Repurposing Mitragynine as Anti-SARS-CoV-2 Agent Evidenced by In Silico Predictive Approach
Keywords:ADME, COVID-19, mitragynine, molecular docking, SARS-CoV-2 RBD
The ongoing coronavirus disease-2019 (COVID-19) catastrophe calls for the development of therapeutic approaches to combat the disease. Therefore, an in silico study was conducted to evaluate druggability capacity of mitragynine, a natural indole alkaloid compound, using adsorption, distribution, metabolism, and excretion (ADME) prediction and molecular docking simulation to the region binding domain of severe acute respiratory coronavirus 2 (SARS-CoV-2 RBD). The pharmacodynamics of mitragynine were evaluated for its druggability using SwissADME software, and molecular docking simulation was performed using using AutoDock software, using SARS-CoV-2 RBD (PDB ID: 6M0J) as the protein target retrieved from Protein Data Bank (PDB). ADME predicted that this compound has excellent druggability, transport properties, and pharmacokinetics, following Lipinski’s rule of five. Mitragynine is also nonmutagenic based on the AMES toxicity test. No PAINS alert was observed and synthetic acceptability score was 4.49, suggesting a moderately synthesised compound. Through the molecular docking approach, mitragynine successfully docked the binding site of SARS-CoV-2 RBD with a binding energy of -6.3kcal/mol and formed hydrogen bonds with the residue N501, which is one of the residues at the binding site of RBD. These findings, together with other therapeutic qualities of mitragynine warrant for more research into molecular dynamics, in vitro, and in vivo studies in COVID-19 therapy.
World Health Organization. (2021). WHO Coronavirus Disease (COVID-19) Dasboard; 27 November 2021. Available online at https://covid19.who.int/.
Yuen, K. S., Ye, Z. W., Fung, S. Y., Chan, C. P., Jin, D. Y. (2020). SARS-CoV-2 and COVID-19: the most important research questions. Cell Biosci. 10, 40.
World Health Organization. (2021). Therapeutics and COVID-19: living guideline; 27 November 2021. Available online at https://www.who.int/publications/i/item/WHO-2019-nCoV-therapeutics-2021.3.
Aygün, İ., Kaya, M., Alhajj, R. (2020). Identifying side effects of commonly used drugs in the treatment of COVID 19. Sci Rep., 10, 21508.
Nhean, S., Varela, M. E., Nguyen, Y. N., Juarez, A., Huynh, T., Ud,h. D. et al. (2021). COVID-19: A review of potential treatments (corticosteroids, remdesivir, tocilizumab, bamlanivimab/etesevimab, and casirivimab/imdevimab) and pharmacological considerations. J Pharm Prac., 1-11.
Self, W. H., Semler, M. W., Leither, L. M. et al. (2020). Effect of Hydroxychloroquine on clinical status at 14 days in hospitalized patients with COVID-19: A randomized clinical trial. JAMA, 324(21), 2165-2176.
Metastasio, A., Prevete, E., Singh, D., Grundmann, O., Prozialeck, W. C., Veltri, C. et al. (2020). Can Kratom (Mitragyna speciosa) alleviate COVID-19 pain? a case study. Front Psych., 11, 1298.
Todd, D. A., Kellogg, J. J., Wallace, E. D., Flores-Bocanegra, L., Tanna, R. S., McIntosh, S. et al. (2020). Chemical composition and biological effects of kratom (Mitragyna speciosa): In vitro studies with implications for efficacy and drug interactions. Sci Rep., 10, 19158.
Flores-Bocanegra, L., Huzefa, A., Raja, T. N., Graf, M. A., Wallace, E. D., Hematian, et al. (2020). The Chemistry of Kratom [Mitragyna speciosa]: Updated Characterization Data and Methods to Elucidate Indole and Oxindole Alkaloid. J Nat Prod., 83(7, 2165-2177.
Firmansyah, A., Sundalian, M., Taufiq, M. Kratom (2020). (Mitragyna speciosa Korth) for a new medicinal: a review of pharmacological and compound analysis. Biointer Res Appl Chem., 11(2), 9704-9797.
Terence, T. Y. C., Lim, X. Y., Hemahwathy, C. K., June, C. L., Nur Salsabeela, M. R., Nurmaziah, M. S. (2020). Review: The potential use of kratom (Mitragyna speciosa) in coronavirus diseases (COVID-19). Globinmed.;1-16.
Rout, J., Swain, B. C., Tripathy, U. (2020). In silico investigation of spice molecules as potent inhibitor of SARS-CoV-2. J Biomolec Struct Dynam., 1-12.
Rutwick, S. U., Praveen, N. (2021). A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery. Virus Dis., 32(1), 1-9.
Daina, A., Michielin, O., Zoete, V. Swiss. (2017). ADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep., 7, 42717.
