You are here:
A Multiple Linear Regression Approach for Kinetics of Hydrolysis: Carboxylic Acid Ester Substructure Compounds
Citation:
Lazare, J., C. Stevens, AND E. Weber. A Multiple Linear Regression Approach for Kinetics of Hydrolysis: Carboxylic Acid Ester Substructure Compounds. ORISE Mini Symposium for National Postdoc Appreciation Week (NPAW), Athens, GA, September 19 - 20, 2023.
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 report on the development and evaluation of QSAR models for predicting rates of hydrolysis of carboxylic acid ester substructure compounds. 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:
Regulatory organizations such as the U.S. Environmental Protection Agency (EPA) assess the potential environmental persistence of manufactured chemicals based on their physicochemical properties and transformation rates. Hydrolytic degradation is an important environmental transformation process in the aquatic environment. During the chemical registration process, manufacturers may supply data on hydrolysis rates and products, or predictive models can be used to estimate the hydrolysis rate constant based on molecular structure. We report on the development of QSAR models for predicting hydrolysis reaction rates of carboxylic acid ester substructure compounds to be incorporated into the Chemical Transformation Simulator (CTS), a web-based tool developed by EPA to provide predicted physicochemical properties, transformation pathways, and environmental transformation half-lives for organic chemicals. These QSAR models have an intuitive multiple-linear regression approach with descriptors associated with protonation, quantum chemical, and steric parameters.