12th Annual Diabetes Technology Meeting

November 8-10, 2012: Bethesda, MD Most Memorable Talks – Draft

Executive Highlights

This report includes nine particularly memorable talks from Diabetes Technology Meeting as identified by members of the Close Concerns team. We hope this list will serve as a starting point for those who have limited time to go through the full report. The conference was brimming with rich device presentations, and we had the difficult task of whittling down our favorites to a notable nine (in no particular order):

  1. A talk in which Dr. Frank Doyle III (University of California, Santa Barbara, Santa Barbara, CA) outlined the NIH DP3-grant-funded Ambulatory Control project’s path to an outpatient study set to commence in mid-2014.
  2. A data-filled update from Dr. Edward Damiano (Boston University, Boston, MA) on his group’s bi- hormonal artificial pancreas system.
  3. The first-patient results of a closed-loop efficacy trial assessing a new modular algorithm, presented by Dr. Boris Kovatchev’s (University of Virginia Health System, Charlottesville, VA).
  4. An update on Padova’s closed-loop projects by Dr. Claudio Cobelli (University of Padova, Padova, Italy).
  5. The Keynote Address by Dr. Jeffrey Shuren (Director, CDRH, FDA, Silver Spring, Maryland) on the challenges and progress of the CDRH in medical device regulation.
  6. Dr. Thomas Peyser’s (VP, Science and Technology, Dexcom) presentation on two new metrics to measure glycemic variability.
  7. A talk by Dr. Steve Prestrelski (Chief Science Officer, Xeris Pharmaceuticals, Austin, TX) on Xeris’ non-aqueous stabilized glucagon.
  8. A direct comparison of three CGM devices, presented by the JDRF Student Award Winner, Dr. Yoeri Luijf (Academic Medical Centre, Amsterdam, The Netherlands).
  9. A results presentation by Dr. Joseph Lucisano (President and CEO, GlySens Inc) of an in-human feasibility study of the first generation GlySens implantable glucose sensor.

Detailed Discussion and Commentary

Artificial Pancreas: Engineering Aspects


Frank Doyle III, PhD (University of California, Santa Barbara, Santa Barbara, CA)

Laying out the near-term goals of the NIH DP3-grant funded Ambulatory Control project, Dr. Frank Doyle III described the control algorithm developed for the project’s first outpatient studies. The algorithm, called periodic zone model predictive control (PZMPC), is similar to UCSB’s initial zone MPC algorithm in that insulin delivery reverts back to a fixed basal rate unless CGM values start to trend outside of a specified glycemic zone. However, with PZMPC the boundaries on this zone gradually shift at night, from the daytime zone of 80-140 mg/dl to an overnight zone of 110-220 mg/dl. To further mitigate the risk of nocturnal hypoglycemia, overnight insulin delivery is constrained to be no more than 150% of the basal rate. In silico modeling suggests that such an algorithm should enable excellent overnight safety, and on November 2 PZMPC was successfully tested for the first time in a human patient. Dr. Doyle said that next steps include additional feasibility evaluations at UCSB/Sansum in the coming weeks and months, larger in-clinic studies in the spring and summer of 2013, supplemental in silico testing in late 2013, and eight-week outpatient experiments starting in mid-2014.

  • Starting with the basic on-off controller described by Kadish in 1963, Dr. Doyle reviewed the history of algorithm development in closed-loop glucose control research. Later the precursors to today’s proportional-integrative-derivative controllers were developed by Albisser et al. (1974), Clemens (1979), and Fischer et al. (1980). In 1996 Dr. Doyle worked on the first application of model predictive control (MPC) for glucose control, and in 2001 Dr. Roman Hovorka’s group introduced non-linear MPC to artificial pancreas research. Promising work has been done with other algorithmic approaches such as pole-placement, H- infinity, adaptive, and fuzzy logic, Dr. Doyle noted. To supplement this overview he presented a slide with all but the most recent published clinical trials of artificial pancreas studies: these included four studies of PID algorithms, one with a PD/PI algorithm, one with a PD-based controller that also dosed glucagon, one hybrid MPC-/PD-driven insulin/glucagon system, 11 using MPC-based algorithms, and one with zone MPC.
  • One of the latest developments in MPC-based glycemic management has been UCSB’s zone MPC algorithm (Grosman et al., J Diabetes Sci Tech 2012), whereby insulin delivery changes from a pre-set basal rate only if the patient’s CGM values leave the target zone or are predicted to leave the target zone (80-140 mg/dl, in initial applications). Dr. Doyle noted that zone MPC has been demonstrated feasible in both a 12-patient UCSB/Sansum study of fully closed-loop control (Zisser et al., ADA 2012) and in larger industry trials of an artificial pancreas precursor product (Mackowiak et al., ADA 2012).
  • Dr. Doyle described the rationale for and design of a periodic zone model predictive control (ZMPC) closed-loop algorithm. Wide agreement exists that closed-loop control should be relaxed overnight in order to minimize risk of hypoglycemia, he explained; the question is just what approach to use. One approach would be to turn off the controller altogether or “de- tune” it so dramatically the controller never takes action (e.g., by widening the target zone to go all the way up to 1,000 mg/dl). As an alternative, UCSB researchers have designed the PZMPC algorithm that smoothly adjusts the boundaries of the zone from their daytime values (80-140 mg/dl) to establish a wider zone overnight (110-220 mg/dl). The PZMPC algorithm also puts a hard constraint on how much insulin can be delivered, even in hyperglycemia: no more than 50% of basal rate. The UVa/Padova simulator was used to compare PZMPC, traditional zone MPC, and a regime that switches to a fixed basal rate at night; PZMPC led to more overnight hyperglycemia but less overnight hypoglycemia (Gondhalekar, Dassau, Doyle III Eur Control Conf 2013).
  • The initial clinical evaluation of PZMPC closed-loop control will enroll 5-12 of the 12 patients who participated in the first study of UCSB’s original zone MPC algorithm (Zisser et al., ADA 2012). Except for the difference in control algorithms, the experimental design is identical to that of the zone MPC study (day-and-night study with unannounced meals, unannounced exercise, skipped lunch). The first patient tested PZMPC on November 2, 2012 and experienced favorable glycemic control (including less overnight insulin delivery – possibly safer if a patient were not frequently testing, Dr. Doyle noted).
  • Dr. Doyle closed with a look to the future of the NIH DP3-grant-funded Ambulatory Control project, a collaboration of artificial pancreas researchers at UCSB, the Sansum Diabetes Research Institute, the University of Virginia, and the Mayo Clinic. He reminded the audience that the five-year, $4.5-million initiative is designed to develop closed-loop systems that respond to glucose on a scale of minutes, adapt to day-to-day and week-to-week glycemic changes, and “monitor and supervise” glucose control over months and months.
    • Communication between the hardware and algorithms will occur through the UCSB/Sansum Artificial Pancreas System (APS) platform, which Dr. Doyle said is being ported to a new iDevice framework so that it can run on mobile phones (as opposed to laptops or tablets as previously used). In addition to the PZMPC controller, the system will incorporate the UCSB/Sansum Health Monitoring System (HMS) safety algorithm, which can send text messages and graphical alerts to physicians for remote monitoring. Dr. Doyle also noted that the FDA has approved the inclusion of fingersticks with Bayer’s Contour Next BG meter in an upcoming UCSB/Sansum closed-loop study – hopefully the first step toward outpatient studies that fingerstick tests as the sole reference values.
    • Dr. Doyle looked forward to upcoming clinical studies in the DP3 project, which will begin inpatient closed-loop studies at the University of Virginia, Sansum Diabetes Research Institute, and Mayo Clinic during the spring and summer of 2013. Each study will include two closed-loop sessions: one for behavioral initialization of the individual patients, and another with “behavioral adaptation.” Additional in silico are slated for late 2013 as a prelude to the main event: eight-week, case-controlled outpatient comparisons of closed-loop and open-loop control.



