The day was packed in Automated Insulin Delivery: Medtronic provided a clearer closed-loop pipeline than we’ve seen of late, headlined by plans for four key product launches in “1 year”: (i) 670G with Bluetooth, mobile app, and a 2-6 year old indication; (ii) the 780G with auto boluses, an optional set point at 100 mg/dl, and remote software updating (FLAIR trial to begin in coming months); (iii) a 7-day wear infusion set (!); and (iv) non-adjunctive CGM with fewer fingersticks (Day 1 calibration). A new low-cost and 50% smaller pump is also in the pipeline (2+ years away). We also got a first look at 670G pivotal data in 2-6 year olds: a 0.5% decline in A1c (baseline: 8.0%) and an outstanding +2 hours/day spent in 70-180 mg/dl (from 55% to 64% - huge for parents and kids). The most stunning AID data came from the SMILE study, testing the MiniMed 640G (predictive low glucose suspend) in those with hypoglycemia unawareness; after six months, suspend-before-low drove 79% less time <55 mg/dl and a remarkable 84% reduction in severe hypoglycemia – wow! In more unexpected news, FDA’s Dr. Courtney Lias said that it’s “likely” the Agency will find a way to create a lower-risk class II iController pathway for AID algorithms, and Cambridge’s Dr. Roman Hovorka announced plans to commercialize the CamAPS FX closed loop system (Cambridge MPC algorithm, Dexcom G6, and Dana R/RS pumps).
CGM and Decision Support also had a big day: Dr. Rich Bergenstal made one of the most important observations of the meeting so far, emphasizing that we will never flatten the population A1c curve if we keep giving these devices only to people with A1cs of 7.2% and bringing them down to 6.9%. The diabetes ecosystem has to do things differently with CGM, he stressed, and the field clearly finds his ideas compelling. In the meantime, Dexcom shared its near-term pipeline – five upcoming G6 app enhancements, confirmed product details on G6 Pro (fully disposable, blinded or real-time), and reiteration that G7 is “on track” to launch by the end of next year. We didn’t get to hear interim DreaMed Advisor Pro data as we were hoping for, but will sometime after ADA.
The Beyond A1c thread was strong today, as Prof. Battelino reviewed findings from Tuesday’s ATTD-led time-in-range consensus meeting, (Kelly and Brian attended from Close Concerns and Keaton from dQ&A – all were beyond impressed), Dr. Irl Hirsch presented fascinating data on different groups’ readiness to move beyond A1c, and Dr. Roy Beck implored pharma manufacturers to include intermittent CGM in their CVOTs.
On the therapy side, a Sanofi-sponsored symposium honed in on basal insulin and hypoglycemia: Dr. Robert Ritzel suggested the titration-period hypoglycemia difference detected in BRIGHT (Toujeo vs. Tresiba) could be a reflection of stronger fasting glucose lowering from Tresiba, and Dr. Pratik Choudhary emphasized the potential of next-gen basals in lowering population-level hypoglycemia risk. We saw the first data from the A-1 study of One Drop with either Afrezza or injectable mealtime – a clear win for MannKind. Additionally, Dr. Des Schatz offered an update on type 1 biomarkers (including his own impressive emerging data on IGF-1 and -2), and Prof. Phillip Moshe presented a DEPICT analysis of MAGE (baseline/change in). Also included below are highlights from a morning session on protein therapies (Drs. Cliff Bailey and Tim Heise on incretin and glucose-responsive insulin), Dr. Stephanie Amiel on hypoglycemia in clinical trials, and a series of talks on hyperglycemia and cognitive function.
Hello from a day #2 of ATTD 2019 in Berlin, where we had a busy day at tech’s biggest gathering. Read on below for our top highlights in AID & Pumps, CGM & Decision Support, Beyond A1c, and Diabetes Therapy. Also, don’t miss our day #1 highlights, and check out our preview for what’s coming tomorrow.
- Automated Insulin Delivery and Pump Highlights
- 1. Medtronic Closed-Loop Pipeline: “1 Year” = 670G with Bluetooth, 2-6 Year Indication; Auto Bolus 780G; 7-Day Wear set; “2+ Years” = Personalized Closed-Loop, phone control, lower-cost system
- 2. FDA’s Dr. Lias Optimistic Agency Can Create a Lower-Risk Class II iController Pathway for AID, Reviews Special Controls of New ACE Pump Designation
- 3. SMILE Study of 640G: Stunning 84% Reduction in Severe Hypoglycemia in High-Risk Population in Six-Month RCT
- 4. MiniMed 670G Pivotal in 2-6 Year Olds: 0.5% A1c Decline (Baseline 8%); +2 Hour/Day TIR Gain; No Change in hypoglycemia; Consistent with 7-13-year data
- 5. Tandem/Dexcom Control-IQ Pivotal to Report at ADA – 95% Complete, not a Single Participant Has Dropped Out At Any Site (N=168)
- 6. Dr. Roman Hovorka’s Plans to Commercialize CamAPS FX Closed Loop System with Cambridge Algorithm, Dexcom G6 CGM, Dana R/RS Pumps; Closed Loop Data in 1-7-Year-Olds Find ~70% Time-in-Range
- 7. MiniMed 780G Has Optional Set Points of 120 or 100 mg/dl; NIH-Funded FLAIR Study (670G vs. 780G) to Start Within Months
- 8. Medtronic CareLink 670G Data from 60,394 Type 1s (69% TIR, 75% in Auto Mode) and 2,443 Type 2s (74% TIR; 76% in Auto Mode) in Real World
- 9. McGill Embarking on Two New Studies: JDRF-Funded, Free-Living Study of Pramlintide-Insulin System, and Inpatient Study of Triple Hormone AID (Fiasp + Pramlintide + Glucagon)
- 10. NIH/NIDDK Roadmap to Implantable IP AID; JDRF Survey Finds Stakeholders Have High Bar For Adoption of IP Systems (A1c 6-6.5%, 90%-95% TIR, no hypo!); Dr. Eyal Dassau’s IP AID Proof of Concept
- 11. Prof. John Pickup: Giving Pumps to All Type 1 Adults with A1c ≥8.5% Modeled to be Cost-Saving in UK After Five Years
- 12. Helmsley Funding Harvard Work on Glucose, Insulin, Lactate, Cortisol Sensing; iAPS System Overview; Six Women Enrolled in NIH-Funded Closed Loop Pregnancy Study
- CGM and Decision Support
- 1. Dexcom Pipeline Updates: “Hey Siri, What’s my Glucose?” First G6 Pro picture (fully disposable transmitter, real-time/blinded); G7 by end of 2020
- 2. Dr. Bergenstal Adamant That We Need to Make CGM Access More Equitable if We Want to Flatten Population A1c Curve; Wishes for T2D Decision Support
- 3. Dr. Lias Leaves Door Open on Regulatory Path for CGM-Based Bolus Calculators (Depends on Impact of Point Inaccuracy); Overviews Nuances of Decision Support Regulation – Many Questions Remain!
- 4. CGM-Based Decision Support for MDI Patients: It Only Works if You Use It – And Not Everyone Is in the TypeZero/Dexcom/NN Study
- 5. Dr. Roy Beck Urges Inclusion of Intermittent CGM in Future CVOTs
- 6. DreaMed Advisor Pro Multicenter RCT (Advice4U) to Report Sometime After ADA; Fully Enrolled, ~50% Completed; Dr. Forlenza’s Take on Time-Saving
- Beyond A1c Highlights
- 1. ATTD Time-in-Range Consensus Targets Preliminarily Set for People with T1D/T2D, Frail People with T1D/T2D, Pregnant Type 1s, and Pregnant Type 2s/GDM; Audience’s Reactions
- 2. Dr. Hirsch Surveys HCPs: Patients & Endos Ready to Embrace Time-in-Range, but PCPs Still on the Fence
- 3. Researchers Describe Detrimental Effects of Hyperglycemia on Cognitive Function; Update on DirecNet from Yale’s Dr. Weinzimer
- 4. Prof. Amiel on Key Challenges to Studying Hypoglycemia in Clinical Trials
- Diabetes Therapy Highlights
- 1. Dr. Ritzel Discusses a Hypothesis for Titration-Period Hypoglycemia Differences in Head-t0-Head BRIGHT Study (Toujeo vs. Tresiba), Building a Case for Clinical Relevance of Results
- 2. A-One Study Finds 0.52% Absolute A1c Improvement with One Drop Premium + Afrezza vs. One Drop Premium + Injected Insulin
- 3. Type 1 Biomarker Update from Dr. Des Schatz: Low Neutrophil Count (Innate Immunity Defects), Pancreas Volume, IGFs As Emerging Pieces of Early Detection and Prevention Puzzle
- 4. DEPICT Post-Hoc Examines Impact of Baseline MAGE on Farxiga-Driven Improvements in A1c, Glucose Variability
- 5. Dr. Choudhary Highlights Potential for Population-Level Impact with Next-Gen Basals While Acknowledging Adaptability of AID for Nuanced Adjustments
- 6. Drs. Bailey & Heise Look to the Future of Diabetes Therapy, Highlighting Potential and Progress in Multi-Receptor Incretin Therapies and Glucose-Responsive Insulin
Automated Insulin Delivery and Pump Highlights
Medtronic Diabetes VP of R&D Ali Dianaty provided a clearer and more ambitious closed-loop pipeline than we’ve seen, headlined by specific plans for four product launches in “1 year”: (i) 670G with Bluetooth, mobile app, and a 2-6 year old indication; (ii) the 780G with auto boluses and remote software updating for in-warranty pumps (80% time-in-range, mean glucose of 135 mg/dl); (iii) a 7-day wear infusion set (!); and (iv) CGM with fewer fingerstick calibrations (just on day 1) and an insulin-dosing claim. The key slides below show the feature details, offering far more nuance and specificity than we heard at either JPM or in Medtronic’s 4Q18 call this week – this was great to see from Ali. Of note, the Bluetooth-enabled 670G with a 2-6 year-old indication will launch separately and before the MiniMed 780G with auto-boluses; as of JPM, we had thought Bluetooth wouldn’t come until the 780G. The timeline to launch a 7-day wear set in the next year was new and seems very aggressive. (In its booth, Unomedical told us this 7-day wear technology is not the coated Lantern, which is currently in a larger study at Stanford; see DTM). For context, the precursor Mio Advance has been FDA-cleared and available in Europe for nearly a year, but still has not launched in the US. (The companies are still building capacity, according to Unomedical.) This was the first time we heard the goal of a mean glucose of 135 mg/dl for the 780G with auto boluses, which would bring the average down by ~15 mg/dl from the 670G and represent another ~0.5% A1c reduction. (Technically, JPM and 4Q18 called for a 780G launch within the next 14 months – i.e., by April 2020.) Within the next year, Medtronic expects a 10x reduction in Auto Mode exits and half the alarms/alerts as on 670G – very big wins and will be terrific news to share with patients and parents and partners. Finally, it would be a huge gain if Medtronic could get to day 1 calibration for its CGM within “1 year,” bringing it closer to the no-cal G6 and Libre and obtaining Medicare coverage at the same time. (It’s unclear if Medtronic plans to go for iCGM at the same time, as was stated at JPM.)
