Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
Chemosphere. 2014 Feb;96:188-94. doi: 10.1016/j.chemosphere.2013.10.039. Epub 2013 Nov 9.
The acute toxicity predictive models are vitally important for the toxicological information used in the ecological risk assessments. In this study, we used Verhaar classification scheme to group compounds into five modes of toxic action. The quantum chemical descriptors that characterize the electron donor-acceptor property of the compounds were introduced into the theoretical linear solvation energy relationship (TLSER) models. The predictive models have relatively larger data sets, which imply that they cover a wide applicability domain (AD). All models were developed following the Organization for Economic Co-operation and Development (OECD) QSAR models development and validation guidelines. The adjusted determination coefficient (Radj(2)) and external explained variance (QEXT(2)) of the models were ranging from 0.707 to 0.903 and 0.660 to 0.858, respectively, indicating high goodness-of-fit, robustness and predictive capacity. The cavity term (McGowans volume) was the most significant descriptor in the models. Moreover, the electron donor-acceptor (E-TLSER) models are comparable to the TLSER models for the toxicity prediction to fathead minnow. Thus, the E-TLSER models developed can be used to predict acute toxicity of new compounds within the AD.
急性毒性预测模型对于生态风险评估中使用的毒理学信息至关重要。在本研究中,我们使用 Verhaar 分类方案将化合物分为五种毒性作用模式。将表征化合物电子供体-受体性质的量子化学描述符引入理论线性溶剂化能关系(TLSER)模型中。预测模型具有相对较大的数据集,这意味着它们涵盖了广泛的适用性域(AD)。所有模型都是按照经济合作与发展组织(OECD)QSAR 模型开发和验证指南开发的。模型的调整确定系数(Radj(2))和外部解释方差(QEXT(2))分别在 0.707 到 0.903 和 0.660 到 0.858 之间,表明具有较高的拟合优度、稳健性和预测能力。腔项(McGowans 体积)是模型中最重要的描述符。此外,对于预测食蚊鱼的毒性,电子供体-受体(E-TLSER)模型与 TLSER 模型相当。因此,开发的 E-TLSER 模型可用于 AD 内预测新化合物的急性毒性。