Raimondo Sandy, Lilavois Crystal R, Nelson S Lexi, Koehrn Kara, Fay Kellie, Eisenreich Karen, Nolan Emily Vebrosky, Green Chris, Bressette James
Office of Research and Development, Gulf Ecosystem Measurement and Modeling Division, US Environmental Protection Agency, Gulf Breeze, FL, United States.
Office of Chemical Safety Office Pollution Prevention, Office of Pollution Prevention and Toxics, US Environmental Protection Agency, Washington, DC, United States.
Integr Environ Assess Manag. 2025 Jan 1;21(1):184-194. doi: 10.1093/inteam/vjae006.
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.
美国环境保护局致力于实施新方法学（NAMs），以加强化学物质危害评估的科学基础。根据《有毒物质控制法》（TSCA）进行的化学评估通常在测试数据有限的情况下进行，非常适合应用新方法学。种间相关性估计（ICE）模型是两种物种之间敏感性的对数线性最小二乘回归，可根据替代物种的敏感性估计未测试物种的急性毒性。种间相关性估计模型已针对多种化学作用模式开发并验证，但此前尚未评估其在TSCA化学评估中的应用。我们使用ICE模型和五种化学物质的实测急性值数据集，增加了推导关注浓度（CoC）所依据的分类学多样性。关注浓度是使用TSCA风险评估中通常采用的方法得出的，包括对最敏感物种应用评估因子，以及在至少有八个物种由实测数据代表的情况下建立物种敏感性分布。将这些关注浓度与通过补充ICE预测值的数据集得出的关注浓度进行比较，同时将ICE预测的物种平均急性值（SMAV）与其各自的实测值进行比较。种间相关性估计模型分别对87%和92%的实测值预测的SMAV在5倍和10倍因子范围内。仅根据实测数据以及补充了ICE预测毒性的数据得出的关注浓度通常在五倍以内，显示出可比的保护效果。补充了ICE的数据集在物种敏感性分布方面的分类学多样性显著高于实测数据，提供了一种数据驱动的方法来降低不确定性，并可能减少对评估因子的需求。种间相关性估计模型有望作为一种新方法学，改善TSCA下化学评估中包含的分类学代表性。
