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.
Water Res. 2011 Jan;45(3):1463-71. doi: 10.1016/j.watres.2010.11.006. Epub 2010 Nov 11.
(Benzo)triazoles are distributed throughout the environment, mainly in water compartments, because of their wide use in industry where they are employed in pharmaceutical, agricultural and deicing products. They are hazardous chemicals that adversely affect humans and other non-target species, and are on the list of substances of very high concern (SVHC) in the new European regulation of chemicals - REACH (Registration, Evaluation, Authorization and Restriction of Chemical substances). Thus there is a vital need for further investigations to understand the behavior of these compounds in biota and the environment. In such a scenario, physico-chemical properties like aqueous solubility, hydrophobicity, vapor pressure and melting point can be useful. However, the limited availability and the high cost of lab testing prevents the acquisition of necessary experimental data that industry must submit for the registration of these chemicals. In such cases a preliminary analysis can be made using Quantitative Structure-Property Relationships (QSPR) models. For such an analysis, we propose Multiple Linear Regression (MLR) models based on theoretical molecular descriptors selected by Genetic Algorithm (GA). Training and prediction sets were prepared a priori by splitting the available experimental data, which were then used to derive statistically robust and predictive (both internally and externally) models. These models, after verification of their structural applicability domain (AD), were used to predict the properties of a total of 351 compounds, including those in the REACH preregistration list. Finally, Principal Component Analysis was applied to the predictions to rank the environmental partitioning properties (relevant for leaching and volatility) of new and untested (benzo)triazoles within the AD of each model. Our study using this approach highlighted compounds dangerous for the aquatic compartment. Similar analyses using predictions obtained by the EPI Suite and VCCLAB tools are also compared and discussed in this paper.
（苯并）三唑类化合物广泛应用于工业，如医药、农业和除冰产品，因此它们分布于环境中，主要存在于水体中。它们是对人类和其他非目标物种有害的化学品，被列入欧盟新的化学品法规-REACH（注册、评估、授权和限制化学物质）的高关注物质（SVHC）清单。因此，需要进一步研究以了解这些化合物在生物群和环境中的行为。在这种情况下，物理化学性质如水溶性、疏水性、蒸气压和熔点可能会很有用。然而，实验室测试的有限可用性和高成本阻止了行业获得注册这些化学品所需的必要实验数据。在这种情况下，可以使用定量构效关系（QSPR）模型进行初步分析。为此，我们提出了基于遗传算法（GA）选择的理论分子描述符的多元线性回归（MLR）模型。训练和预测集是通过分割可用的实验数据预先准备的，然后使用这些数据来得出统计上稳健且可预测的（内部和外部）模型。在验证其结构适用性域（AD）后，这些模型用于预测总共 351 种化合物的性质，包括 REACH 预注册清单中的化合物。最后，对预测进行主成分分析，以根据每个模型的 AD 对（苯并）三唑类化合物的环境分配性质（与浸出和挥发性相关）进行排序。我们使用这种方法的研究强调了对水生环境有危险的化合物。本文还比较和讨论了使用 EPI Suite 和 VCCLAB 工具获得的预测进行类似分析的情况。
