Artificial Pancreas Technologies: New Tools to Improve Diabetes Care Today and Tomorrow
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1 Artificial Pancreas Technologies: New Tools to Improve Diabetes Care Today and Tomorrow Mark D. DeBoer, MD, MSc., MCR University of Virginia Center for Diabetes Technologies October 2017
2 Learning Objectives 1) List the mechanical components of an artificial pancreas system. 2) List commercially-available systems with progressively added features of artificial pancreas technology. 3) Identify 3 sources of delay inherent to how an AP system responds to blood glucose excursions.
3 Disclosures 1) I have no relevant financial disclosures related to the content of this presentation. 2) Many of the Artificial Pancreas technologies presented are not currently approved by the FDA (outside of research protocols).
4 The landscape of Type 1 diabetes Real Ideal
5 The landscape of Type 1 diabetes Real Ideal
6 The landscape of Type 1 diabetes I want my old pancreas back! No worry about hypoglycemia. Food flexibility. No blood sugar checking. Real Sports without distraction. A good night s sleep. Ideal
7 The landscape of Type 1 diabetes Real What do I need to do to get a consistently normal blood sugar?!? Ideal Barriers: Variability in food. Variability in activity. Variability in symptoms. Variability in communication. High HbA1c s. Sub-optimal sleep.
8 The landscape of Type 1 diabetes Real What do I need to do to get a consistently normal blood sugar?!? Ideal Barriers: Variability in food. Variability in activity. Variability in symptoms. Variability in communication. High HbA1c s. Sub-optimal sleep.
9 The landscape of Type 1 diabetes Real, circa 1984 Real Urine glucose No blood glucose meters Insulin pumps rare Ideal Insulins with overnight peaks
10 The landscape of Type 1 diabetes Real, circa 1960 Real Pig insulin Follow symptoms only No home glucose assessments Ideal
11 The Previous Century Backpack insulin & glucagon pump Intravenous glucose control: Albisser et al; Mirouze, Selam et al. Pfeiffer et al. The Minimal Model of Glucose Kinetics. Bergman & Cobelli, AJP, 1979 Subcutaneous Continuous Glucose Monitoring Minimed CGMS, 1999 Blood glucose meters & insulin pumps becoming smaller 1920s 1960s 1970s 1980s 1990s 2000s The Auto Syringe (Dean Kamen) Models of diabetes becoming larger & more complex Insulin discovered Frederick Banting Ames Reflectance Meter First use of Insulin pumps Tamborlane et al; Pickup et al.
12 The Artificial Pancreas 40 Years Ago
13 Trying to bridge the chasm Real X Ideal
14 Trying to bridge the chasm Real Insulin pumps: Up-sides Variation of basal insulin delivery Painless delivery for smaller doses Down-sides More things to go wrong Attached to hardware 24-7 Ideal
15 Trying to bridge the chasm Real CGM: Up-sides View trends predict lows/highs Alarm to wake for lows overnight Ideal Down-sides Need to calibrate Delay in reading, not as accurate Teens: cost/benefit
16 Basic Design of AP Systems Traditional Insulin Delivery Insulin Request Insulin Pump Insulin Delivery
17 Basic Design of AP Systems Glucose: CGM Insulin Parameters: basal rate, carb ratio, correction factor,, total daily insulin Insulin-on-Board G = k. G + k. G U E + k k. G k. I + p 2 p 1 t ii t p1 p2 p p3 d G = k. G + k. G t 1 t 2 p ( ) ( ) ( m0 + mx. ) m0 G p G sc = ksc Gsc V g I = m + m. I + m. I + k. I + k. I p 2 4 p 1 l a1 sc1 a2 sc2 I = m + m. I + m. I l 1 3 l 2 I p I1 = ki I1 Vi I d = ki( Id I1 ) V V X G K p + G Model: State Estimates I p X = p2u X Ib V i J( t) I sc1 = kd. Isc1 ka1. Isc1+ BW I sc1 = kd. Isc1 ka2. Isc2 Q sto1 = kgri. Qsto1+ M( t) Q sto2 = kempt. Qsto2+ kgri. Qsto1 Q gut = kabs. Qgut + kempt. Qsto2 t t fk.. Q abs BW Glucose Prediction gut Insulin Decision
