Advanced Hypoglycemia Prevention Capabilities in Fuzzy Logic Artificial Pancreas

Award Information
Agency: Department of Health and Human Services
Branch: N/A
Contract: 1R43DK093070-01
Agency Tracking Number: R43DK093070
Amount: $788,183.00
Phase: Phase I
Program: SBIR
Awards Year: 2011
Solicitation Year: 2011
Solicitation Topic Code: NIDDK
Solicitation Number: DK10-008
Small Business Information
17904 NE 26TH ST, REDMOND, WA, 98052-
DUNS: 142975601
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 (425) 985-0103
Business Contact
Phone: (425) 985-0103
Research Institution
DESCRIPTION (provided by applicant): The ultimate technical objective of this research is the development of an Artificial Pancreas (AP) controller that improves metabolic control and decreases glycemic excursions by robustly preventing hypoglycemic episodes. Proportional-integral-derivative control (PID) and model predictive control (MPC) have been widely considered to be promising candidate for glucose control. However, PID and MPC methods are dependent on models, and a good model for T1DM is not easy todevelop because of a number of physiological limitations, e.g., unmeasured meal size and frequent insulin sensitivity variations. Model-free approaches, such as fuzzy logic (FL) control offer a different and promising direction for improved glycemic control. A further benefit of FL is the low computational needs it requires compared to traditional controllers, which lead to smaller CPUs, lower power requirements and smaller batteries, all of which lead to a more usable AP system. This research seeks to improve the blood sugar control and hypoglycemia prevention capabilities of the controller used in our 2010-11 JDRF-funded clinical trial. Specifically, Part 1 of this research will use the UVA simulator to evaluate four specific potential improvements to ourpresent FL controller: 1), a Low Glucose Suspend (LGS) feature employing reactive and predictive algorithms; 2) novel controller personalization and online adaptation features; 3), dosing matrices tailored for fast vs. slow insulin responders; and 4), improved controller safety when encountering CGM sensor anomalies in the clinical environment. Where possible, clinical data from our JDRF-funded trial will be used to further validate the new controller features. The feasibility of the proposed improvements has been demonstrated by initial research. Part 2 of this research, occurring during the second year of this grant, focuses on the clinical evaluation of those features under various parameters, to establish the next major configuration of the FL controller. The technical question for Part 2 is whether the alterations in the controller in silico can be translated into actual improvement in humans. The primary goal is the avoidance of hypoglycemia. Successful completion of these studies could lead to the development of a fully closed loop, commercially available artificial pancreas. The resulting FL controller software product will be offered to commercial diabetes medical device manufactures as a choice for an AP control algorithm. PUBLIC HEALTH RELEVANCE: Successful completion of this study could lead to the development of a safe and effective, fully closed- loop, commercially available, artificial pancreas (AP). The resulting FL controller software product will be offered to commercial diabetes medical device manufactures as a choice for an AP control algorithm.

* information listed above is at the time of submission.

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