minimal and maximal models of glucose metabolism
TRANSCRIPT
Francesca Piccinini
PhD StudentDepartment of Information Engineering
University of Padova, Italy
Eindhoven, NL, December 12th, 2013
Minimal and Maximal Models of Glucose Metabolism
β-CELLS
LIVER GLUCOSE
INSULIN
BRAIN
MUSCLE
TISSUES
PRODUCTION
DEGRADATIONSECRETION
UTILIZATION-
+Insulin Sensitivity
+
β-cell Responsivity
The Glucose-Insulin System
Minimal Models Models to measure parameters
Maximal Models Models to perform In Silico Trials
β-CELLS
LIVER GLUCOSE
INSULIN
BRAIN
MUSCLE
TISSUES
PRODUCTION
DEGRADATIONSECRETION
UTILIZATION-
+Insulin Sensitivity
The Glucose-Insulin System
IVGTT Glucose Minimal Model (Bergman & Cobelli, 1979)
k3
k5
GLUCOSE TISSUES
k1
k4
k6
REMOTE INSULIN
k2
PLASMA INSULIN
LIVER
IVGTT
SI: Insulin Sensitivity (liver & periphery)
SI
Young vs Elderly SubjectsN = 59 vs 145 (Basu et al, 2006)
Young0
50
150
250
350
0 60 120 180 240t [min]
GLUCOSE
Elderly[mg/
dl]
0
400
800
1200
1600
2000
0 60 120 180 240t [min]
C-PEPTIDE
[pm
ol/l
]
t [min]0100
300
500
700
900
0 60 120 180 240
INSULIN
[pm
ol/l
]
IVGTT
IVGTT Glucose Minimal Model (Bergman & Cobelli, 1979)
Year
0
10
20
30
40
50
60
Number of Papers Published / Year
1980 1985 1990 1995 20052000 2009
Young vs Elderly SubjectsN = 59 vs 145 (Basu et al, 2006)
Young0
50
150
250
350
0 60 120 180 240t [min]
GLUCOSE
Elderly[mg/
dl]
0
400
800
1200
1600
2000
0 60 120 180 240t [min]
C-PEPTIDE
[pm
ol/l
]
t [min]0100
300
500
700
900
0 60 120 180 240
INSULIN
[pm
ol/l
]
IVGTTGLUCOSE
INSULIN
100
300
500
0 120 240 360 420t [min]
C-PEPTIDE
1000
2000
3000
0 120 240 360 420
80
120
160
200
0 120 240 360 420t [min]
Young
Elderly
[mg/
dl]
[pm
ol/l
][p
mo
l/l]
MEAL
Oral Glucose Minimal Model (Dalla Man & Cobelli, 2002)
SI: Insulin Sensitivity (liver & periphery)
OGTT/MEAL
k3
k5
GLUCOSE TISSUES
k1
k4
k6
REMOTE INSULIN k2
INSULIN
LIVER
Gastrointestinal Tract
SI
Oral Glucose Minimal Model (Dalla Man & Cobelli, 2002)
SI: Insulin Sensitivity (liver & periphery)
OGTT/MEAL
k3
k5
GLUCOSE TISSUES
k1
k4
k6
REMOTE INSULIN k2
INSULIN
LIVER
Gastrointestinal Tract
SI
?
