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A quantitative systems pharmacology approach, incorporating a novel liver model, for predicting pharmacokinetic drug-drug interactions

Cherkaoui-Rbati, Mohammed; Paine, Stuart; Littlewood, Peter; Rauch, Cyril

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Authors

Mohammed Cherkaoui-Rbati

STUART PAINE Stuart.Paine@nottingham.ac.uk
Professor of Pharmacometrics

Peter Littlewood

CYRIL RAUCH CYRIL.RAUCH@NOTTINGHAM.AC.UK
Associate Professor



Contributors

Jinn-Moon Yang
Editor

Abstract

All pharmaceutical companies are required to assess pharmacokinetic drug-drug interactions (DDIs) of new chemical entities (NCEs) and mathematical prediction helps to select the best NCE candidate with regard to adverse effects resulting from a DDI before any costly clinical studies. Most current models assume that the liver is a homogeneous organ where the majority of the metabolism occurs. However, the circulatory system of the liver has a complex hierarchical geometry which distributes xenobiotics throughout the organ. Nevertheless, the lobule (liver unit), located at the end of each branch, is composed of many sinusoids where the blood flow can vary and therefore creates heterogeneity (e.g. drug concentration, enzyme level). A liver model was constructed by describing the geometry of a lobule, where the blood velocity increases toward the central vein, and by modeling the exchange mechanisms between the blood and hepatocytes. Moreover, the three major DDI mechanisms of metabolic enzymes; competitive inhibition, mechanism based inhibition and induction, were accounted for with an undefined number of drugs and/or enzymes. The liver model was incorporated into a physiological-based pharmacokinetic (PBPK) model and simulations produced, that in turn were compared to ten clinical results. The liver model generated a hierarchy of 5 sinusoidal levels and estimated a blood volume of 283 mL and a cell density of 193 × 106 cells/g in the liver. The overall PBPK model predicted the pharmacokinetics of midazolam and the magnitude of the clinical DDI with perpetrator drug(s) including spatial and temporal enzyme levels changes. The model presented herein may reduce costs and the use of laboratory animals and give the opportunity to explore different clinical scenarios, which reduce the risk of adverse events, prior to costly human clinical studies.

Journal Article Type Article
Acceptance Date Aug 11, 2017
Online Publication Date Sep 14, 2017
Publication Date Sep 14, 2017
Deposit Date Apr 19, 2018
Publicly Available Date Apr 19, 2018
Journal PLoS ONE
Electronic ISSN 1932-6203
Publisher Public Library of Science
Peer Reviewed Peer Reviewed
Volume 12
Issue 9
Article Number e0183794
DOI https://doi.org/10.1371/journal.pone.0183794
Public URL https://nottingham-repository.worktribe.com/output/882901
Publisher URL http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183794

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