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Rapid Experimental Estimates of Physicochemical Properties to Inform Models and Testing
Citation:
Nicolas, C., K. Mansouri, K. Phillips, Chris Grulke, A. Richard, A. Williams, J. Rabinowitz, K. Isaacs, A. Yau, AND J. Wambaugh. Rapid Experimental Estimates of Physicochemical Properties to Inform Models and Testing. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 636:901-909, (2018). https://doi.org/10.1016/j.scitotenv.2018.04.266
Impact/Purpose:
This manuscript describes data collected by the ExpoCast contract. We use this data to evaluate the ability of QSAR models to make predictions for new chemicals, and how differences between predicted and measured values might impact aspects of risk such as pharmacokinetics.
Description:
Chemical structures and their properties are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize thousands of environmental chemicals, but experimental values are often lacking. Quantitative structure-property relationship (QSPR) models are routinely used to fill these data gaps. However, all QSPR models are partly limited by the data available for model development that determines its limits of chemical space or applicability domain. To both evaluate available models and provide new data for re-calibration, experimental estimates of five physicochemical properties (octanol-water partition coefficient, vapor pressure, water solubility, Henry’s law constant, and the acid dissociation constant) were attempted for 200 chemicals. Where successful, these estimates were compared against traditional measurements and QSPR models used in risk assessment. Pilot compounds were selected to provide a structurally diverse mix of chemicals with and without measured values. The evaluated models for the octanol-water partition coefficient and vapor pressure have similar predictive accuracies for chemicals with new experimental data. Except in the case of Henry’s law constant, estimates had less concordance with their previous measurements than their respective model predictions. A case study is presented that demonstrates the impact of the new data on toxicokinetic model parameters.
URLs/Downloads:
DOI: Rapid Experimental Estimates of Physicochemical Properties to Inform Models and Testing