Identifiable prediction animal model for the bi-hormonal intraperitoneal artificial pancreas

• Published in: Journal of process control Vol. 121; pp. 13 - 29
• Main Authors: Benam, Karim Davari, Khoshamadi, Hasti, Åm, Marte Kierulf, Stavdahl, Øyvind, Gros, Sebastien, Fougner, Anders Lyngvi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2023
• Subjects: Bi-hormonal artificial pancreas; Biological system modeling; Biological systems; Diabetes; Parameter identification; Bi-hormonal artificial pancreas; Diabetes; Biological system modeling; Parameter identification; Biological systems
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2022.11.008

To achieve a fully automatic artificial pancreas (AP), i.e., an AP without the need for meal announcements, the intraperitoneal (IP) route is explored. This route has faster dynamics than the typical subcutaneous (SC) route. Model predictive control (MPC) is the most promising control algorithm, but it requires a predictive and identifiable model. This paper presents the design of such a model for MPC-based dual hormone IP APs. This model is trained and tested on recorded data from anesthetized pigs. Animal experiments show that the saturation of the hepatic first-pass effect is essential in how IP insulin and IP glucagon affect glucose levels. These physiological phenomena must be modeled to estimate the system behavior for various conditions. This, in turn, increases the number of parameters and complicates system identification. The availability of rich experimental data from 26 animal trials motivated the design of a technique to exploit this prior information to ensure the identifiability of our model. Through this technique, most parameters were either modeled as body weight functions or common among animals. The correlation between parameter values and body weight is discovered utilizing prior data from various animal experiments, such as blood glucose, plasma insulin, and glucagon levels, in which hormones were administered intraperitoneally or intravenously. This method simplifies the system identification for every new subject while keeping the model’s essential details that improve the prediction capability relative to comparable models. The model can be exploited in MPC or any other model-based controller of a bi-hormonal IP AP. It can also be used as a simulator to develop control approaches for single and bi-hormonal IP APs. •A nonlinear model of blood glucose level in type 1 diabetes, developed for closed loop control.•Includes intraperitoneal insulin and glucagon infusion.•Includes dynamics of the hepatic first-pass effect.•Improved identifiability using prior information, to fit the model to animal experiments.•Improved prediction compared to other models suggested in literature.