), and the smaller, low-cost, second-gen device was slated for “late 2020/early 2021.” Dexcom’s upcoming investor day on December 4 should hopefully give more specifics on the Verily plans.

  • Citing the acquisition of TypeZero, Mr. Simpson noted the team is already working on a next-gen closed loop algorithm with “full automation” (no meal announcement!), simplified startup and meal announcements, optimization to increase time-in-range, and personalized delivery based on a patient’s data signature. Seeing both “meal announcement” and “no meal announcement” implied to us that the system will integrate manual meal announcement when a user does so, though it will be able to cope without it – perhaps via more aggressive automated correction boluses. The first-gen version of this algorithm is in its pivotal trial within Tandem’s Control-IQ system, slated for a summer 2019 launch.

  • TypeZero/Dexcom continue to work on better tools for MDI users, including a trend-adjusted bolus calculator, actionable insights, smart pen integration, and both real-time and retrospective therapy guidance. No timing was shared in this talk. The 3Q18 call said pilots of this technology are ongoing, with a broader commercial rollout “certainly by 2020.” Dr. Marc Breton’s talk on Day #2 gave a great overview of the technology, which is leaps better than the guidance MDI users have now.

14-Day FreeStyle Libre Accuracy Results: 10.3 mg/dl MAD for Glucose ≤60 mg/dl, Day 1 MARD vs. YSI: 10.8%

Abbott’s Dr. Marc Taub presented data from the 14-day FreeStyle Libre accuracy study (n=95). The four-center study in 95 adults with diabetes (n=80 type 1s; n=15 type 2s on insulin) found 90.7% of sensor results within ±20 mg/dl / 20% of the reference value. Specifically, for glucose <80 mg/dl, a strong 89.9% of sensor readings were within ±20 mg/dl, and for glucose ≥80 mg/dl, 90.7% of sensor readings were within ±20% of the reference value. 89.5% of sensor results were in Zone A of the Consensus Error Grid, and 100% of sensor results were within Zones A and B. The 14-day version is actually a bit more accurate than the 10-day version, with an overall MARD vs. YSI of 9.4% vs. the 9.7% in the 10-day studies. The similar version in Europe had a higher MARD vs. BGM of 11.4% in the pivotal CE Mark trial. In this study, while accuracy remained relatively stable and consistent over the 14-day period, MARD was slightly higher on day 1 (10.8%) before dropping sharply to 8.5% on day 6. We were interested to see accuracy metrics by glucose level, as accuracy in hypoglycemia has been an area of concern for FreeStyle Libre. Mean Absolute Difference (MAD) was a strong 10.3 mg/dl for glucose ≤60 mg/dl (n=84) and 10.0 mg/dl for glucose 61-80 mg/dl (n=354). As Dr. Nicholas Argento pointed out during Q&A, participants wore two FreeStyle Libre sensors in the study, and Abbott used the longest lasting sensor to analyze the data, whereas the FDA used the primary sensor (the first applied sensor for each patient). The interpretation distinction led to a discrepancy between the MARD reported in the FDA’s SSED (10.1%) and Abbott’s press release (9.4%). In response, Dr. Taub claimed that the accuracy study was not designed to determine duration of wear and that Abbott’s analysis was pre-specified. Last we heard from Abbott in August, the company is “currently working” with the FDA to address the identified discrepancies.

  • Dr. Taub also described the process by which FreeStyle Libre sensors undergo individual calibration adjustment. He explained that over 80 parameters of manufacturing are automatically inspected and used along with their correlation to the sensor sensitivity to calculate individual adjustments for each sensor in a given lot. For example, in vivo measurements have shown the sensor signal to be inversely correlated to membrane thickness and directly correlated to the enzyme layer area. While these adjustments are fairly minimal, Dr. Taub noted that they “very subtly boost performance.” It is remarkable that Abbott does individual sensor calibration in the factory; to support 1 million patients globally, the company is likely making over 20 million sensors each year.

Medtronic Leveraging EIS to “Self-Calibrate” CGMs, Reducing Calibration to 2-3x/Week While Reducing MARD by ~0.5%

Medtronic’s Dr. Andrea Varsavsky detailed efforts to leverage a glucose-independent diagnostic already being used in Guardian Sensor 3 – electrochemical impedance spectroscopy (EIS) – to “self-calibrate” the system via an algorithm, potentially reducing manual calibration burden. EIS is currently used in Medtronic Guardian Sensor 3 to monitor the current status of the sensor and the tissue in which it is embedded; it’s also already used to eliminate outliers by detecting sharp changes in sensor accuracy, and it therefore serves to improve accuracy of the system. But, Dr. Varsavsky said, there’s more EIS potential to be reaped in the form of real-time self-calibration. In an in vitro demonstration, Medtronic researchers modulated manufacturing parameters to generate an array of sensors with various glucose sensitivity levels; the team saw a strong correlation between impedance and sensor sensitivity, leaving potential for sensor algorithms to self-calibrate a CGM (i.e., correct for baseline sensitivity discrepancies in real-time with needing factory calibration). The correlation exists, albeit in slightly messier form, in vivo, as demonstrated by a proof-of-concept where researchers compared no-calibration models of in vivo Guardian 3 data with and without EIS signals. Models with EIS consistently outperformed models without EIS by 2%-5%-points on MARD, establishing that EIS is “a useful signal to help with self-calibration.” In another demonstration, Medtronic trained an algorithm on a massive data set comprised of 1,799 subjects, 5,505 sensors, and 131,138 paired CGM-reference points. Applying this algorithm to a separate set of ~600 sensors led to an ~80% reduction in calibrations (from 2-3/day -> 2-3 calibrations/week) along with a 0.5%-point lower MARD relative to Guardian Sensor 3. One huge advantage to this work is that it doesn’t entail any changes to sensor design or manufacturing, just leveraging existing technology in Guardian Sensor 3. Dr. Varsavsky implied that future sensors will be designed specifically to leverage EIS in a bigger way, potentially pushing calibration and accuracy improvements even further. Absent from the talk were any mentions of commercial plans – updates to next-gen Guardian Connect or Guardian Sensor 3? EIS-based self-calibration baked into the next-gen Project Harmony sensor (see ADA poster) or the Project Unity sensor? Regardless, this seems to be a very cool development for Medtronic, who seems significantly behind Abbott and Dexcom on factory calibration.

Waveform Cascade 1 CGM Slated for EU Launch in 3Q19, US Pivotal in September 2019; Gives Reading Every Minute, Rechargeable Transmitter and Reusable Inserter (Needle-Free!) Both With 2-Year Life; Mean MARD 12.8% So Far, Further Studies Underway

WaveForm’s (AgaMatrix) Dr. Misha Rebec provided some new details and data on the Cascade 1 CGM, which is now slated for a potential EU launch in 3Q19 and an FDA filing in December 2019. The company had previously hoped for a CE Mark in 2018. The sensor has a decent feature set, though is not approaching the accuracy of its factory calibrated competitors. We’ve heard rumors of a potential lower-cost profile, though moving lower that FreeStyle Libre seems hard to believe. WaveForm has pushed back its timelines meaningfully over the last year – will it meet its expectations moving forward? Can it reasonable compete in Europe with a one-cal/day sensor when two factory-calibrated sensors are already available (FreeStyle Libre and G6)?

  • Cascade CGM system details (see pictures of the sensor, inserter, and app): We had previously heard that the sensor would be 14-day wear, average one calibration per day (it will launch with one per day in Europe), no receiver (BLE smartphone communication), limited interferences (e.g., no acetaminophen issues), one-hour warmup time or less (45 minutes in ongoing studies), and “painless” insertion. Dr. Rebec added that the sensor gives a reading every minute (vs. every five minutes with Dexcom and Medtronic) and the transmitter is rechargeable and reusable for up to two years. The inserter itself is also reusable for up to two years, and remarkably, there is no needle involved in the insertion – the 280 um-diameter filament itself pierces the skin in a “virtually painless” process. The sensor is first loaded into the inserter, three buttons are pressed, then the inserter is lifted, leaving the inserted sensor behind. Dr. Rebec also showed a number of pictures of the app.

  • Timing Updates: WaveForm anticipates a “potential” CE Mark and EU product launch in 3Q19. A US pivotal study (100 participants at three sites) is expected from September to November 2019 ahead of a December 2019 FDA filing. In the interim, the company expects to: complete EU safety and effectiveness study in January; submit technical clinical files in EU in 2Q19; submit an IDE in the US for “15-day study” in March; conduct user interface studies in the US/UK to augment EU performance data in February/March; and submit pivotal study protocol to FDA in June.

  • Study Progress and Accuracy Data: WaveForm conducted seven studies in 2017-2018, including six over the last 18 months. Mean MARD across those studies (n=87 total) was 12.8% (median: 9.3%). On the Consensus Error Grid, 85.2% of readings fell within in Zone A and 13.6% fell within Zone B (98.8% together). There were 87 participants in total, and 95% of sensors made it the full study period – only two of the studies investigated 14-day wear, where sensor survival was an impressive 96%. Dr. Rebec also referred to recently completed studies: one comparing accuracy to Dexcom G5 (11.0% MARD for Cascade vs. 12.2% for G5; 10-day wear), one comparing accuracy to FreeStyle Libre (11.9% MARD for Cascade vs. 14.5% for Abbott; 14-day wear), and an EU pre-pivotal study with 20 subjects and 14-day use (12.6% MARD). The company is now in the midst of an n=60, three-site safety and effectiveness study, which will comprise part of the CE mark submission package. Each subject wears two Cascade sensors for 14 days and has five in-clinic days during which YSI is used to calibrate the sensor ­– obviously this does not resemble real-world use and we find it hard to believe that regulators would accept a protocol in which the comparator method is also used for calibration. (In Q&A, Adam asked about this, and Dr. Rebec said that the protocol is “in agreement with what our discussion with FDA has been on this topic.” We find that very hard to believe.) The first site for this study in Slovenia (n=20) completed in mid-October. At this site, 85% of sensors survived for the full 14 days and mean sensor MARD was 11.2%: 11.7% between 80-180 mg/dl, 9.3% between 180-240 mg/dl, and 11.9% in >240 mg/dl (<80 mg/dl wasn’t shown). MARD was 10.8% on day 1, 11.5% on day 4, 9.3% on day 7, 12.0% on day 10, and drifted up to 13.3% by day 14.

