Virtual Screening to Identification The Active Compounds from Kratom (Mitragyna speciosa) as Analgetics

Ihsanul Arief(1*), Inderiyani Inderiyani(2)

(1) Akademi Farmasi Yarsi Pontianak
(2) Akademi Farmasi Yarsi Pontianak
(*) Corresponding Author

Abstract

Kratom (Mytragina speciosa) is one of the endemic plants in Southeast Asia in general and Kalimantan in particular which is proven to have activities as an analgesic, sedative (antidepressant), and, antiobesity, breast anticancer, antinociceptive, CYP450 induction, anti-inflammatory, opiate, antimicrobial, and antioxidant. However, no report states the active compounds from kratom leaves that have these activities. Therefore, this study will identify the active compounds contained in kratom leaves with a virtual screening process responsible for analgesic activity along with the prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The virtual screening process was performed based on molecular tethering, while the prediction of ADMET properties was done with the help of a web server. The results obtained showed that the compound (-)-epicatechin was most responsible for the analgesic activity of kratom leaves. The ADMET profile of this compound predicts that it has good bioavailability and is non-toxic.

Keywords

Kratom, Mytragina speciosa, analgetics, virtual screening, ADMET

Full Text:

PDF

References

Asiedu S.O., Kwofie S.K., Broni E. and Wilson M.D., 2021, Computational identification of potential anti-inflammatory natural compounds targeting the p38 mitogen-activated protein kinase (Mapk): Implications for covid-19-induced cytokine storm, Biomolecules, 11 (5), 653.

Banerjee P., Eckert A.O., Schrey A.K. and Preissner R., 2018, ProTox-II: a webserver for the prediction of toxicity of chemicals, Nucleic Acids Research, 46 (W1), W257–W263.

BIOVIA, 2021, Dassault Systèmes, Discovery Studio Visualizer, v21.1.0.20298, San Diego: Dassault Systèmes, 2021,

das Chagas Pereira de Andrade F. and Mendes A.N., 2020, Computational analysis of eugenol inhibitory activity in lipoxygenase and cyclooxygenase pathways, Scientific Reports, 10 (1), 1–14.

Daina A., Michielin O. and Zoete V., 2017, SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules, Scientific Reports, 7 (1), 42717.

Dallakyan S. and Olson A.J., 2015, Small-molecule library screening by docking with PyRx, Methods in Molecular Biology, 1263, 243–250.

Elhenawy A.A., AL-Harbi L.M., El-Gazzar M.A., Khowdiary M.M., ouidate A., Alosaimi A.M. and elhamid Salim A., 2019, Naproxenylamino acid derivatives: Design, synthesis, docking, QSAR and anti-inflammatory and analgesic activity, Biomedicine and Pharmacotherapy, 116 (March), 109024.

Elhenawy A.A., Al-Harbi L., Moustafa G., El-Gazzar M.A., Abdel-Rahman R.F. and Salim A.E., 2019, Synthesis, comparative docking, and pharmacological activity of naproxen amino acid derivatives as possible anti-inflammatory and analgesic agents, Drug Design, Development and Therapy, Volume 13, 1773–1790.

Endriyatno N.C. and Walid M., 2022, Studi In Silico Kandungan Senyawa Daun Srikaya (Annona squamosa L.)Terhadap Protein Dihydrofolate Reductase Pada Mycobacterium tuberculosis, Pharmacon: Jurnal Farmasi Indonesia, 19 (1), 87–98.

Ertl P. and Schuffenhauer A., 2009, Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions, Journal of Cheminformatics, 1 (1), 1–11.

Firmansyah A., Sundalian M. and Taufiq M., 2020, Kratom (Mitragyna speciosa Korth) for a New Medicinal: a Review of Pharmacological and Compound Analysis, Biointerface Research in Applied Chemistry, 11 (2), 9704–9718.

Goldstein D.M., Soth M., Gabriel T., Dewdney N., Kuglstatter A., Arzeno H., Chen J., Bingenheimer W., Dalrymple S.A., Dunn J., Farrell R., Frauchiger S., La Fargue J., Ghate M., Graves B., Hill R.J., et al., 2011, Discovery of 6-(2,4-Difluorophenoxy)-2-[3-hydroxy-1-(2-hydroxyethyl)propylamino]-8-methyl-8 H -pyrido[2,3- d ]pyrimidin-7-one (Pamapimod) and 6-(2,4-Difluorophenoxy)-8-methyl-2-(tetrahydro-2 H -pyran-4-ylamino)pyrido[2,3- d ]pyrimidin-7(8 H )-one (R1487) , Journal of Medicinal Chemistry, 54 (7), 2255–2265.

