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

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 tomultidrug resistance issues in a diversity of diseases. Brea...

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Autores principales: Gantner, Melisa Edith, Di Ianni, Mauricio Emiliano, Ruiz, María Esperanza, Talevi, Alan, Bruno Blanch, Luis Enrique
Formato: Articulo
Lenguaje:Inglés
Publicado: 2013
Materias:
Ciencias Exactas
modelos computacionales
BCRP
algoritmos
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/70605
https://www.hindawi.com/journals/bmri/2013/863592/
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-70605
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
modelos computacionales
BCRP
algoritmos
spellingShingle Ciencias Exactas
modelos computacionales
BCRP
algoritmos
Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis Enrique
Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
topic_facet Ciencias Exactas
modelos computacionales
BCRP
algoritmos
description 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 tomultidrug 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 systemconditions, 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 randomsubsamples ofDragonmolecular 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.
format Articulo
Articulo
author Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis Enrique
author_facet Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis Enrique
author_sort Gantner, Melisa Edith
title Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
title_short Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
title_full Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
title_fullStr Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
title_full_unstemmed Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
title_sort development of conformation independent computational models for the early recognition of breast cancer resistance protein substrates
publishDate 2013
url http://sedici.unlp.edu.ar/handle/10915/70605
https://www.hindawi.com/journals/bmri/2013/863592/
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