Development of a Support Vector Machine-Based System to Predict Whether a Compound Is a Substrate of a Given Drug Transporter Using Its Chemical Structure.

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.

Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data.

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.

Automated extraction of information on chemical-P-glycoprotein interactions from the literature.

Knowledge of the interactions between drugs and transporters is important for drug discovery and development as well as for the evaluation of their clinical safety. We recently developed a text-mining system for the automatic extraction of information on chemical-CYP3A4 interactions from the literature. This system is based on natural language processing and can extract chemical names and their interaction patterns according to sentence context.

Structure-activity relationship modeling for predicting interactions with pregnane X receptor by recursive partitioning.

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.

Automated information extraction and structure-activity relationship analysis of cytochrome P450 substrates.

Information on CYP-chemical interactions was comprehensively explored by a text-mining technique, to confirm our previous structure-activity relationship model for CYP substrates (Yamashita et al. J. Chem.