Edward Damiano, PhD (Boston University, Boston, MA)

Dr. Edward Damiano provided the latest update on his team’s quickly moving bi-hormonal work, a very valuable update from what we last heard at Children with Diabetes in July. Most notably, Dr. Damiano discussed the status of the five-day outpatient study that we’ve been looking forward to for over a year. The IDE for the controller device (an iPhone 4S communicating with a Dexcom G4 CGM and two Tandem t:slim pumps) was submitted to the FDA last week and the hope is the study can start in December. It will take place in Boston’s Beacon Hill neighborhood, a three square mile area downtown where subjects will able to roam freely while wearing the device with a nurse chaperone during the day. Dr. Damiano also presented interim results from the third inpatient study that is finishing up 51-hour experiments in 12 adolescents and 12 adults. Half of the study participants are getting adaptive pre-meal priming boluses while the other half are using a fully reactive closed-loop system (i.e., no meal boluses from the user). Notably, the closed-loop algorithm adapts over time, starting quite conservatively and then fine-tuning insulin dosing. The interim results look very solid in nine adolescents and 11 adults by day two of the study – mean blood sugars [projected A1c] of 143 mg/dl [6.6%] in adults and 171 mg/dl [7.6%] in adolescents receiving no meal boluses, improving to 138 mg/dl [6.4%] and 157 mg/dl [7.1%] when adaptive pre-meal boluses were used. We hope Dr. Damiano’s team can continue the momentum pending positive feedback from the FDA and IRB for the outpatient study.

  • Dr. Damiano’s much awaited five-day, transitional, outpatient closed-loop study will hopefully begin in December. The IDE for the controller device (an off-the-shelf iPhone 4S that communicates with a Dexcom G4 CGM and two Tandem pumps) was submitted to the FDA last week. He is awaiting feedback from the Agency and pending IRB approval (fingers crossed!), the study will start next month. It will take place in Beacon Hill, a three square mile neighborhood in downtown Boston. Patients (20 adults) will have the ability to roam freely (with a chaperone) during the day with unrestricted eating and exercise and point of care blood glucose testing. At night, they will sleep with a GlucoScout for reference blood glucose checks.
    • The iPhone 4S will run the control algorithm and communicate with two low-energy Bluetooth Tandem t:slim pumps (insulin and glucagon) and a Dexcom G4 CGM. The G4 will wirelessly stream data into the iPhone through a new custom hardware attachment connected through the 30-pin connector. This was an update over the system we saw at ADA in June and Children with Diabetes in July, which was hardwired to Abbott’s FreeStyle Navigator CGM receiver. Dr. Damiano was wearing the system during the presentation and showed the audience his real-time streamed blood glucose value from the G4 along with the Tandem pumps dosing saline.
    • The Beacon Hill study will test both fully reactive (no meal boluses) closed- loop control and closed-loop control with adaptive pre-meal priming boluses. For the latter, patients will select whether a meal is small, medium, or large, and pre-meal doses will be adapted over time by the algorithm. More broadly, the control algorithm itself will also adapt over time and fine tune dosing based on its performance and changing insulin requirements.
  • Dr. Damiano reviewed the design and interim results from his group’s ongoing third clinical feasibility study in 12 adults and 12 adolescents. The trial involves 51-hour experiments using the Abbott Navigator CGM as the input to laptop-driven insulin and glucagon control. The laptop directs dosing on two Insulet OmniPods. Participants ate six high carbs meals (the level of control achieved given the carb content is quite impressive) and had 30-40 minutes of structured exercise (4,000 heart beats). The algorithm initializes with only the subject’s weight and adapts over time – notable robustness considering both adults and adolescents are taking part in the study. Half of the adolescents and half of the adults receive adaptive priming boluses at meal presentation (i.e., the algorithm automatically changes the size of the pre-meal priming bolus over the course of the study), while the other half are on fully reactive control with no priming bolus.
    • Similar to previous trials, Dr. Damiano’s group tested CGMs head to head: Dexcom’s G4 Platinum and Abbott’s FreeStyle Navigator (first gen) – accuracy was very comparable. Dexcom’s G4 had a MARD of 12.3%, very comparable to the Abbott FreeStyle Navigator’s MARD of 12.6%. The CGMs were compared to blood sampling every 15 minutes. Data was used from eight to 48 hours of closed loop experiments. The CGMs were inserted 24 hours before the first calibration. The system to be used in the new outpatient study (see above) will use the Dexcom G4.
    • Dr. Damiano displayed interim study results, demonstrating good average control and a low prevalence of hypoglycemia. He urged the audience to pay more attention to the slightly better day two numbers since the algorithm takes six to 12 hours to adapt to the patient and establish optimal control. In his view, these numbers are more predictive of how the system would perform for several months. Dr. Damiano also emphasized that the A1c’s achieved in adults and adolescents in both experimental conditions were much better than standard of care. Additionally, hypoglycemia was infrequent, though it remains to be seen if there will be an increase once the outpatient study gets going and patients are not so sedentary.


