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      Two phytocompounds from Schinopsis brasiliensis show promising antiviral activity with multiples targets in Influenza A virus

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          Abstract

          Abstract Influenza A virus, the main flu agent, affects billions of people worldwide. Conventional treatments still present limitations related to drug-resistance and severe side effects. As a result, natural product-derived molecules have been increasingly investigated as prospect drug candidates. Therefore, the aim of this study was to investigate the possible anti-flu activity and to evaluate the toxicity and pharmacokinetic parameters, by in silico approaches, of the Schinopsis brasiliensis Engl. phytochemical compounds. Nine phytocompounds and six antiviral drugs (Amantadine, Umifenovir, Favipiravir, Nitazoxanide, Oseltamivir, Zanamivir) were selected for the analyses against four Influenza A proteins: neuraminidase, polymerase basic protein 2, hemagglutinin and M2 ion channel protein. The molecular docking, the predicted antiviral activity, the predicted toxicity and the pharmacokinetics investigations were conducted. The obtained results demonstrated that Syringaresinol and Cycloartenone display promising in silico antiviral activity (binding energy < 5.0 and ≥ 9.0 kcal/mol) and safety (low toxicity than commercial anti-flu drugs). Overall, this study corroborated the hypothesis that S. brasiliensis barks extract has a biological activity against Influenza A virus. Additionally, Syringaresinol and Cycloartenone have multiple targets in Influenza A virus and showed themselves as the most promising phytocompounds to be isolated and considered for the therapeutic arsenal against the flu.

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          Most cited references43

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          AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading.

          AutoDock Vina, a new program for molecular docking and virtual screening, is presented. AutoDock Vina achieves an approximately two orders of magnitude speed-up compared with the molecular docking software previously developed in our lab (AutoDock 4), while also significantly improving the accuracy of the binding mode predictions, judging by our tests on the training set used in AutoDock 4 development. Further speed-up is achieved from parallelism, by using multithreading on multicore machines. AutoDock Vina automatically calculates the grid maps and clusters the results in a way transparent to the user. Copyright 2009 Wiley Periodicals, Inc.
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            ProTox-II: a webserver for the prediction of toxicity of chemicals

            Abstract Advancement in the field of computational research has made it possible for the in silico methods to offer significant benefits to both regulatory needs and requirements for risk assessments, and pharmaceutical industry to assess the safety profile of a chemical. Here, we present ProTox-II that incorporates molecular similarity, pharmacophores, fragment propensities and machine-learning models for the prediction of various toxicity endpoints; such as acute toxicity, hepatotoxicity, cytotoxicity, carcinogenicity, mutagenicity, immunotoxicity, adverse outcomes pathways (Tox21) and toxicity targets. The predictive models are built on data from both in vitro assays (e.g. Tox21 assays, Ames bacterial mutation assays, hepG2 cytotoxicity assays, Immunotoxicity assays) and in vivo cases (e.g. carcinogenicity, hepatotoxicity). The models have been validated on independent external sets and have shown strong performance. ProTox-II provides a freely available webserver for in silico toxicity prediction for toxicologists, regulatory agencies, computational and medicinal chemists, and all users without login at http://tox.charite.de/protox_II. The webserver takes a two-dimensional chemical structure as an input and reports the possible toxicity profile of the chemical for 33 models with confidence scores, and an overall toxicity radar chart along with three most similar compounds with known acute toxicity.
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              In silico studies on therapeutic agents for COVID-19: Drug repurposing approach

              Aims The severe acute respiratory syndrome coronavirus 2, better known as COVID-19 has become the current health concern to the entire world. Initially appeared in Wuhan, China around December 2019, it had spread to almost 187 countries due to its high contagious nature. Precautionary measures remain the sole obliging tactic to cease the person to person transmissions till any effective method of treatment or vaccine is developed. Amidst the pandemic, research and development of new molecule is labour-intensive and tedious process. Drug repurposing is the concept of identifying therapeutically potent molecule from the library of pre-existing molecules. Materials and methods In the present study, 61 molecules that are already being used in clinics or under clinical scrutiny as antiviral agents are surveyed via docking study. Docking study was performed using Maestro interface (Schrödinger Suite, LLC, NY). Key findings Out of these 61 molecules, 37 molecules were found to interact with >2 protein structures of COVID-19. The docking results indicate that amongst the reported molecules, HIV protease inhibitors and RNA-dependent RNA polymerase inhibitors showed promising features of binding to COVID-19 enzyme. Along with these, Methisazone an inhibitor of protein synthesis, CGP42112A an angiotensin AT2 receptor agonist and ABT450 an inhibitor of the non-structural protein 3-4A might become convenient treatment option as well against COVID-19. Significance The drug repurposing approach provide an insight about the therapeutics that might be helpful in treating corona virus disease.
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                Author and article information

                Journal
                aabc
                Anais da Academia Brasileira de Ciências
                An. Acad. Bras. Ciênc.
                Academia Brasileira de Ciências (Rio de Janeiro, RJ, Brazil )
                0001-3765
                1678-2690
                2021
                : 93
                : suppl 4
                : e20210964
                Affiliations
                [3] Camaragibe Pernambuco orgnameUniversidade de Pernambuco orgdiv1Programa de Pós-Graduação em Odontologia Brazil
                [4] Natal Rio Grande do Norte orgnameUniversidade Federal do Rio Grande do Norte orgdiv1Departamento de Farmácia Brazil
                [1] Arcoverde Pernambuco orgnameUniversidade de Pernambuco orgdiv1Faculdade de Odontologia Brazil
                [2] Garanhuns Pernambuco orgnameUniversidade de Pernambuco orgdiv1Programa de Pós-Graduação em Saúde e Desenvolvimento Socioambiental (PPGSDS) Brazil
                Article
                S0001-37652021000800707 S0001-3765(21)09300000707
                10.1590/0001-3765202120210964
                8db0d3b0-6d4d-4b73-ab00-ab8a202fd991

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 24 September 2021
                : 02 July 2021
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 43, Pages: 0
                Product

                SciELO Brazil

                Categories
                Health Sciences

                protein binding,Bioinformatics,In silico modeling,pharmacokinetics,natural products

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