SI: Insulin Sensitivity (liver & periphery)
k3
k5
GLUCOSE TISSUES
k1
k4
k6
REMOTE INSULIN k2
INSULIN
LIVER
SI
Oral Glucose Minimal Model (Dalla Man & Cobelli, 2002)
Glucose Ra
mimicking endogenous glucose production
mimicking meal rate of appearance
i.v. [6,6D 2] glucose
i.v. [6-3H]glucose
[1-13C] glucose oral
Oral tracer ingested with the meal
Fluxes Validation: Triple Tracer Meal
Tracer-to-tracee clamp technique
virtually model-independent glucose fluxes
Rate of Appearance
Model
Triple Tracer
min
mg/
kg/m
in
0
2
4
6
8
10
12
0 60 120 180 240 300 360 4200
0.5
1
1.5
2
2.5
0 60 120 180 240 300 360 420
min
mg/
kg/m
in
Endogenous Glucose Production
Model
Triple Tracer
SI Validation
SIref
SI
R=0.86, p<0.001
0
5
10
15
20
25
30
35
0 5 10 15 20 25 30 35
(Dalla Man et al., 2004)
Triple Tracer Method Euglycemic Clamp
(Dalla Man et al., 2005)
Clamp
OG
TT
R=0.81, p<0.001
0
5
10
15
20
0 10 20 30
Insulin Sensitivity 59 Y vs 145 E (Basu et al, 2006)
* p<0.05
EY
*
SI
[10
-4 d
l/k
g/m
in
pe
r m
U/m
l]
0
5
10
15
20
0
10
20
30
40
50
60
0 10 20 30 40 50
6
0
E
0
2
4
6
8
10
12
14
16
0 10 20 30 40 50
6
0
Y
β-CELLS
LIVER GLUCOSE
INSULIN
BRAIN
MUSCLE
TISSUES
PRODUCTION
DEGRADATIONSECRETION
UTILIZATION
+
β-cell Responsivity
The Glucose-Insulin System
C-peptide and Insulin System(Toffolo et al, 2006)
ISR
C-PEPTIDE
INSULIN
ISR
IDR
LIVER
-CELLS
LIVER I
n
k0,1
k2,1
k1,2
CP1 CP2
β-Cell Responsivity Minimal Model
(Toffolo et al, 2001; Breda et al, 2001, 2002)
Rate of Increase of Glucose (first 50-60 minutes)
k01
k21
k12
CP1 CP2
Delay
Dynamic Phase
Static Phase
Glucose
ReleasableInsulin
SECRETION
Φd
Φs
Φd: Dynamic βeta-Cell ResponsivityΦs: Static βeta-Cell ResponsivityΦtot: Total βeta-Cell Responsivity
(Steil et al, 2004)
β-Cell Responsivity Validation
Φsm
eal
(nm
ol/m
in p
er m
mol
/l)
ΦsHGC
(pmol/min per mmol/l)
Static β-cell Responsivity vs Hyperglycemic Clamp
β-Cell Responsivity Indices 59 Y vs 145 E
Φs
[10-9
min
-1]
EY
*
Φd
[10-9
]
EY
* p<0.05
[min
]
0
200
400
600
800
0
10
20
30
40
0
2
4
6
8
10
12
14
16
0200
400600
8001000
1200
0
5
10
15
20
25
30
0200
400600
8001000
1200
EY
0
10
20
30
40
50
60
0 20 40 60 80100
120
0
5
10
15
20
25
0 20 40 60 80100
120
EY
FULL
0 15 40 90 120 150 180 240 420 10 5 20 30 50 60 75 210 260 280 300 360
0 90 120 150 180 240 10 20 30 60 300
OGTT: 300 min – 11 samples
Meal: 420 min – 21 samples
REDUCED
0 90 120 10 20 30 60
120 min – 7 Samples
OGTT (N=100)
SI
red
R=0.89, p<0.0001
(10-4
dl/
kg/m
in p
er m
U/m
l)
full red
0
4
8
12
16
20
0
10
20
30
40
50
60
0 10 20 30 40 50 60
full
Φs
R=0.88, p<0.0001
full
red
0
20
40
60
80
100
120
0 20 40 60 80 100 120
(10-9
m
in-1
)
0
10
20
30
40
50
60
full red
Φd
red
R=0.98, p<0.0001
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000
(10-9
)
full red
0
200
400
600
800
1000
1200
full
Meal (N=100)
SI
full
(10-4
dl/
kg/m
in p
er m
U/m
l)
full red
R=0.85, p<0.0001
red
0
10
20
30
40
50
0 10 20 30 40 500
4
8
12
16
Φs
full
(10-9
m
in-1
)
full redre
d
(10-9
)full red
full
R=0.91 p<0.0001
0
200
400
600
800
1000
1200
1400
0 500 1000 1500
0
100
200
300
400
500
600
red
