Predictive Toxicity Models for Chemically Related Substances: A Case Study with Nonionic Alcohol Ethoxylate Surfactant

Abstract

Predictive toxicity models, including interspecies correlation estimation (ICE) models, are reliable alternatives to animal toxicity testing. The ICE models describe mathematical relationships facilitating toxicity prediction from one surrogate test species to a species of unknown sensitivity. The ICE models were developed from curated aquatic toxicity data for 19 nonionic alcohol ethoxylate (AE) surfactants manufactured through the same process. Comparison of AE-ICE predictions with observed values from external validation datasets indicates a reasonable predictive accuracy. Model predictions were also closer to observed values than predictions from previously published ICE models for other substance groups. Comparison of acute fifth percentile hazard concentrations (HC5s) from species sensitivity distributions enhanced with AE-ICE predictions with chronic HC5s published elsewhere produced an acute-to-chronic ratio of 5, which was used to estimate chronic HC5s. With both acute and chronic HC5s for 14 AE substances, regressions were made relative to their respective liposome-water partitioning coefficients (log K lipw ), resulting in HC5-log K lipw relationships useful in estimating HC5s for AE substances with known composition but with limited or unavailable toxicity data. The findings from this case study further demonstrate that ICE models are viable alternatives to toxicity testing and could be used as weight of evidence in hazard assessment evaluations.