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
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
ATP-Binding Cassette (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. Moreover, their overexpression is linked to multidrug resistance issues in a diversity of diseases (e.g. cancer). Breast Cancer Resistance Protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting the 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. Here, 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 non-substrates compiled from literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models have been 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 Receiving Operating Characteristic curves were applied to obtain the best 2-model combination, which presented 82% of overall accuracy in the training set and 74.5% of overall accuracy in the test set. These are remarkable results considering the broad substrate specificity of BCRP. Moreover, Receiving Operating Characteristic curves may be applied to attain an optimal, context-dependent balance between specificity and sensitivity of the model ensemble.
Fil: Gantner, Melisa Edith. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Fil: Di Ianni, Mauricio Emiliano. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Fil: Ruiz, María Esperanza. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biologicas. Catedra de Control de Calidad de Medicamentos; Argentina
Fil: Talevi, Alan. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Fil: Bruno Blanch, Luis. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Materia
BREAST CANCER RESISTANCE PROTEIN
ABC TRANSPORTER
MULTIDRUG RESISTANCE
BCRP SUBSTRATES
2D COMPUTATIONAL MODELS
IN SILICO CLASSIFICATION MODEL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/7529
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/7529
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
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, LuisBREAST CANCER RESISTANCE PROTEINABC TRANSPORTERMULTIDRUG RESISTANCEBCRP SUBSTRATES2D COMPUTATIONAL MODELSIN SILICO CLASSIFICATION MODELhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3ATP-Binding Cassette (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. Moreover, their overexpression is linked to multidrug resistance issues in a diversity of diseases (e.g. cancer). Breast Cancer Resistance Protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting the 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. Here, 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 non-substrates compiled from literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models have been 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 Receiving Operating Characteristic curves were applied to obtain the best 2-model combination, which presented 82% of overall accuracy in the training set and 74.5% of overall accuracy in the test set. These are remarkable results considering the broad substrate specificity of BCRP. Moreover, Receiving Operating Characteristic curves may be applied to attain an optimal, context-dependent balance between specificity and sensitivity of the model ensemble.Fil: Gantner, Melisa Edith. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; ArgentinaFil: Di Ianni, Mauricio Emiliano. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; ArgentinaFil: Ruiz, María Esperanza. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biologicas. Catedra de Control de Calidad de Medicamentos; ArgentinaFil: Talevi, Alan. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; ArgentinaFil: Bruno Blanch, Luis. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; ArgentinaHindawi Publishing Corporation2013-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/7529Gantner, Melisa Edith; Di Ianni, Mauricio Emiliano; Ruiz, María Esperanza; Talevi, Alan; Bruno Blanch, Luis; Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein substrates; Hindawi Publishing Corporation; Biomed; 2013; 6-2013; 1-122314-6141enginfo: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/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:15:28Zoai:ri.conicet.gov.ar:11336/7529instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 12:15:28.848CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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
BREAST CANCER RESISTANCE PROTEIN
ABC TRANSPORTER
MULTIDRUG RESISTANCE
BCRP SUBSTRATES
2D COMPUTATIONAL MODELS
IN SILICO CLASSIFICATION MODEL
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
author Gantner, Melisa Edith
author_facet Gantner, Melisa Edith
Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis
author_role author
author2 Di Ianni, Mauricio Emiliano
Ruiz, María Esperanza
Talevi, Alan
Bruno Blanch, Luis
author2_role author
author
author
author
dc.subject.none.fl_str_mv BREAST CANCER RESISTANCE PROTEIN
ABC TRANSPORTER
MULTIDRUG RESISTANCE
BCRP SUBSTRATES
2D COMPUTATIONAL MODELS
IN SILICO CLASSIFICATION MODEL
topic BREAST CANCER RESISTANCE PROTEIN
ABC TRANSPORTER
MULTIDRUG RESISTANCE
BCRP SUBSTRATES
2D COMPUTATIONAL MODELS
IN SILICO CLASSIFICATION MODEL
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv ATP-Binding Cassette (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. Moreover, their overexpression is linked to multidrug resistance issues in a diversity of diseases (e.g. cancer). Breast Cancer Resistance Protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting the 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. Here, 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 non-substrates compiled from literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models have been 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 Receiving Operating Characteristic curves were applied to obtain the best 2-model combination, which presented 82% of overall accuracy in the training set and 74.5% of overall accuracy in the test set. These are remarkable results considering the broad substrate specificity of BCRP. Moreover, Receiving Operating Characteristic curves may be applied to attain an optimal, context-dependent balance between specificity and sensitivity of the model ensemble.
Fil: Gantner, Melisa Edith. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Fil: Di Ianni, Mauricio Emiliano. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Fil: Ruiz, María Esperanza. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biologicas. Catedra de Control de Calidad de Medicamentos; Argentina
Fil: Talevi, Alan. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
Fil: Bruno Blanch, Luis. Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas. Cátedra de Química Medicinal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Cientifico Tecnológico La Plata; Argentina
description ATP-Binding Cassette (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. Moreover, their overexpression is linked to multidrug resistance issues in a diversity of diseases (e.g. cancer). Breast Cancer Resistance Protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting the 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. Here, 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 non-substrates compiled from literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models have been 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 Receiving Operating Characteristic curves were applied to obtain the best 2-model combination, which presented 82% of overall accuracy in the training set and 74.5% of overall accuracy in the test set. These are remarkable results considering the broad substrate specificity of BCRP. Moreover, Receiving Operating Characteristic curves may be applied to attain an optimal, context-dependent balance between specificity and sensitivity of the model ensemble.
publishDate 2013
dc.date.none.fl_str_mv 2013-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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://hdl.handle.net/11336/7529
Gantner, Melisa Edith; Di Ianni, Mauricio Emiliano; Ruiz, María Esperanza; Talevi, Alan; Bruno Blanch, Luis; Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein substrates; Hindawi Publishing Corporation; Biomed; 2013; 6-2013; 1-12
2314-6141
url http://hdl.handle.net/11336/7529
identifier_str_mv Gantner, Melisa Edith; Di Ianni, Mauricio Emiliano; Ruiz, María Esperanza; Talevi, Alan; Bruno Blanch, Luis; Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein substrates; Hindawi Publishing Corporation; Biomed; 2013; 6-2013; 1-12
2314-6141
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
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
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application/pdf
application/pdf
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.982451

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