R=0.90, p<0.0001
0
10
20
30
40
50
60
70
80
90
0 20 40 60 80 1000
10
20
30
40
Φd
β-CELLS
LIVER GLUCOSE
INSULIN
BRAIN
MUSCLE
TISSUES
PRODUCTION
DEGRADATIONSECRETION
UTILIZATION-
+Insulin Sensitivity
+
β-cell Responsivity
The Glucose-Insulin System
βeta-Cell Responsivity
βeta-Cell Responsivity
Insulin Sensitivity Insulin Sensitivity
Normal ToleranceNormal
NormalReduced
I
II
2
Impaired Tolerance
Increased
Efficiency of the Control: Disposition Index(Bergman & Cobelli, 1981, Cobelli et at, 2007)
Insulin Sensitivity x βeta-Cell Function= Constant
Disposition Index
0
60
120
0 20 40 60 80
βeta-Cell Responsivity
βeta-Cell Responsivity
Insulin Sensitivity Insulin Sensitivity
Y: DI= 459
E: DI=313
Hepatic Insulin Extraction(Toffolo et al, 2006)
ISR
C-PEPTIDE
INSULIN
ISR
IDR
LIVER
-CELLS
LIVER I
n
k0,1
k2,1
k1,2
CP1 CP2
Hepatic Extraction
0.00
0.20
0.40
0.60
0.80
1.00
0 60 120 180 240 300 360 420
ELDERLY
YOUNG
t [min]
0.00
0.20
0.40
0.60
0.80
1.00
EY
*(%)(%
)* p<0.05
Profile
ISR(t) - IDR(t)
ISR(t)HE(t) =
Index
(N=59Y vs 145E)
T
ò
ò0
T
0
ISR(t)dt
ISR(t)dt – IDR(t)dt
HE =òT
0
What Happens If You Add A Tracer?
Tracers Allow Segregation of Glucose Disposal from Production
Oral Glucose Minimal Model (Dalla Man & Cobelli, 2002)
SI: Insulin Sensitivity (liver & periphery)
OGTT/MEAL
k3
k5
GLUCOSE TISSUES
k1
k4
k6
REMOTE INSULIN k2
INSULIN
LIVER
Gastrointestinal Tract
SI
Glucose
Time [min] Time [min]
Stable Tracer Glucose
Time [min]
[mm
ol L
-1]
[mU
ml-1
]
Insulin
Labelled Meal/OGTT
0 60 120 180 240 300 360 420
[mm
ol L
-1]
0
3
4
6
8
10
0
20
40
60
80
0 60 120 180 240 300 360 420
0
0.2
0.4
0.6
0.8
0 60 120 180 240 300 360 420
Disposal Insulin Sensitivity
Liver
Insulin
Glucose
Production Utilization
Tissues
Remote Insulin
SI
“COLD” MINIMAL MODEL
SI: Insulin Sensitivity
(Utilization + Production)
SID: Disposal Insulin Sensitivity
(Utilization Only)
Glucose
Utilization
Remote Insulin
SID
“HOT” MINIMAL MODEL
Tissues
Insulin
SID
* p<0.01
0
5
10
Y E
*
10-4
dl/
kg/m
in p
er m
U/m
l
Meal: 59 Y vs 145 E
20
15
Hepatic Insulin Sensitivity
SIL = SI – SI
DFrom SI and SID
Liver
Insulin
Glucose
Production Utilization
Tissues
Remote Insulin
SI
“COLD” MINIMAL MODEL
Insulin
Glucose
Utilization
Remote Insulin
SID
“HOT” MINIMAL MODEL
Tissues
Meal: 59 Y vs 145 E
SID * p<0.01
0
5
10
Y E
*
10-4
dl/
kg/m
in p
er m
U/m
l
20
15
SI
0
5
10
Y E
*
10-4
dl/
kg/m
in p
er m
U/m
l
20
15
SIL
0
2
4
Y E
10-4
dl/
kg/m
in p
er m
U/m
l 8
6
Use in Pathophysiology
1) Role of age and gender (Basu et al, Diabetes 2006)
2) Pathogenesis of Prediabetes (Bock et al, Diabetes 2006)
4) Role of Race (Petersen et al, Proceedings of the National Academy of Science 2006)
8) Diurnal Variation of Glucose Tolerance (Dr. E. Van Cauter, University of Chicago, Chicago, IL)
5) Efficiency of Anti-aging Drugs (Nair et al, New England Journal of Medicine 2006)
7) Children and Adolescent (Calì et al, Diabetes Care 2009)
3) Type 2 Diabetes (Dr. A. Basu, Mayo Clinic Rochester, MN)
6) Effect of DPP-4 Inhibitors (Dalla Man et al, Diabetes Care 2009)
Role of age and gender (Basu et al, 2006)
