Challenges and progress in the development of a closed-loop artificial pancreas

Pursuit of a closed-loop artificial pancreas that automatically controls the blood glucose of individuals with type 1 diabetes has intensified during the past six years. Here we discuss the progress and challenges in the major steps towards a closed-loop system. Continuous insulin infusion pumps have been widely available for over two decades, but “smart pump” technology has made the devices easier to use and more powerful. Continuous glucose monitoring (CGM) technology has improved and the devices are more widely available. A number of approaches are currently under study for fully closed-loop systems; most manipulate only insulin, while others manipulate insulin and glucagon. Algorithms include on-off (for prevention of overnight hypoglycemia), proportional-integral-derivative (PID), model predictive control (MPC) and fuzzy logic based learning control. Meals cause the major “disturbance” to blood glucose, and we focus on approaches that our group has developed to predict when a meal is likely to be consumed and its effect.

[1]  Cesar C. Palerm,et al.  Physiologic insulin delivery with insulin feedback: A control systems perspective , 2011, Comput. Methods Programs Biomed..

[2]  H Peter Chase,et al.  Missed insulin meal boluses and elevated hemoglobin A1c levels in children receiving insulin pump therapy. , 2004, Pediatrics.

[3]  Eyal Dassau,et al.  In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type 1 Diabetes Mellitus , 2009, Journal of diabetes science and technology.

[4]  F. Doyle,et al.  Quest for the Artificial Pancreas: Combining Technology with Treatment , 2010, IEEE Engineering in Medicine and Biology Magazine.

[5]  B Wayne Bequette,et al.  Continuous Glucose Monitoring: Real-Time Algorithms for Calibration, Filtering, and Alarms , 2010, 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]  B. Wayne Bequette,et al.  Anticipatory Behavior in Blood Glucose Control: Using Meal Prior Probabilities to Prepare for Future Meal Disturbances , 2011 .

[8]  D. Cox,et al.  Symmetrization of the Blood Glucose Measurement Scale and Its Applications , 1997, Diabetes Care.

[9]  R. Hovorka,et al.  Suspended insulin infusion during overnight closed‐loop glucose control in children and adolescents with Type 1 diabetes , 2010, Diabetic Medicine.

[10]  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.

[11]  S. Colagiuri,et al.  The Diabetes Control and Complications Trial , 1983, Henry Ford Hospital medical journal.

[12]  Michael O'Grady,et al.  Continuous glucose monitoring and intensive treatment of type 1 diabetes. , 2008, The New England journal of medicine.

[13]  Roman Hovorka,et al.  Artificial pancreas: an emerging approach to treat Type 1 diabetes , 2009, Expert review of medical devices.

[14]  Bruce A Buckingham,et al.  Outcome Measures for Outpatient Hypoglycemia Prevention Studies , 2011, Journal of diabetes science and technology.

[15]  Stuart A Weinzimer,et al.  New-generation diabetes management: glucose sensor-augmented insulin pump therapy , 2011, Expert review of medical devices.

[16]  Günter Niemeyer,et al.  A Closed-Loop Artificial Pancreas Based on Risk Management , 2011, Journal of diabetes science and technology.

[17]  B Wayne Bequette,et al.  Glucose Clamp Algorithms and Insulin Time-Action Profiles , 2009, Journal of diabetes science and technology.

[18]  W. Ward,et al.  The Benefit of Subcutaneous Glucagon During Closed-Loop Glycemic Control in Rats With Type 1 Diabetes , 2008, IEEE Sensors Journal.

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

[20]  Eyal Dassau,et al.  Modular Artificial β-Cell System: A Prototype for Clinical Research , 2008 .

[21]  Eyal Dassau,et al.  In silico evaluation platform for artificial pancreatic beta-cell development--a dynamic simulator for closed-loop control with hardware-in-the-loop. , 2009, Diabetes technology & therapeutics.

[22]  F. Doyle,et al.  Detection of a Meal Using Continuous Glucose Monitoring , 2008, Diabetes Care.

[23]  Eyal Dassau,et al.  Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring , 2010, Diabetes Care.

[24]  Eyal Dassau,et al.  Bolus calculator: a review of four "smart" insulin pumps. , 2008, Diabetes technology & therapeutics.

[25]  B. Wayne Bequette,et al.  A Closed-Loop Artificial Pancreas Based on MPC: Human-Friendly Identification and Automatic Meal Dis , 2008 .

[26]  C S Hughes,et al.  Anticipating the next meal using meal behavioral profiles: A hybrid model-based stochastic predictive control algorithm for T1DM , 2011, Comput. Methods Programs Biomed..

[27]  Roman Hovorka,et al.  The future of continuous glucose monitoring: closed loop. , 2008, Current diabetes reviews.

[28]  Roman Hovorka,et al.  Automated overnight closed-loop glucose control in young children with type 1 diabetes. , 2011, Diabetes technology & therapeutics.

[29]  Eyal Dassau,et al.  Modeling the Effects of Subcutaneous Insulin Administration and Carbohydrate Consumption on Blood Glucose , 2010, Journal of diabetes science and technology.

[30]  Eyal Dassau,et al.  Proposed Clinical Application for Tuning Fuzzy Logic Controller of artificial Pancreas Utilizing a Personalization Factor , 2010, Journal of diabetes science and technology.

[31]  Hyunjin Lee,et al.  A closed-loop artificial pancreas based on model predictive control: Human-friendly identification and automatic meal disturbance rejection , 2009, Biomed. Signal Process. Control..

[32]  George Dailey,et al.  Effectiveness of sensor-augmented insulin-pump therapy in type 1 diabetes. , 2010, The New England journal of medicine.