3M on Adhesives: Going Beyond 14-Day Wear is “Elusive”

3M’s Dr. Del Lawson provided the rare perspective of an adhesive manufacturer, sharing insights from recent clinical trials and R&D. Most importantly for CGM companies, he noted that >14-day wear with reliable function remains an “elusive” goal, given what is known about adhesives right now. This comment in Q&A certain drew a reaction from the audience: “At 21 days, when you smell what comes off, it is not a healthy situation. If you’re saying, ‘I want to go for 30 days adhesive,’ think again. Please come talk to us, as there are some things we have to fix.” Dr. Lawson spent much of his talk on challenges, with the skin, though there were some interesting technical tidbits too: (i) chest and abdomen are the best wear locations for extended wear; “We have a lot of problems when we do arm studies – door knocks, clothing, etc.”; (ii) having an adhesive “skirt” is very important for extended wear, and a larger skirt is optimal for (e.g., the Dexcom G6 has a large adhesive skirt, meaning the adhesive size is larger than the on-body wearable vs. FreeStyle Libre has a smaller adhesive skirt); and (iii) silicone-based adhesives are less irritating, but don’t stick as well to the skin for extended wear (e.g., Senseonics Eversense). This talk reminded us that improving adhesives is not as simple as making them “less irritating,” since changing the materials can result in meaningful product tradeoffs (e.g., less sticky). Further, hearing from 3M – maker of Post-its and Scotch tape – that 21-day wear is difficult makes us think 14-day wear CGMs may be here for a while – unless companies get an indication that includes over-tape. Adam has used Simpatch over-tape for a long time and found it outstanding; on Amazon, it is now available for Dexcom, Abbott, and Medtronic sensors, with great reviews.

Implantable CGM and Pumps Galore: GlySens Gen 3 in Trials in 2019, Theranova to Launch IP vs. SC Sensing Study with JDRF/Montpellier, HCT Funds First Biorasis Human Implant, Physiologic Devices IP-IP System in Canines in March 2018

Glysens, Theranova, Biorasis, Physiologic Devices, LifeCare, and Dr. Mark Arnold (University of Iowa) all presented updates on their implantable CGM and/0r IP insulin technology. Many of these companies participated in a similar panel at DTM 2016, and it was great to hear that many have made progress on a number of fronts, including technological (miniaturization!), funding, and clinical work. Still, they are all rather early stage and none shared regulatory submission/launch timing – by the time they are more market-ready, what will the needle sensor landscape look like? Wear duration will certainly fall in favor of implantable devices, but will that balance the always-improving user-experience of subcutaneous CGMs (bandage-like wearables?) and apps? And by how much can extended wear implantables drive down cost? Notably absent from the updates session were Capillary Biomedical and Profusa, both of which presented at DTM 2016.

  • GlySens is now on its gen 3 implantable Eclipse 3 ICGM system, with plans to enter clinical trials in 2019. [Note: The “I” in “ICGM” likely means “implantable” rather than the new FDA “iCGM” pathway.] CEO Mr. Bill Markle overviewed the gen 3 system’s features: Fully-implanted (no external components, unlike Senseonics); designed for two-year life; Bluetooth-connectivity and no dedicated receiver (straight to iOS app, with high-fidelity connection up to 6-8 feet); aiming for 1-2 fingersticks/month, though the company may move to 1x/week in the interest of driving MARD down; 40% smaller than the gen 2 device; onboard glucose calculation and data storage. The outpatient/in-office procedure is 15-20 minutes has been performed using just local anesthesia. Mr. Markle showed pictures depicting a barely-visible 1.25-inch incision, along with clean sensors at explant (suggesting minimal or no foreign body response). GlySens’ path has been rife with delays, and the first two generation sensors were never deployed on the market or hit their pivotal/submission milestones. Direct-to-smartphone – so long as it’s reliable – and the smaller form factor are major improvements in gen 3, but it seems like accuracy and possibly lag time and signal stability remain the biggest areas for improvement. Mr. Markle notably didn’t display any data on these fronts, which we found to be a notable omission, given that human trials with the gen 3 system are slated to begin next year.

    • GlySens recently announced the completion of a ~$10 million internally-led round, which should carry the company well into 2019. The funds will support development of the gen 3 implantable CGM (Eclipse) system and “multiple” human trials. We look forward to the readout of the first trial in 2019 – a two-year fully-implanted sensor certainly has its appeal, and due to the potential wear time (4x longer than Eversense XL), we suspect that per-day pricing could come in at a fraction of current CGM.

  • Theranova is set to embark on a “first-of-its-kind” study in collaboration with JDRF and Prof. Eric Renard (Montpellier) investigating intraperitoneal (IP) vs. subcutaneous glucose sensing while delivering insulin intraperitoneally. This is a step on the way to development of a fully-implanted IP automated insulin delivery system with Bluetooth connectivity to allow (at least) monitoring from an app. After reviewing the literature supporting IP insulin delivery (e.g., 7-8x faster kinetics, lower risk of hypo, no need for meal announcements in closed loop), Theranova’s Mr. Chris Hanson matter-of-factly stated: “We figured that if we’re in the peritoneal cavity anyway for insulin delivery, why not sense there as well?” He pointed to literature from both swine and humans demonstrating that glucose-sensing lag time is significantly less-pronounced in the IP vs. subcutaneous space and that the company’s IP sensor can function lag-free for 90 days. As Theranova remains in “stealth mode,” Mr. Hanson didn’t show pictures or specifically describe the company’s implantable CGMs or pumps. As of April 2017, the company had three different sensor mechanisms in development: (i) A glycoenzymatic sensor in the preclinical and clinical stages; (ii) a fluorescent sensor in the preclinical stage; and (iii) an optical sensor in the preclinical and clinical stages. We’ve never heard any details on the company’s implantable pump.


  • From a cost perspective, a surgery-requiring implantable IP seems like it would be more expensive than subcutaneous sensor and insulin infusion combo upfront, but Prof. Eric Renard pointed out in Q&A that regular human insulin does the trick in the IP space. “Regular insulin in the peritoneal cavity is at least as quick as fast-acting insulin in the subcutaneous space. You don’t need fast-acting. Analogs were designed for quick subcutaneous absorption, but you don’t need this for IP. Also, the distribution of insulin is different – you can increase the insulin infusion without inducing hypoglycemia.” Due to this lower hypoglycemia risk, Mr. Hanson asserted that IP closed-loop algorithms might not have to be as complicated in design as those for subcutaneous.

  • At this point, there are no insulins approved in the US for IP delivery, while Sanofi’s Insuman is approved in Europe. Even if it should be approved in the US, Sanofi’s Dr. Eric Petreto pointed to a couple of barriers in IP insulin last year: (i) Implantable insulins are further along than pump technology; said Dr. Petreto: “Can we do highly concentrated insulin? Yes. Can we do it thermostable? Yes. Can we deliver it? Big question mark.” (ii) The current formulation of Insuman is only stable for 45 days in a pump. Both Thermalin (preclinical) and Arecor (preclinical) have U1000 insulins in development. 

  • Biorasis announced that the Helmsley Charitable Trust will fund the first human transplantation of its long-term, injectable/implantable CGM, “Glucowizzard.” Notably, Glucowizzard is expected to be the sensing element for Physiologic Device’s IP-IP closed loop system (see updates below). Like GlySens, Biorasis has shrunk its sensor significantly in the past two years – as it stands, the CGM is 0.75 x 6 mm, small enough to be injected through and harvested back using a needle. The vision is to inject the rice-grain-sized sensor on the top side of the wrist such that a watch can power it with light and receive glucose measurements sent from an LED light source through the skin (Dr. Fotios Papadimitrakopoulos said the team has developed its own watch, though a consumer smartwatch could theoretically be used with a customized underside attachment). Glucowizzard has thus far been tested in rats, mini-pigs, rabbits, and dogs, performing with: (i) high stability out to 55 days – as of 2016, the company wanted to extend wear time to six months, so it has a ways to go; (ii) 9.8% error (MARD, we assume) over 14 days (100% in Consensus Error Grid zones A+B); and (iii) a lag time of 5-10 minutes. Dr. Papadimitrakopoulos devoted a solid chunk of the talk to efforts to combat the foreign body response to extend wear. Localized, low-dose dexamethasone release from “microspheres” – similar to the approach taken by Senseonics – has suppressed inflammation and fibrosis for two months in rabbits and six months in rats in studies. At this stage, the single-day does of dexamethasone is 75x smaller than the minimum therapeutic dose – safe by any measure. At this stage, Biorasis is not approaching Senseonics’ accuracy and wear time, though the wrist form-factor to power/receive data is attractive.

