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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/70605
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, 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 |
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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 |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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application/pdf |
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SEDICI (UNLP) - Universidad Nacional de La Plata |
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