[33]  W. Kenneth Ward,et al.  A Review of Closed-Loop Algorithms for Glycemic Control in the Treatment of Type 1 Diabetes , 2009, Algorithms.

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

[35]  Anirban Roy,et al.  The effect of insulin feedback on closed loop glucose control. , 2011, The Journal of clinical endocrinology and metabolism.

[36]  B. Wayne Bequette,et al.  Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data , 2007, Journal of diabetes science and technology.

[37]  F. Doyle,et al.  Prevention of Nocturnal Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension , 2010, Diabetes Care.

[38]  B. Wayne Bequette,et al.  Multivariate statistical analysis to detect insulin infusion set failure , 2011, Proceedings of the 2011 American Control Conference.

[39]  L. Magni,et al.  Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: Preliminary Studies in Padova and Montpellier , 2009, Journal of diabetes science and technology.

[40]  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.

[41]  J. Leahy,et al.  Fully Automated Closed-Loop Insulin Delivery Versus Semiautomated Hybrid Control in Pediatric Patients With Type 1 Diabetes Using an Artificial Pancreas , 2008 .

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

[43]  Stuart A Weinzimer,et al.  Is an automatic pump suspension feature safe for children with type 1 diabetes? An exploratory analysis with a closed-loop system. , 2009, Diabetes technology & therapeutics.

[44]  Malgorzata E. Wilinska,et al.  Overnight Closed-Loop Insulin Delivery with Model Predictive Control: Assessment of Hypoglycemia and Hyperglycemia Risk Using Simulation Studies , 2009, Journal of diabetes science and technology.

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

[46]  Claudio Cobelli,et al.  GIM, Simulation Software of Meal Glucose—Insulin Model , 2007, Journal of diabetes science and technology.

[47]  David Kerr,et al.  Insulin Pump Therapy With Automated Insulin Suspension in Response to Hypoglycemia , 2011, Diabetes Care.

[48]  Jessica R Castle,et al.  Amperometric Glucose Sensors: Sources of Error and Potential Benefit of Redundancy , 2010, Journal of diabetes science and technology.

[49]  Howard Zisser,et al.  Practical issues in the identification of empirical models from simulated type 1 diabetes data. , 2007, Diabetes technology & therapeutics.

[50]  F. Ovalle,et al.  Continuous Glucose Monitoring and Intensive Treatment of Type 1 Diabetes , 2009 .

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

[52]  J Hans DeVries,et al.  Postprandial Glycemic Excursions with the Use of a Closed-Loop Platform in Subjects with Type 1 Diabetes: A Pilot Study , 2010, Journal of diabetes science and technology.

[53]  Dale E. Seborg,et al.  I. Glucose control strategies for treating type 1 diabetes mellitus , 2007 .

[54]  Günter Niemeyer,et al.  Statistical Hypoglycemia Prediction , 2008, Journal of diabetes science and technology.

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

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

[57]  S. Genuth,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. , 1993, The New England journal of medicine.

[58]  John B Welsh,et al.  Designing the modern pump: engineering aspects of continuous subcutaneous insulin infusion software. , 2010, Diabetes technology & therapeutics.

[59]  Francis J Doyle,et al.  Experimental Evaluation of a Recursive Model Identification Technique for Type 1 Diabetes , 2009, Journal of diabetes science and technology.

[60]  B. Bequette,et al.  A tutorial on biomedical process control , 2007 .

[61]  W Kenneth Ward,et al.  A novel insulin delivery algorithm in rats with type 1 diabetes: the fading memory proportional-derivative method. , 2005, Artificial organs.

[62]  D. Rogers,et al.  The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus , 1994 .

[63]  Janet M. Allen,et al.  Closed-loop in children with type 1 diabetes: specific challenges. , 2011, Diabetes research and clinical practice.

[64]  S. Patek,et al.  Closed-Loop Artificial Pancreas Using Subcutaneous Glucose Sensing and Insulin Delivery and a Model Predictive Control Algorithm: The Virginia Experience , 2009, Journal of diabetes science and technology.

[65]  C. C. Palerm,et al.  Closed-Loop Insulin Delivery Using a Subcutaneous Glucose Sensor and Intraperitoneal Insulin Delivery , 2009, Diabetes Care.

[66]  F. Cameron,et al.  Preventing hypoglycemia using predictive alarm algorithms and insulin pump suspension. , 2009, Diabetes technology & therapeutics.

[67]  J. Stockman,et al.  Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial , 2011 .

[68]  Howard C. Zisser,et al.  The OmniPod Insulin Management System: the latest innovation in insulin pump therapy , 2010, Diabetes therapy : research, treatment and education of diabetes and related disorders.

[69]  Bruce Buckingham,et al.  Response to nocturnal alarms using a real-time glucose sensor. , 2005, Diabetes technology & therapeutics.

[70]  E. Atlas,et al.  MD-Logic Artificial Pancreas System , 2010, Diabetes Care.

[71]  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.

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

[73]  Eyal Dassau,et al.  Control to Range for Diabetes: Functionality and Modular Architecture , 2009, Journal of diabetes science and technology.

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

[75]  G. Niemeyer,et al.  Probabilistic Evolving Meal Detection and Estimation of Meal Total Glucose Appearance , 2009, Journal of diabetes science and technology.

[76]  B. Wayne Bequette,et al.  Mean Glucose Slope, Principal Component Analysis Classification to Detect Insulin Infusion Set Failure , 2011 .

[77]  Roman Hovorka,et al.  Closing the loop: the adicol experience. , 2004, Diabetes technology & therapeutics.

[78]  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.