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ToxSTAR: drug-induced liver injury prediction tool for the web environment

Hyun Kil Shin1,2, Hang-Suk Chun1, Sangwoo Lee1

  • 1Department of Predictive toxicology, Korea Institute of Toxicology, 34114 Daejeon, Republic of Korea.

Bioinformatics (Oxford, England)
|July 28, 2022
PubMed
Abstract

Insights

ToxSTAR offers structural similarity analysis and predictive models for drug-induced liver injury (DILI) subtypes. This tool aids in understanding liver damage mechanisms and predicting cholestasis, cirrhosis, hepatitis, and steatosis.

Area of Science:

  • Toxicology
  • Drug Discovery
  • Computational Chemistry

Background:

  • Drug-induced liver injury (DILI) presents a significant challenge in predictive toxicology due to complex mechanisms and reactive metabolites.
  • Identifying DILI subtypes like cholestasis, cirrhosis, hepatitis, and steatosis is crucial for patient safety and drug development.

Purpose of the Study:

  • To introduce ToxSTAR, a platform for DILI analysis and prediction.
  • To provide tools for predicting DILI subtypes based on drug and metabolite structures.

Main Methods:

  • Utilizes structural similarity analysis for DILI assessment.
  • Employs in-house prediction models for four DILI subtypes: cholestasis, cirrhosis, hepatitis, and steatosis.

Main Results:

  • ToxSTAR enables prediction of DILI subtypes based on molecular structures.
  • The platform facilitates DILI analysis through structural comparisons.

Conclusions:

  • ToxSTAR is a valuable resource for predicting and analyzing drug-induced liver injury.
  • The platform supports researchers in understanding DILI mechanisms and improving drug safety assessments.

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