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

Year : 2024 | Volume :13 | Issue : 01 | Page : 54-62
By

Abhimanyu Chauhan

Chakresh Kumar Jain

  1. Research Scholar Department of Biotechnology, Jaypee Institute of Information Technology Uttar Pradesh India
  2. Associate Professor Department of Biotechnology, Jaypee Institute of Information Technology Uttar Pradesh India

Abstract

Major Depressive Disorder (MDD), a globally discussed mental health condition, has drawn significant attention due to its unique and intricate nature. It is marked by 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, oversleeping. This underscores the pressing demand for alternative therapeutic approaches. In this study, a network biology approach was employed to identify candidate genes associated with MDD. Among the identified genes, Brain-derived neurotrophic factor (BDNF) emerged as a potential target for further investigation. BDNF was subjected to molecular docking studies, utilizing various drugs 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. Expanding our exploration to 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 these natural compounds as effective alternatives to synthetic drugs. Furthermore, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties and 2D structures of these phytochemicals were analysed 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: Drugs, natural compound, molecular docking, MDD, marijuana

[This article belongs to Research & Reviews : Journal of Computational Biology(rrjocb)]

How to cite this article: Abhimanyu Chauhan, Chakresh Kumar Jain. Unveiling Nature’s Potential: In-Silico Exploration and Identification of Herbal Remedies for Major Depressive Disorder Through Molecular Interaction Studies. Research & Reviews : Journal of Computational Biology. 2024; 13(01):54-62.
How to cite this URL: Abhimanyu Chauhan, Chakresh Kumar Jain. Unveiling Nature’s Potential: In-Silico Exploration and Identification of Herbal Remedies for Major Depressive Disorder Through Molecular Interaction Studies. Research & Reviews : Journal of Computational Biology. 2024; 13(01):54-62. Available from: https://journals.stmjournals.com/rrjocb/article=2024/view=151308

Browse Figures

References

  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 Med 2013;10. https://doi.org/10.1371/JOURNAL.PMED.1001547.
  2. Depressive disorder (depression) [Internet]. World Health Organization; [cited 2024 May 11]. 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. Sci Rep 2023;13:4180. https://doi.org/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;61:510–23. https://doi.org/10.1177/0706743716659416.
  5. Ormel J, Oldehinkel AJ, Nolen WA, Vollebergh W. Psychosocial Disability Before, During, and After a Major DepressiveEpisode. Arch Gen Psychiatry 2004;61:387. https://doi.org/10.1001/archpsyc.61.4.387.
  6. Vahia VN. Diagnostic and statistical manual of mental disorders 5: A quick glance. Indian J Psychiatry 2013;55:220–3. https://doi.org/10.4103/0019-5545.117131.
  7. Fries GR, Saldana VA, Finnstein J, Rein T. Molecular pathways of major depressive disorder converge on the synapse. Mol Psychiatry 2023;28:284–97. https://doi.org/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 Feb 9;9(1). doi:10.1038/s41392-024-01738-y
  9. Basar MdA, Hosen MdF, Kumar Paul B, Hasan MdR, Shamim SM, Bhuyian T. Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach. Inform Med Unlocked 2023;37:101174. https://doi.org/10.1016/j.imu.2023.101174.
  10. Chandre R, Upadhyay BN, Murthy KHHVSSN. Clinical evaluation of Kushmanda Ghrita in the management of depressive illness. Ayu 2011;32:230–3. https://doi.org/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;3:459–63. https://doi.org/10.4161/viru.21104.
  12. Rao VS, Srinivas K, Sujini GN, Kumar GNS. Protein-Protein Interaction Detection: Methods and Analysis. Int J Proteomics 2014;2014:1–12. https://doi.org/10.1155/2014/147648.
  13. Mering C v. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 2003;31:258–61. https://doi.org/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 Res 2003;13:2498–504. https://doi.org/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 Syst Biol 2014;8. https://doi.org/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 Res 2023;51:D1373–80. https://doi.org/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. J Chem Inf Model 2021;61:3891–8. https://doi.org/10.1021/acs.jcim.1c00203.
  18. Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov Today Technol 2004;1:337–41. https://doi.org/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. J Chem Inf Model 2012;52:3099–105. https://doi.org/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. Sci Rep 2017;7:42717. https://doi.org/10.1038/srep42717.
Regular Issue Subscription Original Research
Volume 13
Issue 01
Received April 23, 2024
Accepted May 22, 2024
Published May 28, 2024
Views: 23