Francis J. Doyle - US grants

DESCRIPTION (provided by applicant): The long term objective of this work is to develop new algorithmic approaches to optimize the delivery of insulin in an automated fashion to people with type 1 diabetes. Specifically, we aim to develop a strategy, inspired by run-to-run control theory established by the chemical process industries, that "learns" from the previous sequence of glucose responses to insulin dosing (over the course of days), and optimally predicts the appropriate strategy for the forthcoming day. The notion of a "cycle" in engineering will be extended to manage the 24 hour routine of repeated meals, activities, and sleep cycles and the corresponding dosing of insulin. The algorithm will be tested in both simulation and clinical trials for robustness to sensor noise, uncertainty in the patient characterization, variability in the timing of the postprandial glucose peak, and variability in the carbohydrate content in the meals. The Specific Aims of this project are to: i) construct predictive patient sensitivity models for calculation of optimal insulin dosing from elevated (or depressed) glucose levels, ii) develop run-to-run algorithm for insulin bolus dosing to provide corrections in subsequent days based on previous history of glucose levels and insulin dosage, and iii) evaluate the robustness of the algorithm through meal challenges of varying carbohydrate content. The aims will blend prototype algorithms that are drawn from systems engineering with validation in a series of clinical tests. The proposed collaboration between systems engineers and renowned diabetes researchers in an established clinical research setting will allow a novel fusion of methods that can be truly characterized as "innovative". The medical collaborators in the proposal are located at the prestigious Sansum Medical Research Institute, which is located less than 10 miles from the campus of the University of California, Santa Barbara. The exchange of personnel will be facilitated, allowing the student and post-doc supported on this project to work at both the institute and the university over the span of the project.

DESCRIPTION (provided by applicant): The long term objective of this work is to develop new algorithmic approaches to optimize the delivery of insulin in an automated fashion to people with type 1 diabetes. Specifically, novel approaches to patient characterization will be developed to predict glucose profiles under various conditions of stress, exploiting developments in pattern recognition from the engineering literature. The net result will be the development of an algorithm that predicts the dosages of insulin delivered to the patient by the clinical team. This will involve the identification of recurring patterns of glucose response to meal and other stimuli. The algorithm will be tested in both simulation and clinical trials for varying degrees of patient stress and meal stimuli, as well as robustness to sensor noise and patient characterization uncertainty. The specific aims of this project are to: i) characterize a group of type 1 diabetic patients in terms of their glucose profiles, ii) develop algorithms for insulin dosing based on pattern recognition, iii) mimic the dual phase of insulin secretion related to meals through post-meal regulation of insulin infusion in an inpatient setting, iv) repeat the methods under pharmacologically-induced stress states and following exercise, and v) develop advanced model-based control strategy for glucose regulation under conditions of type 1 diabetes. The aims will blend prototype algorithms that are drawn from systems engineering with validation in a series of clinical tests. The proposed collaboration between systems engineers and renowned diabetes researchers in an established clinical research setting will allow a novel fusion of methods that can be truly characterized as "bench to bedside". The medical collaborators in the proposal are located at the prestigious Sansum Diabetes Research Institute, which is located less than 10 miles from the campus of the University of California, Santa Barbara. The exchange of personnel will be facilitated, allowing the student and post-doc supported on this project to work at both the institute and the University.

Dynamic energy budget (DEB) models describe the rates at which individual organisms assimilate and use energy and essential elements such as carbon, nitrogen and phosphorus. The resulting theory offers a powerful conceptual framework for relating suborganismal (biochemical, genetic, and physiological) processes to organismal performance (growth, reproduction, mortality), and thereby to ecological and evolutionary change. This project will explore the consequences at many levels of biological organization of a broad range of biochemical and physiological control mechanisms. There will be parallel efforts at developing new, general theory and on one particular application: the biology of stony corals. In that system, the interactions of a host, its symbionts and both intra-cellular and extracellular microbial communities create a context where traditional distinctions between levels of biological organization fail, and where the time scales of physiological, ecological, and evolutionary processes overlap.

