Integrated assessment of developmental toxicity of antibiotic pollutants: Machine learning prediction, zebrafish...

Developmental toxicity induced by environmental pollutants, particularly antibiotics, is often insidious and underestimated due to bioaccumulation and subsequent oral intake. This study developed a machine learning-based predictive strategy by constructing and comparing four models using Morgan fingerprints on a curated dataset of developmental toxicants. The optimal random forest model achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval: 0.83-0.91), an accuracy of 0.80, and a Matthews correlation coefficient of 0.59. Applying the ensemble model to 2,341 antibiotics identified miconazole as the highest-probability candidate (average probability 0.97). In zebrafish embryo toxicity assays, exposure to miconazole at low concentrations (0.3 and 3.0 µM) did not result in statistically significant mortality up to 72 h post-fertilization, whereas the high concentration (30 µM) caused significantly elevated mortality at 48 and 72 h (p < 0.05 compared to control). Network toxicology and molecular docking revealed that miconazole may interact with key targets AKT1 and BRAF, potentially perturbing the chemical carcinogenesis-reactive oxygen species signaling pathway. These integrated findings indicate that miconazole exhibits potential developmental toxicity, warranting further mechanistic and long-term exposure studies before drawing definitive regulatory conclusions.