The Next Generation of Artificial Pancreas Control Algorithms

Abstract

Creating a wearable artificial pancreas (AP) by closing the loop between a glucose sensor and an insulin infusion pump has the potential to significantly impact the complications associated with and improve the quality of life of diabetic individuals. Despite recent progress on glucose sensor and insulin infusion technologies, control algorithms built on the simple glucose value efferent and insulin dose afferent model are not efficient and reliable. Based on glucose regulatory mechanisms known to date, their impairment in the diabetic state, and fundamental principles of control theory, some corrections to the present course of research are proposed to facilitate the removal of this barrier. A greater emphasis on model predictive controllers or controllers that exploit a mathematical representation, or model, of the patient's own physiology is proposed. Whole-body physiologically based pharmacokinetics-pharmacodynamics-type models hold the best odds for enabling a successful closed-loop AP. However, two major improvements to the diabetes modeling state of the art are required to make them practical for daily care: integrating hypothalamus-pituitary-adrenal axis and gastrointestinal tract submodels. Although there are simple representations of these in current existence, large concerted efforts between experimentalists and modelers will be required to enhance their accuracy. Finally, changes in hardware that complements controller performance are suggested. For instance, the development of dual control inputs of insulin and glucagon could relax tolerances on controller accuracy.

Personalized Medicine for Diabetes

“…For example, autoantibodies can be measured to predict type 1 diabetes 14 and adipokines 15,16 can be measured to predict type 2 diabetes. Finally, systems biology 17,18 is an approach that measures interactions between the components of biological systems and how these interactions give rise to the function and behavior of that system. Systems biology analyzes complex data from multiple sources by utilizing such tools as transcriptomics (which assesses gene expression measurements), proteomics (which completely identifies proteins and protein expression patterns of a cell or tissue), metabolomics (which identifies and measures all the small molecule metabolites within a cell or tissue), and glycomics (which identifies all carbohydrates in a cell or tissue).…”

Indirect iterative learning control: Application on artificial pancreatic β-cell

“…Excessive insulin delivery can cause hypoglycemia, which will cause impaired brain functions or even death [8]. Therefore, regulating the insulin delivery automatically to achieve normal glucose level is an interesting and important problem.…”

Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell

“…To reduce the glucose level, exogenous insulin delivery is required for subjects with T1DM. However, excessive insulin infusion can result in hypoglycemia (low blood glucose concentration), which will cause impaired brain functions or even death 11. Hence, managing the insulin delivery to achieve normal glucose levels is a daily challenge.…”