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Predicting in vitro assays related to liver function using probabilistic machine learning.

Flavio M Morelli¹, Marian Raschke², Natalia Jungmann²

¹R&D Machine Learning Research, Bayer AG, Pharmaceuticals Division, Berlin, Germany; Department of Mathematics and Computer Science, Free University of Berlin, Berlin, Germany.

|May 21, 2025

Summary

This study integrates multiple data types into a probabilistic framework to predict liver toxicity in vitro, quantifying prediction uncertainty for safer drug development and reduced animal testing.

Keywords:

Bayesian Modeling Hepatotoxicity In Vitro Assays Multimodal Integration Probabilistic Machine Learning Uncertainty Quantification

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

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

Published on: January 31, 2022

Area of Science:

Toxicology
Computational Biology
Pharmacology

Background:

Machine learning (ML) is increasingly used in toxicology, but limited data necessitates quantifying the uncertainty of in silico predictions.
Reliable decision-making in toxicological assessments requires robust methods for uncertainty quantification.

Purpose of the Study:

To develop and evaluate a probabilistic framework for predicting in vitro liver assay outcomes using integrated data modalities.
To quantify the uncertainty associated with these in silico predictions.
To integrate predictions into an estimation of drug-induced liver injury (DILI) probability.

Main Methods:

Systematic comparison of various probabilistic methods for predicting in vitro liver function assays.
Integration of multiple data modalities: chemical descriptors, gene expression, and morphological profiles.
Generation of new experimental data for reactive oxygen species generation and hepatocyte toxicity assays.

Main Results:

Demonstrated the performance of different data modalities within the probabilistic framework.
Successfully integrated the framework and in vitro assay predictions to estimate DILI probability.
Provided novel experimental data for hepatocyte toxicity and reactive oxygen species generation.

Conclusions:

Uncertainty quantification is crucial for reliable in silico toxicity predictions.
The developed probabilistic framework enhances the prediction of in vitro assays and DILI risk.
This approach can contribute to a safer drug development process and reduce animal testing.