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Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
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Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

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Analytical and Omics-Based Advances in the Study of Drug-Induced Liver Injury

Thomas Kralj1, Kim L R Brouwer2, Darren J Creek1

  • 1Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.

Toxicological Sciences : an Official Journal of the Society of Toxicology
|June 4, 2021
PubMed

Insights

Drug-induced liver injury (DILI) poses a significant challenge in drug development. Understanding DILI

Area of Science:

  • Hepatology
  • Toxicology
  • Pharmacology

Background:

  • Drug-induced liver injury (DILI) affects millions annually and is a major cause of drug candidate failure.
  • Understanding DILI's biological mechanisms is crucial for minimizing its future prevalence.
  • Toxicity and safety concerns are the second-leading reason for drug candidate attrition.

Purpose of the Study:

  • To review analytical techniques for characterizing and investigating DILI mechanisms.
  • To discuss omics-based approaches for deeper biological insight into DILI.
  • To explore potential predictive biomarkers for DILI.

Main Methods:

  • Review of qualitative and quantitative analytical techniques.
  • Discussion of in vitro and multiplexed assays for DILI characterization.
  • Exploration of omics-based techniques (genomics, transcriptomics, proteomics, metabolomics).

Main Results:

  • In vitro and multiplexed assays characterize drug hepatotoxicity and DILI association.
  • Omics techniques provide qualitative and quantitative insights into DILI mechanisms.
  • Omics reveal genetic susceptibilities, gene expression changes, and protein/metabolite abundance alterations.

Conclusions:

  • Analytical techniques, especially omics, are vital for understanding DILI mechanisms.
  • Characterizing DILI through various assays aids in drug development.
  • Identifying predictive biomarkers can help mitigate DILI risks.
Keywords:
DILIcholestasismetabolomicsproteomicssteatosistranscriptomics

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