Prediction of drug distribution in rat and human

UoM administered thesis: Phd

  • Authors:
  • Helen Graham

Abstract

Many methods exist in the literature for the prediction of pharmacokinetic parameterswhich describe drug distribution in rat and human, such as tissue-to-plasma partitioncoefficients (Kps) and volume of distribution (Vss). However, none of these methods makeuse of the in vivo information obtained at the early stages of the drug developmentprocess in the form of plasma concentration vs. time profiles. The overall aim of thepresented study was to improve upon an existing Kp prediction method by making use ofthe distribution information contained within this experimental data. Chapter 2 shows thatKp values can be successfully obtained experimentally, but that this process is expensiveand time-consuming. Chapter 3 compares six Kp prediction methods taken from theliterature for their ability to predict the Kp values of 80 drugs. The Rodgers et al. modelwas found to be the most accurate, with over 77% of predictions within 3-fold ofexperimental values. This Chapter also discusses the Vss prediction ability of some ofthese methods, with the Poulin & Theil and Rodgers et al. models shown to be the mostaccurate predictors for rat Vss and human Vss respectively. Chapter 4 investigates therelationship between muscle Kp and the Kps of all other tissues, to show that experimentalmuscle Kp can be used as a surrogate from which all other non-adipose Kp values can bepredicted. However, the predictions made using this method were shown to be lessaccurate than predictions made by the Rodgers et al. model for the same dataset of drugs.A relationship was identified between muscle Kp and tumour Kp in rat, suggesting apotential way to predict tumour Kp in the future. In Chapter 5, a novel method is developedwhereby Kp predictions made by the Rodgers et al. model are updated using priorinformation obtained from the in vivo concentration-time profile. These updated values arethen used within a physiologically-based pharmacokinetic (PBPK) model and are shown inChapter 6 to generate improved predictions for other pharmacokinetic parameters such asVss and clearance in both rat and human. 100% of human Vss predictions made by themost accurate of the novel methods presented here were within 3-fold of experimentalvalues, compared to 68.8% of predictions made by the Rodgers et al. model. The workpresented here has highlighted the need for a more accurate method for the prediction ofKp values, and has addressed this need by generating a model which improves upon themost accurate Kp prediction method currently found in the literature. This will lead to anincrease in confidence in the use of predicted pharmacokinetic parameters within PBPKmodelling.

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Original languageEnglish
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Award date1 Aug 2012