In oncology, there is a need to optimize drug treatment for efficient eradication of tumors, minimization of adverse effects (AEs), and prolonging patient survival. Pharmacometric models can be developed to streamline information between drug development phases, describe and quantify response to treatment, and determine dose regimens that balance toxicity and efficacy. In this thesis, data from trastuzumab emtansine (T-DM1) and taxane drug treatment were used to develop pharmacometric models of pharmacokinetics (PK), AEs, anti-tumor response, and survival, supporting drug development. T-DM1 is an antibody-drug conjugate (ADC) for treatment of human epidermal growth factor receptor 2 (HER2)–positive breast cancer. ADCs are a relatively new class of oncologic agents, and contain multiple drug-to-antibody ratio (DAR) moieties in their dose product. The complex distribution of T-DM1 was elucidated through PK models developed using in vitro and in vivo rat and cynomolgus monkey DAR data. Mechanism–based PK/pharmacodynamic (PKPD) models were also developed for T-DM1 that described the AEs thrombocytopenia (TCP) and hepatotoxicity in patients receiving T-DM1. Variable patterns of platelet and transaminase (ALT and AST) response were quantified, including an effect of Asian ethnicity that was related to higher incidences of TCP.  Model simulations, comparing dose intensities (DI) and Grade 3/4 incidences between the approved T-DM1 dose (3.6 mg/kg every three weeks) and weekly regimens, determined that 2.4 mg/kg weekly provided the highest DI. Docetaxel and paclitaxel are taxane treatment options for HER2–negative breast cancer. Tumor response data from these treatments were used to develop a mechanism–based model of tumor quiescence and drug–resistance. Subsequently, a parametric survival analysis found that tumor baseline and the model–predicted time to tumor growth (TTG) were predictors of overall survival (OS). This tumor and OS modeling approach can be applied to other anticancer treatments with similar patterns of drug–resistance. Overall, the pharmacometric models developed within this thesis present new modeling approaches and provide understanding on ADC PK and PKPD (TCP and hepatotoxicity), as well as drug–resistance tumor response. These models can inform simulation strategies and clinical study design, and be applied towards dose finding for anticancer drugs in development, especially ADCs.

With approximately nine million new cases and the attributable cause of death of an estimated two millions people every year there is an urgent need for new and effective drugs and treatment regimens targeting tuberculosis. The tuberculosis drug development pathway is however not ideal, containing non-predictive model systems and unanswered questions that may increase the risk of failure during late-phase drug development. The aim of this thesis was hence to develop pharmacometric tools in order to optimize the development of new anti-tuberculosis drugs and treatment regimens. The General Pulmonary Distribution model was developed allowing for prediction of both rate and extent of distribution from plasma to pulmonary tissue. A distribution characterization that is of high importance as most current used anti-tuberculosis drugs were introduced into clinical use without considering the pharmacokinetic properties influencing drug distribution to the site of action. The developed optimized bronchoalveolar lavage sampling design provides a simplistic but informative approach to gathering of the data needed to allow for a model based characterization of both rate and extent of pulmonary distribution using as little as one sample per subject. The developed Multistate Tuberculosis Pharmacometric model provides predictions over time for a fast-, slow- and non-multiplying bacterial state with and without drug effect. The Multistate Tuberculosis Pharmacometric model was further used to quantify the in vitro growth of different strains of Mycobacterium tuberculosis and the exposure-response relationships of three first line anti-tuberculosis drugs. The General Pharmacodynamic Interaction model was successfully used to characterize the pharmacodynamic interactions of three first line anti-tuberculosis drugs, showing the possibility of distinguishing drug A’s interaction with drug B from drug B’s interaction with drug A. The successful separation of all three drugs effect on each other is a necessity for future work focusing on optimizing the selection of anti-tuberculosis combination regimens. With a focus on pharmacokinetics and pharmacodynamics, the work included in this thesis provides multiple new methods and approaches that individually, but maybe more important the combination of, has the potential to inform development of new but also to provide additional information of the existing anti-tuberculosis drugs and drug regimen.

Resistant bacteria are becoming more and more of a problem but treatment with antibiotics in combination may overcome and prevent resistance development. The combination of meropenem and colistin against Pseudomonas aeruginosa has been proposed as a promising treatment to increase the bactericidal effect and a synergistic effect has been proposed. A Pharmacokinetic-Parmacodynamic (PKPD) model that describes the dynamics of bacteria kill could be used to evaluate if the effects are additive or not. The model could later also be used to find optimal dosing for both of the antibiotics used alone or in combination with each other. The aim of the present study was to develop a PKPD model that describes the bactericidal activity of the two antibiotics, both in mono-therapy and in combination. The data were from in vitro static time kill-curve experiments that had been conducted on two strains of Pseudomonas aeruginosa; the wild-type (ATCC 27853) and the resistant-type (PL0603761). Resistance was observed in the experimental data and thus it had to be taken into account in the modelling. PKPD models were fitted to the bacterial counts in NONMEM with pharmacodynamic compartments for susceptible and resting bacteria. In the resting compartment the bacteria could not be killed. The bacteria moved into the resting compartment from the susceptible compartment when a certain concentration of bacteria was obtained. A pharmacokinetic compartment characterized changes in drug concentrations and the drug degradation during the experimental time was considered. Two different drug effects were tried on the susceptible bacteria, linear effect and Emax models.. The resistance development occurring during the experiments was described by two compartments where the parameter kon determined the rate of onset of resistance development. In the final model, kon was found to either be concentration-independent or dependent, depending on antibiotics and bacteria. The degree of resistance development produced an overall inhibitory effect on the drug effect. The growth rate was estimated to be lower and the EC50 to be higher for the resistant compared to the wild-type bacteria. The model was used to predict the expected time-kill curve if the effect of the two drugs are additive when combining the two drugs. The observed  bacteria kill was lower than the model predicted for the wild-type bacteria. For the resistant bacteria the assumption of additive bacteria kill for the two drugs-seemed adequate.

