Department of Chemistry, University of Toronto Scarborough, Toronto, Ontario, Canada.
Environ Int. 2010 Aug;36(6):514-20. doi: 10.1016/j.envint.2010.03.010. Epub 2010 May 6.
Screening is widely used to prioritize chemicals according to their potential environmental hazard, as expressed in the attributes of persistence, bioaccumulation (B), toxicity and long range transport potential (LRTP). Many screening approaches for B and LRTP rely on the categorization of chemicals based on a comparison of their equilibrium partition coefficients between octanol and water (K(OW)), air and water (K(AW)) and octanol and air (K(OA)) with a threshold value. As experimental values of the properties are mostly unavailable for the large number of chemicals being screened, the use of quantitative structure-property relationships (QSPRs) and other computational chemistry methods becomes indispensable. Predictions by different methods often deviate considerably, and flawed predictions may lead to false positive/negative categorizations. We predicted the partitioning properties of 529 chemicals, culled from previous prioritization efforts, using the four prediction methods EPI Suite, SPARC, COSMOtherm, and ABSOLV. The four sets of predictions were used to screen the chemicals against various LRTP and B criteria. Screening results based on the four methods were consistent for only approximately 70% of the chemicals. To further assess whether the means of estimating environmental phase partitioning has an impact, a subset of 110 chemicals was screened for elevated arctic contamination potential based on single-parameter and poly-parameter linear free energy relationships respectively. Different categorizations were observed for 5 out of 110 chemicals. Screening and categorization methods that rely on a decision whether a chemical's predicted property falls on either side of a threshold are likely to lead to a significant number of false positive/negative outcomes. We therefore suggest that screening should rather be based on numerical hazard or risk estimates that acknowledge and explicitly take into account the uncertainties of predicted properties.
筛选被广泛用于根据化学物质的潜在环境危害对其进行优先级排序，这些危害表现在持久性、生物蓄积性 (B)、毒性和长距离迁移潜力 (LRTP) 等属性上。许多用于 B 和 LRTP 的筛选方法依赖于基于化学物质的平衡分配系数 (Kow)、空气-水分配系数 (Kaw) 和辛醇-空气分配系数 (Koa) 与阈值的比较对化学物质进行分类。由于大量被筛选的化学物质的特性实验值大多不可用，因此需要使用定量结构-性质关系 (QSPR) 和其他计算化学方法。不同方法的预测结果往往存在很大差异，有缺陷的预测可能导致错误的正/负分类。我们使用 EPI Suite、SPARC、COSMOtherm 和 ABSOLV 这四种预测方法，对之前优先排序工作中筛选的 529 种化学物质的分配特性进行了预测。使用这四种方法对这些化学物质进行了针对各种 LRTP 和 B 标准的筛选。只有大约 70%的化学物质的四种方法的筛选结果是一致的。为了进一步评估估计环境相分配的方法是否有影响，我们根据单参数和多参数线性自由能关系分别对 110 种化学物质的亚组进行了筛选，以评估其北极地区污染升高的潜在风险。对于 110 种化学物质中的 5 种，观察到了不同的分类。基于化学物质预测属性是否落在阈值的任一侧进行筛选和分类的方法可能会导致大量的错误正/负结果。因此，我们建议筛选应该基于数值风险评估，该评估应承认并明确考虑预测属性的不确定性。
