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

Area of Science:

• Analytical Chemistry
• Separation Science
• Computational Chemistry

Background:

• Enantioseparation using capillary electrophoresis is crucial but predicting chiral selector efficacy is difficult.
• Current methods often rely on time-consuming trial-and-error experiments.
• Developing predictive models can streamline the selection of chiral selectors for racemic compounds.

Purpose of the Study:

• To develop the first quantitative structure-property relationship (QSPR) model for predicting enantioresolution (Rs).
• To assess the utility of highly sulfated β-cyclodextrin (HS-β-CD) as a chiral selector in electrokinetic chromatography (EKC).
• To simplify the prediction of chiral selector performance using readily available structural data.

Main Methods:

• A discriminant partial least squares (PLS)-based QSPR approach was employed.
• The model utilized four structural descriptors: lgD, polar surface area (PSA), hydrogen bond donors (HBD), and acceptors (HBA).
• A Box-Behnken experimental design was proposed for optimizing experimental variables (HS-β-CD concentration, temperature, pH).

Main Results:

• A consistent and predictive QSPR model was established for estimating enantioresolution (Rs).
• The model successfully predicts Rs using only four accessible structural properties of compounds.
• The study provides an explicit equation for predicting enantioresolution with HS-β-CD.

Conclusions:

• This QSPR model offers a significant advancement in predicting the performance of HS-β-CD for enantioseparation in EKC.
• The predictive model reduces the need for extensive experimental screening, saving time and resources.
• The proposed optimization strategy further enhances the efficiency of achieving desired enantioresolution.