In silico ADME/T prediction of novel potential inhibitors against dengue virus
Keywords:Dengue; Denv; In silico; Computational tools; Molecular modeling.
Dengue is an emerging disease with a major impact on public health, with millions of viral infections occurring annually, for which there is still no effective therapy. The present study aims to predict the physicochemical, pharmacokinetic and toxicological properties of candidates for drugs against dengue. 17 candidates for anti-dengue drugs were developed on the PubChem Sketcher V. 2.4® platform. The physical-chemical parameters were quantified on the Molinspiration® platform. Subsequently, the pharmacokinetic parameters were measured using the SwissADME® tool. Finally, the OSIRIS Property Explorer® platform was used to determine the toxicological effect of anti-dengue candidates. Compounds 8 and 14 did not violate any of the rules instituted by Lipinski. All other compounds showed more than one violation, with compounds 5, 7 and 9-11 showing up to 3 violations. As for the pharmacokinetic evaluation, of the compounds designed here only compounds 13 and 14 showed high gastrointestinal absorption. Compounds 2, 15 and 17 have at least a high-risk score for one of the toxicity factors for mutagenesis, tumorigenesis, irritating and reproductive effects. Compounds 1-4 have at least an intermediate risk score for one of the toxicity factors. All other compounds have low risk scores for one of the toxicity factors. The in silico prediction made in that study indicated that compounds 13 and 14 are the most promising for being possible anti-dengue candidates and useful for future screening in tests performed on cells and animals.
Beesetti, H., Khanna, N., & Swaminathan, S. (2016). Investigational drugs in early development for treating dengue infection. Expert Opinion on Investigational Drugs, 25 (9), 1059–69. https://doi.org/10.1080/13543784.2016.1201063
Brady, O. J., & Hay, S. I. (2020). The global expansion of dengue: How aedes aegypti mosquitoes enabled the first pandemic arbovirus. Annual Review of Entomology, 65, 191-208. https://doi.org/10.1146/annurev-ento-011019-024918
Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7. https://doi.org/10.1038/srep42717
Dighe, S. N., Ekwudu, O., Dua, K., Chellappan, D. K., Katavic, P. L., & Collet, T. A. (2019). Recent update on anti-dengue drug discovery. European Journal of Medicinal Chemistry, 176, 431-455. https://doi.org/10.1016/j.ejmech.2019.05.010
Ferreira, L. L. G., & Andricopulo, A. D. (2019). ADMET modeling approaches in drug discovery. Drug Discovery Today, 24(5) 1157-65. https://doi.org/10.1016/j.drudis.2019.03.015
Gil, A.C. (2010). Como elaborar projetos de pesquisa. 5. ed. Atlas. São Paulo. Brasil.
Guy, B., Noriega, F., Ochiai, R. L., L’azou, M., Delore, V., Skipetrova, A., Verdier, F., Coudeville, L., Savarino, S., & Jackson, N. (2017). A recombinant live attenuated tetravalent vaccine for the prevention of dengue. Expert Review of Vaccines, 16(7), 671–683. https://doi.org/10.1080/14760584.2017.1335201
Guzman, M. G., Gubler, D. J., Izquierdo, A., Martinez, E., & Halstead, S. B. (2016). Dengue infection. Nature Reviews Disease Primers, 2(1), 1–25. https://doi.org/10.1038/nrdp.2016.55
Hage-Melim, L. I. da S., Federico, L. B., de Oliveira, N. K. S., Francisco, V. C. C., Correia, L. C., de Lima, H. B., Gomes, S. Q., Barcelos, M. P., Francischini, I. A. G., & da Silva, C. H. T. de P. (2020). Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19. Life Sciences, 256, 117963. https://doi.org/10.1016/j.lfs.2020.117963
Halim, S. A., Khan, S., Khan, A., Wadood, A., Mabood, F., Hussain, J., & Al-Harrasi, A. (2017). Targeting Dengue Virus NS-3 Helicase by Ligand based Pharmacophore Modeling and Structure based Virtual Screening. Frontiers in Chemistry, 5. https://doi.org/10.3389/fchem.2017.00088
Kirchmair, J., Göller, A. H., Lang, D., Kunze, J., Testa, B., Wilson, I. D., Glen, R. C., & Schneider, G. (2015). Predicting drug metabolism: Experiment and/or computation? Nature Reviews Drug Discovery, 14 (6), 387-404.https://doi.org/10.1038/nrd4581
Lakatos, E. M.; Marconi, M.A (2011). Metodologia científica. São Paulo, Brasil.
