Network toxicology reveals key genes of amiodarone induced pulmonary fibrosis: based on machine learning and SHAP analysis - PubMed
14 hours ago
- #pulmonary fibrosis
- #machine learning
- #network toxicology
- Amiodarone (AMD) is a Class III antiarrhythmic drug with a serious adverse effect of inducing pulmonary fibrosis (AIPF).
- The study used network toxicology, machine learning (ML), and in vitro validation to identify key genes in AIPF.
- Eight candidate hub genes were identified, with CTSK, ADORA3, and AGER being the most important predictors according to SHAP analysis.
- Molecular docking and dynamics simulations confirmed stable binding between AMD and the core targets (CTSK, ADORA3, AGER).
- In vitro experiments showed AMD treatment upregulated CTSK and downregulated ADORA3 and AGER in BEAS-2B cells, enhancing cell migration and invasion.
- The findings suggest CTSK, ADORA3, and AGER play key roles in AIPF pathogenesis, offering potential biomarkers and therapeutic targets.