The goal of this project is to develop an intraperitoneally implanted, closed-loop glucose-monitoring / insulin- delivery device (aka artificial pancreas) for people with type 1 diabetes. An artificial pancreas could improve patients short- and long-term health and quality of life, and it could decrease overall healthcare costs. However, most current artificial-pancreas prototypes use subcutaneous insulin pumps and subcutaneous glucose sensors, which are associated with significant lag times (~60 minutes and ~10 minutes, respectively). Furthermore, insulin infusion sites and sensors require replacement every several days, contributing to variability in insulin absorption and glucose measurement. These lags and variabilities impose a ceiling on the performance of any artificial pancreas that uses subcutaneous insulin delivery and glucose sensing. Fortunately, these challenges could be overcome by an implantable artificial pancreas that uses both intraperitoneal insulin delivery and intraperitoneal glucose sensing - an IP-IP AP. Compared to subcutaneous insulin, intraperitoneal insulin is faster and more predictable in both onset and offset; compared to subcutaneous sensing, intraperitoneal sensing appears more accurate and timely for tracking blood glucose changes. Intraday variability is minimal in the intraperitoneal environment, and re-implantation would be required only once per year (with the pump re-filled transcutaneously once every three months). Looking toward real-world impact, the ultimate goal of this research is development of a fully closed-loop, commercializable device with total costs that are identical (or lower) compared to current pump/CGM therapy. Development of an IP-IP AP will be a multidisciplinary effort. Thus, the project team includes: a company developing fluorescence-based implantable glucose sensors; a company developing intraperitoneally implantable, miniaturized insulin pumps; a company developing a heat-stable, highly concentrated insulin analog; a company with expertise in intraperitoneal glucose sensing; an academic institution with expertise in glucose-control algorithms; and a clinical research institut with expertise in diabetes technology. The project has five specific aims: 1) To adapt an existing fluorescence-based glucose sensor for integration into an intraperitoneal insulin catheter, and to build roughly 50 sensor-catheters for animal and human studies; 2) To develop and manufacture a next-generation implantable, MEMS-based insulin pump that can be wirelessly operated as part of a closed-loop system; 3) To confirm that the heat-stable insulin analog is compatible with use in an intraperitoneal pump at 37 C for up to one year, and to characterize the analog's intraperitoneal PK and PD; 4) To optimize a closed-loop control algorithm for intraperitoneal sensing and intraperitoneal insulin delivery, and to validate this algorithm with i silico simulations; 5) To validate the implanted artificial pancreas in swine studies, and to perform a pilot study in humans.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Type 1 Diabetes Targeted Research Award (DP3)
Project #
7DP3DK101068-02
Application #
8809846
Study Section
Special Emphasis Panel (ZDK1-GRB-N (O1))
Program Officer
Arreaza-Rubin, Guillermo
Project Start
2013-09-30
Project End
2016-08-31
Budget Start
2013-11-12
Budget End
2016-08-31
Support Year
2
Fiscal Year
2013
Total Cost
$2,249,990
Indirect Cost
$348,487
Name
University of California Santa Barbara
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
094878394
City
Santa Barbara
State
CA
Country
United States
Zip Code
93106
Chakrabarty, Ankush; Zavitsanou, Stamatina; Doyle, Francis J et al. (2018) Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems. IEEE Trans Biomed Eng 65:575-586
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
Lee, Joon Bok; Dassau, Eyal; Gondhalekar, Ravi et al. (2016) Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control. Ind Eng Chem Res 55:11857-11868
Huyett, Lauren M; Mittal, Rowena; Zisser, Howard C et al. (2016) Preliminary Evaluation of a Long-Term Intraperitoneal Glucose Sensor With Flushing Mechanism. J Diabetes Sci Technol 10:1192-4
Huyett, Lauren M; Dassau, Eyal; Zisser, Howard C et al. (2015) Design and Evaluation of a Robust PID Controller for a Fully Implantable Artificial Pancreas. Ind Eng Chem Res 54:10311-10321