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23 (1-3), 3-25. https://doi.org/10.1016/S0169-409X(96)00423-1
Madden, J. C., Enoch, S. J., Paini, A., & Cronin, M. T. D. (2020). A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications. Alternatives to laboratory animals : ATLA, 48(4), 146–72. https://doi.org/10.1177/0261192920965977
Murugesan, A., & Manoharan, M. (2019). Dengue virus. In Emerging and Reemerging Viral Pathogens: Volume 1: Fundamental and Basic Virology Aspects of Human, Animal and Plant Pathogens. 1, 281-359. https://doi.org/10.1016/B978-0-12-819400-3.00016-8
N. Powers, C., & N. Setzer, W. (2016). An In-Silico Investigation of Phytochemicals as Antiviral Agents Against Dengue Fever. Combinatorial Chemistry & High Throughput Screening, 19(7), 516–536. https://doi.org/10.2174/1386207319666160506123715
Patrick, Í., Amorim, S., Ramos Pestana, E., José, S., & Mendes, F. (2017). Predição do metabolismo do candidato a fármaco cinamaldeído: Uma abordagem in silico. Revista Ceuma Perspectivas, 30(1), 111–120. http://smartcyp.sund.ku.dk/.
Pollett, S., Melendrez, M. C., Maljkovic Berry, I., Duchêne, S., Salje, H., Cummings, D. A. T., & Jarman, R. G. (2018). Understanding dengue virus evolution to support epidemic surveillance and counter-measure development. Infection, Genetics and Evolution, 62, 279–95. https://doi.org/10.1016/j.meegid.2018.04.032
Rai, J., & Kaushik, K. (2018). Reduction of Animal Sacrifice in Biomedical Science & Research through Alternative Design of Animal Experiments. Saudi Pharmaceutical Journal, 26(6), 896–902.https://doi.org/10.1016/j.jsps.2018.03.006
Rao, V. S., & Srinivas, K. (2011). Modern drug discovery process: An in silico approach. Journal of Bioinformatics and Sequence Analysis, 2(5), 89–94. http://www.academicjournals.org/JBSA
Thangarasu, P., Thamarai Selvi, S., & Manikandan, A. (2018). Unveiling novel 2-cyclopropyl-3-ethynyl-4-(4-fluorophenyl)quinolines as GPCR ligands via PI3-kinase/PAR-1 antagonism and platelet aggregation valuations; development of a new class of anticancer drugs with thrombolytic effects. Bioorganic Chemistry, 81, 468–480. https://doi.org/10.1016/j.bioorg.2018.09.011
Vavougios, G. D., Zarogiannis, S. G., Krogfelt, K. A., Gourgoulianis, K., Mitsikostas, D. D., & Hadjigeorgiou, G. (2018). Novel candidate genes of the PARK7 interactome as mediators of apoptosis and acetylation in multiple sclerosis: An in silico analysis. Multiple Sclerosis and Related Disorders, 19, 8–14. https://doi.org/10.1016/j.msard.2017.10.013
Vera, A. A. (1989). Metodologia da pesquisa científica. 8th ed. São Paulo. Brasil.
Vergara, S. C. (2006) Projetos e relatórios de pesquisa em administração. 5th ed. São Paulo. Brasil.
Vukic, V. R., Loncar, D. M., Vukic, D. V., Jevric, L. R., Benedekovic, G., Francuz, J., Kojic, V., Karadzic Banjac, M. Z., & Popsavin, V. (2019). In vitro antitumor activity, ADME-Tox and 3D-QSAR of synthesized and selected natural styryl lactones. Computational Biology and Chemistry, 83, 107112. https://doi.org/10.1016/j.compbiolchem.2019.107112
Waman, V. P., Kolekar, P., Ramtirthkar, M. R., Kale, M. M., & Kulkarni-Kale, U. (2016). Analysis of genotype diversity and evolution of Dengue virus serotype 2 using complete genomes. PeerJ, 8. https://doi.org/10.7717/peerj.2326
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