18 Basic Design of AP Systems Glucose: CGM Insulin Parameters: basal rate, carb ratio, correction factor,, total daily insulin Insulin-on-Board G = k. G + k. G U E + k k. G k. I + p 2 p 1 t ii t p1 p2 p p3 d G = k. G + k. G t 1 t 2 p ( ) ( ) ( m0 + mx. ) m0 G p G sc = ksc Gsc V g I = m + m. I + m. I + k. I + k. I p 2 4 p 1 l a1 sc1 a2 sc2 I = m + m. I + m. I l 1 3 l 2 I p I1 = ki I1 Vi I d = ki( Id I1 ) V V X G K p + G Model: State Estimates I p X = p2u X Ib V i J( t) I sc1 = kd. Isc1 ka1. Isc1+ BW I sc1 = kd. Isc1 ka2. Isc2 Q sto1 = kgri. Qsto1+ M( t) Q sto2 = kempt. Qsto2+ kgri. Qsto1 Q gut = kabs. Qgut + kempt. Qsto2 t t fk.. Q abs BW Glucose Prediction gut Insulin Decision
19 Basic Design of AP Systems Closed Loop Control AP circa 2009 Patient
20 UVa s DiAs: the Diabetes Assistant System Status User Touch Controls Hypoglycemia Traffic Light USS Messages Hyperglycemia Traffic Light
21 UVa s DiAs: the Diabetes Assistant Plot screen CGM trace Bolus marker Basal profile Basal delivery
22 UVa s DiAs: the Diabetes Assistant 3G or WiFi Connection DiAs Smart Phone Dexcom G4 Receiver Wireless using Dexcom Share Devices near the subject Bluetooth Wireless connection Roche Tandem t:slim Accu-Chek insulin pump Combo Dexcom G4 Sensor Devices worn by the subject
23 AP Strategy-Iterative: Increases in Automation Kowalski AJ. Can we really close the loop and how soon? Accelerating the availability of an artificial pancreas: A roadmap to better diabetes outcomes. Diabetes Technol Ther, 11:S113-S119, 2009 Safety System USS Virginia Meal Control Module
24 Threshold suspend: Medtronic Bergenstal N Engl J Med 2013
25 Predictive suspend: Medtronic MiniMed 640G
26 Predictive suspend: Medtronic Low BG s: 21% nights plgs 33% control MiniMed 640G Maahs Diab Care 2014
27 Bedside AP: UVa UVA (Sue Brown, Stacey Anderson, Marc Breton, Boris Kovatchev); Padova, Italy (Daniella Bruttomesso, Simone Del Favero, Claudio Cobelli) Randomized cross-over design; two 5-day sessions Control condition: CGM + pump (usual control) Experimental condition: Daytime CGM + pump; Nighttime closed-loop control (11PM-7AM); No meal restrictions; Alcohol permitted; No intensive exercise; Driving restricted to 25 miles during the day; Primary Outcome: Time within target range mg/dl at wakeup (7AM); Control Algorithm: USS Virginia with nightly system (person) reset to target of 120mg/dl at 7AM N=40 participants Brown JCEM 2017
28 Bedside AP: Nighttime glucose control Overnight: Average glucose was reduced by ~30mg/dl; Percent time in target increased by 25%. No adverse events. Open-loop (mean; quartiles) Closed-loop (mean; quartiles) Time in mg/dl 42.6% (OL) vs. 49.5%(CL) Time in mg/dl 38.5% (OL) vs. 82.4%(CL)
29 Bedside AP: Outcomes 40 participants closed-loop vs. sensor-augmented pump therapy Sensor- Augmented Pump Closed-Loop Control P-value Average Blood Glucose at 7AM Average Blood Glucose overall (mg/dl) Percent time within mg/dl < NS 42.9% 51.7% Percent time below 70mg/dl 4.3% 2.5% Overnight Control Correlated with Control the Next Day (r=0.4, p=0.008):
30 Challenges: Missed meal/snack UVA (Danny Cherñavvsky, Mark DeBoer, Marc Breton, Boris Kovatchev) Adolescents age years Admission 30 gram snack without insulin Lunch with under-bolus of insulin Discharge 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
31 Challenges: Missed meal/snack Cherñavvsky, DeBoer et al. Ped Diab 2014
32 Challenges: Missed meal/snack Cherñavvsky, DeBoer et al. Ped Diab 2014
33 Challenges: Missed meal/snack Cherñavvsky, DeBoer et al. Ped Diab 2014
34 Challenges: Missed meal/snack Conclusions: 1. The AP can eventually compensate for missed insulin for food, but it takes time. 2. For tight control, some mealtime identification is likely here to stay for now. Cherñavvsky, DeBoer et al. Ped Diab 2014
