Departamento de Química Analítica, Universitat de València, Burjassot, Valencia, Spain.
Departamento de Química Analítica, Universitat de València, Burjassot, Valencia, Spain.
J Chromatogr A. 2018 Sep 14;1567:111-118. doi: 10.1016/j.chroma.2018.06.061. Epub 2018 Jun 25.
To the best of our knowledge, the prediction of the enantioresolution ability of polysaccharides-based stationary phases in liquid chromatography for structurally unrelated compounds has not been previously reported. In this study, structural information of neutral and basic compounds is used to model their enantioresolution levels obtained from an immobilised cellulose tris(3,5-dichlorophenylcarbamate) stationary phase in reversed phase conditions. Thirty-four structurally unrelated chiral drugs and pesticides, from seven families, are studied. Categorical enantioresolution levels (RsC, 0 = no baseline enantioresolution and 1 = baseline enantioresolution) are established from the experimental enantioresolution values obtained at a fixed experimental conditions. From 58 initial structural variables, three topological parameters (two of them connected to the chiral carbon), and six molecular descriptors (one of them also related with the chiral carbon), are selected after a discriminant partial least squares refinement process. The molar total charge of the molecule at the working pH is the most important variable. The relationships between RsC and the most important structural variables and the drug/pesticide family are evaluated. An explicit model is proposed to anticipate the RsC levels, which provides 100% of correct anticipations. A criterion is introduced to alert about the compounds that should not be anticipated.
据我们所知,先前尚未有报道预测多糖固定相在反相条件下对结构上无关联的化合物的对映体拆分能力。在这项研究中,使用中性和碱性化合物的结构信息来模拟它们在固定化纤维素三(3,5-二氯苯基氨基甲酸酯)固定相上获得的对映体拆分水平。研究了来自七个家族的 34 种结构上无关联的手性药物和农药。从实验获得的对映体拆分值在固定实验条件下,建立了分类对映体拆分水平(RsC,0=无基线对映体拆分,1=基线对映体拆分)。在经过判别式偏最小二乘精炼过程后,从 58 个初始结构变量中选择了三个拓扑参数(其中两个与手性碳有关)和六个分子描述符(其中一个也与手性碳有关)。分子在工作 pH 值下的总摩尔电荷是最重要的变量。评估了 RsC 与最重要的结构变量和药物/农药家族之间的关系。提出了一个明确的模型来预测 RsC 水平,该模型提供了 100%的正确预测。引入了一个标准来提醒那些不应预测的化合物。