Inhibition of α-Glycosidase by Lippia dulcis Trevir. (Verbenaceae) Preparations, Quantification of Verbascoside, and Study of Its Molecular Docking

This study aimed to quantify verbascoside (VEB), perform molecular docking studies of VEB with the α-glucosidase (GL) of Bacillus stearothermophilus, and evaluate the inhibition of the enzyme by L. dulcis preparations. The substrate concentration and presence of reduced glutathione were evaluated for their effect on the in vitro inhibition of the GL enzyme. Assays were also performed in the presence and absence of simulated gastric fluid. The antidiabetic fractions 2 and 3 were the most inhibited GL, but their activity were significantly decreased in the presence of gastric fluid. Chromatographic analyses confirmed the predominant presence of VEB in the samples. The samples had VEB concentrations between 49.9 and 243.5 mg/g. Simulation of the molecular docking of VEB were consistent with its GL-inhibitory activity. It can conclude that the crude ethanol extract and fractions show inhibitory activity against the GL enzyme.

Theoretical insights into the effect of halogenated substituent on the electronic structure and spectroscopic properties of the favipiravir tautomeric forms and its implications for the treatment of COVID-19

In this study, we systematically investigated the electronic structure, spectroscopic (nuclear magnetic resonance, infrared, Raman, electron ionization mass spectrometry, UV-Vis, circular dichroism, and emission) properties, and tautomerism of halogenated favipiravir compounds (fluorine, chlorine, and bromine) from a computational perspective. Additionally, the effects of hydration on the proton transfer mechanism of the tautomeric forms of the halogenated favipiravir compounds are discussed. Our results suggest that spectroscopic properties allow for the elucidation of such tautomeric forms. As is well-known, the favipiravir compound has excellent antiviral properties and hence was recently tested for the treatment of new coronavirus (SARS-CoV-2). Through in silico modeling, in the current study, we evaluate the role of such tautomeric forms in order to consider the effect of drug-metabolism in the inhibition process of the main protease (M^pro) and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 virus. According to the molecular docking, all halogenated compounds presented a better interaction energy than the co-crystallized active ligand (−3.5 kcal mol⁻¹) in the viral RdRp, in both wild-type (−6.3 to −6.5 kcal mol⁻¹) and variant (−5.4 to −5.6 kcal mol⁻¹) models. The variant analyzed for RdRp (Y176C) decreases the affinity of the keto form of the compounds in the active site, and prevented the ligands from interacting with RNA. These findings clearly indicated that all these compounds are promising as drug candidates for this molecular target.

In this study, we systematically investigated the electronic structure, spectroscopic properties, and tautomerism of halogenated favipiravir compounds (fluorine, chlorine, and bromine) from a computational perspective.

Effect of drug metabolism in the treatment of SARS-CoV-2 from an entirely computational perspective

Understanding the effects of metabolism on the rational design of novel and more effective drugs is still a considerable challenge. To the best of our knowledge, there are no entirely computational strategies that make it possible to predict these effects. From this perspective, the development of such methodologies could contribute to significantly reduce the side effects of medicines, leading to the emergence of more effective and safer drugs. Thereby, in this study, our strategy is based on simulating the electron ionization mass spectrometry (EI-MS) fragmentation of the drug molecules and combined with molecular docking and ADMET models in two different situations. In the first model, the drug is docked without considering the possible metabolic effects. In the second model, each of the intermediates from the EI-MS results is docked, and metabolism occurs before the drug accesses the biological target. As a proof of concept, in this work, we investigate the main antiviral drugs used in clinical research to treat COVID-19. As a result, our strategy made it possible to assess the biological activity and toxicity of all potential by-products. We believed that our findings provide new chemical insights that can benefit the rational development of novel drugs in the future.

4D-QSAR models applied to the study of TGF- β1 receptor inhibitors

BACKGROUND

The quantitative structure-activity relationship is an analysis method that can be applied for designing new molecules. In 1997, Hopfinger and coworkers developed the 4D-QSAR methodology aiming to eliminate the question of which conformation to use in a QSAR study. In this work, the 4D-QSAR methodology was used to quantitatively determine the influence of structural descriptors on the activity of aryl pyrimidine derivatives as inhibitors of the TGF-β1 receptor. The members of the TGF-β subfamily are interesting molecular targets, since they play an important function in the growth and development of cell cellular including proliferation, apoptosis, differentiation, epithelial-mesenchymal transition (EMT), and migration. In late stages, TGF-β exerts tumor-promoting effects, increasing tumor invasiveness, and metastasis. Therefore, TGF-β is an attractive target for cancer therapy.

OBJECTIVE

The major goal of the current research is to develop 4D-QSAR models aiming to propose new structures of aryl pyrimidine derivatives.