35 Challenges: Exercise UVA (Marc Breton, Sue Brown, Stacey Anderson, Boris Kovatchev) Breton Diab Tech Ther 2014
36 Challenges: Exercise UVA (Marc Breton, Sue Brown, Stacey Anderson, Boris Kovatchev) Breton Diab Tech Ther 2014
37 Challenges: Exercise UVA (Marc Breton, Sue Brown, Stacey Anderson, Boris Kovatchev) Breton Diab Tech Ther 2014
38 Challenges: Exercise UVA (Marc Breton, Sue Brown, Stacey Anderson, Boris Kovatchev) Breton Diab Tech Ther 2014
39 AP Challenges: Exercise UVA, VCU (Mark DeBoer, Gary Francis, Marc Breton); Funding Adolescents age years, randomized cross-over: AP and exercise with & without heart rate monitor input Admission to research unit, begin AP Light breakfast Lunch Exercise cycle: 15 min 5 15 min 5 15 min HR goal: 140 Dinner Snack Sleep Discharge 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 24:00 02:00 04:00 06:00 08:00 DeBoer Ped Diab 2016
40 Challenge: Exercise UVa (Marc Breton, Mark DeBoer), VCU (Gary Francis) DeBoer Ped Diab 2016
41 Challenge: Snow skiing UVa (Marc Breton, Boris Kovatchev), Barbara Davis (David Maahs) NIDDK DP3 DK ( ) January 2016: Five-Day Ski Camp on Closed-Loop Control Wintergreen, Virginia, elevation 3,515 (1,071 meters); April 2016: Five-Day Ski Camp on Closed-Loop Control Breckenridge, Colorado, elevation 12,840 (3,914 meters)
42 Challenge: Snow skiing UVa (Marc Breton, Boris Kovatchev), Barbara Davis (David Maahs) Average glucose and interquartile range: Experimental Control Ski Evening activity Activity (steps) Experimental Exercise Intensity: Low Moderat e Control High Overall time in range (70-180mg/dl) 71.3% 64.7% Time in range second half of night, 3-7AM 84.6% 66.2% Time below 70 mg/dl 1.8% 3.2% Breton Diab Care 2017
43 Challenge: Young children UVa (Mark DeBoer, Daniel Chernavvsky) 12 children age 5-8 years DiAs Control-to-Range Controller Families arrive to resort; young child AP system placed Meals, activities Meals, activities AP system removed, family discharged Sleep Sleep Sleep Saturday Day 1 Day 2 Day 3 Day 4 DeBoer Diab Tech & Ther 2017
44 Challenge: Young children UVa (Mark DeBoer, Daniel Chernavvsky) 12 children age 5-8 years Screening visit; CGM sensor placed Pump, BG information downloaded, CGM sent back Day -3 Day -2 Day -1 AP Camp Day +1 Day +2 Day +3 Home care Day 1 Day 2 Day 3 Day 4 Home care Families arrive to resort; young child AP system placed Meals, activities Meals, activities AP system removed, family discharged Sleep Sleep Sleep Saturday Day 1 Day 2 Day 3 Day 4 DeBoer Diab Tech & Ther 2017
45 Challenge: Young children UVa (Mark DeBoer, Daniel Chernavvsky) 12 children age 5-8 years Families arrive to resort; young child AP system placed Meals, activities Meals, activities AP system removed, family discharged Sleep Sleep Sleep Saturday Day 1 Day 2 Day 3 Day 4 DeBoer Diab Tech & Ther 2017
46 Challenge: Young children UVa (Mark DeBoer, Daniel Chernavvsky) Percent time in range ** Percent time in range mg/dl *** Differences adjusted for total steps: ** p<0.01 *** p<0.001 NS p> AP period Home care period 0 AP period Home care period 40 >250 mg/dl 3 <70 mg/dl Percent time in range *** Percent time in range 2 1 NS Total events <70 per participant: AP Home AP period Home care period 0 AP period Home care period DeBoer DTT 2017
47 Results: Mean BG Mean Glucose Total D aily Insulin Average glucose (mg/dl) *** 152 m g/dl 190 m g/dl Total D aily Insulin (units/d) NS 18.6 units/d 19.9 units/d 0 AP period Home care period 0 AP period Home care period DeBoer DTT 2017
48 DeBoer DTT 2017 Results: Lock-out screens 0/12 parents reported that their child discovered the password or were found entering insulin doses or settings unsupervised.
