Computational targeting of TGFβ1 in pulmonary fibrosis following thoracic surgery for emergency nursing postoperative respiratory care

Postoperative pulmonary fibrosis following thoracic surgery is associated with impaired respiratory recovery, prolonged hospitalization, and increased postoperative complications. Transforming growth factor-beta 1 TGF-β1 signaling plays a central role in fibrotic progression through regulation of fibroblast activation, extracellular matrix deposition, and inflammatory responses. The present study employed a computational drug repurposing approach to identify potential modulators of TGF-β receptor I kinase associated with pulmonary fibrosis. Molecular docking, ADMET profiling, Density Functional Theory analysis, and 500 ns molecular dynamics simulations were performed to evaluate selected compounds targeting TGF-β receptor I kinase. Among the screened compounds, Imatinib demonstrated favorable docking interactions and maintained a comparatively consistent interaction profile during the molecular dynamics simulation period. MM-GBSA analysis suggested energetically favorable ligand–receptor interactions in the equilibrated simulation state, while ADMET profiling indicated acceptable pharmacokinetic and drug-likeness characteristics. In addition, Density Functional Theory analysis provided insights into the electronic properties of Imatinib relevant to molecular interaction behavior. Although the findings are based entirely on computational analyses, the study highlights the potential relevance of molecular targeting strategies against TGF-β1 signaling in pulmonary fibrosis following thoracic surgery. Further experimental and clinical validation studies are required to confirm the therapeutic relevance of these findings and their potential implications for emergency nursing postoperative respiratory care.