Enzyme induction is a process whereby a molecule enhances the expression of enzymes. If the affected enzymes are involved in the elimination of a drug, this may result in a drug interaction. Induction is therefore of major concern during drug development and in clinical practice. The induction process depends on the half-life of the induced enzyme, the pharmacokinetics of the inducing agent, and the relationship between the inducer’s concentration and the induction stimulus. The aim of the conducted research was to investigate these key aspects of enzyme induction and the consequences that induction has for substrate elimination. Successful investigations of the induction process presuppose the existence of appropriate methods for the estimation of the metabolic activity. Enzyme activity measurements can be conducted in tissues with low enzyme content using the analytical method presented here. A model was developed describing the changes in the pharmacokinetics of clomethiazole and its metabolite NLA-715, that are attributable to carbamazepine induction. The consequences of the induction was explained using a mechanistic approach, acknowledging food-induced changes in the blood flow to the liver, and interpreting in vitro generated metabolic information. The time course of the induction process was examined in two investigations. In the first of these, the pharmacokinetics of the autoinducing drug phenobarbital and its effect on several enzymes were described in rats. This was accomplished by integrating the bidirectional interaction between drug and enzymes in a mechanistic manner. In the final investigation, the time course of the increase and cessation in enzyme activity was studied in healthy volunteers treated with carbamazepine. This investigation allowed the half-lives of CYP3A and CYP1A2 to be estimated. The key aspects of the enzyme induction process have been examined using mechanistic induction models. These novel models improve the understanding of the induction process and its consequences for substrate elimination.

Cefuroxime is a renally eliminated antibiotic used against a variety of different bacterial infections. The pharmacokinetics (PK) for cefuroxime was studied in 97 hospitalized patients using population analysis. To be able to measure cefuroxime in human serum a new sensitive analytical method was developed using mass spectrometry detection. The method was validated and shown to be sensitive and selective. Cystatin C was found to be a better covariate for cefuroxime clearance compared to the traditionally used creatinine clearance (CLcr). This relation might be useful when designing dosing strategies for cefuroxime and other renally eliminated drugs. The time-courses of the biomarkers C-reactive protein (CRP), serum amyloid A (SAA), interleukin-6 (IL-6) and body temperature were studied for the first 72 hours of cefuroxime treatment and was related to the duration of illness previous treatment with cefuroxime and to time to step-down of treatment. When duration of illness was short, CRP and SAA were showed increasing levels. None of the biomarkers could be used to differentiate between early or late step-down of therapy. By use of known PK and pharmacodynamic (PD) principles, dosing strategies based on CLcr for cefuroxime were estimated using minimization of a risk function. The risk function was constructed with the aim to expose patients to cefuroxime concentration above minimum inhibitory concentration (MIC) for 50 % of the dosing interval and to minimize the amount of drug administered in excess to reach the aim. Based on evaluation using wild type MIC distributions for Escherichia coli and Streptococcus pneumoniae improved dosing strategies were selected. In vitro experiments were performed exposing Streptococcus pyogenes to constant concentration of benzylpenicillin, cefuroxime, erythromycin, moxifloxacin or vancomycin. A semi-mechanistic PK/PD model characterizing the time-course of the antibacterial effect was developed using all data simultaneously. Internal validation showed the model being predictive and robust.

Paclitaxel (Taxol®) is now widely used against breast, ovarian and non-small-cell lung cancer. Anticancer agents generally have narrow therapeutic indices, often with myelosuppression (mainly neutropenia) as dose-limiting side effect. A further complicating factor is that paclitaxel when given as Taxol® has a nonlinear pharmacokinetic (PK) behaviour in plasma. Identifying risk groups more sensitive to chemotherapy due to either a PK or pharmacodynamic (PD) interindividual variability is of importance. The aim of the thesis was to develop predictive mechanism-based PK and PD models applicable for paclitaxel. PK and PK/PD models were developed for patient data from studies with relatively frequent sampling or sparse sampling schedules. Population analyses were performed using the software NONMEM. A pharmacokinetic model describing unbound, total plasma and blood concentrations of paclitaxel from known binding mechanisms was developed and validated. The nonlinear PK in plasma could to a large extent be explained by the micelle forming vehicle Cremophor EL (CrEL) and the unbound drug showed linear PK. Besides a binding component directly proportional to concentrations of CrEL, the model included both linear and nonlinear binding components in plasma and blood. Further, relations between the PK parameters and different demographic factors, including polymorphisms in the cytochrome P450s involved in paclitaxel metabolism, were investigated. A semi-physiological PD model for chemotherapy-induced myelosuppression was developed and applied to different anticancer drugs. The model included a self-renewal for proliferating cells, transit compartments describing the delay in observed myelosuppression and a feedback parameter reflecting the effect on the bone marrow from growth factors that can result in an overshoot in white blood cells. The system-related parameters estimated showed consistency across drugs and the difference in the drug-related parameter reflected the relative bone marrow toxicity of the drugs. Relations between demographic factors and the PD parameters were identified. The developed mechanism-based models promote a better understanding of paclitaxel PK and PD and may be used as tools in dosing individualisation and in development of dosing strategies for new administration forms and new drugs in the same area.

