The Artificial Pancreas

Transcription

1 SAGLB.DIA (2). Approved October 215 The Artificial Pancreas For scientific and medical purposes only

2 The pancreas responds to changes in blood glucose by releasing insulin (β cells) to lower blood glucose or glucagon ( cells) to increase blood glucose Euglycemia requires cross talk between multiple signalling pathways 1 Glucose output by liver Glucose-mediated insulin secretion 5. mmol/l (9 mg/dl) Glucose targets the liver Glycogen Insulin targets tissues Increased glucose uptake by tissues 3.9 mmol/l (7 mg/dl) Increased glucose uptake by liver (stored as glycogen) Glucose-mediated glucagon secretion Insulinotropic function Glucagonotropic function 1. Adapted from Herman MA, Kahn BB. J Clin Invest 26;116: Georg Thieme Verlag KG

3 Plasma glucose concentration (mg/1 ml) The feasibility of automated control of blood glucose was demonstrated by several groups in the 197s 2 Plasma glucose responses to the OGTT in 7 healthy persons (±SD) 1 2 Plasma glucose responses to the OGTT in a T1DM patient controlled by a prototype artificial pancreas 1 1 g glucose orally Patient: blood sugar Time (minutes) Time (minutes) Early systems in animal 2 and human 1 experiments relied on IV administration of glucose and insulin; carefully supervised inpatient conditions were required OGTT, oral glucose tolerance test T1DM, type 1 diabetes 1. Kerner W, et al. Horm Metab Res 1976;8: Georg Thieme Verlag KG 2. Albisser AM, et al. Diabetes 1974;23:389 96

4 The open-loop model of glucose control 1 External input External output CGM Insulin infusion CGM, continuous glucose monitoring Glucose metabolism 1. Adapted from Guidance/GuidanceDocuments/UCM25935.pdf [Accessed 21 October 215]

5 In an artificial pancreas, a control algorithm closes the loop between CGM data and insulin pump delivery 1 Internal control CGM Insulin infusion Glucose metabolism 1. Adapted from Guidance/GuidanceDocuments/UCM25935.pdf [Accessed 21 October 215]

6 Components of a closed-loop system: Algorithms

7 Infused insulin Control algorithms: Proportional-integral-derivative (PID) mimics the beta cell response to glucose Hyperglycemic clamp model glucose step up Rate of change component [D(n)] adjusts insulin infusion rate in response to change in measured glucose level Incremental component [I(n)] increases insulin infusion rate in proportion to difference between target and measured glucose levels Proportional component [P(n)] maintains insulin infusion rate in proportion to difference between target and measured glucose levels D(n) I (n) P (n) Time The three components of PID regulate insulin infusion in response to measured glucose levels; the different elements reflect basal and prandial insulin secretion 1 1. Steil GM, et al. Diabetes 26;55: American Diabetes Association [Diabetes], American Diabetes Association, [26]. Copyright and all rights reserved. Material from this publication has been used with the permission of American Diabetes Association

8 Infused insulin Glucose level Control algorithms: Model predictive control (MPC) calculates future glucose levels and responds accordingly Past Present Future Measured Predicted Target Prediction horizon Predicted Measured Time Control horizon The rate and extent of insulin infusion is regulated by modelled prediction of future glucose levels; the model is reset at each new measurement using a defined horizon 1 1. Bequette BW. J Diabetes Sci Technol 213;7: Reprinted from J Diabetes Sci Technol, Vol 7, Bequette BW, Comparison of dual-hormone artificial pancreas, singlehormone artificial pancreas, and conventional insulin pump therapy for glycaemic control in patients with type 1 diabetes: an open-label randomised controlled crossover trial, , Copyright (213), with permission from Elsevier

