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.

中文翻译：

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使用多元回归、交叉读取和 ML 算法深入了解纳米颗粒对水生生物的毒性：Daphnia magna 和 Danio rerio 的预测模型


由于纳米颗粒 （NP） 在快速增长的纳米技术行业中的众多应用，其生产最近变得更加普遍。尽管纳米颗粒的应用越来越广泛，但由于它们不可避免地会释放到环境中，因此人们对其环境影响非常担忧。随着对水生生物风险的增加，D. magna 和斑马鱼 （Danio rerio） 已成为水生环境中金属氧化物基纳米颗粒 （MeOxNPs） 风险评估和生态毒理学研究的重要淡水模式生物。通过体内或体外实验评估与每个 NP 相关的风险是不可行的。作为替代方案，采用计算机模拟方法来评估 NP 毒性。为了评估这种性能，我们从数据库和文献综述中收集了数据，以开发基于多元回归、跨读法 （RA） 和机器学习 （ML） 算法的模型，遵循 OECD（经济合作与发展组织）的原则进行 QSAR 建模。这项工作旨在使用简单的元素周期表衍生描述符来研究哪些特征是 D. magna 和 Danio rerio 纳米毒性的重要驱动因素。此外，与传统的 QSAR 模型相比，我们还检查了跨读派生相似性测量的有效性。从模型 1 获得的结果推断，纳米颗粒的大小、金属的数量、金属氧化物中存在的金属的核心环境以及金属的氧化数在纳米颗粒对 D. magna 的毒性的最终表达中起着关键作用。 另一方面，较高分子量（金属核心）的存在和氧的存在会影响酶抑制活性。酶抑制与斑马鱼胚胎的孵化能力相关，因此，ZHE1 的抑制似乎是导致孵化延迟的因素。该研究强调了开发可转移、可重复和易于解释的模型的重要性，以早期识别导致水生毒性的纳米颗粒特征。