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

Area of Science:

• Environmental Chemistry
• Chemical Kinetics
• Quantitative Structure-Activity Relationships (QSAR)

Background:

• Halogenated aliphatic hydrocarbons pose environmental risks due to their persistence.
• Understanding their transformation kinetics is crucial for risk assessment and remediation strategies.
• Anaerobic sediment-water systems are key environments for the degradation of these compounds.

Purpose of the Study:

• To develop and compare Quantitative Structure-Activity Relationships (QSARs) for predicting the reductive transformation rates of halogenated aliphatic hydrocarbons.
• To assess the predictive capabilities of different QSAR methods, including multiple linear regression, partial least squares (PLS), and a transition state approach.
• To provide predictions for untested compounds to aid in environmental risk assessment and priority setting.

Main Methods:

• Statistical experimental design and multivariate chemical characterization were used to select training and validation sets.
• Multiple linear regression QSAR was developed using readily available descriptors.
• A multivariate QSAR was constructed using the partial least squares (PLS) method with 36 physicochemical descriptors.
• A transition state QSAR was developed using quantum chemically calculated activation energies to model reaction rate constants.

Main Results:

• The multiple linear regression QSAR showed poor predictive capabilities on the validation set due to the small training set size.
• Both the PLS relationship and the transition state QSAR could predict rate constants within one order of magnitude.
• The transition state QSAR corroborated the assumed reaction mechanism for reductive dehalogenation.

Conclusions:

• The transition state QSAR and PLS models offer valuable predictive capabilities for halogenated hydrocarbon transformation rates.
• These QSAR models can support environmental risk assessment and priority setting for remediation efforts.
• The study validates the reaction mechanism for reductive dehalogenation through the transition state QSAR approach.