Nanjing Tech University, Nanjing, 211816, China.
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing, 210042, China.
BMC Bioinformatics. 2021 Mar 24;22(1):151. doi: 10.1186/s12859-020-03903-w.
A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset.
In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools.
QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.
有许多用于水生毒性的预测模型，但这些计算工具在风险评估中的准确性和易用性仍需要进一步研究。本研究评估了七种用于水蚤和鱼类的计算工具的性能：ECOSAR、T.E.S.T.、丹麦 QSAR 数据库、VEGA、KATE、Read Across 和 Trent Analysis。中国 37 种优先控制化学品（PCCs）和 92 种新化学品（NCs）被用作验证数据集。
在使用实验值与估计值相差 10 倍的标准对 PCCs 进行定量评估时，VEGA 在预测水蚤和鱼类急性毒性方面的准确性最高，在考虑 AD 后，准确性分别为 100%和 90%。KATE、ECOSAR 和 T.E.S.T.的性能相似，准确性略低于 VEGA。以 QSAR 为主要机制的丹麦 Q.D.的准确性是上述工具中最低的。Read Across 和 Trent Analysis 在所有测试的计算工具中性能最低。模型对 NCs 的预测能力低于 PCCs，可能是因为这些模型从未出现在训练集中，ECOSAR 比其他计算工具表现更好。
基于 QSAR 的计算工具在预测水蚤和鱼类急性毒性方面比类别方法（Read Across 和 Trent Analysis）具有更高的预测准确性。类别方法（Read Across 和 Trent Analysis）需要专家知识才能有效利用。ECOSAR 在 PCCs 和 NCs 中表现良好，应在风险评估和优先活动中推广应用。我们建议在开发计算模型时应考虑多种数据和水溶性的分布。需要更智能的计算工具和测试来识别化学品的危害。