  • Physiologic Devices was not on the panel, but did have a poster showing that the pre-clinical ThinPump IP-IP closed loop system was implanted in canines in March 2018. The feasibility trial used a Harvard algorithm and was conducted in collaboration with Vanderbilt’s Dr. Justin Gregory and UCSB/Verily’s Dr. Howard Zisser. In the canine model, IP glucose sensing lag time vs. YSI was just 3 minutes at baseline; at 50 days, lag was six minutes with dexamethasone and nine minutes without dexamethasone, suggesting the corticosteroid coating attenuates the foreign body response. As a reminder, ThinPump has a fully automatic closed loop mode, requires no skin-mounted and is expected to have a 10+ year implant life (requiring refills every three to six months). US and EU clinical trials are slated for 2021, as of the last update in April 2017.

    • Physiologic Devices is currently seeking $3 million in a convertible note round. We also overheard in hallway chatter that the company is working with Thermalin on U1000 insulin – we first heard at the NIH AP Workshop in 2016 that the company had a U1000 monomeric analog in preclinical development for miniaturized artificial pancreas systems.

  • Dr. Andreas Pfützner provided an update on LifeCare’s osmotic sensor, SenCell. Within the miniature sensor (2 x 1 x 0.5 cm), there are two compartments separated by a semi-permeable membrane: In one, there is dextran bound to ConA (a protein, which can be toxic and will therefore be replaced by a different agent). When glucose enters the compartment, it displaces the dextran, increasing the amount of free sugar molecules in the compartment and thereby increasing osmotic pressure. Because the physics are not chemistry-consuming, the sensor could theoretically last indefinitely. SenCell has undergone preclinical proof-of-concept studies with a small working sensor model. In a portion of the talk that elicited plentiful “oohs” and “ahs,” Dr. Pfützner said that LifeCare has been working with CantiMED, which 3-D prints nano-pressure sensors. CantiMED uses an “electron print microscope,” which lets gas flow into a chamber, and where an electron beam makes contact with the gas molecules, the matter solidifies – using this method, one can print with great precision on any substrate down to a resolution of 10 nm! We found the nano-sensing method to be pretty neat: At baseline osmotic pressure, electrons hop from the anode to the cathode of the sensor, resulting in a certain resistance. At higher osmotic pressure (due to higher glucose concentration), the sensing element is now strained, so there’s a change in resistivity, which can then be quantified and correlated to the glucose signal. LifeCare has shown that the force on the membrane (which is directly related to glucose concentration) has a linear relationship to electric signal. The goal is to have each miniaturized implant carry ~16 nanosensors to lend redundancy. Dr. Pfützner didn’t give any timing on the product, but shared that 3-D printing of batches en masse could “decrease the cost of production to very, very, very low amounts. We’re glad to see a very different sensing approach with low-cost potential!

  • University of Iowa’s Dr. Mark Arnold discussed his group’s recent work using near-infrared spectroscopy to detect glucose in a diabetes setting and urea in a hemodialysis setting. Dr. Arnold’s work with glucose-sensing is compelling, but we were perhaps most intrigued by the dialysis work. His lab is using the optical sensor to optimize dialysis “dosage” (time) by continuously tracking urea concentration in the dialysis output – once the concentration drops below a threshold, clinicians can be assured that the dose is sufficient. Theoretically, this tool could help cut down on time, cost, and maximize turnover in dialysis centers – a huge quality of life burden and high-cost for the system. Dr. Arnold said that he ultimately hopes to get the sensor down to the size of a photonic chip (which comfortably fits on a fingertip) and a multidisciplinary team is now needed “to take it farther to get to the point where we can put it in people.”

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?

EyeSense FiberSense 29-Day Wear, 1 Cal/Day Needle CGM Demonstrates 14.7% MARD Vs. YSI; EU Pivotal Trial Slated for 2H19, Potential Launch in Late 2020

A single-center, prospective study (n=18) investigated the performance and safety of FiberSense, a real-time, 29-day wear CGM with a percutaneous fiber optic glucose sensor equipped with ConA-based chemistry. Participants wore the FiberSense either on their abdomen (n=9) or upper arm (n=9) for up to 29 days and also wore the Dexcom G4 as a comparator for one week of the study. Accuracy was comparable between the two systems: FiberSense MARD vs. YSI with one fingerstick/day was 14.7% as compared to 15.7% for the G4. %20/20 analyses were also comparable (73.5% vs. 73.2%). 99.5% of FiberSense glucose readings were within Zones A and B of the Consensus Error grid, with 73.9% in Zone A. There were no serious device-related adverse events, and no severe sensor site reactions were observed. Based on our own experiences with CGM and the 3M adhesive talk today (see above), we are surprised that 29-day wear was seen in this study and wonder how replicable that would be in the real world without significant over-tape.  

  • An EU pivotal trial is expected to initiate in 2H19 (the poster also referenced plans for pivotal trials in the US and China) with a CE Mark expected in 2020 and a potential launch in late 2020. The company expects to launch the 29-day sensor with Bluetooth direct to the phone and at a price point potentially below that of Abbott’s FreeStyle Libre – presumably that all hinges on the indicated wear time. The device has a warmup of a “few hours” and can pair with either a smartphone app or a dedicated receiver. It is clearly early stages for FiberSense, though we’re always interested to see new players pushing to develop innovative, longer-lasting sensors.

CGM in the Hospital: Telemetry to Central Nursing Station is the “Holy Grail”; Large, Outcomes-Focused Studies Needed

Mayo Clinic’s Dr. Curtis Cook covered CGM in the hospital, concluding (as expected) that large, multi-center, randomized controlled trials are needed to look at outcomes – how does using CGM impact mortality, length of stay, hyperglycemic/hypoglycemic episodes, and cost? Yes! – we’d love to see something like NICE SUGAR repeated with modern-day CGM, tested in thousands of patients, using connectivity, and funded by large foundations or NIH. (Another possibility is to add automated insulin delivery, as Dr. Roman Hovorka has done in smaller studies; see below.) To date, most CGM studies in the hospital have looked at accuracy and been limited by small sample size and single-center location. Dr. Cook noted some of the challenges to implementing CGM for hospital blood sugar management, including staff training, patient selection, no FDA approval, IT challenges, cost, and safety. Still, he believes that “Glucometry” (glucose telemetry) is the “holy grail” – continuous measurement and transmission of CGM data from bedside to a centralized station that could be monitored by nurses/trained professionals (just like cardiac monitoring). “As an endocrinologist, I would love to see that, especially because glucometry can provide healthcare providers with more glucose data points, trend, and rate of change. He noted the pilot “Glucose Telemetry System” study published in JDST (mentioned at ENDO Fellows), which tested a Dexcom G4 and central nursing station monitoring system in n=5 type 2s in the general hospital ward. CGM alarms were set up for glucose readings <85 mg/dl. Over the four days of CGM observation, patients spent 65% of the time in-range (“70-179 mg/dl”) and only 0.3% of the time <70 mg/dl. No patients had a CGM glucose value <54 mg/dl. A larger Glucose Telemetry System study at the VA (see our May coverage) is now recruiting. For more on this topic, see Adam’s article about his recent hospital stay and scary lessons learned – including the desperate need for CGM.

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.

Automated Insulin Delivery

Medtronic 670G User Experience Study from Mayo Clinic/ASU – “Varied” and “Contradictory” Experiences; Lots of Manual Mode Use Often/Always

An independent Mayo Clinic/ASU study shared fascinating user experience data from a questionnaire given to n=21 MiniMed 670G users recruited from an outpatient endocrinology clinic (mean age: 44 years; A1c: 6.7%; 5 months of experience on 670G). Responses were quite varied across most questions. On the plus side, two-thirds of users expressed “a high level of satisfaction” with the 670G. However, satisfaction was highly mixed on Auto Mode itself: 52% of respondents were satisfied or very satisfied; 24% were neutral (neither satisfied nor dissatisfied); and 24% were dissatisfied or very dissatisfied. Notably, 62% of respondents said they used manual mode “often” or “always” (i.e., not in closed loop); a smaller 24% used manual mode “rarely”; and only 14% “never” used manual mode (i.e., they were always in closed loop). The least liked features of the 670G were physical design or structural issues (24%); perceived need for increased user input (24%); and high alert frequency (19%). Equally interesting, 43% of respondents reported “better blood glucose control” and 29% liked 670G’s “ease of use”, though many verbatims noted the opposite reactions – that 670G was too conservative or was difficult to use. A notable 14% of users had nothing to say about “most liked” features of Auto Mode, and another 14% had not used it. These data from a small study (in low-A1c patients) certainly illustrate the mixed real-world experiences with the hybrid closed loop – some love it, some find it conservative or demanding. We’d love to see these questionnaires administered in a consistent, structured way across devices – comparisons on user experience would be highly valuable. See key tables below.

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.”

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.

Cambridge’s Dr. Roman Hovorka on Fully Closed Loop in the Hospital – Very Powerful Data, New n=40 study

Cambridge’s Dr. Roman Hovorka summarized the team’s stunning data on use of fully automated insulin delivery in the hospital general ward, including a new study just completed in patients on parenteral/enteral nutrition (n=40). The glucose results were again incredible in patients on up to 15 days of full closed loop (subcutaneous CGM/pump), summarized in the first modal day plot below. He also reviewed the team’s powerful closed loop study in inpatient type 2s presented at ADA (simultaneously published in NEJM!), as well as the EASD poster showing a profound ~9.3-hour/day improvement in time-in-range in the sub-population of hemodialysis patients (n=19) – that’s over a 30-percentage point improvement in time-in-range, the largest the team has even seen. Dr. Hovorka emphasized that the Cambridge system is fully automated (no meal announcement), highly adaptive (very in the hospital, given insulin sensitivity changes), and has seen impressive patient satisfaction. On the latter, 100% of patients in the NEJM study (62/62) would recommend it to a friend/family member entering the hospital. Beyond the general ward, Dr Hovorka also believes there is a role for closed loop in the critically ill, leveraging IV insulin/dextrose infusion and subcutaneous CGM. The team published a 2013 study to this effect (Leelarathna et al., Critical Care), again noting “enormous” difference in time-in-range with closed loop. We cannot wait to see larger studies of closed loop in the hospital in both non-critical care and critical care – the outcomes and cost savings should be a slam dunk, assuming a study is large enough to power outcomes like length of stay, morbidity/mortality, and readmissions.

