QSAR-Based Estimation of Species Sensitivity Distribution Parameters: An Exploratory Investigation

Abstract

Ecological risk assessments are hampered by limited availability of ecotoxicity data. The present study aimed to explore the possibility of deriving species sensitivity distribution (SSD) parameters for nontested compounds, based on simple physicochemical characteristics, known SSDs for data-rich compounds, and a quantitative structure-activity relationship (QSAR)-type approach. The median toxicity of a data-poor chemical for species assemblages significantly varies with values of the physicochemical descriptors, especially when based on high-quality SSD data (from either acute median effect concentrations or chronic no-observed-effect concentrations). Beyond exploratory uses, we discuss how the precision of QSAR-based SSDs can be improved to construct models that accurately predict the SSD parameters of data-poor chemicals. The current models show that the concept of QSAR-based SSDs supports screening-level evaluations of the potential ecotoxicity of compounds for which data are lacking. Environ Toxicol Chem 2019;38:2764-2770. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

Keywords: Ecotoxicity data; Median effect concentration; No-observed-effect concentration; Species sensitivity distribution parameters.

Figure 1

Illustrating the robustness (considering the significance [slope] and the precision [variability of Y for a given X] of the models) of the model‐predicted μ and σ (upper and lower figures, respectively) given the species sensitivity distribution parameters of tested compounds. The predicted toxicity endpoints (Y) were plotted against the measured ecotoxicity parameters (X) for both the high‐quality data set (in red) and the moderate‐quality data set (in black). Corresponding root mean square errors, R ² values, and sample size for the training set and test set are shown. The line represents the 1:1 relationship of X and Y, and dashed lines represent a factor of 10 under‐ or overestimation by the model. RMSE = root mean square errors; EC50 = median effect concentration; NOEC = no‐observed‐effect concentration.