Peer Reviewed Open Access Journal
Obesity is a chronic metabolic disorder associated with serious health complications, including cardiovascular diseases, type 2 diabetes mellitus, and dyslipidemia. The increasing prevalence of obesity has created a need for safer and more sustainable therapeutic alternatives. Plant-derived waste materials are rich sources of bioactive compounds and offer significant potential for drug discovery. The present study investigated the anti-obesity potential of phytoconstituents obtained from amla leaves (Phyllanthus emblica) using an in-silico approach. Obesity-related target proteins (PDB IDs: 9MIZ and 9VG4) involved in lipid metabolism and adipogenesis were selected for molecular docking studies. Ligand structures were retrieved from the PubChem database and docked using AutoDock Vina. Protein–ligand interactions were analyzed using Discovery Studio Visualizer. Drug-likeness and pharmacokinetic properties were evaluated through ADME analysis, while toxicity prediction was performed to assess safety profiles. The results demonstrated that several phytoconstituents exhibited favourable binding affinities toward both target proteins and formed stable interactions with key amino acid residues. Furthermore, selected compounds satisfied Lipinski’s Rule of Five and Ghose filter criteria, showed acceptable ADME characteristics, and exhibited low predicted toxicity. These findings suggest that phytoconstituents derived from plant waste materials possess promising anti-obesity potential and may serve as lead candidates for further development. However, experimental validation through in vitro and in vivo studies is required to confirm their efficacy and safety.
obesity, molecular docking, plant waste, Phyllanthus emblica, ADME, toxicity prediction, anti-obesity activity
Apovian, C. M., Guo, X.-R., Hawley, J. A., Karmali, S., Loos, R. J. F., & Waterlander, W. E. (2023). Approaches to addressing the rise in obesity levels. Nature Reviews Endocrinology, 19(2), 76–81. https://doi.org/10.1038/s41574-022-00777-1
Ankalikar, A., Viswanathswamy, A. H., & Undale, V. R. (2025). Comparative study of aqueous and alcoholic extracts of roots of Bauhinia variegata Linn. on cafeteria diet induced obesity. Indian Journal of Pharmaceutical Education and Research, 59(1 Suppl.), S367–S374. https://doi.org/10.5530/ijper.20254509
Baliga, M. S., Bhat, H. P., Pai, R. J., Boloor, R., & Palatty, P. L. (2011). The chemistry and medicinal uses of the underutilized Indian fruit tree Garcinia indica Choisy (kokum): A review. Food Research International, 44(7), 1790–1799. https://doi.org/10.1016/j.foodres.2011.01.064
Ballinger, A., & Peikin, S. R. (2002). Orlistat: Its current status as an anti-obesity drug. European Journal of Pharmacology, 440(2–3), 109–117. https://doi.org/10.1016/S0014-2999(02)01422-X
Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018). ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318
Berman, H. M. (2000). The Protein Data Bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
Bingul, A. A., Ercan, S., & Pirinccioglu, N. (2025). Development of new inhibitor candidates for SARS-CoV-2 3CLpro (main protease): A molecular docking and molecular dynamics simulation study. Journal of Molecular Graphics and Modelling, 140, 109133. https://doi.org/10.1016/j.jmgm.2025.109133
Blüher, M. (2020). Metabolically healthy obesity. Endocrine Reviews, 41(3). https://doi.org/10.1210/endrev/bnaa004
Bray, G. A., Kim, K. K., & Wilding, J. P. H. (2017). Obesity: A chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obesity Reviews, 18(7), 715–723. https://doi.org/10.1111/obr.12551
Butt, S. S., Badshah, Y., Shabbir, M., & Rafiq, M. (2020). Molecular docking using Chimera and AutoDock Vina software for nonbioinformaticians. JMIR Bioinformatics and Biotechnology, 1(1), e14232. https://doi.org/10.2196/14232
Chang, H.-Y., Chen, S.-Y., Lin, J.-A., Chen, Y.-Y., Chen, Y.-Y., Liu, Y.-C., & Yen, G.-C. (2024). Phyllanthus emblica fruit improves obesity by reducing appetite and enhancing mucosal homeostasis via the gut microbiota–brain–liver axis in HFD-induced leptin-resistant rats. Journal of Agricultural and Food Chemistry, 72(18), 10406–10419. https://doi.org/10.1021/acs.jafc.4c01226
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, 42717. https://doi.org/10.1038/srep42717
Dallakyan, S., & Olson, A. J. (2015). Small-molecule library screening by docking with PyRx. In Chemical Biology: Methods and Protocols (pp. 243–250). https://doi.org/10.1007/978-1-4939-2269-7_19
