Computational Discovery of Novel Trypanosomicidal Drug-like Chemicals by Using Bond-based Non-stochastic and Stochastic Quadratic Maps and Linear Discriminant Analysis.

Identification In Silico and In Vitro of Novel Trypanosomicidal Drug‐Like Compounds

“…The general data set used in this study was the same that we utilize in previous works (24,25), and it consists of 440 compounds of great structural variation, 143 of which are actives and 297 are inactive against trypanosome. For active compounds, it is remarkable that the wide variability of drugs and mechanisms of action in the training and prediction sets assures adequate extrapolation power (For details about the data set, please see Section 3 of supporting information).…”

“…The approach [known as tomocomd acronym of TOpological MOlecular COMputer Design ] ( a , 17–19) allows us to perform rational in silico molecular design (selection/identification) and quantitative structure–activity/property relationship (QSAR/QSPR) studies. In fact, this scheme has been successfully applied to the prediction of several physical, physicochemical, chemical, pharmacokinetical, toxicological as well as biological properties (20–25). Furthermore, these molecular descriptors (MDs) have been extended to consider three‐dimensional (3D) features of small‐/medium‐sized molecules based on the trigonometric‐3D‐chirality‐correction factor approach (26–31).…”

Using entropy of drug and protein graphs to predict FDA drug-target network: Theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

“…The tested drugs (new compounds and reference inhibitors) inhibited the control enzymatic MAO activities and the inhibition was concentration dependent. The corresponding IC 50 [62]. Briefly, liver adult flukes collected from bile ducts of naturally infected cows were washed twice, first in sterile saline solution containing antibiotics (100 IU/ mL penicillin and 100 mg streptomycin) and glucose (2 mg/mL) at 38 C, and then in RPMI 1640 medium, supplemented with 20 mM HEPES, 0.3 g/L L-glutamine, 2 g/L sodium bicarbonate and antibiotics at 38 C under 5% CO 2 in air.…”

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

“…The models showed reliable predictive capability with high accuracies (87− 95%), good specificity (66−85%), and high sensitivity (88− 100%) for both training and test sets, hence capable of accurately discriminating between antitrypanosomally active and inactive compounds. 123 The two models have proven useful and have consequently been applied in the rational discovery of new trypanosomicidal compounds. In one of the reported studies, Castillo-Garit et al applied the developed QSAR models in the in silico predictions of the antiepimastigote (AE) activity of nine synthesized compounds and their epimastigote inhibitory activity experimentally determined.…”

“…In one of the reported studies, Castillo-Garit et al applied the developed QSAR models in the in silico predictions of the antiepimastigote (AE) activity of nine synthesized compounds and their epimastigote inhibitory activity experimentally determined. 123 This work led to identification of four compounds with >70% inhibitory activity against epimastigotes at 100 μg/mL. When evaluated for epimastigote inhibition and cytotoxicity against mammalian cells at 10 μg/mL in vitro, compounds 65 and 66 (Figure 14) showed attractive antitrypanosomal activity (AE = 81.31 and 78.22%, respectively) and high selectivity indices (cytotoxicity = 1.37 and 15.44%, respectively) comparable to that of the clinically used drug nifurtimox (AE = 85.45%, cytotoxicity = 0.6%).…”