Optimising Translational Aspects of Physiologically-based Pharmacokinetic Modelling and Simulation to Aid Precision Dosing in Liver Cirrhosis

Abstract

Background: Cirrhosis is a chronic illness that reduces liver functions including drug metabolism. Many drugs that are available in the market lack dosage guidance for hepatic impairment patients. Including those patients in the early phases of clinical trials can be risky if safe doses were not used. Whilst precision medicine is not a new concept, optimising in silico modelling such as physiologically-based pharmacokinetic (PBPK) modelling and simulation (M&S), can assist in informing drug labelling. This area is quickly growing over the last years, especially for special patient populations. Methods: Literature reviews were performed to understand the current situation with drug dosing in hepatic impairment, the quality of current PBPK models, and identify the gaps. Experimentally, scaling factors for converting clearance data from in vitro to in vivo in cirrhosis were determined. LC-MS/MS-based targeted proteomics were implemented to quantify the abundances of 51 drug-metabolising enzymes and transporters (DME&T) in 32 liver samples from cirrhosis patients at different degrees of disease severity compared to 14 normal control samples. Results: Microsomal and cytosolic protein contents decreased in cirrhosis relative to control samples and varied according to associated liver pathologies. Disease perturbation factor (DPF) reconciled differences in absolute abundances between various proteomic data analysis methods. Specifically designed heavy-labelled concatenated unique peptides from target proteins showed good performances as internal standards with the samples. Abundances of most DME&T per gram liver were lower by 30-50% in mild, 40-70% in moderate, and 50-98% in severe cirrhosis groups compared to controls. DPF, used as a scalar for protein abundances in PBPK models for repaglinide, dabigatran etexilate, and zidovudine, helped to enhance models’ predictive performance. Conclusion: This thesis, to our knowledge, provides the first comprehensive quantification of relevant DME&T in all stages of liver cirrhosis. This helps the development of existing in silico cirrhosis models to inform drug labelling and recommends dose adjustments in scenarios that have not been studied clinically.

Date of Award	1 Aug 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Jill Barber (Supervisor), Amin Rostami-Hochaghan (Supervisor) & Adam Darwich (Supervisor)