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Summary: Pregnane X receptor (PXR) is a ligand-activated nuclear factor that upregulates the expression of proteins involved in the detoxification and clearance of xenobiotics, primarily cytochrome P450 3A4 (CYP3A4). Structure-activity relationship (SAR) analysis of PXR agonists is useful for avoiding unwanted pharmacokinetics due to drug-drug interactions. To perform large-scale ligand-based SAR modeling, we systematically collected information on chemical-PXR interactions from the PubMed database by using the text mining system we developed, and merged it with screening data registered in the PubChem BioAssay database and other published data. Curation of the data resulted in 270 human PXR agonists and 248 non-agonists. After the entire data set was divided into training and testing data sets, the training data set comprising 415 data entries (217 positive and 198 negative instances) was analyzed by a recursive partitioning method. The classification tree optimized by a cross-validation pruning algorithm gave an accuracy of 79.0%, and, for the external testing data set, could correctly classify PXR agonists and non-agonists at an accuracy of 70.9% Descriptors chosen as splitting rules in the classification tree were generally associated with electronic properties of molecules, suggesting they had an important role in the modes of interaction.

The aim of this study was to develop an in silico prediction system to assess which of 7 categories of drug transporters (organic anion transporting polypeptide [OATP] 1B1/1B3, multidrug resistance-associated protein [MRP] 2/3/4, organic anion transporter [OAT] 1, OAT3, organic cation transporter [OCT] 1/2/multidrug and toxin extrusion [MATE] 1/2-K, multidrug resistance protein 1 [MDR1], and breast cancer resistance protein [BCRP]) can recognize compounds as substrates using its chemical structure alone. We compiled an internal data set consisting of 260 compounds that are substrates for at least 1 of the 7 categories of drug transporters. Four physicochemical parameters (charge, molecular weight, lipophilicity, and plasma unbound fraction) of each compound were used as the basic descriptors. Furthermore, a greedy algorithm was used to select 3 additional physicochemical descriptors from 731 available descriptors. In addition, transporter nonsubstrates tend not to be in the public domain; we, thus, tried to compile an expert-curated data set of putative nonsubstrates for each transporter using personal opinions of 11 researchers in the field of drug transporters. The best prediction was finally achieved by a support vector machine based on 4 basic and 3 additional descriptors. The model correctly judged that 364 of 412 compounds (internal data set) and 111 of 136 compounds (external data set) were substrates, indicating that this model performs well enough to predict the specificity of transporter substrates.

Induction of cytochrome P450 3A4 (CYP3A4) expression is often implicated in clinically relevant drug-drug interactions (DDI), as metabolism catalyzed by this enzyme is the dominant route of elimination for many drugs. Although several DDI models have been proposed, none have comprehensively considered the effects of enzyme transcription/translation dynamics on induction-based DDI. Rifampicin is a well-known CYP3A4 inducer, and is commonly used as a positive control for evaluating the CYP3A4 induction potential of test compounds. Herein, we report the compilation of in vitro induction data for CYP3A4 by rifampicin in human hepatocytes, and the transcription/translation model developed for this enzyme using an extended least squares method that can account for inherent inter-individual variability. We also developed physiologically based pharmacokinetic (PBPK) models for the CYP3A4 inducer and CYP3A4 substrates. Finally, we demonstrated that rifampicin-induced DDI can be predicted with reasonable accuracy, and that a static model can be used to simulate DDI once the blood concentration of the inducer reaches a steady state following repeated dosing. This dynamic PBPK-based DDI model was implemented on a new multi-hierarchical physiology simulation platform named PhysioDesigner.