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Title: Prediction of drug distribution in rat and human
Author: Graham, Helen Sarah
ISNI:       0000 0004 2723 9265
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2012
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Many methods exist in the literature for the prediction of pharmacokinetic parameters which describe drug distribution in rat and human, such as tissue-to-plasma partition coefficients (Kps) and volume of distribution (Vss). However, none of these methods make use of the in vivo information obtained at the early stages of the drug development process in the form of plasma concentration vs. time profiles. The overall aim of the presented study was to improve upon an existing Kp prediction method by making use of the distribution information contained within this experimental data. Chapter 2 shows that Kp values can be successfully obtained experimentally, but that this process is expensive and time-consuming. Chapter 3 compares six Kp prediction methods taken from the literature for their ability to predict the Kp values of 80 drugs. The Rodgers et al. model was found to be the most accurate, with over 77% of predictions within 3-fold of experimental values. This Chapter also discusses the Vss prediction ability of some of these methods, with the Poulin & Theil and Rodgers et al. models shown to be the most accurate predictors for rat Vss and human Vss respectively. Chapter 4 investigates the relationship between muscle Kp and the Kps of all other tissues, to show that experimental muscle Kp can be used as a surrogate from which all other non-adipose Kp values can be predicted. However, the predictions made using this method were shown to be less accurate 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 a potential way to predict tumour Kp in the future. In Chapter 5, a novel method is developed whereby Kp predictions made by the Rodgers et al. model are updated using prior information obtained from the in vivo concentration-time profile. These updated values are then used within a physiologically-based pharmacokinetic (PBPK) model and are shown in Chapter 6 to generate improved predictions for other pharmacokinetic parameters such as Vss and clearance in both rat and human. 100% of human Vss predictions made by the most accurate of the novel methods presented here were within 3-fold of experimental values, compared to 68.8% of predictions made by the Rodgers et al. model. The work presented here has highlighted the need for a more accurate method for the prediction of Kp values, and has addressed this need by generating a model which improves upon the most accurate Kp prediction method currently found in the literature. This will lead to an increase in confidence in the use of predicted pharmacokinetic parameters within PBPK modelling.
Supervisor: Aarons, Leon. ; Galetin, Aleksandra. Sponsor: BBSRC ; AstraZeneca (CASE studentship)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Pharmacokinetics ; PBPK modelling ; Partition coefficients ; Volume of distribution