Using network pharmacology to systematically deduce the molecular mechanism of the selective perinucleolar compartment inhibitor ML246

Metarrestin (ML246), an orally bioavailable synthetic molecule, selectively disrupts the perinucleolar compartment (PNC) structure and shows preclinical promise for metastatic cancer therapy. However, its precise molecular mechanism remains unclear. We employed network pharmacology analysis integrating topological assessments, protein-protein interaction (PPI) network construction, molecular docking, and molecular dynamics (MD) simulations to elucidate ML246’s mechanistic actions. Reverse pharmacophore matching identified 25 oncogenic targets (fit score > 0.502). These targets were analyzed through STRING database integration and Cytoscape visualization for network topology analysis. Molecular Complex Detection (MCODE) clustering identified critical modules, followed by CytoHubba analysis for hub protein identification. Gene ontology (GO) enrichment analysis (ClueGO) revealed enriched signaling pathways. The ML246-rewired PPI network comprised 121 nodes and exhibited a scale-free topology and substantial, indicating high biological connectivity. Ten distinct sub-clusters functional modules were identified through MCODE analysis. Fourteen enriched signaling pathways were predominantly linked to cancer biology, particularly cell cycle regulation and transcription. The integration of topological, pathway, and molecular docking analyses identified eight key regulatory proteins: CDKN1B, CCND1, SMAD3, CCND3, FOXO1, CTNNB1, PCNA, and PIK3CA. Molecular docking identified CDKN1B as the highest-affinity ML246 target (ΔG = -7.87 kcal/mol, Ki = 1.70 µM), while 100 ns molecular dynamics simulation showed stable complex formation. Pan-cancer expression analysis demonstrated that CDKN1B elevated in low-grade gliomas, pancreatic adenocarcinoma, and thymoma. Our comprehensive network pharmacology study shows a robust mechanistic framework for ML246 action, identifying CDKN1B as a critical primary regulatory target. The findings combine computational predictions with experimental validation, advancing understanding of ML246’s therapeutic potential and supporting rational drug development strategies for advanced cancer treatment and management.