ARC Arnot Research & Consulting, Toronto, Ontario M4C 2B4, Canada.
AES Armitage Environmental Sciences, Ottawa, Ontario K1L 8C3, Canada.
Environ Sci Process Impacts. 2024 Nov 13;26(11):1986-1998. doi: 10.1039/d4em00485j.
Per- and polyfluoroalkyl substances (PFAS) are chemicals of high concern and are undergoing hazard and risk assessment worldwide. Reliable physicochemical property (PCP) data are fundamental to assessments. However, experimental PCP data for PFAS are limited and property prediction tools such as quantitative structure-property relationships (QSPRs) therefore have poor predictive power for PFAS. New experimental data from Endo 2023 are used to improve QSPRs for predicting poly-parameter linear free energy relationship (PPLFER) descriptors for calculating water solubility (), vapor pressure (VP) and the octanol-water (), octanol-air () and air-water () partition ratios. The new experimental data are only for neutral PFAS, and the QSPRs are only applicable to neutral chemicals. A key PPLFER descriptor for PFAS is the molar volume and this work compares different versions and makes recommendations for obtaining the best PCP predictions. The new models are included in the freely available IFSQSAR package (version 1.1.1), and property predictions are compared to those from the previous IFSQSAR (version 1.1.0) and from QSPRs in the US EPA's EPI Suite (version 4.11) and OPERA (version 2.9) models. The results from the new IFSQSAR models show improvements for predicting PFAS PCPs. The root mean squared error (RMSE) for predicting log expected values from quantum chemical calculations was reduced by approximately 1 log unit whereas the RMSE for predicting log and log was reduced by 0.2 log units. IFSQSAR v.1.1.1 has an RMSE one or more log units lower than predictions from OPERA and EPI Suite when compared to expected values of log , log and log for PFAS, except for EPI Suite predictions for log which have a comparable RMSE. Recommendations for future experimental work for PPLFER descriptors for PFAS and future research to improve PCP predictions for PFAS are presented.
全氟和多氟烷基物质（PFAS）是高度关注的化学物质，正在全球范围内进行危害和风险评估。可靠的物理化学性质（PCP）数据是评估的基础。然而，PFAS 的实验性 PCP 数据有限，因此定量结构-性质关系（QSPR）等属性预测工具对 PFAS 的预测能力较差。来自 Endo 2023 的新实验数据用于改进预测多参数线性自由能关系（PPLFER）描述符的 QSPR，以计算水溶解度（）、蒸气压（VP）以及辛醇-水（）、辛醇-空气（）和空气-水（）分配比。新的实验数据仅适用于中性 PFAS，QSPR 仅适用于中性化学品。PFAS 的一个关键 PPLFER 描述符是摩尔体积，这项工作比较了不同版本，并为获得最佳 PCP 预测提出了建议。新模型包含在免费的 IFSQSAR 软件包（版本 1.1.1）中，并将属性预测与之前的 IFSQSAR（版本 1.1.0）以及美国环保署的 EPI Suite（版本 4.11）和 OPERA（版本 2.9）模型中的 QSPR 进行了比较。新的 IFSQSAR 模型的结果表明，预测 PFAS PCP 的改进。从量子化学计算预测对数预期值的均方根误差（RMSE）降低了约 1 个对数单位，而预测对数和对数的 RMSE 降低了 0.2 个对数单位。与 PFAS 的对数、对数和对数预期值相比，IFSQSAR v.1.1.1 的 RMSE 比 OPERA 和 EPI Suite 的预测低一个或多个对数单位，除了 EPI Suite 对 log 的预测，其 RMSE 相当。提出了对 PFAS 的 PPLFER 描述符进行未来实验工作和改进 PFAS 的 PCP 预测的未来研究的建议。
