Analyzing the Molecular Mechanisms of Passiflora foetida Aerial Parts Extract in Managing Inflammatory Pain: A Network Pharmacology and Bioinformatics Approach

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dc.contributor.author Sewwandi, L.H.C.
dc.contributor.author Wasana, P.W.D.
dc.date.accessioned 2024-09-12T04:05:10Z
dc.date.available 2024-09-12T04:05:10Z
dc.date.issued 2024-07-05
dc.identifier.citation Sewwandi, L.H.C., & Wasana, P.W.D. (2024). Analyzing the Molecular Mechanisms of Passiflora foetida Aerial Parts Extract in Managing Inflammatory Pain: A Network Pharmacology and Bioinformatics Approach. Proceedings of the 2nd International Research Symposium of the Faculty of Allied Health Sciences University of Ruhuna, Galle, Sri Lanka, 03. en_US
dc.identifier.issn 2659-2029
dc.identifier.uri http://ir.lib.ruh.ac.lk/handle/iruor/17463
dc.description.abstract Background: Inflammatory pain (IP) poses a significant global health challenge, with peripheral tissue damage and inflammation identified as its primary causes. Passiflora foetida (PF) is widely recognized for its analgesic properties in Ayurvedic medicine. However, the mechanisms underlying the analgesic potential of PF have not been fully elucidated. Objectives: To analyze the molecular mechanisms of the aqueous extract of PF aerial parts in managing IP using a network pharmacology and bioinformatics approach Methods: The scientific literature was reviewed to identify bioactive compounds in PF. Compound-target genes were identified using Swiss Target Prediction and Similarity Ensemble Approach Search Server. Target genes relevant to IP were sourced from DisGeNET, Online Mendelian Inheritance in Man (OMIM), and Gene Card databases. VENNY 2.1 software was used to determine the common genes between PF and IP, followed by GO_KEGG pathway enrichment analysis. Protein-Protein Interaction (PPI) network parameters were visualized and analyzed using STRING database, Cytoscape and Cytohubba. Results: The screening identified 22 bioactive compounds involved in 801 PF targets and 1068 targets related to IP, with 163 intersection genes (8%). The PPI network contained 163 nodes and 3108 edges, with the top ten interacting hub genes being IL-6, TP53, CASP3, HIF1A, BCL2, JUN, NFKB1, TNF, MMP9, and PTGS2 genes. GO analysis revealed that biological processes with higher enrichment are predominantly linked to the regulation of inflammatory response, cellular response to chemical stress, and response to oxidative stress. KEGG pathway enrichment analysis revealed 179 significantly enriched pathways modulated by PF, including pathways associated with pain such as MAPK, relaxin signaling pathways, PD-L1 expression, and the PD-1 checkpoint pathway. Conclusions: The study results indicate the potential of PF extract to modulate multiple pathways associated with pain pathophysiology, providing valuable insights for future research and potential therapeutic interventions. en_US
dc.language.iso en en_US
dc.publisher FAHS en_US
dc.subject Inflammatory pain en_US
dc.subject Network pharmacology en_US
dc.subject Passiflora foetida en_US
dc.title Analyzing the Molecular Mechanisms of Passiflora foetida Aerial Parts Extract in Managing Inflammatory Pain: A Network Pharmacology and Bioinformatics Approach en_US
dc.type Article en_US


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