Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage

Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this t...

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Detalhes bibliográficos
Autores: Gantner, Melisa Edith, Peroni, Roxana Noemi, Morales, Juan Francisco, Villalba, Maria Luisa, Ruiz, María Esperanza, Talevi, Alan
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:Argentina
Recursos:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/65912
Acesso em linha:http://hdl.handle.net/11336/65912
Access Level:acceso abierto
Palavra-chave:Atp Binding Cassette Transporter
Drug Resistance, Multiple/Drug Effects
Drug Design
Computational Biology/Methods
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
id AR_0785caea418a41bc37f2ca01c9befe49
oai_identifier_str oai:ri.conicet.gov.ar:11336/65912
network_acronym_str AR
network_name_str Argentina
repository_id_str
dc.title.none.fl_str_mv Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
title Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
spellingShingle Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
Gantner, Melisa Edith
Atp Binding Cassette Transporter
Drug Resistance, Multiple/Drug Effects
Drug Design
Computational Biology/Methods
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
title_short Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
title_full Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
title_fullStr Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
title_full_unstemmed Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
title_sort Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
dc.creator.none.fl_str_mv Gantner, Melisa Edith
Peroni, Roxana Noemi
Morales, Juan Francisco
Villalba, Maria Luisa
Ruiz, María Esperanza
Talevi, Alan
author Gantner, Melisa Edith
author_facet Gantner, Melisa Edith
Peroni, Roxana Noemi
Morales, Juan Francisco
Villalba, Maria Luisa
Ruiz, María Esperanza
Talevi, Alan
author_role author
author2 Peroni, Roxana Noemi
Morales, Juan Francisco
Villalba, Maria Luisa
Ruiz, María Esperanza
Talevi, Alan
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Atp Binding Cassette Transporter
Drug Resistance, Multiple/Drug Effects
Drug Design
Computational Biology/Methods
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
topic Atp Binding Cassette Transporter
Drug Resistance, Multiple/Drug Effects
Drug Design
Computational Biology/Methods
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
description Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.
publishDate 2017
dc.date.none.fl_str_mv 2017-08
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/11336/65912
Gantner, Melisa Edith; Peroni, Roxana Noemi; Morales, Juan Francisco; Villalba, Maria Luisa; Ruiz, María Esperanza; et al.; Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage; American Chemical Society; Journal of Chemical Information and Modeling; 57; 8; 8-2017; 1868-1880
1549-9596
CONICET Digital
CONICET
url http://hdl.handle.net/11336/65912
identifier_str_mv Gantner, Melisa Edith; Peroni, Roxana Noemi; Morales, Juan Francisco; Villalba, Maria Luisa; Ruiz, María Esperanza; et al.; Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage; American Chemical Society; Journal of Chemical Information and Modeling; 57; 8; 8-2017; 1868-1880
1549-9596
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/10.1021/acs.jcim.7b00016
info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.jcim.7b00016
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
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
_version_ 1799194740573339648
spelling Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design StageGantner, Melisa EdithPeroni, Roxana NoemiMorales, Juan FranciscoVillalba, Maria LuisaRuiz, María EsperanzaTalevi, AlanAtp Binding Cassette TransporterDrug Resistance, Multiple/Drug EffectsDrug DesignComputational Biology/Methodshttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1Breast Cancer Resistance Protein (BCRP) is an ATP-dependent efflux transporter linked to the multidrug resistance phenomenon in many diseases such as epilepsy and cancer and a potential source of drug interactions. For these reasons, the early identification of substrates and nonsubstrates of this transporter during the drug discovery stage is of great interest. We have developed a computational nonlinear model ensemble based on conformational independent molecular descriptors using a combined strategy of genetic algorithms, J48 decision tree classifiers, and data fusion. The best model ensemble consists in averaging the ranking of the 12 decision trees that showed the best performance on the training set, which also demonstrated a good performance for the test set. It was experimentally validated using the ex vivo everted rat intestinal sac model. Five anticonvulsant drugs classified as nonsubstrates for BRCP by the model ensemble were experimentally evaluated, and none of them proved to be a BCRP substrate under the experimental conditions used, thus confirming the predictive ability of the model ensemble. The model ensemble reported here is a potentially valuable tool to be used as an in silico ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug-drug interactions.Fil: Gantner, Melisa Edith. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Peroni, Roxana Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Farmacológicas. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Investigaciones Farmacológicas; ArgentinaFil: Morales, Juan Francisco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Villalba, Maria Luisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Ruiz, María Esperanza. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaFil: Talevi, Alan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Departamento de Ciencias Biológicas; ArgentinaAmerican Chemical Society2017-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/65912Gantner, Melisa Edith; Peroni, Roxana Noemi; Morales, Juan Francisco; Villalba, Maria Luisa; Ruiz, María Esperanza; et al.; Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage; American Chemical Society; Journal of Chemical Information and Modeling; 57; 8; 8-2017; 1868-18801549-9596CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://pubs.acs.org/doi/10.1021/acs.jcim.7b00016info:eu-repo/semantics/altIdentifier/doi/10.1021/acs.jcim.7b00016info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2024-05-08T13:35:23Zoai:ri.conicet.gov.ar:11336/65912instacron: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:34982024-05-08 13:35:23.655CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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