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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/7529
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:42:47Zoai: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:34982025-09-29 10:42:47.906CONICET 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/ |
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application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf 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 |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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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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13.070432 |