Network pharmacology and molecular docking reveal mechanisms of amiodarone-induced pulmonary fibrosis

Pulmonary fibrosis is a common end-stage outcome of various chronic lung diseases, characterized by excessive extracellular matrix deposition, alveolar structural destruction, and progressive loss of pulmonary function. Despite advances in understanding its pathogenesis, effective therapeutic options remain scarce, highlighting the need for novel strategies. Amiodarone, a widely prescribed antiarrhythmic drug, is associated with pulmonary fibrosis as a severe adverse effect; however, its molecular mechanisms remain incompletely understood. Network pharmacology, combined with molecular docking, has recently emerged as a powerful approach to systematically uncover key targets and pathways underlying drug-induced organ toxicity. This study aimed to elucidate the potential mechanisms of amiodarone-induced pulmonary fibrosis by integrating network pharmacology analysis and molecular docking, thereby providing a theoretical basis for future mechanistic studies and potential preventive or therapeutic strategies. Network pharmacology and molecular docking approaches were applied to explore the mechanisms of amiodarone-induced pulmonary fibrosis. Potential amiodarone targets were predicted using publicly available databases, while pulmonary fibrosis-related genes were retrieved from GeneCards, DisGeNET, and OMIM. Common drug-disease targets were identified through Venn diagram analysis. Protein-protein interaction (PPI) networks were constructed using STRING, and hub genes were determined through topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways involved. Molecular docking was performed to assess the binding affinity of amiodarone to key hub proteins. Finally, the predicted mechanisms were summarized and interpreted based on the integrated network and docking results." A total of 101 KEGG pathways were enriched for the intersection of amiodarone and pulmonary fibrosis targets. PPI network analysis identified eight key hub genes: ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1, and ADRB2. GO enrichment analysis indicated that these targets are primarily involved in membrane-to-nucleus signaling, regulation of phosphorylation, and chromatin remodeling. KEGG pathway analysis highlighted significant enrichment in EGFR/ErbB signaling, VEGF signaling, and renin secretion pathways. Molecular docking suggested favorable binding of amiodarone to the predicted target proteins, Based on docking-score-derived estimates,ABCB1 and AKT1 showed the strongest predicted binding (estimated Kd ≈ 0.37 μM), followed by ERBB2 (≈ 2.9 μM) and ADRB2 (≈ 7.0 μM). Collectively, these findings suggest a signaling framework in which membrane receptor activation propagates through tyrosine kinase cascades to regulate gene expression, thereby linking extracellular stimuli to transcriptional control in the pathogenesis of pulmonary fibrosis. This study systematically explored the potential mechanisms of amiodarone-induced pulmonary fibrosis by integrating network pharmacology, enrichment analysis, and molecular docking. Eight hub targets (ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1, and ADRB2) and three critical signaling pathways (EGFR/ErbB signaling, VEGF signaling, and renin secretion) were identified, providing new insights into the complex mechanisms of amiodarone-associated pulmonary toxicity. The proposed membrane-to-nucleus signaling framework, supported by network topology and docking-based predictions, may help explain coordinated cellular responses implicated in fibrotic progression and could inform the prioritization of targets for future experimental validation and therapeutic development. Collectively, these findings extend our understanding of drug-associated pulmonary fibrosis and provide a rationale for future studies aimed at risk stratification and potential preventive or therapeutic strategies for amiodarone-related pulmonary complications.