9 Control algorithms: Fuzzy logic accounts for individual patient treatment approaches Current and prior glucose levels: Rate of change in glucose level Direction of change in glucose level Duration of change in glucose level Change in insulin dosing (1) Pre-processing step Prediction of future glucose levels (3) Control to Target module Conversion of % change to units or units/hr Modelling of target and insulin dynamics/safety (2) Control to Range module Range: 8 12 mg/dl Input: Future/Past glucose levels and trends Glucose parameters: very low to very high Output: % change in basal rate and bolus portion, based on glucose parameters Output decisions defined by fuzzy logic (between and 1) Fuzzy logic rules incorporate HCP decision making and are non-binary, i.e. not Yes versus no, but How much? 1 HCP, health care professional 1. Adapted from Atlas E, et al. Diabetes Care 21;33:172 6

10 Control algorithms: BiAP employs a complex mathematical model of beta cell physiology INPUTS CONTROLLER SAFETY CHO & I:CHO Bolus calculator 7% s.c. sensor glucose Reinternalization r Priming RRP Forecast s.c. sensor glucose Forecast Mobilization and docking Docked pool β-cell model p p Fusion f Release Fused pool (F) Kiss & run k m SR = mf K L.G.S. Basal insulin profile 7% Insulin feedback CHO, carbohydrate; I:CHO, insulin:carbohydrate; RRP, readily releasable pool; SR, secretion rate; K, tuning gain; L.G.S., low glucose suspend; s.c., subcutaneous The use of a non-minimal/complex model of insulin secretion allows for a more accurate representation of the bi-phasic nature of insulin secretion 1 BiAP, bio-inspired artificial pancreas 1. Reddy M, et al. Diabetes Technol Ther 214;16:55 7 Georg Thieme Verlag KG

11 Components of a closed-loop system: Pumps

12 Pumps Pumps vary according to manufacturer Animas, OmniPod, Medtronic, Roche, Tandem Insulin is stored in a reservoir and delivered subcutaneously via an infusion set 1 The pump is programmed manually by the patient, based on CGM or SMBG readings Sensor-augmented pumps can communicate wirelessly with CGM sensor, to stop insulin supply at threshold glucose levels 2 SMBG, self monitoring of blood glucose 1. Valla V, Exp Diabetes Res 21: Tauschmann M, et al. Expert Opin Drug Deliv 214;11:943 55

13 Survival of infusion set Infusion sets Major cause of pump failure Failure rates similar between Teflon and steel needle catheters in a randomized crossover study of 2 patients 1 Typical failures include: 1,2 Kinking (Teflon only) Failed correction dose Pain, itching, infection, induration Accidental removal Loss of adhesion Priming errors Blockages ST, Sure-T steel catheter QS, Quick-Set Teflon catheter Survival curve for all causes of infusion set failure 1 ST QS Time (days) 1. Patel PJ, et al. Diabetes Technol Ther 214;16:15 9 The publisher for this copyrighted material is Mary Ann Liebert, Inc. publishers 2. Tauschmann M, et al. Expert Opin Drug Deliv 214;11:943 55

14 The sensor augmented pump has been an important step in the development of the artificial pancreas 247 patients received sensor-augmented insulin-pump therapy ± threshold-suspend feature for 3 months Mean AUC for nocturnal hypoglycemic events 1 Mean AUC for nocturnal hypoglycemic events (mg/dl x min) Percentage reduction (%) p value Threshold-suspend group (N=121) Control group (N=126) Run-in phase 1547 ± ± 195 Study phase 98 ± ± p<.1 Sensor glucose <7 mg/dl 1 Threshold-suspend group (N=121) Nocturnal Control group (N=126) Day and night combined Threshold-suspend group (N=121) Control group (N=126) 6 to <7 mg/dl (%) to <6 mg/dl (%) <5 mg/dl (%) Threshold suspension lowers the occurrence of nocturnal hypoglycemia without affecting glycemic control AUC, area under curve 1. Bergenstal RM, et al. N Engl J Med 213;369:224 32

