A central aim in drug development is to ensure that the new drug is efficacious and safe in the intended patient population. Mathematical models describing the pharmacokinetic-pharmacodynamic (PK-PD) properties of a drug are valuable to increase the knowledge about drug effects and disease and can be used to inform decisions. The aim of this thesis was to develop mechanism-based PK-PD-disease models for important safety and efficacy biomarkers used in anti-diabetic drug development. Population PK, PK-PD and disease models were developed, based on data from clinical studies in subjects with varying degrees of renal function, non-diabetic subjects with insulin resistance and patients with type 2 diabetes mellitus (T2DM), receiving a peroxisome proliferator-activated receptor (PPAR) α/γ agonist, tesaglitazar. The PK model showed that a decreased renal elimination of the metabolite in renally impaired subjects leads to increased levels of metabolite undergoing interconversion and subsequent accumulation of tesaglitazar. Tesaglitazar negatively affects the glomerular filtration rate (GFR), and since renal function affects tesaglitazar exposure, a PK-PD model was developed to simultaneously describe this interrelationship. The model and data showed that all patients had decreases in GFR, which were reversible when discontinuing treatment. The PK-PD model described the interplay between fasting plasma glucose (FPG), glycosylated haemoglobin (HbA1c) and haemoglobin in T2DM patients. It provided a mechanistically plausible description of the release and aging of red blood cells (RBC), and the glucose dependent glycosylation of RBC to HbA1c. The PK-PD model for FPG and fasting insulin, incorporating components for β-cell mass, insulin sensitivity and impact of disease and drug treatment, realistically described the complex glucose homeostasis in the heterogeneous patient population. The mechanism-based PK, PK-PD and disease models increase the understanding about T2DM and important biomarkers, and can be used to improve decision making in the development of future anti-diabetic drugs.

<p>Cyclooxygenase-2 (COX-2) catalyses prostaglandin synthesis from arachidonic acid during inflammation. COX-2 is expressed in the normal brain and is induced in neurological disorders. There is evidence that COX-2 is involved in secondary events leading to cell death in the brain. The first objective was to study the expression of COX-2 in the brain after kainate (KA)-induced limbic seizures and brain trauma caused by controlled cortical contusion (CCC) and fluid percussion injury (FPI). COX-2 mRNA and protein were strongly induced by limbic seizures in the hippocampus, amygdala and piriform cortex. CCC and FPI resulted in an upregulation of COX-2 mainly in the dentate gyrus and cortex, with differences in expression levels in these regions between the models. The second objective was to evaluate the effects of selective COX-2 inhibitors on delayed cell death. Limbic seizures induced cell death in parts of the hippocampus, amygdala and functionally connected regions. Treatment with the selective COX-2 inhibitor rofecoxib 8 h after KA injection significantly reduced hippocampal cell death. Pre-treatment with the COX-2 inhibitor nimesulide augmented acute seizures with increased mortality and thus the effect of nimesulide on delayed cell death could not be evaluated. Effects of rofecoxib on trauma-induced cell death were studied in the FPI model. FPI induced delayed cell death mainly in the ipsilateral cortex and bilaterally in the dentate gyrus. Rofecoxib treatment, starting directly after injury was caused, had no protective effect against cell death. </p><p>The results suggest that COX-2 inhibition may be both detrimental and beneficial and largely dependent on the time schedule of treatment. COX-2 inhibitors might thus be of value as a neuroprotective treatment approach, provided that the role of COX-2 and the time course of effects of its metabolites in the brain are elucidated.</p>

Cyclooxygenase-2 (COX-2) catalyses prostaglandin synthesis from arachidonic acid during inflammation. COX-2 is expressed in the normal brain and is induced in neurological disorders. There is evidence that COX-2 is involved in secondary events leading to cell death in the brain. The first objective was to study the expression of COX-2 in the brain after kainate (KA)-induced limbic seizures and brain trauma caused by controlled cortical contusion (CCC) and fluid percussion injury (FPI). COX-2 mRNA and protein were strongly induced by limbic seizures in the hippocampus, amygdala and piriform cortex. CCC and FPI resulted in an upregulation of COX-2 mainly in the dentate gyrus and cortex, with differences in expression levels in these regions between the models. The second objective was to evaluate the effects of selective COX-2 inhibitors on delayed cell death. Limbic seizures induced cell death in parts of the hippocampus, amygdala and functionally connected regions. Treatment with the selective COX-2 inhibitor rofecoxib 8 h after KA injection significantly reduced hippocampal cell death. Pre-treatment with the COX-2 inhibitor nimesulide augmented acute seizures with increased mortality and thus the effect of nimesulide on delayed cell death could not be evaluated. Effects of rofecoxib on trauma-induced cell death were studied in the FPI model. FPI induced delayed cell death mainly in the ipsilateral cortex and bilaterally in the dentate gyrus. Rofecoxib treatment, starting directly after injury was caused, had no protective effect against cell death. The results suggest that COX-2 inhibition may be both detrimental and beneficial and largely dependent on the time schedule of treatment. COX-2 inhibitors might thus be of value as a neuroprotective treatment approach, provided that the role of COX-2 and the time course of effects of its metabolites in the brain are elucidated.

In risk assessment of risk chemicals, variability in susceptibility in the population is an important aspect. The health hazard of a pollutant is related to the internal exposure to the chemical, i.e. the target dose, rather than the external exposure. The target dose may be calculated by physiologically based pharmacokinetic (PBPK) modeling. Furthermore, variability in target dose may be estimated by introducing variability in the physiological, anatomical, and biochemical parameters of the model. Data on these toxicokinetic model parameters may be found in the scientific literature. Since the early seventies, a large number of experimental inhalation studies of the kinetics of several volatiles in human volunteers have been performed at the National Institute for Working Life in Solna. To this day, only very limited analyses of these extensive data have been performed. A Bayesian analysis makes it possible to merge a priori knowledge from the literature with the information in experimental data. If combined with population PBPK modeling, the Bayesian approach may yield posterior estimates of the toxicokinetic parameters for each subject, as well as for the population. One way of producing these estimates is by so-called Markov-chain Monte Carlo (MCMC) simulation. The aim of the thesis was to apply the MCMC technique on previously published experimental data. Another objective was to assess the reliability of PBPK models in general by the combination of the extensive data and Bayesian population techniques. The population kinetics of methyl chloride, dichloromethane, toluene and styrene were assessed. The calibrated model for dichloromethane was used to predict cancer risk in a simulated Swedish population. In some cases, the respiratory uptake of volatiles was found to be lower than predicted from reference values on alveolar ventilation. The perfusion of fat tissue was found to be a complex process that needs special attention in PBPK modeling. These results provide a significant contribution to the field of PBPK modeling of risk chemicals. Appropriate statistical treatment of uncertainty and variability may increase confidence in model results and ultimately contribute to an improved scientific basis for the estimation of occupational health risks.