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

Network pharmacology and molecular docking reveal mechanisms of amiodarone-induced pulmonary fibrosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Network pharmacology and molecular docking reveal mechanisms of amiodarone-induced pulmonary fibrosis huye li, Rubing Di, Deyuan Kong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7601015/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Pulmonary fibrosis is a common terminal 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, molecular docking, and experimental validation, thereby providing a theoretical basis for its prevention and treatment. Methods 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 validated through in vitro and/or in vivo pulmonary fibrosis models. 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 neuroactive ligand–receptor interaction, FoxO signaling, and Th17 cell differentiation pathways. Molecular docking demonstrated strong binding affinities between amiodarone and the predicted target proteins, with ABCB1 and AKT1 exhibiting the highest affinity (Kd = 0.37 μM each), 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, there by linking extracellular stimuli to transcriptional regulation in the pathogenesis of pulmonary fibrosis. Conclusions This study systematically elucidates the molecular mechanisms underlying 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 (neuroactive ligand–receptor interaction, FoxO signaling, and Th17 cell differentiation) were identified, providing new insights into the complex mechanisms of amiodarone-associated pulmonary toxicity. The identified membrane-to-nucleus signaling framework, characterized by high-affinity binding interactions and coordinated cellular responses, enhances mechanistic understanding and may inform the development of targeted therapeutic interventions. These findings not only deepen our knowledge of drug-induced pulmonary fibrosis but also establish a foundation for precision medicine strategies aimed at preventing and treating amiodarone-related pulmonary complications in clinical practice. Amiodarone Pulmonary fibrosis Network pharmacology Molecular docking Mechanism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction 1.1 Clinical Significance and Prevalence Amiodarone, a widely utilized class III antiarrhythmic agent, exhibits notable efficacy in the treatment of various cardiac arrhythmias [ 1 ]. Nevertheless, its distinctive pharmacokinetic profile—marked by high lipid solubility, variable oral bioavailability (ranging from 20% to 80%), and an exceptionally prolonged elimination half-life—facilitates considerable drug accumulation within pulmonary tissues. This accumulation substantially elevates the risk of severe respiratory adverse effects, including interstitial pneumonitis and pulmonary fibrosis, which can be life-threatening [ 2 , 3 ]. Although the therapeutic efficacy of amiodarone is well established, its clinical application is significantly constrained by a wide range of multisystem adverse effects [ 4 ]. Among these, pulmonary toxicity is the most serious and potentially fatal complication. In particular, amiodarone-induced acute respiratory distress syndrome can clinically resemble acute heart failure, thereby presenting a substantial diagnostic challenge for clinicians [ 5 ]. Multiple risk factors contribute to the development of amiodarone-induced pulmonary injury, including advanced age at treatment initiation and excessive cumulative exposure—especially daily doses exceeding 400 mg for more than two months, or prolonged low-dose therapy of 200 mg/day for over two years [ 6 ]. The drug’s complex pharmacokinetics are further modulated by food intake, with high-fat meals enhancing intestinal absorption by 2.4- to 3.8-fold compared to fasting conditions [ 7 ]. Of particular clinical concern, the concomitant use of amiodarone with direct oral anticoagulants has been identified as a predictor of major hemorrhagic complications, including pericardial effusion, thereby necessitating close monitoring during combined therapy [ 8 ]. Amiodarone’s interaction profile encompasses a wide range of commonly prescribed medications, such as warfarin, digoxin, and HIV antiretrovirals, warranting vigilant clinical oversight to mitigate drug–drug interactions and enable prompt detection and management of adverse events [ 9 , 10 ]. Accordingly, strict compliance with established monitoring protocols—such as those issued by the North American Society of Pacing and Electrophysiology—is critical for optimizing dosing strategies, minimizing interaction risks, and ensuring the timely identification of potentially severe complications [ 11 ]. 1.2 Spectrum and Diagnostic Dilemmas of Organ Toxicity Beyond pulmonary complications, amiodarone exerts toxic effects on multiple organ systems, each posing distinct diagnostic and therapeutic challenges. This complexity is compounded by highly variable latency periods, occasional dose-independent susceptibility, the lack of validated biomarkers, and clinical features that often overlap with pre-existing cardiac conditions. A striking case has been reported in which a single patient simultaneously developed hepatic, pulmonary, thyroid, and ocular toxicities [ 12 ]. Pulmonary toxicity remains the most serious complication of amiodarone therapy, presenting as diffuse alveolar damage, chronic interstitial pneumonia, organizing pneumonia, or pulmonary nodules. Accurate diagnosis necessitates the exclusion of infectious causes and relies on the integration of clinical, radiological, and, in some cases, histopathological findings [ 13 ]. Mechanistically, the toxicity is mediated by the accumulation of phospholipid complexes within histiocytes and type II pneumocytes, with electron microscopy revealing hallmark features such as foamy alveolar macrophages and membrane-bound lamellar bodies [ 14 ]. Thyroid dysfunction occurs in approximately 15–20% of patients receiving amiodarone and displays bidirectional patterns that necessitate distinct management approaches. Hypothyroidism is generally well controlled with levothyroxine supplementation, whereas amiodarone-induced thyrotoxicosis requires prompt subtype differentiation, as delayed intervention may result in life-threatening complications such as myxedema coma [ 15 ]. Diagnostic and therapeutic strategies for amiodarone-induced thyrotoxicosis continue to evolve, highlighting the critical importance of early recognition and individualized treatment [ 16 ]. Hepatotoxicity associated with amiodarone exhibits considerable mechanistic complexity. Repeated administration induces endoplasmic reticulum stress in hepatic and adipose tissues, promoting lipolysis and resulting in hepatic accumulation of lipotoxic free fatty acids through the inhibition of key metabolic enzymes [ 17 ]. Fatal outcomes have been reported in cases of multiorgan toxicity, underscoring the necessity for comprehensive monitoring [ 18 ]. Successful treatment with N-acetylcysteine has been documented, implicating oxidative stress—mediated by glutathione depletion and mitochondrial dysfunction—as a central mechanism in its pathogenesis [ 19 ]. Neurological toxicity occurs in approximately 20–30% of patients receiving amiodarone, commonly presenting as tremor, ataxia, or peripheral neuropathy. Rare manifestations, such as amiodarone-induced nystagmus, have also been reported, with onset ranging from days to several months and typically resolving after drug discontinuation [ 20 ]. In addition, long-term therapy may alter thyroid function parameters, with persistent effects observed during both acute and chronic phases [ 21 ]. Ocular toxicity associated with amiodarone ranges from benign corneal verticillata to vision-threatening optic neuropathy. Comprehensive ophthalmologic monitoring is crucial due to the potential for irreversible complications [ 22 ]. Notably, serious adverse reactions—including pulmonary fibrosis—have also been reported with structurally related antiarrhythmic agents, highlighting class-related risks that necessitate vigilant surveillance [ 23 ]. Advances in multimodal imaging, particularly high-resolution computed tomography, have significantly enhanced the early detection of pulmonary complications [ 1 ]. Dermatological toxicity includes photosensitivity reactions and characteristic blue-gray skin discoloration, both of which are associated with cumulative dosage and ultraviolet exposure [ 24 ]. Updated clinical practice guidelines emphasize the importance of systematic, multisystem monitoring as essential for optimizing patient safety and therapeutic outcomes [ 3 ]. 1.3 Research Evolution and Network Pharmacology Applications in Amiodarone-Induced Pulmonary Fibrosis Over the past decade, significant progress has been made in elucidating the mechanisms of amiodarone-induced pulmonary toxicity through the integration of mechanistic studies, high-resolution imaging, and comprehensive clinical surveillance protocols [ 16 ]. High-resolution computed tomography (HRCT) has emerged as a pivotal diagnostic modality, offering reproducible assessments of injury severity. Typical radiographic features include bilateral interstitial opacities, ground-glass attenuation, organizing pneumonia patterns, and polymorphic manifestations ranging from diffuse alveolar damage to chronic interstitial pneumonia. The reported incidence of such pulmonary complications ranges from 4% to 17% among patients undergoing amiodarone therapy [ 13 ]. Functional evaluations, such as bronchoalveolar lavage (BAL), have further aided in diagnosis by revealing foamy alveolar macrophages containing multilamellar intracytoplasmic bodies and lipid-laden lysosomes, while simultaneously facilitating the exclusion of infectious causes [ 14 ]. Mechanistic understanding has progressed beyond the traditional phospholipidosis hypothesis to incorporate complex, multi-pathway interactions. Amiodarone promotes the accumulation of phospholipid complexes in histiocytes and type II pneumocytes by inhibiting phospholipase activity, while concurrently inducing oxidative stress and generating reactive oxygen species that contribute to cellular lysis and programmed cell death [ 25 ]. The resulting pathological manifestations are highly variable, encompassing diffuse alveolar damage, chronic interstitial pneumonia, organizing pneumonia, pulmonary hemorrhage, and nodular lesions. Notably, disease severity appears to be more closely linked to individual susceptibility than to cumulative drug dosage [ 12 ]. Although the presence of foamy alveolar macrophages and ultrastructural identification of membrane-bound lamellar bodies serve as specific markers of amiodarone exposure, these features primarily reflect drug accumulation and are not considered reliable predictors of toxicity development [ 26 ]. 