Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of P-Glycoprotein
In silico classification of new compounds for certain properties is a useful tool to guide further experiments or compound selection. Interaction of new compounds with the efflux pump P-glycoprotein (P-gp) is an important drug property determining tissue distribution and the potential for drug-drug interactions. We present three datasets on substrate, inhibitor, and inducer activities for P-gp (n = 471) obtained from a literature search which we compared to an existing evaluation of the Prestwick Chemical Library with the calcein- AM assay (retrieved from PubMed). Additionally, we present decision tree models of these activities with predictive accuracies of 77.7 % (substrates), 86.9 % (inhibitors), and 90.3 % (inducers) using three algorithms (CHAID, CART, and C4.5). We also present decision tree models of the calcein-AM assay (79.9 %). Apart from a comprehensive dataset of P-gp interacting compounds, our study provides evidence of the efficacy of logD descriptors and of two algorithms not commonly used in pharmacological QSAR studies (CART and CHAID).
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Document Type: Research Article
Publication date: 01 May 2009
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- Current Drug Metabolism aims to cover all the latest and outstanding developments in drug metabolism and disposition. The journal serves as an international forum for the publication of timely reviews in drug metabolism. Current Drug Metabolism is an essential journal for academic, clinical, government and pharmaceutical scientists who wish to be kept informed and up-to-date with the latest and most important developments. The journal covers the following areas:
In vitro systems including CYP-450; enzyme induction and inhibition; drug-drug interactions and enzyme kinetics; pharmacokinetics, toxicokinetics, species scaling and extrapolations; P-glycoprotein and transport carriers; target organ toxicity and interindividual variability; drug metabolism and disposition studies; extrahepatic metabolism; phase I and phase II metabolism; recent developments for the identification of drug metabolites and adducts.