Current excessive use and abuse of antibiotics has resulted in increasing bacterial resistance to common treatment options which is threatening to deprive us of a pillar of modern medicine. In this work methods to optimize the use of existing antibiotics and to help development of new antibiotics were developed and applied. Semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) models were developed to describe the time course of the dynamic effect and interaction of combinations of antibiotics. The models were applied to illustrate that colistin combined with a high dose of meropenem may overcome meropenem-resistant P. aeruginosa infections. The results from an in vivo dose finding study of meropenem was successfully predicted by the meropenem PKPD model in combination with a murine PK model, which supports model based dosage selection. However, the traditional PK/PD index based dose selection was predicted to have poor extrapolation properties from pre-clinical to clinical settings, and across patient populations. The precision of the model parameters, and hence the model predictions, is dependent on the experimental design. A limited study design is dictated by cost and, for in vivo studies, ethical reasons. In this work optimal design (OD) was demonstrated to be able to reduce the experimental effort in time-kill curve experiments and was utilized to suggest the experimental design for identification and estimation of an interaction between antibiotics. OD methods to handle inter occasion variability (IOV) in optimization of individual PK parameter estimates were proposed. The strategy was applied in the design of a sparse sampling schedule that aim to estimate individual exposures of colistin in a multi-centre clinical study. Plasma concentration samples from the first 100 patients have been analysed and indicate that the performance of the design is close to the predicted. The methods described in this thesis holds promise to facilitate the development of new antibiotics and to improve the use of existing antibiotics.

Tuberculosis is an ancient infectious disease and a leading cause of death globally. Preclinical research is important for defining drugs and regimens which should be carried forward to human studies. This thesis aims to characterize the population pharmacokinetics and exposure-response relationships of anti-tubercular drugs alone and in combinations, and to suggest experimental designs for preclinical settings. The population pharmacokinetics of rifampicin, isoniazid, ethambutol and pyrazinamide were described for the first time in two mouse models. This allowed for linking the population pharmacokinetic model to the Multistate Tuberculosis Pharmacometric (MTP) model for biomarker response, which was used to characterize exposure-response relationships in monotherapy. Pharmacodynamic interactions in combination therapies were quantitatively described by linking the MTP model to the General Pharmacodynamic Interaction (GPDI) model, which provided estimates of single drug effects together with a quantitative model-based evaluation framework for evaluation of pharmacodynamic interactions among drugs in combinations. Synergism (more than expected additivity) was characterized between rifampicin and ethambutol, while antagonism (less than expected additivity) was characterized between rifampicin and isoniazid in combination therapies. The new single-dose pharmacokinetic design with enrichened individual sampling was more informative than the original design, in which only one sample was taken from each mouse in the pharmacokinetic studies. The new oral zipper design allows for informative pharmacokinetic sampling in a multiple-dose administration scenario for characterizing pharmacokinetic-pharmacodynamic relationships, with similar or lower bias and imprecision in parameter estimates and with a decreased total number of animals required by up to 7-fold compared to the original design. The optimized design for assessing pharmacodynamic interactions in the combination therapies, which was based on EC20, EC50 and EC80 of the single drug, provided lower bias and imprecision than a conventional reduced four-by-four microdilution checkerboard design at the same total number of samples required, which followed the 3Rs of animal welfare. In summary, in this thesis the population pharmacokinetic-pharmacodynamic models of first-line drugs in mice were characterized through linking each population pharmacokinetic model to the MTP model. Pharmacodynamic interactions were quantitatively illustrated by the MTP-GPDI model. Lastly, experimental designs were optimized and recommended to both pharmacokinetic and pharmacodynamic studies for preclinical settings.

Pharmacometric modelling is widely used in many aspects related to type 2 diabetes mellitus (T2DM), for instance in the anti-diabetes drug development, and in quantifying the disease progression of T2DM. The aim of this thesis were to improve the design of early phase anti-diabetes drug development studies with the focus on the power to identify mechanism of drug action (MoA), and to characterize and quantify the progression from prediabetes to overt diabetes, both the natural progression and the progression with diet and exercise interventions, using pharmacometrics modelling. The appropriateness of a study design depends on the MoAs of the anti-hyperglycaemic drug. Depending on if the focus is power to identify drug effect or accuracy and precision of drug effect, the best design will be different. Using insulin measurements on top of glucose has increase the power to identify a correct drug effect, distinguish a correct MoA from the incorrect, and to identify a secondary MoA in most cases. The accuracy and precision of drug parameter estimates, however, was not affected by insulin. A natural diabetes disease progression model was successfully added in a previously developed model to describe parameter changes of glucose and insulin regulation among impaired glucose tolerance (IGT) subjects, with the quantification of the lifestyle intervention. In this model, the assessment of multiple short-term provocations was combined to predict the long-term disease progression, and offers apart from the assessment of the onset of T2DM also the framework for how to perform similar analysis. Another previously published model was further developed to characterize the weight change in driving the changes in glucose homeostasis in subjects with IGT. This model includes the complex relationship between dropout from study and weight and glucose changes. This thesis has provided a first written guidance in designing a study for pharmacometrics analysis when characterizing drug effects, for early phase anti-diabetes drug development. The characterisation of the progression from prediabetes to overt diabetes using pharmacometrics modelling was successfully performed. Both the natural progression and the progression with diet and exercise interventions were quantified in this thesis.