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).
Hammann
Felix
F
Department of Clinical Pharmacology and Toxicology, University Hospital Basel, University of Basel, Switzerland.
Gutmann
Heike
H
Jecklin
Ursula
U
Maunz
Andreas
A
Helma
Christoph
C
Drewe
Juergen
J
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000037:antagonists & inhibitors / Q000096:biosynthesis / Q000378:metabolism; D003198:Computer Simulation; D003663:Decision Trees; D004790:Enzyme Induction; D005452:Fluoresceins / Q000378:metabolism; D021281:Quantitative Structure-Activity Relationship; D013379:Substrate Specificity
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D005452:Fluoresceins; C085925:calcein AM