Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
Joint Authors
Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis E.
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-08-01
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Gantner, Melisa Edith& Di Ianni, Mauricio Emiliano& Ruiz, María Esperanza& Talevi, Alan& Bruno Blanch, Luis E.. 2013. Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates. BioMed Research International،Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-1005300
Modern Language Association (MLA)
Gantner, Melisa Edith…[et al.]. Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates. BioMed Research International No. 2013 (2013), pp.1-12.
https://search.emarefa.net/detail/BIM-1005300
American Medical Association (AMA)
Gantner, Melisa Edith& Di Ianni, Mauricio Emiliano& Ruiz, María Esperanza& Talevi, Alan& Bruno Blanch, Luis E.. Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates. BioMed Research International. 2013. Vol. 2013, no. 2013, pp.1-12.
https://search.emarefa.net/detail/BIM-1005300
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1005300