Yang Siyun, Kar Supratik
Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA.
Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA.
Sci Total Environ. 2024 Jan 10;907:167991. doi: 10.1016/j.scitotenv.2023.167991. Epub 2023 Oct 26.
The Toxic Substances Control Act (TSCA) mandates the Environmental Protection Agency (EPA) to document chemicals entering the US. Due to the vast range of toxicity endpoints, experimental toxicological study for all chemicals is impossible to conduct. To address this, in silico methods like QSAR and read-across are strategically used to prioritize testing for chemicals lacking ecotoxicity data. Aquatic toxicity is one of the most critical endpoints directly related to aquatic species, mainly fish, followed by direct to indirect effects on humans through drinking water and fish as food, respectively. Therefore, we have employed the ToxValDB database to curate acute LC toxicity data for three Tilapia species covering two different genera, an ideal species for aquatic toxicity testing. Employing the curated dataset, we have developed multiple robust and predictive QSAR and quantitative read-across structure-activity relationship (q-RASAR) models for Tilapia zillii, Oreochromis niloticus, and Oreochromis mossambicus which helped to understand the toxicological mode of action (MoA) of the modeled chemicals and predict the aquatic toxicity of new untested chemicals followed by toxicity data gap filling. The best three QSAR models showed encouraging statistical quality in terms of determination coefficient R (0.94, 0.74, and 0.77), cross-validated leave-one-out Q (0.90, 0.67 and 0.70), and predictive capability in terms of R (0.95, 0.77, and 0.74) for T. zillii, O. niloticus, and O. mossambicus datasets, respectively. The developed best mathematical models were used for the prediction of aquatic toxicity in terms of pLC for 297 untested organic chemicals across three major Tilapia species ranging from 1.841 to 8.561 M in terms of environmental risk assessment.
《有毒物质控制法》（TSCA）要求美国环境保护局（EPA）记录进入美国的化学品。由于毒性终点范围广泛，对所有化学品进行实验毒理学研究是不可能的。为了解决这个问题，定量构效关系（QSAR）和类推法等计算机模拟方法被战略性地用于对缺乏生态毒性数据的化学品进行测试优先级排序。水生毒性是与水生物种直接相关的最关键终点之一，主要是鱼类，其次分别是通过饮用水和作为食物的鱼类对人类产生直接到间接的影响。因此，我们利用ToxValDB数据库整理了涵盖两个不同属的三种罗非鱼的急性LC毒性数据，这是水生毒性测试的理想物种。利用整理后的数据集，我们为吉利罗非鱼、尼罗罗非鱼和莫桑比克罗非鱼开发了多个稳健且具有预测性的QSAR和定量类推结构活性关系（q-RASAR）模型，这些模型有助于理解建模化学品的毒理学作用模式（MoA），预测新的未测试化学品的水生毒性，随后填补毒性数据空白。最好的三个QSAR模型在决定系数R（分别为0.94、0.74和0.77）、留一法交叉验证Q（分别为0.90、0.67和0.70）以及吉利罗非鱼、尼罗罗非鱼和莫桑比克罗非鱼数据集的预测能力（分别为0.95、0.77和0.74）方面显示出令人鼓舞的统计质量。所开发的最佳数学模型用于预测297种未测试有机化学品在三种主要罗非鱼物种中的水生毒性，以pLC表示，环境风险评估范围为1.841至8.561 M。
