Modelling physico-chemical properties of (benzo)triazoles, and screening for environmental partitioning

(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. (C) 2010 Elsevier Ltd. All rights reserved.