1. A mechanism-based disease progression
model to analyse long-term treatment
effects on disease processes underlying
type 2 diabetes
Workshop
Yvonne Rozendaal
y.j.w.rozendaal@tue.nl
“The interplay of fat and carbohydrate metabolism
with application in Metabolic Syndrome and Type 2
Diabetes”
December 12th 2013

2. Introduction
• Disease progression
– multi-scale problem
– how to assess/measure?
• Treatment interventions
– effect of treatment on disease progression?
short-term vs long-term
• How to simulate adaptations & interventions?
2

3. Type 2 Diabetes Mellitus (T2DM)
• Impaired beta-cell function
• Reduced insulin sensitivity
chronic loss of
glycemic control
• Monitoring glycemic control: biomarkers
– FPG: fasting plasma glucose
secondary
– FSI: fasting serum insulin glycemic markers
– HbA1c: glycosylated hemoglobin
primary glycemic marker
how to derive
disease status?
3

4. T2DM treatment
• hypoglycemic effect: short-term
– immediate symptomatic effects on glycemic
control
• inhibitory effect on disease progression:
long-term
– protect against T2DM progression
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5. Objective
disease progression
progressive loss of beta-cell
function and insulin sensitivity
metabolic biomarkers
adaptations in
biological network
FPG
FSI
HbA1c
treatment interventions
pharmacological therapy
computational model:
description and quantification of inputs
minimal model
disease progression model
test functionality of method on minimal model:
 human vs. mouse
 glucose vs. lipid metabolism
introduction to ADAPT
application of ADAPT
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6. Modelling disease progression (1)
• Disentangle treatment effects
– long-term
loss of beta-cell function
and insulin sensitivity
– short-term
anti-hyperglycemic effects
• Computational model:
study & quantify
time-course effects
de Winter et al. (2006) J Pharmacokinet Pharmacodyn,33(3):313-343
disease progression model
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application of ADAPT
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7. PK/PD modelling
• PharmacoKinetic-PharmacoDynamic modelling
• Simple kinetics are modelled using
minimal/macroscopic models
• e.g. absorption profiles
disease progression model
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8. T2DM disease progression model (1)
glucose – insulin – HbA1c
• Model components
– FPG: fasting plasma glucose
– FSI: fasting serum insulin
– HbA1c: glycosylated hemoglobin
• Physiological FPG-FSI homeostasis:
– feedback between FSI and FPG
FPG stimulates FSI production: FSI production rate ∝ FPG concentration
– feed-forward between FPG and HbA1c
HbA1c production rate ∝ to FSI concentration
disease progression model
introduction to ADAPT
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9. T2DM disease progression model (2)
model structure
B: beta-cell function
(disease status) k
in
EFB: treatment effect
on insulin secretion
S: insulin sensitivity
(disease status)
k in
EFS: insulin sensitizing
effect of treatment
k in
disease progression model
FSI
k out
homeostatic
feed-backs
FPG
k out
feed-forward
HbA1c
introduction to ADAPT
k out
application of ADAPT
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10. T2DM disease progression model (3)
model equations
treatment specific
factor of insulinsecretogogues
d FSI
disease status:
fraction of remaining beta-cell function
EF B B ( FPG
dt
d FPG
k in FPG
dt
EF S S FSI
d HbA
1c
dt
FPG k in HbA
treatment specific
factor of insulinsensitizers
disease progression model
3 . 5 ) k in FSI
FSI k out FSI
FPG k out FPG
HbA
1c
1c
k out HbA
1c
disease status:
fraction of remaining insulin sensitivity
introduction to ADAPT
application of ADAPT
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11. T2DM disease progression model (1)
disease status
• Beta-cell function
1
B
fraction of remaining
beta-cell function
1
exp( b 0
shift of disease
progression curve
• Insulin sensitivity
fraction of remaining
hepatic insulin-sensitivity
S
1
1
exp( s 0
rB t )
slope of
disease
progression
curve
rS t )
• Assumption: asympotically decrease over time
disease progression model
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12. Model comparison with data (1)
• Long-term (1y) follow-up of treatment-naïve T2DM
patients
• 3 treatment arms: monotherapy with different
hypoglycemic agents
– pioglitazone: insulin sensitizer
• enhances peripheral glucose uptake
• reduces hepatic glucose production
– metformin: insulin sensitizer
• decreases hepatic glucose production
– gliclazide: insulin secretogogue
• stimulates insulin secretion by the pancreatic beta-cells
disease progression model
introduction to ADAPT
application of ADAPT
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13. FPG [mmol/L]
Model comparison with data (2)
disease progression model
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14. Reproduction of results (1)
 Metabolic biomarkers over time
although initial
decrease, glycemic
control still gradually
decreases over time
disease progression model
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15. Reproduction of results (2)
 Disease status
gliclazide:
insulin secretogogue
pioglitazone & metformin:
insulin sensitizers
however, morphology
of disease progression
