School of Chemical Engineering, National Technical University of Athens, Athens, Greece.
School of Chemical and Process Engineering, University of Leeds, Leeds, United Kingdom.
Nanotoxicology. 2021 May;15(4):446-476. doi: 10.1080/17435390.2021.1872113. Epub 2021 Feb 15.
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.
从财务角度来看，利用计算方法（如纳米定量构效关系或纳米读码）来预测纳米材料的危害是一种很有吸引力的方法，尤其是在需要进行体内测试的情况下，从伦理角度来看更是如此。在本工作中，我们采用了先进的机器学习技术，包括堆叠模型集成，为建模金属和金属氧化物纳米材料的毒性创建纳米定量构效关系工具，这些纳米材料既有涂层的也有无涂层的，并且具有多种不同的核心成分，在不同的剂量浓度下在胚胎斑马鱼上进行了测试。我们使用计算和实验描述符，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学反应相关联。我们的研究结果表明，对于金属和金属氧化物纳米材料组，核心化学成分、浓度以及取决于纳米材料表面和介质组成的特性（如 ζ 电位和团聚体大小）是影响毒性的重要因素，尽管不同变量的排序对确切的分析方法和建模数据敏感。我们的广义纳米定量构效关系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，并可能有助于摆脱动物测试范式。然而，需要进行未来的实验研究，以使用一致的协议生成具有类似高质量数据的可比数据，对于经过良好表征的纳米材料，应根据本文建模的数据集中的协议进行。这将使我们有前途的集成建模方法的预测能力能够在大型、多样化和真正外部的数据集中得到稳健评估。
