Evaluation of computational docking to identify pregnane X receptor agonists in the toxcast database

Background: The pregnane X receptor (PXR) is a key transcriptional regulator of many genes [e.g., cytochrome P450s (CYP2C9, CYP3A4, CYP2B6), MDR1] involved in xenobiotic metabolism and excretion. Objectives: As part of an evaluation of different approaches to predict compound affinity for nuclear hormone receptors, we used the molecular docking program GOLD and a hybrid scoring scheme based on similarity weighted GoldScores to predict potential PXR agonists in the ToxCast database of pesticides and other industrial chemicals. We present some of the limitations of different in vitro systems, as well as docking and ligand-based computational models. Methods: Each ToxCast compound was docked into the five published crystallographic structures of human PXR (hPXR), and 15 compounds were selected based on their consensus docking scores for testing. In addition, we used a Bayesian model to classify the ToxCast compounds into PXR agonists and nonagonists. hPXR activation was determined by luciferase-based reporter assays in the HepG2 and DPX-2 human liver cell lines. Results: We tested 11 compounds, of which 6 were strong agonists and 2 had weak agonist activity. Docking results of additional compounds were compared with data reported in the literature. The prediction sensitivity of PXR agonists in our sample ToxCast data set (n = 28) using docking and the GoldScore was higher than with the hybrid score at 66.7%. The prediction sensitivity for PXR agonists using GoldScore for the entire ToxCast data set (n = 308) compared with data from the NIH (National Institutes of Health) Chemical Genomics Center data was 73.8%. Conclusions: Docking and the GoldScore may be useful for prioritizing large data sets prior to in vitro testing with good sensitivity across the sample and entire ToxCast data set for hPXR agonists.