Berthod L, Whitley D C, Roberts G, Sharpe A, Greenwood R, Mills G A
AstraZeneca Global Environment, Alderley Park, Macclesfield SK10 4TG, UK; School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan Road, Portsmouth, Hampshire PO1 2DT, UK.
School of Pharmacy and Biomedical Sciences, University of Portsmouth, St Michael's Building, White Swan Road, Portsmouth, Hampshire PO1 2DT, UK.
Sci Total Environ. 2017 Feb 1;579:1512-1520. doi: 10.1016/j.scitotenv.2016.11.156. Epub 2016 Dec 3.
Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (K). Experimental K values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log K to log K for each charge class showed weak correlations (maximum R=0.51 for positively charged) with no overall correlation for the combined dataset (R=0.04). Weaker correlations were found when relating log K to log D. Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R=0.62-0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure K limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of K would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments.
了解废水处理过程中药物在污水污泥上的吸附情况对于理解其环境归宿和风险评估至关重要。吸附程度由污泥/水分配系数（K）定义。从文献中收集了初级污泥和活性污泥中活性药物成分（n = 148）的实验K值（n = 297）。这些化合物根据其在pH7.4时的电荷进行分类（44种不带电荷、60种带正电荷、28种带负电荷和16种两性离子）。每个电荷类别中log K与log K的单变量模型显示出较弱的相关性（带正电荷的最大R = 0.51），组合数据集无总体相关性（R = 0.04）。当将log K与log D相关联时发现相关性较弱。使用编码一系列物理化学性质的三组分子描述符（分子操作环境、VolSurf和ParaSurf），通过逐步回归、偏最小二乘法和贝叶斯人工神经网络（ANN）推导多变量模型。ANN获得了最佳预测性能，使用完整数据集时这些描述符的R = 0.62 - 0.69。使用更复杂的Vsurf和ParaSurf描述符相比分子操作环境描述符几乎没有改善。通过自动相关性确定在ANN模型中确定的最具影响力的描述符突出了疏水性、电荷和分子形状效应在这些吸附质 - 吸附剂相互作用中的重要性。用于测量K的不同污水污泥的异质性限制了仅根据药物的物理化学性质预测吸附的能力。测量K的测试材料的标准化将提高不同研究数据的可比性，从长远来看导致更好质量的环境风险评估。