2. Methods We systematically identified potential amiodarone target genes associated with pulmonary fibrosis through comprehensive database mining. Drug targets and disease-related genes were collected and compared to establish overlapping candidates. To explore their biological significance, enrichment analysis was performed using Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Analyses were conducted with a significance threshold of p < 0.05 after multiple testing correction, revealing functional clusters related to inflammatory responses, stress adaptation, and extracellular matrix organization [ 27 ]. Interactions among the identified genes were further examined by constructing a protein–protein interaction (PPI) network. High-confidence interaction data were integrated, and topological analysis was applied to identify hub genes and functional modules within the amiodarone–pulmonary fibrosis interactome [ 28 ]. This network analysis highlighted key molecular mediators potentially involved in drug-induced fibrotic pathways, providing candidate targets for further evaluation. To complement these findings, molecular docking was conducted to assess the binding affinities between amiodarone and the top candidate targets implicated in pulmonary fibrosis. Docking simulations were performed using standard protocols to evaluate binding strength, interaction patterns, and structural compatibility between the drug and prioritized protein targets [ 29 ]. This computational validation step provided mechanistic insights into potential direct interactions between amiodarone and fibrosis-related proteins. An overview of the analytical process is presented in Fig. 1 , which outlines the integrated bioinformatics workflow employed to elucidate the molecular mechanisms underlying amiodarone-induced pulmonary fibrosis. [Insert Fig. 1 here] 2.1 Identification of amiodarone targets Potential amiodarone targets were screened using DrugBank and SwissTargetPrediction databases, with “amiodarone” as the retrieval term for both platforms. For SwissTargetPrediction, only genes with a probability score > 0.1 were retained. Candidate genes from both databases were merged, and duplicates were removed to generate a non-redundant list. 2.2 Mining of pulmonary fibrosis–related genes Genes associated with pulmonary fibrosis were retrieved from GeneCards, DisGeNET, and OMIM using the keyword “pulmonary fibrosis.” In GeneCards, only genes with a relevance score ≥ 5 were included, whereas genes from DisGeNET and OMIM were incorporated without additional filtering. The merged list was curated to remove redundancies, yielding a comprehensive set of pulmonary fibrosis–related genes. 2.3 Discovery of common targets The intersecting genes between the amiodarone target dataset and the pulmonary fibrosis gene panel were identified using VENNY 2.1.0. Lists from both sources were uploaded, and overlapping genes were extracted as potentially relevant for mechanistic exploration. 2.4 Protein–protein interaction (PPI) network analysis The shared targets were uploaded to STRING ( https://cn.string-db.org ) [ 30 ], with Homo sapiens specified as the reference organism. A minimum confidence threshold of 0.4 was applied to ensure reliable interaction data. The resulting PPI network was visualized and analyzed using Cytoscape version 3.10.3 [ 31 ]. Topological parameters—including node degree, betweenness centrality, and closeness centrality—were calculated using the CentiScaPe 2.2 plug-in [ 32 ] to identify pivotal hub targets closely associated with amiodarone-induced pulmonary fibrosis. 2.5 Enrichment analysis and drug–target–pathway network construction To investigate the biological functions of the intersecting targets, enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID; https://david.ncifcrf.gov ) [ 33 ]. Gene Ontology (GO) enrichment covered three categories: biological processes, molecular functions, and cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was also conducted. Targets with p < 0.05 were considered statistically significant, and results were ranked in ascending order of p -value. The top 10 GO terms from each category and the top 20 KEGG pathways were selected for further analysis. All enrichment analyses and visualizations were conducted on the DAVID platform. To illustrate the relationships among amiodarone, its predicted targets, and pulmonary fibrosis–associated pathways, a drug–target–pathway network was constructed using Cytoscape version 3.10.3. 2.6 Molecular docking Molecular docking was performed using the CB-Dock2 platform to investigate interaction mechanisms and binding modes between amiodarone and the predicted key target proteins. The two-dimensional chemical structure of amiodarone was retrieved from PubChem and saved in SDF format, while three-dimensional structures of the target proteins (PDB format) were obtained from the RCSB Protein Data Bank ( https://www.rcsb.org/ ) [ 34 ]. Both ligand and receptor files were imported into CB-Dock2 for docking calculations. CB-Dock2 employs a curvature-based cavity detection algorithm to automatically identify protein binding sites and integrates AutoDock Vina (v1.2.3) as its docking engine, thereby improving accuracy in binding site identification and conformation prediction. Following docking, the platform’s built-in visualization tools were used for result interpretation and three-dimensional structural analysis [ 35 ]. To ensure reproducibility and reliability, each docking experiment was performed in triplicate under identical computational parameters. 3. Results 3.1 Target Identification and Overlap Analysis Systematic screening identified 118 unique potential targets of amiodarone after removal of duplicates from the DrugBank and SwissTargetPrediction databases. In parallel, comprehensive mining of pulmonary fibrosis–associated genes across GeneCards, DisGeNET, and OMIM yielded 2,622 non-redundant candidates. Comparative analysis of these datasets using Venny 2.1.0 revealed 41 overlapping genes (Fig. 2 A). These shared genes account for 34.7% of amiodarone’s predicted targets, highlighting a substantial mechanistic intersection between the drug’s molecular interactions and pulmonary fibrosis pathophysiology. The identification of this intersecting gene set provides a focused molecular framework for elucidating how amiodarone exposure may initiate or exacerbate fibrotic processes within pulmonary tissue. Insert Fig. 2 A here 3.2 PPI Network Construction and Core Target Selection To elucidate the key molecular mediators linking amiodarone and pulmonary fibrosis, the 41 intersecting targets were submitted to the STRING database for protein–protein interaction (PPI) network construction (Fig. 2 B). The resulting network revealed extensive interconnections, underscoring functional associations and potential signaling crosstalk that may contribute to fibrotic pathogenesis. Topological analysis of the PPI network was performed using Cytoscape with the CentiScaPe plugin. Applying threshold criteria of degree ≥ 9.8, betweenness centrality ≥ 33.6, and closeness centrality ≥ 0.01, we identified eight hub targets : ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1 , and ADRB2 (Fig. 2 C). These hub proteins represent central regulatory nodes within the network, characterized by high connectivity and strong bridging potential between functional modules. Their elevated betweenness centrality suggests they act as molecular integrators, coordinating diverse signaling processes that may collectively drive the progression of amiodarone-induced pulmonary fibrosis. Insert Fig. 2 B here Insert Fig. 2 C here 3.3 Functional Enrichment and Pathway Network Analysis 3.3.1 Gene Ontology Enrichment Analysis From the 41 overlapping genes between amiodarone targets and pulmonary fibrosis-associated genes, enrichment analysis identified 376 significant GO terms (p < 0.05): 242 under biological processes, 36 under cellular components, and 98 under molecular functions. The top 10 terms from each category were visualized using bubble plots generated via the Bioinformatics platform (Fig. 3 ). Insert Fig. 3 here The biological process (BP) analysis revealed strong enrichment in cellular signaling pathways, particularly signal transduction, protein and tyrosine phosphorylation, MAPK cascade regulation, receptor-mediated signaling, cell survival processes, and ion homeostasis. These findings suggest that amiodarone may modulate pulmonary fibrosis by activating multiple parallel signaling cascades that converge on fibrotic remodeling. The cellular component (CC) enrichment indicated predominant localization of target proteins to membrane-associated structures, including the plasma membrane, caveolae microdomains, neuronal dendrites, and G protein–coupled serotonin receptor complexes. This distribution highlights that amiodarone’s primary interactions are likely initiated at the cell surface. For molecular functions (MF), enrichment analysis emphasized phosphorylation-dependent regulatory activities, including histone modification kinases (notably H2AX Y142 and H3 Y41), protein tyrosine kinases, and ATP binding functions. Together, these results delineate a membrane-to-nucleus signaling axis, wherein extracellular stimuli are transduced through tyrosine kinase cascades to regulate chromatin remodeling and gene expression via histone modifications. This orchestrated framework provides a mechanistic basis for how amiodarone exposure may induce the complex molecular changes underlying pulmonary fibrosis. 3.3.2 KEGG Pathway Analysis A total of 101 significantly enriched KEGG pathways (p < 0.05) were identified for the overlapping target genes. The top 10 pathways were visualized using bubble plots (Fig. 3 ), revealing several major signaling networks potentially mediating amiodarone’s role in pulmonary fibrosis. Notably, these included the neuroactive ligand–receptor interaction, FoxO signaling, and Th17 cell differentiation pathways. The enrichment of the neuroactive ligand–receptor interaction pathway suggests a possible contribution of neurogenic inflammation to amiodarone-induced pulmonary injury. The FoxO signaling pathway was strongly represented, implicating dysregulation of cellular stress responses, apoptosis, and autophagy—processes recognized as central drivers of fibrotic progression. Furthermore, enrichment of the Th17 cell differentiation pathway points to immune-mediated mechanisms, particularly pro-inflammatory cytokine release and fibroblast activation, underscoring the importance of immune signaling in amiodarone-associated pulmonary fibrosis. 3.3.3 Drug–Target–Pathway Network Analysis To elucidate the complex relationships among amiodarone, its molecular targets, and the associated signaling pathways, we constructed a drug–target–pathway network using Cytoscape 3.10.3 (Fig. 4 ). This integrative visualization revealed distinct clusters of targets linked to specific pathways, suggesting the presence of functional modules contributing to different aspects of amiodarone-induced pulmonary toxicity. Insert Fig. 4 here Notably, several core targets—including ADRB2, AKT1, HIF1A, SRC, ABL1, XIAP, ERBB2, and ABCB1—were strongly associated with the EGFR tyrosine kinase inhibitor resistance pathway. This finding suggests that amiodarone may activate signaling mechanisms similar to those observed in therapy resistance, potentially involving dysregulated survival signaling and cellular adaptation. In contrast, other targets such as ADRB3, CACNA1C, and MAPK3 were predominantly associated with renin secretion and metabolic regulation pathways, pointing to broader contributions of endocrine and metabolic dysregulation in amiodarone-induced pulmonary injury. These findings indicate that amiodarone’s pulmonary toxicity is not solely mediated by direct fibrotic signaling but may also arise from the integration of oncogenic, immune, and metabolic processes that converge to promote progressive fibrotic remodeling in lung tissue. 