In “2+ years,” Medtronic expects five product launches: (i) personalized closed loop (>85% time-in-range, mean glucose of <130 mg/dl, just received FDA breakthrough device designation); (ii) smartphone control (expanding on the remote monitoring app); (iii) a 50% smaller sensor with easier insertion; (iv) a lower-cost pump that is 50% smaller (new in the pipeline; it looks like half the size of an insulin pen); and (v) an all-in-one sensor/insulin infusion set with factory calibration (fingerstick replacement). The latter two are very ambitious and we’ll be curious to see how far beyond two years those are – Medtronic’s pipeline and timelines are often a moving target. Still, we’re glad to see a low-cost system is on the map now, as that is clearly where the field must go to reach better care for all patients and particularly to impact patients from particularly low-income and marginalized populations.
FDA’s Dr. Courtney Lias provided additional color on the special controls of the new class II ACE pump designation, and commented that “we’ll see, but it’s likely” the Agency will be able to create a lower-risk class II category for a standalone AID controller. Remarks on FDA’s evaluation of a possible class II “iController” were all of two sentences, but it is notable that Dr. Lias believes such a designation is possible. Currently, control algorithms are the critical class III components of AID systems, which determine how much insulin to give (or not) at set intervals. A recent Tidepool blog post notes that the nonprofit is working on the iController pathway with Loop; we suspect Tandem and Dexcom might try to go this route with Control-IQ (our speculation). We wonder what the special controls would be like for this category; obviously there’d need to be a tremendous degree of transparency on how the algorithm works, what it targets, and what kind of CGM accuracy it needs. We wonder how FDA would intend to handle incremental changes to an algorithm (if at all) – what would need a new submission and what would not? Perhaps these algorithms will be regulated similar to class II bolus calculators or basal insulin titration apps. In some respects, AID controllers are even less risky than those devices, since they only dictate the delivery of small increments of insulin at a given time, they rely on CGM data points trended over time, and they remove the human from the equation (this piece could also add risk, depending on the context). Regardless, an iController path would be the final domino, fully enabling a plug-and-play, interoperable ecosystem of component, 510(k) regulated parts. We can’t believe how fast this is moving! Special kudos to FDA for its ambition for patients and the field – the field continues to be impressed by how quickly they are moving and how collaborative a partner they are to the field.
The ACE pump category was created last week when FDA authorized marketing of Tandem’s t:slim X2 as the first interoperable insulin pump; these pumps can be used as part of an automated insulin delivery (AID) system or as a standalone pump (with/without CGM). The primary advantage of the designation is that t:slim X2 and all future ACE pumps will be allowed to integrate into AID systems without having to first perform additional clinical trials and submit new PMAs. Dr. Lias highlighted these clear benefits to sponsors, other component developers, and patients. In Q&A, she suggested that an ACE pump could theoretically also be used to send data to other connected devices/decision support apps so the pump company wouldn’t have to be involved in the app submission. As far as regulatory is concerned, she emphasized that when a manufacturer designs an ACE pump, it must support that purpose across the board (e.g., in recall activities, software maintenance activities, and device modifications). The special controls themselves do not have a performance standard – unlike for iCGM – and Dr. Lias noted that there’s actually not a big difference in terms of required testing between a standard pump and an ACE pump. In both cases, basal and bolus accuracy, occlusion detection, drug compatibility, etc. must be demonstrated. However, ACE pumps must: (i) have procedures in place to ensure secure and reliable data transmission to/from digitally connected devices (including processes for authentic communications, necessary state levels such as battery and reservoir levels, risk mitigation design, and plans for how complaints are handled); (ii) incorporate failsafe design features (e.g., safe basal) and enable data logging to facilitate device failure investigations when multiple devices are involved; and (iii) provide more specific information on pump performance than what is normally given (e.g., so that AID algorithm manufacturers can see that the device is capable of delivering appropriate insulin rates and doses).
Dr. Pratik Choudhary (King’s College London) shared stunning outcomes from the SMILE study, testing Medtronic’s MiniMed 640G (predictive low glucose suspend) in a critical population – those at high-risk for severe hypoglycemia. The six-month trial randomized hypoglycemia-unaware patients to the 640G pump alone (n=77; blinded CGM) or the MiniMed 640G suspend-before-low with real-time CGM (n=76). The primary endpoint showed tremendous impact: a 73% reduction in events <55 mg/dl, 38% shorter events, and 79% less time spent <55 mg/dl with suspend-before-low. An even bigger headline came on the secondary endpoints: an 84% reduction in the rate of severe hypoglycemia – 52 events/100 patient-years with a pump alone vs. 8.5 events/100 patient-years with the 640G’s suspend before low. Wow! Time <70 mg/dl declined by a full one hour per day on 640G with suspend before low (8.1% to 2.8%) vs. no meaningful change on pump alone (9.4% to 9.1%). Talk about the power of predictive low glucose suspend! There was significant between-group change in A1c levels, a sign that the 640G wasn’t causing rebound hyperglycemia, also a big deal. See the full outcomes below, including a plot of how the changes compare to other studies in hypoglycemia unaware population. These data are a tremendous win for the field, especially because studies like PROLOG (Tandem Basal-IQ) and ASPIRE excluded this high-risk population.
As an aside, we loved hearing the new terminology: “technology monotherapy” (pump or CGM) or “technology dual therapy” (sensor-augmented pump). This just came out in a Diabetes UK position statement in Diabetic Medicine and co-authored by Drs. Pratik Choudhary, Partha Kar, and colleagues. We hadn’t heard it put this way before, but it makes a lot of sense – take a prescribing approach from the drug world and apply it to technology.
Yale’s Dr. Jennifer Sherr presented the first topline look at the MiniMed 670G pivotal study in 2-6 year olds over three-months of use (n=46, n=6,697 patient days). Results were nearly identical to the 7-13 year-old cohort: a 0.5% decline in A1c (baseline: 8.0%); an outstanding +2 hours/day spent in 70-180 mg/dl (from 55% to 64%); and no change in time <70 mg/dl (from 3.6% to 3.5%). After three-months on the 670G, 52% of patients had an A1c <7.5%, up from 32% at run-in. There were zero DKA or severe hypoglycemia events. Dr. Sherr noted that this very young population is “hard” to manage, given all the variable – snacking, unpredictable eating, various caregivers, etc. The slide below nicely summarizes the glycemic impact of different technology studies, plotting A1c (Y-axis) vs. time-in-range (X-axis); the arrow indicates the change from baseline to study-end. Dr. Sherr noted that CGM clearly offers benefit on both dimensions (blue dots), though closed loop (green=670G; purple=other systems) really moves the needle further down to the right (i.e., lower eA1c, higher time-in-range). “Sensors provide a lot of help, but it’s not until we automate insulin delivery that we get into more targeted ranges.” We’ll see the full study results Friday during an oral presentation. As of its R&D pipeline presentation (see above), Medtronic expects to launch the 2-6 year old indication for 670G in “1 year,” combined with Bluetooth and a remote monitoring app.
Dr. Boris Kovatchev shared that Tandem/Dexcom’s Control-IQ/G6 pivotal study (14+ years) is 95% completed, will wrap up in mid-April, and results will be presented on Sunday of ADA 2019. The six-month study has randomized n=168 people over six months (2:1, closed-loop to SAP), and not a single study participant has dropped out at any site – a remarkable vote of confidence in the next-gen hybrid closed loop with automatic boluses and the no-calibration G6. Mean A1c is 7.6% (wide range of <7% to 10.5%), with 21% of participants on MDI and 30% previously not on CGM. Wow, is this data going to be exciting! We should hear more about timing next week in Tandem’s 4Q18 call. (Previously, a “Summer 2019” launch was expected – can Tandem get the PMA submitted and approved quickly enough? We suspect so, as this algorithm has a lot of data and experience behind it.) Also in new news, a pivotal trial for Control-IQ in pediatrics (6-13 years) is expected to start recruiting next week – this will be important to get going as Medtronic moves ahead with the 2-6 year-old indication (see above). Also on Sunday of ADA 2019, we’ll hear data from the French FreeLife Kid artificial pancreas study – testing the same Tandem Control-IQ/G6 configuration at five sites in France (n=97 randomized). In the past year, >50,000 days of data have been logged on Control-IQ, tripling the amount at this time last year. Wow!
The Project Nightlight study (n=60) – which recently switched to Control-IQ – also shows consistently positive outcomes (74% time-in-range, 1.5% time <70), especially in the switch to the t:slim X2/G6 configuration. The ten-month (!) study will end this April, comparing evening/overnight closed loop vs. 24/7 closed loop vs. sensor-augmented pump. See interim CGM outcomes below – of note is the comparison between the middle column (Control-IQ on Android phone, Roche pump, Dexcom G4) vs. the right-most column (commercial-ready t:slim X2/G6 with embedded Control-IQ). Dr. Kovatchev noted the marked six-percentage-point improvement in time-in-range (+1 hour per day) just from switching the hardware, a reminder that form factor and reliable communication will help dramatically.