  • Cambridge’s hospital algorithm targets 100-180 mg/dl (5.6-10 mmol/l) and often delivers 10-20 units per hour or more. The highest dose was 70 units per hour, indicating just how much insulin resistance is seen in the hospital! Algorithms in the hospital must be highly adaptive, since changes in insulin sensitivity can be significant and frequent.

  • Cambridge is currently using the FreeStyle Navigator 2 CGM (no acetaminophen interference), a subcutaneous insulin pump, a CGM receiver, and a tablet running the control algorithm. We don’t believe FreeStyle Navigator 2 is widely available commercially (if at all), and we wonder if Cambridge will move to a different CGM for future studies.

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.

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)

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.

Smart Pens, Decision Support, Digital Health, and BGM

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).

DreaMed Advisor Pro Indication Updated to Support Pump Setting Adjustments Based on SMBG Alone; Helmsley-Funded Pivotal Study Underway and Nearly Fully Enrolled (n=112)

Schneider Children’s Prof. Moshe Phillip shared that the DreaMed Advisor Pro indication has been updated to support insulin pump settings adjustment for individuals on SMBG alone (Advisor Pro “Version B”), complementing the initially-FDA cleared CGM version (“Version A”). We’re not sure whether Version B is yet available to clinics who are already using Advisor Pro through Glooko, nor if it has received a CE mark. Prof. Phillip presented unpublished data supporting the SMBG indication, the SMBG-EXPERT study, which is analogous in nature to the CGM-EXPERT study (formally published in June). Whereas the CGM-EXPERT study presented expert diabetes clinicians with CGM traces and assessed level of agreement on pump settings adjustment between providers vs. the DreaMed Advisor, the SMBG-EXPERT study did the same with 15 patients’ SMBG profiles (average of 6 fingersticks/day). Interestingly, there was greater inter-HCP and HCP-Advisor agreement when presented with SMBG, suggesting that access to more data (with CGM) leads to a wider variety of treatment approaches that the algorithm can more finely assess. In SMBG-EXPERT, physicians agreed with each other on the direction of basal rate adjustment 50% of the time and with the Advisor 51% of the time; providers agreed with each other on direction of carb ratio adjustment 54% of the time and with the Advisor 55% of the time; providers agreed with each other on direction of correction factor adjustment 50% of the time and with the advisor 47% of the time. In CGM-EXPERT, these levels of directional agreement were all in the 40s%. (It is striking how inconsistently providers agree with each other!) Prof. Phillip et al. performed a retrospective analysis (n=434 cases) to compare the level of agreement between CGM-based recommendations and SMBG-based recommendations when both data streams were available. There was a linear relationship between average number of SMBG per day and agreement in direction of insulin dose adjustment: At 3 SMBG/day, agreement between Advisor Version A and Version B was 55%, while at 9 SMBG/day, agreement rose to ~65%. Prof. Phillip commented that at times, Advisor Version B will decline to offer a recommendation if there is insufficient fingerstick data. We’re still amazed that the probability that two experts’ recommendations for directionality of insulin adjustment alone is essentially the same as a coin-flip; this makes a strong case for a software-based algorithms like Advisor Pro that can take some bias out of the process (not to mention time!), add highly objective data analysis, and ideally learn over time to optimize recommendations.

  • Prof. Phillip brilliantly laid out the potentially disparate goals of providers and patients in a clinic visit (table below) and quantified time constraints facing diabetologists. A 2017 paper found that it took providers 18 minutes to analyze pump data alone, 13 minutes for MDI data, 15 minutes for bolus calculators, and 11 minutes for meter data. This is obviously impossible to squeeze into even a 30-minute appointment, let alone focusing on the aspects of diabetes that truly matter to people with diabetes! Prof. Phillip also calculated that if his practice made weekly phone calls to titrate insulin, it would require 11 extra providers (15 minutes per phone call x 1,700 patients -> 425 hours/week -> 53 work days/week -> 11 extra providers)! To put a face on the statistics, two nights prior to his talk, a diabetologist told Prof. Phillip that he takes patient charts home every night because he can’t finish the work in practice. He told him that “visits are more complicated now that we have data from CGM, pumps, meters, food diabetes…we have to cope with all that data, and also listen to the patient, refer them to an expert for other conditions, deal with their other diseases and medications…” For anyone who’s read Dr. Atul Gawande’s recent long-form piece in The New Yorker (“Why Doctors Hate Their Computers”), the comments from Prof. Phillip’s friend should ring a bell.

Patient expectations from the clinic visit

Clinician expectations from the clinic visit

  • “Get rid of my diabetes”

  • Show interest, sympathy, understanding

  • Praise me on achievements

  • Be tolerable to my under-achievements

  • Remember what I told you during previous meetings

  • Adjust perfectly my blood glucose levels

  • Make sure we prevent complications

  • Take “them” off my back

  • Cure my diabetes

  • Perfect A1c

  • No episodes of DKA or severe hypoglycemia

  • Negative results on routine periodical tests

  • No evidence of complications

  • Great spirit

  • No social issues

  • No special needs

  • Reasonable time in the office

  • Prof. Phillip noted that the Helmsley-funded multi-site pivotal study of Advisor Pro is underway and “3-4 patients short” of full enrollment (n=112). The update came in Q&A in response to an endocrinologist’s question about who is “correct” – expert HCPs or the Advisor? As of June, DreaMed hoped to report results from the first three months of the trial by the end of 2018 or into 1Q19 – the latter seems more likely at this point, perhaps at ATTD.

  • As at a meeting in August, Prof. Phillip said that “once we show it to clinicians and they use it and like it and trust it, there’s not a reason to not give it directly to patients.” He has never publicly attached a timeframe to a patient-facing, non-provider-gated pump settings adjustment app. At AACE 2017, Dr. Phillip said that Advisor Pro support for MDI is also in development. We haven’t heard updates on the MDI front since, though DreaMed also announced a collaboration with Schneider Children’s Medical Center and Harvard School of Engineering and Applied Sciences to develop products to optimize insulin delivery for type 1 patients on MDI and fingersticks.

Dr. Kovatchev Explains Vision for How “Digital Twin” Can Guide Care in Future; UVA/Dexcom Work on Integrating into EMR

UVA’s Dr. Boris Kovatchev explained his vision for how VIP (Virtual Image of the Patient) therapeutics – generating a “digital twin” of each patient – will drive diabetes care in the future. The basic idea is to use available signals to map each individual to one or more virtual subjects in an in silico (virtual) population, run algorithms to see how treatment parameters can be optimized for those simulated subjects, and then apply them back to the real-life patient. For example, the system will intake medical record and CGM data; Dr. Kovatchev provided the example of a 62-year-old male with type 1 diabetes and an 11.7% A1c who, over a six-hour period, experienced prolonged hypoglycemia pre-meal, a large glucose excursion following the meal, and hypoglycemia again following the meal. The system knows that the individual has a basal rate of 2.0 unit/hour and ate 62 grams of carbs for the meal, bolusing six units to cover it. A digital twin, whose glucose trace was nearly identical to the individual’s when given the same amounts and timing of insulin and carbohydrate, was identified within the in silico population. After running simulations seeking to minimize hypoglycemia and hyperglycemia, the algorithm suggested that the individual, if faced with the same scenario, reduce basal rate by 10% and reduce premeal bolus by 2 units (and ideally give it earlier). Had the individual followed this strategy in the first place, the simulation suggests his mean blood glucose would have been 105 mg/dl instead of 79 mg/dl, hypoglycemia would’ve been avoided, and he’d have had a mean amplitude of glucose excursion (MAGE) of 111 mg/dl vs. 121 mg/dl. It’s worth pointing out that the VIP approach as described is only descriptive of what happened and not yet prescriptive, but if the digital twin can be faithfully identified or engineered in silico, it’s not difficult to imagine how Dr. Kovatchev and team would be able to give tailored advice for therapy adjustment, especially insulin dosing around meals and exercise.

  • Over the past year, UVA’s Diabetes Technology Clinical Program and Dexcom’s Clarity team have implemented a program that allows patients to consent to summary CGM reports getting pulled right into their providers’ EMR; the initiative is “in the final stages of testing.” The patient can consent in the very first meeting with the provider, at which point a report is generated and presented to the doctor. At follow-up appointments, since the patient has already consented, the provider can simply pull up a summary CGM report. On a similar note at ATTD, Dexcom’s Dr. Nate Heintzman showed a screenshot of an EHR-integrated Clarity experience, which was already live at Children’s Hospital LA. As we understand it, Dexcom’s latest partnership with Validic could accelerate and begin to democratize provider access to CGM data within current workflows.

  • Remember that first snowboard jump on G6 and Tandem’s Control-IQ that Dr. Kovatchev displayed at ATTD? It turns out that 12-year-old boy was also equipped with an activity tracker, and his glucose and activity data were sent directly to the cloud and relayed to the Diabetes Technology Center so a report could be generated in PDF form. The ski study in February was a successful pilot test of the technology and the team is “moving on with that.” 

Glytec’s Glucommander Reduces Adjusted Length of Stay by 18%, Cuts Severe Hypo by 73% in Critical Care Unit; Safe and Effective for IV Insulin in Labor and Delivery

Glytec presented two retrospective posters at DTM demonstrating strong outcomes in the hospital setting for its insulin titration software.

