Optimal pharmacotherapy requires an understanding of the dose-exposure (pharmacokinetics or PK) to response (pharmacodynamic or PD) relationship. Little is known about the influence of obesity on this dynamic system as PK studies in obesity have been largely descriptive rather than explanatory. This has led to a paucity of dosing guidelines for the obese, and arbitrary dose selection in the clinic. There is a need to quantify the impact of obesity on drug clearance (CL) to ensure that exposure is matched across patients of different body compositions, thereby improving therapeutic outcomes and minimising adverse events. The global aim of this thesis was to use prior published data and new clinical trial data to understand how body composition impacts upon drug CL and renal function, and to determine how clinical study design influences the identification of these relationships. Chapter 2 of this thesis determined if conventional body size descriptors that have been used to scale drug doses to body size were appropriate. In the clinical setting, a body size descriptor commonly used for determining dose requirements is total body weight (WT), based on the assumption that physiological function and PK parameters vary according to body size. However, dosing algorithms based on WT might be unsuitable for the obese due to their altered body composition which, if inaccurate, could ultimately lead to overdoses. Alternative body size descriptors such as body surface area and ideal body weight have been used, but are limited when extrapolated to obese patients as they do not take into account the covariates required to describe differences in body composition between individuals. In contrast, it was demonstrated that lean body weight (LBW), as derived by Janmahasatian et al, had the potential to scale CL across a wide range of body compositions. This literature review and systematic analysis of previously published obesity data led to the proposal of a hypothesis that body composition is sufficient to explain the influence of obesity on drug CL and that dosing for obese patients should be based on LBW. When conducting clinical studies, the selection of an appropriate body size descriptor for scaling doses across individuals of different body compositions can be aided by a study design that allows for the identification of parameter-covariate relationships which are transportable to the obese. Chapter 3 of this thesis quantified the probability of identifying these parameter-covariate relationships as a function of differing study designs. Demographics were generated using a multivariate lognormal covariate distribution with truncation at different WT limits under both a non-stratified and stratified design. PK data were simulated from a 1-compartment, first order input, first order elimination model with LBW as the covariate on CL, termed the ‘True Model’. The ‘False Model’ had WT as the covariate on CL. Both models were fitted to the simulated data and the preferred model was selected based on the difference in objective function values. Each design was evaluated under differing magnitudes of random effects, as well as under a D-optimal sparse sampling scheme. It was shown under a simulation platform that the use of stratification and a wide covariate range enhanced the probability of selecting the true covariate from two competing covariate models. The aforementioned findings regarding LBW and stratification were used to design a new clinical study investigating the influence of obesity on renal drug elimination pathways. This work forms Chapters 4 and 5 of this thesis. Non-obese and obese healthy volunteers were recruited using a study design stratified for LBW. These subjects were administered a combination of four renal markers for the simultaneous assessment of various renal processes. One of the renal markers was para-aminohippuric acid (PAH), which provides an estimation of renal plasma flow (RPF). A population PK model was developed for PAH, which revealed that body size alone was insufficient to explain variability in RPF across healthy individuals of a large range of body compositions, although LBW emerged as the preferred covariate (p=0.053) among the body size descriptors tested. This weak covariate effect was in contrast with prior research supporting the use of LBW in normalising the effect of obesity on glomerular filtration rate (GFR), implying that body composition could play a greater role in influencing GFR than RPF. This thesis has applied new methods to the design of drug CL studies in obesity, and offered results and future directions to maximise the information gained from such clinical studies. A better understanding of alterations in PK and physiological function arising from changes in body composition should aid in optimising dose adjustments for obese patients, which is of great importance given the increasing prevalence of obesity in today’s society.

The kidneys have a significant role in drug elimination through both metabolic and excretory routes. Despite a recent paradigm shift towards systems pharmacology approaches, prediction of renal drug disposition using 'bottom-up' and mechanistic modelling approaches remains underdeveloped. Lack of 'gold-standard' in vitro assays and corresponding in vitro-in vivo extrapolation (IVIVE) approaches for prediction of renal metabolic (CLR,met) and excretory (CLR) clearances contribute to this. A comprehensive literature analysis of quantitative physiological data to inform renal IVIVE scaling factors and systems parameters relevant for physiologically based pharmacokinetic (PBPK) kidney models was initially performed to identify existing knowledge gaps. Following this, microsomal protein content in dog kidney cortex (MPPGK) and liver (MPPGL) were measured in 17 samples from the same animal. Mean dog MPPGK (44.0 mg/ g kidney) and MPPGL (63.6 mg/ g liver) obtained using glucose-6-phosphatase activity as the microsomal protein marker where systematically higher than when CYP content was used as the marker (33.9 mg/ g kidney and 41.1 mg/ g liver respectively). Dog MPPGK was lower than MPPGL, with no direct correlation between the organs. In addition to dog, MPPGK and cytosolic protein per gram kidney (CPPGK) were obtained from 31 human samples, which represent the largest dataset currently available. Mean human MPPGK (25.7 mg/ g kidney) and CPPGK (52.7 mg/ g kidney), were measured using glucose-6-phosphatase and glutathione-S-transferase activities as recovery markers, respectively. Activity of prepared kidney microsomes was assessed using mycophenolic acid glucuronidation as a marker. Novel scaling factor of 25.7 mg/ g kidney was applied for IVIVE of mycophenolic acid microsomal glucuronidation data, resulting in a 2-fold increase in scaled intrinsic clearance compared with data scaled by the commonly used literature MPPGK value (12.8 mg/ g kidney). In addition to the microsomal scaling factor, several elements of a modified stereology method were developed for quantifying human proximal tubule cellularity. The methods included implementation of a systematic uniform random sampling protocol and investigation of tinctorial and immunohistochemistry based staining approaches that could be used identify and count proximal tubule cells in histology sections. A range of mechanistic models for prediction of CLR via either tubular reabsorption or active secretion were developed. A novel 5-compartment model for prediction of tubular reabsorption and CLR from Caco-2 apparent permeability data was developed. This model accounted for relevant physiological complexities of the kidney, such as regional differences in tubular filtrate flow rates and tubular surface area, including consideration of the impact of microvilli. The model predicted the CLR of 45 drugs with overall good accuracy (geometric mean fold error of 1.96), although a systematic under-prediction was noted for basic drugs. The novel 5-compartment model represents an important addition to the IVIVE toolbox for physiologically-based prediction of renal tubular reabsorption and CLR and can be implemented in the more complex mechanistic kidney models, as shown in the case of prediction of urine flow dependent CLR of theophylline and caffeine. Final part of the Thesis focused on the refinement of digoxin PBPK kidney model and its ability to predict effect of aging and renal impairment on digoxin CLR. The analysis has identified that reducing either the proximal tubule cellularity or OATP4C1 abundance parameters in the mechanistic model recovers well observed reduced tubular secretion and CLR of digoxin in renal impairment populations whereas no effect of modification of P-gp abundance was observed. Conversely, reducing the proximal tubule cellularity, OATP4C1 abundance or P-gp abundance parameters in the model resulted in negligible change, decreased or increased accumulation of digoxin in proximal tubule cells, respectively. In conclusion, the current study provides to date the most comprehensive kidney microsomal and cytosolic metabolic scaling factors, together with revised database on renal physiological data necessary for quantitative prediction of renal drug disposition. Mechanistic modelling work shown here has highlighted a need for physiological data from different population groups to inform kidney model parameters, in order to improve the scope and utility of such models within the systems pharmacology paradigm.