Although Dr. Roman Hovorka presented a slew of exciting closed loop data, the most unexpected update came when he briefly mentioned plans to commercialize a new AID system comprised of the Cambridge MPC algorithm, Dexcom G6, and Dana R/RS pumps equipped with data streaming to Glooko. While the specific path to market is unclear, Dr. Hovorka explained that the new system, called CamAPS FX, will be used in all of Cambridge’s upcoming studies slated to begin in Q1-Q2 of this year (listed below), including NIH-funded Dan05 (n=130 6-18 year-olds; six months), KidsAP02 (n=66 2-7 year-olds; four months), and a trial in pregnancy (n=90; time unspecified). We were particularly excited to see Dan05 on this list – as of ATTD last year, recruitment was placed on hold as investigators awaited manufacturing of the study device. At the time, the closed loop included a modified Medtronic 640G and Enlite 3 CGM; it appears the investigators decided to hold off in favor of the commercial-ready-looking CamAPS FX.
Dr. Hovorka presented results from a small randomized controlled crossover trial (n=23) investigating the benefits of diluting insulin in a closed loop system for very young children (1-7 years-old). The hypothesis, Dr. Hovorka explained, was that by diluting insulin, higher accuracy might be achieved. However, the three-week study found no additional benefit of dilution insulin, with both groups demonstrating a solid time-in-range of ~70% and “acceptable” time <70 mg/dl of 4.5%. Results from the study were published in the January edition of Diabetes Care. The data will inform the longer, four-month KidsAP02 (n=66) study investigating the CamAPS FX closed loop system in children 2-7-years old. Per clinicaltrials.gov, the study is not yet recruiting and is estimated to begin in March and end in June 2020.
Dr. Hovorka shared strong closed loop data from a 12-week randomized controlled parallel trial (n=86 type 1s) published in The Lancet in October. Participants were ≥6 years-old (50% >18 years-old) and sub-optimally managed (baseline A1c: 8.3%). The closed loop group (Enlite 3 sensor, modified Medtronic 640G pump, and Cambridge algorithm on Android phone) achieved 65% time-in-range (70-180 mg/dl), spending a significant~2.6 hours more in-range than the 54% in the control group (sensor-augmented pump therapy). Improvements in time-in-range were observed across all time-in-range values – generally a 10%-15% difference between the two groups across and a difference of nearly 20 percentage points among users with the highest time-in-range between groups. Closed loop drove a minor 12-minute difference in the reduction of time <70 mg/dl (-1.4% vs. -0.2%) and a significant 2.5-hour difference in the reduction of time >180 mg/dl (-13.2% vs. -7.5%). As seen in the modal day plot, most of the benefit came overnight. Those in the closed loop group also achieved a significant 0.4% difference in A1c reduction as compared to the control group: closed loop participants decreased A1c by 0.9% (baseline: 8.3%), and control participants decreased A1c by 0.5% (baseline: 8.2%). Improvements did not differ between children and adults.
With such a wealth of data collected across a diversity of age groups, Dr. Hovorka was able to demonstrate that young children exhibit the greatest variability in insulin requirements. Results are pending publication and show those ≤6 years-old to have a significantly greater coefficient of variation (CV) for insulin as compared to 13-17-year-olds and ≥18 year-olds during the day and night. Dr. Hovorka believes the data are a strong case for closed loop adoption in the very young. We agree that the pediatric population is where huge benefits could be had, particularly given the strong pediatric CGM uptake (51% penetration) in the new T1D Exchange data. Medtronic’s 670G is now approved for those ≥7 years-old (and we saw encouraging data in ages 2-6 today), Insulet is testing its Horizon closed loop system in 2-6 year-olds, Beta Bionics received FDA IDE approval to test its insulin-only Bionic Pancreas in those ≥6 years-old, Bigfoot enrolled children ≥7 years-old in its feasibility trial, and Diabeloop plans to begin pediatric clinical trials in children 6-12 years-old.
Dr. Rich Bergenstal shared that, beyond a design for improved user experience (“85%, 90% of time in auto mode”) and automatic correction boluses, the next-gen MiniMed 780G hybrid closed loop system will allow users to opt in to a blood glucose set point of 100 mg/dl (default will still be 120 mg/dl, as it is with 670G). This is the first time we’re hearing of the optional more aggressive target of 100 mg/dl, but it makes sense; the 670G’s glucose target is 120 mg/dl, but its mild auto-basal aggressiveness means it achieves a mean glucose of ~150 mg/dl. The goal for the 780G is a mean glucose of 135 mg/dl. Dr. Bruce Bode earlier said that the NIH-funded FLAIR study of 670G vs. 780G would begin in May – in slightly more tempered fashion, Dr. Bergenstal stated that the study would begin “within months … we’re waiting for final approval.” His fingers are crossed that the system will be available by ATTD 2020 (Medtronic has guided for before April 2020), despite a number of delays since the initial plan to commence FLAIR in 2017. As a reminder, FLAIR (n=112) is a crossover study (12 weeks on each system) with a primary outcome of superiority for time ≥180 mg/dl during the day (6 am-midnight). Dr. Bergenstal emphasized that the study is looking at “adolescents, those on MDI alone, the postprandial period, and those on 670G who are not at goal. We’re focusing on time-in-range, not really A1c. The baseline A1c will probably be higher than 7.2% or 7.4%.” It certainly seems that this system will be challenged in a way that 670G wasn’t (as a first-gen product in a safety study), which is a necessary part of extending beyond early adopters – we can’t wait to see how it fares!
Dr. Bruce Bode presented Medtronic 670G data uploaded to CareLink from 60,394 type 1 patients and 2,443 type 2 patients in the US as of January 2019. Both groups saw increased time-in-range (70-180 mg/dl) after initiating auto mode, with type 1s increasing time-in-range from 61% to 69% (+2.1 hours) and type 2s increasing from 66% to 73% (+1.7 hours). Time spent in auto mode was comparable for type 1s (75%) and type 2s (76%) – both slightly lower than recommended by many KOLs and that seen in the pivotal (closer to 80%). After breaking out the data by age group, Dr. Bode noted that those ≥65 years-old (n=3,594) achieved the highest time-in-range (74%) and the greatest time spent in auto mode (80%). Indeed, time-in-range and time spent in auto mode increased across age groups, with the youngest age group (≤17 years-old) spending 61% of the day in-range and 64% of the day in auto mode.
Dr. Bode also demonstrated a CareLink feature capable of comparing national data to specific territories and even specific practices. In the example below, 670G patients (n=349) from Dr. Bode’s clinic in Atlanta achieved a slightly higher time-in-range of 73% when in auto mode as compared to the national average (n=69,373) of 72% time-in-range when in auto mode.
After presenting the dual hormone (pramlintide + insulin) closed loop data he initially detailed at ADA, McGill’s Dr. Ahmad Haidar shared that his group has two additional studies on tap:
A second JDRF-funded study (n=130) with the same dual hormone system, but this time for two-weeks at home and with no carb counting. This system will only have a qualitative meal announcement (“I’m eating”). The primary outcome of this trial, Dr. Haidar told us afterward, is quality of life.
A non-inferiority study of a fully-automated, triple hormone (Fiasp + pramlintide + glucagon) artificial pancreas. The control group in this trial is a single hormone AID system with Fiasp. The inpatients will have to wear three separate pumps with insulin, pramlintide, and glucagon, respectively, so Dr. Haidar emphasized that this is purely a probing, academic question at this point. Wow could this be amazing! Data from the first test is displayed below; red traces are glucose, blue traces are insulin, purple numbers are pramlintide (infused at fixed ratio to insulin), and green bars are glucagon.
Dr. Haidar reasoned that the insulin-and-pramlintide system might be “the next logical treatment in patients approaching, not yet achieving, A1c targets with the insulin system alone.” Literature shows that for a high A1c, the relative contribution of postprandial glucose (vs. fasting glucose) to A1c is 30%; for A1c near 7%, the relative contribution is 70%. Therefore, as A1c decreases, postprandial glucose contributes more than fasting glucose to A1c. This is a strong argument (with historical underpinnings from Prof. Monnier – see our EASD 2012 report) and opens up a potentially interesting commercial strategy, should a company take the idea and run with it, though the field is moving away from A1c, so we’d be curious to see a similar analysis with time-in-range (we imagine it’d turn out the same).
In the preliminary inpatient data presented at ADA, use of pramlintide (amylin analog) on top of rapid acting insulin during a 24-hour, in-clinic, JDRF-funded study boosted time in 70-180 mg/dl by nearly three hours/day vs. insulin alone. There were no increases in hypoglycemia or side effects. In a post-trial survey, 19/26 (73%) individuals agreed or strongly agreed that they would use a co-formulation product of rapid insulin + pramlintide if it were on the market. Only three (~12%) disagreed or strongly disagreed. This study used the Class AP algorithm that Lilly licensed for its in-development hybrid closed loop – we can’t help but wonder whether Lilly will pursue this dual-hormone approach (unsurprisingly, the company has said they are keeping an open mind with regards to closed loop system design).
NIDDK’s Dr. Guillermo Arreaza-Rubin proposed several areas of improvement for developing a more efficient, user-friendly, and cost-effective new generation of implantable Intraperitoneal (IP) AID systems. Technical needs included: (i) a proper CGM partner that is either subcutaneous or IP sensing (although the final version should be IP); (ii) better insulins that are stable in pumps and do not aggregate or clog; (iii) smaller pumps with longer batteries, potentially lasting 10 years; (iv) enhanced catheter design to prevent occlusion; and (v) algorithms adaptable for IP delivery. To this end, he provided an IP closed loop product roadmap, identifying a minimum viable product and a final, fully integrated implanted AID system. Key differences include: (i) the transition between an iCGM to an integrated IP sensor; and (ii) moving from a six-week refill interval to no less than three-month interval. It’s difficult to tell just how far down the line even the minimum viable product may be. According to Dr. Arreaza-Rubin’s predictions, reaching this point alone will require the development of a small implantable pump, IP closed loop algorithm, and a reliable, intra-body Bluetooth system agnostic to CGM site.
