Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML...

The production of nanoparticles (NPs) has recently become more prevalent owing to their numerous applications in the fast-growing nanotechnology industry. Although nanoparticles have growing applications, there is a significant concern over their environmental impact due to their inevitable release into the environment. With the increasing risk to aquatic organisms, D. magna and zebrafish (Danio rerio) have been preferred as important freshwater model organisms for risk assessment and ecotoxicological studies on metal oxide-based nanoparticles (MeOxNPs) in aquatic environments. It is unfeasible to assess the risks associated with every single NP through in vivo or in vitro experiments. As an alternative, in silico approaches are employed to evaluate the NP toxicity. To evaluate such performance, we have collected data from databases and literature reviews to develop models based on multivariate regression, read-across approach (RA), and machine learning (ML) algorithms following the principles of OECD (Organization for Economic Cooperation and Development) for QSAR modeling. This work has aimed to investigate which features are important drivers of nanotoxicity in D. magna and Danio rerio using simple periodic table-derived descriptors. Further, we have examined the effectiveness of read-across-derived similarity measures compared to traditional QSAR models. The results obtained from model 1 infers that nanoparticles' size, the number of metals, the core environment of the metal present in the metal oxide, and the oxidation number of the metal play a key role in the final expression of toxicity of nanoparticles to D. magna. On the other hand, the presence of higher molecular weight, the core of the metal, and the presence of oxygen influence the enzyme inhibition activity. The enzyme inhibition is correlated with the ability of zebrafish embryos to hatch, and therefore, the inhibition of ZHE1 seems to be the factor driving hatch delay. The study emphasized the importance of developing transferable, reproducible, and easily interpretable models for the early identification of nanoparticle features contributing to aquatic toxicity.