Machine-learning Assisted Insights into Cytotoxicity of Zinc Oxide Nanoparticles

Abstract

Abstract Zinc oxide nanoparticles (ZnO NPs) are commercially used as an active ingredient or a color additive in foods, pharmaceuticals, sun protection lotions, and cosmetic products. While the use of ZnO NPs in everyday products has not been linked to any serious health issues so far, the scientific evidence generated for their safety is not conclusive and, in most cases, could not be validated further in in vivo settings. To settle controversies arising from inconsistent in vitro findings in previous research focusing on the toxicity ZnO NPs, we combined the results of 25+ independent studies. One way analysis of variance (ANOVA) and classification and regression tree (CART) algorithm were used to pinpoint intrinsic and extrinsic factors influencing cytotoxic potential of ZnO in nanoscale. Particle size was found to have the most significant impact on the cytotoxic potential of ZnO NPs, with 10 nm identified as a critical diameter below which cytotoxic effects were elevated. As expected, strong cell type-, exposure duration- and dose-dependency were observed in cytotoxic response of ZnO NPs, highlighting the importance of assay optimization for each cytotoxicity screening. Our findings also suggested that ≥12 hours exposure to NPs resulted in cytotoxic responses irrespective of the concentration. Considering the cumulative nature of research processes where advances are made through subsequent investigations over time, such meta-analytical approaches are critical to maximizing the use of accumulated data in nano-safety research.