JDRF’s Ms. Jackie Le Grand presented results from 66 interviews with development experts, physicians, payers, and patients to assess the market appetite for an implantable closed loop system. The biggest finding? When asked what outcomes would be necessary to convince patients with type 1 diabetes to adopt an implantable closed loop system, responders provided fairly rigorous standards: A1c of 6.0%-6.5%, time-in-range of 90%-95%, no time <70 mg/dl, and ~5% time >180 mg/dl. Concerns regarding the procedure and cost would also have to be addressed. (Such outcomes are already possible with low-carb eating and an existing system like Loop.) Ms. Le Grand generally characterized physicians as viewing implantable closed loop systems “positively,” noting that the primary driving force was its potential to reduce hypoglycemia – that said, current closed loop already does this. General anesthesia was a major concern amongst physicians, as was durability in the event of a trauma. Durability was also a major concern for patients, in addition to undergoing surgery and the related risks. Patients also rated the system’s potential to reduce hypoglycemia and hyperglycemia as key clinical benefits and emphasized quality of life advantages, such as reduced burden (i.e., carrying fewer devices – we must say it’s hard to see this as a “major burden” in this world). On this latter point, Ms. Le Grand noted that “the thing we hear most from payers” is the need for “really strong evidence that drives results.” Payers are looking at “the bigger picture” and want to see peer-reviewed journal articles, improvement over the standard of care, and reductions in hospitalizations and emergency department visits. Ms. Le Grand believes Medicare and Medicaid are likely to eventually cover an implantable AP system, with type 1s getting clear preference over type 2s. We aren’t sure this kind of system can show improvement over traditional closed loop systems in development except for people challenged by extreme hypoglycemia – we aren’t sure what size of market this is but believe it’s small.
Picking up right where he left off at the JDRF/Helmsley Closed Loop Intraperitoneal (IP) workshop in 2017, Harvard University’s Dr. Eyal Dassau explained that following early feasibility evaluations of an implantable automated insulin delivery system in canines, a new PID controller was designed to enhance closed loop performance. Recently published data from a simulation show that the new PID controller developed from the canine outcomes resulted in a “sharp delivery similar to a bolus” following a large (90g carbs) meal. As compared to a PID controller from an earlier model, the canine-developed controller achieved significantly superior time-in-range (97% vs. 90%), tighter glycemic control (time 80-120 mg/dl was 73% vs. 55%) and time >180 mg/dl (3% vs. 10%). Next, Dr. Dassau compared the new PID controller between an IP insulin delivery-IP sensor system and an IP insulin delivery-subcutaneous system. In this simulation, both unannounced meals and exercise were included. The two systems produced similar time-in-range, but the IP-IP system generated “much tighter control,” with greater time spent between 80-120 mg/dl (77% vs. 67%). Both systems were superior as compared to a simulated IP-IP system using the previous algorithm. Dr. Dassau believes the data, published in Journal of Process Control, serve as a proof of concept for IP-IP closed loop, despite only being generated from a simulation.
In the heat of a very futuristic-sounding AID session, King’s College London’s Prof. John Pickup wondered aloud: “Can we afford the future?” While the audience held a collective breath, Prof. Pickup presented bits from a recently-published budget impact analysis showing that insulin pumps, at least, are affordable for the type 1 masses in the UK. In the paper, he and his colleagues model that bringing down the A1cs of all individuals who started with baseline ≥8.5% to ≤7% by any means would save the country £687 million (~$896 million) over the course of five years; that’s ~$7,300 per person! Those dollars, he said, are therefore equivalent to the money available or any effective, cost-neutral treatment strategy. [As a side note, this pan-population A1c reduction would reduce chronic complications by a factor of ~5.5 and DKA by a factor of 14.5.] Giving all of these individuals (type 1 adults with elevated A1c) pumps would thus become cost-saving after five years, according to the authors’ data. These results stemmed from a highly comprehensive budget impact model, where complications, severe hypoglycemia, and even lost productivity are accounted for. Sadly, this analysis didn’t examine CGM or AID, which we suspect would be even more cost-sparing.
Selected Questions and Answers
Q: Have you considered the number of new healthcare providers to handle the increase in new technologies?
Prof. Pickup: Yes, it would vary from one center to another. At least in the UK, we are forced to provide the same level of care with the same healthcare provider resources. I don’t think that’s actually a particular factor in the budget impact. Of course, it would depend on the diabetes technology and amount of extra education necessary. But for insulin pump therapy, we assumed it would be the exact same level of healthcare provider input as for MDI.
Dr. Anders Dhyr Toft (Novo Nordisk CVP of Commercial Innovation): As we’re increasing the number of people wearing sensors, we’re increasingly aware of time-in-range. It’s a better measure of control than A1c … are NICE or other organizations in the UK, the home of cost-effectiveness calculations, looking at incorporating time-in-range in models?
Pickup: The answer to that is not yet. I think there’s probably, in NICE circles, more skepticism than there is in this audience. [Laughter] There’s more work to be done, as you are beginning to do, in linking time-in-range to risk of complications. One issue I think is the regulatory authorities…the other is the assertion that there’s some link between time-in-range and glycemic variability and I’m interested in what Dr. Bergenstal is saying about that. A lot of clarification needs to be done in that area, in assuming that time-in-range is as clinically meaningful in (a) complication risk and (b) what you do clinically about a poor time in range. There’s a lot of work to be done I think in clarifying that message for time-in-range. We’re not there yet.
Dr. Dassau detailed a Helmsley-funded project to include additional sensors for biomarkers such as glucose, lactate, insulin, and cortisol in future closed loop systems. In collaboration with UCSD, Dr. Dassau is currently testing a proof of concept for an insulin sensor based on capillary glucose. We’re intrigued by the prospect of fine-tuning AID with additional inputs. Given the myriad factors (42!) that impact blood glucose, it seems logical that the next step is incorporating some of these components.
Dr. Dassau emphasized the need for human-centric design in closed loop therapy, reviewing the Interoperable Artificial Pancreas System (iAPS) smartphone app, capable of interfacing with CGMs, pumps, and algorithms while running on an unlocked smartphone. Results from a small in-clinic study (n=6 type 1 adults) published last month in DT&T found only “moderate improvement” in time-in-range, although Dr. Dassau was quick to note this was likely due to the extreme food and exercise challenges. Participants completed one week of sensor-augmented pump use followed by 48 hours of closed loop therapy with iAPS, Dexcom G5 CGM, and either a Tandem t:slim or Insulet Omnipod pump. Per the DT&T paper, patients can use iAPS to request an insulin bolus, log various activities, and view current glycemic status, including insulin-on-board. iAPS also provides alarms for system malfunctions and synchronizes data with a remote server for remote monitoring. Intriguingly, iAPS can interface with multiple algorithms, which could be used as party of a safety net for monitoring hypoglycemia, or updated throughout a study. Dr. Dassau mentioned that this proof of concept system for iAPS was then tested in a two-week outpatient randomized crossover trial, receiving “excellent feedback” from users.
As of January, six pregnant women with type 1 diabetes are enrolled in Dr. Dassau’s longitudinal, observation study of insulin requirements and sensor use during pregnancy. The cohort will be followed from before 16 weeks through six weeks postpartum. The NIH/NIDDK-funded study, first described at DTM 2018, will be led by Dr. 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. The aim is to develop and evaluate a pregnancy-specific artificial pancreas in a series of in-clinic and transitional environments followed by evaluation in an at-home clinical trial. We’re excited to see a focus on technology in pregnancy – despite very positive CONCEPTT results, CGM is still not considered standard of care during pregnancy, so closed loop therapy unfortunately seems a bit far down the line.
CGM and Decision Support
Dexcom CTO Jake Leach provided a pipeline update, including five upcoming G6 app enhancements (e.g., “Hey Siri, What’s my glucose?”); the first pictures and product details on the G6 Professional (fully disposable, blinded or real-time); confirmation that G7 will launch by the end of next year in collaboration with Verily (full launch in 2021); a new “On the Bright Side” notification for Dexcom Clarity (Bright Spots coming to CGM!); and news that smart pens will send insulin data directly into the G6 app. Details and pictures are below!
Dexcom has five near-term enhancements planned for the G6 app: (i) a Siri Shortcuts feature – “Hey Siri, What’s my glucose?” “Your glucose is 110 mg/dl and flat” (nice convenience!); (ii) adding up to 10 followers on Dexcom Share; (iii) the ability to launch the Dexcom Clarity data management app from within the separate G6 app; (iv) a new G6 watch face complication for Apple Watch Series 4; (v) a 24-hour reminder before sensor expiration; and the ability for Android users to share data with Google Fit (similar to Apple Health). Direct-to-Apple Watch remains in development, and Mr. Leach admitted it has “taken a while” because Dexcom “really wants to get the user experience right.” No timing was shared, though CEO Kevin Sayer previously said to expect a launch this year.
Following FDA clearance in announced in 3Q18, we finally got to see the new “Dexcom G6 Pro” (professional version) – it includes a fully disposable G6 transmitter and 10-day sensor (factory calibrated), an ability to run in blinded or real-time mode, a reader to download the data in clinic after 10 days, and the ability for users to get real-time CGM data through the same G6 app. (Based on the Pro transmitter, the G6 will run in a simplified mode.) As of JPM, G6 Pro is expected to launch sometime this year. This is a huge upgrade over Dexcom’s current professional (G4) CGM, bringing it mostly on par with Abbott’s FreeStyle Libre Pro in terms of simplicity – a fully disposable transmitter and no calibration were the critical moves. G6 Pro is ahead of Libre Pro with the ability for real-time data, though behind on wear time (10 days vs. 14 days). We wonder if G6 Pro will allow for more “CGM sampling” – giving many Pro sensor to HCPs to encourage people to trial real-time CGM for 10 days. G6 Pro should also fit nicely with virtual clinics like Onduo that use CGM “sprints” (episodic wear) in type 2 diabetes. The sensor is the same as the current G6, a plus for manufacturing and accuracy.
Mr. Leach confirmed that G7 – the Dexcom/Verily Gen 2 product – will launch starting at the end of next year (2020), with a broader launch “throughout the world” in 2021. Fully disposable electronics, a “significant cost reduction,” a much smaller profile, no-calibration, real-time app transmission via Bluetooth, and extended wear (14 or 15 days, per JPM) are still the plan for G7. All of this is identical since last month and Dexcom’s December Investor Meeting – great to see consistency, since the goal posts on this product have moved. Gen 1 Verily was not mentioned, as expected following JPM. Mr. Leach specifically said G7 will be more suited for clinical impact in new markets (high performance, reliability, low cost, disposable): hospital prediabetes, gestational diabetes, type 2 diabetes on oral medications only (perfect for wearing for a period of time to titrate drugs, verify correct doses), obesity (behavior modification, how different foods impact the body).
