在现代,化学品及其产品已被广泛使用,如农业,healthcare,食物,化妆品,制药,家用产品,服装行业,等。这些化学物质找到了到达水生生态系统的途径(直接/间接),并对水生物种造成严重的慢性和长期的毒性作用,然后也转化为人类。由于实验测试成本高,无法获得日常使用的许多化学品的长期和慢性毒性数据,时间投资,以及大量动物祭祀的要求。因此,计算机模拟方法(例如,QSAR(定量结构-活性关系))是慢性和长期毒性预测的最佳选择。目前的工作提供了多端点(五个端点:chronic_LOEC,延长_14D_LC50,延长_14D_NOEC,延长的_21D_LC50,延长的_21D_NOEC)QSAR模型,用于解决化学物质对鱼类的长期和慢性水生毒性(O.latipes)。所开发模型的统计结果(R2=0.738-0.869,QLOO2=0.712-0.831,Q(F1)2=0.618-0.731)表明它们是稳健的,可靠,可重复,准确,和预测。导致化学物质对O.latipes的长期和慢性毒性的一些特征如下:存在取代的苯,疏水性,不饱和度,电负性,长链片段的存在,共轭时存在更多的原子,以及卤素原子的存在。另一方面,亲水性和图形密度描述符会延迟化学物质对O.latipes的水生慢性和长期毒性。还使用开发的模型筛选了来自DrugBank数据库的PPDB(农药特性数据库)以及实验和研究类别的药物。因此,这些多端点模型将有助于填补数据差距,并提供广泛的适用性。因此,这项研究将有助于对未测试和新的有毒化学品/药物/农药的长期和慢性毒性进行计算机QSAR(定量结构-活性关系)预测(非动物测试),设计和开发环保,小说,和更安全的化学品,并帮助保护水生生态系统免受有毒和危险化学品的影响。
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_LC50, prolonged_14D_NOEC, prolonged_21D_LC50, prolonged_21D_NOEC) QSAR models for addressing the prolonged and chronic aquatic toxicity of chemicals toward fish (O. latipes). The statistical results (R2 =0.738-0.869, QLOO2 =0.712-0.831, Q(F1)2 =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.