curves unknown...
disease progression model
introduction to ADAPT
application of ADAPT
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16. Introduction to ADAPT (1)
• Phenotype transition over time
treatment interventions
medication, surgery, ...
disease progression
phenotype A
phenotype B
which adaptations
occur?
• Analysis of Dynamic Adaptations in Parameter
Trajectories
Tiemann et al. (2011). BMC Syst Biol,26(5):174
Tiemann et al. (2013). PLoS Comput Biol,9(8):e1003166
disease progression model
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17. Introduction to ADAPT (2)
• Phenotype transition:
– gradual, long-term processes
– measurements at metabolome level
• Adaptation at proteome and transcriptome level
• Model at metabolome level
• Time-dependency implemented using timevarying parameters
disease progression model
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18. Modelling phenotype transition (1)
 long-term discrete data: different phenotypes
treatment
disease progression
disease progression model
introduction to ADAPT
application of ADAPT
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19. Modelling phenotype transition (2)
 long-term discrete data: different phenotypes
 estimate continuous data: cubic smooth spline
introduce artificial
intermediate phenotypes
disease progression model
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20. Modelling phenotype transition (3)
 long-term discrete data: different phenotypes
 estimate continuous data: cubic smooth spline
 incorporate uncertainty in data: multiple describing functions
disease progression model
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21. Parameter estimation (1)
 steady state model
disease progression model
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22. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
minimize difference between data and model simulation
disease progression model
introduction to ADAPT
application of ADAPT
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23. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
disease progression model
introduction to ADAPT
application of ADAPT
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24. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
disease progression model
introduction to ADAPT
application of ADAPT
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25. Parameter estimation (2)
 steady state model
 iteratively calibrate model to data: estimate parameters over time
disease progression model
introduction to ADAPT
application of ADAPT
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26. Estimated parameter trajectories
 effect of parameter adaptations on underlying processes?
down-regulation
up-regulation
stochastic
behaviour...
unaffected
physiologically
unrealistic
disease progression model
introduction to ADAPT
application of ADAPT
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27. Possible applications for ADAPT
• Unravel which processes in network might be
responsible for phenotype transition
• Guide new experiment design
• Define possible pharmacological targets
disease progression model
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28. Application of ADAPT in
disease progression model
fraction of beta-cell function:
time-dependent parameter
d FSI
B ( FPG
dt
d FPG
k in FPG
dt
S FSI
d HbA
dt
1c
3 . 5 ) k in FSI
FSI k out FSI
time-constant
parameters
FPG k out FPG
FPG k in HbA
HbA1c
1c
k out HbA
1c
fraction of insulin sensitivity:
time-dependent parameter
disease progression model
introduction to ADAPT
application of ADAPT
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29. Disease progression model
vs. application of ADAPT (1)
 Metabolic biomarkers over time
treatment with pioglitazone
FPG & FSI:
ADAPT reproduces
model predictions
HbA1c:
performance ADAPT
disease progression model
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application of ADAPT
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30. Disease progression model
vs. application of ADAPT (2)
 Parameter trajectories: disease status
treatment with pioglitazone
ADAPT suggests dynamic
disease progression curves
rather than pre-defined
mathematical functions by
de Winter et al.
disease progression model
introduction to ADAPT
application of ADAPT
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31. Disease progression model
vs. application of ADAPT (2)
 Parameter trajectories: disease status
treatment with pioglitazone
ADAPT suggests dynamic
disease progression curves
rather than pre-defined
mathematical functions by
de Winter et al.
disease progression model
introduction to ADAPT
application of ADAPT
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32. Conclusions & Future work
• Disease progression model & ADAPT approach both
useful for monitoring disease status
• ADAPT
– applicable to both mice/human, glucose/lipoprotein
metabolism and multiscale models
– more dynamically correct representation of beta-cell
function and insulin sensitivity using ADAPT
• However;
– How to disentangle disease progression effects from hypoglycemic effects?
– How to estimate time-varying parameters in conjunction with time-constant
parameters?
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33. Acknowledgements