Comparison of Seven in Silico methods for Evaluating of Ecotoxicological Acute Toxicity of Daphnia magna and Pimephales promelas: Case study on Chinese Priority Controlled Chemicals

Background: The acute toxicity on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). Currently, a number of predictive models for aquatic toxicity are available, however, the accuracy of in silico tools in priority assessment and risk assessment still remains to be further studied. Herein, this study evaluated the performance of seven Quantitative Structure–Activity Relationship (QSAR) in silico methods (Danish QSAR Database, Ecological Structure Activity Relationships, KAshinhou Tool for Ecotoxicity on PAS, Toxicity Estimation Software Tool, QSAR Toolbox, Read Across, and Virtual models for property Evaluation of chemicals within a Global Architecture) for assessing acute aquatic toxicity to Daphnia magna and Pimephales promelas using the first batch list of Priority Controlled Chemicals in China. Results: Based on the values for the median lethal dose and the US Environmental Protection Agency’s acute aquatic toxicity categories of concern, the acute toxicity grade was classified into six categories. According to the comparative prediction results, the accuracy of the Daphnia magna toxicity categories prediction was 25%–56%, the correlation coefficient ranged from 0.1236 to 0.6349, and the correlation coefficients of the applicability domain were 0.040 and 0.5148. The corresponding values for the Pimephales promelas toxicity categories prediction were 22%–44%, 0.1495–0.4144, 0.2156 and 0.6793. Conclusion: As the structure of chemicals of first batch list of Priority Controlled Chemicals in China are complex, the accuracy of model prediction is low, which depends on the quality of the constructed model and application domain. Although in silico methods can be used to preliminarily estimate aquatic toxicity, experimental data validation is still required for prioritizing environmental hazards assessments and risk assessments.

In silicoestimation of chemical aquatic toxicity on crustaceans using chemical category methods

A series ofin silicomodels were developed to estimate chemical acute aquatic toxicity on crustaceans using machine learning methods combined with molecular fingerprints.