Dexcom will soon launch new features in Clarity, including an “On the Bright Side” notification – automatically identifying days where users achieve their goals and receive a notification. We’re elated to see this Bright Spot pattern recognition coming to CGM, something we have advocated for over the past two years. Mr. Leach showed the same screens of the MDI decision support with TypeZero – including on-demand sleep and exercise dosing advice and a CGM-informed bolus calculator – but did not share timing. See Dr. Marc Breton’s presentation below for an update on that study.
For the first time, Mr. Leach said smart pens will send insulin data directly into the G6 app and onto Clarity – a huge simplicity win. We might have expected Dexcom to pull the insulin data from other apps (e.g., from the smart pen app itself), but this is excellent news for combining the two data sets on the phone in one app from the start. The talk showed both Lilly and Novo Nordisk as smart pen partners.
IDC’s Dr. Rich Bergenstal wrote at this time last year, “CGM: Transforming diabetes management step by step!” The recent very concerning T1D Exchange data – in which CGM penetration increased but number of aggregate A1c statistics worsened –has led him to question whether the “!” at the end of his former statement should be amended to a “?”, and to rethink effective implementation of CGM. Indeed, he believes that there is more the system can do to support patients at every stage of CGM use: Starting CGM, wearing CGM, using CGM, and effectively using CGM. His strongest point came with regards to starting CGM – “It’s not just giving CGM to those with A1cs of 7.2% and getting them down to 6.9%. We won’t see a flattening of the curve if we only do that. We need to get to patients with A1cs of 9%, 10%, and 11% if we want to actually flatten the curve. We need to be more equitable, regardless of A1c, ability to pay, race, or ethnicity. It’ll take more work on our part but we’ve got to make it happen if we’re going to flatten that curve.” His following bullets were all the more important when taking these additional populations into account. On wearing CGM, he advocated for greater support for patients, down to the granularity of helping individuals determine which adhesive and sensor location works best for them (we believe the Cecilia Health-Jaeb Center-Helmsley pilot holds promise here). On using CGM, he encouraged clinicians to support patients in looking at their sensor data, including setting goals for time-in-range, hypoglycemia reduction, and (maybe) A1c. This bucket also includes having patients share data with family, if they are comfortable doing so. Finally, he hearkened back to the CGM-adaptation of Daniel Kahneman’s work (Thinking Fast and Slow) that he unveiled at ENDO 2018. Read the ENDO report for more details, but in brief, “thinking fast” refers to real-time corrective action and use of trend arrows, and “thinking slow” refers to retrospective analysis, pattern control, and AGP – both are necessary for optimal CGM use, he argues.
Dr. Bergenstal outlined his vision for useful decision support in type 2 diabetes. In type 2 diabetes, he pointed to the recent ADA/EASD treatment algorithm, claiming that it’s useful in that it tells the clinician what to do, but doesn’t support them in following the recommendations. He raised an interesting paradox: The algorithm is truly patient-centered, offering granular guidance for all sorts of presentations of type 2 diabetes, but that quality also makes it inherently more complex and difficult to adhere to. “I would like for a decision support tool in my EMR to tell me that based on a bunch of criteria, my patient should be on a GLP-1, and tell me which GLP-1 is covered for that patient, and what would be a reasonable starting dose. That would help many PCPs try to follow an algorithm that sounds simple but is very complicated in the office.” Beyond automated decision support, he added, “It also would be bad to have a nurse who could train on injection and handle follow-up with a phone call to monitor side effects or effectiveness.
Dr. Atul Gawande’s (CEO of new Amazon-Berkshire Hathaway-JPM healthcare venture) recent New Yorker article, “Why Doctors Hate Their Computers,” resonated with Dr. Bergenstal (and countless other physicians). Pulling language from the article, he posited that we need decision support systems in diabetes that make the right care simpler and not more complicated while maintaining the human connection.
FDA’s Dr. Courtney Lias acknowledged that there are many questions about the regulatory path for diabetes decision support tools in the US (both inside and outside the Agency), part of which was apparent in her musings on the use of CGM to inform bolus calculators: “What if we use CGM instead of BGM to put values into blood glucose calculators? That’d be very useful, now that CGMs are used more often. The question is what is the outcome of algorithm? There is some imprecision with SMBG point values, but there’s a different bias for CGM – a larger bias – so a point glucose value is not as accurate as a single point value from BGM. So we’d be looking at information to understand whether CGM-based calculators should be looked at the same as BGM – what is the impact of the inaccuracy of CGM on the output insulin doses? We’re talking to a lot of people about this, and we’ll hopefully have information soon. If it’s low risk, then maybe we can apply standard blood glucose calculator regulations to CGM – if the recommendations are not always great, then new calculators will probably be developed to incorporate trend and other CGM information to enable that sort of feature.” We guess class II is very possible here, at least for any iCGM with a non-adjunctive indication. Plus, the FDA cleared DreaMed Advisor Pro clinical decision support software for optimizing pump settings based on retrospective CGM data (and now SMBG data) created a new class II medical device category (“Insulin Therapy Adjustment Device”). One of our questions at the time was whether real-time, patient-facing, CGM-based decision support would fall into this new category; it sounds like this question remains to be fully addressed. We also wonder how the iCGM designation will play into this – might some bolus calculators (and possibly basal titration apps) be cleared for explicit use with iCGMs that have demonstrated consistently high performance? Our guess is that CGM-based bolus calculators will take advantage of the wealth of trend and historical data offered by continuous glucose tracking; doing so might require greater algorithmic complexity, though it could also be kept simple based on the dose adjustment trend guidelines published for both Dexcom and FreeStyle Libre. Between Abbott, Bigfoot, Companion Medical, DreaMed, Lilly, Glytec, Dexcom (TypeZero), and Novo Nordisk, we can think of quite a few players who might be willing to work with the FDA to push the envelope here.
Does a 510(k) decision support submission necessitate the presentation of clinical data? “Depends on what the product does.” She explained that an app that simple gives nutrition and exercise advice is probably not regulated, while an app with standard insulin-dosing bolus calculator might entail data on human factors, and a new algorithm (i.e., DreaMed’s Advisor Pro) “might need clinical data to show safety.”
Further exploring other nuances of decision support regulation, Dr. Lias touched on intended use (what does the app do?), transparency, and intended user. Software that provides more general recommendations, is more transparent in its decision-making processes, and is intended for expert users (HCPs, nurses) might be subjected to less FDA scrutiny; on the other hand, software that provides highly specific advice (e.g., insulin dose), has more opacity in how it arrived at its recommendation, and is patient-facing may be scrutinized more thoroughly, but also has potential to make a bigger impact on the lives of people with diabetes.
We were glad to hear Dr. Lias voice her strong support for integrated devices and decision support tools. “Integrated devices are key. Diabetes devices that are designed to work together, including for decision support, will invariably lead to better outcomes. The more integrated a bolus calculator is with a pump, the better outcomes there’ll be. For example, you’ll get better estimates from a bolus calculator that knows the insulin on board than you would from one that doesn’t.” Likewise, she began her talk by empathizing with the nurses who don’t really understand insulin dosing, but are asked to support inpatients, including those who do not have diabetes and so can’t tell if a dose is way off.
UVA’s Dr. Marc Breton summarized mixed top-line outcomes from the ongoing study testing TypeZero dosing decision support, Dexcom G5 CGM, and Novo Nordisk NFC-enabled smart pens in MDI users vs. CGM and manual insulin dosing. The first-of-its-kind study has n=77 completed and n=2 ongoing, so final statistical outcomes were not shared. At a high level, decision support did not lead to big benefits in the overall population. Time-in-range (70-180) was a solid ~60% in both groups at baseline (with ~4.5% time <70 mg/dl), and both groups improved by a similar amount during the study. According to Dr. Breton, this was due to the introduction of CGM and insulin degludec titration, which occurred in both groups. TypeZero’s MDI decision support did not drive further benefit – a smart CGM-based bolus calculator, basal and bolus titration, bedtime and exercise advice, and hypoglycemia warnings. Why? As with any technology, decision support only benefitted those who actually used it – and use varied quite a bit in this study. A sizeable 25% of the decision support group used the bolus calculator less than twice per day, and the bedtime advice was barely used at all – 29% never used it and 48% used it less than once every three weeks. The same was true of the exercise advice. On the plus side, those who used decision support consistently had less time-in-hypoglycemia (<70 mg/dl) – based on the graph (picture #3 below), time <70 went from ~6% at baseline to ~3% in the group defined as “users.” “Our system provided the expected benefits, but you had to use it a minimum number of times per day.” Dr. Breton noted that one app update during the study meaningfully changed satisfaction, a reminder that user experience (UX) is going to be critical in this domain. (In fact, UX might be more critical, since MDI decision support requires opening an app and following the advice vs. an AID system that just runs in the background.) Full data will presumably come at ADA.
Dr. Breton also emphasized that this study was very hard to recruit for – to achieve 77 completers, the study team had to contact 751 people! Apparently, many MDI users were not interested in having to carb count and do intensive insulin therapy – and these were people who agreed to be contacted. We see huge potential for CGM-based MDI decision support, but learning is clearly still in the early stages. (To roughly approximate it, we’d say this field is where AID was three to five years ago.) As with most studies, we’d note the baseline management was better – mean A1c of ~7.4%-7.5%; time-in-range of 60%; CV of 35% - relative to the average person with type 1. The study also used NFC-enabled pens that had to be manually scanned before meals – more onerous than seamless Bluetooth-enabled options.
5. Dr. Roy Beck Urges Inclusion of Intermittent CGM in Future CVOTs
Acknowledging that it would probably be unethical to repeat the DCCT as an RCT with CGM, Jaeb’s Dr. Roy Beck urged pharmaceutical manufacturers to use CGM in future CVOTs: “It would’ve been great if CVOTs had had CGM, but that was not done. It’s never too late! Any of you embarking on large CVOTs, there’d be incredible value in having blinded CGM in them at intervals.” Though he didn’t expressly volunteer, we imagine he and his Jaeb colleagues would be more than happy to assist (or at minimum, advise) with the administration and data aspects of such an ambitious venture. We point out that most CVOTs started a long time ago, when current CGMs were not possible to use. He pointed to the PERL RCT of allopurinol to reduce the progression of kidney disease, which uses CGM and is expected to complete this summer. In that study, investigators are deploying blinded CGM (Libre Pro) “every three or six” months over the course of the study, in order to determine how well CGM metrics, namely time-in-range, predicts progression of kidney disease. This will be great to see. With the upcoming 2019 launch of G6 Pro, there is another disposable device well-suited for clinical trial monitoring. We wonder if there’s a role for other funding bodies – NIH? Helmsley? – to provide CGM for more of these studies – and the brainpower to analyze and synthesize the data?
