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

[ Vol. 10 , Issue. 4 ]

Author(s):

Felix Hammann, Heike Gutmann, Ursula Jecklin, Andreas Maunz, Christoph Helma and Juergen Drewe   Pages 339 - 346 ( 8 )

Abstract:


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

Affiliation:

Department of Clinical Pharmacology and Toxicology, University Hospital of Basel, Petersgraben 4, CH-4031 Basel Switzerland.



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