15 Components of a closed-loop system: CGM

16 MARD % (SD) Development of the artificial pancreas requires CGM systems that approach the clinical performance of SMBG systems CGMs measure glucose in interstitial fluid Less accurate than SMBG Concerns with accuracy at hypoglycemic levels (BG <4. mmol/l) Dexcom G4 p<.1 13,87 17,85 2,4 34,69 All data Enlite p=.41 Glucose <4. mmol/l 1 1 New 55 software improves the accuracy of the Dexcom G4: Overall MARD (9% vs 13%), accuracy at low BG levels, and accuracy over time 2 1. Adapted from Matuleviciene V. Diabetes Technol Ther 214;16: BG, blood glucose 2. MARD, mean absolute relative difference CGM to SMBG [Accessed 16 March 215]

17 Non-invasive CGM (NGM) systems NGM obviates the need for a strip this makes the repeated measurements highly cost-effective, but lacks the precision and specificity of BG meters 1 Two NGM systems in development: HG1-c (raman spectroscopy) 2 Comparable precision with BG meters Smartphone compatible Cost-effective compared with fingerstick testing Available in the US for investigational use only GlucoTrack TM (ultrasonic, electromagnetic, heat capacity) 3 High precision Improvements in calibration/algorithms required Not yet available Currently no data to support the use of non-invasive CGM in a closed-loop system 1. Vashist SK. Diagnostics 213;3: Keenan DB. J Diabetes Sci Technol 21,4: Harman-Boehm I. J Diabetes Sci Technol 29;3:253 6

18 CGM systems are approaching the clinical performance of SMBG systems CGM accuracy should not limit the development of an artificial pancreas Systems such as Dexcom G4 have improved accuracy in the hypoglycemic range Comparing CGM with YSI in a study of 51 patients found: Mean absolute relative difference: 9.% Average differences improved with duration of wear (1.7%, 8.% and 8.5% on Days 1, 4 and 7, respectively) Clinical accuracy was 92.9% for both hypoglycemia and hyperglycemia Pearson correlation coefficient between CGM and YSI was.97 (p<.1) When comparing CGM with SMBG the results were similar Mean absolute relative difference: 11.2% Average differences improved with duration of wear (12.7%, 1.9% and 9.9% on Days 1, 4 and 7, respectively) Clinical accuracy was 85.4% for both hypoglycemia and hyperglycemia YSI, Yellow Springs Instrument Bailey TS, et al. J Diabetes Sci Technol 215;9:29 14

19 Developments in closed-loop systems for the outpatient setting

20 Percentage (%) mg/dl Percentage (%) The Florence closed-loop system is reliable, safe and effective in maintaining glucose levels in a real-life setting Early start closed-loop delivery (n=8) Study outcomes 1 Hypoglycemia 1 Late start closed-loop delivery (n=8) Hyperglycemia p=.36 p= p= p= p= * * * p= * * p=1. The Florence system utilizes a MPC algorithm and was evaluated in 16 adolescents with T1DM at a clinical research facility Low blood glucose index was greater in the early closed-loop cohort vs. the late closed group cohort (.9 vs..3, respectively; P=.28) 1. Elleri D, et al. Pediatr Diabetes 212;13:449 53

21 Real-time remote monitoring is an important consideration in artificial pancreas development: The DiAs approach 1 DiAs smart phone: 1 CGM and insulin delivery traces are shown. Traffic lights representing hyper- and hypoglycemia risk are also shown 2 The artificial pancreas sends data in real time to a remote server. 2 Data are stored and can be monitored remotely 3 Multiple patient monitoring screen: 2 Here, two patients are monitored at one time. The system alerts that one patient is at risk for hypoglycemia DiAs, Diabetes Assistant 1. Kovatchev BP, et al. Diabetes Care 213;36: Place J, et al. J Diabetes Sci Technol 213;7:

