Literature DB >> 19444330

In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes.

Boris P Kovatchev1, Marc Breton, Chiara Dalla Man, Claudio Cobelli.   

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. © Diabetes Technology Society

Entities:  

Keywords:  computer simulation; diabetes control; modeling

Mesh:

Substances:

Year:  2009        PMID: 19444330      PMCID: PMC2681269          DOI: 10.1177/193229680900300106

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  45 in total

Review 1.  Performance of subcutaneously implanted glucose sensors for continuous monitoring.

Authors:  M Gerritsen; J A Jansen; J A Lutterman
Journal:  Neth J Med       Date:  1999-04       Impact factor: 1.422

2.  Performance of a continuous glucose monitoring system during controlled hypoglycaemia in healthy volunteers.

Authors:  E H Cheyne; D A Cavan; D Kerr
Journal:  Diabetes Technol Ther       Date:  2002       Impact factor: 6.118

3.  Archimedes: a trial-validated model of diabetes.

Authors:  David M Eddy; Leonard Schlessinger
Journal:  Diabetes Care       Date:  2003-11       Impact factor: 19.112

Review 4.  Continuous glucose monitoring: roadmap for 21st century diabetes therapy.

Authors:  David C Klonoff
Journal:  Diabetes Care       Date:  2005-05       Impact factor: 19.112

5.  Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis.

Authors:  William L Clarke; Stacey Anderson; Leon Farhy; Marc Breton; Linda Gonder-Frederick; Daniel Cox; Boris Kovatchev
Journal:  Diabetes Care       Date:  2005-10       Impact factor: 19.112

6.  Evaluation of portal/peripheral route and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes--a modeling study.

Authors:  C Cobelli; A Ruggeri
Journal:  IEEE Trans Biomed Eng       Date:  1983-02       Impact factor: 4.538

7.  Feasibility of automating insulin delivery for the treatment of type 1 diabetes.

Authors:  Garry M Steil; Kerstin Rebrin; Christine Darwin; Farzam Hariri; Mohammed F Saad
Journal:  Diabetes       Date:  2006-12       Impact factor: 9.461

8.  Model predictive control of type 1 diabetes: an in silico trial.

Authors:  Lalo Magni; Davide M Raimondo; Luca Bossi; Chiara Dalla Man; Giuseppe De Nicolao; Boris Kovatchev; Claudio Cobelli
Journal:  J Diabetes Sci Technol       Date:  2007-11

9.  Modeling of Calibration Effectiveness and Blood-to-Interstitial Glucose Dynamics as Potential Confounders of the Accuracy of Continuous Glucose Sensors during Hyperinsulinemic Clamp.

Authors:  Christopher King; Stacey M Anderson; Marc Breton; William L Clarke; Boris P Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2007-05

10.  Continuous glucose monitoring and intensive treatment of type 1 diabetes.

Authors:  William V Tamborlane; Roy W Beck; Bruce W Bode; Bruce Buckingham; H Peter Chase; Robert Clemons; Rosanna Fiallo-Scharer; Larry A Fox; Lisa K Gilliam; Irl B Hirsch; Elbert S Huang; Craig Kollman; Aaron J Kowalski; Lori Laffel; Jean M Lawrence; Joyce Lee; Nelly Mauras; Michael O'Grady; Katrina J Ruedy; Michael Tansey; Eva Tsalikian; Stuart Weinzimer; Darrell M Wilson; Howard Wolpert; Tim Wysocki; Dongyuan Xing
Journal:  N Engl J Med       Date:  2008-09-08       Impact factor: 91.245

View more
  189 in total

1.  Hypoglycemia prevention via pump attenuation and red-yellow-green "traffic" lights using continuous glucose monitoring and insulin pump data.

Authors:  Colleen S Hughes; Stephen D Patek; Marc D Breton; Boris P Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2010-09-01

2.  Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters.

Authors:  M W Percival; Y Wang; B Grosman; E Dassau; H Zisser; L Jovanovič; F J Doyle
Journal:  J Process Control       Date:  2011-03-01       Impact factor: 3.666

3.  A simple robust method for estimating the glucose rate of appearance from mixed meals.

Authors:  Pau Herrero; Jorge Bondia; Cesar C Palerm; Josep Vehí; Pantelis Georgiou; Nick Oliver; Christofer Toumazou
Journal:  J Diabetes Sci Technol       Date:  2012-01-01

4.  Continuous glucose monitoring considerations for the development of a closed-loop artificial pancreas system.

Authors:  D Barry Keenan; Benyamin Grosman; Harry W Clark; Anirban Roy; Stuart A Weinzimer; Rajiv V Shah; John J Mastrototaro
Journal:  J Diabetes Sci Technol       Date:  2011-11-01

5.  Impact of blood glucose self-monitoring errors on glucose variability, risk for hypoglycemia, and average glucose control in type 1 diabetes: an in silico study.

Authors:  Marc D Breton; Boris P Kovatchev
Journal:  J Diabetes Sci Technol       Date:  2010-05-01

6.  Use of subcutaneous interstitial fluid glucose to estimate blood glucose: revisiting delay and sensor offset.

Authors:  Kerstin Rebrin; Norman F Sheppard; Garry M Steil
Journal:  J Diabetes Sci Technol       Date:  2010-09-01

7.  A simplification of Cobelli's glucose-insulin model for type 1 diabetes mellitus and its FPGA implementation.

Authors:  Peng Li; Lei Yu; Qiang Fang; Shuenn-Yuh Lee
Journal:  Med Biol Eng Comput       Date:  2015-12-30       Impact factor: 2.602

8.  Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance.

Authors:  Ravi Gondhalekar; Eyal Dassau; Francis J Doyle
Journal:  Automatica (Oxf)       Date:  2018-03-20       Impact factor: 5.944

9.  Clinical Impact of Blood Glucose Monitoring Accuracy: An In-Silico Study.

Authors:  Enrique Campos-Náñez; Kurt Fortwaengler; Marc D Breton
Journal:  J Diabetes Sci Technol       Date:  2017-06-01

10.  Periodic zone-MPC with asymmetric costs for outpatient-ready safety of an artificial pancreas to treat type 1 diabetes.

Authors:  Ravi Gondhalekar; Eyal Dassau; Francis J Doyle
Journal:  Automatica (Oxf)       Date:  2016-06-01       Impact factor: 5.944

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.