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Research Articles

Molecular docking and dynamics simulation of Orthosiphon stamineus against SGLT1 and SGLT2

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Pages 13663-13678 | Received 13 Aug 2022, Accepted 06 Feb 2023, Published online: 30 Mar 2023

References

  • Abdul-Ghani, M. A., Norton, L., & DeFronzo, R. A. (2015). Renal sodium-glucose cotransporter inhibition in the management of type 2 diabetes mellitus. American Journal of Physiology-Renal Physiology, 309(11), F889–F900. https://doi.org/10.1152/ajprenal.00267.2015
  • Ashraf, K., Sultan, S., & Adam, A. (2018). Orthosiphon stamineus Benth. is an outstanding food medicine: Review of phytochemical and pharmacological activities. Journal of Pharmacy & Bioallied Sciences, 10(3), 109–118.
  • Avanza, T. (2020). Forxiga cardiovascular outcomes benefit approved in China (Cision). Update, 8, 01.
  • Borad, M. A., Jethava, D. J., Bhoi, M. N., Patel, C. N., Pandya, H. A., & Patel, H. D. (2020). Novel isoniazid-spirooxindole derivatives: Design, synthesis, biological evaluation, in silico ADMET prediction and computational studies. Journal of Molecular Structure, 1222, 128881. https://doi.org/10.1016/j.molstruc.2020.128881
  • Bordoli, L., Kiefer, F., Arnold, K., Benkert, P., Battey, J., & Schwede, T. (2009). Protein structure homology modeling using SWISS-MODEL workspace. Nature Protocols, 4(1), 1–13. https://doi.org/10.1038/nprot.2008.197
  • Coffey, A., & Meek, A. (XXXX). JARDIANCE® (empagliflozin) becomes the first and only approved treatment in Canada for adults with chronic heart failure regardless of ejection fraction.
  • Costa, C. R., Olivi, P., Botta, C. M., & Espindola, E. L. (2008). Toxicity in aquatic environments: discussion and evaluation methods. Química Nova, 31(7), 1820–1830. https://doi.org/10.1590/S0100-40422008000700038
  • Fatima, S., Gupta, P., Sharma, S., Sharma, A., & Agarwal, S. M. (2020). ADMET profiling of geographically diverse phytochemical using chemoinformatic tools. Future Medicinal Chemistry, 12(1), 69–87. https://doi.org/10.4155/fmc-2019-0206
  • Filimonov, D. A., Akimov, D. V., & Poroikov, V. V. (2004). The method of self-consistent regression for the quantitative analysis of relationships between structure and properties of chemicals. Pharmaceutical Chemistry Journal, 38(1), 21–24. https://doi.org/10.1023/B:PHAC.0000027639.17115.5d
  • Fretwell, T., & Bell, A. (2019). Mundipharma announces launch of INVOKANA® and VOKANAMET® in Norway for the treatment of type 2 diabetes as part of exclusive distribution agreement with Janssen.
  • Goddard, T. D., Huang, C. C., & Ferrin, T. E. (2007). Visualizing density maps with UCSF Chimera. Journal of Structural Biology, 157(1), 281–287.
  • Gohlke, H., Hendlich, M., & Klebe, G. (2000). Knowledge-based scoring function to predict protein-ligand interactions. Journal of Molecular Biology, 295(2), 337–356.
  • Gupta, M., Kant, K., Sharma, R., & Kumar, A. (2018). Evaluation of in silico anti-parkinson potential of β-asarone. Central Nervous System Agents in Medicinal Chemistry, 18(2), 128–135. https://doi.org/10.2174/1871524918666180416153742
  • Haas, B., Eckstein, N., Pfeifer, V., Mayer, P., & Hass, M. D. S. (2014). Efficacy, safety and regulatory status of SGLT2 inhibitors: focus on canagliflozin. Nutrition & Diabetes, 4(11), e143. https://doi.org/10.1038/nutd.2014.40
  • Hakami, A. R. (2022). Targeting the RBD of omicron variant (B. 1.1. 529) with medicinal phytocompounds to abrogate the binding of spike glycoprotein with the hACE2 using computational molecular search and simulation approach. Biology, 11(2), 258. https://doi.org/10.3390/biology11020258
  • Han, Y., Zhang, J., Hu, C. Q., Zhang, X., Ma, B., & Zhang, P. (2019). In silico ADME and toxicity prediction of ceftazidime and its impurities. Frontiers in Pharmacology, 10, 434. https://doi.org/10.3389/fphar.2019.00434
  • Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 1–17. https://doi.org/10.1186/1758-2946-4-17
  • Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., & De Fabritiis, G. (2017). DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 33(19), 3036–3042. https://doi.org/10.1093/bioinformatics/btx350
