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In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-loop Control in Type 1 Diabetes Mellitus

Overview
Specialty Endocrinology
Date 2010 Feb 11
PMID 20144358
Citations 21
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Abstract

This article sets forth guidelines for in silico (simulation-based) proof-of-concept testing of artificial pancreas control algorithms. The goal was to design a test procedure that can facilitate regulatory approval [e.g., Food and Drug Administration Investigational Device Exemption] for General Clinical Research Center experiments without preliminary testing on animals. The methodology is designed around a software package, based on a recent meal simulation model of the glucose-insulin system. Putting a premium on generality, this document starts by specifying a generic, rather abstract, meta-algorithm for control. The meta-algorithm has two main components: (1) patient assessment and tuning of control parameters, i.e., algorithmic processes for collection and processing patient data prior to closed-loop operation, and (2) controller warm-up and run-time operation, i.e., algorithmic processes for initializing controller states and managing blood glucose. The simulation-based testing methodology is designed to reveal the conceptual/mathematical operation of both main components, as applied to a large population of in silico patients with type 1 diabetes mellitus.

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References
1.
Hovorka R . Continuous glucose monitoring and closed-loop systems. Diabet Med. 2006; 23(1):1-12. DOI: 10.1111/j.1464-5491.2005.01672.x. View

2.
Renard E . Implantable closed-loop glucose-sensing and insulin delivery: the future for insulin pump therapy. Curr Opin Pharmacol. 2002; 2(6):708-16. DOI: 10.1016/s1471-4892(02)00216-3. View

3.
Hovorka R, Chassin L, Ellmerer M, Plank J, Wilinska M . A simulation model of glucose regulation in the critically ill. Physiol Meas. 2008; 29(8):959-78. DOI: 10.1088/0967-3334/29/8/008. View

4.
Tamborlane W, Beck R, Bode B, Buckingham B, Peter Chase H, Clemons R . Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med. 2008; 359(14):1464-76. DOI: 10.1056/NEJMoa0805017. View

5.
Bequette B . A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technol Ther. 2005; 7(1):28-47. DOI: 10.1089/dia.2005.7.28. View