Development and Validation of a Computational Model Ensemble for the Early Detection of BCRP/ABCG2 Substrates during the Drug Design Stage.
J Chem Inf Model
; 57(8): 1868-1880, 2017 08 28.
Article
en En
| MEDLINE
| ID: mdl-28708399
ABSTRACT
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Simulación por Computador
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Diseño de Fármacos
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Biología Computacional
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Transportador de Casetes de Unión a ATP, Subfamilia G, Miembro 2
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Proteínas de Neoplasias
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Screening_studies
Límite:
Animals
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Humans
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Male
Idioma:
En
Año:
2017
Tipo del documento:
Article