  • Jaén-Gil, A., Buttiglieri, G., Benito, A., Mir-Tutusaus, J. A., Gonzalez-Olmos, R., Caminal, G., Barceló, D., Sarrà, M., & Rodriguez-Mozaz, S. (2021). Combining biological processes with UV/H2O2 for metoprolol and metoprolol acid removal in hospital wastewater. Chemical Engineering Journal, 404, 126482. https://doi.org/10.1016/j.cej.2020.126482
  • Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., Wang, J., Yu, B., Zhang, J., & Bryant, S. H. (2016). PubChem substance and compound databases. Nucleic Acids Research, 44(D1), D1202–D1213. https://doi.org/10.1093/nar/gkv951
  • Koche, D., Shirsat, R., & Kawale, M. A. H. E. S. H. (2016). An overerview of major classes of phytochemicals: their types and role in disease prevention. Hislopia Journal, 9(1/2), 0976–2124.
  • Lagunin, A., Zakharov, A., Filimonov, D., & Poroikov, V. (2011). QSAR modelling of rat acute toxicity on the basis of PASS prediction. Molecular Informatics, 30(2–3), 241–250. https://doi.org/10.1002/minf.201000151
  • Laskowski, R. A., MacArthur, M. W., Moss, D. S., & Thornton, J. M. (1993). PROCHECK: a program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26(2), 283–291. https://doi.org/10.1107/S0021889892009944
  • Li, G., Shao, K., & Umeshappa, C. S. (2019). Recent progress in blood-brain barrier transportation research. Brain Targeted Drug Delivery System, 33–51.
  • Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64, 4–17. https://doi.org/10.1016/j.addr.2012.09.019
  • Macalalad, M. A. B., & Gonzales III, A. A. (2021). In-silico screening and identification of phytochemicals from Centella asiatica as potential inhibitors of sodium-glucose co-transporter 2 for treating diabetes. Journal of Biomolecular Structure and Dynamics, 40(22), 12221–12238.
  • Mahanthesh, M. T., Ranjith, D., Yaligar, R., Jyothi, R., Narappa, G., & Ravi, M. V. (2020). Swiss ADME prediction of phytochemicals present in Buteamonosperma (Lam.) Taub. Journal of Pharmacognosy and Phytochemistry, 9(3), 1799–1809.
  • Marino, A. M., Yarde, M., Patel, H., Chong, S., & Balimane, P. V. (2005). Validation of the 96 well Caco-2 cell culture model for high throughput permeability assessment of discovery compounds. International Journal of Pharmaceutics, 297(1–2), 235–241. https://doi.org/10.1016/j.ijpharm.2005.03.008
  • May, M., & Schindler, C. (2016). Clinically and pharmacologically relevant interactions of antidiabetic drugs. Therapeutic Advances in Endocrinology and Metabolism, 7(2), 69–83. https://doi.org/10.1177/2042018816638050
  • McElvany, K. D. (2009). FDA requirements for preclinical studies. In Clinical trials in the neurosciences (Vol. 25, pp. 46–49). Karger Publishers.
  • Meng, X. Y., Zhang, H. X., Mezei, M., & Cui, M. (2011). Molecular docking: a powerful approach for structure-based drug discovery. Current Computer Aided-Drug Design, 7(2), 146–157. https://doi.org/10.2174/157340911795677602
  • Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841–1846.
  • Nelinson, D. S., Sosa, J. M., & Chilton, R. J. (2021). SGLT2 inhibitors: a narrative review of efficacy and safety. Journal of Osteopathic Medicine, 121(2), 229–239. https://doi.org/10.1515/jom-2020-0153
  • Nguyen, N. T., Nguyen, T. H., Pham, T. N. H., Huy, N. T., Bay, M. V., Pham, M. Q., Nam, P. C., Vu, V. V., & Ngo, S. T. (2020). Autodock vina adopts more accurate binding poses but autodock4 forms better binding affinity. Journal of Chemical Information and Modeling, 60(1), 204–211. https://doi.org/10.1021/acs.jcim.9b00778
  • Oany, A. R., Ahmad, S. A. I., Siddikey, M. A. A., Hossain, M. U., & Ferdoushi, A. (2014). Computational structure analysis and function prediction of an uncharacterized protein (I6U7D0) of Pyrococcus furiosus Com1. Austin J Comput Biol Bioinform, 1(2), 5.
  • Ojo, O. A., Ojo, A. B., Okolie, C., Nwakama, M.-A C., Iyobhebhe, M., Evbuomwan, I. O., Nwonuma, C. O., Maimako, R. F., Adegboyega, A. E., Taiwo, O. A., Alsharif, K. F., & Batiha, G. E.-S. (2021). Deciphering the interactions of bioactive compounds in selected traditional medicinal plants against Alzheimer’s diseases via pharmacophore modeling, auto-QSAR, and molecular docking approaches. Molecules, 26(7), 1996. https://doi.org/10.3390/molecules26071996
  • Oliva, F., Musiani, F., Giorgetti, A., De Rubeis, S., Sorokina, O., Armstrong, J. D., … Ruggerone, P. (2022). ModelingeNvironment for Isoforms (MoNvIso): A general platform to predict structural determinants of protein isoforms in genetic diseases. bioRxiv, 2022-04.
