Dry selection and wet evaluation for the rational discovery of new anthelmintics

Abstract

Helminths infections remain a major problem in medical and public health. In this report, atom-based 2D bilinear indices, a TOMOCOMD-CARDD (QuBiLs-MAS module) molecular descriptor family and linear discriminant analysis (LDA) were used to find models that differentiate among anthelmintic and non-anthelmintic compounds. Two classification models obtained by using non-stochastic and stochastic 2D bilinear indices, classified correctly 86.64% and 84.66%, respectively, in the training set. Equation 1(2) correctly classified 141(135) out of 165 [85.45%(81.82%)] compounds in external validation set. Another LDA models were performed in order to get the most likely mechanism of action of anthelmintics. The model shows an accuracy of 86.84% in the training set and 94.44% in the external prediction set. Finally, we carry out an experiment to predict the biological profile of our ‘in-house’ collections of indole, indazole, quinoxaline and cinnoline derivatives (∼200 compounds). Subsequently, we selected a group of nine of the theoretically most active structures. Then, these chemicals were tested in an invitro assay and one good candidate (VA5-5c) as fasciolicide compound (100% of reduction at concentrations of 50 and 10 mg/L) was discovered.