MATERIALS AND METHODS

Molecular dynamics simulation was carried out to generate the conformational ensemble profile of a data set with aryl pyrimidine derivatives. The conformations were overlaid into a three-dimensional cubic box, according to the three-ordered atom alignment. The occupation of the grid cells by the interaction of pharmacophore elements provides the grid cell occupancy descriptors (GCOD), the dependent variables used to build the 4D-QSAR models. The best models were validated (internal and external validation) using several statistical parameters. Docking molecular studies were performed to better understand the binding mode of pyrimidine derivatives inside the TGF-β active site.

RESULTS

The 4D-QSAR model presented seven descriptors and acceptable statistical parameters (R2 = 0.89, q2 = 0.68, R2pred = 0.65, r2m = 0.55, R2P = 0.68 and R2rand = 0.21) besides pharmacophores groups important for the activity of these compounds. The molecular docking studies helped to understand the pharmacophoric groups and proposed substituents that increase the potency of aryl pyrimidine derivatives.

CONCLUSION

The best QSAR model showed adequate statistical parameters that ensure their fitness, robustness, and predictivity. Structural modifications were assessed, and five new structures were proposed as candidates for a drug for cancer treatment.

Theoretical Studies Aimed at Finding FLT3 Inhibitors and a Promising Compound and Molecular Pattern with Dual Aurora B/FLT3 Activity

FLT3 and dual Aurora B/FLT3 inhibitors have shown relevance in the search for promising new anticancer compounds, mainly for acute myeloid leukemia (AML). This study was designed to investigate the interactions between human FLT3 in the kinase domain with several indolin-2-one derivatives, structurally similar to Sunitinib. Molegro Virtual Docker (MVD) software was utilized in docking analyses. The predicted model of the training group, considering nineteen amino acid residues, performed in Chemoface, achieved an R² of 0.82, suggesting that the binding conformations of the ligands with FLT3 are reasonable, and the data can be used to predict the interaction energy of other FLT3 inhibitors with similar molecular patterns. The MolDock Score for energy for compound 1 showed more stable interaction energy (-233.25 kcal mol^-1) than the other inhibitors studied, while Sunitinib presented as one of the least stable (-160.94 kcal mol^-1). Compounds IAF70, IAF72, IAF75, IAF80, IAF84, and IAF88 can be highlighted as promising derivatives for synthesis and biological evaluation against FLT3. Furthermore, IAF79 can be considered to be a promising dual Aurora B/FLT3 inhibitor, and its molecular pattern can be exploited synthetically to search for new indolin-2-one derivatives that may become drugs used in the treatment of cancers, including AML.

Mitochondriotropic action and DNA protection: Interactions between phenolic acids and enzymes

The protective action of caffeic (CA) and syringic (SA) acids on the genotoxicity exercised by snake venoms was investigated in this study. Molecular interactions between phenolic acids and the enzyme succinate dehydrogenase were also explored. In the electrophoresis assay, SA did not inhibit the genotoxicity induced by the venom. However, CA partially inhibited DNA degradation. In the comet assay, SA and CA exerted an inhibitory effect on the venom-induced fragmentation. Succinate dehydrogenase presented, in computational analyzes, favorable energies to the molecular bond to both the malonic acid and the phenolic compounds evaluated. In the enzymatic activity assays, SA inhibited succinate dehydrogenase and interfered in the interaction of malonic acid. Meanwhile, CA potentiated the inhibition exerted by the malonic acid. The results suggest transient interactions between toxins present in venoms and phenolic acids, mainly by hydrogen interactions, which corroborate with the data from previous works.

Molecular Modelling of Potential Candidates for the Treatment of Depression

A lot of research initiatives in the last decades have been focused on the search of new strategies to treat depression. However, despite the availability of various antidepressants, current treatment is still far from ideal. Unwanted side effects, modest response rates and the slow onset of action are the main shortcomings. As a strategy to improve symptomatic relief and response rates, the dual modulation of the serotonin transporter and the histamine H₃ receptor by a single chemical entity has been proposed in the literature. Accordingly, this work aims to elucidate key structural features responsible for the dual inhibitory activity of the hexahydro-pyrrolo-isoquinoline derivatives. For this purpose, two approaches were employed, four-dimensional quantitative structure-activity relationship (4D-QSAR) and molecular docking. The 4D-QSAR models for both receptors allowed the identification of the pharmacophore groups critical for the modelled biological activity, whereas the binding mode of this class of compounds to the human serotonin transporter was assessed by molecular docking. The findings can be applicable to design new antidepressants.

