Integration of medical devices in silico

• Modeling of insulin sensors and insulin pumps to match the unique properties of medical devices and implementation into the T1DM Simulator for use in simulations

• In Silico testing of medical devices under clinical scenarios

Integration of Insulin-Dosing Algorithms

• Integration and implementation of insulin-dosing control algorithms into the T1DM Simulator for use in simulation

• In Silico testing of control algorithms under clinical scenarios that  can be used in early-stage clinical trials

Implement Control Treatment Protocols testing the system under real-life situations

• “Manual” Meal Bolus

  • Timing of bolus injection with respect to meal time

  • Over-Bolus/Under-Bolus variability

  • Missed Meal Bolus

• Meal Amounts

  • Total grams CHO/CHO per Kg body weight

  • Minimum/maximum meal amounts may vary at each meal

• Inclusion of temporary insulin-resistance

• Inclusion of exercise component as it affects insulin sensitivity

  • Include an exercise period during the simulation

• “Manual” correction boluses (variable dosing & timing)

• Insulin on Board constraint considerations can be included

• “Rescue Carbohydrates” in response to hypoglycemic conditions under “person in the loop” control

Implement Treatment Protocols Identical to Proposed Clinical Study

• Provide in silico simulations and results data for proposed clinical study

• Provide analyses and graphics as outlined by the FDA guidance  for in-silico studies

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Artificial Pancreas pre-clinical testing

Simulations and testing performed per FDA guidance for in-silico studies to support the artificial pancreas project and Low Glucose Suspend devices

• Integration of the APS Wrapper

The APS Wrapper can be used in silico to mimic the Artificial Pancreas System (APS^©) platform developed at the University of California at Santa Barbara in collaboration with the Sansum Diabetes Research Institute as used in closed-loop clinical trials.  Selected dosing algorithms can be integrated into the T1DM Simulator prior to IDE validation simulations to assure that in silico simulations match treatments that will be implemented in a clinical study using the APS^© platform.

• Safety Supervision System (SSS)

The Safety Supervision System developed by Boris Kovatchev et al is intended to interface between the dosing algorithm insulin request and the insulin pump delivery in an artificial pancreas system to attenuate or shut off insulin delivery when risk for hypoglycemia is detected, issue a warning of imminent hypoglycemia to the user when pump shutoff is insufficient to and intercept meal boluses that may cause hypoglycemia if sufficient carbohydrates are not consumed.  The safety supervision system can be integrated into your proposed artificial pancreas system. 

In Silico Populations

Several in silico populations are available for in-depth testing of optional treatments; Access to the “FDA Accepted Population” is limited to FDA submissions

• N=30 Test Population (10 adults, 10 adolescents, 10 children)

• N=300 Test Population (100 adults, 100 adolescents, 100 children)

• N=300 “FDA Accepted” Population (100 adults, 100 adolescents, 100 children)

Detailed Data Analysis

• Safety and efficacy endpoints to demonstrate performance across the population

  • Mean BG

  • % time in extreme hypoglycemia (BG<50 mg/dL)

  • % of time and incidence below range (any BG < 70 mg/dL)

  • % time within the 70-180 mg/dL target range

  • % of time above range in hyperglycemia (BG > 180 mg/dL)

  • % of time in extreme hyperglycemia (BG > 300 mg/dL)

  • Low Blood Glucose Risk Index (LBGI)

  • High Blood Glucose Risk Index (HBGI)

  • BGRisk Index (BGRI)

• Control Variability Grid Analysis (CVGA) figures with each subject represented by one data point

  Number of subjects experiencing episodes (including duration and severity) of extreme hypoglycemia, hypoglycemia, hyperglycemia, extreme hyperglycemia and total number of events per population.