Subjects and Protocols
38 Elderly Men (EM), 29 Elderly Women (EW), 10 Young Men (YM), 11 Young Women (YW) underwent a labelled mixed meal.
Elderly vs Young
YoungElderly
0
4
8
12
16
20
10-4
dl/
kg/m
in p
er μ
U/m
l
SI
*
* p<0.05
10-4
dl/
kg/m
in p
er μ
U/m
l
SI
0
4
8
12
16
WomenMen
Men vs Women
Use in Pathophysiology
1) Role of age and gender (Basu et al, Diabetes 2006)
2) Pathogenesis of Prediabetes (Bock et al, Diabetes 2006)
4) Role of Race (Petersen et al, Proceedings of the National Academy of Science 2006)
8) Diurnal Variation of Glucose Tolerance (Dr. E. Van Cauter, University of Chicago, Chicago, IL)
5) Efficiency of Anti-aging Drugs (Nair et al, New England Journal of Medicine 2006)
7) Children and Adolescent (Calì et al, Diabetes Care 2009)
3) Type 2 Diabetes (Dr. A. Basu, Mayo Clinic Rochester, MN)
6) Effect of DPP-4 Inhibitors (Dalla Man et al, Diabetes Care 2009)
OGTT in IFG vs NFGN=32 vs 28
(Bock et al, 2006)Plasma Glucose
0
3
6
9
12
15
-60 0 60 120 180 240 300 360
mm
ol/l
IFG NFG
minutes
0
250
500
750
-60 0 60 120 180 240 300 360
IFGNFG
0
1
2
3
4
5
-60 0 60 120 180 240 300 360
IFGNFG
Plasma Insulin
Plasma C-peptide
nmol
/l
pmol
//l
minutesminutes
0
400
800
1200
10-1
4 dl/
kg
/min
-2 p
er p
mo
l/L
0
400
800
1200
10
-14 d
l/kg
/min
-2 p
er p
mo
l/L
0
4000
8000
12000
16000
10-1
4 dl/
kg
/min
pe
r p
mo
l/L
DI
DId
DIs
Disposition Indices
IFG NGT NFG IGT IFG IGT IFG DM
*
*
*
*
*
*
*
*
*
*
*
*
NFGNGT
IFG
IFG NGT
IFG NGT
NFG IGT
NFG IGT
IFG IGT
IFG IGT
IFG DM
IFG DM
NFGNGT
IFG
NFGNGT
IFG
Use in Pathophysiology
1) Role of age and gender (Basu et al, Diabetes 2006)
2) Pathogenesis of Prediabetes (Bock et al, Diabetes 2006)
4) Role of Race (Petersen et al, Proceedings of the National Academy of Science 2006)
8) Diurnal Variation of Glucose Tolerance (Dr. E. Van Cauter, University of Chicago, Chicago, IL)
5) Efficiency of Anti-aging Drugs (Nair et al, New England Journal of Medicine 2006)
7) Children and Adolescent (Calì et al, Diabetes Care 2009)
3) Type 2 Diabetes (Dr. A. Basu, Mayo Clinic Rochester, MN)
6) Effect of DPP-4 Inhibitors (Dalla Man et al, Diabetes Care 2009)
SI
Φd Φs
DI
10-5
dl/
kg/m
in p
er p
mol
/l10
-9
10-9
min
-110
-14 d
l/kg
/min
2 per
pm
ol/l
NormalDiabetic
0
5
10
15
20
25
30
*
0
200
400
600
800
*
0
10
20
30
40
50
60
0
400
800
1200
1600
*
*
*
NormalDiabetic
NormalDiabetic NormalDiabetic
Type 2 Diabetes(Basu A. et al 2009)
