<p>Progress in individualized drug treatment is of increasing importance, promising to avoid much human suffering and reducing medical treatment costs for society. The strategy is to maximize the therapeutic effects and minimize the negative side effects of a drug on individual or group basis. To reach the goal, interactions between the human body and different drugs must be further clarified, for instance by using mathematical models. Whether clinical studies or laboratory experiments are used as primary sources of information, greatly</p><p>influences the possibilities of obtaining data. This must be considered both prior and during model development and different strategies must be used. The character of the data may also restrict the level of complexity for the models, thus limiting their usage as tools for individualized treatment.</p><p>In this thesis work two case studies have been made, each with the aim to develop a model for a specific human-drug interaction. The first case study concerns treatment of inflammatory bowel disease with thiopurines, whereas the second is about treatment of ovarian cancer with paclitaxel. Although both case studies make use of similar amounts of experimental data, model development depends considerably on prior knowledge about the systems, the character of the data and the choice of modelling tools. All these factors are presented for</p><p>each of the case studies along with current results. Further, a system for classifying different but related models is also proposed with the intention that an increased understanding will contribute to advancement in individualized drug dosage.</p> / Report code: LiU-Tek-Lic-2007:41.

Mathematical modelling is playing an increasingly important role in helping us to understand biological systems. The construction of biological models typically requires the use of experimentally-measured parameter values. However, varying degrees of uncertainty surround virtually all parameters in these models. Sensitivity analysis is one of the most important tools for the analysis of models, and shows how the outputs of a model, such as concentrations and reaction fluxes, are dependent on the parameters which make up the input. Unfortunately, small changes in parameter values can lead to the results of a sensitivity analysis changing significantly. The results of such analyses must therefore be interpreted with caution, particularly if a high degree of uncertainty surrounds the parameter values. Global sensitivity analysis methods can help in such situations by allowing sensitivities to be calculated over a range of possible parameter values. However, these techniques are computationally expensive, particularly for larger, more detailed models. Software was developed to enable a number of computationally-intensive modelling tasks, including two global sensitivity analysis methods, to be run in parallel in a high-throughput computing environment. The use of high-throughput computing enabled the run time of these analyses to be drastically reduced, allowing models to be analysed to a degree that would otherwise be impractical or impossible. Global sensitivity analysis using high-throughput computing was performed on a selection of both theoretical and physiologically-based models. Varying degrees of parameter uncertainty were considered. These analyses revealed instances in which the results of a sensitivity analysis were valid, even under large degrees of parameter variation. Other cases were found for which only a slight change in parameter values could completely change the results of the analysis. Parameter uncertainties are a real problem in biological systems modelling. This work shows how, with the help of high-throughput computing, global sensitivity analysis can become a practical part of the modelling process.

Progress in individualized drug treatment is of increasing importance, promising to avoid much human suffering and reducing medical treatment costs for society. The strategy is to maximize the therapeutic effects and minimize the negative side effects of a drug on individual or group basis. To reach the goal, interactions between the human body and different drugs must be further clarified, for instance by using mathematical models. Whether clinical studies or laboratory experiments are used as primary sources of information, greatly influences the possibilities of obtaining data. This must be considered both prior and during model development and different strategies must be used. The character of the data may also restrict the level of complexity for the models, thus limiting their usage as tools for individualized treatment. In this thesis work two case studies have been made, each with the aim to develop a model for a specific human-drug interaction. The first case study concerns treatment of inflammatory bowel disease with thiopurines, whereas the second is about treatment of ovarian cancer with paclitaxel. Although both case studies make use of similar amounts of experimental data, model development depends considerably on prior knowledge about the systems, the character of the data and the choice of modelling tools. All these factors are presented for each of the case studies along with current results. Further, a system for classifying different but related models is also proposed with the intention that an increased understanding will contribute to advancement in individualized drug dosage. / <p>Report code: LiU-Tek-Lic-2007:41.</p>