研究报告

  • 田一琳,季力,于海瀛.细胞色素P450酶代谢新烟碱类农药N-脱烷基反应的机制研究:以噻虫嗪为例[J].环境科学学报,2022,42(3):468-476

  • 细胞色素P450酶代谢新烟碱类农药N-脱烷基反应的机制研究:以噻虫嗪为例
  • Mechanisms of cytochrome P450 enzymes catalyzed N-dealkylation of neonicotinoid pesticides: A case study of thiamethoxam
  • 基金项目:国家自然科学基金(No.21677133,21806144);浙江省自然科学基金(No.LY20B070005)
  • 作者
  • 单位
  • 田一琳
  • 浙江大学环境与资源学院,杭州 310058
  • 季力
  • 浙江大学环境与资源学院,杭州 310058
  • 于海瀛
  • 浙江师范大学地理与环境科学学院, 金华 321004
  • 摘要:生物转化是影响有机污染物毒性的重要过程,细胞色素P450酶在其中起着关键作用.新烟碱类农药对于哺乳动物毒性较低,但是经过P450酶的转化后,可能会产生毒性增强的代谢产物,提高环境和健康风险.本研究以噻虫嗪(TMX)为例,采用计算模拟的方法研究P450酶催化下TMX的代谢路径和产物,探讨该类污染物N-脱烷基的反应机制.计算结果表明,P450酶代谢TMX有两条不同路径:一条是经过两次O-亚甲基羟基化后通过水桥开环生成毒性减小的噻虫胺(CLO);另一条是通过氢提取/羟基反弹羟基化甲基的过程,然后经水桥转移氢质子发生N-脱甲基反应,生成去甲基噻虫嗪(dm-TMX),据前人实验报道该产物会导致肝肿瘤发生率的增加,具有明显的健康风险.鉴于其他烷基胺类化合物具有类似的代谢机理,进一步通过C—H键解离能(BDEC-H)与氢提取反应的活化能(ΔE*)建立了N-脱烷基化反应活性的预测模型,R2 = 0.932,平均绝对误差MAE = 1.75 kcal·mol-1,模型的稳健性良好,可以快速有效地为该类污染物的代谢活性研究提供数据支持,为进一步研究其健康风险奠定基础.
  • Abstract:Cytochrome P450 enzymes take effect on the toxicity of organic pollutants during their Biotransformation. Neonicotinoid pesticides are less toxic to mammals, however, their metabolites during biotransformation by P450 enzymes may are more toxic, leading to environmental and health risk. In this work, thiamethoxam (TMX) was taken as an example to study the metabolic pathways and products under P450 enzyme catalysis by computational simulation, and the mechanism of N-dealkylation of the pollutants with similar structures. The computational results indicate there are two different metabolic pathways of TMX catalyzed by P450 enzymes: producing less toxic clothianidin (CLO) through two O-methylene hydroxylation and water-assisted ring cleavage; generating desmethyl-thiamethoxam (dm-TMX) in the N-demethylation reactionvia H-abstraction/OH-rebound and water-mediated proton transfer, which increases the incidence of hepatocellular adenomas and carcinomas, according to previous experimental reports. Since other alkylamines have similar metabolic mechanisms, a predictive model of N-dealkylation reactivity was further developed based on the C—H bond dissociation energy (BDEC-H) and the activation energy (ΔE*) of the H-abstraction. Mean absolute error (MAE) is 1.75 kcal·mol-1. The model has good statistical performance (R2 = 0.936) and robustness, which can be used to quickly and effectively provide data for the metabolic activity of these pollutants and enlighten future research of health risk.

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