Synthesis and SAR of new isoxazole-triazole bis-heterocyclic compounds as analogues of natural lignans with antiparasitic activity.

Despite the impressive scientific and technological advances of recent decades, no effective treatment is currently available for Chagas disease. Our research group has been studying the design and synthesis of analogues of natural lignans aiming to identify compounds with antiparasitic activity. This article reports the synthesis of 42 novel bis-heterocyclic derivatives and the structure-activity relationship study conducted based on results of biological assays against Trypanosoma cruzi amastigotes. Thirty-seven compounds were active, and eight of them had GI50 values lower than 100 µM (GI50 88.4-12.2 µM). A qualitative structure activity relationship study using three dimensional descriptors was carried out and showed a correlation between growth inhibitory potency and the presence of bulky hydrophobic groups located at rings A and D of the compounds. Compound 3-(3,4-dimethoxyphenyl)-5-((4-(4-pentylphenyl)-1H-1,2,3-triazol-1-yl)methyl)isoxazole (31) was the most active in the series (GI50 12.2 µM), showing, in vitro, low toxicity and potency similar to benznidazole (GI50 10.2 µM). These results suggest that this compound can be a promising scaffold for the design of new trypanocidal compounds.

Four-dimensional structure-activity relationship model to predict HIV-1 integrase strand transfer inhibition using LQTA-QSAR methodology.

Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN strand transfer inhibitors (INSTI), containing the ß-diketo acid (DKA) substructure, was carried out. The GROMACS molecular dynamic package was used to obtain a conformational ensemble profile (CEP) and LQTA-QSAR was employed to calculate Coulomb and Lennard-Jones potentials and to generate the field descriptors. The partial least-squares (PLS) regression model using 14 field descriptors and 8 latent variables (LV) yielded satisfactory statistics (R2= 0.897, SEC = 0.270, and F = 72.827), good performance in internal (QLOO2 = 0.842 and SEV = 0.314) and external prediction (Rpred2 = 0.839, SEP = 0.384, AREpred = 4.942%, k = 0.981, k' = 1.016, and |R02 ­ R0'2 = 0.0257). The QSAR model was shown to be robust (leave-N-out cross validation; average QLNO2 = 0.834) and was not built by chance (y-randomization test; R2 intercept = 0.109; Q2 intercept = -0.398). Fair chemical interpretation of the model could be traced, including descriptors related to interaction with the metallic cofactors and the hydrophobic loop. The model obtained has a good potential for aid in the design of new INSTI, and it is a successful example of application of LQTA-QSAR as an useful tool to be used in computer-aided drug design (CADD).

Quantitative structure-activity relationship and machine learning studies of 2-thiazolylhydrazone derivatives with anti-Cryptococcus neoformans activity.

Cryptococcus neoformans is a fungus responsible for infections in humans with a significant number of cases in immunosuppressed patients, mainly in underdeveloped countries. In this context, the thiazolylhydrazones are a promising class of compounds with activity against C. neoformans. The understanding of the structure-activity relationship of these derivatives could lead to the design of robust compounds that could be promising drug candidates for fungal infections. Specifically, modern techniques such as 4D-QSAR and machine learning methods were employed in this work to generate two QSAR models (one 2D and one 4D) with high predictive power (r2 for the test set equals to 0.934 and 0.831, respectively), and one random forest classification model was reported with Matthews correlation coefficient equals to 1 and 0.62 for internal and external validations, respectively. The physicochemical interpretation of selected models, indicated the importance of aliphatic substituents at the hydrazone moiety to antifungal activity, corroborating experimental data.Communicated by Ramaswamy H. Sarma.

Insights on 3D Structures of Potential Drug-targeting Proteins of SARS-CoV-2: Application of Cavity Search and Molecular Docking.

The emergence of the COVID-19 has caused public health problems worldwide and there is no effective pharmacological treatment for this disease. Research on 3D models of proteins and the search for active molecular sites are important tools to assist in the discovery of effective antiviral drugs to combat COVID-19. To address this problem, the 3D protein structures of SARS-CoV 2 were analyzed and submitted to cavities research, evaluation of their druggabillity and liganbility, and applied to molecular docking studies with potential ligand candidates actually assayed against COVID-19. Eight druggable potential cavity sites were determined in model structures' PDB code, 6W4B, 6VWW, 6W01, 6M3M, and 6VYO, and these are the good alternatives to be characterized as targets for antiviral compounds. The good cavity model of the protease 3D structure was used in molecular docking, and this allowed verifying the theoric interactions of this protein and lopinavir and ritonavir antiviral drugs. These results may assist in the use of 3D protein models in drug design studies aiming to develop drugs against the COVID-19 pandemic.

Synthesis of (+)-(2R,3S,4R)-2,3,4-trihydroxycyclohexanone from D-glucose.

We have described the synthesis of (+)-(2R,3S,4R)-2,3,4-trihydroxycyclohexanone by the reduction of a keto-conduritol derivative, the latter being prepared in five steps from (-)-(2S,3R,4S,5S)-2,3,4-tribenzyloxy-5-hydroxycyclohexanone, which is in turn readily synthesized from D-glucose.

Multivariate SAR and QSAR of cucurbitacin derivatives as cytotoxic compounds in a human lung adenocarcinoma cell line.

This article describes structure-activity relationship (SAR/QSAR) studies on the cytotoxic activity in a human lung adenocarcinoma cell line (A549) of 43 cucurbitacin derivatives. Modeling was performed using the methods partial least squares with discriminant analysis (PLS-DA) and PLS. For both studies, the variables were selected using the ordered predictor selection (OPS) algorithm. The SAR study demonstrated that the presence or absence of cytotoxic activity of the cucurbitacins could be described using information derived from their chemical structures. The QSAR study displayed suitable internal and external predictivity, and the selected descriptors indicated that the observed activity might be related to electrophilic attack on cellular structures or genetic material. This study provides improves the understanding of the cytotoxic activity of cucurbitacins and could be used to propose new cytotoxic agents.