In-silico target prediction and pathway analysis of propranolol as a potential therapeutic agent for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains lethal despite multitargeted tyrosine kinase inhibitors and immunotherapy, motivating the repurposing of safe, widely available agents. To delineate the anti-HCC potential of propranolol through an in-silico network pharmacology and molecular structure-based study, 70 intersecting potential anti-HCC targets were retrieved from the SwissTargetPrediction and GeneCards databases. Protein-protein interaction (PPI) analysis identified a network of 64 interconnected nodes exhibiting a high average node degree of 9.84, highlighting target centrality. Subsequent hub analysis isolated nine pivotal proteins (SRC, EGFR, CCND1, JAK2, ERBB2, PARP1, CDK4, CDK2, CHEK1) with degree centrality values exceeding 23.2, more than twice the network average. Gene Ontology and KEGG enrichment analyses underscored robust involvement in oncogenic pathways, including PI3K-Akt, MAPK, and immune checkpoints. Molecular docking revealed strong binding affinities of propranolol toward key kinases, notably JAK2 (-8.14 kcalmol-1), ERBB2 (-7.80 kcalmol-1), EGFR (-7.76 kcalmol-1), and CDK2 (-7.44 kcalmol-1). Molecular dynamics simulations confirmed the complex stability, with RMSD values stably maintained below 4.5 Å over 100 ns simulations. The sustained hydrogen-bond occupancy ranged from 30% to 68% per trajectory, corroborating stable ligand engagement. Collectively, these factorial results provide compelling evidence that propranolol may interact with core oncogenic kinase cluster and potential modulation of the critical signaling cascades implicated in HCC pathogenesis. Collectively, these computational findings support the hypothesis that propranolol possesses the molecular characteristics of a viable therapeutic candidate for HCC, thereby substantiating the need for rigorous experimental and translational investigation to validate its clinical potential.