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Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver

Marina Garcia de Lomana1, Domenico Gadaleta2, Marian Raschke3

  • 1Bayer AG, Pharmaceuticals, 42113 Wuppertal, Germany.

Chemical Research in Toxicology
|March 10, 2025
PubMed

Insights

Drug-induced liver injury (DILI) prediction is improved using multi-task machine learning models. These models analyze 28 in vitro liver toxicity endpoints to forecast potential DILI risks early in drug development.

Area of Science:

  • Pharmacology and Toxicology
  • Computational Chemistry
  • Drug Development

Background:

  • Drug-induced liver injury (DILI) is a significant challenge in drug development, leading to failures and withdrawals.
  • The complex mechanisms of DILI make it difficult to predict, necessitating advanced predictive tools.
  • Understanding liver pharmacokinetics and pharmacodynamics is crucial for mitigating DILI risks.

Purpose of the Study:

  • To explore the potential of multi-task learning for predicting 28 in vitro liver toxicity endpoints.
  • To develop machine learning models for predicting molecular events triggering DILI.
  • To create a 'Virtual Liver Safety Profile' for compound prioritization and mode of action elucidation.

Main Methods:

  • Collected a comprehensive dataset of 28 in vitro liver toxicity and DILI-specific endpoints.
  • Applied multi-task learning and ensemble modeling for prediction.
  • Incorporated uncertainty estimation to define model applicability domains.
  • Evaluated models using public compound data for Bile salt export pump (BSEP) inhibition and phospholipidosis.

Main Results:

  • Demonstrated the benefits of ensemble modeling for predicting liver toxicity endpoints.
  • Established an applicability domain for the predictive models using uncertainty estimation.
  • Analyzed relationships between various endpoints and DILI.
  • Validated model performance on specific assays like BSEP inhibition.

Conclusions:

  • Multi-task learning models show promise for predicting DILI and related liver endpoints.
  • The 'Virtual Liver Safety Profile' can aid in early-stage drug safety assessment.
  • These models support assay prioritization and understanding of DILI mechanisms.

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