A Composite Model of Glucagon-Glucose Dynamics for In Silico Testing of Bihormonal Glucose Controllers

Background: The utility of simulation environments in the development of an artificial pancreas for type 1 diabetes mellitus (T1DM) management is well established. The availability of a simulator that incorporates glucagon as a counterregulatory hormone to insulin would allow more efficient design of bihormonal glucose controllers. Existing models of the glucose regulatory system that incorporates glucagon action are difficult to identify without using tracer data. In this article, we present a novel model of glucagon-glucose dynamics that can be easily identified with standard clinical research data. Methods: The minimal model of plasma glucose and insulin kinetics was extended to account for the action of glucagon on net endogenous glucose production by incorporating a new compartment. An existing subcutaneous insulin absorption model was used to account for subcutaneous insulin delivery. The same model of insulin pharmacokinetics was employed to model the pharmacokinetics of subcutaneous glucagon absorption. Finally, we incorporated an existing gastrointestinal absorption model to account for meal intake. Data from a closed-loop artificial pancreas study using a bihormonal controller on T1DM subjects were employed to identify the composite model. To test the validity of the proposed model, a bihormonal controller was designed using the identified model. Results: Model parameters were identified with good precision, and an excellent fitting of the model with the experimental data was achieved. The proposed model allowed the design of a bihormonal controller and demonstrated its ability to improve glycemic control over a single-hormone controller. Conclusions: A novel composite model, which can be easily identified with standard clinical data, is able to account for the effect of exogenous insulin and glucagon infusion on glucose dynamics. This model represents another step toward the development of a bihormonal artificial pancreas.

[1]  Josep Vehí,et al.  Robust Fault Detection System for Insulin Pump Therapy Using Continuous Glucose Monitoring , 2012, Journal of diabetes science and technology.

[2]  Christofer Toumazou,et al.  A Bio-Inspired Glucose Controller Based on Pancreatic β-Cell Physiology , 2012, Journal of diabetes science and technology.

[3]  Robert G. Sutherlin,et al.  A Bihormonal Closed-Loop Artificial Pancreas for Type 1 Diabetes , 2010, Science Translational Medicine.

[4]  C. Cobelli,et al.  In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[5]  Roman Hovorka,et al.  Closed-loop insulin delivery: from bench to clinical practice , 2011, Nature Reviews Endocrinology.

[6]  Ahmad Haidar,et al.  Glucose-responsive insulin and glucagon delivery (dual-hormone artificial pancreas) in adults with type 1 diabetes: a randomized crossover controlled trial , 2013, Canadian Medical Association Journal.

[7]  C. Cobelli,et al.  Validation of mathematical models of complex endocrine-metabolic systems. A case study on a model of glucose regulation , 1983, Medical and Biological Engineering and Computing.

[8]  W. Kenneth Ward,et al.  Novel Use of Glucagon in a Closed-Loop System for Prevention of Hypoglycemia in Type 1 Diabetes , 2010, Diabetes Care.

[9]  Roman Hovorka,et al.  Simulation Environment to Evaluate Closed-Loop Insulin Delivery Systems in Type 1 Diabetes , 2010, Journal of diabetes science and technology.

[10]  B Wayne Bequette,et al.  Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms , 2010, Journal of diabetes science and technology.

[11]  Y. Z. Ider,et al.  Quantitative estimation of insulin sensitivity. , 1979, The American journal of physiology.

[12]  P. Cryer,et al.  Hypoglycaemia: The limiting factor in the glycaemic management of Type I and Type II Diabetes* , 2002, Diabetologia.

[13]  Garry M. Steil,et al.  Identification of Intraday Metabolic Profiles during Closed-Loop Glucose Control in Individuals with Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[14]  E. Atlas,et al.  Automatic learning algorithm for the MD-logic artificial pancreas system. , 2011, Diabetes technology & therapeutics.

[15]  Howard Zisser,et al.  Glucose Estimation and Prediction through Meal Responses Using Ambulatory Subject Data for Advisory Mode Model Predictive Control , 2007, Journal of diabetes science and technology.

[16]  Zvi Roth,et al.  A Complex System Model of Glucose Regulatory Metabolism , 2006, Complex Syst..

[17]  I. Mühlhauser,et al.  Pharmacokinetics and Bioavailability of Injected Glucagon: Differences Between Intramuscular, Subcutaneous, and Intravenous Administration , 1985, Diabetes Care.

[18]  R. Hovorka,et al.  Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.

[19]  Giovanni Sparacino,et al.  Modeling the Error of Continuous Glucose Monitoring Sensor Data: Critical Aspects Discussed through Simulation Studies , 2010, Journal of diabetes science and technology.

[20]  John Thomas Sorensen,et al.  A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes , 1985 .

[21]  Ewart R. Carson,et al.  The mathematical modeling of metabolic and endocrine systems : model formulation, identification, and validation , 1983 .

[22]  Eyal Dassau,et al.  Zone Model Predictive Control: A Strategy to Minimize Hyper- and Hypoglycemic Events , 2010, Journal of diabetes science and technology.

[23]  Mihalis G. Markakis,et al.  Computational study of an augmented minimal model for glycaemia control , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Claudio Cobelli,et al.  Cellular modeling: insight into oral minimal models of insulin secretion. , 2010, American journal of physiology. Endocrinology and metabolism.

[25]  Paolo Vicini,et al.  Use of oral glucose minimal model-derived index of insulin sensitivity in subjects with early type 1 diabetes mellitus. , 2008, Metabolism: clinical and experimental.

[26]  W. Ward,et al.  A review of artificial pancreas technologies with an emphasis on bi‐hormonal therapy , 2013, Diabetes, obesity & metabolism.

[27]  Claudio Cobelli,et al.  Insulin sensitivity by oral glucose minimal models: validation against clamp. , 2005, American journal of physiology. Endocrinology and metabolism.