<p>In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. </p><p>Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. </p><p>The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.</p>

<p>This thesis investigates the potential of using positron emission tomography (PET) to study the biosynthesis and release of serotonin (5HT) at the brain serotonergic neuron. As PET requires probe compounds with specific attributes to enable imaging and quantification of biological processes, emphasis was placed on the evaluation of these attributes. </p><p>The experiments established that the 5HT transporter radioligand [¹¹C]-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile, [¹¹C]DASB, is suitable for imaging and quantification of transporters in rats and rhesus monkeys. In addition, the binding of [¹¹C]DASB in brain tissue is decreased when 5HT concentrations are increased by tranylcypromine administration. The sensitivity of [¹¹C]DASB binding, under these experimental conditions, to increased endogenous 5HT concentrations demonstrates the potential of in vivo monitoring of 5HT release in rat and monkey models.</p><p>The irreversible binding of 5-hydroxy-L-[β-¹¹C]tryptophan, [¹¹C]HTP, in the monkey brain was lower in the presence of NSD1015, which was used to inhibit the decarboxylase step in 5HT synthesis. [¹¹C]HTP seems thus to have potential for tracking changes in the activity of this biosynthesis enzyme. In contrast, the accumulation of [¹¹C]HTP was unaffected by clorgyline, which was used to inhibit metabolism of the probe in the brain. This appears to indicate that elimination of the main metabolite from the brain could be negligible and thus will not alter [¹¹C]HTP quantification. The extent and distribution of the irreversible binding of a substrate for the first enzyme in 5HT formation, α-[¹¹C]methyl-L-tryptophan, [¹¹C]AMT, was different from those for [¹¹C]HTP. This suggests that the two studied probe compounds provide estimates related to the enzyme activity of different steps in the 5HT biosynthesis pathway. </p><p>A reference tissue version of the Patlak method for the analysis of data obtained by PET was also developed. This approach takes into account irreversible binding in the reference region and appears, therefore, to yield more reliable parameter estimates than the conventional reference Patlak analysis. The method is recommended for parameter estimation of [¹¹C]HTP data when no metabolite-corrected plasma curve is available. </p><p>Knowledge of altered 5HT synthesis and release in disease states and the consequences for effective pharmacotherapy can improve our knowledge of the aetiology of certain psychiatric and neurological diseases and enhance our ability to design more effective drugs.</p>

<p> Malaria, Human Immunodeficiency Virus (HIV) and tuberculosis (TB) are global health problems having their worst situation in sub-Saharan Africa. Consequently, concomitant use of antimalarial, antiretroviral and antitubercular drugs may be needed, resulting in a potential risk of drug-drug interactions.</p><p>Cytochrome P-450 (CYP) enzyme induction/inhibition may lead to drug-drug interactions and can be detected by probe drugs. An analytical method was developed for the quantitation of mephenytoin, CYP2B6 and CYP2C19 probe, and its metabolites. </p><p>Induction/inhibition of principal CYP enzymes by the antimalarials; artemisinin, dihydroartemisinin, arteether, artemether and artesunate, was evaluated using the 4-hour plasma concentration ratios of probe drugs and their metabolites along with modelling the population pharmacokinetics of S-mephenytoin and its metabolites. The extent of change in enzymatic activities was different among the antimalarials, with artemisinin having strongest capacity for induction and inhibition, consequently, the strongest potential risk for drug-drug interactions. </p><p>Drug-drug interactions between the antitubercular rifampicin and the antiretrovirals nevirapine and lopinavir were assessed, in TB/HIV patients, by developing population pharmacokinetic models. Rifampicin increased nevirapine oral clearance. Simulations suggested that increasing the nevirapine dose to 300 mg twice daily when co-administered with rifampicin, would result in nevirapine concentrations above subtherapeutic levels, with minimum exposure above the recommended maximum concentration. Lopinavir is co-formulated with ritonavir in the ratio of 4:1. In children, increasing ritonavir dose four times did not completely compensate the enhancement of lopinavir oral clearance caused by rifampicin. However, the predicted lopinavir trough concentration was above the recommended minimum therapeutic concentration.</p><p>The work presented in this thesis followed an investigation line though not done for a particular drug. First the CYP enzymes involved in the interaction are identified. Afterwards, the expected drug-drug interaction is investigated where the potentially interacting drugs are concomitantly administered and an adjustment in the dose regimen is proposed that is subsequently evaluated.</p>

