Abstract

The possibility of employing computational approaches like nano-QSAR or nano-read-across to predict nanomaterial hazard is attractive from both a financial, and most importantly, where in vivo tests are required, ethical perspective. In the present work, we have employed advanced Machine Learning techniques, including stacked model ensembles, to create nano-QSAR tools for modeling the toxicity of metallic and metal oxide nanomaterials, both coated and uncoated and with a variety of different core compositions, tested at different dosage concentrations on embryonic zebrafish. Using both computed and experimental descriptors, we have identified a set of properties most relevant for the assessment of nanomaterial toxicity and successfully correlated these properties with the associated biological responses observed in zebrafish. Our findings suggest that for the group of metal and metal oxide nanomaterials, the core chemical composition, concentration and properties dependent upon nanomaterial surface and medium composition (such as zeta potential and agglomerate size) are significant factors influencing toxicity, albeit the ranking of different variables is sensitive to the exact analysis method and data modeled. Our generalized nano-QSAR ensemble models provide a promising framework for anticipating the toxicity potential of new nanomaterials and may contribute to the transition out of the animal testing paradigm. However, future experimental studies are required to generate comparable, similarly high quality data, using consistent protocols, for well characterized nanomaterials, as per the dataset modeled herein. This would enable the predictive power of our promising ensemble modeling approaches to be robustly assessed on large, diverse and truly external datasets.

摘要

使用纳米 QSAR 或纳米交叉读取等计算方法来预测纳米材料危害的可能性从财务角度和最重要的是，从需要体内测试的伦理角度来看都是有吸引力的。在目前的工作中，我们采用了先进的机器学习技术，包括堆叠模型集成，以创建纳米 QSAR 工具，用于模拟金属和金属氧化物纳米材料的毒性，包括涂层和未涂层​​以及具有各种不同的核心成分，在对胚胎斑马鱼的不同剂量浓度。使用计算和实验描述符，我们确定了一组与纳米材料毒性评估最相关的特性，并成功地将这些特性与在斑马鱼中观察到的相关生物反应相关联。我们的研究结果表明，对于一组金属和金属氧化物纳米材料，核心化学成分、浓度和特性取决于纳米材料表面和介质成分（例如 zeta 电位和团聚体尺寸）是影响毒性的重要因素，尽管不同变量的排序不同对精确的分析方法和建模的数据敏感。我们的广义纳米 QSAR 集成模型为预测新纳米材料的毒性潜力提供了一个有前途的框架，并可能有助于从动物测试范式中过渡出来。然而，未来的实验研究需要根据此处建模的数据集，使用一致的协议，为特征良好的纳米材料生成可比较的、类似的高质量数据。