Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis.
Herein we present results of a quantitative structure-activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the present approach is finally satisfactorily applied to the virtual evaluation of 9 already synthesized in house compounds. The in vitro antitrypanosomal activity of this series against epimastigote forms of Trypanosoma cruzi is assayed. The model is able to predict correctly the behaviour for the majority of these compounds. Four compounds (FER16, FER32, FER33 and FER 132) showed more than 70% of epimastigote inhibition at a concentration of 100 microg/mL (86.74%, 78.12%, 88.85% and 72.10%, respectively) and two of these chemicals, FER16 (78.22% of AE) and FER33 (81.31% of AE), also showed good activity at a concentration of 10 microg/mL. At the same concentration, compound FER16 showed lower value of cytotoxicity (15.44%), and compound FER33 showed very low value of 1.37%. Taking into account all these results, we can say that these three compounds can be optimized in forthcoming works, but we consider that compound FER33 is the best candidate. Even though none of them resulted more active than Nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.
Applied Chemistry Research Center, Faculty of Chemistry-Pharmacy, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba. jacgarit@yahoo.es
Juan Alberto
JA
Castillo-Garit
Maria C
MC
Vega
Miriam
M
Rolon
Yovani
Y
Marrero-Ponce
Vladimir V
VV
Kouznetsov
Diego Fernando Amado
DF
Torres
Alicia
A
Gómez-Barrio
Alfredo Alvarez
AA
Bello
Alina
A
Montero
Francisco
F
Torrens
Facundo
F
Pérez-Giménez
D002470:Cell Survival / Q000187:drug effects; D002478:Cells, Cultured; D017076:Computer-Aided Design; D016002:Discriminant Analysis; D055808:Drug Discovery / Q000379:methods*; D015233:Models, Statistical; D015394:Molecular Structure; D021281:Quantitative Structure-Activity Relationship; D014344:Trypanocidal Agents / Q000737:chemistry* / Q000494:pharmacology*; D014349:Trypanosoma cruzi / Q000187:drug effects
D014344:Trypanocidal Agents