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Exploring Adverse Event Associations of Predicted PXR Agonists Using the FAERS Database

  • 0Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo 204-8588, Japan.
International Journal of Molecular Sciences +

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August 14, 2025

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August 14, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Pregnane X receptor (PXR) activation is linked to cardiac risks. Machine learning identified PXR agonist activity in drugs associated with cardiovascular adverse events, aiding drug safety assessments.

Area Of Science

  • Pharmacology
  • Toxicology
  • Computational Biology

Background

  • Pregnane X receptor (PXR) is a nuclear receptor regulating drug metabolism and physiological functions.
  • The full impact of PXR activation on adverse events remains incompletely understood.
  • PXR's role in drug-induced toxicity requires further investigation.

Purpose Of The Study

  • To develop a machine learning model for predicting PXR agonist activity.
  • To assess the association between PXR agonist activity and adverse drug events.
  • To identify potential cardiovascular risks linked to PXR activation.

Main Methods

  • Developed a machine learning model to predict PXR agonist activity.
  • Applied the model to drugs in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database.
  • Analyzed predicted PXR agonist-drug interactions against reported adverse events using statistical risk assessment (lnROR, -logp).

Main Results

  • The machine learning model successfully predicted PXR agonist activity.
  • Statistically significant risks for multiple cardiac disorders were identified for PXR agonist-active drugs.
  • Specific drug-cardiac event associations revealed elevated risk profiles.

Conclusions

  • PXR activation is implicated in cardiovascular adverse effects.
  • Machine learning models can aid in predicting drug-induced cardiovascular risks.
  • Early identification of PXR-mediated risks can enhance drug safety evaluations.

Keywords:

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