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Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects.

Lu Liu¹, Li Fu¹, Jin-Wei Zhang¹

¹Xiangya School of Pharmaceutical Sciences , Central South University , Changsha , People's Republic of China.

Molecular Pharmaceutics

|November 27, 2018

Summary

A new tiered system predicts drug-induced liver injury (hepatotoxicity) using machine learning. This tool enhances drug development by identifying potential liver damage and severity early, reducing market withdrawals.

Keywords:

SAR adverse hepatic effects hepatotoxicity random forest toxicity risk assessment

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Area of Science:

Drug discovery and development
Computational toxicology
Pharmacology

Background:

Hepatotoxicity is a primary reason for drug withdrawal, necessitating improved prediction methods.
Accurate and efficient prediction systems are crucial to mitigate drug attrition due to liver injury.

Purpose of the Study:

To develop a multi-level hepatotoxicity prediction system integrating machine learning and adverse hepatic effects (AHEs).
To predict hepatotoxicity existence, severity, and specific AHEs for compounds.

Main Methods:

Collected and curated 15,873 compound-AHE pairs involving 2017 compounds and 403 AHEs.
Developed 27 random forest models across three endpoint levels (hepatotoxicity, severity, specific AHEs).
Integrated models into a tiered prediction system and validated using external datasets.

Main Results:

Achieved prediction accuracies from 67.0% to 78.2% and AUCs from 0.715 to 0.875.
The tiered system enables simultaneous inference of hepatotoxicity, severity, and AHEs.
External validation confirmed the system's predictive capabilities.

Conclusions:

The developed tiered system provides robust prediction of compound hepatotoxicity, severity, and specific adverse hepatic effects.
This system aids researchers in confirming hepatotoxicity, analyzing risks, and selecting appropriate in vitro experiments.
The system is available as a flow framework for broader accessibility in drug safety assessment.