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A Multiple Linear Regression Approach for the Estimation of Carboxylic Acid Ester and Lactone Alkaline Hydrolysis Rate Constants
Citation:
Lazare, J., C. Stevens, AND E. Weber. A Multiple Linear Regression Approach for the Estimation of Carboxylic Acid Ester and Lactone Alkaline Hydrolysis Rate Constants. 2022 SETAC NA Annual Meeting, Pittsburgh, PA, November 13 - 17, 2022.
Impact/Purpose:
Organic contaminants dissolved in groundwater, surface water, and runoff may be transformed into new molecules through hydrolysis reactions. Knowledge about the potential formation of hydrolysis transformation products of organic contaminants is important for chemical risk assessment performed by regulatory agencies, research scientists, and chemical manufacturers. Rates of hydrolytic transformations can be predicted using Quantitative Structure Activity Relationships (QSARs). This presentation will describe a QSAR model developed for predicting rates of hydrolysis of carboxylic acid esters and lactones, which are common chemical classes found in pharmaceutical, industrial and agricultural chemicals. The QSAR models will be implemented in the Chemical Transformation Simulator (CTS), a web-based software tool developed by ORD/CEMM to predict transformation pathways of organic contaminants in the environment.
Description:
Predictive models are important to regulatory organizations such as the U.S. Environmental Protection Agency (EPA) for addressing data gaps in chemical risk and exposure assessment. Quantitative Structure Activity Relationships (QSARs) can be used to estimate physicochemical properties and transformation rates when experimental values are lacking. A multiple linear regression (MLR) approach is used to develop robust QSARs for predicting base-catalyzed hydrolysis rate constants of carboxylic acid esters and lactones. The approach is intuitive and easy to interpret. Models are being developed primarily based on underlying concepts from linear free energy relationships (LFER) and the use of electronic parameters for reactivity. These parameters include protonation (pkA), charge (and electronegativity), Hückel analysis (charge density), and steric (both topological and geometrical) parameters. Various combinations of independent descriptors are being explored collectively. The developed models have shown significantly improved performance compared to some popular hydrolysis models (HYDROWIN and SPARC). The models will be implemented in EPA’s Chemical Transformation Simulator (CTS) platform, and a similar approach will be used to develop QSAR models for predicting transformation rates of chemicals with other hydrolyzable functional groups.