Use in Pathophysiology
1) Role of age and gender (Basu et al, Diabetes 2006)
2) Pathogenesis of Prediabetes (Bock et al, Diabetes 2006)
4) Role of Race (Petersen et al, Proceedings of the National Academy of Science 2006)
8) Diurnal Variation of Glucose Tolerance (Dr. E. Van Cauter, University of Chicago, Chicago, IL)
5) Efficiency of Anti-aging Drugs (Nair et al, New England Journal of Medicine 2006)
7) Children and Adolescent (Calì et al, Diabetes Care 2009)
3) Type 2 Diabetes (Dr. A. Basu, Mayo Clinic Rochester, MN)
6) Effect of DPP-4 Inhibitors (Dalla Man et al, Diabetes Care 2009)
Efficiency of Anti aging Drug
- 87 elderly men e 57 elderly women underwent a mixed meal test
- After a 2 yr DHEA or Testosterone same test
SI
0
5
10
15
20
10^-
4 dl
/kg/
min
per
uU
/ml
Pre Post
Women
Placebo DHEA
Pre Post Pre Post
Men
Placebo DHEA
Pre Post
Testosterone
Pre Post
Use in Pathophysiology
1) Role of age and gender (Basu et al, Diabetes 2006)
2) Pathogenesis of Prediabetes (Bock et al, Diabetes 2006)
4) Role of Race (Petersen et al, Proceedings of the National Academy of Science 2006)
8) Diurnal Variation of Glucose Tolerance (Dr. E. Van Cauter, University of Chicago, Chicago, IL)
5) Efficiency of Anti-aging Drugs (Nair et al, New England Journal of Medicine 2006)
7) Children and Adolescent (Calì et al, Diabetes Care 2009)
3) Type 2 Diabetes (Dr. A. Basu, Mayo Clinic Rochester, MN)
6) Effect of DPP-4 Inhibitors (Dalla Man et al, Diabetes Care 2009)
Children and Adolescents (Calì et al, Diabetes Care 2009)
GLUCOSE
0
20
40
60
80
100
120
140
160
180
0 30 60 90 120 150 180
min
mg/
dl
NGT_NFG
NGT_IFG
IGT_NFG
IGT_IFG
INSULIN
0
50
100
150
200
250
300
350
400
450
0 30 60 90 120 150 180
min
uU/m
l
C-PEPTIDE
0
1000
2000
3000
4000
5000
6000
7000
0 30 60 90 120 150 180
min
pmol
/l
SI
0
1
2
3
4
5
6
7
8
10-4
dl/
kg/m
in p
er u
U/m
l
NGT_NFG
NGT_IFG
IGT_NFGIGT_IFG
* *^
*
* p<0.05 vs NGT-NFG; ^ p<0.05 vs NGT-IFG; # p<0.05 vs IGT-NFG
Φd
0
500
1000
1500
2000
2500
3000
10-9
*^
*Φs
0
15
30
45
60
75
90
10-9
min
-1
*#
*
Minimal Models Parameters measurement
Maximal Models In Silico Trials
Background Models to Simulate:
often not possible, appropriate, convenient or desirable to perform experiments in humans, e.g. testing of glucose sensors and insulin infusion algorithms for closed loop control during normal life condition
Can Models to Measure be used as Models to Simulate for in Silico Trial?
