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Related Experiment Video

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Free Radicals in Chemical Biology: from Chemical Behavior to Biomarker Development

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Published on: April 15, 2013

Refining Drug-Induced Cholestasis Prediction: An Explainable Consensus Model Integrating Chemical and Biological

Palle S Helmke¹, Gerhard F Ecker¹

¹Department of Pharmaceutical Sciences, University of Vienna, 1090 Vienna, Austria.

Journal of Chemical Information and Modeling

|May 27, 2025

Summary

This study developed a computational model to predict drug-induced cholestasis (DIC), a type of liver injury. The model integrates chemical structures, liver targets, and transporter data, improving early drug safety assessment and reducing animal testing.

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Area of Science:

Pharmacology and Toxicology
Computational Chemistry
Systems Biology

Background:

Drug-induced liver injury (DILI), especially drug-induced cholestasis (DIC), poses a significant challenge in early drug development.
Minimizing animal testing through the 3R principle (Replacement, Reduction, Refinement) is crucial for ethical and efficient drug safety assessment.

Purpose of the Study:

To develop and validate a computational method for predicting drug-induced cholestasis (DIC).
To integrate diverse data sources including chemical substructures, liver-expressed targets, pathways, and hepatic transporter inhibition for enhanced prediction accuracy.

Main Methods:

Utilized PubChem substructure fingerprints and biological data from liver-expressed targets and pathways.
Incorporated nine hepatic transporter inhibition models and employed undersampling to address class imbalance in public cholestasis data.
Applied target prediction tools to enrich the compound-target interaction matrix and employed an expanded consensus model with probability range filtering.

Main Results:

The baseline model combining chemical, pathway, and transporter data achieved a Matthews correlation coefficient (MCC) of 0.29 and sensitivity of 0.79 via 10-fold cross-validation.
Feature importance analysis identified albumin as a potential target associated with cholestasis.
The refined method, using an expanded consensus model and probability filtering, improved prediction with an MCC of 0.38 and sensitivity of 0.80.

Conclusions:

The developed computational approach effectively predicts drug-induced cholestasis by integrating chemical and biological descriptors.
The model offers a reliable and explainable tool for early drug safety assessment, supporting decision-making and potentially reducing reliance on animal testing.
Further investigation into identified targets like albumin is warranted to deepen understanding of cholestasis mechanisms.