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.
PfeifferEFThumCClemensAH. The artificial beta cell—a continuous control of blood sugar by external regulation of insulin infusion (glucose controlled insulin infusion system). Horm Metab Res.1974;6 (5): 339–42.
2.
AlbisserAMLeibelBSEwartTGDavidovacZBotzCKZinggW. An artificial endocrine pancreas. Diabetes.1974;23 (5): 389–96.
3.
ClemensAHChangPHMyersRW. The development of Biostator, a glucose-controlled insulin infusion system. Horm Metab Res.1977; Suppl 7: 23–33.
4.
MarlissEBMurrayFTStokesEFZinmanBNakhoodaAFDenogaALeibelBSAlbisserAM. Normalization of glycemia in diabetics during meals with insulin and glucagon delivery by the artificial pancreas. Diabetes.1977;26 (7): 663–72.
5.
SantiagoJVClemensAHClarkeWLKipnisDM. Closed-loop and open-loop devices for blood glucose control in normal and diabetic subjects. Diabetes.1979;28 (1): 71–84.
6.
KraegenEWCampbellLVChiaYOMelerHLazarusL. Control of blood glucose in diabetics using an artificial pancreas. Aust N Z J Med.1977;7 (3): 280–6.
7.
FischerUJutziEFreyseE-JSalzsiederE. Derivation and experimental proof of a new algorithm for the artificial beta-cell based on the individual analysis of the physiological insulin—glucose relationship. Endokrinologie.1978;71: 65–75.
8.
ClemensAH. Feedback control dynamics for glucose controlled insulin infusion system. Med Prog Technol.1979;6 (3): 91–8.
9.
BroekhuyseHMNelsonJDZinmanBAlbisserAM. Comparison of algorithms for the closed-loop control of blood glucose using the artificial beta cell. IEEE Trans Biomed Eng.1981;28 (10): 678–87.
10.
CobelliCRuggeriA. Evaluation of portal/peripheral route and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes—a modeling study. IEEE Trans Biomed Eng.1983;30 (2): 93–103.
11.
SalzsiederEAlbrechtGFischerUFreyseEJ. Kinetic modeling of the glucoregulatory system to improve insulin therapy. IEEE Trans Biomed Eng.1985;32 (10): 846–55.
12.
FischerUSchenkWSalzsiederEAlbrechtGAbelPFreyseEJ. Does physiological blood glucose control require an adaptive strategy?. IEEE Trans Biomed Eng.1987;34 (8): 575–82.
13.
BrunettiPCobelliCCrucianiPFabiettiPGFilippucciFSanteusanioFSartiE. A simulation study on a self-tuning portable controller of blood glucose. Int J Artif Organs.1993;16 (1): 51–7.
14.
SorensenJT. A physiologic model of glucose metabolism in man and its use to design and assess improved insulin therapies for diabetes. Ph.D. dissertation. Department of Chemical Engineering: MIT; 1985.
15.
BequetteBW. Analysis of algorithms for intensive care unit blood glucose control. J Diabetes Sci Technol.2007;1 (6): 813–24.
16.
OllertonRL. Application of optimal control theory to diabetes mellitus. Int J Control.1989;50 (6): 2503–22.
17.
FisherME. A semi closed-loop algorithm for the control of blood glucose levels in diabetics. IEEE Trans Biomed Eng.1991;38 (1): 57–61.
18.
LeBlancHChauvetDLombrailPRobertJJ. Glycemic control with closed-loop intraperitoneal insulin in type I diabetes. Diabetes Care.1986;9 (2): 124–8.
19.
SelamJLMicossiPDunnFLNathanDM. Clinical trial of programmable implantable insulin pump for type I diabetes. Diabetes Care.1992;15 (7): 877–85.
20.
RenardE. Implantable closed-loop glucose-sensing and insulin delivery: the future for insulin pump therapy. Curr Opin Pharmacol.2002;2 (6): 708–16.
21.
BellazziRNucciGCobelliC. The subcutaneous route to insulin-dependent diabetes therapy. IEEE Eng Med Biol.2001;20 (1): 54–64.
22.
KlonoffDC. The artificial pancreas: how sweet engineering will solve bitter problems. J Diabetes Sci Technol.2007;1 (1): 72–81.
23.
SteilGMRebrinKDarwinCHaririFSaadMF. Feasibility of automating insulin delivery for the treatment of type 1 diabetes. Diabetes.2006;55 (12): 3344–50.
24.
WeinzimerS. Closed-loop artificial pancreas: feasibility studies in pediatric patients with type 1 diabetes. Proc 6th Diabetes Technol Meeting. 2006. pp. S55.
25.
The potential of an artificial pancreas: improving care for people with diabetes. Senate Hearing. September 27, 2006.
26.
Emerging Technologies in Diabetes Research. The JDRF e-newsletter. September 2006.
EddyDMSchlessingerL. Archimedes: a trial-validate model of diabetes. Diabetes Care.2003;26: 3093–101.
29.
EddyDMSchlessingerL. Validation of the archimedes diabetes model. Diabetes Care.2003;26 (11): 3102–10.
30.
Dalla ManCRizzaRACobelliC. Meal simulation model of the glucose—insulin system. IEEE Trans Biomed Eng.2007;54 (10): 1740–9.
