Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.
Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.
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DOI：
10.1021/tx900189p
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发表时间：
2009-12
影响因子：
4.1
通讯作者：
Tropsha, Alexander
中科院分区：
文献类型：
--
作者：
Zhu, Hao;Martin, Todd M.;Ye, Lin;Sedykh, Alexander;Young, Douglas M.;Tropsha, Alexander
很少有定量构效关系（QSAR）研究成功地模拟了大型、多样的啮齿动物毒性终点。在这项研究中，编制了7385种化合物及其最保守致死剂量（LD50）值的综合数据集。一个组合的QSAR方法已被用于开发大和预测模型的急性毒性的大鼠口服暴露于化学品。为了在本研究中生成的模型的预测能力与商业毒性预测器TOPKAT（计算机辅助毒性预测技术）之间进行公平的比较，选择了整个数据集的建模子集，其中包括TOPKAT训练集中使用的所有3,472种化合物。其余3913个TOPKAT训练集中不存在的化合物被用作外部验证集。建立了5种不同类型的QSAR模型。外部验证集的预测精度采用实际LD50值与预测LD50值线性回归的决定系数R2进行估计。大多数模型采用的适用域阈值总体上提高了外部预测精度，但预期会导致化学空间覆盖率下降；根据适用域阈值的不同，R2范围为0.24 ~ 0.70。最终，通过使用所有5种模型对每种化合物的预测LD50进行平均，建立了几个共识模型。与单个成分模型相比，共识模型为外部验证数据集提供了更高的预测精度和更高的覆盖率。本研究建立的经验证的共识LD50模型可作为体内急性毒性的可靠计算预测因子。
Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. In this study, a comprehensive dataset of 7,385 compounds with their most conservative lethal dose (LD50) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire dataset was selected that included all 3,472 compounds used in the TOPKAT’s training set. The remaining 3,913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R2 of linear regression between actual and predicted LD50 values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R2 ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD50 for every compound using all 5 models. The consensus models afforded higher prediction accuracy for the external validation dataset with the higher coverage as compared to individual constituent models. The validated consensus LD50 models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.
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影响因子：
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