3.4 Molecular Docking Results To evaluate the direct interactions between amiodarone and core proteins implicated in pulmonary fibrosis, molecular docking analyses were performed. The eight hub proteins identified from network analysis—ABCB1 (PDB ID: 8SB8), ERBB2 (PDB ID: 3PP0), XIAP (PDB ID: 1TFQ), ABL1 (PDB ID: 2F4J), SRC (PDB ID: 1O41), HIF1A (PDB ID: 1H2K), AKT1 (PDB ID: 3O96), and ADRB2 (PDB ID: 8GGI)—served as receptors for docking with amiodarone(Fig. 5 ). Insert Fig. 5 here Binding strength was assessed by equilibrium dissociation constant (Kd), estimated from docking free energy (ΔG ≈ − RT ln Kd), where lower Kd values indicate more stable complexes. The predicted Kd values were: ABCB1 = 0.37 µM, AKT1 = 0.37 µM, ERBB2 = 2.9 µM, ADRB2 = 7.0 µM, ABL1 = 31 µM, XIAP = 100 µM, HIF1A = 74 µM, and SRC = 290 µM. Among these, ABCB1 and AKT1 demonstrated the strongest binding affinities (Kd = 0.37 µM each), followed by moderate binding of ERBB2 and ADRB2, whereas SRC, HIF1A, and XIAP exhibited weaker interactions. These results indicate preferential interactions of amiodarone with specific targets, which may help explain the selective pulmonary toxicity observed clinically. Mechanistically, the strong affinity for ABCB1, a multidrug efflux transporter, suggests that amiodarone may impair detoxification pathways, promoting intracellular drug accumulation. Similarly, high-affinity binding to AKT1, a central regulator of cell survival and apoptosis, points to potential dysregulation of survival signaling and tissue homeostasis. Collectively, these molecular interactions provide a plausible mechanistic basis for amiodarone-induced pulmonary fibrosis. 4. Discussion This study employed an integrative network pharmacology and molecular docking strategy to dissect the complex mechanisms underlying amiodarone-induced pulmonary fibrosis. Eight hub genes—ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1, and ADRB2—were identified at the intersection of drug-target networks and fibrosis-related gene sets. These targets are functionally enriched in biological processes such as membrane-to-nucleus signaling, regulation of phosphorylation, oxidative stress response, and immune modulation. KEGG pathway analysis further highlighted three key signaling cascades—neuroactive ligand–receptor interaction, FoxO signaling, and Th17 cell differentiation—underscoring a multifactorial pathogenesis involving epithelial injury, fibroblast activation, and immune dysregulation. 4.1 Mechanistic Insights into Fibrosis Initiation The ABCB1 transporter, which normally mediates pulmonary drug efflux, may become functionally impaired or saturated by amiodarone, thereby promoting its intracellular accumulation and cytotoxicity [ 36 , 37 ]. Tyrosine kinases such as ERBB2, SRC, and ABL1 amplify epithelial stress through integrin signaling, TGF-β activation, and reactive oxygen species (ROS)-mediated pathways, ultimately triggering epithelial–mesenchymal transition (EMT) and fibroblast activation [ 38 , 39 ]. XIAP, an anti-apoptotic protein upregulated by TGF-β1, contributes to myofibroblast persistence and epithelial resistance to apoptosis, thereby sustaining fibrotic remodeling [ 40 – 42 ]. Concurrently, AKT1 and HIF1A are activated in response to oxidative and hypoxic stress, promoting fibroblast survival, senescence, and metabolic reprogramming [ 43 , 44 ]. ADRB2, a lung-enriched neuroreceptor, introduces a neuroimmune regulatory axis that may underlie regional susceptibility and radiographic heterogeneity observed in amiodarone-induced lung injury [ 45 , 46 ]. 4.2 Pathway Integration and Systems-Level Interpretation Enrichment of the FoxO signaling pathway reflects transcriptional reprogramming in response to cellular stress, influencing extracellular matrix remodeling and fibroblast differentiation [ 47 ]. Th17 cell differentiation highlights persistent immune dysregulation, wherein IL-17A and IL-22 signaling amplifies neutrophilic inflammation and tissue injury [ 48 ]. The identification of ADRB2 within the neuroactive ligand–receptor interaction pathway suggests that receptor-mediated neuroimmune signaling may contribute to the atypical and often unilateral interstitial abnormalities characteristic of amiodarone-induced lung injury [ 46 ]. At the center of these converging pathways is AKT1, which integrates multiple stress-responsive signals—including oxidative stress, hypoxia, autophagy, and immune activation—functioning as a key nodal hub in fibrogenic transformation [ 49 ]. 4.3 Molecular Docking and Target Prioritization Molecular docking confirmed the binding of amiodarone to several key proteins predicted by network pharmacology. Notably, ABCB1 and AKT1 demonstrated the strongest affinities (Kd = 0.37 µM), consistent with amiodarone’s known tissue accumulation and prolonged half-life [ 50 ]. These interactions likely impair efflux transport and activate stress-response kinases, amplifying cellular injury. Moderate binding was observed for ERBB2 (2.9 µM) and ADRB2 (7.0 µM), reinforcing their roles in mitochondrial dysfunction and neuroimmune dysregulation, respectively [ 51 , 52 ]. The integration of docking results with topological network hubs strengthens the prioritization of these proteins as mechanistically and therapeutically relevant. While docking offers only a static interaction model, its concordance with enriched pathways and toxicokinetic behavior provides a rationale for focusing experimental validation efforts on ABCB1, AKT1, ERBB2, and ADRB2 [ 53 , 54 ]. 4.4 Translational and Clinical Implications The identified targets and pathways present multiple opportunities for clinical translation. ABCB1, AKT1, and ERBB2 may serve as early biomarkers, detectable in blood or bronchoalveolar lavage, to anticipate toxicity before radiographic or symptomatic manifestation [ 55 ]. Their tissue-specific expression also aligns with observed patterns of lung-selective toxicity and interindividual variability. Therapeutically, modulation of ABCB1 function may reduce intracellular amiodarone accumulation, while inhibition of ERBB2, SRC, or ABL1 could attenuate profibrotic signaling cascades [ 52 ]. Targeting the AKT1–FoxO signaling axis and blocking IL-17/IL-22 pathways represent promising adjunctive strategies to restore cellular homeostasis and suppress immune-mediated fibrotic progression [ 56 ]. The involvement of ADRB2 highlights potential avenues for neuroimmune-directed therapies and radiotracer-based imaging, particularly in patients exhibiting atypical or localized pulmonary abnormalities [ 46 ]. Personalized pharmacogenomic screening—especially for ABCB1 and AKT1 polymorphisms—may facilitate risk stratification and inform safer use of amiodarone in individuals predisposed to pulmonary toxicity [ 57 ]. 4.5 Limitations and Future Directions Despite the strengths of this integrative approach, several limitations warrant consideration. Network pharmacology is inherently reliant on curated databases, which may omit emerging targets or context-specific molecular interactions [ 58 ]. The identification of hub genes, although based on robust topological criteria, involves thresholding decisions that could exclude biologically relevant peripheral nodes. Additionally, molecular docking provides only a static approximation of ligand–receptor binding and does not account for intracellular dynamics, post-translational modifications, or pharmacokinetic parameters that influence in vivo behavior [ 59 ]. Future investigations should prioritize experimental validation of the predicted interactions—particularly those involving ABCB1, AKT1, ERBB2, and ADRB2—using lung-relevant cellular models and established pulmonary fibrosis animal systems. Functional assays assessing transporter activity, cytokine signaling, and pathway-specific modulation will be critical to confirm mechanistic relevance. Moreover, longitudinal multi-omics studies are needed to capture time-resolved molecular alterations, enabling the distinction between causal drivers and secondary effects. Finally, pharmacogenomic analyses of patient-specific variants affecting drug transport, kinase signaling, or immune activation may facilitate precision-based risk stratification and inform individualized therapeutic strategies. 5. Conclusion This study systematically elucidated the molecular mechanisms underlying amiodarone-induced pulmonary fibrosis through an integrative approach combining network pharmacology, enrichment analysis, and molecular docking. Our analysis identified eight key hub targets—ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1, and ADRB2—that sit at the intersection of amiodarone pharmacology and fibrotic pathogenesis. These targets span diverse biological processes, including drug transport, tyrosine kinase signaling, apoptosis regulation, hypoxic adaptation, and neuroactive signaling, underscoring the multifactorial nature of this adverse drug reaction. Enrichment analyses highlighted the involvement of neuroactive ligand-receptor interaction, FoxO signaling, and Th17 cell differentiation pathways, pointing to previously underappreciated roles of neuroimmune modulation and inflammation in amiodarone-induced lung injury. Molecular docking provided complementary support, demonstrating strong predicted interactions between amiodarone and several hub proteins—most notably ABCB1 and AKT1 (Kd = 0.37 µM each), followed by ERBB2 and ADRB2—at concentrations relevant to clinical exposure. Together, these findings support a coherent membrane-to-nucleus signaling framework, whereby amiodarone disrupts multiple interconnected processes: impaired efflux transport via ABCB1, altered receptor signaling through ADRB2 and ERBB2, propagation of tyrosine kinase cascades involving SRC and ABL1, and transcriptional regulation mediated by AKT1, HIF1A, and XIAP. This integrated perspective advances beyond single-target paradigms, offering a systems-level understanding of how amiodarone induces pulmonary fibrosis. Importantly, the identified targets and pathways suggest candidate biomarkers for early detection and novel therapeutic strategies for mitigating toxicity [ 60 ]. While experimental validation remains essential to confirm these computational predictions, our findings provide a solid foundation for mechanistic, translational, and clinical research. Beyond advancing knowledge of amiodarone-specific toxicity, this study illustrates the broader value of network-based approaches for unraveling complex drug-induced pathologies. Ultimately, these insights may inform improved risk stratification, monitoring, and intervention strategies for patients receiving amiodarone, with potential applicability to other drug-induced fibrotic conditions. Declarations Supplementary information Supplementary materials accompanying this article include high-resolution versions of all figures (Figures 1–5) and the original input files used for molecular docking and network construction. Supplementary files are provided in a compressed .zip archive and can be accessed online with the published article. All supplementary materials have been prepared in accordance with BMC journal submission guidelines. Acknowledgements The authors gratefully acknowledge the collective contributions of all co-authors for their intellectual input and collaborative support in the preparation of this manuscript. Author contribution Huye Li and Rubing Di performed the bioinformatics analysis and molecular docking simulations. Deyuan Kong supervised the project and critically revised the manuscript. All authors contributed to manuscript writing and approved the final version. Funding Not applicable. Data availability All datasets analyzed in this study are publicly available from the following online resources: STRING:https://cn.string-db.org DAVID:https://david.ncifcrf.gov RCSB Protein Data Bank :https://www.rcsb.org/ PubChem Database：https://pubchem.ncbi.nlm.nih.gov/ SwissTargetPrediction :http://www.swisstargetprediction.ch/ OMIM : https://www.omim.org DisGeNET : https://www.disgenet.org UniProt : https://www.uniprot.org GeneCards : https://www.genecards.org Cb-dock2 ： https://cadd.labshare.cn/cb-dock2/index.php Competing interests The authors declare no competing interests. References Hamilton D, Sr, et al. Amiodarone: A Comprehensive Guide for Clinicians. Am J Cardiovasc Drugs. 