Patil, V. M., Gaurav, A., Garg, P., Masand, N. (2021). Non-cancer to anti-cancer: investigation of human ether-a-go-go-related gene potassium channel inhibitors as potential therapeutics. J Egypt Nat Cancer Inst., 33(1), 33.
Daina, A., Michielin, O., Zoete, V. Swiss. (2019). Target Prediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res., 47(1), 357-364.
Liu, X., Zhang, B., Jin, Z., Yang, H., Rao, Z. (2020). The crystal structure of COVID-19 main protease in complex with an inhibitor N3. Nature, 582, 289-293.
Zhao, Y., Fang, C., Zhang, Q., Zhang, R., Zhao, X., Duan, Y. et al. (2021). Crystal structure of SARS-CoV-2 main protease in complex with protease inhibitor PF-07321332. Protein Cell, 1-5.
Trott, O., Olson, A. J. (2010). Auto Dock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comp Chem., 31, 455-461.
Wang, M., Cao, R., Zhang, L., Yang, X., Liu, J., Xu, M. et al. (2020). Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res., 30, 269-71.
Fan, Q., Zhang, B., Ma, J., Zhang, S. (2020). Safety profile of the antiviral drug remdesivir: An update. Biomedic Pharmacother., 30, 110532.
Yoo, S., Yang, H. C., Lee, S., Shin, J., Min, S., Lee, E. et al. (2020). A deep learning-based approach for identifying the medicinal uses of plant-derived natural compounds. Front Pharmacol., 11, 584875.
Islam, M. T., Sarkar, C., El-Kersh, D. M. et al. (2020). Natural products and their derivatives against coronavirus: A review of the non-clinical and pre-clinical data. Phytother Res., 34(10), 2471-2492.
Mandal, A., Jha, A. K., Hazra, B. (2021). Plant products as inhibitors of coronavirus 3CL protease. Front Pharmacol., 12, 583387.
Silva, J., Rocha, M. N., Marinho, E. M., et al. (2021). Evaluation of the ADME, toxicological analysis and molecular docking studies of the anacardic acid derivatives with potential antibacterial effects against staphylococcus aureus. J Anal Pharm Res., 10(5), 177-194. ‘
Benet, L. Z., Hosey, C. M., Ursu, O., Oprea, T. I. (2016). The Rule of 5 and drugability. Adv Drug Deliv Rev., 101, 89-98.
Lipinski, C. A., Lombardo, F., Dominy, B. W., Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev., 46, 3-26.
Ramanathan, S., Parthasarathy, S., Murugaiyah, V., Magosso, E., Tan, S. C., Mansor, S. M. (2015). Understanding the physicochemical properties of mitragynine, a principal alkaloid of Mitragyna speciosa, for preclinical evaluation. Molecules, 20, 4915-4927.
Rutkowska, E., Pajak, K., Jóźwiak, K. (2013). Lipophilicity--methods of determination and its role in medicinal chemistry. Acta Pol Pharm., 70(1), 3-18.
Sepay, N., Hoque, A., Mondal, R., Halder, U. C., Muddassir, M. (2020). In silico fight against novel coronavirus by finding chromone derivatives as inhibitor of coronavirus main proteases enzyme. Struct Chem., 31(5), 1831-1840.
Maximo da Silva, M., Comin, M., Santos Duarte, T., Foglio, M. A., De Carvalho, J. E., Do Carmo Vieira, M., Nazari Formagio, A. S. (2015). Synthesis, antiproliferative activity and molecular properties predictions of galloyl derivatives. Molecules, 20(4), 5360-5373.
Paul, A. (2019). Drug absorption and bioavailability. In: Raj G., Raveendran R. (eds). Introduction to basics of pharmacology and toxicology. Springer, Singapore, 81-88.
Pardridge, W. M. (2012). Drug transport across the blood-brain barrier. J Cereb Blood Flow Metab., 32(11), 1959-1972.
Sychev, D., Ashraf, G. M., Svistunov, A., Maksimov, M., Tarasov, V., Chubarev, V. N., Otdelenov, V. A., Denisenko, N. P., Barreto. G. E., Aliev, G. (2018). The cytochrome P450 isoenzyme and some new opportunities for the prediction of negative drug interaction in vivo. Drug Des Devel Ther., 12, 1147-1156.
Lynch, T., Price, A. P. (2007). The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects. Am Physic, 76(3), 391-39.
Gurusamy, U., Shewade, D. G. (2014). Pharmacogenomics in India. Handbook Pharmacogen Strat Medic. 46, 1037-1059.
Palleria, C., Di Paolo, A., Giofrè, C., Caglioti, C., Leuzzi, G., Siniscalchi, A. et al. (2013). Pharmacokinetic drug-drug interaction and their implication in clinical management. J Res Medic Sci., 18(7), 601-610.