This new theory will have wide biological and ecological applicability. Potential users of the new approach include the Long-Term Ecological Research (LTER) community within the United States who require theory to compare and contrast critical biological processes at many levels of organization, both within and among sites. There are detailed plans for close collaboration with researchers at the Moorea Coral Reef LTER. There are distinctive educational opportunities deriving from the collaboration between researchers at a research university (University of California, Santa Barbara) and a teaching oriented, minority-serving institution (California State University Northridge). These include cooperative efforts in graduate student training, new modules for undergraduate classes, and the direct involvement of minority students in developing new theory and its empirical tests.

DESCRIPTION (provided by applicant): This proposal seeks to build on a promising line of research that has already resulted in a working prototype of a closed-loop artificial pancreas (AP) that combines continuous glucose sensing with automated insulin delivery via a control algorithm. The AP consists of three components: a continuous glucose sensor, a continuous subcutaneous insulin infusion pump, and artificial pancreas software (APS). The APS is in the final stages of review by the Food and Drug Administration for use in fully automated closed-loop control clinical trials (Master File MAF-1625 ). The specific aims are: 1) Development of the Artificial Pancreas to optimize insulin delivery and minimize meal-related excursions in glucose, 2) Expansion of the Artificial Pancreas to include alterations and individual differences in insulin sensitivity, and 3) Further development of the algorithms using multi-parametric model predictive control (mpMPC) in order to reduce online computation, develop effective online monitoring strategies to ensure that the AP is operating properly, and accommodate multiple input systems. In the quest to achieve our overall goal of a completely automated closed-loop device for insulin delivery, we will utilize a staged approach in which the clinical studies also have a concurrent engineering design approach in order to refine our AP. In silico testing and human in-clinic testing will be used to validate each step of model development. The ultimate goal of this line of research is a functional AP that will provide around-the-clock glucose regulation through controlled insulin delivery in response to detected patterns of changes in glucose levels in order to achieve optimal glucose regulation in subjects with type 1 diabetes. Special consideration will be given to the amount of potentially available insulin from prior infusions (insulin-on-board) in setting constraints for subsequent insulin delivery. The proposal describes multi-parametric model predictive control approaches to glycemic regulation and extensive in silico and clinical validation of the AP under overnight, meal, and exercise conditions. This application grows out of a long-standing collaboration between systems engineers at the University of California, Santa Barbara (UCSB) and research physicians specializing in diabetes research at the prestigious Sansum Diabetes Research Institute (Sansum) located less than ten miles away. This team has distinguished itself as a major contributor to the artificial pancreas literature and as an international leader in diabetes technology. PUBLIC HEALTH RELEVANCE: This project aims to develop a functional artificial pancreas that will provide around-the-clock glucose regulation through controlled insulin delivery in response to detected patterns of changes in glucose levels in order to achieve optimal glucose regulation in subjects with type 1 diabetes. Special consideration will be given to the amount of potentially available insulin from prior infusions (insulin-on-board) in setting constraints for subsequent insulin delivery. The proposal describes multi-parametric model predictive control approaches to glycemic regulation and extensive in silico and clinical validation of the AP under overnight, meal, and exercise conditions.

DESCRIPTION (provided by applicant): The mammalian suprachiasmatic nucleus (SCN), required for daily cycles in behavior and physiology. How the cells of the SCN synchronize to coordinate behavior is largely unknown. We have established a collaborative program combining experimental and computational methods to study large numbers of circadian oscillators, their connections, and the real-time kinetics by which they self-synchronize and respond to perturbations in environmental timing cues. To understand circadian regulation within the brain, we must understand the topology and types of interactions between circadian neurons. Aim 1 will monitor the network of SCN oscillators as they synchronize during fetal development, during entrainment, following a phase shift, and after restoration of cell-cell communication in the adult SCN. Using novel wavelet-based time series analyses, we will estimate the strength and direction of individual connections in the SCN. Aim 2 will use graph theory and spatial statistics to quantify network features of the developing and adult SCN. These analyses will define the mechanisms of synchronization during normal development and following environmental perturbations and the relative contributions of local, regional or global coupling which contribute to period precision. Aim 3 will compare the performance of the SCN networks under the four conditions with both deterministic and stochastic model networks. The computational models will investigate the effects of intrinsic noise and cell-cell heterogeneity on circadian synchronization. Revealing how circadian oscillators interact to generate a coherent rhythmic output will have important clinical implications for prevention and treatment of circadian rhythm disruptions, including mood and sleep disorders.

