Early life stage (ELS) toxicity testing in fish is a crucial test procedure used to evaluate the long-term effects of a wide range of chemicals, including pesticides, industrial chemicals, pharmaceuticals, and food additives. This test is particularly important for screening and prioritizing thousands of chemicals under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) regulation. In silico methods can be used to estimate the toxicity of a chemical when no experimental data is available and to reduce the cost, time, and resources involved in the experimentation process. In the present study, we developed predictive Quantitative Structure-Activity Relationship (QSAR) models to assess chronic effects of chemicals on ELS in fish. Toxicity data for ELS in fish was collected from two different sources, i.e. J-CHECK and eChemPortal, which contain robust study summaries of experimental studies performed according to OECD Test Guideline 210. The collected data included two types of endpoints – the No Observed Effect Concentration (NOEC) and the Lowest Observed Effect Concentration (LOEC), which were utilized to develop the QSAR models. Six different partial least squares (PLS) models with various descriptor combinations were created for both endpoints. These models were then employed for intelligent consensus-based prediction to enhance predictability for unknown chemicals. Among these models, the consensus model – 3 (Q2F1 = 0.71, Q2F2 = 0.71) and individual model – 3 (Q2F1 = 0.80, Q2F2 = 0.79) exhibited most promising results for both the NOEC and LOEC endpoints. Furthermore, these models were validated experimentally using experimental data from nine different industrial chemicals provided by Global Product Compliance (Europe) AB. Lastly, the models were used to screen and prioritize chemicals obtained from the Pesticide Properties (PPDB) and DrugBank databases.

中文翻译：

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根据 OECD 测试指南 210 对鱼的早期生命阶段毒性进行智能共识预测


鱼类早期 （ELS） 毒性测试是一项重要的测试程序，用于评估各种化学品的长期影响，包括杀虫剂、工业化学品、药品和食品添加剂。该测试对于根据化学品注册、评估、授权和限制 （REACH） 法规筛选和优先处理数千种化学品尤为重要。计算机模拟方法可用于在没有实验数据可用时估计化学品的毒性，并减少实验过程中涉及的成本、时间和资源。在本研究中，我们开发了预测定量构效关系 （QSAR） 模型来评估化学物质对鱼类 ELS 的慢性影响。鱼类中 ELS 的毒性数据是从两个不同的来源收集的，即 J-CHECK 和 eChemPortal，其中包含根据 OECD 测试指南 210 进行的实验研究的稳健研究摘要。收集的数据包括两种类型的终点——无观察到效应浓度 （NOEC） 和最低观察到效应浓度 （LOEC），它们被用于开发 QSAR 模型。为两个端点创建了六个不同的偏最小二乘法 （PLS） 模型，这些模型具有不同的描述符组合。然后，这些模型被用于基于共识的智能预测，以提高未知化学品的可预测性。在这些模型中，共识模型 – 3 （Q2F1 = 0.71， Q2F2 = 0.71） 和单个模型 – 3 （Q2F1 = 0.80， Q2F2 = 0.79） 在 NOEC 和 LOEC 终点上都显示出最有希望的结果。此外，这些模型使用 Global Product Compliance （Europe） AB 提供的 9 种不同工业化学品的实验数据进行了实验验证。 最后，这些模型用于筛选和优先考虑从农药特性 （PPDB） 和 DrugBank 数据库获得的化学品。