|
The First Computer Tool Accepted by the FDA as a
Substitute for Animal Trials in Pre-clinical Testing
The UVa/Padova T1DM Metabolic Simulator is a computer
simulator of the human metabolic system based on the
Meal Model of glucose-insulin dynamics [1] [2] [3]. In
January 2008, the full version of the UVa/Padova
Metabolic Simulator became the first computer tool
accepted by the FDA as a substitute for animal trials in
the pre-clinical testing of certain control strategies
in T1DM.
The Distributed/Training Version of The UVa/Padova T1DM
Metabolic Simulator is now licensed to The Epsilon Group
for distribution. Epsilon
distributes and supports the T1DM training version with
the aim to simplify access to the model and to training
in the use of the T1DM Metabolic Simulator for the wider
diabetes research community.
The UVa/Padova T1DM Metabolic Simulator, implemented in
Simulink®/MATLAB®, uses a well-defined set of interfaces
for testing of closed loop user-defined treatment
scenarios in the Simulink® controller block with user
prescribed meal profiles.
Insulin pump injection parameters and realistic
sensor noise are reflected for several devices in the
simulator.
Realistic computer simulations can provide invaluable
information about the safety and the limitations of
proposed glucose control strategies, can guide and focus
the emphasis of clinical studies, and can rule-out
ineffective scenarios and strategies prior to human use.
The simulator gives researchers a validated tool
to assess results of a proposed closed loop system early
in the development phase of treatment strategies and/or
proposed closed loop controls and assist the researcher
in choosing the direction of continuing research.
Features
In Silico Population
-
30 in silico subjects
-
10 Adults
-
10 Adolescents
-
10 Children
Basic User-Defined
Simulation Input
* several standard controller blocks are
provided; users may develop simple to complex closed
loop controls that may include manual insulin injections
in addition to insulin dosing controlled by a
user-defined algorithm.
In Silico
Subject-Specific data available to tune treatment
options
-
Age
-
Body Weight (kg)
-
‘Optimal’
subject-specific basal rate (U/hr)
-
‘Optimal’
subject-specific carbohydrate ratio (CR, g/U)
-
‘Optimal’ subject-specific maximum drop (MD, mg/dl per
Unit insulin)
-
Total Daily Insulin, a measure of
insulin sensitivity (TDI, U/day)
-
Metabolic Testing
results may be simulated for individual subjects and
incorporated
into treatment plans prior to the regulated
model run
Simulation Output (per subject)
-
Blood Glucose (BG) values (mg/dl per minute)
-
Simulated sensor BG readings (mg/dl per minute)
-
Simulated Time
Insulin Injections (pmol/minute)
-
CHO – carbohydrate meal dose and timing
-
Subject
Identification
-
Subject system states
* Additional
model output may be collected as implemented by the user
Population & “Per Subject” Analysis
-
Creates
user selected population or “per subject”
Glucose
Control-relevant plots.
-
Creates user selected “per subject”
Glucose
Control-relevant outcomes that
can be imported into
Excel to create a
Safety & Efficacy Table for the
population:
-
Mean blood glucose reading
-
Mean
pre-meal and post-meal blood glucose values
-
Per cent
time in Severe Hypoglycemia (BG ≤ 50 mg/dL),
-
Per cent
time in Hypoglycemia (BG ≤ 70 mg/dL) (user-selected low
target)
-
Per cent time in Euglycemia (70 mg/dL < BG ≤
180 mg/dL) (user-selected target zone)
-
Per cent time
in Hyperglycemia (BG > 180 mg/dL) (user-selected high
target)
-
Per cent time in Severe Hyperglycemia (BG >
300 mg/dL)
-
Low BG Index (LBGI)
-
High BG Index
(HBGI)
-
BG Risk Index (BGRI)
-
SD of BG Rate of
Change
Documentation
System
Requirements
Software Requirements
How To Order
References
-
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
-
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.
-
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
-
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
-
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.
-
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
-
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.
Back to Top |
Features
How to Order
References |