  • In a retrospective study (n=382) from Riverside Medical Center, Glytec’s Glucommander software reduced length of stay, blood glucose, hypoglycemia, and bounce-backs to critical care unit after transition to the general ward. 174 of the patients were dosed insulin off of Glucommander recommendations, while 208 were dosed through standard (paper) protocols. Albeit retrospective, and the fact that the Glucommander group had significantly lower starting A1c (8.1% vs. 8.7%) and higher starting blood glucose (300 mg/dl vs. 264 mg/dl), the clinical outcomes were encouraging. With Glucommander, there was 73% less incidence of severe hypoglycemia (0.11% vs. 0.41%), 39% less mild/moderate hypoglycemia (2.34% vs. 3.85%), 20.7 mg/dl lower mean glucose (166.6 mg/dl vs. 187.3 mg/dl), and 31.2 mg/dl lower final critical care unit blood glucose (154.7 mg/dl vs. 186 mg/dl). Length of stay index (actual length of stay vs. expected length of stay) was 1.12 days with Glucommander vs. 1.37 days with standard protocols, suggesting an 18% reduction in length of critical care unit stay (adjusted for expected stay length). Further, there were no cases of bounce-back to the critical care unit following transition to general ward in the Glucommander group, while 1-2 patients had to revert per month in the non-Glucommander arm. Cost-effectiveness data were not presented, but we have to imagine they would be favorable from the reductions in length of stay and severe hypoglycemia alone. Our back-of-the-envelope calculations: Assuming the average length of stay in the critical care unit is three days at a price of $20,000 per day, then the hospital could save $5,000 per patient by using Glucommander. We know from Adam’s recent hospital stay (along with many reader stories) that diabetes management is miles away from optimal in the hospital setting – software like Glucommander could make a huge difference. We wonder how the outcomes from Glytec would compare to those from closed loop; the Cambridge group has published a couple of high profile studies of fully closed loop in non-critical care settings (ADA 2018; ADA 2016).

  • A second poster showed that Glucommander was safe and effective in 204 young women during labor and delivery in a Honolulu facility from November 2015-June 2018. Median time on the system was seven hours and time-to-target was six hours, with average initial blood glucose of 152 mg/dl dropping to 113 mg/dl by the end of the period. Mean first day blood glucose was 128 mg/dl vs. mean last day blood glucose of 117 mg/dl. There was no comparator arm here, but the poster showed that 90+% of readings across subjects were in 70-180 mg/dl; 7.2% were >180 mg/dl, and 0.53% were <54 mg/dl.

mySugr Excellent Real-World Data from US mySugr Bundle Users: -16 mg/dl mean glucose, TIR +2 hrs/day; AGP-Style Data Visualization for SMBG

We noticed two mySugr posters of interest, one on using an evidence-based continuous probability estimation to generate an AGP-style data visualization from SMBG measurements, and the other showing real-world data from US mySugr Bundle users. See below for the details.

  • To determine the real-world changes in glycemic control following use of the mySugr Bundle (mySugr app + unlimited strips + CDE coaching), data from 52 US participants (56% type 1, 37% type 2, 6% LADA) were evaluated over four months. Mean blood glucose dropped significantly by 16 mg/dl (baseline: 154 mg/dl) and time-in-range improved significantly from 64.5% to 73.0% (+2 hours/day). Significant declines in time-in-hyperglycemia (-8.9%) and eA1c (-0.43%; baseline: 6.7%) were also observed, and SMBG checking frequency increased by 17.5% from 5.8 fingersticks/day to 6.2 fingersticks/day. A clinically relevant change in eA1c (defined by EMA guidelines as ≥0.3%) was achieved in 31% of the population. A subgroup analysis found that for those with baseline eA1c ≥6.7% (n=26), mean blood glucose significantly declined by 40 mg/dl (baseline: 186 mg/dl) and glycemic standard deviation decreased by 14 mg/dl (baseline: 71 mg/dl). For the subgroup with baseline eA1c <6.7%, no significant changes were observed, suggesting that the positive effects of the mySugr Bundle are enhanced in those who are less well-controlled. Positive trend data taken two months after bundle registration but before Bundle initiation indicate that the initiation of CDE coaching may positively impact the sustainability of glycemic improvements, as well as contribute to longer-term benefits. Importantly, the participant population was broad: 77% were using insulin, only 19% were using insulin pumps, and 23% were on non-insulin therapies. The results are very encouraging, and we’d be interested in seeing outcomes across a larger population.

  • In an attempt to develop an AGP-style glycemic data visualization tool for SMBG patients, the mySugr team applied a kernel density estimation (KDE) to obtain a continuous probability for glycemic events from SMBG measurements. A kernel essentially determines the probability of a glycemic event (i.e., hypoglycemia, hyperglycemia, in-range) being present before and after the time of measurement. By summing all kernels, the likelihood of recorded events can be shown over a day. The poster notes a limitation for KDE in that it uses non-periodic data, resulting in a non-continuity of the estimated probabilities. By switching to a Van-Mises Kernel, the probabilities can be translated into a continuous transition. To evaluate the accuracy of the model, 14-day CGM datasets were randomly subsampled to generate artificial SMBG datasets. The CGM and SMBG datasets were plugged into the model and compared for fit, revealing that increasing SMBG measurements from once daily to three/day provided the greatest error reduction, while a plateau was reached at ~4 fingersticks/day. The investigators therefore chose four measurements/day over a duration of 14 days as the minimum requirement for the model. Given the immense utility of AGP both for patients and providers, we’re excited to see efforts aimed at expanding its benefits to SMBG users. It also seems to be a first step at eventual glucose prediction for SMBG users. One Drop has a version of this for type 2 non-insulin users, though obviously doing so in those with more glucose fluctuation is exceedingly more difficult.

Dr. Jane Jeffrie Seley: Smart Pen Caps “Easier in Some Ways” than Smart Pens (More Freedom of Choice of Insulin); Reusable Smart Pens with Manual-Fill Cartridges Coming Down the Pike?

New York Presbyterian’s Dr. Jane Jeffrie Seley highlighted a selection of players in the smart insulin pen/pen cap landscape, punctuated by her enthusiasm for the growing market: “I really welcome this into my practice – some people think too much data is a curse, but I think it’s a blessing for us.” She noted that caps are “easier in some ways” than durable pens since they might give patients more freedom of choice with respect to insulins and continued use of disposable pens. She foreshadowed that, in the future, some reusable pens will allow users to manually fill cartridges with any insulin of their choice (with a syringe, like a pump reservoir); she didn’t elaborate, but we imagine patients would be given a connected pen and an empty cartridge and then prescribed vials of insulin, which may be lower hassle than prescribing pre-filled cartridges. For smart pens, Dr. Seley prefers disposable pen add-ons, which are easier to learn to use since they don’t require the patient to re-load the insulin cartridge, though we’d note that they still require the user to transfer the cap from disposable pen to pen and to recharge the cap. The beauty, as Dr. Seley pointed out, is there is a bounty of devices in development, which will allow patients (and payers) to choose from a suite of options. Many providers – particularly educators – are understandably eager to have granular data on insulin injection history, but Dr. Seley emphasized that a number of pieces have to fall into place in order for a fast ramp among consumers. First and foremost, cost and reimbursement – one audience member put a finer point here during Q&A, claiming that large numbers of her type 2 patients decline to use apps as soon as they find out doing so would boost the cost of their data plans (this is important and not often talked about!). Dr. Seley believes cost and reimbursement will improve over time, as outcomes and cost-savings are demonstrated and pharma players transition their bases over to connected devices. In addition to access, Dr. Seley wondered how much training devices will require – how intuitive will it be to use them, and how much provider time will it take to get patients up and running? Lastly, the patient has to perceive that the device has added value to their routines (or at least not adding burden) – considering most dose capture devices are integrated into the injection device and passively upload dose, there is not too much friction associated with their use; however, this needs to be shown in a wide variety of patients, rather than current early adopters. Naturally, adding in features that patients regard as value-adds – e.g., bolus calculators and titration – will make them stickier. 

  • Dr. Seley spoke briefly about six companies manufacturing smart pens or caps:

    • Companion Medical InPen. According to Dr. Seley, the Bluetooth-enabled InPen offers valuable features such as an insulin bolus calculator that incorporates insulin on board (IOB), dose reminders and temperature alerts. In her view, the pen’s one-year battery life is a limitation. New to us, though the pen’s different color options currently serve to provide options, in the future, different colors may correlate to different insulins (i.e., basal or bolus). InPen currently only supports prandial insulin cartridges (Novolog and Humalog), to our knowledge.

    • Common Sensing Gocap. The Bluetooth-enabled dose capturing pen cap (Gocap) remains in limited supply, direct from the company currently, though Dr. Seley imagines it will scale soon. She noted that the Lantus, Apidra, and Novolog compatible caps are now available, and positioned the 510(k)-exempt status – Gocap doesn’t have a bolus calculator – as both an advantage (no FDA hurdles) and a detriment (less value to the user).

    • Emperra Esysta pen. Currently only available in the EU.

    • DiabNext Clipsulin. The pen attachment will be launching globally this month, as of EASD, though timing with this company is always a moving target. Dr. Seley highlighted that the device works with almost every insulin pen available, logs in one-unit increments only (different attachment needed for two-unit or half-unit dosing in the future?), and stores 200 doses on the device itself and all data in app.

    • Lilly and Novo Nordisk. “Both companies are working on connected health, sharing as much data as possible. This is what we want: everything in one place, we can look at one chart, and have all the data that we need there. One way to download, one way to look – that’s how we’ll integrate all this into our practice.”

