@article {Hammann:2009:1389-2002:339,
title = "Development of Decision Tree Models for Substrates, Inhibitors, and Inducers of P-Glycoprotein",
journal = "Current Drug Metabolism",
parent_itemid = "infobike://ben/cdm",
publishercode ="ben",
year = "2009",
volume = "10",
number = "4",
publication date ="2009-05-01T00:00:00",
pages = "339-346",
itemtype = "ARTICLE",
issn = "1389-2002",
url = "https://www.ingentaconnect.com/content/ben/cdm/2009/00000010/00000004/art00003",
doi = "doi:10.2174/138920009788499021",
keyword = "MDR1, decision trees, QSAR, Multidrug resistance, P-glycoprotein, Calcein AM assay",
author = "Hammann, Felix and Gutmann, Heike and Jecklin, Ursula and Maunz, Andreas and Helma, Christoph and Drewe, Juergen",
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). ",
}