22 Unihormonal closed-loop control in an outpatient setting

23 The patient-operated, closed-loop DiAs system reduced hypoglycemia compared with open-loop pump therapy in a supervised outpatient setting 1 2 patient crossover trial No significant difference (p >.1) between the open-loop and closed-loop cohorts for secondary endpoints percent of time in the target range of mmol/l [7 18 mg/dl] percent of time >18 mg/dl glucose variability total mean carbohydrate content/person/session total insulin delivered/person/session 3 2,5 2 1,5 1,5 Primary endpoints in 2 patients 45 Open-loop p=.21 4 Closed-loop p>.1 p= p= except average blood glucose 8.45 vs 9.96 mmol/l [p=.42] 1. Kovatchev BP, et al. Diabetes Care 214;37:

24 The patient-operated, closed-loop DiAs system reduced hypoglycemia compared with open-loop pump therapy in a supervised outpatient setting 1 2 patient crossover trial No significant difference (p >.1) between the open-loop and closed-loop cohorts for secondary endpoints percent of time in the target range of mmol/l [7 18 mg/dl] percent of time >18 mg/dl glucose variability total mean carbohydrate content/person/session total insulin delivered/person/session Primary endpoints in 2 patients Open-loop Closed-loop p=.3 P> p= p= except average blood glucose 8.45 vs 9.96 mmol/l [p=.42] 1. Kovatchev BP, et al. Diabetes Care 214;37: ; American Diabetes Association [Diabetes Care], American Diabetes Association, [214]. Copyright and all rights reserved. Material from this publication has been used with the permission of American Diabetes Association

25 The patient-operated, closed-loop DiAs system reduced hypoglycemia compared with open-loop pump therapy in a supervised outpatient setting 1 2 patient crossover trial No significant difference (p >.1) between the open-loop and closed-loop cohorts for secondary endpoints percent of time in the target range of mmol/l [7 18 mg/dl] percent of time >18 mg/dl glucose variability total mean carbohydrate content/person/session total insulin delivered/person/session Primary endpoints in 2 patients Open-loop Closed-loop p=.3 P> p= p= except average blood glucose 8.45 vs 9.96 mmol/l [p=.42] 1. Kovatchev BP, et al. Diabetes Care 214;37:

26 Percentage of time The DiAs system improved glycemic control and reduced nocturnal hypoglycemia compared with open-loop pump therapy during the course of a diabetes camp 1 2 patient crossover trial More time in the target glucose range *p<.5 * Per protocol closed-loop (nights, n=41) Sensor-augmented pump (nights, n=39) Less time in hypoglycemic range * * * <5 <6 < Glucose range (mg/dl) 1. Ly TT, et al. Diabetes Care 214;37:231 6

27 mg/dl The MD-Logic artificial pancreas is controlled by a fuzzy logic algorithm and has demonstrated tighter glucose control with less nocturnal hypoglycemia vs open-loop at a diabetes camp 1 Primary endpoints in 54 patients 56 patient single night crossover trial 25 Artificial pancreas Control 18 Artificial pancreas Control Glucose levels were significantly more stable More hypoglycemia AEs were reported in the control group vs. the artificial pancreas group (28 vs. 19 [daytime], and 19 vs. 6 [nighttime]) Total number of overnight episodes of glucose levels <63 mg/dl ,4 14,4 Overnight glucose level Bars indicate IQR 1. Phillip M, et al. N Engl J Med 213;368:824 33

28 Percentage (%) Percentage (%) Long-term home use of the MD-Logic system was found to be effective in reducing nocturnal hypoglycemia and in improving overnight glycemic control 1 24 patient crossover trial of MD-Logic system vs sensor-augmented pump Significantly reduced overnight hypoglycemia Increased time in target glucose range Paired difference for the primary endpoint in the ITT population, median % (IQR) p= Paired difference for the secondary endpoints in the ITT population, median % (IQR) p= p= p= Time glucose level spent <7 mg/dl -15 ITT, intent to treat; Paired difference is closed loop minus control 1. Nimri R, et al. Diabetes Care 214;37:325 32

