Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of P-Glycoprotein

Author(s): Felix Hammann, Heike Gutmann, Ursula Jecklin, Andreas Maunz, Christoph Helma, Juergen Drewe

Journal Name: Current Drug Metabolism

Volume 10 , Issue 4 , 2009

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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).

Keywords: P-glycoprotein, MDR1, Multidrug resistance, Calcein AM assay, QSAR, decision trees

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Article Details

Year: 2009
Page: [339 - 346]
Pages: 8
DOI: 10.2174/138920009788499021
Price: $65

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