A mechanism-based disease progression model to analyse long-term treatment effects on disease processes underlying type 2 diabetes Workshop “The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2 Diabetes” December 12 th 2013 Yvonne Rozendaal [email protected]
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A mechanism-based disease progression model to analyse long-term treatment
effects on disease processes underlying type 2 diabetes
Workshop“The interplay of fat and carbohydrate metabolism with application in Metabolic Syndrome and Type 2
disease progression model introduction to ADAPT application of ADAPT
Modelling phenotype transition (3)
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long-term discrete data: different phenotypes estimate continuous data: cubic smooth spline incorporate uncertainty in data: multiple describing functions
disease progression model introduction to ADAPT application of ADAPT
Parameter estimation (1)
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steady state model
disease progression model introduction to ADAPT application of ADAPT
Parameter estimation (2)
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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
Parameter estimation (2)
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steady state model iteratively calibrate model to data: estimate parameters over time
disease progression model introduction to ADAPT application of ADAPT
Parameter estimation (2)
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steady state model iteratively calibrate model to data: estimate parameters over time
disease progression model introduction to ADAPT application of ADAPT
Parameter estimation (2)
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steady state model iteratively calibrate model to data: estimate parameters over time
disease progression model introduction to ADAPT application of ADAPT
Estimated parameter trajectories
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up-regulation
down-regulation
unaffectedstochastic
behaviour...
effect of parameter adaptations on underlying processes?
physiologically unrealistic
disease progression model introduction to ADAPT application of ADAPT
Possible applications for ADAPT
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• Unravel which processes in network might be responsible for phenotype transition
• Guide new experiment design
• Define possible pharmacological targets
disease progression model introduction to ADAPT application of ADAPT
Application of ADAPT indisease progression model
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1cHbA1cHbA
FPG
FPG
FSIFSI
HbA1cFPGt
HbA
FPGFSIt
FPG
FSI)5.3FPG(t
FSI
1c
outin
out
in
outin
kkd
d
kS
k
d
d
kkBd
d
fraction of beta-cell function:time-dependent parameter
fraction of insulin sensitivity:time-dependent parameter
time-constantparameters
disease progression model introduction to ADAPT application of ADAPT
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Metabolic biomarkers over timetreatment with pioglitazone
Disease progression modelvs. application of ADAPT (1)
disease progression model introduction to ADAPT application of ADAPT
HbA1c:performance ADAPT
FPG & FSI:ADAPT reproduces model predictions
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Parameter trajectories: disease statustreatment with pioglitazone
Disease progression modelvs. application of ADAPT (2)
disease progression model introduction to ADAPT application of ADAPT
ADAPT suggests dynamic disease progression curves rather than pre-defined mathematical functions by de Winter et al.
Disease progression modelvs. application of ADAPT (2)
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Parameter trajectories: disease statustreatment with pioglitazone
disease progression model introduction to ADAPT application of ADAPT
ADAPT suggests dynamic disease progression curves rather than pre-defined mathematical functions by de Winter et al.
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