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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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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...
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Related Experiment Video

Updated: Dec 25, 2025

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank

Robert Ancuceanu1, Marilena Viorica Hovanet1, Adriana Iuliana Anghel1

  • 1Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, 020956 Bucharest, Romania.

International Journal of Molecular Sciences
|March 25, 2020
PubMed
Summary

Predicting drug-induced liver injury (DILI) from chemical structures is crucial for drug safety. This study developed stacked machine learning models using the DILIrank dataset, achieving superior performance in identifying non-hepatotoxic compounds.

Keywords:
DILIDILIrankQSARdrug hepatotoxicityin siliconested cross-validationvirtual screening

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Area of Science:

  • Computational toxicology
  • Drug safety assessment
  • cheminformatics

Background:

  • Drug-induced liver injury (DILI) poses significant challenges in drug development.
  • Predicting hepatotoxicity from chemical structures aligns with the trend towards in silico alternatives for non-clinical testing.
  • The DILIrank dataset provides a valuable resource for ranking drug hepatotoxicity risk.

Purpose of the Study:

  • To develop and validate predictive models for drug-induced liver injury using the DILIrank dataset.
  • To explore stacked machine learning approaches for enhancing hepatotoxicity prediction accuracy.
  • To apply the developed models for virtual screening of large compound libraries.

Main Methods:

  • Computation of molecular descriptors using Dragon 7.0 software.
  • Implementation of various feature selection and machine learning algorithms in R.
  • External model validation using nested (double) cross-validation.
  • Ensemble modeling through stacking and meta-models.

Main Results:

  • A total of 78 models with good performance were selected and stacked.
  • Stacked models demonstrated slightly superior performance compared to previously published models.
  • Virtual screening of over 100,000 compounds from the ZINC database was performed.
  • Approximately 20% of screened compounds were predicted as non-hepatotoxic.

Conclusions:

  • The developed stacked models offer a promising approach for predicting drug-induced liver injury.
  • In silico prediction of hepatotoxicity can aid in early drug safety assessment and compound prioritization.
  • This study successfully identified a significant subset of non-hepatotoxic compounds through virtual screening.