Through the majority of his talk, Dr. Beck reviewed evidence that: (i) time-in-range is largely a measure of hyperglycemia; (ii) ~14 days of CGM are needed in order for derived metrics to approximate those compiled out to 90 days; (iii) 30%, 50%, and 70% times-in-range roughly equate to A1cs of 9%, 8%, and 7%; (iv) for a given percent increase in time-in-range, the decline in A1c is greater when baseline A1c is higher; and (v) higher time-in-range (as measured by quarterly 7-point SMBG profiles) in the DCCT correlated with lower microvascular complication burden (Diabetes Care 2018). One astute attendee from Sweden asked if Dr. Beck had performed the DCCT time-in-range analysis while holding A1c constant (i.e., at a given A1c, did greater time-in-range predict fewer complications?) and vice versa. The answer was no, but Dr. Beck said it was a good idea.
Selected Question and Answer
Prof. Stephanie Amiel: Would FDA accept the DCCT post-hoc analysis as adequate support for time-in-range as a useful metric in diabetes?
Dr. Beck: That was the impetus for trying to do this analysis. So yes, I hope so. We’ve been coordinating with JDRF in terms of communication with the FDA, because we want a concerted effort from the community in dealing with FDA on this issue. It’s very different talking hypo with the device side vs. the drug side of FDA, because the device folks have been dealing with CGM metrics for a while. It’s the drug side that we really have to convince. But hopefully we’ll present our analyses and make inroads there. That’s the hope.
Drs. Lori Laffel (Joslin) and Greg Forlenza (Barbara Davis), investigators in the multicenter, Helmsley-funded Advice4U study of DreaMed’s Advisor Pro clinical decision support software, shared a status update on the trial and a clinician’s perspective of using the software, respectively. Unfortunately, though we were hoping for an interim readout of three-month data, DreaMed now tells us that we won’t see any results until the full six-month read out at a post-ADA meeting (perhaps ISPAD?). At this stage, the trial is fully enrolled (n=112) and ~50% have already completed the study. In the study, there have been ~600 in-person or telemedicine visits between the two groups, 83% of which have resulted in pump settings change recommendations. The remaining 17% of visits were skipped or did not have recommendations due to a host of reasons (insufficient data, technical difficulties, time change snags, and device malfunctions). But, Dr. Laffel emphasized, success with the system has improved as the study has progressed; while the percent of visits with no data generation approached 25% at visit three, this number had fallen to ~10% by visit nine. Dr. Laffel conveyed her excitement for this particular study and the idea of remote monitoring/decision support in general: “In the DCCT, they had monthly visits and weekly telephone contacts. Who has that much time? We wonder why we haven’t seen those results translated into clinical practice today – we’re lacking the personnel, expertise, and resources. This highlights to me why remote, automated, decision support could be helpful.”
Dr. Forlenza explained in no uncertain terms where the value in Advisor Pro lies: “In a typical visit, we would spend ~1/3 of our time analyzing that profile, playing technician and tuning the data. But with a system like this, we could actually practice the art of medicine, figuring out how to incorporate better behaviors into patients’ lives instead of just turning knobs and flipping switches.” His ~1/3 estimate rings true with recent findings that it takes providers 18 minutes to analyze pump data, and Drs. Bruce Buckingham and Desmond Schatz estimated during a recent CWD panel that they spend a remarkable 45-60 minutes in each patient visit – most not even looking at the data. Of course, as many have pointed out, this is far more than most people get with their endocrinologist or (most likely) PCP.
Beyond A1c Highlights
Prof. Tadej Battelino reported on the discussions and outcomes of the ATTD Consensus meeting on time-in-range, which took place this past Tuesday. Read our report on the consensus points and other takeaways from the day’s proceedings. A fascinating Q&A followed, during which attendees raised a number of concerns – all of which were voiced at the meeting earlier this week. Below, we’ve bolded the audience comments, followed by a summary of the panel’s response.
A couple of people wondered what the guidelines meant for (i) people not on CGM, and (ii) resource-poor nations. Prof. Battelino said this was a very important question but didn’t necessarily know the answer; he threw out the idea of making targets for percent of 7-pt SMBG profiles in range. At the consensus gathering, we frequently heard the idea that in setting these goals, the experts may elevate the number of patients using CGM. But ultimately, Prof. Battelino hopes competition will eventually drive cost down or incentivize lower-cost CGM manufacturing.
Dr. Des Schatz: “I’d love to see a majority of my patients getting to 70% time-in-range, but I’m seeing adverse effects on academic performance. What’s happening is, kids are looking at this and are disappointed in themselves, and parents are wondering whether it’s really the right thing to do. What is the effect of setting a high expectation?” Dr. Beck recalled hearing this concern from many pediatric endocrinologists at Tuesday’s gathering; the group decided that it should set an ambitious target, but also encourage patients to make meaningful 5% improvements (one hour per day) in their time-in-range numbers. (Prof. Simon Heller spoke up at the consensus meeting and on the panel, voicing concern that those non-experienced endocrinologists or PCPs caring for people with diabetes might end up “beating their patients up” with the consensus targets.) The lofty target was established partially due to ethical concerns – how could a target be relaxed to a level that is known to be harmful to people? This is a tough argument – all goals have downsides. Read Adam’s diaTribe take on time-in-range goals and why he focuses more on “process goals.” Also see diaTribe’s 2017 piece on expert opinions on CGM, which didn’t change all that much by the Consensus meeting – and number will only improve with more wearing AID.
Dr. Schatz elaborated on his position after the meeting in an email: “What I am concerned about is the (lack of) importance of small measures of success that will continue to help patients ultimately realize goals. This is real time - not 3 month HbA1c. So, asking for 5% improvement every 3 months would be reasonable e.g., 40%-45%. I have found that for treating overweight and obese patients - we set long term goal but define short term measures of success. Behavior is hard to change but make it realistic and it is more likely to become long lasting.”
As was raised at the Consensus meeting on Tuesday, an attendee wondered how the consensus intended to handle varied performances/ accuracies of different CGMs. The consensus does not address this issue at all. Prof. Battelino hopes that there will be harmonization among manufacturers in the future, and Dr. Beck commented that (i) average CGM metrics tend to be “right on” even if individual points may not have the same accuracy as BGMs; and (ii) for the purposes of a clinical trial, the control and intervention groups use the same sensor so bias is theoretically controlled for.
UW’s Dr. Irl Hirsch shared results from an email survey he conducted (n=62, 66% response rate) asking whether patients, endos, and PCPs are ready to change from A1c to time-in-range: Though he conceded that this is “not the type of work the Jaeb Center would do,” the answer leaned in favor of “yes” for patients and diabetes specialists, but was a resounding “no” for primary care doctors. As shown on the slide below, 65% of all respondents felt that patients were ready to jump ship (from A1c), while 59% felt that endocrinologists were ready. (We hope that it’s not actually “jumping ship” – we’ve always thought about time in range supplementing A1c rather than replacing it.) Zero respondents – “not a single person in the survey” – thought non-endos were ready to adopt time-in-range over A1c though our bet is that some of the difference is a matter of exposure. The vast majority of type 2s and a significant percentage of type 1s in the US (estimated at 70% but unknown) see a PCP for their diabetes care, so this perceived resistance from the primary care community may be a barrier for the time-in-range and broader beyond-A1c movement. Again, we doubt it’s true resistance but more an absence of familiarity. And indeed, Dr. Hirsch was cautiously optimistic. He described a long road ahead for time-in-range to gain traction, but highlighted a recurring pattern wherein endocrinologists adopt a new innovation in diabetes and PCPs later follow suit. This is happening slowly but surely for CGM; Dr. Hirsch mentioned that many general internists have been asking him of late how to order a CGM, because their patients are asking for it (he attributed growing patient awareness to direct-to-consumer advertising in the US – no doubt some is also due to word-of-mouth news among patients). Indeed, continued patient-facing education on the utility of time-in-range as a diabetes metric should have spillover effects on PCPs. We look very forward to the day when all patients/providers involved in diabetes care are comfortable with time-in-range; obviously getting CGM to those groups will be the first step. During Q&A, one audience member asked what can be done to accelerate the transition from A1c to TIR, and Dr. Hirsch emphasized that this movement must be all-hands-on-deck. “It took us a long time – years, maybe decades – to teach A1c and what that number is. This is a bit more complicated, but the good news is, it’s doable. We need to start in the medical schools, teach this in CME, and it’s just got to be an all-out effort.”
Dr. Hirsch sent out 94 surveys and garnered 62 individual responses. Of these respondents, 53% were adult endocrinologists, 8% were pediatric endocrinologists, 10% were PCPs, and 29% were nurse practitioners and/or CDEs. Notably, everyone answered all three questions about the TIR-readiness of (i) patients; (ii) endos; and (iii) non-endo HCPs. Across the board, 89% of providers felt that non-endos aren’t ready for a change to time-in-range, and 11% said “maybe” (even though maybe wasn’t listed as an option – people wrote it in). We wonder the degree to which the endo crowd was a bit dismissive of PCPs.
Dr. Hirsch also received unsolicited comments from survey respondents, and he displayed several slides-worth (see below for an example). The theme of patient-related comments seemed to be that newly-diagnosed individuals (especially children) should learn TIR from the get-go, although it may be hard for longtime diabetes vets to make the transition (this also seems like speculation). One internist commented “internists are too lazy,” which sparked laughter from the audience, though this gets at an important point – PCPs are busy enough as it is that simplicity and minimal burden are critical. Another comment implied that time-in-range will be much more confusing than A1c, but Dr. Hirsch disagreed and so do we. Time-in-range is out of 100% - higher is better; A1c is on a more confusing scale. Time-in-range also affects patients’ quality of life every day, is more intuitive than a weighted average calculated every three months based on hemoglobin glycation. A major point, as well, of CGM, is how it can help you do better by identifying the right therapy – the numbers are clearly not an end in and of themselves.
