pgp - publications

Predict more pgp - ligand interactions now!


1. Bioorg Med Chem. 2012 Mar 29. [Epub ahead of print]

Fingerprint-based in silico models for the prediction of P-glycoprotein
substrates and inhibitors.

Poongavanam V, Haider N, Ecker GF.

University of Vienna, Department of Medicinal Chemistry, Althanstrasse 14, 1090
Vienna, Austria.

P-Glycoprotein (P-gp, ABCB1) plays a significant role in determining the ADMET
properties of drugs and drug candidates. Substrates of P-gp are not only subject
to multidrug resistance (MDR) in tumor therapy, they are also associated with
poor pharmacokinetic profiles. In contrast, inhibitors of P-gp have been
advocated as modulators of MDR. However, due to the polyspecificity of P-gp,
knowledge on the molecular basis of ligand-transporter interaction is still poor,
which renders the prediction of whether a compound is a P-gp
substrate/non-substrate or an inhibitor/non-inhibitor quite challenging. In the
present investigation, we used a set of fingerprints representing the
presence/absence of various functional groups for machine learning based
classification of a set of 484 substrates/non-substrates and a set of 1935
inhibitors/non-inhibitors. Best models were obtained using a combination of a
wrapper subset evaluator (WSE) with random forest (RF), kappa nearest neighbor
(kNN) and support vector machine (SVM), showing accuracies >70%. Best P-gp
substrate models were further validated with three sets of external P-gp
substrate sources, which include Drug Bank (n=134), TP Search (n=90) and a set
compiled from literature (n=76). Association rule analysis explores the various
structural feature requirements for P-gp substrates and inhibitors.

Copyright © 2012 Elsevier Ltd. All rights reserved.

PMID: 22595422 [PubMed - as supplied by publisher]