  • Patel, C. N., Jani, S. P., Jaiswal, D. G., Kumar, S. P., Mangukia, N., Parmar, R. M., Rawal, R. M., & Pandya, H. A. (2021). Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations. Scientific Reports, 11(1), 1–13. https://doi.org/10.1038/s41598-021-99165-4
  • Pontius, J., Richelle, J., & Wodak, S. J. (1996). Deviations from standard atomic volumes as a quality measure for protein crystal structures. Journal of Molecular Biology, 264(1), 121–136.
  • Poroikov, V. V., Filimonov, D. A., Borodina, Y. V., Gloriozova, T. A., Sitnikov, V. B., Sadovnikov, S. V., & Sosnov, A. V. (2004). Quantitative relationships between structure and delayed neurotoxicity of chemicals studied by the Self-Consistent Regression method using the PASS program. Pharmaceutical Chemistry Journal, 38(4), 188–190. https://doi.org/10.1023/B:PHAC.0000038416.33964.dc
  • Prasad, S., Sajja, R. K., Naik, P., & Cucullo, L. (2014). Diabetes mellitus and blood-brain barrier dysfunction: an overview. Journal of Pharmacovigilance, 2(2), 125.
  • Rachmania, R. A., Supandi, S., & Larasati, O. A. (2015). Analisis in-silico senyawa diterpenoid lakton herba sambiloto (Andrographis Paniculata Nees) pada reseptor alpha-glucosidase sebagai antidiabetes tipe II. Pharmacy: Jurnal Farmasi Indonesia (Pharmaceutical Journal of Indonesia), 12(2), 210–222.
  • Rai, H., Barik, A., Singh, Y. P., Suresh, A., Singh, L., Singh, G., Nayak, U. Y., Dubey, V. K., & Modi, G. (2021). Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19. Molecular Diversity, 25(3), 1905–1927. https://doi.org/10.1007/s11030-021-10188-5
  • Rajalakshmi, R., Lalitha, P., Sharma, S. C., Rajiv, A., Chithambharan, A., & Ponnusamy, A. (2021). In Silico studies: Physicochemical properties, drug score, toxicity predictions and molecular docking of organosulphur compounds against Diabetes mellitus. Journal of Molecular Recognition, 34(11), e2925. https://doi.org/10.1002/jmr.2925
  • Ramachandran, S., Kota, P., Ding, F., & Dokholyan, N. V. (2011). Automated minimization of steric clashes in protein structures. Proteins: Structure, Function, and Bioinformatics, 79(1), 261–270. https://doi.org/10.1002/prot.22879
  • Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard III, W. A., & Skiff, W. M. (1992). UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. Journal of the American Chemical Society, 114(25), 10024–10035. https://doi.org/10.1021/ja00051a040
  • Rose, A. S., & Hildebrand, P. W. (2015). NGL Viewer: a web application for molecular visualization. Nucleic Acids Research, 43(W1), W576–W579. https://doi.org/10.1093/nar/gkv402
  • Sharma, A., Mittal, S., Aggarwal, R., & Chauhan, M. K. (2020). Diabetes and cardiovascular disease: inter-relation of risk factors and treatment. Future Journal of Pharmaceutical Sciences, 6(1), 1–19. https://doi.org/10.1186/s43094-020-00151-w
  • Singh, M., & Kumar, A. (2018). Risks associated with SGLT2 inhibitors: an overview. Current Drug Safety, 13(2), 84–91. https://doi.org/10.2174/1574886313666180226103408
  • Singh, M., Sharma, R., & Kumar, A. (2019). Safety of SGLT2 inhibitors in patients with diabetes mellitus. Current Drug Safety, 14(2), 87–93. https://doi.org/10.2174/1574886314666190206164647
  • Tahara, A., Takasu, T., Yokono, M., Imamura, M., & Kurosaki, E. (2016). Characterization and comparison of sodium-glucose cotransporter 2 inhibitors in pharmacokinetics, pharmacodynamics, and pharmacologic effects. Journal of Pharmacological Sciences, 130(3), 159–169. https://doi.org/10.1016/j.jphs.2016.02.003
  • Thai, K. M., Ngo, T. D., Tran, T. D., & Le, M. T. (2013). Pharmacophore modeling for antitargets. Current Topics in Medicinal Chemistry, 13(9), 1002–1014. https://doi.org/10.2174/1568026611313090004
  • Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615–2623. https://doi.org/10.1021/jm020017n
  • Vyas, V., Jain, A., Jain, A., & Gupta, A. (2008). Virtual screening: a fast tool for drug design. ScientiaPharmaceutica, 76(3), 333–360.
  • Wang, Q., Wang, J., Li, N., Liu, J., Zhou, J., Zhuang, P., & Chen, H. (2022). A systematic review of Orthosiphon stamineus Benth in the treatment of diabetes and its complications. Molecules, 27(2), 444. https://doi.org/10.3390/molecules27020444
  • Wu, P., Nielsen, T. E., & Clausen, M. H. (2016). Small-molecule kinase inhibitors: an analysis of FDA-approved drugs. Drug Discovery Today, 21(1), 5–10. https://doi.org/10.1016/j.drudis.2015.07.008

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