Eberhardt, J., Santos-Martins, D., Tillack, A. F., & Forli, S. (2021). AutoDock Vina 1.2.0: New docking methods, expanded force field, and Python bindings. Journal of Chemical Information and Modeling, 61(8), 3891–3898. https://doi.org/10.1021/acs.jcim.1c00203
Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55–68. https://doi.org/10.1021/cc9800071
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, 17. https://doi.org/10.1186/1758-2946-4-17
Hasani-Ranjbar, S., Nayebi, N., Larijani, B., & Abdollahi, M. (2009). A systematic review of the efficacy and safety of herbal medicines used in the treatment of obesity. World Journal of Gastroenterology, 15(25), 3073–3085. https://doi.org/10.3748/wjg.15.3073
Kar, P., Oriola, A., & Oyedeji, A. (2024). Toward understanding the anticancer activity of the phytocompounds from Eugenia uniflora using molecular docking, in silico toxicity and dynamics studies. Advances and Applications in Bioinformatics and Chemistry, 17, 71–82. https://doi.org/10.2147/AABC.S473928
Kim, S., Chen, J., Cheng, T., et al. (2021). PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Research, 49(D1), D1388–D1395. https://doi.org/10.1093/nar/gkaa971
Kitchen, D. B., Decornez, H., Furr, J. R., & Bajorath, J. (2004). Docking and scoring in virtual screening for drug discovery: Methods and applications. Nature Reviews Drug Discovery, 3(11), 935–949. https://doi.org/10.1038/nrd1549
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. Advanced Drug Delivery Reviews, 46(1–3), 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0
Loos, R. J. F., & Yeo, G. S. H. (2022). The genetics of obesity: From discovery to biology. Nature Reviews Genetics, 23(2), 120–133. https://doi.org/10.1038/s41576-021-00414-z
Mirunalini, S., & Krishnaveni, M. (2010). Therapeutic potential of Phyllanthus emblica (amla): The Ayurvedic wonder. Journal of Basic and Clinical Physiology and Pharmacology, 21(1), 93–105. https://doi.org/10.1515/JBCPP.2010.21.1.93
Morris, G. M., Huey, R., Lindstrom, W., et al. (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
Nazish, I., & Ansari, S. H. (2018). Emblica officinalis: Anti-obesity activity. Journal of Complementary and Integrative Medicine, 15(2). https://doi.org/10.1515/jcim-2016-0051
Pagadala, N. S., Syed, K., & Tuszynski, J. (2017). Software for molecular docking: A review. Biophysical Reviews, 9(2), 91–102. https://doi.org/10.1007/s12551-016-0247-1
Pettersen, E. F., Goddard, T. D., Huang, C. C., et al. (2004). UCSF Chimera—A visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13), 1605–1612. https://doi.org/10.1002/jcc.20084
Prananda, A. T., Dalimunthe, A., Harahap, U., et al. (2023). Phyllanthus emblica: A comprehensive review of its phytochemical composition and pharmacological properties. Frontiers in Pharmacology, 14, Article 1288618. https://doi.org/10.3389/fphar.2023.1288618
Saltiel, A. R., & Olefsky, J. M. (2017). Inflammatory mechanisms linking obesity and metabolic disease. Journal of Clinical Investigation, 127(1), 1–4. https://doi.org/10.1172/JCI92035
Ubale, S., Gurunani, S., & Pawar Desai, T. A. (2026). Insilico evaluation of Arachis hypogaea (Linn) for its potential antiobesity activity. International Journal of Scientific Research in Science and Technology, 13(3), 648–660. https://doi.org/10.32628/IJSRST26133185
Shedame, S. P., Tajane, S. N., & Gurunani, S. (2026). Anthelmintic potential of few phytoconstituents: An in-silico approach. Journal of Medicinal Plants Studies, 14(3), 4–13. https://doi.org/10.22271/plants.2026.v14.i3a.2098
Sharma, P., Joshi, T., Joshi, T., Chandra, S., & Tamta, S. (2020). In silico screening of potential antidiabetic phytochemicals from Phyllanthus emblica against therapeutic targets of type 2 diabetes. Journal of Ethnopharmacology, 248, 112268. https://doi.org/10.1016/j.jep.2019.112268
Singh, E., Sharma, S., Pareek, A., Yadav, S., Sharma, S., & Dwivedi, J. (2011). Phytochemistry, traditional uses and cancer chemopreventive activity of amla (Phyllanthus emblica): The sustainer. Journal of Applied Pharmaceutical Science, 1(1), 176–183.
Trott, O., & Olson, A. J. (2010). 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. https://doi.org/10.1002/jcc.21334
World Health Organization. (2026). Overweight and obesity.
Wilding, J. P. H., Batterham, R. L., Calanna, S., et al. (2021). Once-weekly semaglutide in adults with overweight or obesity. New England Journal of Medicine, 384(11), 989–1002. https://doi.org/10.1056/NEJMoa2032183
