Modelling of drug-effect on time-varying biomarkers
Licentiate thesis, 2018
and clinical phases of drug trials. Mathematical modelling can lead to improved
understanding of the underlying biological mechanisms, help in finding shortcomings
of experimental design and suggest improvements, or be an effective tool
in simulation-based analyses. This thesis addresses the modelling of time-varying
biomarkers both with and without drug-treatment. Pharmacokinetic/pharmacodynamic
models were used to describe observed drug concentrations and biomarkers.
These are modelled in the framework of compartmental modelling described by
ordinary differential equations.
This thesis contains two papers in manuscript-form. In the first paper, a metaanalysis
was performed of an existing model and previously published data for the
stress-hormone cortisol and the drug dexamethasone. Cortisol exhibits a circadian
rhythm, resembling oscillations, and is therefore a time-varying target for treatment.
The aim was to utilize the model for prediction of the outcome of a medical
test used in veterinary treatments on horses. In addition to model parameters,
inter-individual variability was modelled and estimated in a Bayesian framework.
This allowed simulation of test outcomes for the whole population, which in turn
were used to evaluate available test protocols and suggest improvements.
In the second paper, an improved model was constructed for the cytokine TNFα
after challenge with LPS in addition to intervention treatment. TNFα is not measurable
in healthy subjects but release into blood plasma can be provoked by
challenge with LPS. The result is a short-lived turnover of TNFα. A test compound
targeting intervention of TNFα release was included in the study. Comprehensive
experimental data from two studies was available and allowed to model features of
TNFα release, that were not addressed in previously published models. The final
model was then used to analyse the current experimental design and correlations
between LPS challenge and test compound effectiveness. The paper provides
suggestions for future experimental designs.
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Probability Theory and Statistics
C3SE (Chalmers Centre for Computational Science and Engineering)
Chalmers University of Technology
Room Pascal, Chalmers tvärgata 3
Opponent: Peter Gennemark, AstraZeneca, Sverige