Integration of QSAR models for bioconcentration suitable for REACH
Integration of QSAR models for bioconcentration suitable for REACH
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DOI：
10.1016/j.scitotenv.2013.03.104
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发表时间：
2013-07-01
影响因子：
9.8
通讯作者：
Benfenati, Emilio
中科院分区：
文献类型：
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作者：
Gissi, Andrea;Nicolotti, Orazio;Benfenati, Emilio
QSAR（定量结构活性关系）模型可以替代或减少REACH要求的动物试验，是一种有价值的替代方法。特别是，一些终点，如生物浓度因子（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种流行的BCF模型以获得更可靠的预测。特别地，本文提出的集成模型依赖于最常用的两个BCF模型（CAESAR和Meylan）的预测，以及VEGA软件提供的适用性领域指数（ADI）。使用一套简单的规则，集成模型选择最可靠和保守的预测，并丢弃可能的异常值。通过这种方式,预测的851种化合物中ANTARES供应量数据集,综合模型揭示r2(确定系数)的0.80,0.61的RMSE(均方根误差)日志单位和灵敏度为76%,有相当大的改善对凯撒(r2 = 0.63; RMSE = 0.84日志单位;灵敏度55%)和Meylan (r2 = 0.66; RMSE = 0.77日志单位;灵敏度65%)没有丢弃太多预测(118 851)。重要的是，单独考虑新集成ADI内的化合物，R-2增加到0.92，灵敏度增加到85%，RMSE为0.44 log单位。最后，使用适当设定的安全阈值来监测所谓的“可疑”化合物，这些化合物是在边界附近预测的通常用于区分非生物累积性物质和生物累积性物质的化学物质，允许获得灵敏度等于100%的综合模型。(C) 2013 Elsevier B.V.版权所有
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%. (C) 2013 Elsevier B.V. All rights reserved.