Abstract
Amikacin, an aminoglycoside antibiotic for severe Gram-negative infections, is limited by dose-dependent nephrotoxicity. However, its acute kidney injury (AKI) risk profile and underlying molecular mechanisms remain insufficiently characterized in real-world settings. This study integrated real-world data with computational biology approaches. Pharmacovigilance analysis was performed using the FDA Adverse Event Reporting System (FAERS) to identify the risk of acute AKI associated with amikacin. Network toxicology was utilized to screen shared targets, while molecular docking and dynamics simulations were conducted to evaluate binding interactions. The expression of core genes was validated using GEO datasets. Disproportionality analysis indicated a significant amikacin-AKI association. Injectable formulation posed higher risk than inhalation (OR = 7.47). Male sex and age ≤ 65 years were independent risk factors. Network toxicology identified IL1B, CXCL8, SIRT1, and PTGS2 as hub genes. Molecular docking showed strong binding (SIRT1, - 7.789 kcal/mol; PTGS2, - 9.467 kcal/mol), with dynamics indicating stability over 100 ns. GEO analysis corroborated the predicted upregulation of IL1B and CXCL8 in AKI, and further supported the involvement of PTGS2, which was significantly upregulated in a cisplatin‑induced AKI model. This study delineates the risk profile of amikacin-associated AKI and elucidates a molecular mechanism involving multi-target interactions in renal injury induction, thereby offering a theoretical basis and identifying potential molecular targets for further investigation into clinical risk mitigation strategies.