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

The
pregnane X receptor (PXR) is a ligand-activated transcription
factor that acts as a master regulator of metabolizing enzymes and
transporters. To avoid adverse drug–drug interactions and diseases
such as steatosis and cancers associated with PXR activation, identifying
drugs and chemicals that activate PXR is of crucial importance. In
this work, we developed ligand-based predictive computational models
for both rat and human PXR activation, which allowed us to identify
potentially harmful chemicals and evaluate species-specific effects
of a given compound. We utilized a large publicly available data set
of nearly 2000 compounds screened in cell-based reporter gene assays
to develop Bayesian quantitative structure–activity relationship
models using physicochemical properties and structural descriptors.
Our analysis showed that PXR activators tend to be hydrophobic and
significantly different from nonactivators in terms of their physicochemical
properties such as molecular weight, logP, number of rings, and solubility.
Our Bayesian models, evaluated by using 5-fold cross-validation, displayed
a sensitivity of 75% (76%), specificity of 76% (75%), and accuracy
of 89% (89%) for human (rat) PXR activation. We identified structural
features shared by rat and human PXR activators as well as those unique
to each species. We compared rat in vitro PXR activation
data to in vivo data by using DrugMatrix, a large
toxicogenomics database with gene expression data obtained from rats
after exposure to diverse chemicals. Although in vivo gene expression data pointed to cross-talk between nuclear receptor
activators that is captured only by in vivo assays,
overall we found broad agreement between in vitro and in vivo PXR activation. Thus, the models developed
here serve primarily as efficient initial high-throughput in silico screens of in vitro activity.