29 Time in target (%) Mean glucose overnight (mmoi/l) Unsupervised overnight closed-loop insulin delivery at home can be more effective at improving glucose control than open-loop 1 25 patient crossover trial 8 12 Overnight closed-loop therapy controlled by a MPC algorithm vs open-loop therapy Time in target glucose range was significantly higher with closed-loop therapy Control Closed loop Individual values of time when glucose was in target glucose range of mmol/l Control Closed loop Individual values of mean overnight glucose 1. Thabit H, et al. Lancet Diabetes Endocrinol 214;2:71 9; Reprinted from Lancet Diabetes Endocrinol, Vol.2, Thabit H, et al., Pages.71 9, Copyright (214), with permission from Elsevier.

30 Bihormonal closed-loop control

31 Glucose (mg/dl) Insulin (U/hr) and glucagon (µg/min) delivery rates Glucose (mg/dl) Insulin delivery rate (U/hr) Glucagon given by algorithm during impending hypoglycemia is effective in preventing most cases of hypoglycemia 1 14 patient trial Closed-loop insulin + placebo vs insulin + glucagon (bihormonal) Glucagon was delivered rapidly (high-gain) or slowly (low-gain) High-gain glucagon reduced hypoglycemic events and need for carbohydrate treatment Summary of glucose levels (means ± SE), insulin delivery rate, and for glucagon studies, the glucagon delivery rate Blood glucose Meals Insulin delivery rate Study time (minutes) Blood glucose Meals Insulin delivery rate Glucagon delivery rate Glucose Glucose Study time (minutes) 1. Castle JR, et al. Diabetes Care 21;33:1282 7; American Diabetes Association [Diabetes Care] American Diabetes Association, [21]. Copyright and all rights reserved. Material from this publication has been used with the permission of American Diabetes Association

32 Mean glucose levels (mg/dl) The bionic pancreas improved glycemic control with fewer hypoglycemic episodes compared with an insulin pump 1 Mean glucose levels in adults and adolescents 2 adults and 32 adolescents participated in testing 2 18 p<.1 p=.4 Bionic pancreas Control Automated, bihormonal, bionic pancreas consisting of: iphone G4 Platinum GCM t:slim infusion pumps Reductions in mean glucose level and hypoglycemia occurred vs control (own existing insulin pump) Adults Adolescents 1. Russell SJ, et al. N Engl J Med 214;371:313 25

33 Bihormonal control additional considerations: Glucagon is unstable Glucagon cannot be kept in a portable pump long-term 1 A comparison of approaches showed that the unihormonal AP may be sufficient for overnight control 1 Conventional insulin pump therapy (n=29) p value* Singlehormone artificial pancreas (n=3) Patients with at least one hypoglycemic event p value Dualhormone artificial pancreas (n=29) p value During whole study 24 (83%) <.1 5 (17%).73 6 (21%).14 period Overnight 1 (34%).4 (%).. (%).4 Exerciseinduced 11 (38%).4 2 (7%).38 4 (14%).15 Number of hypoglycemic events Total Overnight Exerciseinduced Number of half-hourly overnight plasma glucose measurements, mmol/l (%) <4. 7 (12.7).. 25 (4.).. 11 (2.).. < (6.).. 9 (1.6).. 2 (.4).. < (4.).. 6 (1.1).. 1 (.2).. *Conventional vs single; Single vs dual; Dual vs conventional Reprinted from Lancet Diabetes Endocrinol, Vol 3, Haidar A, et al. Pages 17 26, Copyright (215), with permission from Elsevier.

