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Assessment of Toxicity-Normalized Species Sensitivity Distributions (SSDn) for Grouped Chemical Hazard Estimation
Citation:
Lambert, F., Sandy Raimondo, AND M. Barron. Assessment of Toxicity-Normalized Species Sensitivity Distributions (SSDn) for Grouped Chemical Hazard Estimation. Society of Environmental Toxicology and Chemistry (SETAC), Portland, Oregon, November 14 - 18, 2021.
Impact/Purpose:
The purpose of the presentation is to communicate new EPA science on using computational methods to estimate hazards of chemicals that have limited toxicity data. The impact of the research is that it evaluates a new approach method (NAM) that has the potential for developing hazard estimates for chemicals without sufficient toxicity data by grouping toxicologically similar compounds. This NAM has the potential to reduce animal testing and for developing protective water concentrations for chemicals that to date have had insufficient toxicity data to allow water quality criteria development.
Description:
New approach methods are being developed to address the challenges of reducing animal testing and assessing risks to the diversity of species in aquatic environments with minimal data. The toxicity-normalized species sensitivity distribution (SSDn) approach is a novel method for developing compound-specific hazard concentrations using data for toxicologically similar chemicals. This approach first develops a SSDn composed of acute toxicity values for multiple related chemicals that have been normalized by the sensitivity of a common species tested with each compound. A toxicity-normalized hazard quotient (HC5n) is then computed from the 5th percentile of the SSDn. Chemical-specific HC5 values are determined by back-calculating with the HC5n and the chemical-specific toxicity data for the normalization species. The accuracy and uncertainty of the SSDn approach was evaluated for nine transition metals and compared to HC5s derived for the individual metals using conventional single chemical SSDs. We identified several guiding principles for this method that, when applied, resulted in highly accurate HC5 values based on comparisons with results from single metal SSDs. The SSDn approach shows promise for developing statistically robust hazard concentrations when adequate taxonomic representation is not available for a single chemical.