Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Aquat Toxicol. 2024 Aug;273:106985. doi: 10.1016/j.aquatox.2024.106985. Epub 2024 Jun 1.
In the modern era, chemicals and their products have been used everywhere like agriculture, healthcare, food, cosmetics, pharmaceuticals, household products, clothing industry, etc. These chemicals find their way to reach the aquatic ecosystem (directly/indirectly) and cause severe chronic and prolonged toxic effects to aquatic species which is also then translated to human beings. Prolonged and chronic toxicity data of many chemicals that are used daily is not available due to high experimentation testing costs, time investment, and the requirement of a large number of animal sacrifices. Thus, in silico approaches (e.g., QSAR (quantitative structure-activity relationship)) are the best alternative for chronic and prolonged toxicity predictions. The present work offers multi-endpoint (five endpoints: chronic_LOEC, prolonged_14D_LC, prolonged_14D_NOEC, prolonged_21D_LC, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R =0.738-0.869, Q =0.712-0.831, Q =0.618-0.731) of the developed models show that they were robust, reliable, reproducible, accurate, and predictive. Some of the features that are responsible for prolonged and chronic toxicity of chemicals towards O. latipes are as follows: the presence of substituted benzene, hydrophobicity, unsaturation, electronegativity, the presence of long-chain fragments, the presence of a greater number of atoms at conjugation, and the presence of halogen atoms. On the other hand, hydrophilicity and graph density descriptors retard the aquatic chronic and prolonged toxicity of chemicals toward O. latipes. The PPDB (pesticide properties database) and experimental and investigational classes of drugs from the DrugBank database were also screened using the developed model. Thus, these multi-endpoint models will be helpful for data-gap filling and provide a broad range of applicability. Therefore, this research will aid in the in silico QSAR (quantitative structure-activity relationship) prediction (non-animal testing) of the prolonged and chronic toxicity of untested and new toxic chemicals/drugs/pesticides, design and development of eco-friendly, novel, and safer chemicals, and help to protect the aquatic ecosystem from exposure to toxic and hazardous chemicals.
在现代，化学物质及其产品已经广泛应用于农业、医疗保健、食品、化妆品、制药、家居产品、服装行业等领域。这些化学物质会以直接或间接的方式进入水生生态系统，并对水生物种造成严重的慢性和长期毒性影响，而这些影响也会传递给人类。由于高实验测试成本、时间投入和大量动物牺牲的要求，许多日常使用的化学物质的长期和慢性毒性数据并不完备。因此，基于计算机的方法（例如定量构效关系（QSAR））是预测慢性和长期毒性的最佳选择。本工作提供了针对鱼类（斑马鱼）的化学物质长期和慢性水生毒性的多终点（慢性 LOEC、延长 14 天 LC、延长 14 天 NOEC、延长 21 天 LC、延长 21 天 NOEC）QSAR 模型。所开发模型的统计结果（R=0.738-0.869，Q=0.712-0.831，Q=0.618-0.731）表明，这些模型具有稳健性、可靠性、可重现性、准确性和预测性。导致化学物质对斑马鱼产生长期和慢性毒性的一些特征如下：取代苯的存在、疏水性、不饱和性、电负性、长链片段的存在、共轭原子数量较多、卤原子的存在。另一方面，亲水性和图密度描述符会减缓化学物质对斑马鱼的水生慢性和长期毒性。此外，还使用开发的模型对农药属性数据库（PPDB）和药物银行数据库中的实验和研究类药物进行了筛选。因此，这些多终点模型将有助于填补数据空白，并提供广泛的适用性。因此，这项研究将有助于对未经测试和新的有毒化学物质/药物/农药的长期和慢性毒性进行基于计算机的 QSAR（定量构效关系）预测（非动物测试）、设计和开发环保、新颖、更安全的化学品，并有助于保护水生生态系统免受有毒有害物质的侵害。
