University of Antwerp, Department of Biology, Ecophysiology, Biochemistry and Toxicology, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
Chemosphere. 2010 Sep;81(2):177-86. doi: 10.1016/j.chemosphere.2010.06.059. Epub 2010 Jul 15.
Aquatic toxicity information is essential in environmental risk assessment to determine the potential hazards and risks of new and existing chemicals. Prediction and modelling techniques, such as quantitative structure activity relationships (QSAR) and species sensitivity distributions (SSDs), are applied to fill data gaps and to predict, assess and extrapolate the toxicity of chemicals. In this study, both techniques (i.e. the ECOSAR programme as QSAR tool and SSDs) were assessed for a set of polar narcotic structural analogues that differ in their degree of chloro-substitution (aniline, 4-chloroaniline, 3,5-dichloroaniline and 2,3,4-chloroaniline). The acute toxicity of these compounds was tested in one prokaryote species (Escherichia coli) and three eukaryote aquatic species (Pseudokirchneriella subcapitata, Daphnia magna and Danio rerio). Consequently, the experimental acute toxicity data were compared to the QSAR predictions made by the ECOSAR programme and compared to the species sensitivity modelling results. Large interspecies differences in sensitivity were observed (D. magna>P. subcapitata>D. rerio>E. coli). 4-Chloroaniline acted as an outlier in P. subcapitata toxicity. Whereas in D. magna, toxicity decreased rather than increased with increasing logK(ow) of the test compounds. In general, large interchemical and interspecies differences in toxicity of these relatively simple chemical structures were observed. Moreover, this species variation could not entirely be characterized by the ECOSAR tool. SSD modelling is particularly focussed on species variations and emphasis is put on protecting those species that are most affected by chemical exposure. Compared to QSARs, SSDs offer broader perspectives regarding species sensitivity ranking, however, in this study they could only be applied for aniline and 4-chloroaniline.
水生毒性信息对于环境风险评估至关重要,可用于确定新的和现有的化学物质的潜在危害和风险。预测和建模技术,如定量构效关系(QSAR)和物种敏感度分布(SSD),被应用于填补数据空白,并预测、评估和推断化学物质的毒性。在这项研究中,这两种技术(即 ECOSAR 程序作为 QSAR 工具和 SSD)都被用于一组极性麻醉结构类似物,这些类似物在氯取代程度上有所不同(苯胺、4-氯苯胺、3,5-二氯苯胺和 2,3,4-三氯苯胺)。这些化合物的急性毒性在一个原核生物物种(大肠杆菌)和三个真核水生物种(拟南芥、大型溞和斑马鱼)中进行了测试。因此,将实验急性毒性数据与 ECOSAR 程序进行的 QSAR 预测进行了比较,并与物种敏感性建模结果进行了比较。观察到物种间敏感性存在较大差异(大型溞>拟南芥>斑马鱼>大肠杆菌)。4-氯苯胺在拟南芥毒性中表现为异常值。而在大型溞中,毒性随着测试化合物的 logKow 值的增加而降低而不是增加。一般来说,这些相对简单化学结构的毒性在不同化学物质和不同物种之间存在较大差异。此外,这种物种变异不能完全用 ECOSAR 工具来描述。SSD 建模特别关注物种变异,并强调保护那些受化学暴露影响最大的物种。与 QSAR 相比,SSD 提供了更广泛的物种敏感性排序视角,然而,在本研究中,它们只能应用于苯胺和 4-氯苯胺。