Veltri, C., Grundmann, O. (2019). Current perspectives on the impact of Kratom use. Subs Abuse Rehab., 10, 23-3.
Uslu, H., Koparir, P., Sarac, K., Karatepe, A. (2021). ADME predictions and molecular docking study of some compounds and drugs as potential inhibitors of COVID-19 main protease: A virtual study as comparison of computational results. Medic Sci Int Medic J., 10(1): 18.
Wu, F., Zhou, Y., Li, L., Shen, X., Chen, G., Wang, X. et al. (2020). Computational Approaches in Preclinical Studies on Drug Discovery and Development. Front Chem., 11(8), 726.
Sukumaran, S. K., Ranganatha, R., Chakravarthy, S. (2016). High-throughput approaches for genotoxicity testing in drug development: recent advances. Int J High Throu Screen, 6, 1-12.
Ertl, P., Schuffenhauer. A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform., 1(1), 8.
Lee, H. M., Yu, M. S., Kazmi, S. R., Oh, S. Y., Rhee, K. H., Bae, M. A. et al. (2019). Computational determination of hERG-related cardiotoxicity of drug candidates. BMC Bioinform., 20(10), 250.
Ashraf, S. A., Elkhalifa, A., Mehmood, K., Adnan, M., Khan, M. A., Eltoum, N. E. et al. Multi-targeted molecular docking, pharmacokinetics, and drug-likeness evaluation of okra-derived ligand abscisic acid targeting signaling proteins involved in the development of diabetes. Molecules, 26(19), 5957.
Gfeller, D., Grosdidier, A., Wirth, M., Daina, A., Michielin, O., Zoete, V. (2014). Swiss Target prediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res., 42, 32-38.
Cortegiani, A., Ingoglia, G., Ippolito, M., Giarratano, A., Einav, S. (2020). A systematic review on the efficacy and safety of chloroquine for the treatment of COVID-19. J Crit Care, 57, 27-283.
Hage-Melim, L., Federico, L. B., de Oliveira, N., Francisco, V., Correia, L. C., de Lima, H. B. et al. (2020). Virtual screening, ADME/Tox predictions and the drug repurposing concept for future use of old drugs against the COVID-19. Life Sci., 256, 117963.
Kondo, H., Fujimoto. K. J., Tanaka, S., Deki, H., Nakamura, T. (2015). Theoretical prediction and experimental verification on enantioselectivity of haloacid dehalogenase L-DEX YL with chloropropionate. Chem Phy Lett., 623, 101-107.
Pantsar, T., Poso, A. (2018). Binding affinity via docking: fact and fiction. Molecules, 23(8), 1899.
Tai, W., He, L., Zhang, X. et al. (2020). Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: implication for development of RBD protein as a viral attachment inhibitor and vaccine. Cell Mol Immunol., 17(6), 613-620.
Çubuk, H., Özbİl, M. (2021). Comparison of clinically approved molecules on SARS-CoV-2 drug target proteins: a molecular docking study. Turk J Chem., 4, 35-41.
Adegbola, P. I., Fadahunsi, O. S., Adegbola, A. E. et al. (2021). In silico studies of potency and safety assessment of selected trial drugs for the treatment of COVID-19. In Silico Pharmacol., 9, 45.
Yusuf, A. J., Abdullahi, M. I., Musa, A. M., Abubakar, H., Amali, A. M., Nasir, A. H. (2022). Potential inhibitors of SARS-CoV-2 from Neocarya macrophylla (Sabine) Prance ex F. White: Chemoinformatic and molecular modeling studies for three key targets. Turk J Pharm Sci., 19, 202-212.
Ismail, E. M., Shantier, O. A., Mohammed, S. W., Musa, M. S., Osman, H. H., Mothana, W. (2021). Quinoline and quinazoline alkaloids against covid-19: an in silico multitarget approach. J Chem., 1-11.
Vesga, L. C., Ruiz-Hernández, C. A., Alvarez-Jacome, J. J., Duque, J. E., Rincon-Orozco, B., Mendez-Sanchez, S. C. (2022). Repurposing of four drugs as anti-SARS-CoV-2 agents and their interactions with protein targets. Sci Pharm., 90(2), 24.
Khelfaoui, H., Harkati, D., Saleh, B. A. (2020). Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2. J Biomol Struct Dynam., 1-17.
Kong, W. M., Chik, Z., Ramachandra, M., Subramaniam, U., Aziddin, R. E. R., Mohamed, Z. (2011). Evaluation of the effects of Mitragyna speciosa alkaloid extract on cytochrome P450 enzymes using a high throughput assay. Molecules,16(9), 7344-7356.
Copyright (c) 2022 Fatahiya Mohamed Tap, Nor Hafizah Zakaria, Fadzilah Adibah Abdul Majid, Moyeenul Huq AKM, Jamia Azdina Jamal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.