Network pharmacology based investigation of the multi target mechanisms of Murraya koenigii (curry leaves) in non-alcoholic steatohepatitis (NASH)
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Harsh Kashyap
Abstract
Objectives
Non-alcoholic steatohepatitis (NASH) is a multifactorial chronic liver disease with limited treatment options. Murraya koenigii has reported hepato-protective effects against NASH in humans, but the bioactive compounds and mechanisms remain unknown. In this study, we explored the bioactive compounds of M. koenigii and their potential mechanisms for combating NASH using network pharmacology and molecular docking approach.
Methods
Using online platforms such as IMPPAT, GeneCards, KEGG, and DisGeNET, we identified the phytochemicals, target proteins, and genes associated with NASH. Through Venn diagram analysis, we determined 31 common targets between the bioactive compounds and NASH-related genes. Gene Ontology (GO) and KEGG pathway enrichment analyses further revealed the key targets and underlying mechanisms. Molecular docking validated the binding interactions between the identified phytochemicals and core target proteins.
Results
Nine key targets (NFKB, HSP90, TLR4, ESR1, STAT3, MTOR, HIF1A, PI3KA, and PKCD) were identified that interacts with 16 selected phytochemicals. Molecular docking studies indicated phytochemicals, Osthole and Spathulenol to be the promising binders to the core targets especially NF-kB and STAT3. The results represented the muti-target, multi-compound and multi-pathway mechanisms of M. koenigii against NASH.
Conclusions
This study provides the evidence for further research into the mechanisms of M. koenigii bioactive compounds as complementary therapies for NASH. Our study also identifies the novel drug candidates based on M. koenigii active compounds.
Acknowledgments
The authors are thankful to Shaheed Rajguru College of Applied Sciences, University of Delhi, for providing the research facility to conduct the study.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors contributed to the study, accepted responsibility for the entire content of this manuscript, and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Not applicable.
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Conflict of interest: The authors declare no conflict of interest.
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Research funding: There are no funding sources.
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Data availability: Not applicable.
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