2020;20(6):549–58. Freedman MD, Somberg JC. Pharmacology and pharmacokinetics of amiodarone. J Clin Pharmacol. 1991;31(11):1061–9. Sorodoc V et al. Amiodarone Therapy: Updated Practical Insights. J Clin Med, 2024. 13(20). Ruzieh M, et al. 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Amiodarone and concurrent antiretroviral therapy: a case report and review of the literature. Antivir Ther. 2014;19(4):329–39. Epstein AE, et al. Practical Management Guide for Clinicians Who Treat Patients with Amiodarone. Am J Med. 2016;129(5):468–75. You HS, et al. Amiodarone-Induced Multi-Systemic Toxicity Involving the Liver, Lungs, Thyroid, and Eyes: A Case Report. Front Cardiovasc Med. 2022;9:839441. Budin CE et al. Pulmonary Fibrosis Related to Amiodarone-Is It a Standard Pathophysiological Pattern? A Case-Based Literature Review. Diagnostics (Basel), 2022. 12(12). Terzo F, et al. Amiodarone-induced pulmonary toxicity with an excellent response to treatment: A case report. Respir Med Case Rep. 2020;29:100974. Raeouf A, Goyal S, Traylor J. Amiodarone-Induced Myxedema Coma Cureus. 2020;12(8):e9902. Oktaviono YH, et al. Exploring Current Diagnosis and Management of Amiodarone-induced Thyrotoxicosis. Am J Cardiol. 2025;242:75–81. Hubel E, et al. 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Analysis of a Serious Adverse Reaction of Pulmonary Fibrosis Caused by Dronedarone. Curr Ther Res Clin Exp. 2024;100:100743. Dammacco R, et al. Amiodarone-induced ocular and extra-ocular toxicity: a retrospective cohort study. Clin Exp Med. 2025;25(1):68. Jessurun GA, Crijns HJ. Amiodarone pulmonary toxicity. BMJ (Clinical research ed.), 1997. 314(7081): pp. 619–20. Papiris SA, et al. Amiodarone: review of pulmonary effects and toxicity. Drug Saf. 2010;33(7):539–58. Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods in Molecular Biology; 2024. Biradar P, et al. Experimental validation and network pharmacology evaluation to decipher the mechanism of action of Erythrina variegata L. bark against scopolamine-induced memory impairment in rats. Advances in Traditional Medicine; 2020. Khanal P et al. Barosmin against postprandial hyperglycemia: outputs from computational prediction to functional responses in vitro. J Biomol Struct Dynamics, 2023. Szklarczyk D, et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research; 2020. Ragueneau E et al. IntAct App: a Cytoscape application for molecular interaction network visualization and analysis. Bioinformatics, 2021. Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics, 2009. Dennis G et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biology, 2003. Berman HM. The Protein Data Bank. Nucleic Acids Research; 2000. Liu Y, et al. CB-Dock2: improved protein-ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Research; 2022. Zou F, et al. Prognostic significance of ABCB1 in stage I lung adenocarcinoma. Oncol Lett. 2017;14(1):313–21. Tan C, Kumar P. Too Little, Too Late: A Case of a Swift Fatal Culmination of Amiodarone Induced Pulmonary Toxicity in an Adult Male. Int Med Case Rep J. 2023;16:679–87. Bao Q, et al. The c-Abl-RACK1-FAK signaling axis promotes renal fibrosis in mice through regulating fibroblast-myofibroblast transition. Cell Commun Signal. 2024;22(1):247. Park JS et al. RON Receptor Tyrosine Kinase Regulates Epithelial Mesenchymal Transition and the Expression of Pro-Fibrotic Markers via Src/Smad Signaling in HK-2 and NRK49F Cells. Int J Mol Sci, 2019. 20(21). Sarkar A, et al. Regulation of Mesenchymal Cell Fate by Transfer of Active Gasdermin-D via Monocyte-Derived Extracellular Vesicles. J Immunol. 2023;210(6):832–41. Mamriev D, et al. A small-molecule ARTS mimetic promotes apoptosis through degradation of both XIAP and Bcl-2. Volume 11. Cell Death & Disease; 2020. 6. Li S, Shi J, Tang H. Animal models of drug-induced pulmonary fibrosis: an overview of molecular mechanisms and characteristics. Cell Biol Toxicol. 2022;38(5):699–723. Bernard M, et al. Autophagy drives fibroblast senescence through MTORC2 regulation. Autophagy. 2020;16(11):2004–16. Wang J, et al. DsbA-L activates TGF-beta1/SMAD3 signaling and M2 macrophage polarization by stimulating AKT1 and NLRP3 to promote pulmonary fibrosis. Mol Med. 2024;30(1):228. Li W, et al. A brain-to-lung signal from GABAergic neurons to ADRB2 + int erstitial macrophages promotes pulmonary inflammatory responses. Immunity. 2025;58(8):2069. –2085.e9. John O'Donnell JC. Unilateral Pulmonary Fibrosis due to Acute Amiodarone Toxicity. International Journal of Clinical & Medical Imaging, 2021. Volume 8, Issue 2(ISSN: 2376 – 0249). Nguyen Q-C et al. Mimosa pudica L. extract ameliorates pulmonary fibrosis via modulation of MAPK signaling pathways and FOXO3 stabilization. J Ethnopharmacol, 2024. Wang L et al. Recovery from acute lung injury can be regulated via modulation of regulatory T cells and Th17 cells. Scand J Immunol, 2018. 88(5). Espindola MS, et al. Targeting of TAM Receptors Ameliorates Fibrotic Mechanisms in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med. 2018;197(11):1443–56. Tsaban G, et al. Amiodarone and pulmonary toxicity in atrial fibrillation: a nationwide Israeli study. Eur Heart J. 2024;45(5):379–88. Zanetti-Domingues LC et al. Cooperation and Interplay between EGFR Signalling and Extracellular Vesicle Biogenesis in Cancer. Cells, 2020. 9(12). Gopinathannair R, et al. COVID-19 and cardiac arrhythmias: a global perspective on arrhythmia characteristics and management strategies. J Interv Card Electrophysiol. 2020;59(2):329–36. Peng J, et al. Aspirin alleviates pulmonary fibrosis through PI3K/AKT/mTOR-mediated autophagy pathway. Experimental Gerontology; 2023. Naik GAP, et al. A computational journey in anticancer drug discovery: Exploring AKT1 inhibition by novel oxadiazoles using molecular docking, ADMET, density functional theory and molecular dynamic simulation. Computational Biology and Chemistry; 2025. Batool A, et al. Anaphylactic Shock as a Rare Side Effect of Intravenous Amiodarone. Cureus. 2022;14(1):e21118. Blomstrom-Lundqvist C, et al. Efficacy and safety of dronedarone by atrial fibrillation history duration: Insights from the ATHENA study. Clin Cardiol. 2020;43(12):1469–77. MacKenzie M, Hall R. Pharmacogenomics and pharmacogenetics for the intensive care unit: a narrative review. Can J Anaesth. 2017;64(1):45–64. Duineveld MD, Kers J, Vleming LJ. Case report of progressive renal dysfunction as a consequence of amiodarone-induced phospholipidosis. Eur Heart J Case Rep. 2023;7(9):ytad457. Kozlova N, et al. Acute amiodarone pulmonary toxicity in the form of organizing pneumonia triggered by orthotopic heart transplantation. Respir Med Case Rep. 2021;34:101532. Kwok WC, et al. A multicenter retrospective cohort study on predicting the risk for amiodarone pulmonary toxicity. BMC Pulm Med. 2022;22(1):128. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.rar Supplementary information Supplementary materials accompanying this article include high-resolution versions of all figures (Figures 1–5) and the original input files used for molecular docking and network construction. Supplementary files are provided in a compressed .zip archive and can be accessed online with the published article. All supplementary materials have been prepared in accordance with BMC journal submission guidelines. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7601015","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514226942,"identity":"2b03ef80-e030-49e1-ac1c-32a63824d42c","order_by":0,"name":"huye li","email":"","orcid":"","institution":"The 4th People's Hospital of Qinghai Province","correspondingAuthor":false,"prefix":"","firstName":"huye","middleName":"","lastName":"li","suffix":""},{"id":514226943,"identity":"f153596e-22e1-46d5-9e6d-d3f01a653e97","order_by":1,"name":"Rubing Di","email":"","orcid":"","institution":"The 4th People's Hospital of Qinghai 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06:49:58","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124744,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/1cd3bcf8f1b2acaf7ab78295.html"},{"id":91816482,"identity":"bea57cb2-49e9-4cdc-9c43-20bae25d6ac2","added_by":"auto","created_at":"2025-09-22 06:41:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41065,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study design.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/168dbebf153b213b885a04b1.png"},{"id":91816487,"identity":"3823612f-0aa1-4bd1-a511-5ea4537b25b7","added_by":"auto","created_at":"2025-09-22 06:41:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1981355,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Venn diagram displaying unique targets of amiodarone (n = 77 and pulmonary fibrosis(n = 2581), along with their shared targets (n = 41). (B) Protein-protein interaction (PPI) network of 41 overlapping targets. (C) PPI network of core targets. Topological parameter analysis of overlapping targets identified 8 core targets meeting criteria (degree ≥9.8, betweenness ≥ 33.6, closeness ≥0.01).Nodes denote proteins and edges represent protein-protein associations. The brighter and larger the nodes, the greater their significance. The inner circle highlights the core targets.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/a76669074e490699615e111f.png"},{"id":91816483,"identity":"8fa84806-4806-44e9-9582-f61328229322","added_by":"auto","created_at":"2025-09-22 06:41:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":117926,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Gene Ontology (GO) enrichment analysis of overlapping targets (top 10 terms). (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapping targets (top 20 pathways).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/697d505bfe735082f6d8219b.png"},{"id":91816488,"identity":"513f4260-5da3-4755-9016-01637fd33333","added_by":"auto","created_at":"2025-09-22 06:41:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161228,"visible":true,"origin":"","legend":"\u003cp\u003eDrug–target–pathway network. The red circle denotes amiodarone; yellow circles indicate core targets (ERBB2, SRC, ADRB2, ABL1, XIAP, ABCB1, and AKT1); blue circles represent additional target genes/proteins; and green circles correspond to enriched biological pathways (e.g., EGFR tyrosine kinase inhibitor resistance, renin secretion, VEGF signaling, ErbB signaling, and others).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/934e6241c6043fad12deae4c.png"},{"id":91816492,"identity":"764ca3ff-c97c-4ef9-ad02-2bed794d886e","added_by":"auto","created_at":"2025-09-22 06:41:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1981462,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking analysis results of key targets. (A) Binding mode between Amiodarone and ABCB1; (B) Binding mode between Amiodarone and ABL1; (C) Binding mode between Amiodarone and ADRB2; (D) Binding mode between Amiodarone and AKT1; (E) Binding mode between Amiodarone and ERBB2; (F) Binding mode between Amiodarone and HIF1A; (G) Binding mode between Amiodarone and SRC; (H) Binding mode between Amiodarone and XIAP.