49 Montpellier UVA Padova NIH JDRF Helmsley Charitable Trust Santa Barbara
50 Bedside AP: at home Artificial Pancreas: Pump/CGM only:
51 Dual-hormone system Boston U: (El-Khatib, Russell, Magyar, Sinha, McKeon, Nathan, Damiano) Requires: Glucose: CGM Glucagon Decision Insulin Parameters: BR, CR, CF, TDD Model: State Estimates Glucose Prediction Insulin Decision Insulin-on- Board
52 Dual-hormone system Boston U: (El-Khatib, Russell, Magyar, Sinha, McKeon, Nathan, Damiano) Adolescents Adults Mealtime glucagon Total insulin 20% El-Khatib JCEM 2014
53 Recent Closed-Loop Studies at a Glance Source of Data Duration of Closed-Loop Control Medtronic 670G safety trial 1 Number of participants 124 Algorithm Automation Algorithm Description Sensor/Pump JDRF Pilot trial of long-term closed-loop control 2,3 Home use of bihormonal bionic pancreas 4 3 months 6 months 11 days Basal Rate Only PID with insulin feedback Medtronic MiniMed 670G System 30 (Phase 1) 14 (Phase 2) Basal Rate and Correction Boluses Model-based sliding target Dexcom G4 with Software Roche insulin pump 1 Bergenstal RM, Garg S, Weinzimer SA, et al.; Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients With Type 1 Diabetes. JAMA 2016; 316: Anderson SM, Raghinaru D, Pinsker JE, et al.; Multinational Home Use of Closed-Loop Control Is Safe and Effective. Diabetes Care 2016; 39: (Phase 1) 3 Kovatchev B, Cheng P, Anderson SM, et al.; Feasibility of Long-Term Closed-Loop Control: A Multicenter 6- Month Trial of 24/7 Automated Insulin Delivery. Diabetes Technol Ther 2017; 19: (Phase 2) 4 El-Khatib FH, Balliro C, Hillard MA, et al.; Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicenter randomised crossover trial. Lancet 2017; 389: Insulin + Glucagon - Dexcom G4 Platinum + two Tandem t:slim insulin pumps
54 Recent Closed-Loop Studies at a Glance Source of Data Duration of Closed-Loop Control Medtronic 670G safety trial 1 Number of participants 124 Algorithm Automation Algorithm Description Time within range mg/dl JDRF Pilot trial of long-term closed-loop control 2,3 Home use of bihormonal bionic pancreas 4 3 months 6 months 11 days Basal Rate Only PID with insulin feedback 30 (Phase 1) 14 (Phase 2) Basal Rate and Correction Boluses Model-based sliding target 39 Insulin + Glucagon 72% 77% 78% Insulin injection U/kg/day Glucagon injection none none 0.51 mg/day - Time below 70 mg/dl 2.9% (42 minutes/day) 1.3% (19 minutes/day) 1.8% (26 minutes/day) Time below 60 mg/dl - 0.3% 0.6% Time below 50 mg/dl 0.4% 0.1% 0.1%
55 Recent Closed-Loop Studies at a Glance Source of Data Duration of Closed-Loop Control Medtronic 670G safety trial 1 Number of participants 124 Algorithm Automation Algorithm Description Time within range mg/dl JDRF Pilot trial of long-term closed-loop control 2,3 Home use of bihormonal bionic pancreas 4 3 months 6 months 11 days Basal Rate Only PID with insulin feedback 30 (Phase 1) 14 (Phase 2) Basal Rate and Correction Boluses Model-based sliding target 39 Insulin + Glucagon 72% 77% 78% Insulin injection U/kg/day Glucagon injection none none 0.51 mg/day - Time below 70 mg/dl 2.9% (42 minutes/day) 1.3% (19 minutes/day) 1.8% (26 minutes/day) Time below 60 mg/dl - 0.3% 0.6% Time below 50 mg/dl 0.4% 0.1% 0.1%
56 Overall AP Challenges 1. CGM accuracy (e.g. at extremes), failure 2. Too Many Delays Data transmission ~1-5 min Cobelli et al, Diabetes 2011
57 Overall AP Challenges 1. CGM accuracy (e.g. at extremes), failure 2. Too Many Delays 3. Complexity/connectivity of devices Algorithmic Solutions: Can be modeled into the algorithm Detection of sensor failures Revert to Open Loop mode with system failure
58 AP Timeline Within about 5 years Web-based remote monitoring/alert system Steps: Definitive safety studies Establishment of final system Industry agreements FDA approval Speedier timing: European approval Approval of AP technologies besides closed-loop
59 Lingering Questions Will adolescents be willing to increase their diabetes-related effort for the gain of automated insulin delivery? Will well-controlled individuals start unhealthy practices, expecting the system to compensate?
60 Bridging the canyon Real Ideal
61 Bridging the canyon I want my old pancreas back! No worry about hypo s. Food flexibility. No blood sugar checking. Real Sports without distraction. A good night s sleep. X X +/ X +/ Ideal
62 Bridging the canyon Real Ideal
63 Bridging the canyon Real Ideal
64 Bridging the canyon Real Ideal
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