Moore Dwayne R J, Breton Roger L, MacDonald Drew B
The Cadmus Group, Suite 204, 411 Roosevelt Avenue, Ottawa, Ontario K2A 3X9, Canada.
Environ Toxicol Chem. 2003 Aug;22(8):1799-809. doi: 10.1897/00-361.
Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.
一些监管项目依靠定量构效关系（QSAR）模型来预测对生物群的毒性效应。许多现有的QSAR模型能够预测多种物质对生物群的影响，尤其是对水生生物群的影响。监管项目面临的困难在于为其新物质和现有物质项目选择合适的一个或多个QSAR模型进行应用。我们评估了六个QSAR建模软件包的模型性能：生态构效关系（ECOSAR）、TOPKAT、概率神经网络（PNN）、计算神经网络（CNN）、风险评估工具（ASTER）系统的QSAR组件以及基于结构指数集的优化方法（OASIS）系统。使用一个包含130种物质的测试数据集，这些物质未被纳入所考虑的QSAR模型的训练数据集中，我们将对黑头呆鱼96小时半数致死浓度（LC50）的模型预测值与AQUIRE数据库中可获得的相应实测毒性值进行了比较。该测试数据集大量包含低分子量和低官能度的中性有机物。测试数据集中的许多物质还具有非极性麻醉作用模式和/或被氯化。多种统计量（相关系数、线性回归分析的斜率和截距、对数预测值与对数实测毒性之间的平均绝对差和平方差，以及预测值在实测毒性值的2、5、10、100和1000倍范围内的百分比）表明，对于包含130种物质的完整测试数据集，PNN模型具有最佳的模型性能。其余模型的排名顺序取决于所采用的统计量。TOPKAT在其最佳预测空间内对物质也具有出色的模型性能。然而，测试数据集中只有37%的物质落在这个最佳预测空间内。
