Contemporary studies focus increasingly on the development of artificial pancreas (AP) - an engineering system known as closed-loop control (CLC). The final goal - an ambulatory AP - has the potential to make a tremendous impact on the health and lives of people with type 1 diabetes. Our interdisciplinary international team has been at the forefront of CLC development, creating models, in silico testing platform, safety and control algorithms that represent the state of the art in AP development today. With this project, we bring the quest for ambulatory CLC to a new level, proposing to merge for the first time three key aspects of the optimal control in type 1 diabetes: human behavior, physiology and engineering Our primary goal is to build, test, and validate a new ambulatory CLC system that is informed by, and is adaptive to, real-time changes in behavior and physiology. Our underlying hypothesis is: the rate of behavioral events and the ensuing metabolic responses can be divided into hierarchical time scales, which can be translated into a modular engineering hierarchy with clearly identifiable and tractable control goals at each time scale. Phase 1 - Building assessment algorithms and control modules (primary time scale minutes-hours): We will first characterize the relationships of psycho-behavioral markers and acute behavioral events (e.g. meals, exercise) with the magnitude of physiological response and the need for real-time adaptation of CLC. Engineering tools will be designed responsible for the patient safety and prevention of hypoglycemia and for the 'health'of the AP system on both local and remote levels. We will develop a framework to address system transitions instigated by behavioral challenges and will conduct innovative physiological experiments to assess """"""""dawn"""""""" phenomenon, glucose fluxes following complex carbohydrate meal, and hepatic glucagon sensitivity. Phase 2 - Judging the effect size of control components (primary time scale days-weeks): We will engineer an adaptive learning algorithm that recognizes patients'bio-behavioral patterns, such as meal and exercise timing, and diurnal variation in insulin sensitivity. Coordinated clinical studies and large-scale in silico experiments will estimate the effect size of inclusion into CLC of initialization and real-time adaptation control components. Specifically, we will assess the effect of using physiological and behavioral: (i) markers to initialize CLC and (ii) profiles to adjust insulin boluses and basal rate. Phase 3 - System validation and trial of long-term ambulatory CLC: A final multi-center trial will validate our system in patients'natural environment in preparation for its ultimate translation into clinical practice. The primary hypothesis driving Phase 3 is: compared to state-of-the-art sensor augmented open loop therapy, closed-loop control will reduce the frequency of hypoglycemia and will increase the time spent within the target range of 70-180 mg/dl, without compromising average glycemic control as measured by HbA1c.
The artificial pancreas based on closed-loop control, has the potential to make a tremendous impact on the health and lives of people with type 1 diabetes. The development of this technology has made significant strides over the last five years;however, it is still in infancy, currently being tested in inpatient clinical-research center setting. As the transition is made from the clinic to outpatient trials and then to approved ambulatory devices, additional strategies will need to be developed to optimize control and individualize treatment, requiring creative, medically-inspired engineering design and safety monitoring.
|Howsmon, Daniel P; Baysal, Nihat; Buckingham, Bruce A et al. (2018) Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas. J Diabetes Sci Technol 12:599-607|
|Ozaslan, Basak; Patek, Stephen D; Grabman, Jesse H et al. (2018) Body Mass Index Effect on Differing Responses to Psychological Stress in Blood Glucose Dynamics in Patients With Type 1 Diabetes. J Diabetes Sci Technol 12:657-664|
|Schiavon, Michele; Dalla Man, Chiara; Cobelli, Claudio (2018) Insulin Sensitivity Index-Based Optimization of Insulin to Carbohydrate Ratio: In Silico Study Shows Efficacious Protection Against Hypoglycemic Events Caused by Suboptimal Therapy. Diabetes Technol Ther 20:98-105|
|Cao, Zhixing; Gondhalekar, Ravi; Dassau, Eyal et al. (2018) Extremum Seeking Control for Personalized Zone Adaptation in Model Predictive Control for Type 1 Diabetes. IEEE Trans Biomed Eng 65:1859-1870|
|Gondhalekar, Ravi; Dassau, Eyal; Doyle 3rd, Francis J (2018) Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance. Automatica (Oxf) 91:105-117|
|Forlenza, Gregory P; Deshpande, Sunil; Ly, Trang T et al. (2017) Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial. Diabetes Care 40:1096-1102|
|Huyett, Lauren M; Ly, Trang T; Forlenza, Gregory P et al. (2017) Outpatient Closed-Loop Control with Unannounced Moderate Exercise in Adolescents Using Zone Model Predictive Control. Diabetes Technol Ther 19:331-339|
|Pinsker, Jordan E; Lee, Joon Bok; Dassau, Eyal et al. (2017) Response to Comment on Pinsker et al. Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas. Diabetes Care 2016;39:1135-1142. Diabetes Care 40:e4-e5|
|Laguna Sanz, Alejandro J; Doyle 3rd, Francis J; Dassau, Eyal (2017) An Enhanced Model Predictive Control for the Artificial Pancreas Using a Confidence Index Based on Residual Analysis of Past Predictions. J Diabetes Sci Technol 11:537-544|
|Forlenza, Gregory P; Deshpande, Sunil; Ly, Trang T et al. (2017) Erratum. Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial. Diabetes Care 2017;40:1096-1102. Diabetes Care 40:1606|
Showing the most recent 10 out of 61 publications