CGM Average 


BG Average (mg/dl)

[Projected A1d]

% BG Values

< 70 mg/dl




Day 1

Day 2

Day 1

Day 2

Day 1

Day 2


Adults No Meal Bolus (n=5)










Adolescents No Meal Bolus (n=6)










Adults Auto Meal Bolus (n=5)










Adolescents Auto Meal Bolus (n=3)










  • Dr. Damiano briefly touched on his team’s second clinical feasibility study (just published in Diabetes Care), highlighting that children are much different from adults. Initially, the closed-loop algorithm performed well in six adults: an overall mean blood glucose of 158 mg/dl (68% in the range of 70-180 mg/dl, 0.7% <70 mg/dl) and a mean of 123 mg/dl overnight (93% in the range of 70-180 mg/dl, 0.5% <70 mg/dl). However, when the same system was brought into children, it could not get them in range – average BGs were 180-190 mg/dl with the same controller. The team iterated the algorithm and eventually generated a more adaptive system, which has since been used in the third feasibility study (see above) and will be part of the Beacon Hill study (also described above). It is initiated with only the subject’s weight and comes online with conservative dosing that adapts over time.



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

In an engaging, data-driven presentation, Dr. Boris Kovatchev presented the first-patient results from a closed-loop efficacy trial using a new modular algorithm and the latest device technologies. The algorithm used an enhanced control-to-range system comprised of: 1) an insulin on board (IOB) tracking module; 2) a range control module; and 3) a safety supervision module. The algorithm ran on a DiAs smart phone (a portable AP system developed at the University of Virginia by Dr. Patrick Keith- Hynes that has a modified medical-grade Android OS platform designed for AP application), which connected to a Dexcom G4 Platinum receiver via USB and wirelessly to the Tandem t:slim pump via low energy Bluetooth. (The DiAs also could be monitored remotely over 3G or Wi-Fi connection.) The study, which just commenced a week ago, is being conducted at four sites, with five patients at each site. In a crossover design, patients were randomized to either open- or closed-loop control (run on the DiAs) for a 40-hour session. Dr. Kovatchev presented the very first patient experience. Over 40 hours with closed- loop control, the participant was in range (70-180 mg/dl) 83.4% of the time, never below 60 mg/dl, in range overnight (80-140 mg/dl from 11:00 pm to 7:00 am) a notable 100% of the time, and >180 mg/dl 14.4% of the time. While Dr. Kovatchev said he knew he shouldn’t present data from just one person, we were sure glad he did! After seeing the promising first tracing, we can’t help but look forward to when full results emerge.

  • Dr. Kovatchev began by detailing the specifics of the Diabetes Assistant (DiAs) portable AP platform. Notably, the system has a medical grade Android operating system designed for AP applications. (The operating system and graphical user interface are deposited in the FDA master file MAF 2109, “AP Mobile Medical Platform.”) DiAs was developed at the University of Virginia by Dr. Patrick Keith-Hynes. The system can wirelessly communicate with an insulin pump and CGM and can operate multiple control algorithms. It’s color touch screen features a home screen with hypoglycemia and hyperglycemia “traffic lights” to inform the patient whether intervention is needed, and various system statuses (e.g., battery time, whether there is connection to the pump or sensor). DiAs can be used for closed-loop or open-loop control, and can enable remote monitoring (even simultaneous real-time remote monitoring of several patients, as Dr. Howard Zisser [Sansum Diabetes Research Institute, Santa Barbara, CA] demonstrated – he controlled several patients from a single iPad). For a deeper delve into the user interface, please see page three of our DTM 2011 Day #2-3 report at http://www.closeconcerns.com/knowledgebase/r/5f40a09f.
  • Early closed-loop feasibility studies with DiAs demonstrated the ability to maintain inter-device communication. The system consisted of DiAs, a communication box (Google Galaxy Nexus phone), and iDex (an Insulet OmniPod PDM integrated with the Dexcom Seven Plus CGM). Across four centers (UVA, Padova, Montpellier, and Sansum Diabetes Research Institute), patients received both open- and closed-loop control using DiAs. Inter-device communication was maintained 98.9% of the time in open-loop control (out of 277 patient hours) and 97.1% of the time in closed-loop control (out of 550 patient hours).
    • After 13 hours of open-loop control, participants had closed-loop control for 29 hours. The closed-loop control was two-fold: during the day the system implemented control-to-range and overnight the system was in safety mode (i.e., more relaxed control to reduce hypoglycemia risk).
  • Remote monitoring using DiAs connected to Dexcom’s G4 sensor reduced nocturnal hypoglycemia in a trial in young children at three summer camps sessions (n=20/camp [n=10 G4 + DiAs; n=10 G4 only]). Total study time was 1360 hours, of which remote monitoring was operational for 1314 hours (97%). For a deeper delve into the study, please see our coverage of Dr. Bruce Buckingham’s (Stanford University, Stanford, CA) dedicated presentation on the trial on page 12 of our EASD Day #2 Highlights report at http://www.closeconcerns.com/knowledgebase/r/ee283b0b.
  • Just last week, a multi-center efficacy trial of closed-loop control using a control-to- range algorithm and the newest generation devices commenced. Participating centers include UVA Center for Diabetes Technology, Sansum Diabetes Research Institute (UC Santa Barbara), Padova (Italy), and Montpellier (France). This randomized crossover study consists of one 40-hour session each of open- and closed-loop control (DiAs runs both). Five patients are enrolled per site; patients are responsible for system communications.
    • The modular control-to-range algorithm is comprised of three modules: 1) an IOB tracking module (UCSB); 2) a range control module (Pavia); and 3) a safety supervision module (UVA). Importantly, the algorithm allows for enhanced control-to- range during the day for intensive treatment, but relaxes control overnight.
    • The closed-loop system consists of DiAs smart phone, which connects by USB to the Dexcom G4 receiver and by low power Bluetooth to the Tandem t:slim. The G4 Receiver, of course, wirelessly communicates to the G4 sensor. Dr. Kovatchev said that to the best of his knowledge, this was the first time a closed-loop used the G4 and t:slim.
  • Results from the first patient tracing were encouraging, with 83.4% of time in target range (70-180 mg/dl) and 100% of time in target range (80-140 mg/dl) overnight. Dr. Kovatchev drew attention to the accuracy of the G4 – the 12 fingersticks shown seemed to fall closely in line with the G4 tracer. Further, Dr. Kovatchev highlighted the “traffic light” system of DiAs, with a color charting beneath the tracer showing hypoglycemia lights. Dr. Kovatchev noted two examples: 1) when the blood sugar was rapidly declining the safety system picked up the event at 140 mg/dl, the yellow hypoglycemic light came on, and insulin delivery was cut; 2) when the blood sugar reached ~90 mg/dl, the red late came on indicating that carbohydrates were needed and hypoglycemia was avoided.

