Journal
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Volume 69, Issue 2, Pages 899-909Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2021.3109096
Keywords
Glucose; Biosensors; Insulin; Biological system modeling; Computational modeling; Pancreas; Mathematical model; Artificial pancreas; biosensor; control algorithm; diabetes; MEA; UVA; padova T1DMS
Categories
Funding
- Fonds Europeen de Developpement Regional (FEDER) [3538519]
- French Agence Nationale de la Recherche (ANR) [ANR-18-CE17-0005-01]
- Agence Nationale de la Recherche (ANR) [ANR-18-CE17-0005] Funding Source: Agence Nationale de la Recherche (ANR)
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This study validates the concept of using inborn pancreatic islet algorithms in an artificial pancreas in silico, demonstrating successful regulation of blood glucose levels in virtual type 1 diabetic patients using an electrophysiological biosensor.
Objective: Current treatment of type 1 diabetes by closed-loop therapy depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore nutrient homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurements. This work aims at investigating the performance of the biosensor in a blood glucose control loop as potential in silico proof-of-concept. Methods: Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inborn regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Subsequently, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. Results: With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as efficient as standard treatments, and outperformed them under challenging conditions. Conclusion: This work validates the concept of using inborn pancreatic islets algorithms in an artificial pancreas in silico. Significance: Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.
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