QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology (DBSF), University of Insubria , via J.H. Dunant 3, Varese, 21100, Italy.
Environ Sci Technol. 2011 Oct 1;45(19):8120-8. doi: 10.1021/es101181g. Epub 2010 Oct 19.
The majority of perfluorinated chemicals (PFCs) are of increasing risk to biota and environment due to their physicochemical stability, wide transport in the environment and difficulty in biodegradation. It is necessary to identify and prioritize these harmful PFCs and to characterize their physicochemical properties that govern the solubility, distribution and fate of these chemicals in an aquatic ecosystem. Therefore, available experimental data (10-35 compounds) of three important properties: aqueous solubility (AqS), vapor pressure (VP) and critical micelle concentration (CMC) on per- and polyfluorinated compounds were collected for quantitative structure-property relationship (QSPR) modeling. Simple and robust models based on theoretical molecular descriptors were developed and externally validated for predictivity. Model predictions on selected PFCs were compared with available experimental data and other published in silico predictions. The structural applicability domains (AD) of the models were verified on a bigger data set of 221 compounds. The predicted properties of the chemicals that are within the AD, are reliable, and they help to reduce the wide data gap that exists. Moreover, the predictions of AqS, VP, and CMC of most common PFCs were evaluated to understand the aquatic partitioning and to derive a relation with the available experimental data of bioconcentration factor (BCF).
由于其物理化学稳定性、在环境中的广泛迁移以及难以生物降解，大多数全氟化合物（PFCs）对生物群和环境的风险日益增加。有必要识别和优先考虑这些有害的 PFC，并对其物理化学性质进行特征描述，这些性质控制着这些化学物质在水生生态系统中的溶解度、分布和归宿。因此，收集了关于全氟和多氟化合物的三个重要性质（水溶解度（AqS）、蒸气压（VP）和临界胶束浓度（CMC））的现有实验数据（10-35 种化合物），用于定量结构-性质关系（QSPR）建模。基于理论分子描述符开发了简单而稳健的模型，并进行了外部验证以进行预测性评估。对选定的 PFC 进行模型预测，并将预测结果与可用的实验数据和其他已发表的计算预测结果进行比较。在更大的 221 种化合物数据集上验证了模型的结构适用性域（AD）。对于在 AD 范围内的化学品的预测性质是可靠的，它们有助于缩小存在的广泛数据差距。此外，评估了大多数常见 PFC 的 AqS、VP 和 CMC 的预测值，以了解水分配情况，并与生物浓缩因子（BCF）的可用实验数据建立关系。
