Low-Order Nonlinear Animal Model of Glucose Dynamics for a Bihormonal Intraperitoneal Artificial Pancreas

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 3; pp. 1273 - 1280
• Main Authors: Lopez-Zazueta, Claudia, Stavdahl, Oyvind, Fougner, Anders Lyngvi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Animal models; Animals; Artificial pancreas (AP); Blood; Blood Glucose; Blood levels; Computational modeling; Control methods; Diabetes mellitus; Diabetes mellitus (insulin dependent); Diabetes Mellitus, Type 1 - drug therapy; Disease Models, Animal; Feedback control; Glucagon; Glucose; Homeostasis; Hypoglycemic Agents; Insulin; Insulin Infusion Systems; IP networks; Kinetics; Mathematical models; Model reduction; model validation; Pancreas; Pancreas, Artificial; Parameter identification; Parameter sensitivity; power–law kinetics; Predictive control; Sensitivity; Singular value decomposition; Swine
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3125839

Objective: The design of an Artificial Pancreas (AP) to regulate blood glucose levels requires reliable control methods. Model Predictive Control has emerged as a promising approach for glycemia control. However, model-based control methods require computationally simple and identifiable mathematical models that represent glucose dynamics accurately, which is challenging due to the complexity of glucose homeostasis. Methods: In this work, a simple model is deduced to estimate blood glucose concentration in subjects with Type 1 Diabetes Mellitus (T1DM). Novel features in the model are power-law kinetics for intraperitoneal insulin absorption and a separate glucagon sensitivity state. Profile likelihood and a method based on singular value decomposition of the sensitivity matrix are carried out to assess parameter identifiability and guide a model reduction for improving the identification of parameters. Results: A reduced model with 10 parameters is obtained and calibrated, showing good fit to experimental data from pigs where insulin and glucagon boluses were delivered in the intraperitoneal cavity. Conclusion: A simple model with power-law kinetics can accurately represent glucose dynamics submitted to intraperitoneal insulin and glucagon injections. The reduced model was found to exhibit local practical as well as structural identifiability. Importance: The proposed model facilitates intraperitoneal bi-hormonal model-based closed-loop control in animal trials.