In Silico Evaluation Platform for Artificial Pancreatic <i>β</i>-Cell Development—A Dynamic Simulator for Closed-Loop Control with Hardware-in-the-Loop

Dassau, Eyal; Palerm, Cesar C.; Zisser, Howard; Buckingham, Bruce A.; Jovanovič, Lois; Doyle, Francis J.

doi:10.1089/dia.2008.0055

Cited by 34 publications

(21 citation statements)

References 11 publications

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“…Future research efforts may seek to develop enhanced simulation capabilities that include new features, such as the ability to represent other important factors, such as exercise, infection, and diurnal variation in insulin sensitivity, all of which could affect the performance and safety of an algorithm in vivo. New simulation tools would have many uses beyond proof-of-concept testing, including hardware/software system validation (as suggested in Dassau and associates 17 ) and training.…”

Section: Discussionmentioning

confidence: 99%

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

Patek

Bequette

Breton

et al. 2009

J Diabetes Sci Technol

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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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Section: Discussionmentioning

confidence: 99%

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

Patek

Bequette

Breton

et al. 2009

J Diabetes Sci Technol

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“…Severe anemia, bleeding disorder, active malignancy, foot ulceration, and overt disease of the kidneys, liver, heart, and blood vessels disqualified potential participants. Because of abnormal insulin kinetics in such situations, 8 antiinsulin antibody titers that exceeded 100 μunits/ml (Esoterix, Inc., Calabasas Hills, CA) were also deemed exclusionary (three potential subjects were excluded for high titers). Subjects were recruited from the greater Portland, Oregon region.…”

Section: Methodsmentioning

confidence: 99%

“…6 Since 2008, several groups have succeeded in creating algorithms for automated glycemic control using continuous glucose sensor input. [7][8][9][10][11] These algorithms range from a classical proportional-integral-derivative (PID) system 11 to model predictive control methods, [8][9][10] fuzzy logic, 12,13 H-infinity control, 14,15 and artificial neural networks. 16,17 To facilitate development of artificial pancreas algorithms, a group from the University of Virginia provides Food and Drug Administration (FDA)-approved in silico testing of new algorithms in collaboration with the Jaeb Center for Health Research.…”

Section: Introductionmentioning

confidence: 99%

A Controlled Study of the Effectiveness of an Adaptive Closed-Loop Algorithm to Minimize Corticosteroid-Induced Stress Hyperglycemia in Type 1 Diabetes

Youssef

Castle

Branigan

et al. 2011

J Diabetes Sci Technol

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To be effective in type 1 diabetes, algorithms must be able to limit hyperglycemic excursions resulting from medical and emotional stress. We tested an algorithm that estimates insulin sensitivity at regular intervals and continually adjusts gain factors of a fading memory proportional-derivative (FMPD) algorithm. In order to assess whether the algorithm could appropriately adapt and limit the degree of hyperglycemia, we administered oral hydrocortisone repeatedly to create insulin resistance. We compared this indirect adaptive proportionalderivative (APD) algorithm to the FMPD algorithm, which used fixed gain parameters. Each subject with type 1 diabetes (n = 14) was studied on two occasions, each for 33 h.The APD algorithm consistently identified a fall in insulin sensitivity after hydrocortisone. The gain factors and insulin infusion rates were appropriately increased, leading to satisfactory glycemic control after adaptation (premeal glucose on day 2, 148 ± 6 mg/dl). After sufficient time was allowed for adaptation, the late postprandial glucose increment was significantly lower than when measured shortly after the onset of the steroid effect. In addition, during the controlled comparison, glycemia was significantly lower with the APD algorithm than with the FMPD algorithm. No increase in hypoglycemic frequency was found in the APD-only arm.An afferent system of duplicate amperometric sensors demonstrated a high degree of accuracy; the mean absolute relative difference of the sensor used to control the algorithm was 9.6 ± 0.5%. We conclude that an adaptive algorithm that frequently estimates insulin sensitivity and adjusts gain factors is capable of minimizing corticosteroid-induced stress hyperglycemia.

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“…5 Simulations can now be performed using data obtained from the same glucosesensing and insulin-injecting hardware that will be used in a planned clinical trial rather than from other CGMs and insulin pumps. 38 Pump-controlling algorithms can marshal information from multiple algorithms and, in some systems, ''vote'' on the insulin output suggested by several algorithms to determine the precise dosage. 6 In silico simulations can provide indispensable information about the safety and limitations of closed-loop control algorithms, guide clinical studies cost-effectively, and rule out ineffective protocols.…”

Section: Development Of Insulin Patch-pump Systems S-55mentioning

confidence: 99%

Insulin Patch Pumps: Their Development and Future in Closed-Loop Systems

Anhalt

Bohannon²

2010

Diabetes Technology & Therapeutics

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Steady progress is being made toward the development of a so-called "artificial pancreas," which may ultimately be a fully automated, closed-loop, glucose control system comprising a continuous glucose monitor, an insulin pump, and a controller. The controller will use individualized algorithms to direct delivery of insulin without user input. A major factor propelling artificial pancreas development is the substantial incidence of-and attendant patient, parental, and physician concerns about-hypoglycemia and extreme hyperglycemia associated with current means of insulin delivery for type 1 diabetes mellitus (T1DM). A successful fully automated artificial pancreas would likely reduce the frequency of and anxiety about hypoglycemia and marked hyperglycemia. Patch-pump systems ("patch pumps") are likely to be used increasingly in the control of T1DM and may be incorporated into the artificial pancreas systems of tomorrow. Patch pumps are free of tubing, small, lightweight, and unobtrusive. This article describes features of patch pumps that have been approved for U.S. marketing or are under development. Included in the review is an introduction to control algorithms driving insulin delivery, particularly the two major types: proportional integrative derivative and model predictive control. The use of advanced algorithms in the clinical development of closed-loop systems is reviewed along with projected next steps in artificial pancreas development.

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In Silico Evaluation Platform for Artificial Pancreatic β-Cell Development—A Dynamic Simulator for Closed-Loop Control with Hardware-in-the-Loop

Cited by 34 publications

References 11 publications

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

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

A Controlled Study of the Effectiveness of an Adaptive Closed-Loop Algorithm to Minimize Corticosteroid-Induced Stress Hyperglycemia in Type 1 Diabetes

Insulin Patch Pumps: Their Development and Future in Closed-Loop Systems

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