Chen Shuo, Sun Guohui, Fan Tengjiao, Li Feifan, Xu Yuancong, Zhang Na, Zhao Lijiao, Zhong Rugang
Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China.
Beijing Key Laboratory of Environmental and Viral Oncology, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, PR China; Department of Medical Technology, Beijing Pharmaceutical University of Staff and Workers (CPC Party School of Beijing Tong Ren Tang (Group) co., Ltd.), Beijing 100079, China.
Sci Total Environ. 2023 Jun 10;876:162736. doi: 10.1016/j.scitotenv.2023.162736. Epub 2023 Mar 11.
Fused/non-fused polycyclic aromatic hydrocarbons (FNFPAHs) have a variety of toxic effects on ecosystems and human body, but the acquisition of their toxicity data is greatly limited by the limited resources available. Here, we followed the EU REACH regulation and used Pimephales promelas as a model organism to investigate the quantitative structure-activity relationship (QSAR) between the FNFPAHs and their toxicity for the aquatic environment for the first time. We developed a single QSAR model (SM1) containing five simple and interpretable 2D molecular descriptors, which met the validation of OECD QSAR-related principles, and analyzed their mechanistic relationships with toxicity in detail. The model had good degree of fitting and robustness, and had better external prediction performance (MAE = 0.4219) than ECOSAR model (MAE = 0.5614). To further enhance its prediction accuracy, the three qualified single models (SMs) were used for constructing consensus models (CMs), the best one CM2 (MAE = 0.3954) had a significantly higher prediction accuracy for test compounds than SM1, and also outperformed the T.E.S.T. consensus model (MAE = 0.4233). Subsequently, the toxicity of 252 true external FNFPAHs from Pesticide Properties Database (PPDB) was predicted by SM1, the prediction results showed that 94.84 % compounds were reliably predicted within the model's application domain (AD). We also applied the best CM2 to predict the untested 252 FNFPAHs. Furthermore, we provided a mechanistic analysis and explanation for pesticides ranked as top 10 most toxic FNFPAHs. In summary, all developed QSAR and consensus models can be used as efficient tools for predicting the acute toxicity of unknown FNFPAHs to Pimephales promelas, thus being important for the risk assessment and regulation of FNFPAHs contamination in aquatic environment.
稠合/非稠合多环芳烃(FNFPAHs)对生态系统和人体具有多种毒性作用,但其毒性数据的获取因可用资源有限而受到极大限制。在此,我们遵循欧盟化学品注册、评估、授权和限制法规(REACH),首次以黑头呆鱼作为模式生物,研究FNFPAHs与它们对水生环境毒性之间的定量构效关系(QSAR)。我们开发了一个包含五个简单且可解释的二维分子描述符的单QSAR模型(SM1),该模型符合经合组织(OECD)QSAR相关原则的验证要求,并详细分析了它们与毒性的作用机制关系。该模型具有良好的拟合度和稳健性,并且比ECOSAR模型(平均绝对误差MAE = 0.5614)具有更好的外部预测性能(MAE = 0.4219)。为进一步提高其预测准确性,使用三个合格的单模型(SMs)构建了共识模型(CMs),最佳的CM2(MAE = 0.3954)对测试化合物的预测准确性明显高于SM1,并且也优于T.E.S.T.共识模型(MAE = 0.4233)。随后,通过SM1预测了来自农药特性数据库(PPDB)的252种真实外部FNFPAHs的毒性,预测结果表明,94.84%的化合物在模型应用域(AD)内得到可靠预测。我们还应用最佳的CM2预测未经测试的252种FNFPAHs。此外,我们对毒性排名前十的FNFPAHs农药进行了作用机制分析和解释。总之,所有开发的QSAR和共识模型都可作为预测未知FNFPAHs对黑头呆鱼急性毒性的有效工具,因此对于水生环境中FNFPAHs污染的风险评估和监管具有重要意义。