Dr. Hirsch called attention to his just-published editorial in Diabetes Care advocating for time-in-range. He and co-authors Drs. Jennifer Sherr (Yale) and Korey Hood (Stanford) highlight Dr. Roy Beck’s DCCT analyses correlating TIR (calculated by seven-point SMBG) with microvascular outcomes (ADA coverage). Specifically, a 10% decrease in time-in-range was associated with a 64% increase in risk for retinopathy and a 40% increase in risk for microalbuminuria. The editorial suggests that these findings would have been “even more striking” had CGM been used in the DCCT, and while it doesn’t make practical sense to repeat a DCCT-like study with CGM, this technology should absolutely be incorporated into other diabetes clinical trials.
In back-to-back talks, Dr. Pratik Choudhary (King’s College London) and Dr. Jasna Šuput Omladič (University of Ljubljana, Slovenia) highlighted the negative cognitive effects of acute and chronic hyperglycemia. Dr. Omladič presented preliminary results from her ongoing study of youth with type 1 diabetes (n=40). Participants (20 adolescents age 11-19 with T1D + 20 matched controls) underwent one structural MRI scan and two functional MRI scans – one during a euglycemic clamp and a second, 30 minutes later, during a hyperglycemic clamp. While in the fMRI machine, participants completed the Tower of London task (assesses planning), the Flanker task (assesses attention), and the Visual Spatial Working Memory task (VSWM). Dr. Omladič focused on the last of these, showing how type 1s perform similarly to controls on the VSWM with in-range blood sugar (remembering ~1.7 out of 4 positions on the first try), but perform significantly worse on this test with high blood sugar (remembering ~2.6/4 positions vs. >3 positions, on average). Both groups do better during the second condition because they’ve seen the task once before, but the key takeaway is that a significant difference in working memory emerges between adolescents with type 1 diabetes and those without (no p-value reported). Over a lifetime with type 1 diabetes, acute hyperglycemia accumulates and can have a pronounced impact on cognitive ability. Dr. Choudhary summarized the relevant findings from the DCCT/EDIC: Recurrent episodes of severe hypoglycemia had no significant effect on cognition across several domains (problem-solving, learning, memory/recall, attention, motor speed, etc.), but higher A1c was associated with reduced psychomotor efficiency and motor speed. He concluded that while acute hypoglycemia does impair cognitive function, the much greater consequence on population-level cognition comes from chronic hyperglycemia. This is worth restating – persistent hyperglycemia is more dangerous for brain function than are isolated instances of hypoglycemia – especially in countering the fear of hypoglycemia that keeps patients/HCPs from targeting a lower A1c, or optimizing insulin titration. We don’t want to minimize the importance of avoiding hypo (not at all!), but with the ACP recommending an A1c goal of 8% for type 2s, and with still-prevalent fear of hypo affecting the initiation and titration of insulin in clinical practice, it’s crucial that the field doesn’t forget why it treats high blood glucose in the first place – to minimize complications, including microvascular disease, macrovascular events, and deterioration of cognitive ability.
In the same session, Yale’s Dr. Stuart Weinzimer provided an update on the DirecNet program of type 1s diagnosed at an early age (4-10 years-old). Corroborating his fellow faculty, he explained how long-term hyperglycemia was inversely correlated with IQ as well as learning and memory scores. DirecNet researchers have continued glycemic, cognitive, and neuroanatomical assessments on the study population; two- and four-year follow-up data are forthcoming. Late last year, new results published in Diabetes Care showed how a single episode of DKA can negatively affect multiple aspects of cognition – Dr. Weinzimer listed full-scale IQ, executive function, learning, and memory. Lastly, he announced a pilot study investigating how hybrid closed loop affects the trajectory of neuroanatomical and cognitive development in children with type 1 diabetes, which will hopefully report data within the year.
4. Prof. Amiel on Key Challenges to Studying Hypoglycemia in Clinical Trials
London’s Dr. Stephanie Amiel discussed challenges to studying hypoglycemia in clinical trials, but emphasized that it’s a critical area of research nonetheless. Because severe hypo is relatively rare, RCTs have to enrich their study population with participants at high-risk for hypoglycemia (much like conventional diabetes CVOTs enrolled type 2s with established CV disease). Dr. Amiel presented a dilemma between open vs. anonymous self-report of hypoglycemia: Incidence of hypoglycemia magically drops by 70% when you shift protocol from anonymous to open reporting, but anonymous reporting introduces its own issues, namely that a patient’s understanding of severe hypo may not align with the official definition. Open reporting with a physician or diabetes educator might help patients identify their own hypos more precisely, but then the problem circles back to under-reporting. Dr. Amiel further underscored that patients may have inaccurate recall of their hypoglycemia over several months or a year; this is definitely the case when investigating hypoglycemia unawareness. On a positive note, she shared several examples of successful hypoglycemia RCTs, including HypoDE presented at ATTD 2018 and her own ongoing study of HARPdoc, a new intervention that aims to lower hypoglycemia incidence by addressing patient misconceptions – e.g. “I have to be low all the time because I must not be high.” Dr. Amiel presented very preliminary results (she’d only seen them ~48 hours prior), sparking our curiosity for more detailed data.
Diabetes Therapy Highlights
During a Sanofi-sponsored symposium, Munich’s Dr. Robert Ritzel discussed a hypothesis for the difference in hypoglycemia rates observed during the titration period of BRIGHT (Sanofi’s head-to-head study of Toujeo vs. Novo Nordisk’s Tresiba). As a reminder, BRIGHT data (n=929) presented at ADA 2018 showed that, during the 24-week study’s 12-week titration period, Toujeo was associated with a 26% lower risk of hypoglycemia <70 mg/dl (OR=0.74, 95% CI: 0.57-0.97, p=0.03) and a 37% lower risk of hypoglycemia <54 mg/dl (OR=0.63, 95% CI: 0.40-0.99, p=0.004) compared to Tresiba (see the Diabetes Care publication). However, there was no significant difference in hypoglycemia rates during the following 12-week “maintenance period,” and the somewhat high p-values and fairly wide confidence intervals during the titration period have been pointed out to us. With a transient benefit and no difference in either fasting glucose values or A1c, the field has been less-than-convinced of any clinically meaningful advantage with Toujeo over Tresiba. Still, Dr. Ritzel offered a possible explanation that highlights the importance of individual characteristics of each insulin: Given that hourly data from the titration period show the hypoglycemia differences occurred between ~4 am and ~12 pm, Dr. Ritzel hypothesized in conjunction with the published PK/PD data that Tresiba may have a slightly stronger effect on fasting glucose. In support of this hypothesis, he pointed out that both insulins were injected in the evening and have only small differences in their PD profiles, reasoning that the “highly dynamic” period of titration may allow each insulin to exhibit some different, inherent characteristics more readily.
The hypoglycemia results may seem more meaningful within the context Dr. Ritzel established in the first half of his presentation: A recent observational, retrospective analysis (n=40,627 type 2s) shows that hypoglycemia in the first 12 weeks of basal insulin use significantly predicts risk of hypoglycemia over 24 months (OR=5.71, 95% CI: 4.67-6.99). That said, to our understanding, this analysis did not attempt to control for hypoglycemia risk factors; despite the common aphorism that “hypoglycemia begets hypoglycemia,” this data alone is not convincing evidence of a causative relationship between early and later hypoglycemia – rather, hypoglycemia in the first 3 months is likely a marker of general hypoglycemia risk. To this point, we appreciated Dr. Ritzel’s closing remarks: “I continue to document hypoglycemia data and the initial response when I change or initiate insulin, and I try to incorporate that into the treatment strategy to achieve the patient’s goals long-term.” This could be a key lesson to take away from BRIGHT: A patient’s early experience on insulin is likely indicative of how things will go in the future, and HCPs can adjust treatment strategies accordingly.
Close Concerns' Thoughts
We continue to feel the most important aspect of next-gen basals Tresiba and Toujeo is that both are light-years ahead of first-gen basal insulins on both hypoglycemia risk and the stability/duration of action they offer many patients. On BRIGHT specifically, perhaps Dr. Alice Cheng put it best at EASD 2018 when she said, “I don’t think the big takeaway from BRIGHT is that it demonstrated non-inferiority in terms of A1c reduction between the two next-gen insulins. The big takeaway is that both insulins were able to lower A1c from an average above 8.5% to 7.0% in just 24 weeks.” There’s no doubt in our mind that more patients could benefit from access to either next-gen basal given that astounding data, and we hope comparative studies will offer insight on when, to whom, and how HCPs can best prescribe either agent to improve outcomes and experiences for people with diabetes.
Still, BRIGHT did raise the stakes for Novo Nordisk’s own study comparing Tresiba to Toujeo, and we expect to learn far more about how these next-gen agents stack up once those results are released in 2Q19. Overall, from a patient perspective, we don’t really care too much about the differences since patients won’t have choice anyway in most geographies – we just care that patients who experience hypoglycemia using Lantus, Levemir, NPH, etc. are offered one Of the two next-gen insulins. Standing in contrast to BRIGHT, hypoglycemia is the primary endpoint in Novo Nordisk’s study. From what we’ve seen – particularly today – Sanofi is striving to leverage BRIGHT’s hypoglycemia findings into a strong case for the use of Toujeo over Tresiba, but our sense is that most thought leaders either see them as neutral (see Dr. Cheng’s quote above) or possibly still favor Tresiba ever-so-slightly over Toujeo but believe it doesn’t matter too much since choice is out of their control. Indeed, if one is going to draw comparisons, it’s important to consider BRIGHT’s findings in the context of both agents’ broader profiles – including the fact that Tresiba’s longer duration of action allows for recommended dosing “once daily at any time of day,” compared to at the same time every day for Toujeo, which can add valuable flexibility to a patient’s daily insulin management (as we understand it, for some patients Toujeo can actually work better if taken closer to every 18 hours than 24 hours…).