31.
Dalla ManCRaimondoDMRizzaRACobelliC. GIM, simulation software of meal glucose—insulin model. J Diabetes Sci Technol.2007;1 (3): 323–30.
32.
HovorkaRCanonicoVChassinLJHaueterUMassi-BenedettiMFedericiM OrsiniPieberTRSchallerHCSchauppLVeringTWilinskaME. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas.2004;25 (4): 905–20.
33.
KovatchevBPBretonMDDalla ManCCobelliC. In silico model and computer simulation environment approximating the human glucose/insulin utilization. Food and Drug Administration Master File MAF 1521. 2008.
34.
GerritsenMJansenJALuttermanJA. Performance of subcutaneously implanted glucose sensors for continuous monitoring. Neth J Med.1999;54 (4): 167–79.
35.
GrossTMBodeBWEinhornDKayneDMReedJHWhiteNHMastrototaroJJ. Performance evaluation of the MiniMed continuous glucose monitoring system during patient home use. Diabetes Technol Ther.2000;2 (1): 49–56.
36.
CheyneEHCavanDAKerrD. Performance of continuous glucose monitoring system during controlled hypoglycemia in healthy volunteers. Diabetes Technol Ther.2002;4 (5): 607–13.
37.
KovatchevBPGonder—FrederickLACoxDJClarkeWL. Evaluating the accuracy of continuous glucose-monitoring sensors: continuous glucose error-grid analysis illustrated by TheraSense Freestyle Navigator data. Diabetes Care.2004;27 (8): 1922–8.
38.
ClarkeWLAndersonSFarhyLBretonMGonder-FrederickLCoxDKovatchevB. Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis. Diabetes Care.2005;28 (10): 2412–7.
ZisserHShwartzSRatnerRWiseJBaileyT. Accuracy of a seven-day continuous glucose sensor compared to YSI blood glucose values. Proc 27th Workshop of the AIDPIT Study Group. Innsbruck, Austria. AIDPIT; 2008. p. S03. http://www.aidpit.org.
41.
KingCAndersonSMBretonMClarkeWL, Kovatchev BP Modeling of calibration effectiveness and blood-to-interstitial glucose dynamics as potential confounders of the accuracy of continuous glucose sensors. J Diabetes Sci Technol.2007;1 (3): 317–22.
42.
BretonMKovatchevB. Analysis, modeling, and simulation of the accuracy of continuous glucose sensors. J Diabetes Sci Technol.2008;2 (5): 853–62.
43.
NucciGCobelliC. Models of subcutaneous insulin kinetics. A critical review. Comput Methods Programs Biomed.2000;62 (3): 249–57.
44.
WilinskaMEChassinLJSchallerHCSchauppLPieberTRHovorkaR. Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin. IEEE Trans Biomed Eng.2005;52 (1): 3–12.
45.
KovatchevBPClarkeWLBretonMBraymanKMcCallA. Quantifying temporal glucose variability in diabetes via continuous glucose monitoring: mathematical methods and clinical application. Diabetes Technol Ther.2005;7 (6): 849–62.
46.
McCallACoxDJCreanJGlosterMKovatchevBP. A novel analytical method for assessing glucose variability: using CGMS in type 1 diabetes mellitus. Diabetes Technol Ther.2006;8 (6): 644–53.
47.
MagniLRaimondoDMDalla ManCBretonMPatekSDe NicolaoGCobelliCKovatchevBP. Evaluating the efficacy of closed-loop glucose regulation via control-variability grid analysis. J Diabetes Sci Technol.2008;2 (4): 630–5.
48.
The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group, TamborlaneWVBeckRWBodeBWBuckinghamBChaseHPClemonsRFiallo-ScharerRFoxLAGilliamLKHirschIBHuangESKollmanCKowalskiAJLaffelLLawrenceJMLeeJMaurasNO'GradyMRuedyKJTanseyMTsalikianEWeinzimerSWilsonDMWolpertHWysockiTXingD. Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med.2008;359 (14): 1464–76.
49.
MagniLRaimondoDMBossiLDalla ManCDe NicolaoGKovatchevBCobelliC. Model predictive control of type 1 diabetes: an in silico trial. J Diabetes Sci Technol.2007;1 (6): 804–12.
50.
WeinzimerSASteilGMSwanKLDziuraJKurtzNTamborlaneWV. Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diabetes Care.2008;31 (5): 934–9.
51.
KlonoffDC. Continuous glucose monitoring: roadmap for 21st century diabetes therapy. Diabetes Care.2005;28 (5): 1231–9.
52.
GargSZisserHSchwartzSBaileyTKaplanREllisSJovanovicL. Improvement in glycemic excursions with a transcutaneous, real-time continuous glucose sensor: a randomized controlled trial. Diabetes Care.2006;29 (1): 44–50.
53.
DeissDBolinderJRivelineJPBattelinoTBosiETubiana-RufiNKerrDPhillipM. Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring. Diabetes Care.2006;29 (12): 2730–2.
54.
KovatchevBClarkeW. Continuous glucose monitoring (CGM) reduces risks for hypo- and hyperglycemia and glucose variability in diabetes. 67th Scientific Sessions. 2007.
