QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Structural and Functional Biology (DBSF), University of Insubria, via JH Dunant 3, Varese 21100, Italy.
Chem Res Toxicol. 2010 Mar 15;23(3):528-39. doi: 10.1021/tx900252h.
Fully or partially fluorinated compounds, known as per- and polyfluorinated chemicals are widely distributed in the environment and released because of their use in different household and industrial products. Few of these long chain per- and polyfluorinated chemicals are classified as emerging pollutants, and their environmental and toxicological effects are unveiled in the literature. This has diverted the production of long chain compounds, considered as more toxic, to short chains, but concerns regarding the toxicity of both types of per- and polyfluorinated chemicals are alarming. There are few experimental data available on the environmental behavior and toxicity of these compounds, and moreover, toxicity profiles are found to be different for the types of animals and species used. Quantitative structure-activity relationship (QSAR) is applied to a combination of short and long chain per- and polyfluorinated chemicals, for the first time, to model and predict the toxicity on two species of rodents, rat (Rattus) and mouse (Mus), by modeling inhalation (LC(50)) data. Multiple linear regression (MLR) models using the ordinary-least-squares (OLS) method, based on theoretical molecular descriptors selected by genetic algorithm (GA), were used for QSAR studies. Training and prediction sets were prepared a priori, and these sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the model was verified on a larger set of per- and polyfluorinated chemicals retrieved from different databases and journals. The descriptors involved, the similarities, and the differences observed between models pertaining to the toxicity related to the two species are discussed. Chemometric methods such as principal component analysis (PCA) and multidimensional scaling (MDS) were used to select most toxic compounds from those within the AD of both models, which will be subjected to experimental tests under the EU project CADASTER.
全氟或部分氟化合物，也被称为多氟和聚氟化学品，广泛分布在环境中，并因其在不同的家庭和工业产品中的使用而被释放。这些长链全氟和聚氟化学品中有少数被归类为新兴污染物，其环境和毒理学效应在文献中被揭示出来。这导致了被认为毒性更高的长链化合物的生产转向短链化合物，但对这两种类型的全氟和聚氟化学品的毒性的担忧仍然令人警惕。关于这些化合物的环境行为和毒性的实验数据很少，而且，毒性特征因使用的动物和物种类型而异。定量构效关系（QSAR）首次被应用于短链和长链全氟和聚氟化合物的组合，以通过模拟吸入（LC（50））数据来对两种啮齿动物（大鼠和小鼠）的毒性进行建模和预测。使用基于遗传算法（GA）选择的理论分子描述符的多元线性回归（MLR）模型，基于普通最小二乘（OLS）方法，用于 QSAR 研究。训练集和预测集是预先准备的，这些集用于推导出统计上稳健且具有预测性的（内部和外部）模型。通过从不同的数据库和期刊中检索到的更多全氟和聚氟化学品，对模型的结构适用性域（AD）进行了验证。讨论了所涉及的描述符、模型之间的相似性和差异，以及与两种物种毒性相关的模型。化学计量学方法，如主成分分析（PCA）和多维尺度分析（MDS），用于从两个模型的 AD 内选择最有毒的化合物，这些化合物将在欧盟项目 CADASTER 下进行实验测试。
