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

Autores
Gantner, Melisa Edith; Di Ianni, Mauricio Emiliano; Ruiz, María Esperanza; Talevi, Alan; Bruno Blanch, Luis Enrique
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Facultad de Ciencias Exactas
Materia
Ciencias Exactas
modelos computacionales
BCRP
algoritmos
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/70605

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network_name_str SEDICI (UNLP)
spelling Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein SubstratesGantner, Melisa EdithDi Ianni, Mauricio EmilianoRuiz, María EsperanzaTalevi, AlanBruno Blanch, Luis EnriqueCiencias Exactasmodelos computacionalesBCRPalgoritmosABC 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.Facultad de Ciencias Exactas2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/70605enginfo:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/bmri/2013/863592/info:eu-repo/semantics/altIdentifier/doi/10.1155/2013/863592info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:11:19Zoai:sedici.unlp.edu.ar:10915/70605Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:11:19.865SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
title Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
spellingShingle Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates
Gantner, Melisa Edith
Ciencias Exactas
modelos computacionales
BCRP
algoritmos
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
dc.creator.none.fl_str_mv Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis Enrique
author Gantner, Melisa Edith
author_facet Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis Enrique
author_role author
author2 Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis Enrique
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Exactas
modelos computacionales
BCRP
algoritmos
topic Ciencias Exactas
modelos computacionales
BCRP
algoritmos
dc.description.none.fl_txt_mv 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.
Facultad de Ciencias Exactas
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.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/70605
url http://sedici.unlp.edu.ar/handle/10915/70605
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/bmri/2013/863592/
info:eu-repo/semantics/altIdentifier/doi/10.1155/2013/863592
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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