Dual hormone predictive control for a fully automated intraperitoneal artificial pancreas: Preclinical evaluation in pigs

Fully automated regulation of blood glucose levels (BGL) has been the ultimate goal in the treatment of type 1 diabetes (T1D). In this context, full automation refers to a system that operates without requiring any patient interaction, such as meal or exercise announcements or manual insulin adjustments. However, achieving BGL control without such inputs remains a significant challenge for artificial pancreas (AP) systems, primarily due to the unfavorable mismatch between the time constants of meal absorption and the slower absorption kinetics of subcutaneously administered insulin. In this paper, we propose and test a dual-hormone intraperitoneal (IP) artificial pancreas system — delivering both insulin and glucagon — to explore the challenges and feasibility of achieving fully automated glucose regulation. To this, a predictive control approach was developed and tested in animal experiments. Experiments were conducted in six anesthetized pigs for 12–24 h and in an awake (unanaesthetized) pig for five days. The proposed method achieved a time-in-range (TIR, 3.9–10 mmol/L) of 73.1–94.2%, exceeding the average TIR reported for commercially available hybrid closed-loop systems. For comparison, the Medtronic MiniMed 670G reports a TIR of 70%, the Tandem t:slim X2 with Control-IQ achieves 72%, the Omnipod 5 with Horizon reports 70%, and the Diabeloop G7 achieves 74% TIR. The findings demonstrate that the full automation of BGL control using dual-hormone AP with IP injections is feasible. The paper also discusses the challenges and complexities associated with implementing the dual-hormone IP artificial pancreas system from the ground up. These challenges include addressing BGL measurement, estimation, prediction, and surgical considerations.