Model-based quantification of drug effect is an efficient tool during pre-clinical and clinical phases of drug trials. Mathematical modelling can lead to improved understanding of the underlying biological mechanisms, help in finding short-comings 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 meta-analysis 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.