No
Models to Measure need to be minimal (parsimonious)Models to Simulate need to be maximal (large scale)
47
(mg/
dl)
Glucose
50
100
150
200
250
0 60 120 180 240 300 360 420
(pm
ol/l
)
Insulin
0
100
200
300
400
500
600
0 60 120 180 240 300 360 420
Production
(mg/
kg/m
in)
0
0.5
1
1.5
2
2.5
0 60 120 180 240 300 360 420
t (min)
(mg/
kg/m
in)
Utilization
0
2
4
6
8
10
12
0 60 120 180 240 300 360 420
(pm
ol/k
g/m
in)
t (min)
Secretion
0
2
4
6
8
10
12
14
16
0 60 120 180 240 300 360 420
(mg/
kg/m
in)
Rate of Appearance
0
2
4
6
8
10
12
14
0 60 120 180 240 300 360 420
t (min)
(N=204 Normals)
Fluxes, in addition to concentrations, available
DataRange
New Generation of Simulation Models
Healthy State Meal Simulator
Meal
GLUCOSESYSTEM
GASTRO-INTESTINAL TRACT
LIVER
BETA-CELL
MUSCLE AND ADIPOSE
TISSUE
INSULINSYSTEM
(Dalla Man et al, 2007)
Plasma Glucose
Plasma Insulin
60
80
100
120
140
160
180
0 60 120 180 240 300 360 420
0
100
200
300
400
500
0 60 120 180 240 300 360 420
Secretion
Production
0
2
4
6
8
10
12
0 60 120 180 240 300 360 420
0
2
4
6
8
10
0 60 120 180 240 300 360 420
0
100
200
300
400
500
600
700
0 60 120 180 240 300 360 420
0
0.5
1
1.5
2
0 60 120 180 240 300 360 420
Utilization
Rate of Appearance
Renal Excretion
Identification: System Decomposition & Forcing Function Strategy
Plasma Insulin
LIVER
Plasma Glucose Glucose
Production
BETA CELL
Plasma Insulin
Insulin Secretion
Plasma Glucose
Glucose Rate of Change
Plasma Insulin
Glucose Rate of Appearance
MUSCLE AND ADIPOSE TISSUE
Glucose Production
Plasma Glucose
Glucose Utilization
GASTRO-INTESTINAL TRACTMeal
Glucose Rate of Appearance
Muscle and Adipose Tissue ModelPlasma Insulin (I)
Glucose Rate of Appearance (Ra)
MUSCLE AND ADIPOSE TISSUE
Glucose Production (EGP)
Plasma Glucose (G)
Glucose Utilization (U)
Model
TissuesGt
k21
k12
Kg Ki
G
PlasmaGp
Insulin Action
X
I
EGP
Ra
p2Up2U
U
Gt(t)
U(t)
Vm(X1)
Vm(X2)
Vm(X3)
Km(X3) Km(X2) Km(X1)
X1<X2<X3
52
Utilization
t (min)
U (
mg/
kg/m
in)
Glucose
t (min)
G (
mg/
dl)
0
1
2
3
4
5
6
7
8
9
0 60 120 180 240 300 360 420
data
fit
60
80
100
120
140
160
180
0 60 120 180 240 300 360 420
data
fit
Results
24 h Simulation: average model
0
0t (hours) t (hours)
(mg
/dl)
(mg
/kg
/min
)
(mg
/kg
/min
)
(mg
/kg
/min
)
(pm
ol/k
g/m
in)
(pm
ol/l
)
Glucose Insulin Production
UtilizationRate of Appearance Secretion
0
2
4
6
8
10
12
6 9 12 15 18 21 24
60
80
100
120
140
160
180
3 6 9 12 15 18 21 24
0
100
200
300
6 9 12 15 18 21 24
0
0.6
1.2
1.8
6 9 12 15 18 21 24
0
2
4
6
8
6 9 12 15 18 21 24
0
2
4
6
8
10
6 9 12 15 18 21 24
t (hours)
45 g 70 g 70 g
Rate Constant of Liver Insulin Action
0
20
40
60
80
100
0.0010.011
0.0220.032
0.0430.053
0.064
min^-1
0
20
40
60
80
100
0.0110.022
0.0320.043
0.0530.064
mg/kg/min/(pmol/l)
LiverGlucose Effectiveness
0
20
40
60
80
100
0.0000.003
0.0060.009
0.0120.015
0.018
min^-1
0
20
40
60
80
100
0.000.03
0.050.07
0.090.11
0.13
min^-1
0
20
40
60
80
100
0.000.03
0.060.09
0.120.15
0.18
mg/kg/min per pmol/L
0
20
40
60
80
100
1.251.45
1.651.86
2.062.26
mg/kg
LiverInsulin Sensitivity
Rate Constant of Peripheral Insulin Action
PeripheralGlucose Effectiveness
PeripheralInsulin Sensitivity
Inter-Subject Variability
Generation of virtual subjects
Generation of In Silico Normal Subjects
# 1
Glucose
t (hours)
20 25 30 403530
60
90
120
150
180
(mg/
dl)
200
# 2
Glucose
20 25 30 4035
(mg/
dl)
t (hours)
30
60
90
120
150
180
200
# 3
Glucose
20 25 30 4035
(mg/
dl)
t (hours)
30
60
90
120
150
180
200
# 100
.....
