Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Alberto Castillo-Garit, Juan; del Toro-Cortes, Oremia; Vega, Maria C.; Rolon, Miriam; Rojas de Arias, Antonieta; Casanola-Martin, Gerardo M.; Escario, Jose A.; Gomez-Barrio, Alicia; Marrero-Ponce, Yovani; Torrens, Francisco; Abad, Concepcion

Abstract

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 mu M, while for the benznidazole the IC50 = 54.7 mu M which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds. (C) 2015 Elsevier Masson SAS. All rights reserved.

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Título según WOS: ID WOS:000355712500022 Not found in local WOS DB
Título de la Revista: EUROPEAN JOURNAL OF MEDICINAL CHEMISTRY
Volumen: 96
Editorial: ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
Fecha de publicación: 2015
Página de inicio: 238
Página final: 244
DOI:

10.1016/j.ejmech.2015.03.063

Notas: ISI