Closed Loop Control

Time in range of 70-180 mg/dl


Time above 180 mg/dl


Time in range of 80-140 mg/dl overnight (11:00 pm to 7:00 am)


Number of hypoglycemic episodes below 60 mg/dl




Claudio Cobelli, PhD (University of Padova, Padova, Italy)

Dr. Claudio Cobelli described three ongoing projects to improve closed-loop control: the latest AP@home trials, an improved Dexcom sensor, and an updated simulator. The AP@home consortium is in midway through a set of overnight, partially outpatient experiments (n=12) that use UVa’s latest DiAs controller, a specialized remote monitoring system, an algorithm that incorporates recent insulin delivery information in its control decisions, and a simplified protocol for open-loop insulin dosage at meals. Meanwhile Padova engineers are working with Dexcom to develop a “smart” CGM transmitter with new onboard processing algorithms to detect noise and enhance calibration. Dr. Cobelli also described several recently submitted modifications to the UVa/Padova metabolic simulator, which include a more-physiological nonlinear response to hypoglycemia, a model of glucagon counterregulation, and revised definitions of insulin-to-carbohydrate ratio and correction factor. Ongoing research on the simulator will introduce a new model of sensor error, improve the model of subcutaneous rapid-acting insulin, and attempt to “clone” the results of the initial AP@home studies.

  • Dr. Cobelli explained that several improvements have been introduced in the latest AP@home clinical trial, which is targeted to complete by the end of the year. The crossover-design study uses outpatient closed-loop during the day and inpatient closed-loop control at night; the design also includes exercise and video games. The first four patients have completed the study at Padova, and the trial is planned to conclude with four patients in Montpellier and four in Amsterdam. The DiAs system used in the new trial has been improved over that in the first outpatient European studies, and the MPC “observer” module has beenmodified to monitor the pump for information on insulin delivery (enabling more accurate glycemic predictions). Also, integration of open-loop meal control in the closed-loop scheme has been simplified. Pre-clinical simulations were run on a recently modified simulator (see below), and improvements have been made to the “worst-case analysis” CVGA grid used to tune the controller’s aggressiveness. (Basically, the previous grid scored a particular simulated patient’s performance based only on whichever was worse in a given experiment, the highest hyperglycemic excursion or the lowest hypoglycemic excursion. By contrast the new curvilinear grid would rate an algorithm’s performance differently if a patient’s respective maximum and minimum values were 300 mg/dl and 110 mg/dl, instead of 300 mg/dl and 70 mg/dl, for example.)
    • Dr. Cobelli presented data from one of the patients in the study (“as you can imagine,” he smiled, “I chose the best.”) This patient’s time in target range (70-180 mg/dl) was improved dramatically with closed-loop control (99.9%) compared to open- loop control (72.7%); the mean time in target for closed-loop control in all four patients was in the mid-80% range.
  • In collaboration with Dexcom, Padova’s bioengineering team is exploring improved CGM algorithms for better noise detection (Facchinetti et al., IEEE Trans Biomed Eng 2011) and enhanced calibration (Guerra et al., IEEE Trans Biomed Eng 2012). The published work on these algorithms has involved post-processing sensor data that had already been converted to a glucose signal. However, by building algorithms directly into a future Dexcom “smart” transmitter, Dr. Cobelli hopes to further improve sensor performance and simplify wireless communication in closed-loop systems.
  • Modifications to the Virginia/Padova metabolic simulator were submitted to the FDA on October 17, 2012, and subsequent improvements are already underway. The changes currently under FDA review include a non-linear response to hypoglycemia, a counterregulation model that includes glucagon secretion, kinetics, and action), a new way to define insulin-carbohydrate ratio and correction factor (to mimic the way that real patients would determine these values), and an altered model of absorption parameters. In a CE-EGA of how well actual patient data (n=96) agreed with the old and new simulations, the new simulation performed significantly better in hypoglycemia (and agreed more closely with real data on interquartile range and high and low blood glucose indices, as well).
    • Ongoing work on the simulator includes a new model of CGM error, which is based on data that the Oregon researchers shared from a recent clinical trial (Castle et al., Diabetes Care 2012). This work includes individualized models of blood to interstitial glucose kinetics as well as models of the calibration function, sensor variability, and measurement noise. These components can be analyzed individually to see how errors in each might affect the sensor result. The researchers can also assign various probabilities to the likelihood transient artifacts, error codes due to noise, and disconnection of the sensor from the body, to see what these problems would mean for glycemic control.
    • Padova engineers are also changing their module of subcutaneous insulin kinetics to incorporate data from a clamp study of insulin lispro in 41 patients with type 1 diabetes. Dr. Cobelli thanked Biodel’s Alan Krasner for donating these data, and he expressed hopes that the updated module would be completed by the end of 2012.
    • To conclude, Dr. Cobelli explained that the simulation is being adjusted so that it can “clone” the clinical data from the AP@home CAT Trial. (As a reminder, this dataset includes a total of 141 traces from eight patients.) For example, these modifications would allow the simulation to incorporate intraday variability in glucose absorption and insulin sensitivity, as occurs physiologically.


Keynote Address


Jeffrey Shuren, MD, JD (Director, CDRH, FDA, Silver Spring, Maryland)

We were glad to see the FDA’s CDRH Director Dr. Jeffrey Shuren front and center at DTM in the Friday morning keynote speech. Dr. Shuren emphasized CDRH’s vision: “to give (diabetic) patients access to high quality, safe, and effective devices of public health importance first in the world” – admittedly, we are pretty far from this today. He explained that the division is taking this vision “very seriously” through a number of key steps: collaborating with companies (CGMs, pumps, the artificial pancreas, data management), developing a public-private partnership to focus on regulatory science for medical devices (this sounds encouraging though few details were shared), developing better tools and software, and use of the new innovation pathway and entrepreneur in residence program. Overall, we found Dr. Shuren’s words encouraging to hear, though they were fairly general in scope and light on details (e.g., “we are woem>rking with companies”). However, he also had some very promising and frank comments about FDA’s challenges and how it can better encourage innovation and help companies get devices to market faster – this perspective was really great to hear. In terms of specific diabetes technology, he only mentioned the Medtronic Veo and was very non-committal on its status: “we’ll see where that goes and whether we’ll have that technology for US patients in the near future.”

