Laboratory of Chemistry and Environmental Toxicology, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri, via Giuseppe La Masa 19, 20156 Milan, Italy.
Sci Total Environ. 2013 Jul 1;456-457:325-32. doi: 10.1016/j.scitotenv.2013.03.104. Epub 2013 Apr 24.
QSAR (Quantitative Structure Activity Relationship) models can be a valuable alternative method to replace or reduce animal test required by REACH. In particular, some endpoints such as bioconcentration factor (BCF) are easier to predict and many useful models have been already developed. In this paper we describe how to integrate two popular BCF models to obtain more reliable predictions. In particular, the herein presented integrated model relies on the predictions of two among the most used BCF models (CAESAR and Meylan), together with the Applicability Domain Index (ADI) provided by the software VEGA. Using a set of simple rules, the integrated model selects the most reliable and conservative predictions and discards possible outliers. In this way, for the prediction of the 851 compounds included in the ANTARES BCF dataset, the integrated model discloses a R(2) (coefficient of determination) of 0.80, a RMSE (Root Mean Square Error) of 0.61 log units and a sensitivity of 76%, with a considerable improvement in respect to the CAESAR (R(2)=0.63; RMSE=0.84 log units; sensitivity 55%) and Meylan (R(2)=0.66; RMSE=0.77 log units; sensitivity 65%) without discarding too many predictions (118 out of 851). Importantly, considering solely the compounds within the new integrated ADI, the R(2) increased to 0.92, and the sensitivity to 85%, with a RMSE of 0.44 log units. Finally, the use of properly set safety thresholds applied for monitoring the so called "suspicious" compounds, which are those chemicals predicted in proximity of the border normally accepted to discern non-bioaccumulative from bioaccumulative substances, permitted to obtain an integrated model with sensitivity equal to 100%.
QSAR（定量构效关系）模型可以作为一种替代或减少 REACH 要求的动物测试的有价值的方法。特别是一些终点，如生物浓缩因子（BCF），更容易预测，并且已经开发了许多有用的模型。在本文中，我们描述了如何整合两个流行的 BCF 模型以获得更可靠的预测。特别是，本文提出的集成模型依赖于两个最常用的 BCF 模型（CAESAR 和 Meylan）中的两个预测，以及软件 VEGA 提供的适用性域指数（ADI）。使用一组简单的规则，集成模型选择最可靠和保守的预测，并排除可能的异常值。通过这种方式，对于包含在 ANTARES BCF 数据集中的 851 种化合物的预测，集成模型的 R2（决定系数）为 0.80，RMSE（均方根误差）为 0.61 个对数单位，灵敏度为 76%，与 CAESAR（R2=0.63；RMSE=0.84 对数单位；灵敏度 55%）和 Meylan（R2=0.66；RMSE=0.77 对数单位；灵敏度 65%）相比有了相当大的改进，而没有排除太多的预测（851 种中的 118 种）。重要的是，仅考虑新的集成 ADI 内的化合物，R2 增加到 0.92，灵敏度增加到 85%，RMSE 为 0.44 个对数单位。最后，使用适当设置的安全阈值用于监测所谓的“可疑”化合物，这些化合物是那些预测在通常用于区分非生物累积物质和生物累积物质的边界附近的化学物质，这允许获得灵敏度等于 100%的集成模型。
