Development of QSAAR and QAAR models for predicting fish early-life stage toxicity with a focus on industrial chemicals

We developed models for predicting fish early-life stage (ELS) toxicities oriented to industrial chemicals. The training set was constructed without data from the Office of Pesticide Programs Pesticide Ecotoxicity Database, the main source for the pesticide-biased training set used in our previous work (SAR QSAR Environ. Res. 29:9, 725–742). In addition to the descriptors from the previous study, we also used water solubility to develop the new models, which were evaluated against the test set used in our previous study so that we could focus on the effects of the different training set and the additional descriptor. The statistics for the new models were hardly better than those for the previous models, which suggests, contrary to our expectations, that pesticide-biased data can successfully be used to develop models for predicting the fish ELS toxicities oriented to industrial chemicals. Acute <i>Daphnia magna</i> toxicity was important for the predictive QSAARs in both studies. A distance-based method for defining the applicability domains indicated that water solubility was a key indicator for detecting underestimated chemicals. The comparison of fish ELS toxicities for chemicals presented in different literatures revealed the uncertainty of the experimental data, which may lead to the low predictivity.

我们开发了面向工业化学品的鱼类早期生命阶段（Early-Life Stage, ELS）毒性预测模型。本次构建训练集时，未纳入来自农药项目办公室（Office of Pesticide Programs）农药生态毒性数据库的数据——该数据库正是我们此前工作（发表于《SAR QSAR环境研究》（SAR QSAR Environ. Res. 29:9, 725–742））中所使用的偏向农药类样本的训练集的主要数据源。除沿用此前研究中的分子描述符外，我们还引入了水溶解度以构建新模型；为了单独探究训练集差异与新增描述符的影响，我们采用此前研究的测试集对新模型进行了评估。新模型的统计性能指标仅略优于此前的模型，这与我们的预期相悖，表明偏向农药类的数据集可成功用于构建面向工业化学品的鱼类早期生命阶段毒性预测模型。两项研究中，大型溞（Daphnia magna）急性毒性对预测性定量结构-活性关系（QSAAR）模型均具有重要价值。一种基于距离的适用域定义方法显示，水溶解度是识别低估毒性化学品的关键指标。对不同文献中报道的化学品鱼类早期生命阶段毒性数据进行比对后发现，实验数据存在不确定性，这可能是导致模型预测性能偏低的原因。