Indolin-2-one Derivatives: Theoretical Studies Aimed at Finding More Potent Aurora B Kinase Inhibitors

Background:

Aurora kinases perform important roles in mammals, mainly in cell cycle. Overexpression of these enzymes is related to tumor development and is indicative of worsening of clinical conditions. Aurora kinases are promising targets in the search for new anticancer drugs, in particular, Aurora B. </P><P> Methods: This work was designed to study and understand the interactions between human Aurora B and several indolin-2-one derivatives, structurally similar to sunitinib. MVD software was utilized in docking analyses of indolin-2-one derivatives. Human Aurora B kinase was obtained from the PDB (4AF3) and redocked with hesperadin, which was used as a reference compound. The predicted model of the training group, considering 21 amino acid residues, performed in Chemoface, achieved an R2 of 0.945, suggesting that the binding conformations of the ligands with human Aurora B are reasonable and the data can be used to predict the interaction energy of other Aurora B inhibitors indolin-2-one derivatives.

Results:

MolDock Score energy for compound 1 showed more stable interaction energy (-225.90 kcal.mol-1) then the other inhibitors studied, while sunitinib was the least stable (-135.63 kcal.mol-1). Compounds 1-45, hesperadin and sunitinib, interacted with Glu171 (–NH from indolinonic moiety), and the majority of them with Ala173 (C=O from indolinonic moiety) via hydrogen bonds, thus these two residues are relevant for potency.

Conclusion:

Docking studies and biological activity in literature show subunits likely for structural optimizations, leading to four new proposed derivatives (IAF61, IAF63, IAF66, IAF79) as promising compounds for synthesis and biological evaluation against human Aurora B, validating and ratifying the docking studies.

Multi-objective Optimization of Benzamide Derivatives as Rho Kinase Inhibitors

Despite recent advances in Computer Aided Drug Discovery and High Throughput Screening, the attrition rates of drug candidates continue to be high, underscoring the inherent complexity of the drug discovery paradigm. Indeed, a compromise between several objectives is often required to obtain successful clinical drugs. The present manuscript details a multi-objective workflow that integrates the 4D-QSAR and molecular docking methods in the simultaneous modeling of the Rho Kinase inhibitory activity and acute toxicity of Benzamide derivatives. To this end, the pIC₅₀ /pLD₅₀ ratio is considered as the response variable, permitting the concurrent modeling of both properties and representing a shift from classical step-by-step evaluations. The 4D-QSAR strategy is used to generate the Grid Cell Occupancy Descriptors (GCODs), and Stochastic Gradient Boosting (SGB) and Partial Least Squares (PLS) methods as the model fitting techniques. While the statistical parameters for the PLS model do not meet established criteria for acceptability, the SGB model yields satisfactory performance, with correlation coefficients r² =0.95 and r² pred=0.65 for the training and test set, respectively. Posteriorly, the structural interpretation of the most relevant GCODs according to the SGB model is performed, allowing for the proposal of 139 novel benzamide derivatives, which are then screened using the same model. Of these 9 compounds were predicted to possess pIC₅₀ /pLD₅₀ ratio values higher than those for the employed dataset. Finally, in order to corroborate the results obtained with the SGB model, a docking simulation was formed to evaluate the binding affinity of the proposed molecules to the ROCK2 active site and 3 chemical structures (i. e. p6, p14 and p131) showed higher binding affinity than the most active compound in the training set, while the rest generally demonstrated comparable behavior. It may therefore be concluded that the consensus models that intertwine the 4D-QSAR and molecular docking methods contribute to more reliable virtual screening and compound optimization experiments. Additionally, the use of multi-objective modeling schemes permits the simultaneous evaluation of different chemical and biological profiles, which should contribute to the control a priori of causative factors for the high attrition rates in later drug discovery phases.

3D-QSAR CoMFA/CoMSIA models based on theoretical active conformers of HOE/BAY-793 analogs derived from HIV-1 protease inhibitor complexes

The three-dimensional quantitative structure-activity relationships (3D-QSAR) of a series of HOE/BAY-793 analogs (C(2)-symmetric diol peptidomimetics), developed by Budt and co-workers [Bioorg. Med. Chem. 3 (1995) 559] as inhibitors of HIV-1 protease (HIV-PR), were studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Theoretical active conformers for these peptidomimetics were generated, derived from modeled protease inhibitor complexes, in order to orient the compounds superposition and to afford a consistent alignment. The best CoMFA model (N=27, q(2)=0.637, R(2)=0.991) showed contributions of the steric (45.7%) and electrostatic (54.3%) fields to the activity, while the best CoMSIA model (N=27, q(2)=0.511, R(2)=0.987) showed contributions of the electrostatic (68.5%) and hydrogen bond donor (37.5%) fields. The models were also external validated using four compounds (test set) not included in the model generation process. The statistical parameters from both models indicate that the data are well fitted and have high predictive ability. Moreover, the resulting 3D CoMFA/CoMSIA contour maps provide useful guidance for designing highly active ligands. The CoMFA/CoMSIA models were also compared with previous 4D-QSAR models [E.F.F. da Cunha, M.G. Albuquerque, O.A.C. Antunes, R.B. de Alencastro, QSAR Comb. Sci. 24 (2005), 240-253.].