Evaluation of interspecies correlation estimation models to increase taxonomic diversity while reducing reliance on animal testing for chemicals evaluated under the Toxic Substances Control Act.

Abstract

The U.S. Environmental Protection Agency is committed to the implementation of new approach methodologies (NAMs) to enhance the scientific basis for chemical hazard assessments. Chemical evaluations under the Toxic Substance Control Act (TSCA) are often conducted with limited test data and are well suited for NAMs applications. Interspecies correlation estimation (ICE) models are log-linear least squares regressions of the sensitivity between two species that estimate the acute toxicity of an untested species from the sensitivity of a surrogate. Interspecies correlation estimation models have been developed from and validated for diverse chemical modes of action, but their application in TSCA chemical assessments has not been previously evaluated. We use ICE models and a dataset of measured acute values for five chemicals, increasing the taxonomic diversity from which concentrations of concern (CoCs) are derived. Concentrations of concern were developed using approaches typically applied in TSCA risk evaluations, including application of assessment factors to the most sensitive species and the development of species sensitivity distributions where a minimum of eight species are represented by measured data. These CoCs were compared with those derived from datasets supplemented with ICE-predicted values, as well as comparing ICE predicted species mean acute values (SMAVs) to their respective measured values. Interspecies correlation estimation models predicted SMAVs within a factor of 5 and 10 for 87% and 92% of measured values, respectively. The CoCs developed from measured data only and data supplemented with ICE predicted toxicity were generally within five-fold, showing comparable protection. The taxonomic diversity in the ICE supplemented dataset was substantially higher than the measured data for species sensitivity distributions, providing a data-driven way of reducing uncertainty and potentially reducing the need for assessment factors. Interspecies correlation estimation models show promise as a NAM to improve the taxonomic representation included in chemical evaluations under TSCA.