The method most commonly used in screening of drugs for the treatment of Chagas' disease, microscopic counting of viable trypanosomes, is time-consuming, labor-intensive, and dependent on the observer. Although the tetrazolium dye [MTT; 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] assay is comparatively quick and accurate, it requires careful attention in design as well as in interpretation of the results. Therefore, we examined under various conditions the sensitivity and specificity of the MTT assay versus microscopic counting for determination of the viability of Trypanosoma cruzi for drug-screening purposes. We tested different concentrations of MTT in phenazine methosulfate (PMS) against T. cruzi epimastigotes of the Y strain in different stages of logarithmic growth. In our model, in tests of benznidazole and nifurtimox the optimal concentration of MTT was 2.5 mg/ml of PMS and the optimal incubation period was 75 min. This method detected parasite concentrations of approx. 500,000 epimastigotes/ml (P<0.01), and the linear correlation between absorbance values and numbers of epimastigotes per milliliter was very strong (approx. R = 0.99). The present MTT assay results in faster determination of the activity of compounds, is more objective, and enables testing of several drugs simultaneously.

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Computational approaches are developed to design or rationally select, from structural databases, new lead trichomonacidal compounds. First, a data set of 111 compounds was split (design) into training and predicting series using hierarchical and partitional cluster analyses. Later, two discriminant functions were derived with the use of non-stochastic and stochastic atom-type linear indices. The obtained LDA (linear discrimination analysis)-based QSAR (quantitative structure-activity relationship) models, using non-stochastic and stochastic descriptors were able to classify correctly 95.56% (90.48%) and 91.11% (85.71%) of the compounds in training (test) sets, respectively. The result of predictions on the 10% full-out cross-validation test also evidenced the quality (robustness, stability and predictive power) of the obtained models. These models were orthogonalized using the Randic orthogonalization procedure. Afterwards, a simulation experiment of virtual screening was conducted to test the possibilities of the classification models developed here in detecting antitrichomonal chemicals of diverse chemical structures. In this sense, the 100.00% and 77.77% of the screened compounds were detected by the LDA-based QSAR models (Eq. 13 and Eq. 14, correspondingly) as trichomonacidal. Finally, new lead trichomonacidals were discovered by prediction of their antirichomonal activity with obtained models. The most of tested chemicals exhibit the predicted antitrichomonal effect in the performed ligand-based virtual screening, yielding an accuracy of the 90.48% (19/21). These results support a role for TOMOCOMD-CARDD descriptors in the biosilico discovery of new compounds.

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