Golbamaki A, Cassano A, Lombardo A, Moggio Y, Colafranceschi M, Benfenati E
a Laboratory of Chemistry and Environmental Toxicology , Istituto di Ricerche Farmacologiche Mario Negri - IRCCS , Via La Masa 19, 20156 Milano , Italy.
SAR QSAR Environ Res. 2014;25(8):673-94. doi: 10.1080/1062936X.2014.923041. Epub 2014 Jun 9.
Eight in silico modelling packages were evaluated and compared for the prediction of Daphnia magna acute toxicity from the viewpoint of the European legislation on chemicals, REACH. We tested the following models: Discovery Studio (DS) TOPKAT, ACD/Tox Suite, ADMET Predictor, ECOSAR (Ecological Structure Activity Relationships), TerraQSAR, T.E.S.T. (Toxicity Estimation Software Tool) and two models implemented in VEGA on 480 industrial compounds for 48-h median lethal concentrations (LC50) to D. magna, matching them with experimental values. The quality of the estimates was compared using a standard statistical review and an additional classification approach in which the hazard predictions were grouped using well-defined regulatory criteria. The regression parameters, correlation coefficient being the most influential, showed that four models (ADMET Predictor, DS TOPKAT, TerraQSAR and VEGA DEMETRA) had similar reliability. These performed better than the others, but the coefficient of determination was still low (r2 around 0.6), considering that at least half the predicted compounds were inside the training sets. Additionally, we grouped the results in four defined toxicity classes. TerraQSAR™ gave 60% of correct classifications, followed by DS TOPKAT, ADMET Predictor™ and VEGA DEMETRA, with 56%, 54% and 48%, respectively. These results highlight the challenges associated with developing reliable and easily applied acceptability criteria for the regulatory use of QSAR models to D. magna acute toxicity.
从欧洲化学品法规《化学品注册、评估、授权和限制》（REACH）的角度出发，对八个计算机模拟软件包进行了评估和比较，以预测大型溞的急性毒性。我们测试了以下模型：Discovery Studio（DS）TOPKAT、ACD/Tox Suite、ADMET Predictor、ECOSAR（生态结构活性关系）、TerraQSAR、T.E.S.T.（毒性估计软件工具）以及VEGA中实现的两个模型，针对480种工业化合物对大型溞的48小时半数致死浓度（LC50）进行预测，并将预测值与实验值进行匹配。使用标准统计审查和一种额外的分类方法比较估计质量，在额外的分类方法中，根据明确的监管标准对危害预测进行分组。回归参数（相关系数影响最大）表明，四个模型（ADMET Predictor、DS TOPKAT、TerraQSAR和VEGA DEMETRA）具有相似的可靠性。这些模型的表现优于其他模型，但考虑到至少一半的预测化合物在训练集中，决定系数仍然较低（r2约为0.6）。此外，我们将结果分为四个定义的毒性类别。TerraQSAR™给出了60%的正确分类，其次是DS TOPKAT、ADMET Predictor™和VEGA DEMETRA，分别为56%、54%和48%。这些结果凸显了为将QSAR模型用于监管大型溞急性毒性制定可靠且易于应用的可接受标准所面临的挑战。