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

? DESCRIPTION (provided by applicant): Type 1 diabetes mellitus (T1D) management requires a significant effort by patients and their families to maintain near-normal glycemia. The effort is magnified when patients are young children. Recent technology development, integrated with advanced control design, has proven effective for improving diabetes managements in adults. However, the design of a pediatric artificial pancreas, focusing specifically on requirements of young children with T1D, and their parents, is in early stages and not yet been thoroughly investigated. We will focus on the design and clinical evaluation of a pediatric artificial pancrea system for enhanced diabetes management in young children with T1D. The ultimate goal of the project is to develop, and demonstrate in clinical studies, a safe and effective glucose management system that improves glucose outcomes in young children with T1D, and improves parental quality of life. Phase 1 - Development, refinement, and evaluation of pediatric glucose control strategy - pediatric artificial pancreas: Insulin/Carbohydrate on Board (IOB/COB) models for young children with T1D will be characterized and employed as safety mechanisms within a zone Model Predictive Controller for automatic insulin delivery. The controller will undergo preliminary clinical evaluation, with emphasis on assessing overnight and meal-time glucose control. Phase 2 - Design and evaluation of parental oriented remote advisory and glucose supervision system for open and closed-loop glucose control: We will engineer a parental remote supervision and alert system to reduce the risk of hypoglycemia, and the fear of nocturnal hypoglycemia, and develop adaptive advisory algorithms for glucose control, thereby improving insulin management by parents of young children with T1D. The alert and advisory system will be evaluated in a series of CRC and ambulatory clinical trials for safety and efficacy, as well for its effects on mitigating parental stress and anxiety. Phase 3 - Reduction of nocturnal hypoglycemia events and parental stress and improvement in glycemic control in ambulatory settings: The integrated system will be validated in a pilot clinical trial prior to deployment int practice, and contrasted, by a randomized crossover trial, with the state-of-the-art therapy; sensor augmented pump (SAP) or threshold suspend (TS) technology. The hypothesis of phase 3 is: Compared to SAP or TS, combining a pediatric AP and safety system reduces the hypoglycemia risk and lengthens time spent in the range 70-180 mg/dL, without compromising average glycemic control. Secondary outcomes include the system's effect on parental stress, anxiety, and fear of hypoglycemia.

PROJECT SUMMARY Type 1 diabetes mellitus (T1D) management requires a significant effort by patients and their families to maintain near-normal glycemia. The effort is magnified when patients are young children. Recent technology development, integrated with advanced control design, has proven effective for improving diabetes managements in adults. However, the design of a pediatric artificial pancreas, focusing specifically on requirements of young children with T1D, and their parents, is in early stages and not yet been thoroughly investigated. We will focus on the design and clinical evaluation of a pediatric artificial pancreas system for enhanced diabetes management in young children with T1D. The ultimate goal of the project is to develop, and demonstrate in clinical studies, a safe and effective glucose management system that improves glucose outcomes in young children with T1D, and improves parental quality of life. Phase 1 - Development, refinement, and evaluation of pediatric glucose control strategy ? pediatric artificial pancreas: Insulin/Carbohydrate on Board (IOB/COB) models for young children with T1D will be characterized and employed as safety mechanisms within a zone Model Predictive Controller for automatic insulin delivery. The controller will undergo preliminary clinical evaluation, with emphasis on assessing overnight and meal-time glucose control. Phase 2 - Design and evaluation of parental oriented remote advisory and glucose supervision system for open and closed-loop glucose control: We will engineer a parental remote supervision and alert system to reduce the risk of hypoglycemia, and the fear of nocturnal hypoglycemia, and develop adaptive advisory algorithms for glucose control, thereby improving insulin management by parents of young children with T1D. The alert and advisory system will be evaluated in a series of CRC and ambulatory clinical trials for safety and efficacy, as well for its effects on mitigating parental stress and anxiety. Phase 3 ? Reduction of nocturnal hypoglycemia events and parental stress and improvement in glycemic control in ambulatory settings: The integrated system will be validated in a pilot clinical trial prior to deployment into practice, and contrasted, by a randomized crossover trial, with the state-of-the-art therapy; sensor augmented pump (SAP) or threshold suspend (TS) technology. The hypothesis of phase 3 is: Compared to SAP or TS, combining a pediatric AP and safety system reduces the hypoglycemia risk and lengthens time spent in the range 70-180 mg/dL, without compromising average glycemic control. Secondary outcomes include the system's effect on parental stress, anxiety, and fear of hypoglycemia.

