Model Predictive Control of Type 1 Diabetes

Abstract The paper presents an overview of the most important elements for the synthesis of a Model Predictive Control (MPC) algorithm for the development of an Artificial Pancreas (AP). Three possible control schemes for meal compensation are described and compared. MPC individualization is discussed through different solutions. Of paramount importance, for a realistic in-silico test, is the use of the population simulator, that has been accepted by the Food and Drug Administration (FDA) as a substitute of the preclinical animals studies. The concepts and techniques described in the paper are the core of the AP algorithm that has been used in the last 4 years during about 4000h of clinical experiments.

[1]  B. Bequette A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. , 2005, Diabetes technology & therapeutics.

[2]  R.S. Parker,et al.  A model-based algorithm for blood glucose control in Type I diabetic patients , 1999, IEEE Transactions on Biomedical Engineering.

[3]  L. Schaupp,et al.  On‐line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[4]  C. C. Palerm,et al.  A Run-to-Run Control Strategy to Adjust Basal Insulin Infusion Rates in Type 1 Diabetes. , 2008, Journal of process control.

[5]  Marc D Breton,et al.  Effects of Pulsatile Subcutaneous Injections of Insulin Lispro on Plasma Insulin Concentration Levels , 2008, Journal of diabetes science and technology.

[6]  F. El-Khatib,et al.  Adaptive Closed-Loop Control Provides Blood-Glucose Regulation Using Dual Subcutaneous Insulin and Glucagon Infusion in Diabetic Swine , 2007, Journal of diabetes science and technology.

[7]  Claudio Cobelli,et al.  An integrated mathematical model of the dynamics of blood glucose and its hormonal control , 1982 .

[8]  William L Clarke,et al.  Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. , 2005, Diabetes technology & therapeutics.

[9]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[10]  Giuseppe De Nicolao,et al.  Model predictive control of glucose concentration in type I diabetic patients: An in silico trial , 2009, Biomed. Signal Process. Control..

[11]  L. Magni,et al.  Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis , 2008, Journal of diabetes science and technology.

[12]  Francis J. Doyle,et al.  Run-to-run control of blood glucose concentrations for people with type 1 diabetes mellitus , 2006, IEEE Transactions on Biomedical Engineering.

[13]  L. Magni,et al.  Multinational Study of Subcutaneous Model-Predictive Closed-Loop Control in Type 1 Diabetes Mellitus: Summary of the Results , 2010, Journal of diabetes science and technology.

[14]  Eyal Dassau,et al.  Closed-Loop Control of Artificial Pancreatic $\beta$ -Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control , 2010, IEEE Transactions on Biomedical Engineering.

[15]  Claudio Cobelli,et al.  Oral Glucose Tolerance Test Minimal Model Indexes of β-Cell Function and Insulin Sensitivity , 2001 .

[16]  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.

[17]  C. Cobelli,et al.  A Model of Glucose Production During a Meal , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  R. Rizza,et al.  Effects of Plasma Glucose Concentration on Glucose Utilization and Glucose Clearance in Normal Man , 1981, Diabetes.

[19]  Howard C. Zisser,et al.  Prandial Insulin Dosing Using Run-to-Run Control , 2007, Diabetes Care.

[20]  Marco Forgione,et al.  Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial , 2009, Journal of diabetes science and technology.

[21]  Efstratios N. Pistikopoulos,et al.  Model-based blood glucose control for type 1 diabetes via parametric programming , 2006, IEEE Transactions on Biomedical Engineering.

[22]  Marc D. Breton,et al.  Modular Closed-Loop Control of Diabetes , 2012, IEEE Transactions on Biomedical Engineering.

[23]  Eyal Dassau,et al.  Safety Constraints in an Artificial Pancreatic β Cell: An Implementation of Model Predictive Control with Insulin on Board , 2009, Journal of diabetes science and technology.

[24]  David C Klonoff,et al.  The Artificial Pancreas: How Sweet Engineering Will Solve Bitter Problems , 2007, Journal of diabetes science and technology.

[25]  Giuseppe De Nicolao,et al.  MPC based Artificial Pancreas: Strategies for individualization and meal compensation , 2012, Annu. Rev. Control..

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

[27]  L. Magni,et al.  Model Predictive Control of Type 1 Diabetes: An in Silico Trial , 2007, Journal of diabetes science and technology.

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

[29]  Darrell M. Wilson,et al.  A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator , 2009, Journal of diabetes science and technology.

[30]  Giovanni Sparacino,et al.  Diabetes: Models, Signals, and Control , 2009 .

[31]  R. Sridhar,et al.  A mathematical model for the control mechanism of free fatty acid-glucose metabolism in normal humans. , 1970, Computers and biomedical research, an international journal.

[32]  C. C. Palerm,et al.  Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes , 2009 .

[33]  Manfred Morari,et al.  Model predictive control: Theory and practice - A survey , 1989, Autom..

[34]  E D Lehmann,et al.  A physiological model of glucose-insulin interaction in type 1 diabetes mellitus. , 1992, Journal of biomedical engineering.

[35]  U. Fischer,et al.  Kinetic Modeling of the Glucoregulatory System to Improve Insulin Therapy , 1985, IEEE Transactions on Biomedical Engineering.

[36]  Claudio Cobelli,et al.  A System Model of Oral Glucose Absorption: Validation on Gold Standard Data , 2006, IEEE Transactions on Biomedical Engineering.

[37]  R. Hovorka Continuous glucose monitoring and closed‐loop systems , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[38]  Claudio Cobelli,et al.  Effects of Age and Sex on Postprandial Glucose Metabolism , 2006, Diabetes.

[39]  Claudio Cobelli,et al.  Meal Simulation Model of the Glucose-Insulin System , 2007, IEEE Transactions on Biomedical Engineering.

[40]  E. Carson,et al.  A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study. , 1994, Computer methods and programs in biomedicine.

[41]  Claudio Cobelli,et al.  The hot IVGTT two-compartment minimal model: indexes  of glucose effectiveness and insulin sensitivity. , 1997, American journal of physiology. Endocrinology and metabolism.