Hadi S., Khairunnisa A., Khalifah S.N., Oktaviani S., Sari S.O. and Hapifah U.N., 2021, Skrining Inhibitor NF-κB Combretum indicum dengan Metode Docking, Pharmacon: Jurnal Farmasi Indonesia, 18 (2), 157–163.

Ji J., Zhang R., Li H., Zhu J., Pan Y. and Guo Q., 2020, Analgesic and anti-inflammatory effects and mechanism of action of borneol on photodynamic therapy of acne, Environmental Toxicology and Pharmacology, 75 (January)

Kontoyianni M., 2017, Docking and virtual screening in drug discovery, Methods in Molecular Biology, 1647, 255–266.

Kurniawan A., Siswandono S., Mumpuni E. and Abdillah S., 2022, In Silico Molecular Docking and Toxicity Studies of Bioactive Fucoidan Compound from Brown Seaweed as Potential of Antihypertensive, Pharmacon: Jurnal Farmasi Indonesia, 19 (1), 1–9.

León F., Habib E., Adkins J.E., Furr E.B., McCurdy C.R. and Cutler S.J., 2009, Phytochemical characterization of the leaves of Mitragyna speciosa grown in USA, Natural Product Communications, 4 (7), 907–910.

Lipinski C.A., Lombardo F., Dominy B.W. and Feeney P.J., 2012, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Advanced Drug Delivery Reviews, 64 (SUPPL.), 4–17.

Lipinski C.A., Lombardo F., Dominy B.W. and 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.

Marcou G. and Rognan D., 2007, Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints, Journal of Chemical Information and Modeling, 47 (1), 195–207.

Motiejunas D. and Wade R.C., 2007, Structural, Energetic, and Dynamic Aspects of Ligand–Receptor Interactions, Dalam Taylor, J. B. & Triggle, D. J., eds. Comprehensive Medicinal Chemistry II, Elsevier, pp. 193–213.

Orlando B.J. and Malkowski M.G., 2016, Substrate-selective inhibition of cyclooxygeanse-2 by fenamic acid derivatives is dependent on peroxide tone, Journal of Biological Chemistry, 291 (29), 15069–15081.

Pettersen E.F., Goddard T.D., Huang C.C., Couch G.S., Greenblatt D.M., Meng E.C. and Ferrin T.E., 2004, UCSF Chimera: A visualization system for exploratory research and analysis, Journal of Computational Chemistry, 25 (13), 1605–1612.

Sakamuru S., Attene-Ramos M.S. and Xia M., 2016, Membrane Mitochondrial Potential Assay, Zhu, H. & Xia, M., eds., Springer New York, New York, NY.

Santoso B., 2015, D-Molecular Screening of Diketopiperazine Derivates on Staphylococcus aureus Dehydrosqualene Synthase using Vina, Pharmacon: Jurnal Farmasi Indonesia, 13 (1), 24–29.

Tariq S., Alam O. and Amir M., 2018, Synthesis, p38α MAP kinase inhibition, anti-inflammatory activity, and molecular docking studies of 1,2,4-triazole-based benzothiazole-2-amines, Archiv der Pharmazie - Chemistry in Life Science, 351 (3–4), e1700304.

Trott O. and Olson A.J., 2009, AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading, Journal of Computational Chemistry, 31 (2), 455–461.

Wang Z., Yang H., Wu Z., Wang T., Li W., Tang Y. and Liu G., 2018, In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods, ChemMedChem, 13 (20), 2189–2201.

Xiang M., Cao Y., Fan W., Chen L. and Mo Y., 2012, Computer-Aided Drug Design: Lead Discovery and Optimization, Combinatorial Chemistry & High Throughput Screening, 15 (4), 328–337.

Article Metrics

Abstract view(s): 107 time(s)
PDF: 96 time(s)

Refbacks

  • There are currently no refbacks.