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手性拆分能力的建模高硫酸化 β-环糊精的基本化合物在电泳色谱法。

Modeling the chiral resolution ability of highly sulfated β-cyclodextrin for basic compounds in electrokinetic chromatography.

机构信息

Departamento de Química Analítica, Universidad de Valencia, Burjassot, Valencia, Spain.

出版信息

J Chromatogr A. 2013 Sep 20;1308:152-60. doi: 10.1016/j.chroma.2013.08.003. Epub 2013 Aug 6.

DOI:10.1016/j.chroma.2013.08.003
PMID:23958689
Abstract

Despite the fact that extensive research in the field of enantioseparations by capillary electrophoresis has been carried out, it is difficult to predict whether a concrete chiral selector would be useful for the separation of a racemic compound. Hence, several experimental effort is necessary to test the abilities of individual chiral selectors, usually by trial and error procedures. Thus, the enantioseparation of a new racemate becomes a time- and money-consuming task. In this work, the ability of highly sulfated β-cyclodextrin (HS-β-CD) as chiral selector in electrokinetic chromatography (EKC) is modeled for the first time, using exclusively directly-available structural data of forty compounds (structurally unrelated basic drugs and pesticides). A discriminant partial least squares (PLS)-based quantitative structure-property relationship (QSPR) approach is simplified, resulting in a consistent, predictive and descriptive model. It is converted into an explicit equation able to predict the enantioresolution level (Rs) of new compounds, from four structure properties available in an on-line open database: logarithm of octanol-water partition coefficient estimated at pH 7.4 (lgD), polar surface area (PSA), number of hydrogen bond donors (HBD) and acceptors (HBA). For the cases in which the model predicts good Rs only in concrete experimental conditions, a Box-Behnken experimental design is proposed for the fast PLS-based optimization of the most influential experimental variables: cyclodextrin concentration, temperature and pH.

摘要

尽管在毛细管电泳手性拆分领域已经进行了广泛的研究,但很难预测具体的手性选择剂是否对拆分外消旋化合物有用。因此,需要进行一些实验工作来测试各个手性选择剂的能力,通常采用反复试验的方法。因此,对外消旋新化合物的手性拆分成为一项耗时耗钱的任务。在这项工作中,首次使用四十种化合物(结构上无相关性的碱性药物和农药)的直接可用结构数据,对高度硫酸化的β-环糊精(HS-β-CD)作为电动色谱(EKC)中的手性选择剂的能力进行建模。简化了基于判别偏最小二乘(PLS)的定量结构-性质关系(QSPR)方法,得到了一个一致、可预测和描述性的模型。将其转化为一个显式方程,能够从在线开放数据库中提供的四个结构特性预测新化合物的对映体分辨率水平(Rs):在 pH 7.4 时估算的辛醇-水分配系数的对数(lgD)、极性表面积(PSA)、氢键供体(HBD)和受体(HBA)的数量。对于模型仅在具体实验条件下预测良好 Rs 的情况,提出了 Box-Behnken 实验设计,以便快速基于 PLS 的最有影响的实验变量(环糊精浓度、温度和 pH)的优化。

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