Show simple item record

dc.contributor.author Gantner, Melisa Edith
dc.contributor.author Di Ianni, Mauricio Emiliano
dc.contributor.author Ruiz, María Esperanza
dc.contributor.author Talevi, Alan
dc.contributor.author Bruno Blanch, Luis
dc.date.available 2016-09-07T19:23:23Z
dc.date.issued 2013-06
dc.identifier.citation 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
dc.identifier.issn 2314-6141
dc.identifier.uri http://hdl.handle.net/11336/7529
dc.description.abstract 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.
dc.format application/pdf
dc.language.iso eng
dc.publisher Hindawi Publishing Corporation
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject BREAST CANCER RESISTANCE PROTEIN
dc.subject ABC TRANSPORTER
dc.subject MULTIDRUG RESISTANCE
dc.subject BCRP SUBSTRATES
dc.subject 2D COMPUTATIONAL MODELS
dc.subject IN SILICO CLASSIFICATION MODEL
dc.subject.classification Ciencias de la Información y Bioinformática
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.subject.classification Oncología
dc.subject.classification Medicina Clínica
dc.subject.classification CIENCIAS MÉDICAS Y DE LA SALUD
dc.title Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein substrates
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2016-04-28T17:09:09Z
dc.journal.volume 2013
dc.journal.pagination 1-12
dc.journal.pais Estados Unidos
dc.journal.ciudad Nueva York
dc.description.fil 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
dc.description.fil 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
dc.description.fil 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
dc.description.fil 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
dc.description.fil 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
dc.journal.title Biomed
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/bmri/2013/863592/
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1155/2013/863592


Archivos asociados

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution 2.5 Unported (CC BY 2.5)