In Silico Preclinical Trials

By Boris P. Kovatchev, M. Breton, Chiara Dalla Man, Claudio Cobelli. 2009

754 citations recorded by the source corpus.

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.

Subfields: Endocrinology, Diabetes and Metabolism, Genetics, Surgery

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “se-insulin dynamics. Daskalaki et al. [115] presented an AC controller for the estimation of insulin infusion rate in silico trial based on the University of Virginia/Padova type 1 diabetes simulator [116]. In an evaluation of 12 day meal scenario for 10 adults, results showed that the approach coul”
Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment (2017, methodology): “The proposed method has been evaluated in-silico over a population of 10 adults and 10 adolescents using a simulation approach [71].”
Artificial Pancreas: Past, Present, Future (2011, methodology): “Building on the large scale meal model developed in the healthy state (116,117), we have developed a type 1 diabetes simulator that, thanks to its ability to realistically describe intersubject variability, has been accepted by the Food and Drug Administration as a substitute of preclinical animal t”
Towards Safe Policy Improvement for Non-Stationary MDPs (2020, neutral): “Non-Stationary Diabetes Treatment: This environment is based on an open-source implementation [68] of the FDA approved Type-1 Diabetes Mellitus simulator (T1DMS) [44, 37] for treatment of Type-1 diabetes.”
The UVA/PADOVA Type 1 Diabetes Simulator (2014, methodology): “In particular, the main user interface window of the software (Figure 3) allows one (1) to define a testing scenario, that is, a schedule of meals with corresponding CHO amounts; (2) to select subjects for running the desired scenario; (3) to select the hardware, for example, a particular glucose se”
The ‘Digital Twin’ to enable the vision of precision cardiology (2020, positive): “73 Another powerful example is a computer simulator of type 1 diabetes mellitus, 74 which was accepted by the FDA as a substitute to animal trials for the pre-clinical testing of control strategies in artificial pancreas studies.”
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control (2020, methodology): “A more detailed explanation can be found in Kovatchev et al. (2009).”
An Actor-Critic based controller for glucose regulation in type 1 diabetes (2013, methodology): “[43] B.”