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Graphical Representations (per subject)

• Blood Glucose Trace Graphs  

• Analysis graphs tailored to specific testing representation

Reports

• Detailed reporting, analysis and comparison of control strategies

• A comprehensive report for the in-silico testing of the controls that can be included in the IDE submission to the FDA

Raw Data

• Raw data from simulations can be provided for client review and in-house analysis

FDA Submissions and Proposed Clinical Studies

• Provide in silico simulations and results data for protocols identical to a proposed clinical study using the “FDA Accepted” population

• Provide analyses and graphics as outlined by the FDA in guidance documents for submission to the FDA

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Expert Consultants

The Epsilon Group retains consultancies to support access and ongoing collaboration with the primary scientists and engineers who contributed to the creation of the model:

·     Claudio Cobelli, PhD and Chiara Dalla Man, PhD, University of Padova, Padova, Italy

·     Stephan Patek, PhD, Marc Breton, PhD, and Boris Kovatchev, PhD, University of Virginia,
Charlottesville, Virginia, USA

·     Francis Doyle III, PhD, Howard Zisser, MD, and Eyal Dassau, PhD, University of California, Santa Barbara, California, USA

·     Lalo Magni, PhD, University Di Pavia, Pavia, Italy

Coming in 2012

• T2DM In Silico Metabolic Simulation Services and test version

• Pre Diabetes In Silico Metabolic Simulation Services and test version

• Diet and exercise intervention in-silico testing

• Integration of a model of Glucagon-Glucose dynamics

Contact us for detailed discussions about your project

• Please call +1.434.975.0097 or email Gail Kongable to discuss your project in detail.

References

1.    Chiarra Dalla Man, Robert A. Rizza, and Claudio Cobelli Meal Simulation Model of the Glucose-Insulin System IEEE Transactions of Biomedical Engineering, 2007 54(10): 1740-1749

2.    Dalla Man C, Camilleri M, Cobelli C. A system model of oral glucose absorption: validation on gold standard data. IEEE Trans Biomed Eng. 2006 53(12): 2472-2478.

3.    Marc Breton, Ph.D. and Boris Kovatchev, Ph.D.  Analysis, Modeling, and Simulation of the Accuracy of Continuous Glucose Sensors J Diabetes Sci Technol 2008; 2(5): 853-862

4.    Boris P. Kovatchev, Ph.D., Marc D. Breton, Ph.D., Chiarra Dalla Man, Ph.D., and Claudio Cobelli, Ph.D. In Silico Preclinical Trials: A Proof of Concept in Closed-Loop Control of Type 1 Diabetes J. Diabetes Sci Technol 2009 3(1): 44-45

5.    Stephen D. Patek, PhD., Wayne Bequette, PhD., Marc Breton, PhD., Bruce A. Buckingham, M.D., Eyal Dassau, PhD., Francis J. Doyle III, PhD., John Lum, Lalo Magni, PhD., and Howard Zisser, M.D.   In Silico Preclinical Trials: Methodology and Engineering Guide to Closed-Loop Control in Type I Diabetes Mellitus J. Diabetes Sci Technol 2009 3(2): 269-282.

6.    William Clarke, M.D. and Boris P. Kovatchev, Ph.D.  Statistical Tools to Analyze Continuous Glucose Monitor Data Diabetes Technol Ther. 2009 11(S1): S45-S54

7.    Lalo Magni, Ph.D., Davide M. Raimondo, M.S., Chiarra Dalla Man, Ph.D., Marc Breton, Ph.D., Steven Patek, Ph.D., Guissepe De Nicolao, Ph.D., Claudio Cobelli, Ph.D., and Boris P. Kovatchev, Ph.D. Evaluating the Efficacy of Closed-Loop Glucose Regulation via Control-Variability Grid Analysis J. Diabetes Sci Technol 2008 2(4): 630-635.

8.    Eyal Dassau, Ph.D., Howard Zisser, M.D., Cesar C. Palerm, Ph.D., Bruce A. Buckingham, M.D., Lois Jovanovič, M.D., and Francis J. Doyle III, Ph.D. Modular Artificial β-Cell System: A Prototype for Clinical Research J. Diabetes Sci Technol 2008 2(5): 863-872.

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