To Kimberly T, Truong Lisa, Edwards Sabrina, Tanguay Robert L, Reif David M
Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.
Bioinformatics Graduate Program, North Carolina State University, Raleigh, NC, USA.
NanoImpact. 2019 Apr;16. doi: 10.1016/j.impact.2019.100185. Epub 2019 Nov 1.
Despite the increasing prevalence of engineered nanomaterials (ENMs) in consumer products, their toxicity profiles remain to be elucidated. ENM physicochemical characteristics (PCC) are known to influence ENM behavior, however the mechanisms of these effects have not been quantified. Further confounding the question of how the PCC influence behavior is the inclusion of structural and molecular descriptors in modeling schema that minimize the effects of PCC on the toxicological endpoints. In this work, we analyze ENM physico-chemical measurements that have not previously been studied within a developmental toxicity framework using an embryonic zebrafish model. In testing a panel of diverse ENMs to build a consensus model, we found nonlinear relationships between any singular PCC and bioactivity. By using a machine learning (ML) method to characterize the information content of combinatorial PCC sets, we found that concentration, surface area, shape, and polydispersity can accurately capture the developmental toxicity profile of ENMs with consideration to whole-organism effects.
尽管工程纳米材料（ENM）在消费品中的普及率不断上升，但其毒性特征仍有待阐明。已知ENM的物理化学特性（PCC）会影响ENM的行为，然而这些影响的机制尚未得到量化。在建模方案中纳入结构和分子描述符，以尽量减少PCC对毒理学终点的影响，这进一步混淆了PCC如何影响行为的问题。在这项工作中，我们使用胚胎斑马鱼模型分析了以前在发育毒性框架内未研究过的ENM物理化学测量结果。在测试一组不同的ENM以建立共识模型时，我们发现任何单一PCC与生物活性之间存在非线性关系。通过使用机器学习（ML）方法来表征组合PCC集的信息内容，我们发现浓度、表面积、形状和多分散性在考虑全生物体效应的情况下可以准确地捕捉ENM的发育毒性特征。
