<p>The pharmacokinetics and pharmacodynamics of oxycodone and morphine was investigated and related to the transport across the blood-brain barrier (BBB) in rats. The influence of a P-glycoprotein (P-gp) inhibitor on the plasma pharmacokinetics and pharmacodynamics of oxycodone was evaluated. Microdialysis experiments were conducted to evaluate the unbound pharmacokinetics, including the rate and extent of transport across the BBB, of oxycodone and morphine. Mathematical models were used to assess the pharmacokinetics and also the relationship between pharmacokinetics and pharmacodynamics of the drugs.</p><p>Oxycodone clearance, volume of distribution at steady-state, half-life, total brain tissue concentrations and tail-flick latency were all unaffected when a P-gp inhibitor was co-administered with oxycodone as compared to a control group. The lack of differences between the groups indicates that oxycodone BBB transport is not affected by P-gp inhibition. Investigating the unbound concentrations of oxycodone in brain and blood using microdialysis revealed an exciting finding. At steady-state, the unbound concentration in brain was 3 times higher than in blood (i.e. a K_p,uu of 3), indicating that active influx is involved in the BBB transport of oxycodone. In contrast, the K_p,uu of morphine was estimated to 0.56, which is an indication that active efflux mechanisms are involved in the BBB transport of morphine. This means that based on the same unbound concentration in blood, an approximately 6-fold higher unbound concentration of oxycodone compared to morphine will be reached in the brain. Using pharmacokinetic-pharmacodynamic modelling, the unbound brain concentrations of oxycodone and morphine were correlated to the tail-flick latency in vivo. The relative potency of the drugs was found to be concentration dependent with an infliction point of 55 nM.</p><p>In summary, this thesis emphasise the importance of taking the local brain pharmacokinetics into consideration when investigating the pharmacokinetics and the pharmacokinetic-pharmacodynamic relationships of centrally acting drugs.</p>

<p>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. </p><p>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.</p><p>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. </p><p>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.</p><p>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. </p><p>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.</p>

The transport of drugs across the blood-brain barrier (BBB) has been investigated in different species using morphine and morphine-6-glucuronide (M6G) as model compounds. The influence of probenecid on the BBB transport of morphine and M6G was investigated, and the consequences of meningitis and severe brain injury on the concentrations of morphine in the brain were examined. All data were obtained by microdialysis, and data analysis using mathematical models was emphasised. Morphine is exposed to active efflux at the BBB in rats, pigs and humans. In addition, the half-life of morphine is longer in the brain than in blood in these species. These interspecies similarities show the predictive potential of the two animal models for the BBB transport of morphine in humans. In the pig the exposure of the brain to morphine was higher in the presence of meningitis than when healthy. This was interpreted as a decrease in the active efflux and an increase in the passive diffusion over the injured BBB. In contrast, there was no significant difference in the concentrations of morphine in the “better” (uninjured) or the “worse” (injured) brain tissue in brain trauma patients. The extent of the transport across the BBB is similar for morphine and M6G. However, co-administration of probenecid only increased the brain concentrations of morphine, demonstrating that morphine and M6G are substrates for different efflux transporters at the BBB. An integrated model for the analysis of data obtained by microdialysis was developed. This model makes fewer assumptions about the recovery, the protein binding and the time of the dialysate observation than a previous model and traditional non-compartmental analysis and should, therefore, yield more reliable parameter estimates. Knowledge of the consequences of efflux transporters and disease on the brain concentrations of a drug can be useful for individualising the dosing regimen in patients.

To increase the proportion of patients with successful drug treatment, dose individualisation on the basis of one or several patient characteristics, a priori individualisation, and/or on the basis of feedback observations from the patient following an initial dose, a posteriori individualisation, is an option. Efficient tools in optimising individualised dosing strategies are population models describing pharmacokinetics (PK) and the relation between pharmacokinetics and pharmacodynamics (PK/PD). Methods for estimating optimal dosing strategies, with a discrete number of doses, for dose individualisation a priori and a posteriori were developed and explored using simulated data. The methods required definitions of (i) the therapeutic target, i.e. the value of the target variable and a risk function quantifying the seriousness of deviation from the target, (ii) a population PK/PD model relating dose input to the target variable in the patients to be treated, and (iii) distributions of relevant patient factors. Optimal dosing strategies, in terms of dose sizes and individualisation conditions, were estimated by minimising the overall risk. Factors influencing the optimal dosing strategies were identified. Consideration of those will have implications for study design, data collection, population model development and target definition. A dosing strategy for a priori individualisation was estimated for NXY-059, a drug under development. Applying the estimated dosing strategy in a clinical study resulted in reasonable agreement between observed and expected outcome, supporting the developed methodology. Estimation of a dosing strategy for a posteriori individualisation for oxybutynin, a drug marketed for the treatment of overactive bladder, illustrated the implementation of the method when defining the therapeutic target in terms of utility and responder probability, that is, as a combination of the desired and adverse effects. The proposed approach provides an estimate of the maximal benefit expected from individualisation and, if individualisation is considered clinically superior, the optimal conditions for individualisation. The main application for the methods is in drug development where the methods can be generally employed in the establishment of dosing strategies for individualisation with relevant extensions regarding population model complexity and individualisation conditions.

