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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Detalles Bibliográficos
Autores principales: Gantner, Melisa Edith, Peroni, Roxana N., Morales, Juan Francisco, Villalba, María Luisa, Ruiz, María Esperanza, Talevi, Alan
Formato: Articulo
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Exactas
Biología
computational model ensemble
breast cancer resistance protein
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/105506
https://pubs.acs.org/doi/10.1021/acs.jcim.7b00016
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-105506
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Exactas
Biología
computational model ensemble
breast cancer resistance protein
spellingShingle Ciencias Exactas
Biología
computational model ensemble
breast cancer resistance protein
Gantner, Melisa Edith
Peroni, Roxana N.
Morales, Juan Francisco
Villalba, María Luisa
Ruiz, María Esperanza
Talevi, Alan
Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
topic_facet Ciencias Exactas
Biología
computational model ensemble
breast cancer resistance protein
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 <i>ex vivo</i> 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 <i>in silico</i> ADME filter in computer-aided drug discovery campaigns intended to overcome BCRP-mediated multidrug resistance issues and to prevent drug−drug interactions.
format Articulo
Articulo
author Gantner, Melisa Edith
Peroni, Roxana N.
Morales, Juan Francisco
Villalba, María Luisa
Ruiz, María Esperanza
Talevi, Alan
author_facet Gantner, Melisa Edith
Peroni, Roxana N.
Morales, Juan Francisco
Villalba, María Luisa
Ruiz, María Esperanza
Talevi, Alan
author_sort Gantner, Melisa Edith
title Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage
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
publishDate 2017
url http://sedici.unlp.edu.ar/handle/10915/105506
https://pubs.acs.org/doi/10.1021/acs.jcim.7b00016
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