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Research and Reviews : Journal of Computational Biology Original Research

Unveiling Nature’s Potential: In Silico Exploration and Identification of Herbal Remedies for Major Depressive Disorder Through Molecular Interaction Studies

  1. Abhimanyu Chauhan Department of Biotechnology, Jaypee Institute of Information Technology
  2. Chakresh Kumar Jain Department of Biotechnology, Jaypee Institute of Information Technology

Abstract

Major depressive disorder (MDD), a globally discussed mental health condition, has drawn significant attention because of its unique and intricate nature. This is marked by the enduring presence of negative emotions stemming from a lack of interest, diminished self-esteem, and excessive rumination. Despite the widespread availability of various antidepressant medications, their effectiveness is hindered by low response rates, prolonged treatment durations, and the prevalence of side effects, such as headaches, dizziness, insomnia, and oversleeping. This underscores the pressing need for alternative therapeutic approaches. In this study, a network biology approach was employed to identify the candidate genes associated with MDD. Among the identified genes, brain-derived neurotrophic factor (BDNF) has emerged as a potential target for further investigation. BDNF was subjected to molecular docking studies that utilized various drugs that are commonly prescribed for MDD treatment. Notably, the drug Paroxetine and Duloxetine demonstrated a superior docking score of -9.3 kcal/mol and -8.7 kcal/mol. To expand our exploration of plant-derived natural compounds (phytochemicals), we investigated substances from Brahmi (Bacopa monnieri), Shatavari (Asparagus racemosus), Ash Gourd (Benincasa hispida), and Marijuana (Cannabis). Phytochemicals such as Quercitin, Kaempferol (from Shatavari), and Dronabinol (from Marijuana) exhibited compelling docking scores of -10.6 kcal/mol, -9.9 kcal/mol and -9.6 kcal/mol respectively. These findings suggest the potential of natural compounds as effective alternatives to synthetic drugs. Furthermore, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties and 2D structures of these phytochemicals were analyzed to assess their pharmacokinetic profiles and potential toxicity. This comprehensive analysis underscores the potential of phytochemicals as alternative therapeutic agents for MDD, and emphasizes the importance of further research in this area for the development of effective treatments in mental health.

Keywords

References (20)

  1. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJL, et al. Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLoS Medicine. 2013;10(11):e1001547. doi:10.1371/journal.pmed.1001547
  2. World Health Organization. (2023). Depressive disorder (depression) [online]. World Health Organization: Geneva. Available from: https://www.who.int/news-room/fact-sheets/detail/depression
  3. Kong X, Wang C, Wu Q, Wang Z, Han Y, Teng J, et al. Screening and identification of key biomarkers of depression using bioinformatics. Scientific Reports. 2023;13(1). doi:10.1038/s41598-023-31413-1
  4. Lam RW, McIntosh D, Wang J, Enns MW, Kolivakis T, Michalak EE, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder. The Canadian Journal of Psychiatry. 2016;61(9):510-523. doi:10.1177/0706743716659416
  5. Ormel J, Oldehinkel AJ, Nolen WA, Vollebergh W. Psychosocial Disability Before, During, and After a Major DepressiveEpisode. Archives of General Psychiatry. 2004;61(4):387. doi:10.1001/archpsyc.61.4.387
  6. Vahia V. Diagnostic and statistical manual of mental disorders 5: A quick glance. Indian Journal of Psychiatry. 2013;55(3):220. doi:10.4103/0019-5545.117131
  7. Fries GR, Saldana VA, Finnstein J, Rein T. Molecular pathways of major depressive disorder converge on the synapse. Molecular Psychiatry. 2022;28(1):284-297. doi:10.1038/s41380-022-01806-1
  8. Cui L, Li S, Wang S, Wu X, Liu Y, Yu W, et al. Major depressive disorder: hypothesis, mechanism, prevention and treatment. Signal Transduction and Targeted Therapy. 2024;9(1). doi:10.1038/s41392-024-01738-y
  9. Basar MA, Hosen MF, Kumar Paul B, Hasan MR, Shamim SM, Bhuyian T. Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach. Informatics in Medicine Unlocked. 2023;37:101174. doi:10.1016/j.imu.2023.101174
  10. Murthy KHHVSSN, Chandre R, Upadhyay B. Clinical evaluation of Kushmanda Ghrita in the management of depressive illness. AYU (An international quarterly journal of research in Ayurveda). 2011;32(2):230. doi:10.4103/0974-8520.92592
  11. Wang X, Ren X, Li B, Yue J, Liang L. Applying modularity analysis of PPI networks to sequenced organisms. Virulence. 2012;3(5):459-463. doi:10.4161/viru.21104
  12. Rao VS, Srinivas K, Sujini GN, Kumar GNS. Protein-Protein Interaction Detection: Methods and Analysis. International Journal of Proteomics. 2014;2014:1-12. doi:10.1155/2014/147648
  13. Mering CV. STRING: a database of predicted functional associations between proteins. Nucleic Acids Research. 2003;31(1):258-261. doi:10.1093/nar/gkg034
  14. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research. 2003;13(11):2498-2504. doi:10.1101/gr.1239303
  15. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology. 2014;8(S4). doi:10.1186/1752-0509-8-s4-s11
  16. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2023 update. Nucleic Acids Research. 2022;51(D1):D1373-D1380. doi:10.1093/nar/gkac956
  17. Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling. 2021;61(8):3891-3898. doi:10.1021/acs.jcim.1c00203
  18. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies. 2004;1(4):337-341. doi:10.1016/j.ddtec.2004.11.007
  19. Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G, et al. admetSAR: A Comprehensive Source and Free Tool for Assessment of Chemical ADMET Properties. Journal of Chemical Information and Modeling. 2012;52(11):3099-3105. doi:10.1021/ci300367a
  20. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017;7(1). doi:10.1038/srep42717
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