The pharmacokinetics and pharmacodynamics of oxycodone and morphine was investigated and related to the transport across the blood-brain barrier (BBB) in rats. The influence of a P-glycoprotein (P-gp) inhibitor on the plasma pharmacokinetics and pharmacodynamics of oxycodone was evaluated. Microdialysis experiments were conducted to evaluate the unbound pharmacokinetics, including the rate and extent of transport across the BBB, of oxycodone and morphine. Mathematical models were used to assess the pharmacokinetics and also the relationship between pharmacokinetics and pharmacodynamics of the drugs. Oxycodone clearance, volume of distribution at steady-state, half-life, total brain tissue concentrations and tail-flick latency were all unaffected when a P-gp inhibitor was co-administered with oxycodone as compared to a control group. The lack of differences between the groups indicates that oxycodone BBB transport is not affected by P-gp inhibition. Investigating the unbound concentrations of oxycodone in brain and blood using microdialysis revealed an exciting finding. At steady-state, the unbound concentration in brain was 3 times higher than in blood (i.e. a Kp,uu of 3), indicating that active influx is involved in the BBB transport of oxycodone. In contrast, the Kp,uu of morphine was estimated to 0.56, which is an indication that active efflux mechanisms are involved in the BBB transport of morphine. This means that based on the same unbound concentration in blood, an approximately 6-fold higher unbound concentration of oxycodone compared to morphine will be reached in the brain. Using pharmacokinetic-pharmacodynamic modelling, the unbound brain concentrations of oxycodone and morphine were correlated to the tail-flick latency in vivo. The relative potency of the drugs was found to be concentration dependent with an infliction point of 55 nM. In summary, this thesis emphasise the importance of taking the local brain pharmacokinetics into consideration when investigating the pharmacokinetics and the pharmacokinetic-pharmacodynamic relationships of centrally acting drugs.

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.

The interest for cell-to-cell variation has in recent years increased in a steady pace. Several studies have shown that a large portion of the observed variation in the nature originates from the fact that all biochemical reactions are in some respect stochastic. Interestingly, nature has evolved highly advanced frameworks specialized in dealing with stochasticity in order to still be able to produce the delicate signalling pathways that are present in even very simple single-cell organisms. Such a simple organism is Saccharomyces cerevisiae, which is the organism that has been studied in this thesis. More particulary, the distribution of the transport rate in S. cerevisiae has been studied by a mathematical modelling approach. It is shown that a two-compartment model can adequately describe the flow of a yellow fluorescent protein (YFP) between the cytosol and the nucleus. A profile likelihood (PLH) analysis shows that the parameters in the two-compartment model are identifiable and well-defined under the experimental data of YFP. Furthermore, the result from this model shows that the distribution of the transport rates in the 80 studied cells is lognormal. Also, in contradiction to prior beliefs, no significant difference between recently divided mother and daughter cells in terms of transport rates of YFP is to be seen. The modelling is performed by using both standard two-stage(STS) and nonlinear mixed effect model (NONMEM). A methodological comparison between the two very different mathematical STS and NONMEM is also presented. STS is today the conventional approach in studies of cell-to-cell variation. However, in this thesis it is shown that NONMEM, which has originally been developed for population pharmacokinetic/ pharmacodynamic (PK/PD) studies, is at least as good, or in some cases even a better approach than STS in studies of cell-to-cell variation. Finally, a new approach in studies of cell-to-cell variation is suggested that involves a combination of STS, NONMEM and PLH. In particular, it is shown that this combination of different methods would be especially useful if the data is sparse. By applying this combination of methods, the uncertainty in the estimation of the variability could be greatly reduced.

Receptor occupancy assessed by Positron Emission Tomography (PET) can provide important translational information to help bridge information from one drug to another or from animal to man. The aim of this thesis was to develop nonlinear mixed effects methods for estimation of the relationship between drug exposure and receptor occupancy for the two mGluR5 antagonists AZD9272 and AZD2066 and for the 5HT1B receptor antagonist AZD3783. Also the optimal design for improved estimation of the relationship between drug exposure and receptor occupancy as well as for improved dose finding in neuropathic pain treatment, was investigated. Different modeling approaches were applied. For AZD9272, the radioligand kinetics and receptor occupancy was simultaneously estimated using arterial concentrations as input function and including two brain regions of interest. For AZD2066, a model was developed where brain/plasma partition coefficients from ten different brain regions were included simultaneously as observations. For AZD3783, the simplified reference tissue model was extended to allow different non-specific binding in the reference region and brain regions of interest and the possibility of using white matter as reference was also evaluated. The optimal dose-selection for improved precision of receptor occupancy as well as for improved precision of the minimum effective dose of a neuropathic pain treatment was assessed, using the D-optimal as well as the Ds-optimal criteria. Simultaneous modelling of radioligand and occupancy provided a means to avoid simplifications or approximations and provided the possibility to tests or to relax assumptions. Inclusion of several brain regions of different receptor density simultaneously in the analysis, markedly improved the precision of the affinity parameter. Higher precision was achieved in relevant parameters with designs based on the Ds compared to the D-optimal criterion. The optimal design for improved precision of the relationship between dose and receptor occupancy depended on the number of brain regions and the receptor density of these regions. In conclusion, this thesis presents novel non-linear mixed effects models estimating the relationship between drug exposure and receptor occupancy, providing useful translational information, allowing for a better informed drug-development.

To accurately predict the outcome of a late phase study, pharmacometric models can help in drug development. Making informed decision on which models to use will also facilitate drug development. This can depend on the mechanism of action for the drug as well as stability and runtime factors. This is an investigation of four published semi-mechanistic pharmacometric models to predict glycosylated red blood cells (HbA1c) in a late phase study of an anti-diabetic drug together with an assessment of their stability and power to detect drug effects. Mean plasma glucose (MPG), fasting plasma glucose (FPG) or FPG and fasting serum insulin (FSI) are used together with HbA1c as drivers for change in the models. We find that less complex models, with fewer differential equations, are quicker to run and more stable, and that MPG alone is superior to FPG or FPG and FSI to detect a drug effect. The findings are useful for drug development in the anti-diabetic area, and show that a less mechanistic model performs well under these conditions.

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.