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A multiple linear regression approach to 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 to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, 34(3):183-210, (2023). https://doi.org/10.1080/1062936X.2023.2188608
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 paper will present 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:
Pesticides, pharmaceuticals, and other organic contaminants often undergo hydrolysis when released into the environment; therefore, measured or estimated hydrolysis rates are needed to assess their environmental persistence. An intuitive multiple linear regression (MLR) approach was used to develop robust QSARs for predicting base-catalyzed rate constants of carboxylic acid esters (CAEs) and lactones. We explored various combinations of independent descriptors, resulting in four primary models (two for lactones and two for CAEs), with a total of 15 and 11 parameters included in the CAE and lactone QSAR models, respectively. The most significant descriptors include pKa, electronegativity, charge density, and steric parameters. Model performance is assessed using Drug Theoretics and Cheminformatics Laboratory's DTC-QSAR tool, demonstrating high accuracy for both internal validation (r2 = 0.93 and RMSE = 0.41-0.43 for CAEs; r2 = 0.90-0.93 and RMSE = 0.38-0.46 for lactones) and external validation (r2 = 0.93 and RMSE = 0.43-0.45 for CAEs; r2 = 0.94-0.98 and RMSE = 0.33-0.41 for lactones). The developed models require only low-cost computational resources and have substantially improved performance compared to existing hydrolysis rate prediction models (HYDROWIN and SPARC).
URLs/Downloads:
DOI: A multiple linear regression approach to the estimation of carboxylic acid ester and lactone alkaline hydrolysis rate constants
PubMed Central Link to Manuscript