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Assessment and Development of Multiple Linear Regression Models for Kinetics of Hydrolysis: Carboxylic Acid Ester Substructure Compounds
Citation:
Lazare, J., C. Stevens, AND E. Weber. Assessment and Development of Multiple Linear Regression Models for Kinetics of Hydrolysis: Carboxylic Acid Ester Substructure Compounds. ACS Fall 2023 Meeting, San Francisco, CA, August 13 - 17, 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 esters, carbonates, anhydrides, and their cyclic variants. 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:
Hydrolytic degradation is an important mechanism in aquatic environments. Many of the manufactured chemicals introduced to aquatic environments are organic pollutants (including polymers, plasticizers, etc) that hydrolyze at OC=O functional groups/substructures. To prioritize and identify new persistent organic pollutants (POPs), predictive models are needed to assess the likelihood of hydrolysis of organic pollutants. Various combinations of independent descriptors were explored to derive multiple QSAR models for predicting hydrolysis rate constants, covering carboxylic acid esters, carbonates, anhydrides, and their cyclic variants. The dataset used for assessment of the models is divided into multiple categories including single and multi-ester compounds such as benzoates, phthalates, and long-chain alkyl esters (consisting of eight or more connected saturated carbon atoms). The multiple linear regression (MLR) models perform well in estimating rate constants for various types of single and multi-esters of the extended family of OC=O structure. The models will be implemented in Chemical Transformation Simulator (CTS), a cloud-based web tool for predicting environmental and biological transformation pathways. Comparisons are made to popular hydrolysis models- HYDROWIN and SPARC- when available. Some of the most significant parameters used for developing the models include protonation (pKa), charge, and charge density. Statistical performance of the new models was assessed with Drug Theoretics and Cheminformatics Laboratory’s DTC-QSAR tool.