Results from the Mannkind-funded A-One trial (n=119 type 2s) found One Drop Premium (unlimited test strips and 24/7 in-app support from CDEs) + MannKind’s inhalable mealtime insulin Afrezza (technosphere insulin) to confer a 0.52% absolute A1c improvement as compared to One Drop Premium + current injected insulin over three months (0.93% vs. 0.41%). Importantly, the A1c reduction observed with One Drop + Afrezza is also 0.39% greater than Afrezza without One Drop (-0.55% A1c reduction), demonstrating additive benefit.
Notably, A1c stability or reduction was seen at all baseline A1c’s in the dual intervention arm in both the intent-to-treat and per-protocol analyses, while A1c was stable or rose with One Drop and injected insulin for all baseline A1c’s <8.5 in both analyses (see below). This could be related to less hypoglycemia risk with Afrezza, allowing for tighter glycemic control in this group relative to those receiving injectable insulin. One Drop announced the poster via simultaneous press release.
The study was originally set to randomize 400 people with type 2, and we’re wondering what accounted for the drastic (70%) size reduction. The randomization flow diagram states that 265 people were originally randomized, with major dropout reasons including no longer interested (n=21), lost contact with subject (n=49), and unable to receive prescription (n=67).
We see these results as beneficial for both parties: One Drop garners evidence of compatibility across the spectrum of mealtime insulins while Mannkind has another study in its pocket demonstrating benefit over injectable competitors (the first being the head-to-head STAT study vs. NovoLog). Also, exclusion criteria specified that participants could not have had previous experience with either intervention, making a case that switching to either alone – or even better, both – could be beneficial.
University of Florida’s Dr. Desmond Schatz gave a fantastically comprehensive overview of the known and emerging biomarkers for type 1 development and progression, highlighting recent progress on the roles of innate immunity and pancreas size. On the former, previous research has identified significantly lower neutrophil (an immune cell) counts in autoantibody-positive relatives of people with type 1 or new onset type 1 diabetes, but more recent work has found that the transcriptomic signature for neutrophil count might also be altered in first-degree-relative but autoantibody-negative subjects (age 5-18), suggesting it could serve as a biomarker alongside genetic risk even prior to autoantibody formation. With respect to pancreas volume, a great presentation at ADA 2018 demonstrated a clear downward trend from age-matched controls, to autoantibody positive first-degree relatives of type 1s, to those with type 1 diabetes. Earlier in 2019, this spectrum was expanded to include first-degree relatives without autoantibodies, first degree-relatives with one antibody, and first degree relatives with >2 antibodies – demonstrating a semi-linear relationship between decreasing pancreas size and progression through type 1 risk, though with an apparent levelling-off after one autoantibodies is present (see below). Interestingly, after type 1 onset, there is no correlation between duration of diabetes and relative pancreas volume; the same goes for serum trypsinogen – a biomarker for pancreas volume also shown to decrease in autoantibody positive subjects. Doubtless, further research into these preceding events and a critical mass of broad type 1 biomarker research are both needed to help complete the puzzle (likely also with the use of “big data”) of onset and prevention. To this end, Dr. Schatz briefly highlighted some emerging, unpublished data from his lab suggesting lowered insulin-like-growth-factor-1 (IGF-1) in at-risk, new onset, and established type 1s, as well as decreased IGF-2 levels in only at-risk and new onset individuals (see below). Only adding to the puzzle, Dr. Schatz concluded his talk by reminding the audience that type 1 may progress through different, age-related mechanisms, based on autoantibody incidence. This fascinating Diabetologia paper from the TEDDY study captures the concept well; incidence of different autoantibodies (GADA and IAA, specifically) and their chronology varies by time, with IAA spiking earlier, suggesting multiple pathways to type 1 development.
Prof. Phillip Moshe presented a subgroup analysis of pooled DEPICT data (AZ’s Farxiga in type 1, n=1,591), examining the impact of baseline MAGE. As shown below, when splitting the cohort at the 33rd, 50th, and 66th percentiles, there was no consistent association between baseline MAGE and adjusted mean change at 24 weeks (first figure below). However, the figure supplied in the online abstract (also below) displays the full spectrum of baseline MAGE values and corresponding changes, with consistent patterns between DEPICT-1 and DEPICT-2: These data indicate that patients with progressively higher baseline MAGE saw greater drops in variability – a finding that makes sense, given patients with higher variability at baseline simply have a higher base from which to fall. However, there was no correlation between baseline MAGE and change in A1c at 24 weeks. The sum of these findings is encouraging: The glucose variability (and, ostensibly, time in range) benefits of dapagliflozin aren’t limited to any given subgroup of baseline variability. We would have been interested to see a subgroup analysis examining baseline MAGE and DKA risk, though our understanding from Prof. Moshe was that there is no detectable association. As regulatory approval for SGLT inhibitors (particularly Farxiga) in type 1 marches slowly onward, the field is eager for data that can inform patient selection for both greater benefit and lower DKA risk; and while general consensus guidelines for patient selection have been published, we imagine many HCPs could benefit from more precise guidance.
King’s College’s Dr. Pratik Choudhary affirmed the potential of automated insulin delivery for nuanced basal insulin adjustments while maintaining that improvements in MDI via next-gen basal insulins will have a more profound impact on population health. As he put it, ~30%-40% of people with type 1s are using pumps and/or CGMs and another few percent DIY closed-loop systems, leaving the majority relying on MDI regimens – and using basal insulin (not to mention type 2s on insulin, for which an even smaller fraction are using tech). To this end, the more stable PK/PD profiles and associated lower incidence of severe and nocturnal hypoglycemia of next-gen insulins (compared to first-gen basals, such as Lantus) represent major steps forward on a broad scale. Specifically, he reviewed outcomes from the EDITION (non-inferior A1c reduction with less nocturnal and severe hypoglycemia with Toujeo vs. Lantus), OPTIMIZE (improved A1c and treatment satisfaction with no hypoglycemia increase in type 1), and SPARTA (0.4% greater A1c reduction with Toujeo after switching from twice-daily insulin dosing) studies to underscore improvements of Toujeo over Lantus for both type 1s and type 2s. We would add to these DEVOTE, Novo Nordisk’s CVOT for Tresiba, which established a robust and impressive 40% risk reduction vs. Lantus on severe hypoglycemia and garnered Tresiba a first-ever hypoglycemia label update in the US; though this data is limited to type 2, and though it is not an indication, we still view it as quite meaningful. When you scale the hypoglycemia risk reduction next-gen basals offer to the millions of patients worldwide using basal insulin, the impact on safety, patient quality of life, and healthcare expenditures could be monumental. Dr. Choudhary emphasized that even as AID grows, a significant proportion of people with diabetes will remain who either: (i) cannot access the technology due to cost or other barriers; or (ii) do not wish to use these systems. We disagree slightly with the second – while this is of course true today, as it was with first-gen pumps and CGM, we believe far better diabetes management will make AID the killer app to use pumps and CGM for those that don’t. In other words, although early-stage AID is not for everyone, we believe that later-stage AID will be for a very sizable percentage of people with diabetes that can access it. Access issues will undoubtedly remain but we also believe this will improve over time. Ultimately, both scenarios render further improvements in MDI and basal insulins not only of great importance, but necessary for better population-level outcomes. All this said, he acknowledged that minute differences in basal adjustment, such as those made due to variable parameters like number of steps, are better captured (at the moment) by algorithms rather than insulin innovation, rightfully extolling the potential of AID – reminding us that there is no one-size-fits-all solution for any diabetes issue at the present moment.
Dr. Cliff Bailey gave a master class in multi-target therapies, focusing on incretins in a rundown of noteworthy molecules – see below for a tremendously useful table of the different metabolic effects of nine (!) different incretin receptor agonists he covered. He opened with an emphasis on structure, drawing the distinction between (i) combinations/mixtures that add together two peptides with no physical linkage; (ii) chimeric peptides take epitopes of given proteins and mixing sections together (these seem to be growing increasingly common); and (iii) hybrid/fusion peptides that physically link two peptides together. And while, he says, there are apparently endless opportunities for forming and delivering peptides, there is a limit to how far you can push a receptor: “If you give a receptor a bit too much work to do, it might turn out to be sensitized, and you may stimulate the post-receptor pathways as a result.” All in all, Dr. Bailey’s remarks painted an enthusiastic picture of the potential dual- and multi-agonists hold in diabetes, but with the caution that novel molecules bring unknowns – a dynamic playing out already in the highly-efficacious but possibly hard-to-tolerate molecules we’ve seen from Lilly (tirzepatide, now in phase 3), Sanofi (SAR425899, just discontinued), AZ (MEDI0382, finishing phase 2), and others.
Additionally, Profil’s Dr. Tim Heise reviewed strategies developed to-date in the quest to create a glucose-responsive insulin: “The good news is some of these really seem to work, the bad is – only in mice [so far].” He outlined four primary scientific strategies:
Chemically modified insulins sensitive to glucose: These molecules take existing insulin analogs and add glucose-sensitive components. For example, insulin detemir plus a PBA molecule binds to albumin (inactive state) at low glucose concentration, but releases from albumin at high glucose concentrations.
Glucose-oxidase (GOx) based systems: Utilizing the same enzyme as glucose sensors, GOx systems require the removal of hydrogen peroxide byproducts through a catalase reaction. The movement of glucose into a microgel causes swelling (via electrostatic repulsion), and swelling enables insulin release.
Phenylboronic acid (PBA) systems: PBA forms reversible esters with the diols in glucose, and PBA-linked insulin in a “smart gel” can be delivered via a subcutaneous catheter. When glucose binds to the supply, hydration causes insulin release, but when the depot is dehydrated (hypoglycemic conditions), a skin layer forms over the gel and insulin cannot escape. This system has been tested, with success, in mice, but Dr. Heise cautioned that it may not be highly convenient for patients.
Glucose-binding protein based systems: Dr. Heise closed with the first-ever approach described, a glycosylated system based on competitive binding with glucose – specifically, the binding of glucose to lectin allows for the release of insulin. This became Merck’s first clinical GRI, MK-2640, which was discontinued in phase 1 due to insufficient efficacy and variable patient requirements (see published results).
He closed on a more imminently hopeful note: While the field has to wait for a “smart insulin,” smart pens are here now and stand to make diabetes management and insulin dosing seriously less cumbersome. Game on!
-- by Adam Brown, Ann Carracher, Brian Levine, Payal Marathe, Peter Rentzepis, Maeve Serino, Peninah Benjamin, and Kelly Close