Network pharmacology and in silico analysis of Azadirachta indica phytoconstituents reveal potential hepatoprotective targets and mechanisms.
TL;DR
This integrative computational network pharmacology analysis provides novel insights into the pathogenesis of liver diseases (HT/HI) and identifies potential therapeutic targets, exploring biomarkers for future experimental validation and clinical translation.
Abstract
Network pharmacology and bioinformatics approaches may provide valuable insights into pharmacological effects by enabling a system-level understanding of how drugs interact with biological networks rather than single targets. The study aimed to elucidate the therapeutic mechanisms of Azadirachta indica leaf extract (AILE) in hepatotoxicity and hepato-injury (HT/HI) through the identification of pathways and molecular targets via network pharmacology. Phytochemical profiling of AILE was performed by Gas Chromatography-Mass Spectrometry (GC-MS). SMILES structures of identified phytochemicals were retrieved from PubChem, and ADMET properties were assessed. Five non-hepatotoxic compounds with high absorption were prioritized. Their potential targets and hepatotoxicity-related genes were predicted using SwissTargetPrediction and GeneCards, followed by drug-target network construction in Cytoscape. Hub genes were identified through protein-protein interaction (PPI) analysis (STRING) and enrichment studies (ShinyGO, KEGG). Gene regulatory networks were built using TRRUST and miRNet 2.0, and molecular docking was performed to evaluate target-protein binding affinities. In the results, ADMET profiling identified five candidate phytochemicals: Butane, 1,1-diethoxy-3-methyl (B), 1,1,3-triethoxybutane (T), Propane, 1,1,3-triethoxy (P), Gamma-Sitosterol (G), and Caryophyllene (C), with favorable absorption. A total of 478 potential compound targets (BTPGC) were predicted, while 1243 HT/HI-related genes were identified, of which 73 overlapped as potential therapeutic targets. PPI analysis generated a network of 73 nodes and 582 edges. GO enrichment revealed involvement in lipid response, oxidative response, programmed cell death, and apoptosis. CytoHubba highlighted six hub genes (TNF, CASP3, ESR1, MAPK3, EGFR, and HSP90AA1). TRRUST identified 15 transcription factors, while miRNet predicted four regulatory miRNAs (miR-155-5p, miR-122-5p, miR-328, and miR-16). This integrative computational network pharmacology analysis provides novel insights into the pathogenesis of liver diseases (HT/HI) and identifies potential therapeutic targets, exploring biomarkers for future experimental validation and clinical translation.