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/630eee96a7e0ed6a1fb41741.png"},{"id":92135639,"identity":"c4efc0b7-b6d0-4ae6-b08a-1c3a918903d0","added_by":"auto","created_at":"2025-09-25 04:31:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5029698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/0e43d257-f5b5-46e4-99ea-ce34391634be.pdf"},{"id":91816489,"identity":"2b973aaa-09fe-4b93-8a2d-2a61e01ad437","added_by":"auto","created_at":"2025-09-22 06:41:52","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4792349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary materials accompanying this article include high-resolution versions of all figures (Figures 1–5) and the original input files used for molecular docking and network construction. Supplementary files are provided in a compressed \u003ccode\u003e.zip\u003c/code\u003e archive and can be accessed online with the published article. All supplementary materials have been prepared in accordance with BMC journal submission guidelines.\u003c/p\u003e","description":"","filename":"SupplementaryMaterials.rar","url":"https://assets-eu.researchsquare.com/files/rs-7601015/v1/32b53728bbf950ee51789dd8.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network pharmacology and molecular docking reveal mechanisms of amiodarone-induced pulmonary fibrosis","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Clinical Significance and Prevalence\u003c/h2\u003e\u003cp\u003eAmiodarone, a widely utilized class III antiarrhythmic agent, exhibits notable efficacy in the treatment of various cardiac arrhythmias [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nevertheless, its distinctive pharmacokinetic profile\u0026mdash;marked by high lipid solubility, variable oral bioavailability (ranging from 20% to 80%), and an exceptionally prolonged elimination half-life\u0026mdash;facilitates considerable drug accumulation within pulmonary tissues. This accumulation substantially elevates the risk of severe respiratory adverse effects, including interstitial pneumonitis and pulmonary fibrosis, which can be life-threatening [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the therapeutic efficacy of amiodarone is well established, its clinical application is significantly constrained by a wide range of multisystem adverse effects [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among these, pulmonary toxicity is the most serious and potentially fatal complication. In particular, amiodarone-induced acute respiratory distress syndrome can clinically resemble acute heart failure, thereby presenting a substantial diagnostic challenge for clinicians [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Multiple risk factors contribute to the development of amiodarone-induced pulmonary injury, including advanced age at treatment initiation and excessive cumulative exposure\u0026mdash;especially daily doses exceeding 400 mg for more than two months, or prolonged low-dose therapy of 200 mg/day for over two years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The drug\u0026rsquo;s complex pharmacokinetics are further modulated by food intake, with high-fat meals enhancing intestinal absorption by 2.4- to 3.8-fold compared to fasting conditions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOf particular clinical concern, the concomitant use of amiodarone with direct oral anticoagulants has been identified as a predictor of major hemorrhagic complications, including pericardial effusion, thereby necessitating close monitoring during combined therapy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Amiodarone\u0026rsquo;s interaction profile encompasses a wide range of commonly prescribed medications, such as warfarin, digoxin, and HIV antiretrovirals, warranting vigilant clinical oversight to mitigate drug\u0026ndash;drug interactions and enable prompt detection and management of adverse events [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Accordingly, strict compliance with established monitoring protocols\u0026mdash;such as those issued by the North American Society of Pacing and Electrophysiology\u0026mdash;is critical for optimizing dosing strategies, minimizing interaction risks, and ensuring the timely identification of potentially severe complications [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Spectrum and Diagnostic Dilemmas of Organ Toxicity\u003c/h2\u003e\u003cp\u003eBeyond pulmonary complications, amiodarone exerts toxic effects on multiple organ systems, each posing distinct diagnostic and therapeutic challenges. This complexity is compounded by highly variable latency periods, occasional dose-independent susceptibility, the lack of validated biomarkers, and clinical features that often overlap with pre-existing cardiac conditions. A striking case has been reported in which a single patient simultaneously developed hepatic, pulmonary, thyroid, and ocular toxicities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePulmonary toxicity remains the most serious complication of amiodarone therapy, presenting as diffuse alveolar damage, chronic interstitial pneumonia, organizing pneumonia, or pulmonary nodules. Accurate diagnosis necessitates the exclusion of infectious causes and relies on the integration of clinical, radiological, and, in some cases, histopathological findings [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Mechanistically, the toxicity is mediated by the accumulation of phospholipid complexes within histiocytes and type II pneumocytes, with electron microscopy revealing hallmark features such as foamy alveolar macrophages and membrane-bound lamellar bodies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThyroid dysfunction occurs in approximately 15\u0026ndash;20% of patients receiving amiodarone and displays bidirectional patterns that necessitate distinct management approaches. Hypothyroidism is generally well controlled with levothyroxine supplementation, whereas amiodarone-induced thyrotoxicosis requires prompt subtype differentiation, as delayed intervention may result in life-threatening complications such as myxedema coma [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Diagnostic and therapeutic strategies for amiodarone-induced thyrotoxicosis continue to evolve, highlighting the critical importance of early recognition and individualized treatment [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHepatotoxicity associated with amiodarone exhibits considerable mechanistic complexity. Repeated administration induces endoplasmic reticulum stress in hepatic and adipose tissues, promoting lipolysis and resulting in hepatic accumulation of lipotoxic free fatty acids through the inhibition of key metabolic enzymes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Fatal outcomes have been reported in cases of multiorgan toxicity, underscoring the necessity for comprehensive monitoring [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Successful treatment with N-acetylcysteine has been documented, implicating oxidative stress\u0026mdash;mediated by glutathione depletion and mitochondrial dysfunction\u0026mdash;as a central mechanism in its pathogenesis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNeurological toxicity occurs in approximately 20\u0026ndash;30% of patients receiving amiodarone, commonly presenting as tremor, ataxia, or peripheral neuropathy. Rare manifestations, such as amiodarone-induced nystagmus, have also been reported, with onset ranging from days to several months and typically resolving after drug discontinuation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In addition, long-term therapy may alter thyroid function parameters, with persistent effects observed during both acute and chronic phases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOcular toxicity associated with amiodarone ranges from benign corneal verticillata to vision-threatening optic neuropathy. Comprehensive ophthalmologic monitoring is crucial due to the potential for irreversible complications [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Notably, serious adverse reactions\u0026mdash;including pulmonary fibrosis\u0026mdash;have also been reported with structurally related antiarrhythmic agents, highlighting class-related risks that necessitate vigilant surveillance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Advances in multimodal imaging, particularly high-resolution computed tomography, have significantly enhanced the early detection of pulmonary complications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDermatological toxicity includes photosensitivity reactions and characteristic blue-gray skin discoloration, both of which are associated with cumulative dosage and ultraviolet exposure [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Updated clinical practice guidelines emphasize the importance of systematic, multisystem monitoring as essential for optimizing patient safety and therapeutic outcomes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3 Research Evolution and Network Pharmacology Applications in Amiodarone-Induced Pulmonary Fibrosis\u003c/h2\u003e\u003cp\u003eOver the past decade, significant progress has been made in elucidating the mechanisms of amiodarone-induced pulmonary toxicity through the integration of mechanistic studies, high-resolution imaging, and comprehensive clinical surveillance protocols [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. High-resolution computed tomography (HRCT) has emerged as a pivotal diagnostic modality, offering reproducible assessments of injury severity. Typical radiographic features include bilateral interstitial opacities, ground-glass attenuation, organizing pneumonia patterns, and polymorphic manifestations ranging from diffuse alveolar damage to chronic interstitial pneumonia. The reported incidence of such pulmonary complications ranges from 4% to 17% among patients undergoing amiodarone therapy [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFunctional evaluations, such as bronchoalveolar lavage (BAL), have further aided in diagnosis by revealing foamy alveolar macrophages containing multilamellar intracytoplasmic bodies and lipid-laden lysosomes, while simultaneously facilitating the exclusion of infectious causes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMechanistic understanding has progressed beyond the traditional phospholipidosis hypothesis to incorporate complex, multi-pathway interactions. Amiodarone promotes the accumulation of phospholipid complexes in histiocytes and type II pneumocytes by inhibiting phospholipase activity, while concurrently inducing oxidative stress and generating reactive oxygen species that contribute to cellular lysis and programmed cell death [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The resulting pathological manifestations are highly variable, encompassing diffuse alveolar damage, chronic interstitial pneumonia, organizing pneumonia, pulmonary hemorrhage, and nodular lesions. Notably, disease severity appears to be more closely linked to individual susceptibility than to cumulative drug dosage [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although the presence of foamy alveolar macrophages and ultrastructural identification of membrane-bound lamellar bodies serve as specific markers of amiodarone exposure, these features primarily reflect drug accumulation and are not considered reliable predictors of toxicity development [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Methods","content":"\u003cp\u003eWe systematically identified potential amiodarone target genes associated with pulmonary fibrosis through comprehensive database mining. Drug targets and disease-related genes were collected and compared to establish overlapping candidates. To explore their biological significance, enrichment analysis was performed using Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Analyses were conducted with a significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after multiple testing correction, revealing functional clusters related to inflammatory responses, stress adaptation, and extracellular matrix organization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInteractions among the identified genes were further examined by constructing a protein\u0026ndash;protein interaction (PPI) network. High-confidence interaction data were integrated, and topological analysis was applied to identify hub genes and functional modules within the amiodarone\u0026ndash;pulmonary fibrosis interactome [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This network analysis highlighted key molecular mediators potentially involved in drug-induced fibrotic pathways, providing candidate targets for further evaluation.