ABSTRACT
The nuclear receptor human pregnane X receptor (hPXR) is a ligand-regulated transcription factor that responds to a wide range of endogenous and xenobiotic molecules. Upon activation with ligands, hPXR can increase induction levels of metabolic enzymes. Therefore, hPXR plays a critical role in drug metabolism and excretion. Identifying the molecules that activate this protein can be of great help to predict adverse drug interaction, which, nevertheless, cannot be accurately modeled without taking into account its promiscuous nature, namely, highly flexible protein conformation and multiple ligand orientations. An in silico model was developed to predict the activation of hPXR using the novel pharmacophore ensemble/support vector machine (PhE/SVM) scheme. The predictions by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 32, r(2) = 0.86, q(2) = 0.80, RMSE = 0.37, s = 0.21) and test set (n = 120, r(2) = 0.80, RMSE = 0.25, s = 0.19). In addition, this PhE/SVM model performed equally well for those molecules in the outlier set (n = 8, r(2) = 0.91, RMSE = 0.15, s = 0.12) and completely met with those validation criteria generally adopted to gauge the predictivity of a theoretical model. A mock test also verified its predictivity. When compared with crystal structures, the calculated results are consistent with the published hPXR-ligand cocomplex structure and the plasticity nature of hPXR is also revealed. Thus, this accurate, fast, and robust PhE/SVM model can be utilized for predicting the activation of promiscuous hPXR to facilitate drug discovery and development.
