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BioMed Research International
Volume 2013 (2013), Article ID 863592, 12 pages
http://dx.doi.org/10.1155/2013/863592
Research Article

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

1Medicinal Chemistry, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), Argentinean National Council for Scientific and Technical Research (CONICET), CCT La Plata, Buenos Aires, B1900AJI La Plata, Argentina
2Quality Control of Medications, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), Buenos Aires, B1900AJI La Plata, Argentina
3Biopharmacy, Department of Biological Sciences, Faculty of Exact Sciences, National University of La Plata (UNLP), 47 and 115, Buenos Aires, B1900AJI La Plata, Argentina

Received 30 April 2013; Accepted 25 June 2013

Academic Editor: Jielin Sun

Copyright © 2013 Melisa Edith Gantner et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked to multidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous system conditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to random subsamples of Dragon molecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.