Malaria, Human Immunodeficiency Virus (HIV) and tuberculosis (TB) are global health problems having their worst situation in sub-Saharan Africa. Consequently, concomitant use of antimalarial, antiretroviral and antitubercular drugs may be needed, resulting in a potential risk of drug-drug interactions. Cytochrome P-450 (CYP) enzyme induction/inhibition may lead to drug-drug interactions and can be detected by probe drugs. An analytical method was developed for the quantitation of mephenytoin, CYP2B6 and CYP2C19 probe, and its metabolites. Induction/inhibition of principal CYP enzymes by the antimalarials; artemisinin, dihydroartemisinin, arteether, artemether and artesunate, was evaluated using the 4-hour plasma concentration ratios of probe drugs and their metabolites along with modelling the population pharmacokinetics of S-mephenytoin and its metabolites. The extent of change in enzymatic activities was different among the antimalarials, with artemisinin having strongest capacity for induction and inhibition, consequently, the strongest potential risk for drug-drug interactions. Drug-drug interactions between the antitubercular rifampicin and the antiretrovirals nevirapine and lopinavir were assessed, in TB/HIV patients, by developing population pharmacokinetic models. Rifampicin increased nevirapine oral clearance. Simulations suggested that increasing the nevirapine dose to 300 mg twice daily when co-administered with rifampicin, would result in nevirapine concentrations above subtherapeutic levels, with minimum exposure above the recommended maximum concentration. Lopinavir is co-formulated with ritonavir in the ratio of 4:1. In children, increasing ritonavir dose four times did not completely compensate the enhancement of lopinavir oral clearance caused by rifampicin. However, the predicted lopinavir trough concentration was above the recommended minimum therapeutic concentration. The work presented in this thesis followed an investigation line though not done for a particular drug. First the CYP enzymes involved in the interaction are identified. Afterwards, the expected drug-drug interaction is investigated where the potentially interacting drugs are concomitantly administered and an adjustment in the dose regimen is proposed that is subsequently evaluated.

This thesis investigates the potential of using positron emission tomography (PET) to study the biosynthesis and release of serotonin (5HT) at the brain serotonergic neuron. As PET requires probe compounds with specific attributes to enable imaging and quantification of biological processes, emphasis was placed on the evaluation of these attributes. The experiments established that the 5HT transporter radioligand [11C]-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile, [11C]DASB, is suitable for imaging and quantification of transporters in rats and rhesus monkeys. In addition, the binding of [11C]DASB in brain tissue is decreased when 5HT concentrations are increased by tranylcypromine administration. The sensitivity of [11C]DASB binding, under these experimental conditions, to increased endogenous 5HT concentrations demonstrates the potential of in vivo monitoring of 5HT release in rat and monkey models. The irreversible binding of 5-hydroxy-L-[β-11C]tryptophan, [11C]HTP, in the monkey brain was lower in the presence of NSD1015, which was used to inhibit the decarboxylase step in 5HT synthesis. [11C]HTP seems thus to have potential for tracking changes in the activity of this biosynthesis enzyme. In contrast, the accumulation of [11C]HTP was unaffected by clorgyline, which was used to inhibit metabolism of the probe in the brain. This appears to indicate that elimination of the main metabolite from the brain could be negligible and thus will not alter [11C]HTP quantification. The extent and distribution of the irreversible binding of a substrate for the first enzyme in 5HT formation, α-[11C]methyl-L-tryptophan, [11C]AMT, was different from those for [11C]HTP. This suggests that the two studied probe compounds provide estimates related to the enzyme activity of different steps in the 5HT biosynthesis pathway. A reference tissue version of the Patlak method for the analysis of data obtained by PET was also developed. This approach takes into account irreversible binding in the reference region and appears, therefore, to yield more reliable parameter estimates than the conventional reference Patlak analysis. The method is recommended for parameter estimation of [11C]HTP data when no metabolite-corrected plasma curve is available. Knowledge of altered 5HT synthesis and release in disease states and the consequences for effective pharmacotherapy can improve our knowledge of the aetiology of certain psychiatric and neurological diseases and enhance our ability to design more effective drugs.

Type 2 diabetes is a complex chronic metabolic disorder characterized by hyperglycemia associated with a relative deficiency of insulin secretion and a reduced response of target tissues to insulin. Considerable efforts have been put into the development of models describing the glucose-insulin system. The best known is Bergman’s “minimal” model for glucose, which is estimating glucose concentrations using fixed insulin concentrations as input. However, due to the involved feedback mechanisms, simultaneous modeling of both entities would be advantageous. This is particularly relevant if the model is intended to be used as a predictive tool. The mechanism-based glucose-insulin model presented in this thesis is able to simultaneously describe glucose and insulin profiles following a wide variety of clinical provocation experiments, such as intravenous and oral glucose tolerance tests, clamp studies and sequential meal tests over 24 hours. It consists of sub-models for glucose, labeled glucose and insulin kinetics. It also incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Even if this model is a crude representation of a complex physiological system, its ability to represent the main processes of this system was established by identifying: 1) the difference in insulin secretion and insulin sensitivity between healthy volunteers and type 2 diabetics, 2) the action of incretin hormones after oral administration of glucose, 3) the circadian variation of insulin secretion and 4) the correct mechanism of action of a glucokinase activator, a new oral antidiabetic compound acting on both the pancreas and the liver. These promising results represent a proof of concept of a mechanistic drug-disease model that could play an important role in the clinical development of anti-diabetic drugs.

In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.