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Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models.

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Computational models predict pregnane X receptor (PXR) activation by chemicals, aiding in identifying potential drug interactions and diseases. These models offer efficient in silico screening for PXR activators.

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Area of Science:

  • Pharmacology and Toxicology
  • Computational Chemistry
  • Drug Discovery

Background:

  • The pregnane X receptor (PXR) is a key regulator of drug metabolism and transporter proteins.
  • PXR activation is linked to adverse drug-drug interactions, steatosis, and cancers.
  • Identifying PXR activators is crucial for drug safety and disease prevention.

Purpose of the Study:

  • To develop computational models for predicting rat and human PXR activation.
  • To identify potentially harmful chemicals and assess species-specific PXR activation.
  • To provide an efficient in silico screening tool for PXR activators.

Main Methods:

  • Utilized a dataset of nearly 2000 compounds screened in cell-based reporter gene assays.
  • Developed Bayesian quantitative structure-activity relationship (QSAR) models using physicochemical and structural properties.
  • Employed 5-fold cross-validation for model evaluation.

Main Results:

  • PXR activators were found to be hydrophobic with distinct physicochemical properties compared to nonactivators.
  • Bayesian models achieved high accuracy (89% human, 89% rat) with good sensitivity and specificity.
  • Identified shared and species-specific structural features of PXR activators.
  • Demonstrated broad agreement between in vitro and in vivo PXR activation data in rats.

Conclusions:

  • Developed accurate and efficient in silico models for predicting PXR activation.
  • Models can identify potentially harmful chemicals and evaluate species-specific effects.
  • These computational tools serve as valuable high-throughput screens for in vitro PXR activity.