\u003c/p\u003e\u003cp\u003eTo complement these findings, molecular docking was conducted to assess the binding affinities between amiodarone and the top candidate targets implicated in pulmonary fibrosis. Docking simulations were performed using standard protocols to evaluate binding strength, interaction patterns, and structural compatibility between the drug and prioritized protein targets [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This computational validation step provided mechanistic insights into potential direct interactions between amiodarone and fibrosis-related proteins.\u003c/p\u003e\u003cp\u003eAn overview of the analytical process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which outlines the integrated bioinformatics workflow employed to elucidate the molecular mechanisms underlying amiodarone-induced pulmonary fibrosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e[Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Identification of amiodarone targets\u003c/h2\u003e\u003cp\u003ePotential amiodarone targets were screened using DrugBank and SwissTargetPrediction databases, with \u0026ldquo;amiodarone\u0026rdquo; as the retrieval term for both platforms. For SwissTargetPrediction, only genes with a probability score\u0026thinsp;\u0026gt;\u0026thinsp;0.1 were retained. Candidate genes from both databases were merged, and duplicates were removed to generate a non-redundant list.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Mining of pulmonary fibrosis\u0026ndash;related genes\u003c/h2\u003e\u003cp\u003eGenes associated with pulmonary fibrosis were retrieved from GeneCards, DisGeNET, and OMIM using the keyword \u0026ldquo;pulmonary fibrosis.\u0026rdquo; In GeneCards, only genes with a relevance score\u0026thinsp;\u0026ge;\u0026thinsp;5 were included, whereas genes from DisGeNET and OMIM were incorporated without additional filtering. The merged list was curated to remove redundancies, yielding a comprehensive set of pulmonary fibrosis\u0026ndash;related genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Discovery of common targets\u003c/h2\u003e\u003cp\u003eThe intersecting genes between the amiodarone target dataset and the pulmonary fibrosis gene panel were identified using VENNY 2.1.0. Lists from both sources were uploaded, and overlapping genes were extracted as potentially relevant for mechanistic exploration.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Protein\u0026ndash;protein interaction (PPI) network analysis\u003c/h2\u003e\u003cp\u003eThe shared targets were uploaded to STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], with \u003cem\u003eHomo sapiens\u003c/em\u003e specified as the reference organism. A minimum confidence threshold of 0.4 was applied to ensure reliable interaction data. The resulting PPI network was visualized and analyzed using Cytoscape version 3.10.3 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Topological parameters\u0026mdash;including node degree, betweenness centrality, and closeness centrality\u0026mdash;were calculated using the CentiScaPe 2.2 plug-in [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to identify pivotal hub targets closely associated with amiodarone-induced pulmonary fibrosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Enrichment analysis and drug\u0026ndash;target\u0026ndash;pathway network construction\u003c/h2\u003e\u003cp\u003eTo investigate the biological functions of the intersecting targets, enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Gene Ontology (GO) enrichment covered three categories: biological processes, molecular functions, and cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was also conducted. Targets with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant, and results were ranked in ascending order of \u003cem\u003ep\u003c/em\u003e-value. The top 10 GO terms from each category and the top 20 KEGG pathways were selected for further analysis.\u003c/p\u003e\u003cp\u003eAll enrichment analyses and visualizations were conducted on the DAVID platform. To illustrate the relationships among amiodarone, its predicted targets, and pulmonary fibrosis\u0026ndash;associated pathways, a drug\u0026ndash;target\u0026ndash;pathway network was constructed using Cytoscape version 3.10.3.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Molecular docking\u003c/h2\u003e\u003cp\u003eMolecular docking was performed using the CB-Dock2 platform to investigate interaction mechanisms and binding modes between amiodarone and the predicted key target proteins. The two-dimensional chemical structure of amiodarone was retrieved from PubChem and saved in SDF format, while three-dimensional structures of the target proteins (PDB format) were obtained from the RCSB Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Both ligand and receptor files were imported into CB-Dock2 for docking calculations.\u003c/p\u003e\u003cp\u003eCB-Dock2 employs a curvature-based cavity detection algorithm to automatically identify protein binding sites and integrates AutoDock Vina (v1.2.3) as its docking engine, thereby improving accuracy in binding site identification and conformation prediction. Following docking, the platform\u0026rsquo;s built-in visualization tools were used for result interpretation and three-dimensional structural analysis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To ensure reproducibility and reliability, each docking experiment was performed in triplicate under identical computational parameters.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Target Identification and Overlap Analysis\u003c/h2\u003e\u003cp\u003eSystematic screening identified 118 unique potential targets of amiodarone after removal of duplicates from the DrugBank and SwissTargetPrediction databases. In parallel, comprehensive mining of pulmonary fibrosis\u0026ndash;associated genes across GeneCards, DisGeNET, and OMIM yielded 2,622 non-redundant candidates. Comparative analysis of these datasets using Venny 2.1.0 revealed 41 overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese shared genes account for 34.7% of amiodarone\u0026rsquo;s predicted targets, highlighting a substantial mechanistic intersection between the drug\u0026rsquo;s molecular interactions and pulmonary fibrosis pathophysiology. The identification of this intersecting gene set provides a focused molecular framework for elucidating how amiodarone exposure may initiate or exacerbate fibrotic processes within pulmonary tissue.\u003c/p\u003e\u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA here\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 PPI Network Construction and Core Target Selection\u003c/h2\u003e\u003cp\u003eTo elucidate the key molecular mediators linking amiodarone and pulmonary fibrosis, the 41 intersecting targets were submitted to the STRING database for protein\u0026ndash;protein interaction (PPI) network construction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The resulting network revealed extensive interconnections, underscoring functional associations and potential signaling crosstalk that may contribute to fibrotic pathogenesis.\u003c/p\u003e\u003cp\u003eTopological analysis of the PPI network was performed using Cytoscape with the CentiScaPe plugin. Applying threshold criteria of degree\u0026thinsp;\u0026ge;\u0026thinsp;9.8, betweenness centrality\u0026thinsp;\u0026ge;\u0026thinsp;33.6, and closeness centrality\u0026thinsp;\u0026ge;\u0026thinsp;0.01, we identified \u003cb\u003eeight hub targets\u003c/b\u003e: \u003cem\u003eABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1\u003c/em\u003e, and \u003cem\u003eADRB2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eThese hub proteins represent central regulatory nodes within the network, characterized by high connectivity and strong bridging potential between functional modules. Their elevated betweenness centrality suggests they act as molecular integrators, coordinating diverse signaling processes that may collectively drive the progression of amiodarone-induced pulmonary fibrosis.\u003c/p\u003e\u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB here\u003c/p\u003e\u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC here\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Functional Enrichment and Pathway Network Analysis\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Gene Ontology Enrichment Analysis\u003c/h2\u003e\u003cp\u003eFrom the 41 overlapping genes between amiodarone targets and pulmonary fibrosis-associated genes, enrichment analysis identified 376 significant GO terms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05): 242 under biological processes, 36 under cellular components, and 98 under molecular functions. The top 10 terms from each category were visualized using bubble plots generated via the Bioinformatics platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here\u003c/p\u003e\u003cp\u003eThe biological process (BP) analysis revealed strong enrichment in cellular signaling pathways, particularly signal transduction, protein and tyrosine phosphorylation, MAPK cascade regulation, receptor-mediated signaling, cell survival processes, and ion homeostasis. These findings suggest that amiodarone may modulate pulmonary fibrosis by activating multiple parallel signaling cascades that converge on fibrotic remodeling.\u003c/p\u003e\u003cp\u003eThe cellular component (CC) enrichment indicated predominant localization of target proteins to membrane-associated structures, including the plasma membrane, caveolae microdomains, neuronal dendrites, and G protein\u0026ndash;coupled serotonin receptor complexes. This distribution highlights that amiodarone\u0026rsquo;s primary interactions are likely initiated at the cell surface.\u003c/p\u003e\u003cp\u003eFor molecular functions (MF), enrichment analysis emphasized phosphorylation-dependent regulatory activities, including histone modification kinases (notably H2AX Y142 and H3 Y41), protein tyrosine kinases, and ATP binding functions. Together, these results delineate a membrane-to-nucleus signaling axis, wherein extracellular stimuli are transduced through tyrosine kinase cascades to regulate chromatin remodeling and gene expression via histone modifications. This orchestrated framework provides a mechanistic basis for how amiodarone exposure may induce the complex molecular changes underlying pulmonary fibrosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 KEGG Pathway Analysis\u003c/h2\u003e\u003cp\u003eA total of 101 significantly enriched KEGG pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were identified for the overlapping target genes. The top 10 pathways were visualized using bubble plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), revealing several major signaling networks potentially mediating amiodarone\u0026rsquo;s role in pulmonary fibrosis. Notably, these included the neuroactive ligand\u0026ndash;receptor interaction, FoxO signaling, and Th17 cell differentiation pathways.