Predicting reductive transformation rates of halogenated aliphatic compounds using different QSAR approaches

Abstract

The kinetics of the reductive transformation rates of a set of 17 halogenated aliphatic hydrocarbons in anaerobic sediment-water mixtures are examined using different QSAR methods. Statistical experimental design in combination with multivariate chemical characterization of the compounds was used to select a representative training and validation set. The aim of the QSARs is to generate predictions for priority setting and risk assessment purposes, and to better understand the kinetics of the dehalogenation of aliphatic hydrocarbons. The first QSAR was constructed with multiple linear regression using readily available descriptors. Subsequently, a multivariate QSAR was constructed using the partial least squares (PLS) method with 36 (physico)-chemical descriptors. Finally, a transition state approach has been used in which quantum chemically calculated activation energies for the transition state of the most probable reaction mechanism are used to model the reaction rate constantsk. Because of the relatively small size of the training set (10 compounds) the linear regression QSAR using multiple descriptors does not show good predictive capabilities on the validation set. The PLS relationship and the transition state QSAR are both capable of generating predictions of rate constants within one order of magnitude. Moreover, the transition state QSAR closely follows, and thus corroborates the assumed reaction mechanism for reductive dehalogenation. Predictions for 23 non tested halogenated aliphatics are given and compared using both the PLS and the transition state model.