BASF SE, Limburgerhof, Germany.
Bayer AG, Monheim, Germany.
Sci Total Environ. 2020 Apr 15;713:136667. doi: 10.1016/j.scitotenv.2020.136667. Epub 2020 Jan 13.
Peer-reviewed Transpiration Stream Concentration Factor (TSCF) values were analysed to elucidate whether pH-induced changes in lipophilicity can explain some of the variability in reported TSCF and whether a potential relationship between lipophilicity and TSCF can be described by a simple mathematical model. The data set for this investigation combined TSCF values of 42 non-ionisable and ionisable compounds from hydroponic tests with intact plants and publicly available lipophilicity data for the tested compounds. The data set was not homogenous in terms of molecular weight of the tested compounds, plant species used for testing and experimental conditions, but a strong effect of one of these factors on variation in reported TSCF was not detected. Variation in TSCF was high for the same or similar predicted octanol/water partitioning coefficient (log P) but could be reduced by considering octanol/water distribution coefficients (log D) instead. The TSCF data set was split into a training and a test data set in order to identify and test a best-fit model describing the relationship between log D and TSCF. Comparing different types of models (linear, sigmoidal, Gaussian), the Gaussian model fitted to the training data set after removal of two outliers was identified as best-fit model based on visual assessment and fit statistics (RMSE = 0.20, NSE = 0.57, R = 0.75 (p < 0.001)). The 95% confidence interval around the best-fit model contained about 70% of data points in the training set and the test set, respectively. In conclusion, compound lipophilicity expressed as log D is a more appropriate descriptor of uptake by plant roots and subsequent translocation than log P when ionisable compounds are considered. Furthermore, findings in this study suggest that a relationship exists between log D and TSCF for uptake tests with intact plants which can be described by a simple bell-shaped Gaussian model.
经同行评审的蒸腾流浓缩因子(TSCF)值进行了分析,以阐明 pH 诱导的亲脂性变化是否可以解释部分报道的 TSCF 变异性,以及亲脂性和 TSCF 之间是否存在简单的数学模型描述的潜在关系。本研究的数据集中包括了 42 种非离子化和可离子化化合物的 TSCF 值,这些化合物是在水培试验中用完整植物进行测试,并提供了测试化合物的可公开获取的亲脂性数据。该数据集在测试化合物的分子量、用于测试的植物物种和实验条件方面并不均匀,但没有检测到其中一个因素对报告的 TSCF 变异有强烈影响。对于相同或相似的预测辛醇/水分配系数(log P),TSCF 的变异很大,但考虑辛醇/水分配系数(log D)可以减少这种变异。将 TSCF 数据集分为训练数据集和测试数据集,以确定并测试描述 log D 和 TSCF 之间关系的最佳拟合模型。比较不同类型的模型(线性、S 型、高斯),发现去除两个异常值后,适用于训练数据集的高斯模型是根据视觉评估和拟合统计数据(RMSE=0.20、NSE=0.57、R=0.75(p<0.001))确定的最佳拟合模型。最佳拟合模型的 95%置信区间分别包含训练集和测试集中约 70%的数据点。总之,当考虑可离子化化合物时,用 log D 表示的化合物亲脂性是植物根系吸收和随后转运的更合适的描述符,而不是 log P。此外,本研究的结果表明,对于完整植物的吸收测试,log D 和 TSCF 之间存在关系,可以用简单的钟形高斯模型来描述。