Literature DB >> 28129975

The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury.

Jeffrey L Woodhead1, Paul B Watkins2, Brett A Howell3, Scott Q Siler3, Lisl K M Shoda3.   

Abstract

Idiosyncratic drug-induced liver injury (iDILI) is a serious concern in drug development. The rarity and multifactorial nature of iDILI makes it difficult to predict and explain. Recently, human leukocyte antigen (HLA) allele associations have provided strong support for a role of an adaptive immune response in the pathogenesis of many iDILI cases; however, it is likely that an adaptive immune attack requires several preceding events. Quantitative systems pharmacology (QSP), an in silico modeling technique that leverages known physiology and the results of in vitro experiments in order to make predictions about how drugs affect biological processes, is proposed as a potentially useful tool for predicting and explaining critical events that likely precede immune-mediated iDILI, as well as the immune attack itself. DILIsym, a QSP platform for drug-induced liver injury, has demonstrated success in predicting the presence of delayed hepatocellular stress events that likely precede the iDILI cascade, and has successfully predicted hepatocellular stress likely underlying iDILI attributed to troglitazone and tolvaptan. The incorporation of a model of the adaptive immune system into DILIsym would represent and important advance. In summary, QSP methods can play a key role in the future prediction and understanding of both immune-mediated and non-immune-mediated iDILI.
Copyright © 2016 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Adaptive immune system; Computer modeling; Idiosyncratic DILI; Liver injury; Quantitative systems pharmacology

Mesh:

Substances:

Year:  2016        PMID: 28129975     DOI: 10.1016/j.dmpk.2016.11.008

Source DB:  PubMed          Journal:  Drug Metab Pharmacokinet        ISSN: 1347-4367            Impact factor:   3.614


  18 in total

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Review 10.  Harvesting the promise of AOPs: An assessment and recommendations.

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