  • Dr. Shuren emphasized the major challenges facing regulators and regulatory science: communication, infrastructure, and funding. He explained that regulatory science (the tools, standards, and approaches needed to evaluate safe and effective medical devices) is not well understood or appreciated in the medical device ecosystem. Additionally, most of the evaluation work is scattered throughout the country – it is inefficient (one expert here, one project there) and there is very little investment by the federal government. For comparison, NIH’s FY12 budget for research was $30.7 billion, including $575 million for the NIH’s new Center for Advancing Translational Sciences (NCATS). By contrast, FDA has $15 million for medical device regulatory science (excluding staff). “The disparity is huge,” he noted.
  • Dr. Shuren highlighted classes of diabetes technology as examples of how CDRH is working to get better devices approved sooner. His discussion was fairly general for the most part, more about broad approaches than specific companies or devices.
    • 1) Data management: Dr. Shuren explained that we have meters, CGMs, and pumps collecting data, but clinicians have limited time to look at it and must deal with lots of cables and downloading hassle. For patients, this makes it challenging to manage diabetes. FDA is currently working with companies to bring data management technologies into a single stream of information. Additionally, the Agency is working to develop analytical tools that make interpretation easier. Dr. Shuren’s slide highlighted that “medical cell phone apps [are] coming soon.” We hope this means the finalized mobile medical applications guidance is on the horizon.
    • 2) CGM: Dr. Shuren emphasized that “CGMs are not as accurate as we need them to be” for the closed loop. FDA is working with companies to develop better, more reliable, and more accurate sensors. He stated that companies “are taking on that challenge” and it’s “encouraging to see some of the advances that are hitting the market” – this may have been an indirect reference to Dexcom’s recently approved G4 Platinum.
      • How do we better assess CGM? FDA is working on better in vitro screening of substances that can interfere with CGM readings. Work is also ongoing to better understand the effects of biofouling and how to manipulate a sensor’s surface. The idea is to understand if there are important physiological differences that lead to differences in long-term sensor function. FDA would then provide this feedback to companies to make better technology.
      • Emerging technologies in hospital glucose sensors. FDA is working to understand how changes in physiological pH affect sensor accuracy in the hospital setting. The Agency is also focusing on optical glucose biosensing and minimally invasive sensing.
    • 3) Insulin pumps. Dr. Shuren reminded the audience of the FDA’s 2010 initiative on infusion pumps. Previously, there were thousands of adverse events being reported for infusion pumps, many related to insulin pumps. Interestingly, Dr. Shuren believes the new initiative has resulted in higher quality regulatory submissions. In the two-year period prior to initiative, the Agency cleared 51% of pumps. Now, the FDA is clearing about 70% of them. The FDA has also developed better tools for manufacturers to use and development of a generic insulin infusion pump safety model is ongoing.
    • 4) Artificial pancreas. Dr. Shuren noted, “We’re not there yet because we need better components, but we’re well on our way to getting there.” He emphasized that the Agency is committed to this technology and would “love to see it come to the US first.” The FDA has consolidated the review team in CDRH to help improve oversight over the AP. In the past year, Dr. Shuren highlighted that the FDA has approved four or five clinical trials every single month devoted just to the AP. The Agency has also approved the first outpatient closed loop AP study in the US.
      • On the FDA status of the Medtronic Veo, Dr. Shuren was disappointingly non-committal and fairly vague: “We have an in-house application and we’ll see where that goes and whether we’ll have that technology for US patients in the near future.” He mentioned that it has LGS technology and “is already CE Marked in Europe” – we would of course add that it’s been a three- plus year delay…
      • Dr. Shuren explained that the AP draft guidance documents (subsequently finalized and posted a few hours after his talk) were somewhat unique – usually, the regulatory pathway must catch up with the science. For the AP, it was the other way around: the regulatory pathway needed to get ahead of the science (we would note that this was really not the case with low glucose suspend).
    • 5) Bioartificial pancreas. FDA is working to better understand combination products and is focusing on different ways to have successful encapsulation of pancreatic islet cells.

  • To overcome the challenges of funding and inefficiency, FDA is setting up a public- private partnership with LifeScience Alley. The Agency is working to set up a 501(c)(3) organization that will be separate and only focused on advancing the regulatory science for medical devices. One of the areas will be diabetes. The hope is to get this off the ground in the “near future.” The partnership will allow sharing of resources, dollars, expertise, data, and allow for companies to come together and not run into legal challenges. We hope this could allow for independent testing of devices, especially blood glucose meters, which would jointly be supported by money from all companies.
  • Dr. Shuren closed with a review of the FDA’s innovation pathway, a new route to market for breakthrough technologies. It serves as an “incubator cell” for new approaches and tools to reduce the time and cost of development, assessment, and review of breakthrough (and other) devices. It also transforms how the FDA and innovators work together. Part of the program includes the entrepreneurs in residence program, which invites experts from the medical device industry (VCs, patients, experts, and companies) to the FDA for some of the Agency’s day- to-day work. The pathway includes an application process, a collaboration phase, a clinical trials phase, and market approval. A new version of the pathway was recently launched and Dr. Shuren specifically mentioned that there are three products for end-stage renal disease. He did not specifically address diabetes, though we certainly hope industry experts are taking part in the process.


Questions and Answers

Dr. Yogish Kudva (Mayo Clinic, Rochester, MN): It’s very important to all of us that the US be ahead of the curve and have the first device approvals. What are the lessons we can all learn from how the Medtronic Veo process has gone? Why are we lagging behind?

A: From an FDA perspective, in the past few years, we’ve had challenges on a variety of fronts. Our programs have not been sufficiently predictable, transparent, or timely. We’ve been very public about those challenges. We recognized those problems when I came and we put out two reports on it. We were very frank about the challenges. In early 2011, we developed a plan of action and steps to start fixing the program. Not only has there been lots of progress, but we are now for the first time seeing changes in our performance that we have not seen in some cases for a decade. We will put this data out in the coming weeks on what it’s been like before and where we’re headed now. We’re focused on making our programs better.