34 Units/day Bihormonal control additional considerations: continuous infusion of insulin and amylin proved effective in reducing HbA1c and weight in patients with T1DM and T2DM 1 1 patients switched from insulin to insulin + amylin (SYMLIN) Total daily insulin Start of therapy End of therapy Treatment continued for up to 5 years Average reduction in HbA1c from 8.3% to 6.9% Decrease in average weight from lb to lb Total daily insulin decreased as amylin doses were increased Insulin SYMLIN Amylin conversion of units to g: 1 unit = 6 g 1. Schorr AB, et al. J Diabetes Sci Technol 212;6:157 8

35 Remaining challenges

36 Average maximum exercise induced decline in plasma glucose, mg/dl Reducing exercise-induced hypoglycemia in patients using closed-loop therapy 1 1 T1DM patients underwent 24hr closedloop (CL) and closedloop + heart rate (CL+HR) control, with 3 min of exercise in the afternoon CL+HR control used a modified algorithm and was manually triggered when HR was 125% of resting CL+HR offers a route to reduce exerciseinduced hypoglycemia CL CL+HR p=.22 Hypoglycemia events exercise Low blood glucose index exercise Low blood glucose index overall Time in target (7-18mg/dL) exercise, % Time in target (7-18mg/dL) overall, % CL CL+HR p Breton MD, et al. Diabetes Technol Ther 214;16:56-511

37 Coping with the effect of meals on PPG Post-prandial excursions present a challenge in reactive systems because of the relatively slow action of insulins A meal announcement or bolus insulin is usually required Algorithms that can cope with PPG excursions are in development A fully-automated fuzzy logic controller has been shown to be potentially viable and adaptable by clinicians 1 Supplemental ultrafast-acting inhaled insulins taken at mealtimes may provide a solution An algorithm for this solution is in development 2 PPG, post prandial glucose 1. Mauseth R, et al. Diabetes Technol Ther 213;15: Cengiz E. J Diabetes Sci Technol 212;6:797-81

38 Improved insulin pharmacokinetics are required Factors that effect the absorption rate and action of rapid-acting insulins 1 Current rapid-acting insulins do not work quickly enough to mimic post-meal insulin secretion in healthy subjects Ultrafast-acting insulins will be required for reactive systems Insulin preparation Injection site Physical changes Dose Anatomical location Low blood sugar Physical properties of the Intramuscular injection vs. Ketoacidosis preparation subcutaneous injection Smoking vs. non-smoking Concentration and volume Lipodystrophy Metabolic control Origin of insulin (e.g. Changes in body animal, human, analogue) temperature Physical activity Increase in local blood flow Massage Injury Approaches to increase the absorption rate of rapid-acting insulins 1 Mechanical Increase of local blood flow (e.g. exercise, massage, heat) Alternative routes of insulin administration (e.g. inhalation, intradermal application) Mechanical or enzymatic distribution of insulin into a wider area of subcutaneous tissue Insulin preparation Addition of excipients to the insulin that increase absorption (e.g. excipients that increase the stability of insulin monomers, excipients that increase local blood flow) Novel rapid-acting insulin analogues 1. Adapted from Heinemann L, et al. J Diabetes Sci Technol 212;6:728 42

39 Integration of components into a single commercial device 1 To be suitable for large-scale outpatient studies, the following components need to be packaged into a single device Accurate and reliable CGM Reliable and user-friendly insulin pump Efficacious control algorithms These components are being developed separately within different companies and universities, and it is unlikely that any single company can provide all three components in the near future Business partnerships will be needed in order to integrate the components, perform clinical studies and carry out regulatory filings 1. Peyser T, et al. Ann N Y Acad Sci;214;1311:12 23

40 Summary Recent advances are demonstrating that a commercial artificial pancreas system should be a possibility in the near future The required components are: A CGM: increased accuracy is needed Insulin pump: increased reliability and better infusion sets required Algorithm: efficacious control algorithms need to be developed and identified Other obstacles include: Development of ultrafast-acting insulins Development of business relationships to obtain a commercial device