Lilly’s Connected Pen Study on Missed Meal Boluses Complete; Even “Passive Users” Can Benefit from “Really Rich and Useful” Connected Pen Data

In her discussion surrounding the potential for connected pens to boost patient adherence and improve diabetes outcomes, Lilly’s Ms. Jennal Johnson noted that the company’s 12-week, single-arm smart pen study investigating the frequency and effect of missed meal boluses has been completed (as of July). So far, this study is one of the very few clinical trials that have been conducted in the area – Ms. Johnson also referenced the impressive Joslin study evaluating use of Common Sensing’s Gocap paired with Dexcom’s G4 CGM data (n=31), as well as the ongoing, 24-week crossover Emory study of Insulclock in type 2 diabetes (n=100), anticipated to complete by the end of 2018. We’d also note a very interesting Stanford/UVA/Mt. Sinai poster presented at ADA, showing that 27% of meals had either a late or missed meal bolus in adults and adolescents (n=24) using smart pens. Ms. Johnson listed a few pilots, including Innovation Health’s collaboration with Sanofi to leverage One Drop’s mobile app and Common Sensing’s Gocap, and Novo Nordisk’s NFC-enabled NovoPen 5 Plus pilot in Sweden. She explained that connected pens not only provide richer insulin dose, timing, and temperature data, but if applied appropriately, can yield actionable insights for providers and people with diabetes. She proposed several add-ons for connected pens, advocating for the passive collection of context (e.g., nutrition, exercise, etc.), alerts/reminders (e.g., Companion Medical’s basal insulin reminders), and the integration of CGM to identify missed mealtime boluses in real time. Still, she cautioned industry members in the room to “keep it simple” especially when it comes to patient-facing data visualization. Highlighting the value of pairing previously invisible insulin data with CGM traces, Ms. Johnson urged the audience to remember that the ultimate goal of avoiding hypoglycemia requires more than patients taking an insulin dose; rather, patients must take the right dose at the right time. We were disappointed not to hear a more substantive update on Lilly’s connected pen efforts, which have been notably quiet since the announcement one year ago – we have yet to see what the device looks like that fits on the disposable KwikPen platform. See Lilly’s Blogger Summit from this past May for more details.

  • During Q&A, Ms. Johnson explained the distinction between active and passive users of technology, asserting that even passive users can benefit from connected pens. While active users might use “every bit and part” of a given device, a passive user “sets it on auto pilot and might use one function.” However, Ms. Johnson said, the “data is still coming in and is going to be really rich and useful” in informing healthcare providers in their discussions with patients. This is the beauty of passive dose capture: Unlike other innovative technologies that fundamentally change diabetes management for the patient, once a smart pen/pen cap is paired with a phone, there is little additional lifestyle change foisted upon the patient (unless he/she wants it).

NIH’s Dr. Bremer Touts NIH’s Interest in Visual Analytics, Machine Learning to Prevent Info Overload in Clinical Decision Support; Emphasizes Applicability to Disparate Types of Diabetes

While he purposely didn’t include any RFAs in his talk, NIH’s Dr. Andrew Bremer spoke expectantly about the possibilities that arise when data science, technology, informatics, and phenotyping are incorporated into clinical decision support. He noted that the quantity of data continues to grow, along with (our understanding of) the heterogeneity in diabetes. Accordingly, he applauded efforts from the likes of Dr. Kovatchev and Prof. Phillip, but asked how “we can increase applicability in the field, to help the child with type 2, or the woman with gestational diabetes.” One area that NIH has invested in is visual analytics, consolidating analytical information in interactive, easy-to-understand visual interfaces in order to prevent information overload for providers using clinical decision support tools. He gave the example of Glucolyzer, “an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes.” By hovering over a bar on the screen, users see a prediction of their likely glucose spike if they were to eat a given meal. (The idea is cool though the user interface looks very confusing and overwhelming to us.) Dr. Bremer also said that other advances in data science, such as mechanistic machine learning, are also promising. A fun role for him at the NIH, he concluded, is to play matchmaker – to recognize individuals pushing technology, maybe not in diabetes at the outset, but recognizing where technology is going and how it can be applicable in the field of diabetes.”

Ascensia Contour App data: n=5,870 users, doubled blood glucose checking frequency at 180 days, lower odds of highs/lows

An Ascensia poster presented real-world data from n=5,870 Contour Diabetes app/connected Contour Next One BGM users, comparing app data from use in the first 30 days to >180 days of use. Impressively, blood glucose checking frequency more than doubled from 2.0 times daily in the first 30 days to 4.5 times daily after 180 days (p<0.001) – this sort of increase in engagement is rarely seen in digital health studies this long and is an excellent sign for Ascensia. Hypoglycemia (<70 mg/dl) and hyperglycemia (>180 mg/dl) were also twice as likely in the first 30 days relative to after 180 days of use. Data looked notably similar in other analyses of cutoffs (<50, >250 mg/dl) and multiple events of hypo/hyperglycemia – there was 40%+ more risk of events in the first 30 days vs. >180 days. It’s excellent to see some real-world data from Ascensia, indicating that the app’s pattern recognition and suggestions are engaging BGM users and driving nice glycemic impact.

Top 10 Mobile Apps by Amount of Evidence: Dexcom G5, Senseonics’ Eversense, and WellDoc’s BlueStar Take the Lead

Children’s Mercy Hospital’s Dr. Mark Clements reviewed the top 10 mobile apps by total number of registered trials, journal articles, and meeting items (i.e., abstracts/posters). We were somewhat surprised to see Senseonics’ Eversense coming in so high on the list (#2!), beating out WellDoc’s BlueStar and Fitbit’s app, and well behind Dexcom’s G5. This ranking is particularly surprising given that Fitbit is the wearable device provider for nearly 95% of NIH-funded research studies using a wearable device, suggesting that a large proportion of these studies may not be leveraging the Fitbit app or at least calling it out in the published data. Dr. Clements highlighted the G5/G6 mobile app, expecting that direct-to-Watch communication will increase adoption of Dexcom and other CGM systems. He also referenced Companion Medical’s InPen app, noting that it provides a variety of reminders (e.g., insulin temperature, battery life, dosing, insulin age), and One Drop Mobile, remarking that patients can receive push notifications even outside of the app. Rimidi, he noted, is an example of a platform in which the healthcare team can set individualized reminders for patients. Last, he noted Klue, a new Apple Watch app for automatically detecting meal start and eating speed with an automatic bolus reminder, which we just saw demoed at DiabetesMine last week.

Pops! Diabetes Care Expects 510(k) Clearance for All-in-One, Smartphone-Adhered BGM; Poster Shows 1% A1c Decline at 6 Months (n=5)

A poster from Pops! Diabetes Care indicates that the novel all-in-one BGM and digital platform is currently pending FDA 510(k) clearance, and we confirmed with the company that the milestone is expected before the end of the calendar year, with a launch soon to follow. The poster itself showed that use of the “Pops! one Mobile Platform” reduced A1c by ~1% (baseline: 8.9%) over six months in n=5 adolescents with type 1 diabetes – the study enrolled 50 individuals into the single-armed, prospective trial, but the remaining 45 participants had not yet completed the trial. As shown in the picture below, the whole testing kit can adhere to the back of a smartphone, where three stationary lancets reside next to three strips on a disposable case module (i.e., each module provides three glucose measurement opportunities; see a YouTube demo here). The meter communicates with a companion mobile app via Bluetooth, and the app also allows caregivers and providers to remotely monitor patients’ glucose readings and other information. The system will be sold to payers through a subscription model, with the goal of patients receiving all supplies at no cost. A rep told us there will be some level of automated coaching in the app (e.g., “You got it!”), but the company doesn’t intend to incorporate human coaching at this stage. Pops! CEO Mr. Lonny Stormo has significant experience in the field, having spent the last 11 of his 30 years at Medtronic in VP roles; despite his diabetes expertise and the novel BGM design, making headway in an already tough BGM market will be no easy feat. That said, we really like the form factor focus on a slim BGM that fits onto the phone i.e, (always with someone), especially for those that don’t want/cannot access CGM.

Insulin and Insulin Delivery

Unomedical Extended 10-Day Wear Infusion Set Study Starting Soon at Stanford, Large Multicenter Study in 2019; Set+Sensor Designs In Development

Convatec’s (Unomedical) Dr. Matthias Heschel discussed the company’s infusion set pipeline, sharing that an upcoming Stanford study will test the next-gen “coated” Lantern catheter for extended wear over 10 days. Following mixed seven-day data shared in an ADA poster, Unomedical is ambitiously extending wear further in an upcoming outpatient study at Stanford (not yet listed on ClinicalTrials.gov). As a reminder, the Lantern catheter includes several slits along the side to allow insulin to flow out of multiple places (e.g., for occlusion or kinking), and this next-gen version has a “coating” to suppress the body’s foreign body response over extended wear. The ADA data on coated Lantern in n=16 type 1s showed a trend towards reduced insulin action and more hyperglycemia over time, though “safe” wear out to seven days. We’ll be interested to see if the hyperglycemia/insulin trends are seen in this upcoming outpatient study, particularly with ambitious ten-day wear. Dr. Heschel said a “large multicenter study” of coated Lantern will start in 2019, presumably using lessons from this smaller Stanford study. Notably, the set is expected to remain class II (510(k)), even with the anti-inflammatory coating. Dr. Heschel also briefly covered the gen one version of Lantern (non-coated) as an example of improved catheter reliability. The approach indeed seems like a better version of BD’s FlowSmart, though it’s still unclear when Lantern gen one will launch; perhaps we’ll hear an update at ATTD. Dr. Heschel’s first pipeline slide reviewed the company’s all-in-one novel serter – marketed as Medtronic’s Mio Advance – which is available outside the US and received under-the-radar FDA clearance in March. No US launch timing has been shared on this excellent product, though as of ATTD, “other countries” were expected to launch “later in 2018.” Medtronic told us at EASD that it is seeing major demand for the set outside the US, and the lag time between clearance and US launch reflects the need to build sufficient supply. Will we see a launch this year?

