Pore Souvik, Pelloux Alexia, Bergqvist Anders, Chatterjee Mainak, Roy Kunal
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032, Kolkata, India.
Global Product Compliance (Europe) AB, Ideon Beta 5, Scheelevägen 17, 223 63, Lund, Sweden.
Aquat Toxicol. 2025 Feb;279:107216. doi: 10.1016/j.aquatox.2024.107216. Epub 2024 Dec 19.
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 (Q = 0.71, Q = 0.71) and individual model - 3 (Q = 0.80, Q = 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.
鱼类早期生命阶段（ELS）毒性测试是一种关键的测试程序，用于评估多种化学物质的长期影响，这些化学物质包括农药、工业化学品、药品和食品添加剂。对于根据《化学品注册、评估、授权和限制》（REACH）法规筛选和确定数千种化学品的优先级而言，该测试尤为重要。当没有实验数据可用时，可使用计算机模拟方法来估计化学物质的毒性，并减少实验过程中涉及的成本、时间和资源。在本研究中，我们开发了预测性定量构效关系（QSAR）模型，以评估化学物质对鱼类ELS的慢性影响。鱼类ELS的毒性数据来自两个不同的来源，即J-CHECK和eChemPortal，它们包含根据经合组织测试指南210进行的实验研究的详细研究总结。收集的数据包括两种类型的终点——未观察到效应浓度（NOEC）和最低观察到效应浓度（LOEC），这些数据被用于开发QSAR模型。针对这两个终点，创建了六种不同的具有各种描述符组合的偏最小二乘（PLS）模型。然后，这些模型被用于基于智能共识的预测，以提高对未知化学物质的预测能力。在这些模型中，共识模型 - 3（Q = 0.71，Q = 0.71）和个体模型 - 3（Q = 0.80，Q = 0.79）在NOEC和LOEC终点方面均表现出最有前景的结果。此外，使用全球产品合规（欧洲）有限公司提供的九种不同工业化学品的实验数据对这些模型进行了实验验证。最后，这些模型被用于筛选和确定从农药特性（PPDB）和药物银行数据库中获得的化学物质的优先级。
