A High-Throughput/High-Content Screening Model for Nanotoxicity Assessment Using Autophagy as a Mechanistic Indicator within an AOP in Zebrafish Embryos.

As nanotechnology advances, the widespread use of nanoparticles (NPs) raises concerns regarding their environmental and biological risks. Conventional toxicity assessments remain limited by low throughput and weak linkage to adverse outcomes. Here, we establish a high-throughput/high-content screening (HTS/HCS) zebrafish embryo model for nanotoxicity assessment within an Adverse Outcome Pathway (AOP) framework, linking key events to toxicity outcomes. To standardize exposure conditions, we optimized concentrations and identified deionized (DI) water as a stable medium that preserves NP physicochemical properties without altering acute toxicity. Exposure to silver nanoparticles (Ag NPs) induced significant increases in reactive oxygen species (ROS) and autophagy markers (LC3 and p62). Correlation analysis revealed strong associations between these mechanistic indicators and mortality (ROS: r = 0.97; LC3: r = 0.99; p62: r = 0.92). Using a ROS scavenger and autophagy inhibition, we identified ROS accumulation and autophagy disruption as key events in NP-induced toxicity. Furthermore, application of the platform to ZnO and Cu NPs validated the approach, with consistent ROS/p62-toxicity correlations (ROS: r = 0.84, p = 0.04; p62: r = 0.70, p = 0.01). Together, these results establish a robust, scalable HTS/HCS platform linking AOP-relevant key events to adverse outcomes for mechanism-driven nanotoxicity assessment in environmental and regulatory contexts.