pgp - publications

Predict more pgp - ligand interactions now!

1. PLoS One. 2012;7(3):e33829. Epub 2012 Mar 16.

Prediction of promiscuous p-glycoprotein inhibition using a novel machine
learning scheme.

Leong MK, Chen HB, Shih YH.

Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan.

BACKGROUND: P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that
plays a pivotal role in eliminating xenobiotics by active extrusion of
xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with
the over-expression of P-gp by cells, resulting in increased efflux of
chemotherapeutical agents and reduction of intracellular drug accumulation. It is
of clinical importance to develop a P-gp inhibition predictive model in the
process of drug discovery and development.
METHODOLOGY/PRINCIPAL FINDINGS: An in silico model was derived to predict the
inhibition of P-gp using the newly invented pharmacophore ensemble/support vector
machine (PhE/SVM) scheme based on the data compiled from the literature. The
predictions by the PhE/SVM model were found to be in good agreement with the
observed values for those structurally diverse molecules in the training set
(n = 31, r(2) = 0.89, q(2) = 0.86, RMSE = 0.40, s = 0.28), the test set (n = 88,
r(2) = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 11, r(2) = 0.96,
RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy
when subjected to those validation criteria generally adopted to gauge the
predictivity of a theoretical model.
CONCLUSIONS/SIGNIFICANCE: This accurate, fast and robust PhE/SVM model that can
take into account the promiscuous nature of P-gp can be applied to predict the
P-gp inhibition of structurally diverse compounds that otherwise cannot be done
by any other methods in a high-throughput fashion to facilitate drug discovery
and development by designing drug candidates with better metabolism profile.

PMID: 22439003 [PubMed - in process]