  • Unomedical also has set+sensor and dual-hormone delivery concepts in development. The former includes double-port designs with a two-needle insertion (i.e., like a snake bite) and also ambitious single-port configurations that integrate the CGM sensor within the insulin catheter (i.e., one needle). Unomedical plans to exhibit “selected concepts” at upcoming events. On the dual-hormone front (second picture below), we believe Beta Bionics is building its own dual-hormone set; however, this suggests potential to use a Unomedical version.

  • For context, Medtronic’s 2018 Analyst Meeting expected a “combination set with extended wear” to launch “Beyond” April 2020 (2+ years away). We assume this is in partnership with Unomedical – called “Project Duo” back in June – though it was not specified at the time.

Capillary Biomedical Soon to Start Human Study on Extended Wear SteadiSet with Sprinkler-Like, No-Kink Catheter (3 Side Holes)

TJU’s Dr. Jeffrey Joseph shared an update on Capillary Biomedical’s plans for an extended wear, no-kink (wire reinforced), multi-ported (3 side holes) insulin catheter that provides “consistent absorption of insulin from day to day and dose to dose.” The company has NIH funding to soon begin a first-in-human trial of SteadiSet, gathering reliability, safety, and PK/PD data for extended set wear out to 7 days and potentially beyond. Our coverage of Capillary in August called for a “late 2019” launch of a three-day wear version of SteadiSet, though Dr. Joseph focused on the next-gen extended wear potential in this presentation (7-14 days). He presented data from Capillary’s cool DTM poster (download it here), which compared its set to a standard teflon catheter (Unomedical) over 14 days of wear (!) in pigs. The tissue histology data showed significantly less inflammation with Capillary’s set, both in terms of thickness of the inflammatory layer and total surface area of inflammation (graphs enclosed below). The company believes its soft, flexible, SteadiFlow cannula produces less tissue inflammation due to: (i) less motion-induced tissue trauma (the catheter bends and flexes); and (ii) greater reliability of insulin delivery due to the wire-reinforced cannula that resists kinking and multiple holes (four in total) that produce redundancy. We like how data driven this company is and look forward to seeing if the promising extended wear data in pigs is confirmed in humans.

  • See our August coverage for a deep dive on Capillary Biomedical and the SteadiSet. At the time, the company had raised $2.9 million in seed funding and planned to launch gen one (three-day wear) in “late 2019” through pump partners. The three-day wear version is expected first (easier path to market), followed by an extended wear version that will obviously require more clinical testing.

Pacific Diabetes’ Integrated CGM/Insulin Catheter: MARD of ~12-14% in Small OHSU Study

Pacific Diabetes’ Dr. Bob Cargill shared new accuracy data on the company’s single-port, integrated insulin/CGM sensing catheter. In a small, 10-hour human feasibility study at OHSU (n=8 completers), the sensor demonstrated an MARD of ~12%-14% using a “retrospective” two-point calibration, with 70% of points in Zone A and 26% in Zone B. The company has reportedly seen lower MARDs “down around” 10% in pig studies, which have tested the sensor/catheter out to seven days. Since DTM 2017, Pacific has improved the sensor chemistry/stability and transitioned to a flexible cannula with a 60% smaller cross section. The on-body form factor has improved to a sleeker wearable (see below), targeted to enter a feasibility study in 2Q19; an FDA IDE submission is expected soon. The company has funding from HCT, NIDDK, and JDRF, and is looking for industry partnerships, academic collaborations, and investment. The goal here is ambitious – sensing and infusion in one site with one needle – and we the commercial viability rests on extending insulin infusion length. We cannot imagine moving back to a three-day CGM sensor, which not only puts tremendous pressure on price/margins (10 sensors/month instead of 2-3), but puts a full one-third of the wear-time on day one of insertion. Can Pacific get infusion set length out to 7-14 days? We did not hear anything about that in this talk.

  • As we noted at DTM 2017, one of the first challenges Pacific faced was designing a sensor unbiased by the presence of insulin preservatives at the same site. When exposed to insulin, conventional, platinum-based sensors exhibit a rapid spike resembling hyperglycemia followed by eventual poisoning and loss of sensitivity. By turning to redox-mediated chemistry, Pacific Diabetes developed a sensor capable of functioning normally, showing no significant glucose spike when insulin is infused at the sensing site. Pacific uses a sensor array and different chemistry, which mostly eliminates the artifact with only a brief artificial spike (lasting ~5-15 minutes) following a large bolus.

Light-Induced Insulin Delivery Controlled by CGM; Still Early Stage but Shows Substantial Promise in Rats; Potential for Bihormonal System

University of Missouri’s Dr. Simon Friedman described his team’s fascinating efforts to develop a light-induced insulin delivery system. An insoluble polymer, which when reacted with a specific wavelength of light releases pure insulin, is injected below the skin and stimulated non-invasively through a LED light source controlled by CGM. Dr. Friedman was quick to address the common concern regarding the potential for ambient light to affect the system; according to Dr. Friedman, the light source is 50-100-fold more intense than ambient light, which is already adequately restricted by covering the injection site with the light source. Early results published in 2013 demonstrated that pulses of insulin are released in a predictable fashion upon photolysis of the polymer. In rats, insulin was shown to be “immediately” released after two minutes of radiation, with a very fast peak and decline – in fact, Dr. Friedman highlighted that 30%-40% of the peak insulin level is reached within five minutes. The rats exhibited a glucose reduction in response to the light irradiation, but Dr. Friedman characterized the reduction as “not robust enough.” His team achieved a “more substantial” reduction in glucose by applying a second pulse of light. While Dr. Friedman described this first-generation material as “fine,” his team recently developed a second-generation material that provides five-fold peak insulin while using one-fourth of the light relative to the first-generation version, yielding a 20-fold improvement on a per light basis. Moreover, by attaching a hydrophobic tag to the material, the material is rendered soluble in a mildly acidic injectable formulation, but precipitates in a neutral pH environment (i.e., the skin) – an important consideration for the commercial and clinical feasibility of the therapy. Upon photolysis, native insulin is released, leaving behind a much smaller, more easily absorbed molecule to clear the body than the original polymer. Next steps, according to Dr. Friedman, include investigating photolysis via a longer light wavelength, performing multiple-day trials, and developing a similar system for glucagon delivery. Excitingly, Dr. Friedman’s team has already established light-responsive glucagon release, and Dr. Friedman ultimately sees potential in leveraging the system for bihormonal non-invasive glycemic control. In such a system, insulin and glucagon could be individually controlled by using two distinct wavelengths of light. There was substantial excitement in the room during Dr. Freidman’s talk – we can see why. While obviously in its early stages, this system could radically change insulin delivery for people with diabetes.

Pump vs. MDI in T1D – Drs. Roy Beck and John Pickup Mostly Agree

In the now-common debate on pumps vs. MDI in type 1, Drs. Roy Beck and John Pickup agreed on the big stuff – both technologies are valuable – though discussed different nuances. As we usually see with thoughtful diabetes debate participants, this one didn’t deliver an “either/or” answer, but a “both/and.” See the key points below. To us, the biggest questions in this area are about cadence, connectivity, and automation: (i) when should a type 1 receive a CGM? (i.e., at diagnosis?); (ii) when should a pump be added on top of CGM, and in whom?; and (iii) how will smart pens/CGM compare to pump/CGM? How will automation in both areas change outcomes, quality of life, and treatment burden?

  • CGM – Dr. Beck highlighted the oft-shown T1D Exchange data on A1c by device category (pump vs. MDI, CGM vs. no CGM): while pumpers have a lower A1c than MDI users, in those using CGM, there is no significant A1c difference between pumps and MDI. Looking to clinical trial data, Dr. Beck highlighted the same A1c reductions with MDI and pumps in the JDRF CGM trial, as well as the 2017 JAMA publications from DIAMOND and GOLD (both testing CGM in MDI and showing benefit). Concluding, Dr. Beck noted that “without CGM, a pump wins” – it is associated with lower A1c and a higher percentage meeting the ADA target relative to MDI alone. Once CGM is added, however, “it is a tie” between pump and MDI – mean A1c and percent meeting ADA targets are similar for pump and MDI users, supported by both registry and clinical trial data. In his last slide, he answered what we believe is a better framing of the debate: if you could only have a pump or CGM, which would you choose? Dr. Beck chose CGM, showing pictures of G6, FreeStyle Libre, Eversense, and Guardian Connect.

