Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon.
Combining Experimental Sorption Parameters with QSAR to Predict Neonicotinoid and Transformation Product Sorption to Carbon Nanotubes and Granular Activated Carbon.
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DOI：
10.1021/acsestwater.1c00492
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发表时间：
2022-01-14
期刊：
影响因子：
--
通讯作者：
LeFevre GH
中科院分区：
文献类型：
--
作者：
Webb DT;Nagorzanski MR;Cwiertny DM;LeFevre GH
我们最近发现，类烟碱杀虫药效团的转化改变了活性炭的吸附倾向，其产物吸附量低于母体化合物。为了评估缺乏商业标准的新型转化产品的环境命运，研究人员必须依靠预测方法。在这项研究中，我们结合计算衍生的定量构效关系（QSAR）参数与实验确定的Freundlich分配常数（对吸附到碳纳米管[CNT]和颗粒活性炭[GAC]的KF日志）的类烟碱和类烟碱转化产物的吸附模型。迭代生成基于类烟碱吸附到功能化/非功能化CNT（用于概括/简化类烟碱-GAC相互作用）的QSAR模型，以获得多元线性回归，该多元线性回归可以使用内部和外部验证准确预测类烟碱吸附到CNT（在实验测定值的0.5 log单位内）。log KF，CNT值随后与log KF，GAC相关，其中预测GAC对类烟碱的吸附在实验测定值的0.3个对数单位内。我们应用我们的类烟碱特异性模型来预测一套新的类烟碱转化产物（即，通过水解、生物转化和氯化形成），其不具有市售标准品。我们提出这种建模方法作为一种创新但相对简单的技术来预测高度专业化/独特的极性新兴污染物和/或转化产物的命运，这些污染物和/或转化产物不能通过传统方法准确预测（例如，pp-LFER），并强调了驱动新兴污染物相互作用的分子特性。具有内部/外部验证的类烟碱量身定制的QSAR模型成功地预测了活性炭的吸附，并优于传统的pp-LFER模型，同时为新型转化产物提供了预测能力。
We recently discovered that transformation of the neonicotinoid insecticidal pharmacophore alters sorption propensity to activated carbon, with products adsorbing less than parent compounds. To assess the environmental fate of novel transformation products that lack commercially available standards, researchers must rely on predictive approaches. In this study, we combined computationally derived quantitative structure–activity relationship (QSAR) parameters for neonicotinoids and neonicotinoid transformation products with experimentally determined Freundlich partition constants (log KF for sorption to carbon nanotubes [CNTs] and granular activated carbon [GAC]) to model neonicotinoid and transformation product sorption. QSAR models based on neonicotinoid sorption to functionalized/nonfunctionalized CNTs (used to generalize/simplify neonicotinoid-GAC interactions) were iteratively generated to obtain a multiple linear regression that could accurately predict neonicotinoid sorption to CNTs using internal and external validation (within 0.5 log units of the experimentally determined value). The log KF,CNT values were subsequently related to log KF,GAC where neonicotinoid sorption to GAC was predicted within 0.3 log-units of experimentally determined values. We applied our neonicotinoid-specific model to predict log KF,GAC for a suite of novel neonicotinoid transformation products (i.e., formed via hydrolysis, biotransformation, and chlorination) that do not have commercially available standards. We present this modeling approach as an innovative yet relatively simple technique to predict fate of highly specialized/unique polar emerging contaminants and/or transformation products that cannot be accurately predicted via traditional methods (e.g., pp-LFER), and highlights molecular properties that drive interactions of emerging contaminants. A neonicotinoid-tailored QSAR model with internal/external validation successfully predicted sorption to activated carbon and outperformed traditional pp-LFER models, while enabling predictive power for novel transformation products.
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DOI：
10.1007/s11356-014-3332-7
发表时间：
2015-01
期刊：
Environmental science and pollution research international
影响因子：
--
作者：
Bonmatin JM;Giorio C;Girolami V;Goulson D;Kreutzweiser DP;Krupke C;Liess M;Long E;Marzaro M;Mitchell EA;Noome DA;Simon-Delso N;Tapparo A
通讯作者：
Tapparo A
影响因子：
3
作者：
Goss, KU;Schwarzenbach, RP
通讯作者：
Schwarzenbach, RP
影响因子：
5
作者：
Dai, Yi-jun;Yuan, Sheng;Ni, Jue-ping
通讯作者：
Ni, Jue-ping
影响因子：
10.9
作者：
Klarich, Kathryn L.;Pflug, Nicholas C.;LeFevre, Gregory H.
通讯作者：
LeFevre, Gregory H.
影响因子：
6.1
作者：
Haberhauer, G;Pfeiffer, L;Gerzabek, MH
通讯作者：
Gerzabek, MH