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.

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 (Mpro) 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.

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.

QSAR Study of N-Myristoyltransferase Inhibitors of Antimalarial Agents.

Malaria is a disease caused by protozoan parasites of the genus Plasmodium that affects millions of people worldwide. In recent years there have been parasite resistances to several drugs, including the first-line antimalarial treatment. With the aim of proposing new drugs candidates for the treatment of disease, Quantitative Structure⁻Activity Relationship (QSAR) methodology was applied to 83 N-myristoyltransferase inhibitors, synthesized by Leatherbarrow et al. The QSAR models were developed using 63 compounds, the training set, and externally validated using 20 compounds, the test set. Ten different alignments for the two test sets were tested and the models were generated by the technique that combines genetic algorithms and partial least squares. The best model shows r² = 0.757, q²adjusted = 0.634, R²pred = 0.746, R²m = 0.716, ∆R²m = 0.133, R²p = 0.609, and R²r = 0.110. This work suggested a good correlation with the experimental results and allows the design of new potent N-myristoyltransferase inhibitors.

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.

QSAR Models Guided by Molecular Dynamics Applied to Human Glucokinase Activators.

In this study, quantitative structure-activity relationship studies which make use of molecular dynamics trajectories were performed on a set of 54 glucokinase protein activators. The conformations obtained by molecular dynamics simulation were superimposed according to the twelve alignments tested in a virtual three-dimensional box comprised of 2 Å cells. The models were generated by the technique that combines genetic algorithms and partial least squares. The best alignment models generated with a determination coefficient (r(2)) between 0.674 and 0.743 and cross-validation (q(2)) between 0.509 and 0.610, indicating good predictive capacity. The 4D-QSAR models developed in this study suggest novel molecular regions to be explored in the search for better glucokinase activators.

Application of 4D-QSAR studies to a series of raloxifene analogs and design of potential selective estrogen receptor modulators.

Four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis was applied on a series of 54 2-arylbenzothiophene derivatives, synthesized by Grese and coworkers, based on raloxifene (an estrogen receptor-alpha antagonist), and evaluated as ERa ligands and as inhibitors of estrogen-stimulated proliferation of MCF-7 breast cancer cells. The conformations of each analogue, sampled from a molecular dynamics simulation, were placed in a grid cell lattice according to three trial alignments, considering two grid cell sizes (1.0 and 2.0 Å). The QSAR equations, generated by a combined scheme of genetic algorithms (GA) and partial least squares (PLS) regression, were evaluated by "leave-one-out" cross-validation, using a training set of 41 compounds. External validation was performed using a test set of 13 compounds. The obtained 4D-QSAR models are in agreement with the proposed mechanism of action for raloxifene. This study allowed a quantitative prediction of compounds' potency and supported the design of new raloxifene analogs.

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.].