We need to tackle this issue on science. Most other countries are not safety and effectiveness. The bar is different – other countries don’t need to be effective. We believe that’s important for patients. If that bar becomes irrationally too high, it becomes a barrier. You need to find a sweet spot to have safety and effectiveness, but do it in a way that is rational, timely, reasonable, and not costly. Regulatory science is the linchpin for getting there. Through things like computer models, we can test drive technology without animal studies – that is a game changer. Those are the advances we’re talking about. Better pathways, putting out guidance, and advancing the very science itself.

Dr. Robert Vigersky (Walter Reed National Military Medical Center, Washington, DC): One of the barriers in getting devices into hands of patients is the IRBs at the various institutions. What is the FDA doing to work with IRBs on an individual basis or to put out guidance to get protocols to IRBs?

A: We don’t have authority over IRBs. This is one of the issues on the table for the entrepreneur in residence program. How can we streamline clinical trials? The scope isn’t about what’s solely within the jurisdiction of FDA. It’s anything that we can influence in any way, shape, or form. IRBs are something we’ll look at. Do you move to a central IRB model? It could be a time saver. How much time does it take contracting if you’re doing a 70-site study? Why not have set templates with contracts? That can lead to big efficiencies and not big costs to do.

Peter Rule (OptiScan, Hayward, CA). If we could envision the ideal collaboration between industry and the FDA, with certain endpoints and certain outcomes, and jointly meet them, there would be a high probability of approval. But the current risk benefit standard makes that difficult. It’s hard to power trials as a manufacturer. Do you see the day where there is more refinement around the notion that risk-benefit is a subjective state?

A: At the end of the day, there will always be a little bit of subjectivity. Science is often gray. I think we make a lot of smart decisions, but not as consistently as we could. What is the right kind of assessment – can you get out ahead and work with industry and academia. That’s what we try to do with the AP. Just this past April, we released a final framework on benefit-risk for those seeking PMA or de novo decisions. It’s very patient centric. When technologies come on the market, they’re not coming to be used on you. They are used on patients. We need to be focused on patients’ perception of risk-benefit. With a new technology, you cannot expect it to be a home run on the first iteration. You must take that into account. If you don’t, you will never let it on the market and it will never get better. My staff is applying that to every single PMA and de novo submission. For some technologies, there is a risk that some people would not take. But some would. In that circumstance, if you’re explicit about that risk, let’s let patients and practitioners make that call.

Q: For technologies like glucose sensing, what about an implantable vs. a non-invasive sensor – is there a useful domain for both?

A: You go where the technology takes you. If the technology is good enough that you didn’t need implantable, you wouldn’t use it. If the answer is no, you would still have implantable. It would be a wonderful world if we didn’t have to use as many invasive technologies on patients – we’re a long way from that, but it’s a terrific goal that we should be shooting for.

Dr. David Klonoff (Mills-Peninsula Health Services, San Mateo, CA): Thank you for a really good presentation. It’s nice for us to hear that you really get it and understand the issues that are going on. On behalf of everyone, we want to thank you for the hard work.


New Metrics to Quantify Glycemic Variability


Thomas Peyser, PhD (VP, Science and Technology, Dexcom)

In an elegant talk, Dr. Thomas Peyser presented two unconventional measurements of glycemic variability designed to be simple to understand, describe, and calculate. The first, glycemic variability index (GVI), is simply a measure of the relative length of the line that would be shown in a CGM trace. For example, someone with a perfectly flat CGM trace (never found in nature) would have a GVI of 1.00, and someone with moderate glucose variability would have a GVI of 1.2-1.5. The other metric, patient glycemic status (PGS), is designed to capture both variability and overall glycemic control; it is the multiplicative product of GVI, mean sensor glucose, and percent time spent outside of target range. Dr. Peyser proposed that with further study, GVI and PGS might be found useful as a clinical flag for when patients need help -- a method readily understandable even for non-specialists (an important concern as CGM becomes more widely used outside of endocrinology practices). At the other end of the spectrum, he suggested that the metrics could be used to characterize how well different drugs reduce GV and improve overall glycemic control.

  • Dr. Peyser half-jokingly asked whether anyone in the audience would feel comfortable publicly writing out the equations for glucose standard deviation, MAGE, and CONGA. (No one raised a hand.) He noted that most metrics for glycemic variability are mathematically complicated, whereas most people involved in diabetes seem to feel about glycemic variability the way that Supreme Court Justice Potter Stewart described hard-core pornography: “I know it when I see it.” Glycemic variability has been found to be associated with lower mood and quality of life (Penckofer et al., Diab Technol Ther 2012); Dexcom’s perspective is that wearing CGM should reduce GV (and that patients who cannot reduce their GV using CGM tend to be dissatisfied with the technology).
  • As a relatively simple and intuitive way to quantify glycemic variability, Dr. Peyser proposed adding up the diagonal distance between each pair of consecutive sensor values and then dividing this by the horizontal distance to standardize for any given amount of time. He called this method glucose variability index (GVI) and listed some example values: 1.0 would be a perfectly flat line (never seen in nature), 1.0-1.2 would be found in people without diabetes, 1.2-1.5 would indicate modest variability, and >1.5 would correspond to high GV. Dr. Peyser said that GVI correlates well with MAGE, though it does not penalize single large glucose spikes as much as MAGE does.
  • For a basic topological measurement of overall glycemic control, Dr. Peyser proposed that GVI could be multiplied by mean sensor glucose and the percentage of time spent outside glycemic range: i.e., GVI x MG x (1-PTIR), where MG = mean glucose and PTIR = percent time in range. The resulting method, patient glycemic status (PGS), is conceptually similar to area under the curve but somewhat more precise in the contributions of each aspect of glucose control, Dr. Peyser said. PGS would be at or below 35 for most people without diabetes, people with diabetes and good glycemic status might have PGS 35-100, 100-150 would be a poor PGS, and >150 would indicate “very poor” glycemia. Dr. Peyser said that retrospective data haven’t yet been analyzed to compare PGS and A1c but that PGS > 150 would probably correlate with an A1c above 9.0%.
  • Dr. Peyser acknowledged that GVI and PGS were similar to metrics proposed by Drs. Cynthia Marling, Jay Shubrook, and Frank Schwartz and colleagues at Ohio University (Marling et al., J Diabetes Sci Tech 2011).

