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实验和定量构效关系研究吸附阴离子非甾体抗炎药物与活性炭的相互作用。

Experimental and QSAR studies on adsorptive interaction of anionic nonsteroidal anti-inflammatory drugs with activated charcoal.

机构信息

Division of Semiconductor and Chemical Engineering, Chonbuk National University, 567 Beakje-dearo, Deokjin-gu, Jeonju, Jeonbuk, 561-756, Republic of Korea.

出版信息

Chemosphere. 2018 Dec;212:620-628. doi: 10.1016/j.chemosphere.2018.08.115. Epub 2018 Aug 24.

Abstract

Adsorptive interactions, namely adsorption capacity (q) and affinity (b), between nonsteroidal anti-inflammatory drugs (NSAIDs) in anionic forms and commercial activated charcoal (AC), were estimated by isotherm experiment in a batch, and the properties were modeled based on the concept of quantitative structure-activity relationship (QSAR). Experimental results showed that AC had a high q (0.38-0.67 mmol g) and b (14.03-930.8 L mmol) for the selected NSAIDs. In QSAR modeling, linear free energy relationship (LFER) descriptors of excess molar refraction (E), dipolarity/polarizability (S), and Coulombic interactions of anions (J) were highly related to log q, and the combination of the three terms could predict log q in R of 0.97 and SE of 0.015 log unit. In the case of b, only single B term showed a good correlation with log b in R of 0.81. Additionally, the combination of hydrogen-bonding acceptors (HBAs) and molar volume (MV), which are easily calculable parameters, could also derive good predictability in R = 0.81 and SE = 0.26 log unit. Afterwards, validation of the QSAR models based on the leave-one-out cross-validation (Q) method showed that the models were acceptable.

摘要

吸附相互作用,即阴离子形式的非甾体抗炎药(NSAIDs)与商业活性炭(AC)之间的吸附容量(q)和亲和力(b),通过批量平衡实验进行估计,并基于定量构效关系(QSAR)的概念对其性质进行建模。实验结果表明,AC 对所选 NSAIDs 具有高吸附容量(q)(0.38-0.67mmol/g)和高亲和力(b)(14.03-930.8Lmmol)。在 QSAR 建模中,过量摩尔折射(E)、偶极/极化率(S)和阴离子静电作用(J)的线性自由能关系(LFER)描述符与 log q 高度相关,三个术语的组合可在 R 为 0.97 和 SE 为 0.015 对数单位的情况下预测 log q。在 b 的情况下,只有单一的 B 项与 log b 具有良好的相关性,R 为 0.81。此外,易于计算的氢键受体(HBAs)和摩尔体积(MV)的组合也可以在 R=0.81 和 SE=0.26 对数单位的情况下提供良好的预测能力。随后,基于留一法交叉验证(Q)方法对 QSAR 模型进行验证表明,这些模型是可接受的。

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