Machine learning prioritization of antibiotic residues in aquatic foods reveals exposure-driven genotoxic risk...

Antibiotic residues in aquatic foods pose genotoxic risks. Traditional monitoring focuses on individual Maximum Residue Limit (MRL) compliance, often overlooking cumulative multi-residue risks. We developed an interpretable machine learning (ML) framework integrating surveillance data (3719 samples, 17 antibiotics) with mechanistic validation to prioritize risks in Guangzhou (2021-2023). Exposure metrics and in silico hazard predictions were analyzed via clustering and ranking. Enrofloxacin, sulfamethoxazole, and 3-amino-2-oxazolidinone were prioritized, with risk drivers deconstructed via Explainable AI (SHAP). In L-02 hepatocytes, prioritized mixtures reduced viability (70.21 ± 7.49%), increased apoptosis (7.12 ± 2.75%), and induced DNA damage (tail DNA% 5.25 ± 1.03%) (all p < 0.05). BCL2 overexpression significantly attenuated this damage (tail DNA% 2.90 ± 2.65%, p < 0.05), confirming its role as a key functional mediator. This surveillance-to-mechanism workflow provides a data-driven paradigm for identifying mechanistic biomarkers and prioritizing food safety interventions, surpassing traditional compliance-based monitoring.