Novel Insulins


Steve Prestrelski, PhD (Chief Science Officer, Xeris Pharmaceuticals, Austin, TX)

Dr. Steve Prestrelski gave a comprehensive overview of Xeris’ non-aqueous stabilized glucagon formulation for use in an auto-injector pen for severe hypoglycemia (G-Pen), a mini dose pen for mild/moderate hypoglycemia (G-Pen Mini), and a formulation for the bi-hormonal artificial pancreas. To stabilize glucagon, Xeris is using non-aqueous solutions with biocompatible solvents (DMSO) that are already FDA approved. According to Dr. Prestrelski, the company’s glucagon formulation has stability for two years at room temperature. The compound is currently preclinical, though an IND enabling program has been agreed upon with the FDA and the quicker 505(b)(2) regulatory pathway will be used. Xeris has scaled for clinical production (2,000 syringes), completed the bridging pharmacological/toxicological study, and will begin a phase 2a clinical trial in 1Q13 under PI Dr. Ralph DeFronzo (Texas Diabetes Institute). Data is hopefully expected early next year. The company’s ultimate hope is license the technology – during Q&A, Dr. Prestrelski explained that it could come on the market as soon as 2014 if all goes smoothly. (This would be ahead of Biodel’s stable glucagon formulation, which is expected to be filed with the FDA in mid-2014 per comments at the October 12 Analyst Day.) There is a much less clear timeline and regulatory pathway for Xeris’ G-Pen Mini and the bi-hormonal AP indication, though it is notable that the company is working with the two major insulin-glucagon AP research teams: Drs. Ed Damiano and Steven Russell in Boston and Dr. Ken Ward in Oregon. A clinical trial for the bi-hormonal AP indication is expected to start in 2014 (n=14) under PI Dr. Ken Ward.

  • Instead of a water-based formulation, Xeris is mixing Lilly’s glucagon powder with an FDA approved, biocompatible, non-aqueous solvent. The solvent, DMSO, is already in FDA approved parenteral products and is approved at volumes ~200 times greater than what Xeris is using. According to the company, the novel formulation remains stable and free of fibrillation after incubation at 104 degrees Fahrenheit (40 degrees Celsius) for at least two months, and stability data at room temperature predict a shelf life of at least two years. A 505(b)(2) regulatory pathway will be pursued using the Lilly glucagon kit as the reference product. As a reminder, the company had a late-breaking poster at ADA 2012 (see our extensive report, including an update on the field, at http://www.closeconcerns.com/knowledgebase/r/cc525978). Xeris’ ADA poster can be found at http://xerispharma.com/ADA_Poster_FINAL_5_30.pdf.
  • The G-Pen, an emergency glucagon treatment for hypoglycemia, would reduce the current nine-step glucagon administration process to two steps. Dr. Prestrelski demonstrated use of the EpiPen-like auto-injector on stage: 1) remove the safety cap and 2) press the device against the body. The device will reduce the volume of injection through a five-fold increase in glucagon concentration – the benefit is a one-second injection time and a 200 micro- liter injection volume. The pen will have a two-year expiration date at room temperature.
  • The Xeris glucagon formulation is currently preclinical; a rodent model demonstrated identical PK/PD compared to traditional aqueous formulations. Dr. Prestrelski explained that Cmax, Tmax, and AUC were all equivalent to aqueous control. The glucagon formulation demonstrated rapid absorption (Tmax ~5 minutes) and elevation of blood glucose levels within 15 minutes.
  • The G-Pen Mini for mild-to-moderate hypoglycemia is in very early development and there is no final design as of now. It would be just like an insulin pen (e.g., Novo Nordisk’s FlexPen) but would contain glucagon for self-injection for mild or moderate hypoglycemia. The recommended dose would be ten micrograms per year of age. The G-Pen Mini would hold enough glucagon for 30 or more injections (i.e., enough for one month if injecting once per day). Like the auto-injector, it will have a two-year expiration date at room temperature. Conceptually, the G-Pen Mini is a major win in our view – the ability to precisely dose glucagon could help many patients spend more time in zone and less time on the “roller coaster” pattern of hypoglycemia, carb overtreatment, hyperglycemia, overcorrecting with insulin, hypoglycemia, etc. Development risk certainly exists, given regulatory barriers for new products, but we believe interest will be very high from multiple parties.
  • Xeris is working with “various pump manufacturers,” Drs. Edward Damiano, Steven Russell, and Ken Ward to develop a glucagon formulation for use in a bi- hormonal artificial pancreas. The slide specifically displayed the Tandem t:slim pump, corroborating what we’ve heard at recent conferences about Tandem’s development of a dual- chamber pump. Xeris’ target product profile for a pump is a concentration of 5 mg/ml sold in a 2 ml per vial of glucagon with a two-year expiration date at room temperature. The goal is compatibility with most or all available pumps and stability at 37 degrees Celsius (98.6 degreesFahrenheit) for up to four weeks. The one challenge is that Xeris will need to demonstrate compatibility with pumps due to the non-traditional solvents (“not always easy”).
    • Dr. Prestrelski very briefly mentioned Xeris’ recent NIH/NIDDK Small Business Innovation Research (SBIR) grant to develop its glucagon for use in a bi-hormonal artificial pancreas. The phase 1 grant is for $336,793 specifically for the AP indication. The phase 1 grant is the initial installment of a phase 1-2 fast track SBIR grant, with the potential for a total award of $1.05 million. Phase 2 funding will support IND-enabling preclinical studies and a foundational clinical trial to be conducted at the Oregon Health and Science University under PI Dr. Ken Ward. It is expected to include 14 patients and start in 2014.


Yoeri M. Luijf, MD, MSc (Academic Medical Centre, Amsterdam, The Netherlands)

This year’s JDRF Student Research Award Winner, PhD-candidate Dr. Yoeri Luijf, presented a 20- patient comparison of three CGM sensors: the Abbott FreeStyle Navigator I, the Medtronic Enlite, and the Dexcom G4 Version A (i.e., the version used with the Animas Vibe in Europe, not the more advanced G4 Platinum available in the US). During the in-clinic portion of the study on day one, the G4A’s accuracy was significantly worse than that of the Navigator and Enlite (as measured by mean absolute relative difference, MARD). However, during the home-use period that followed, the Navigator and G4A had statistically similar accuracy, and the Enlite’s was significantly worse. (Dr. Luijf thus hypothesized that the G4A’s warm-up time may be longer than indicated by the manufacturer.) Sensor longevity was also assessed; all three sensors had median lifetimes longer than their respective indicated wear times, but accuracy for sensors that outlasted their indicated wear-time was significantly better for the Dexcom sensor than Abbott’s or Medtronic’s. During Q&A Dr. Luijf said that the researchers will conduct a follow-up study using the G4 Platinum, with the in-clinic portion of the study conducted on day three rather than day one to ensure that all three sensors have fully warmed up.

