Closed-loop Artificial Pancreas: Algorithm Engineering and Clinical Evaluation ABSTRACT This proposal seeks to build on a promising line of research that has already resulted in a working prototype of a closed-loop artificial pancreas (AP) that combines continuous glucose sensing with automated insulin delivery via a control algorithm. The AP consists of three components: a continuous glucose sensor, a continuous subcutaneous insulin infusion pump, and artificial pancreas software (APS). The APS is in the final stages of review by the Food and Drug Administration for use in fully automated closed-loop control clinical trials (Master File MAF-1625 ).
The specific aims are: 1) Development of the Artificial Pancreas to optimize insulin delivery and minimize meal-related excursions in glucose, 2) Expansion of the Artificial Pancreas to include alterations and individual differences in insulin sensitivity, and 3) Further development of the algorithms using multi-parametric model predictive control (mpMPC) in order to reduce online computation, develop effective online monitoring strategies to ensure that the AP is operating properly, and accommodate multiple input systems. In the quest to achieve our overall goal of a completely automated closed-loop device for insulin delivery, we will utilize a staged approach in which the clinical studies also have a concurrent engineering design approach in order to refine our AP. In silico testing and human in-clinic testing will be used to validate each step of model development. The ultimate goal of this line of research is a functional AP that will provide around-the-clock glucose regulation through controlled insulin delivery in response to detected patterns of changes in glucose levels in order to achieve optimal glucose regulation in subjects with type 1 diabetes. Special consideration will be given to the amount of potentially available insulin from prior infusions (insulin-on-board) in setting constraints for subsequent insulin delivery. The proposal describes multi-parametric model predictive control approaches to glycemic regulation and extensive in silico and clinical validation of the AP under overnight, meal, and exercise conditions. This application grows out of a long-standing collaboration between systems engineers at the University of California, Santa Barbara (UCSB) and research physicians specializing in diabetes research at the prestigious Sansum Diabetes Research Institute (Sansum) located less than ten miles away. This team has distinguished itself as a major contributor to the artificial pancreas literature and as an international leader in diabetes technology.

Public Health Relevance

This project aims to develop a functional artificial pancreas that will provide around-the-clock glucose regulation through controlled insulin delivery in response to detected patterns of changes in glucose levels in order to achieve optimal glucose regulation in subjects with type 1 diabetes. Special consideration will be given to the amount of potentially available insulin from prior infusions (insulin-on-board) in setting constraints for subsequent insulin delivery. The proposal describes multi-parametric model predictive control approaches to glycemic regulation and extensive in silico and clinical validation of the AP under overnight, meal, and exercise conditions.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK085628-05
Application #
8531230
Study Section
Special Emphasis Panel (ZDK1-GRB-2 (O2))
Program Officer
Arreaza-Rubin, Guillermo
Project Start
2009-09-30
Project End
2014-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
5
Fiscal Year
2013
Total Cost
$649,624
Indirect Cost
$117,410
Name
University of California Santa Barbara
Department
Type
Organized Research Units
DUNS #
094878394
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106
Ozaslan, Basak; Patek, Stephen D; Grabman, Jesse H et al. (2018) Body Mass Index Effect on Differing Responses to Psychological Stress in Blood Glucose Dynamics in Patients With Type 1 Diabetes. J Diabetes Sci Technol 12:657-664
Gondhalekar, Ravi; Dassau, Eyal; Doyle 3rd, Francis J (2018) Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance. Automatica (Oxf) 91:105-117
Pinsker, Jordan E; Lee, Joon Bok; Dassau, Eyal et al. (2017) Response to Comment on Pinsker et al. Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas. Diabetes Care 2016;39:1135-1142. Diabetes Care 40:e4-e5
Dassau, Eyal; Renard, Eric; Place, Jérôme et al. (2017) Intraperitoneal insulin delivery provides superior glycaemic regulation to subcutaneous insulin delivery in model predictive control-based fully-automated artificial pancreas in patients with type 1 diabetes: a pilot study. Diabetes Obes Metab 19:1698-1705
Huyett, Lauren; Dassau, Eyal; Pinsker, Jordan E et al. (2016) Minority groups and the artificial pancreas: who is (not) in line? Lancet Diabetes Endocrinol :
Colmegna, Patricio H; Sanchez-Pena, Ricardo S; Gondhalekar, Ravi et al. (2016) Switched LPV Glucose Control in Type 1 Diabetes. IEEE Trans Biomed Eng 63:1192-1200
Gondhalekar, Ravi; Dassau, Eyal; Doyle 3rd, Francis J (2016) Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes. Automatica (Oxf) 71:237-246
Gonder-Frederick, Linda A; Grabman, Jesse H; Kovatchev, Boris et al. (2016) Is Psychological Stress a Factor for Incorporation Into Future Closed-Loop Systems? J Diabetes Sci Technol 10:640-6
Colberg, Sheri R; Bevier, Wendy C; Pinsker, Jordan E et al. (2016) Challenges Associated With Exercise Studies in Type 1 Diabetes. J Diabetes Sci Technol 10:993-4
Colmegna, Patricio H; Sánchez-Peña, Ricardo S; Gondhalekar, Ravi et al. (2016) Reducing Glucose Variability Due to Meals and Postprandial Exercise in T1DM Using Switched LPV Control: In Silico Studies. J Diabetes Sci Technol 10:744-53

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