In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes
TLDR
A system for in silico testing of control algorithms that has been shown to represent adequate glucose fluctuations in T1DM observed during meal challenges, and has been accepted by the Food and Drug Administration as a substitute to animal trials in the preclinical testing of closed-loop control strategies.Abstract:
Arguably, a minimally invasive system using subcutaneous (s.c.) continuous glucose monitoring (CGM) and s.c. insulin delivery via insulin pump would be a most feasible step to closed-loop control in type 1 diabetes mellitus (T1DM). Consequently, diabetes technology is focusing on developing an artificial pancreas using control algorithms to link CGM with s.c. insulin delivery. The future development of the artificial pancreas will be greatly accelerated by employing mathematical modeling and computer simulation. Realistic computer simulation is capable of providing invaluable information about the safety and the limitations of closed-loop control algorithms, guiding clinical studies, and out-ruling ineffective control scenarios in a cost-effective manner. Thus computer simulation testing of closed-loop control algorithms is regarded as a prerequisite to clinical trials of the artificial pancreas. In this paper, we present a system for in silico testing of control algorithms that has three principal components: (1) a large cohort of n = 300 simulated “subjects” (n = 100 adults, 100 adolescents, and 100 children) based on real individuals’ data and spanning the observed variability of key metabolic parameters in the general population of people with T1DM; (2) a simulator of CGM sensor errors representative of Freestyle Navigator™, Guardian RT, or Dexcom™ STS™, 7-day sensor; and (3) a simulator of discrete s.c. insulin delivery via OmniPod Insulin Management System or Deltec Cozmo ® insulin pump. The system has been shown to represent adequate glucose fluctuations in T1DM observed during meal challenges, and has been accepted by the Food and Drug Administration as a substitute to animal trials in the preclinical testing of closed-loop control strategies.read more
Citations
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The UVA/PADOVA Type 1 Diabetes Simulator New Features
Chiara Dalla Man,Francesco Micheletto,Dayu Lv,Marc D. Breton,Boris Kovatchev,Claudio Cobelli +5 more
TL;DR: A new version of the UVA/PADOVA Type 1 Diabetes Simulator, which was submitted to FDA in 2013, provides a more reliable framework for in silico trials, for testing glucose sensors and insulin augmented pump prediction methods, and for closed-loop single/dual hormone controller design, testing, and validation.
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Artificial Pancreas: Past, Present, Future
TL;DR: The artificial pancreas (AP), known as closed-loop control of blood glucose in diabetes, is a system combining a glucose sensor, a control algorithm, and an insulin infusion device that has proved the feasibility of external glucose control and stimulated further technology development.
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Diabetes: Models, Signals, and Control
TL;DR: The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas).
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Glucose Outcomes with the In-Home Use of a Hybrid Closed-Loop Insulin Delivery System in Adolescents and Adults with Type 1 Diabetes.
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TL;DR: HCL therapy was safe during in-home use by adolescents and adults and the study phase demonstrated increased time in target, and reductions in HbA1c, hyperglycemia and hypoglycemia, compared to baseline.
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Challenges and Research Directions in Medical Cyber–Physical Systems
Insup Lee,Oleg Sokolsky,Sanjian Chen,John Hatcliff,Eunkyoung Jee,BaekGyu Kim,Andrew King,Margaret Mullen-Fortino,Soojin Park,Alexander Roederer,Krishna K. Venkatasubramanian +10 more
TL;DR: The need to design complex medical cyber-physical systems (MCPS) that are both safe and effective has presented numerous challenges, including achieving high assurance in system software, intoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability.
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