  • The study enrolled 20 patients with type 1 diabetes, who were simultaneously fitted with three different CGM sensors: the Abbott FreeStyle Navigator I, the Medtronic Enlite, and the Dexcom G4 version A – i.e., the sensor approved for use with the Animas Vibe in Europe, not the more-advanced G4 Platinum. On the first day of sensor wear, patients stayed in the clinical research center for a glycemic challenge (breakfast with an insulin bolus that was delayed and then increased). Reference blood glucose values in the clinical research center were taken with YSI. After this in-clinic portion, one of the three sensors was randomly removed so that patients would need to wear only two sensors for the rest of the study. Each patient then wore those two sensors at home, taking fingerstick blood glucose measurements for reference. To assess sensor longevity, patients wore each sensor for as long as they could until apparent technical failure or two consecutive days of mean absolute relative difference (MARD) greater than >25%.
  • During the clinical research center (CRC) portion of the study on day one, the Dexcom G4 A had significantly worse YSI-matched accuracy than either the Abbott FreeStyle Navigator I or the Medtronic Enlite (which were not statistically different from each other). This same pattern was seen in sub-analyses of glucose values below 100 mg/dl and between 100 and 200 mg/dl. For glucose values above 200 mg/dl, however, accuracy did not significantly differ between the sensors.

YSI-matched accuracy in clinic on day one




Navigator I









MARD = Mean absolute relative difference; SD = Standard deviation

  • The median ±interquartile range of sensor longevity were as follows for the Navigator (8.5± 3.5 days), G4 A (10.0± 1.0 days), and Enlite (8.0± 1.5 days). Maximum observed sensor lifetime was 26 days for the Navigator, 82 days for the G4A, and 15 days for the Enlite. (Dr. Luijf noted that three of the Dexcom sensors lasted for over 40 days, though he emphasized that these were outliers.) As a reminder, the indicated wear time is five days for the Navigator I, seven days for the G4A, and six days for the Enlite.
  • During the study’s home phase, accuracy within labeled wear time was statistically significantly worse for the Enlite than the Navigator or G4A; beyond labeled wear time, the G4A’s accuracy was best by a significant margin. Accuracy during home use was assessed by comparison to self-monitoring of blood glucose (SMBG) fingerstick values. 

SMBG-matched accuracy at home


MARD during specified lifetime

MARD after specified lifetime

Navigator I









The Sensor-Tissue Interface: Industry Panel


Joseph Lucisano, PhD (President and CEO, GlySens Inc.)

Dr. Joseph Lucisano presented a promising first-in-human feasibility study of the first-generation GlySens implantable glucose sensor, building on positive 18-month data in pigs (Sci Med Transl 2010). Implanted for six months in insulin-using patients with diabetes (n=6), the glucose-oxidase (GOx)-based sensor was generally well tolerated. Sensor performance was stable over time with calibrations performed once per month (during in-clinic glycemic clamp studies). However, the time offset of the sensor from plasma glucose varied from day-to-day, patient-to-patient, and based on whether glucose was rising or falling. Dr. Lucisano said that GlySens’ second-gen sensor features a longer range for data transmission, more optimal sensitivity to oxygen, and no contact between tissue and the sensor’s enzymes (the source of the only adverse event in the first-gen sensor’s feasibility study). This next-gen sensor is in “the final stage” of development and is anticipated for clinical evaluation in early 2013. The company’s goal for its first commercialized product is yearlong implantation with quarterly calibration.

  • The GlySens sensor is based on the research of Dr. David Gough at the University of California, San Diego and uses a “dual-detector” method. In addition to glucose oxidase, the system uses another enzyme called catalase that breaks down hydrogen peroxide (a byproduct of the glucose oxidase reaction). Both enzymes’ reactions are monitored, which enables sensing to be more specific and also extends the lifetime of the sensor (because hydrogen peroxide accumulation can be deleterious).
  • Tolerability, the feasibility study’s primary endpoint, was generally favorable for six months. The only adverse event was an increase in non-neutralizing antibodies to glucose oxidase in one patient, which led the investigators to explant the sensor at five months. Dr. Lucisano said that this would likely not be an issue in future versions of the system, since tissue- enzyme contact has been precluded in the second-generation sensor. The implant site was initially somewhat painful, but patient-reported data were consistent with better tolerability over time. No incidents of swelling, erythema, itching, or significant discomfort were reported.
  • Dr. Lucisano presented an analysis of time offset (lag or lead) as measured during the monthly glucose clamp studies. When glucose was rising, the mean ± SD lag from plasma glucose values was 9 ± 6 minutes (maximum lag 17 minutes). When glucose was falling, lag was -3 ± 12 minutes (maximum lag 9 minutes). Dr. Lucisano noted that the mean ‘lag’ was negative during falling fronts, meaning that sensor glucose changes actually preceded blood glucose changes. During Q&A he proposed that this likely reflects true physiology rather than a software error, though we have not often heard this phenomenon described for current CGM sensors. (He also acknowledged that the high levels of insulin during the clamp study could have had confounding effects and that this analysis was still important to conduct.) When glucose was falling and in the hypoglycemic range, lag was 7 ± 4 minutes (maximum lag 13 minutes). Dr. Lucisano noted that the time lag did not appear to change over time in a systematic way, but rather to show high inter-day variability throughout the six months. Calibration drift averaged 4% per week; the worst weekly drift observed was 9%.
  • The feasibility study’s overall accuracy analysis included 1,097 sensor glucose values paired with four-times-daily home fingerstick glucose tests. Nearly 30% of fingersticks were excluded because no sensor data were available within 10 minutes of the measurement; Dr. Lucisano indicated that the communication range between the first-generation sensor and receiver was too limited but that this had been corrected in the gen two. The overall Clarke Error Grid Analysis placed 88% of points in either the A or B zones, 11% in the C zone, and 12% in the D zone; overall MARD was roughly 20%. Dr. Lucisano also showed Clarke Error Grid results for a single subject (presumably the one with the best results): for 125 data pairs, that patient had 62.4% in the A zone, 31.2% in the B zone, and 5.6% in the C zone; MARD for this patient was roughly 16%.
  • Dr. Lucisano summarized the status of GlySens’ next-generation sensor, which is being targeted for clinical evaluation in 2013. A smaller sensor is in production, the telemetry signal has been improved, and the sensor’s response range had been broadened.

-- by Adam Brown, Kira Maker, Joseph Shivers and Kelly Close