Quantifying the impact of body composition on drug clearance: influence of study design and implications for dosing in obesity

Phey Yen Han (2009). Quantifying the impact of body composition on drug clearance: influence of study design and implications for dosing in obesity PhD Thesis, School of Pharmacy, The University of Queensland.

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Author Phey Yen Han
Thesis Title Quantifying the impact of body composition on drug clearance: influence of study design and implications for dosing in obesity
School, Centre or Institute School of Pharmacy
Institution The University of Queensland
Publication date 2009-12
Thesis type PhD Thesis
Supervisor Dr Bruce Green
Assoc Prof Carl Kirkpatrick
Total pages 212
Total colour pages 1
Total black and white pages 211
Subjects 11 Medical and Health Sciences
Abstract/Summary 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.
Keyword obesity
lean body weight
body composition
drug clearance
renal plasma flow
clinical study design
population modelling
Additional Notes Page number for colour printing : p. 143

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Created: Sun, 25 Apr 2010, 01:44:10 EST by Ms Phey Yen Han on behalf of Library - Information Access Service