No 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.

BackgroundBreast cancer resistant protein has an essential role in active transport of endogenous substances and xenobiotics across extracellular and intracellular membranes along with P-glycoprotein. It also plays a major role in multiple drug resistance and permeation of blood-brain barrier. Therefore, it is of great importance to derive theoretical models to predict the inhibition of both transporters in the process of drug discovery and development. Hitherto, very limited BCRP inhibition predictive models have been proposed as compared with its P-gp counterpart.Methodology/Principal FindingsAn in silico BCRP inhibition model was developed in this study using the pharmacophore ensemble/support vector machine scheme to take into account the promiscuous nature of BCRP. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those molecules in the training set (n = 22, r 2 = 0.82,  = 0.73, RMSE  =  0.40, s = 0.24), test set (n = 97, q 2 = 0.75–0.89, RMSE  = 0.31, s = 0.21), and outlier set (n = 16, q 2 = 0.72–0.91, RMSE  =  0.29, s = 0.17). When subjected to a variety of statistical validations, the developed PhE/SVM model consistently met the most stringent criteria. A mock test by HIV protease inhibitors also asserted its predictivity.Conclusions/SignificanceIt was found that this accurate, fast, and robust PhE/SVM model can be employed to predict the BCRP inhibition of structurally diverse molecules that otherwise cannot be carried out by any other methods in a high-throughput fashion to design therapeutic agents with insignificant drug toxicity and unfavorable drug–drug interactions mediated by BCRP to enhance clinical efficacy and/or circumvent drug resistance.