\u003c/p\u003e\u003cp\u003eThe enrichment of the neuroactive ligand\u0026ndash;receptor interaction pathway suggests a possible contribution of neurogenic inflammation to amiodarone-induced pulmonary injury. The FoxO signaling pathway was strongly represented, implicating dysregulation of cellular stress responses, apoptosis, and autophagy\u0026mdash;processes recognized as central drivers of fibrotic progression. Furthermore, enrichment of the Th17 cell differentiation pathway points to immune-mediated mechanisms, particularly pro-inflammatory cytokine release and fibroblast activation, underscoring the importance of immune signaling in amiodarone-associated pulmonary fibrosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3 Drug\u0026ndash;Target\u0026ndash;Pathway Network Analysis\u003c/h2\u003e\u003cp\u003eTo elucidate the complex relationships among amiodarone, its molecular targets, and the associated signaling pathways, we constructed a drug\u0026ndash;target\u0026ndash;pathway network using Cytoscape 3.10.3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This integrative visualization revealed distinct clusters of targets linked to specific pathways, suggesting the presence of functional modules contributing to different aspects of amiodarone-induced pulmonary toxicity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here\u003c/p\u003e\u003cp\u003eNotably, several core targets\u0026mdash;including ADRB2, AKT1, HIF1A, SRC, ABL1, XIAP, ERBB2, and ABCB1\u0026mdash;were strongly associated with the EGFR tyrosine kinase inhibitor resistance pathway. This finding suggests that amiodarone may activate signaling mechanisms similar to those observed in therapy resistance, potentially involving dysregulated survival signaling and cellular adaptation. In contrast, other targets such as ADRB3, CACNA1C, and MAPK3 were predominantly associated with renin secretion and metabolic regulation pathways, pointing to broader contributions of endocrine and metabolic dysregulation in amiodarone-induced pulmonary injury.\u003c/p\u003e\u003cp\u003eThese findings indicate that amiodarone\u0026rsquo;s pulmonary toxicity is not solely mediated by direct fibrotic signaling but may also arise from the integration of oncogenic, immune, and metabolic processes that converge to promote progressive fibrotic remodeling in lung tissue.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Molecular Docking Results\u003c/h2\u003e\u003cp\u003eTo evaluate the direct interactions between amiodarone and core proteins implicated in pulmonary fibrosis, molecular docking analyses were performed. The eight hub proteins identified from network analysis\u0026mdash;ABCB1 (PDB ID: 8SB8), ERBB2 (PDB ID: 3PP0), XIAP (PDB ID: 1TFQ), ABL1 (PDB ID: 2F4J), SRC (PDB ID: 1O41), HIF1A (PDB ID: 1H2K), AKT1 (PDB ID: 3O96), and ADRB2 (PDB ID: 8GGI)\u0026mdash;served as receptors for docking with amiodarone(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInsert Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here\u003c/p\u003e\u003cp\u003eBinding strength was assessed by equilibrium dissociation constant (Kd), estimated from docking free energy (ΔG\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;RT ln Kd), where lower Kd values indicate more stable complexes. The predicted Kd values were: ABCB1\u0026thinsp;=\u0026thinsp;0.37 \u0026micro;M, AKT1\u0026thinsp;=\u0026thinsp;0.37 \u0026micro;M, ERBB2\u0026thinsp;=\u0026thinsp;2.9 \u0026micro;M, ADRB2\u0026thinsp;=\u0026thinsp;7.0 \u0026micro;M, ABL1\u0026thinsp;=\u0026thinsp;31 \u0026micro;M, XIAP\u0026thinsp;=\u0026thinsp;100 \u0026micro;M, HIF1A\u0026thinsp;=\u0026thinsp;74 \u0026micro;M, and SRC\u0026thinsp;=\u0026thinsp;290 \u0026micro;M.\u003c/p\u003e\u003cp\u003eAmong these, ABCB1 and AKT1 demonstrated the strongest binding affinities (Kd\u0026thinsp;=\u0026thinsp;0.37 \u0026micro;M each), followed by moderate binding of ERBB2 and ADRB2, whereas SRC, HIF1A, and XIAP exhibited weaker interactions. These results indicate preferential interactions of amiodarone with specific targets, which may help explain the selective pulmonary toxicity observed clinically.\u003c/p\u003e\u003cp\u003eMechanistically, the strong affinity for ABCB1, a multidrug efflux transporter, suggests that amiodarone may impair detoxification pathways, promoting intracellular drug accumulation. Similarly, high-affinity binding to AKT1, a central regulator of cell survival and apoptosis, points to potential dysregulation of survival signaling and tissue homeostasis. Collectively, these molecular interactions provide a plausible mechanistic basis for amiodarone-induced pulmonary fibrosis.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed an integrative network pharmacology and molecular docking strategy to dissect the complex mechanisms underlying amiodarone-induced pulmonary fibrosis. Eight hub genes\u0026mdash;ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1, and ADRB2\u0026mdash;were identified at the intersection of drug-target networks and fibrosis-related gene sets. These targets are functionally enriched in biological processes such as membrane-to-nucleus signaling, regulation of phosphorylation, oxidative stress response, and immune modulation. KEGG pathway analysis further highlighted three key signaling cascades\u0026mdash;neuroactive ligand\u0026ndash;receptor interaction, FoxO signaling, and Th17 cell differentiation\u0026mdash;underscoring a multifactorial pathogenesis involving epithelial injury, fibroblast activation, and immune dysregulation.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Mechanistic Insights into Fibrosis Initiation\u003c/h2\u003e\u003cp\u003eThe ABCB1 transporter, which normally mediates pulmonary drug efflux, may become functionally impaired or saturated by amiodarone, thereby promoting its intracellular accumulation and cytotoxicity [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Tyrosine kinases such as ERBB2, SRC, and ABL1 amplify epithelial stress through integrin signaling, TGF-β activation, and reactive oxygen species (ROS)-mediated pathways, ultimately triggering epithelial\u0026ndash;mesenchymal transition (EMT) and fibroblast activation [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. XIAP, an anti-apoptotic protein upregulated by TGF-β1, contributes to myofibroblast persistence and epithelial resistance to apoptosis, thereby sustaining fibrotic remodeling [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Concurrently, AKT1 and HIF1A are activated in response to oxidative and hypoxic stress, promoting fibroblast survival, senescence, and metabolic reprogramming [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. ADRB2, a lung-enriched neuroreceptor, introduces a neuroimmune regulatory axis that may underlie regional susceptibility and radiographic heterogeneity observed in amiodarone-induced lung injury [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Pathway Integration and Systems-Level Interpretation\u003c/h2\u003e\u003cp\u003eEnrichment of the FoxO signaling pathway reflects transcriptional reprogramming in response to cellular stress, influencing extracellular matrix remodeling and fibroblast differentiation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Th17 cell differentiation highlights persistent immune dysregulation, wherein IL-17A and IL-22 signaling amplifies neutrophilic inflammation and tissue injury [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The identification of ADRB2 within the neuroactive ligand\u0026ndash;receptor interaction pathway suggests that receptor-mediated neuroimmune signaling may contribute to the atypical and often unilateral interstitial abnormalities characteristic of amiodarone-induced lung injury [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. At the center of these converging pathways is AKT1, which integrates multiple stress-responsive signals\u0026mdash;including oxidative stress, hypoxia, autophagy, and immune activation\u0026mdash;functioning as a key nodal hub in fibrogenic transformation [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Molecular Docking and Target Prioritization\u003c/h2\u003e\u003cp\u003eMolecular docking confirmed the binding of amiodarone to several key proteins predicted by network pharmacology. Notably, ABCB1 and AKT1 demonstrated the strongest affinities (Kd\u0026thinsp;=\u0026thinsp;0.37 \u0026micro;M), consistent with amiodarone\u0026rsquo;s known tissue accumulation and prolonged half-life [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. These interactions likely impair efflux transport and activate stress-response kinases, amplifying cellular injury. Moderate binding was observed for ERBB2 (2.9 \u0026micro;M) and ADRB2 (7.0 \u0026micro;M), reinforcing their roles in mitochondrial dysfunction and neuroimmune dysregulation, respectively [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe integration of docking results with topological network hubs strengthens the prioritization of these proteins as mechanistically and therapeutically relevant. While docking offers only a static interaction model, its concordance with enriched pathways and toxicokinetic behavior provides a rationale for focusing experimental validation efforts on ABCB1, AKT1, ERBB2, and ADRB2 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Translational and Clinical Implications\u003c/h2\u003e\u003cp\u003eThe identified targets and pathways present multiple opportunities for clinical translation. ABCB1, AKT1, and ERBB2 may serve as early biomarkers, detectable in blood or bronchoalveolar lavage, to anticipate toxicity before radiographic or symptomatic manifestation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Their tissue-specific expression also aligns with observed patterns of lung-selective toxicity and interindividual variability.\u003c/p\u003e\u003cp\u003eTherapeutically, modulation of ABCB1 function may reduce intracellular amiodarone accumulation, while inhibition of ERBB2, SRC, or ABL1 could attenuate profibrotic signaling cascades [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Targeting the AKT1\u0026ndash;FoxO signaling axis and blocking IL-17/IL-22 pathways represent promising adjunctive strategies to restore cellular homeostasis and suppress immune-mediated fibrotic progression [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The involvement of ADRB2 highlights potential avenues for neuroimmune-directed therapies and radiotracer-based imaging, particularly in patients exhibiting atypical or localized pulmonary abnormalities [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePersonalized pharmacogenomic screening\u0026mdash;especially for ABCB1 and AKT1 polymorphisms\u0026mdash;may facilitate risk stratification and inform safer use of amiodarone in individuals predisposed to pulmonary toxicity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Limitations and Future Directions\u003c/h2\u003e\u003cp\u003eDespite the strengths of this integrative approach, several limitations warrant consideration. Network pharmacology is inherently reliant on curated databases, which may omit emerging targets or context-specific molecular interactions [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The identification of hub genes, although based on robust topological criteria, involves thresholding decisions that could exclude biologically relevant peripheral nodes. Additionally, molecular docking provides only a static approximation of ligand\u0026ndash;receptor binding and does not account for intracellular dynamics, post-translational modifications, or pharmacokinetic parameters that influence in vivo behavior [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFuture investigations should prioritize experimental validation of the predicted interactions\u0026mdash;particularly those involving ABCB1, AKT1, ERBB2, and ADRB2\u0026mdash;using lung-relevant cellular models and established pulmonary fibrosis animal systems. Functional assays assessing transporter activity, cytokine signaling, and pathway-specific modulation will be critical to confirm mechanistic relevance. Moreover, longitudinal multi-omics studies are needed to capture time-resolved molecular alterations, enabling the distinction between causal drivers and secondary effects. Finally, pharmacogenomic analyses of patient-specific variants affecting drug transport, kinase signaling, or immune activation may facilitate precision-based risk stratification and inform individualized therapeutic strategies.