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

? DESCRIPTION (provided by applicant): Clinical Acceptance of the Artificial Pancreas: the International Diabetes Closed Loop (iDCL) Trial In 2009 we put forward the idea that the artificial pancreas (AP) is not a single-function all-in-one device but a network encompassing the patient in a digital treatment ecosystem that can offer different treatment modalities tailored to patient preferences and signal availability. We implemented this idea in the Diabetes Assistant - DiAs - the first AP smart phone suitable for everyday use. After extensive DiAs testing in the U.S. and Europe, we can confirm that reliable technology has been developed and sufficient data have been accumulated to warrant a large-scale study aiming to establish the AP as a clinically accepted treatment for type 1 diabetes. To do so, we propose a multi-center project involving research sites in Virginia, California, New York, Minnesota, Colorado, Florida, Italy, France and Holland, all with extensive AP track record. We will execute two trials: Study 1 - the International Diabetes Closed Loop Trial - will enroll 240 patients with type 1 diabetes to test the safety and efficacy of Control-to-Range (CTR) vs. Sensor-Augmented Pump (SAP). We hypothesize that: SA1: CTR, compared to SAP, will result in: (1) Improved HbA1c without increased incidence of hypoglycemia for those with HbA1c?7.5%; (2) Lower incidence of hypoglycemia without deterioration in HbA1c for those with HbA1c<7.5%; (3) Improved time within the glucose target ranges of 70-180mg/dl during the day and 70- 140mg/dl overnight, and (4) Fewer episodes of severe hypoglycemia, DKA, and other serious adverse events. SA2: Use of DiAs will result in: (1) Reduced fear of hypoglycemia and better quality of life as compared to SAP; (2) System acceptance and positive evaluation of the DiAs graphical user interface and remote monitoring/ automated notification system - the latter will be particularly useful for parents of children with diabetes, and (3) System reliability and usability estimates meeting regulatory acceptance criteria. Study 2 - the Enhanced Control-to-Range Trial - will follow 180 patients who complete Study 1 for additional 6 months to test the incremental effect of enhanced CTR using a next-generation adaptive control algorithm that builds on the well-studied and clinically tested zone model-predictive control strategy. We hypothesize that: SA3: treatment escalation will preserve the glycemic and behavioral benefits of CTR attained in Study 1 and will result in: (1) further improvement in glycemic control, including increased time within glucose target range (70-180mg/dl) during the day, primarily due to improved meal control and lower postprandial blood glucose variability, and (2) further reduced HbA1c for those who did not achieve the target of HbA1c <7.5% on CTR. Our broad objective is to test and validate a multicomponent AP system that can, as required by the NIH program announcement ...satisfy safety and efficacy requirements by regulatory agencies regarding the clinical testing of AP device systems. We will establish that contemporary smart phones provide accessible and user-friendly AP platform that is acceptable by regulatory agencies, facilitates technology proliferation, and gives physicians and patients the freedom to select an optimal treatment for any person, at any particular time.