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Related Concept Videos

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow01:26

Effect of Hepatic Disease on Pharmacokinetics: Drug Dosing and Hepatic Blood Flow

Chronic liver disease significantly impacts drug metabolism due to alterations in hepatic blood flow and enzyme accessibility. This disruption affects the body's pharmacokinetics—the movement and processing of drugs within the system. Key enzymes crucial for metabolizing medications become less accessible, changing how drugs are processed and utilized. Furthermore, liver disease influences the synthesis of plasma proteins, such as albumin and globulins, which play critical roles in drug binding...
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In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess the...

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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
Summary

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.

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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.