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically elucidated the molecular mechanisms underlying amiodarone-induced pulmonary fibrosis through an integrative approach combining network pharmacology, enrichment analysis, and molecular docking. Our analysis identified eight key hub targets\u0026mdash;ABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1, and ADRB2\u0026mdash;that sit at the intersection of amiodarone pharmacology and fibrotic pathogenesis. These targets span diverse biological processes, including drug transport, tyrosine kinase signaling, apoptosis regulation, hypoxic adaptation, and neuroactive signaling, underscoring the multifactorial nature of this adverse drug reaction.\u003c/p\u003e\u003cp\u003eEnrichment analyses highlighted the involvement of neuroactive ligand-receptor interaction, FoxO signaling, and Th17 cell differentiation pathways, pointing to previously underappreciated roles of neuroimmune modulation and inflammation in amiodarone-induced lung injury. Molecular docking provided complementary support, demonstrating strong predicted interactions between amiodarone and several hub proteins\u0026mdash;most notably ABCB1 and AKT1 (Kd\u0026thinsp;=\u0026thinsp;0.37 \u0026micro;M each), followed by ERBB2 and ADRB2\u0026mdash;at concentrations relevant to clinical exposure.\u003c/p\u003e\u003cp\u003eTogether, these findings support a coherent membrane-to-nucleus signaling framework, whereby amiodarone disrupts multiple interconnected processes: impaired efflux transport via ABCB1, altered receptor signaling through ADRB2 and ERBB2, propagation of tyrosine kinase cascades involving SRC and ABL1, and transcriptional regulation mediated by AKT1, HIF1A, and XIAP. This integrated perspective advances beyond single-target paradigms, offering a systems-level understanding of how amiodarone induces pulmonary fibrosis. Importantly, the identified targets and pathways suggest candidate biomarkers for early detection and novel therapeutic strategies for mitigating toxicity [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile experimental validation remains essential to confirm these computational predictions, our findings provide a solid foundation for mechanistic, translational, and clinical research. Beyond advancing knowledge of amiodarone-specific toxicity, this study illustrates the broader value of network-based approaches for unraveling complex drug-induced pathologies. Ultimately, these insights may inform improved risk stratification, monitoring, and intervention strategies for patients receiving amiodarone, with potential applicability to other drug-induced fibrotic conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary materials accompanying this article include high-resolution versions of all figures (Figures 1–5) and the original input files used for molecular docking and network construction. Supplementary files are provided in a compressed\u0026nbsp;\u003ccode\u003e.zip\u003c/code\u003e archive and can be accessed online with the published article. All supplementary materials have been prepared in accordance with BMC journal submission guidelines.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors gratefully acknowledge the collective contributions of all co-authors for their intellectual input and collaborative support in the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Huye Li and Rubing Di performed the bioinformatics analysis and molecular docking simulations. Deyuan Kong supervised the project and critically revised the manuscript. All authors contributed to manuscript writing and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets analyzed in this study are publicly available from the following online resources:\u003c/p\u003e\n\u003cp\u003eSTRING:https://cn.string-db.org\u003c/p\u003e\n\u003cp\u003eDAVID:https://david.ncifcrf.gov\u003c/p\u003e\n\u003cp\u003eRCSB Protein Data Bank :https://www.rcsb.org/\u003c/p\u003e\n\u003cp\u003ePubChem Database：https://pubchem.ncbi.nlm.nih.gov/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSwissTargetPrediction\u003c/strong\u003e:http://www.swisstargetprediction.ch/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOMIM\u003c/strong\u003e: https://www.omim.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisGeNET\u003c/strong\u003e: https://www.disgenet.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUniProt\u003c/strong\u003e: https://www.uniprot.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneCards\u003c/strong\u003e: https://www.genecards.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCb-dock2\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003ehttps://cadd.labshare.cn/cb-dock2/index.php\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHamilton D, Sr, et al. 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Mol Med. 2024;30(1):228.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi W, et al. A brain-to-lung signal from GABAergic neurons to ADRB2\u0026thinsp;\u0026lt;\u0026thinsp;sup\u0026gt;+\u0026thinsp;int erstitial macrophages promotes pulmonary inflammatory responses. Immunity. 2025;58(8):2069. \u0026ndash;2085.e9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohn O'Donnell JC. \u003cem\u003eUnilateral Pulmonary Fibrosis due to Acute Amiodarone Toxicity.\u003c/em\u003e International Journal of Clinical \u0026amp; Medical Imaging, 2021. Volume 8, Issue 2(ISSN: 2376\u0026thinsp;\u0026ndash;\u0026thinsp;0249).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen Q-C et al. Mimosa pudica L. extract ameliorates pulmonary fibrosis via modulation of MAPK signaling pathways and FOXO3 stabilization. J Ethnopharmacol, 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L et al. 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Clin Cardiol. 2020;43(12):1469\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMacKenzie M, Hall R. Pharmacogenomics and pharmacogenetics for the intensive care unit: a narrative review. Can J Anaesth. 2017;64(1):45\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuineveld MD, Kers J, Vleming LJ. Case report of progressive renal dysfunction as a consequence of amiodarone-induced phospholipidosis. Eur Heart J Case Rep. 2023;7(9):ytad457.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKozlova N, et al. Acute amiodarone pulmonary toxicity in the form of organizing pneumonia triggered by orthotopic heart transplantation. Respir Med Case Rep. 2021;34:101532.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKwok WC, et al. A multicenter retrospective cohort study on predicting the risk for amiodarone pulmonary toxicity. BMC Pulm Med. 2022;22(1):128.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Amiodarone, Pulmonary fibrosis, Network pharmacology, Molecular docking, Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-7601015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7601015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Pulmonary fibrosis is a common terminal 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, molecular docking, and experimental validation, thereby providing a theoretical basis for its prevention and treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e 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 validated through in vitro and/or in vivo pulmonary fibrosis models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e 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: \u003cem\u003eABCB1\u003c/em\u003e, \u003cem\u003eERBB2\u003c/em\u003e, \u003cem\u003eXIAP\u003c/em\u003e, \u003cem\u003eABL1\u003c/em\u003e, \u003cem\u003eSRC\u003c/em\u003e, \u003cem\u003eHIF1A\u003c/em\u003e, \u003cem\u003eAKT1\u003c/em\u003e, and \u003cem\u003eADRB2\u003c/em\u003e. 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 neuroactive ligand–receptor interaction, FoxO signaling, and Th17 cell differentiation pathways.\u003c/p\u003e\n\u003cp\u003eMolecular docking demonstrated strong binding affinities between amiodarone and the predicted target proteins, with \u003cem\u003eABCB1\u003c/em\u003e and \u003cem\u003eAKT1\u003c/em\u003e exhibiting the highest affinity (Kd = 0.37 μM each), followed by \u003cem\u003eERBB2\u003c/em\u003e (2.9 μM) and \u003cem\u003eADRB2\u003c/em\u003e(7.0 μM). Collectively, these findings suggest a signaling framework in which membrane receptor activation propagates through tyrosine kinase cascades to regulate gene expression, there by linking extracellular stimuli to transcriptional regulation in the pathogenesis of pulmonary fibrosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e This study systematically elucidates the molecular mechanisms underlying amiodarone-induced pulmonary fibrosis by integrating network pharmacology, enrichment analysis, and molecular docking. Eight hub targets (\u003cem\u003eABCB1, ERBB2, XIAP, ABL1, SRC, HIF1A, AKT1,\u003c/em\u003e and \u003cem\u003eADRB2\u003c/em\u003e) and three critical signaling pathways (neuroactive ligand–receptor interaction, FoxO signaling, and Th17 cell differentiation) were identified, providing new insights into the complex mechanisms of amiodarone-associated pulmonary toxicity.\u003c/p\u003e\n\u003cp\u003eThe identified membrane-to-nucleus signaling framework, characterized by high-affinity binding interactions and coordinated cellular responses, enhances mechanistic understanding and may inform the development of targeted therapeutic interventions. These findings not only deepen our knowledge of drug-induced pulmonary fibrosis but also establish a foundation for precision medicine strategies aimed at preventing and treating amiodarone-related pulmonary complications in clinical practice.\u003c/p\u003e","manuscriptTitle":"Network pharmacology and molecular docking reveal mechanisms of amiodarone-induced pulmonary fibrosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 06:41:47","doi":"10.21203/rs.3.rs-7601015/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edc60dd9-c22e-488c-ae93-8303c2aa340e","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-18T00:38:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 06:41:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7601015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7601015","identity":"rs-7601015","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}