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Screening Priority Factors Determining and Predicting the Reproductive Toxicity of Various Nanoparticles.

Due to the numerous factors (e.g., nanoparticle [NP] properties and experimental conditions) influencing nanotoxicity, it is difficult to identify the priority factors dominating nanotoxicity. Herein, by integrating data from the literature and a random forest model, the priority factors determining reproductive toxicity were successfully screened from highly heterogeneous data. Among 10 factors from more than 18 different NPs, the NP type and the exposure pathway were found to dominantly determine NP accumulation. The reproductive toxicity of various NPs primarily depended on the NP type and the toxicity indicators. Nanoparticles containing major elements (e.g., Zn and Fe) tended to accumulate in rats but induced lower toxicity than NPs containing noble elements. Compared with other exposure pathways, i.p. injection posed significantly higher risks for NP accumulation. By combining similarity network analysis and hierarchical clustering, the sources of highly heterogeneous data were identified, the factor-toxicity dependencies were extracted and visualized, and the prediction of nanotoxicity was then achieved based on the screened priority factors. The present work provides insights for the design of animal experiments and the illustration and prediction of nanotoxicity.