Model-Based Advanced Control of Insulin in T1DM

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DESCRIPTION (provided by applicant): The long term objective of this work is to develop new algorithmic approaches to optimize the delivery of insulin in an automated fashion to people with type 1 diabetes. Specifically, novel approaches to patient characterization will be developed to predict glucose profiles under various conditions of stress, exploiting developments in pattern recognition from the engineering literature. The net result will be the development of an algorithm that predicts the dosages of insulin delivered to the patient by the clinical team. This will involve the identification of recurring patterns of glucose response to meal and other stimuli. The algorithm will be tested in both simulation and clinical trials for varying degrees of patient stress and meal stimuli, as well as robustness to sensor noise and patient characterization uncertainty. The specific aims of this project are to: i) characterize a group of type 1 diabetic patients in terms of their glucose profiles, ii) develop algorithms for insulin dosing based on pattern recognition, iii) mimic the dual phase of insulin secretion related to meals through post-meal regulation of insulin infusion in an inpatient setting, iv) repeat the methods under pharmacologically-induced stress states and following exercise, and v) develop advanced model-based control strategy for glucose regulation under conditions of type 1 diabetes. The aims will blend prototype algorithms that are drawn from systems engineering with validation in a series of clinical tests. The proposed collaboration between systems engineers and renowned diabetes researchers in an established clinical research setting will allow a novel fusion of methods that can be truly characterized as "bench to bedside". The medical collaborators in the proposal are located at the prestigious Sansum Diabetes Research Institute, which is located less than 10 miles from the campus of the University of California, Santa Barbara. The exchange of personnel will be facilitated, allowing the student and post-doc supported on this project to work at both the institute and the University.