A new animal model of insulin-glucose dynamics in the intraperitoneal space enhances closed-loop control performance. (April 2019) - Chakrabarty, Ankush; Gregory, Justin M.; Moore, L. Merkle; Williams, Philip E.; Farmer, Ben; Cherrington, Alan D.; Lord, Peter; Shelton, Brian; Cohen, Don; Zisser, Howard C.; Doyle, Francis J.; Dassau, Eyal
Highlights: State-of-the-art control algorithms delivering insulin subcutaneously (SC) to treat type 1 diabetes (T1D) exhibit slow diffusion and lag. Alternative insulin delivery sites such as the intraperitoneal cavity have demonstrated benefits in prior simulation studies. A transfer function model of insulin-glucose dynamics in the IP space is constructed from canine data. The new transfer function is used to design a PID controller with insulin feedback to prevent controller-induced hypoglycemia. Experiments support knowledge that insulin clearance rate in the IP space faster than in SC; shows potential in next-gen artificial pancreas. Abstract: Current artificial pancreas systems (AP) operate via subcutaneous (SC) glucose sensing and SC insulin delivery. Due to slow diffusion and transport dynamics across the interstitial space, even the most sophisticated control algorithms in on-body AP systems cannot react fast enough to maintain tight glycemic control under the effect of exogenous glucose disturbances caused by ingesting meals or performing physical activity. Recent efforts made towards the development of an implantable AP have explored the utility of insulin infusion in the intraperitoneal (IP) space: a region within the abdominal cavity where the insulin-glucose kinetics are observed to be much more rapid than the SC space. In this paper, a series of canine experiments are used to determine the dynamic association between IP insulin boluses and plasma glucose levels. Data from these experiments are employed to construct a new mathematical model and to formulate a closed-loop control strategy to be deployed on an implantable AP. The potential of the proposed controller is demonstrated via in-silico experiments on an FDA-accepted benchmark cohort: the proposed design significantly outperforms a previous controller designed using artificial data (time in clinically acceptable glucose range: 97.3 ± 1.5% vs. 90.1 ± 5.6%). Furthermore, the robustness of the proposed closed-loop system to delays and noise in the measurement signal (for example, when glucose is sensed subcutaneously) and deleterious glycemic changes (such as sudden glucose decline due to physical activity) is investigated. The proposed model based on experimental canine data leads to the generation of more effective control algorithms and is a promising step towards fully automated and implantable artificial pancreas systems.