Jia Qingzhu, Wang Junli, Yan Fangyou, Wang Qiang
School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China.
School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, 13St. 29, TEDA, 300457, Tianjin, PR China.
Chemosphere. 2022 Mar;291(Pt 2):132980. doi: 10.1016/j.chemosphere.2021.132980. Epub 2021 Nov 20.
Because of the large amount of pesticides discharged into rivers, adverse effects could be induced to aquatic organisms. Daphnia magna is often used as an indicator organism to evaluate the toxicity of pesticides. In this study, a quantitative structure-toxicity relationship (QSTR) model was established based on norm descriptors for predicting the acute toxicity of pesticides to Daphnia magna. The model results showed the good predictability (R = 0.8045, R = 0.8224). The validation results of internal validation, external validation, Y-randomization test and application domain analysis demonstrated the model's stability, reliability and robustness. Therefore, the above results indicate that norm descriptors might be universal for describing the relationship between the toxicity and structures of pesticides compounds. Moreover, some pesticides' toxicities without experimental data were also predicted by this model.
由于大量农药排放到河流中，可能会对水生生物产生不利影响。大型溞常被用作评估农药毒性的指示生物。在本研究中，基于范数描述符建立了定量结构-毒性关系（QSTR）模型，用于预测农药对大型溞的急性毒性。模型结果显示出良好的预测能力（R = 0.8045，R = 0.8224）。内部验证、外部验证、Y-随机化检验和应用域分析的验证结果证明了该模型的稳定性、可靠性和稳健性。因此，上述结果表明范数描述符可能普遍适用于描述农药化合物毒性与结构之间的关系。此外，该模型还预测了一些没有实验数据的农药的毒性。