  • Pump – Dr Pickup started in his usual humorous tone, “I know what some of you are thinking, ‘Pumps are so last year, darling’” He agreed with Dr. Beck that MDI users can get excellent diabetes outcomes – e.g., DCCT participants on pumps had an A1c of 6.6% vs. 7% in MDI users (“Not much worse”). However, such outcomes on MDI are not routinely seen in the average patient, the average clinical trial, and at the average clinic. Dr. Pickup argued persuasively that pumps have been around for over 40 years now, meaning the evidence and knowledge base is much greater than with CGM. We also know a lot about pumps that is “still uncertain” about MDI/CGM: long-term glycemic outcomes on pumps are well established; long-term treatment satisfaction and quality of life is generally good on pumps; the discontinuation rate of pump therapy over the long term is low (<5%); and pumps are effective in many patients struggling on MDI (e.g., disabling hypoglycemia and/or elevated A1c, even after best structured education). (Dr. Pickup used the term “MDI failures,” which is not as patient-friendly as other phrasing.) Dr. Pickup noted several important benefits of pumps, including “special pharmacology of pump delivery” (constant basal, improved predictability, accurate adjustment); alleviating burdensome multiple daily injections (especially for meals, where bolus injections are often missed and raise A1c); covering high-fat, high-protein meals with extended boluses; and coping with frequent, unplanned exercise. Taking a page out of Dr Beck’s book, he even cited the DIAMOND extension phase, where MDI/CGM users crossed over to the Omnipod – time-in-range was 83 minutes/day higher in the pump/CGM group vs. MDI/CGM. Concluding, he noted, “Insulin pumps are still a jolly good choice for treatment in type 1 diabetes.”

    • Dr. Pickup cited “more than 1 million people” using pumps worldwide – a sign of the technology’s uptake – though of course this now also applies to CGM with Abbott’s 1+ million FreeStyle Libre users, likely 350,000+ Dexcom CGM users, and likely over 125,000 Medtronic CGM users. Dr. Pickup estimated 5% of type 1s globally are using pumps, while only 1-2% are on CGM; that math doesn’t quite work out, unless he is not counting FreeStyle Libre as a CGM.

Outcomes Beyond A1c

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. 

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.

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.

Dr. Simon Heller Argues for Hypoglycemia-Induced Arrhythmia Screening in Diabetes; Demonstrated Cardiac Effects of Hypoglycemia in Type 1s and 2s

University of Sheffield’s Dr. Simon Heller presented compelling evidence in favor of screening patients with diabetes for hypoglycemia-induced arrhythmias. In the largest observational study examining the effects of symptomatic hypoglycemia on cardiac arrhythmias, heart rate variability (HRV), and cardiac repolarization in young adults with type 1 diabetes, 37 patients with type 1 diabetes <50 years-old underwent 96-hours of simultaneous ECG and CGM monitoring while continuing their normal activities and diabetes management. The results, published in 2017 in Diabetes Care, found several differences in cardiac responses to hypoglycemia between the day and night. Bradycardia (abnormally slow heart rate) was significantly more common during nocturnal hypoglycemia vs. nocturnal euglycemia (IRR: 6.44; 95% CI: 6.26-6.63; p<0.001) but occurred significantly less frequently during daytime hypoglycemia vs. daytime euglycemia (IRR: 0.02; 95% CI: 0.002-0.26; p=0.002). The frequency of atrial ectopics (premature heart beat) was also significantly higher during daytime hypoglycemia as compared to daytime euglycemia (IRR: 2.29; 95% CI: 1.19-4.39; p=0.013). The investigators also detected cardio-acceleration during daytime hypoglycemia but not during nocturnal hypoglycemia, and confirmed a proarrhythmogenic of hypoglycemia by showing significant extension of the QTc interval and TpTend interval plus a change toward an abnormal T wave shape during the night and day. After reviewing the results further, Dr. Heller realized that the bradycardia had actually been observed in just one individual. Dr. Heller interpreted this finding as indicative of bradycardia as “probably very specific to a few individuals.” Still, he does not find the occurrence to be an anomaly unworthy of investigation; rather, he remains interested in determining which factors led this individual to be more susceptible to bradycardia. Given the evidence above coupled with a demonstrated increased risk of overnight death in young people with type 1 diabetes as compared to those without diabetes, Dr. Heller concluded that there is a case for screening patients with diabetes for hypoglycemia-induced arrhythmias.

  • We’d be very interested to see a cost analysis study for an arrhythmia screening program, possibly coupled with screening for broader CV risk/mortality. At Diabetes Canada, University of Toronto’s Dr. Lawrence Leiter provided a useful review of the literature on the relationships between glycemic variability, hypoglycemia, and CV risk/mortality. As he pointed out, establishing causation may remain a point of debate, as ethical concerns preclude an RCT where people are randomized to have more severe hypoglycemia or greater glycemia variability. Still, the association alone suggests that the issue should be further addressed.

  • A smaller study (n=23) published in Diabetes in 2017 observed cardiac function during experimental hypoglycemia in patients with type 2 diabetes. Participants with type 2 diabetes demonstrated greater heterogeneity of repolarization during hypoglycemia, as demonstrated by T-wave symmetry, despite comparable epinephrine levels as compared to matched controls without diabetes. The investigators concluded that these mechanisms could feasibly contribute to arrhythmias during clinical hypoglycemia.

Regulatory and Big Picture

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.

Dr. George Grunberger’s Poster on PCP and Endocrinologist Perspectives Surrounding Diabetes Technology Use in Type 2 Patients

A BD-funded poster listing Dr. George Grunberger as first author evaluated primary care physician (n=102 PCPs) and endocrinologist (n= 100 ENDOs) perspectives on the use of diabetes technology to optimize intensive insulin therapy in patients with type 2 diabetes. Among other inclusion criteria, PCPs must have personally treated ≥20 patients with type 2 diabetes each month, while ENDOs must have personally treated ≥80 patients with type 2 diabetes each month. At least 25% and 50% of these patients, respectively, must be prescribed insulin therapy. Not surprisingly, the results indicated that ENDOs are more likely to prescribe use of diabetes technology than PCPs. However, both ENDOs and PCPs reported willingness to utilize diabetes technology in patients with type 2 diabetes if A1c targets are not met using basal-bolus therapy. CGM is the most commonly prescribed technology for ENDOs (87%) while traditional tubed insulin pumps are the most commonly prescribed technology for PCPs (74%). ENDOs report substantially more comfort initiating pump therapy than PCPs: nearly all ENDOs were “comfortable” or “very comfortable” with initiating tubed insulin pumps (96%) and wearable tube-free patch devices (93%) whereas more PCPs felt “comfortable” or “very comfortable” initiating a wearable tube-free patch (74%) than a tubed insulin pump (52%). The latter point is interesting, as despite the preference for wearable patch delivery devices, more PCPs report prescribing tubed pumps (74%) far more often than patch devices (23%); definitely an opportunity for Insulet, Valeritas, BD, CeQur, etc. When asked to rank the top four features that deter/prevent participants from using diabetes technology, both groups included device complexity and patient acceptance (ENDOs ranked complexity as #4 while PCPs ranked it as the #1 deterrent). We were somewhat surprised to see cost/insurance coverage listed as the #1 deterrent for ENDOs but not included at all for PCPs. Given that the vast majority of type 2 diabetes patients receive their diabetes care from PCPs, many leaders in the space have advocated for increased efforts to facilitate technology uptake among general practitioners. From the results of the survey, it would appear that this might best be achieved by decreasing device complexity and cutting down on the extra time and training required by the device. Abbott’s FreeStyle Libre has made great strides in checking these boxes – it remains to be seen if insulin delivery devices can get to that point of simplicity.

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.

Injection Therapies Show Higher Persistence than Orals (though both are low); >50% of Type 2s Don’t Receive A1c Test Within One Year of Initiating Treatment

McKinsey’s Ms. Nisha Subramanian showed data demonstrating significant variation in adherence across therapy type in diabetes. She noted a “huge, steep drop off” at the 2-3-month mark across multiple disease areas, not just diabetes, hypothesizing that in diabetes the trend is likely due to an initial influx of multiple add-on therapies and difficulties surrounding expectation setting. (We’d also point to “long-term” motivators to “avoid complications” as not very effective or engaging.) Notably, she found that injection therapies have a higher persistence rate than orals (except for SGLT-2s over GLP-1s), possibly because injectables require a more rigorous schedule that patients find easier to follow. It’s also possible that an injectable “feels” like a more important medication, is deployed in people with more advanced disease who approach their care with greater urgency, or that those medications (especially insulin) have a more noticeable effect on how the individual feels day-to-day. The steepest adherence drop-off past six months tends to be within orals and non-insulin medication. For non-insulin therapies, SGLT-2s show slightly higher persistence over GLP-1s and DPP-4s through 10 months. On average, ~40-50% of patients stop taking a given therapy within the first year, which is the real headline stat of this stalk and a very concerning one.

  • Ms. Subramanian shared sobering and surprising statistics: 57% of patients with type 2 diabetes do not receive an A1c test within a year of initiating treatment; and of the patients who do receive an A1c test and are found to be not at goal, ~70% do not experience therapy escalation. Moreover, many patients initiate with non-standard-of-care treatments (e.g., ~13% type 2 patients start sulfonylureas as their first line therapy). As we heard more than one attendee proclaim today, we need to go back to basics of education and emotional support!

  • Earlier in the day, we received some fast facts from Dr. Deborah Greenwood on adherence:

    • 25%-40% of patients do not fill their primary prescription.

    • 45% of patients self-report not taking their medication and not persisting at six months.

    • 18%-26% discontinue insulin therapy within 12 months.

    • HCPs describe only 20%-30% of patients as “very successful” with insulin therapy.

    • 38% of patients self-report missed, mistimed, or reduced doses in the last 30 days.

YSI 2300 being phased out, no plans for next-gen 2900 to seek FDA clearance; Talk to FDA for acceptable comparator methods

In a talk from FDA’s Dr. Lisa Landree, we learned that the popular YSI 2300 analyzer is “being phased” out by the company, and oddly, there are “no plans” to get FDA clearance for the next-gen YSI 2900. She noted that there are “many” FDA-cleared lab-based methods that can be used for accuracy comparison of BGMs and CGMs; anyone planning to use the YSI 2300 as a comparator in the future should talk to the FDA.


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