Glucose
20 25 30 4035
(mg/
dl)
t (hours)
30
60
90
120
150
180
200
Healthy State Simulator12 Differential Equations, 26 Parameters
Pre-diabetes Simulator
12 Differential Equations, 26 Parameters
Type 2 Simulator12 Differential Equations, 26 Parameters
Type 1 Simulator
13 Differential Equations, 26 Parameters
What is an Artificial Pancreas?
SavesYears
Traditional vs. Accelerated Development
Concept
Animal Trials
Clinical Trials
Product
Concept
In Silico Trials
Clinical Trials
Product
Model of Type 1 Diabetes
Insulin Pump
SCInsulinKinetics
Secretion
BETA-CELL
Renal Excretion
Meal
Rate of Appearance
Production UtilizationPLASMA GLUCOSE
GASTRO-INTESTINAL TRACT
LIVERMUSCLE
& ADIPOSE TISSUE
PLASMA INSULIN
Degradation• Tested against, and showing excellent agreement with:
Common clinical knowledgeLab traces of induced
hypoglycemiaField data of children with
T1DM
61
02468
10
0 60 120180 240 300360 420
Artificial Pancreas In Silico Trial
Renal Excretion
Meal
Rate of Appearance
Production Utilization
Plasma Glucose
6080
100120140160180
0 60 120 180 240 300 360 420
PLASMA GLUCOSE
GASTRO-INTESTINAL TRACT
LIVER
Plasma Insulin
0
100
200
300
400
500
0 60 120180 240 300 360 420
MUSCLE AND ADIPOSE TISSUE
PLASMA INSULIN
Degradation
00.51
1.52
0 60 120180 240 300360420
02468
1012
0 60 120 180 240 300 360 420
SecretionBETA-CELL
0100200300400500600700
0 60 120 180 240 300 360 420
SubcutaneousInsulin Infusion Pump
SensorControllerController A Sensor IController B Sensor IIController C Sensor IIIController D Sensor IV
SubcutaneousInsulin Infusion Pump A
SubcutaneousInsulin Infusion Pump B
SubcutaneousInsulin Infusion Pump C
SubcutaneousInsulin Infusion Pump D
In Silico Selection of Control Strategy:Proportionl-Integral-Derivative vs Model Predictive Control
Health T1DM + PID controller
T1DM + Model Predictive Control
Bloo
d G
luco
se (m
g/dl
)
Time (hours)0 12 24 36 48 60 72
January 18, 2008:Simulation accepted by FDAas substitute to animal trials
(Master file #1521)
Concept
Animal Trials
Clinical Trials
Product
Concept
In Silico Trials
Clinical Trials
Product
Traditional vs. Accelerated Development
Regulatory Approval for clinical trialsbased entirely on in silico testing:
April 17, 2008 (UVA IDE);May 20, 2008 (Padova EC)
Conclusions
• Importance of System Models in Diabetes
• Minimal Models:
• Powerful Tools to Measure & Understand the Pathophysiology of Diabetes
• Maximal Models:
• Importance of prediabetes, type 2 and type 1 diabetes simulators for in silico trials