QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland.
QSAR Lab, ul. Trzy Lipy 3, Gdańsk, Poland; Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, Wita Stwosza 63, 80-308, Gdansk, Poland.
Chemosphere. 2024 Sep;364:143146. doi: 10.1016/j.chemosphere.2024.143146. Epub 2024 Aug 23.
The bioconcentration factor (BCF) is an important parameter that gives information regarding the ability of a contaminant to be taken up by organisms from the water. Per- and polyfluoroalkyl substances (PFAS) are widespread in the environment, causing concern regarding their impact on human health. Due to the lack of available bioaccumulation data for most compounds in the PFAS group, we developed a quantitative structure-property relationship (QSPR) model to predict the log BCF for fish (taxonomic class Teleostei), based on experimental data available for the most studied 33 representatives of this group of compounds. Furthermore, we implemented the developed model to predict log BCF for an external dataset of 2209 PFAS. Consequently, 1045 PFAS were found not to be bioaccumulative, 208 were classified as bioaccumulative, and 956 were predicted to be very bioaccumulative. Finally, we obtained the high correlation (R = 0.844) between the log BCFs obtained in laboratory and field studies for 13 PFAS. In silico analyses indicate that PFAS bioconcentration depends on the size (chain length - number of CF groups in alkyl tail/chain) of a molecule, as well as on the atomic distribution properties. In general, long-chain PFAS - above 8 and 6 carbon atoms for perfluorinated carboxylic acids (PFCAs)and perfluorinated sulfonic acids (PFSAs), respectively - tend to bioconcentrate more compared to the short-chain ones. In conclusion, predicting BCF on fish is possible for a wide range of fluorinated compounds, which can be further used for estimating PFAS behavior in the environment.
生物浓缩因子 (BCF) 是一个重要的参数,提供了有关污染物从水中被生物体吸收的能力的信息。全氟和多氟烷基物质 (PFAS) 在环境中广泛存在,引起了人们对其对人类健康影响的关注。由于大多数 PFAS 组化合物缺乏可用的生物累积数据,我们开发了一种定量结构-性质关系 (QSPR) 模型,以根据该组最受研究的 33 种代表性化合物的可用实验数据预测鱼类的 log BCF(分类学类硬骨鱼)。此外,我们实施了开发的模型来预测 2209 种 PFAS 的外部数据集的 log BCF。结果,发现 1045 种 PFAS 不易生物累积,208 种被归类为生物累积,956 种被预测为非常生物累积。最后,我们获得了在实验室和野外研究中 13 种 PFAS 的 log BCF 之间的高相关性(R=0.844)。计算机分析表明,PFAS 的生物浓缩取决于分子的大小(烷基尾/链中 CF 基团的数量-链长)以及原子分布特性。一般来说,长链 PFAS-对于全氟羧酸 (PFCAs) 和全氟磺酸 (PFSAs),分别为 8 个和 6 个碳原子以上-与短链相比,更容易浓缩。总之,对于广泛的氟化化合物,可以预测鱼类的 BCF,这可以进一步用于估计 PFAS 在环境中的行为。