A hierarchical screening approach to enantiomeric separation

Area of Science:

• Analytical Chemistry
• Chromatography
• Computational Chemistry

Background:

• Chiral separations are crucial in pharmaceutical development.
• Screening chiral stationary phases (CSPs) for enantiomer separation is often time-consuming.
• Computational approaches can optimize chromatographic method development.

Purpose of the Study:

• To develop a hierarchical screening strategy for CSPs using computational methods.
• To identify a minimal set of complementary CSPs for efficient enantiomer separation.
• To evaluate quantitative structure enantioselective models (QSERs) for predicting chiral separation.

Main Methods:

• Utilized a dataset of 110 chiral compounds tested on 15 CSPs with two modifiers.
• Applied combinatorial algorithms, principal component analysis (PCA), and correlation matrices for data analysis.
• Evaluated QSERs, incorporating separation factors from multiple CSPs.

Main Results:

• Identified a primary set of eight CSPs with complementary enantioselective properties.
• QSER accuracy was limited by chemical diversity and training set size.
• Combining separation factors from various CSPs significantly improved QSER accuracy.

Conclusions:

• The hierarchical screening strategy effectively prioritizes CSPs for enantiomer separation.
• Computational models, especially when combining data from multiple CSPs, can aid in selecting optimal columns.
• This approach accelerates the development of supercritical fluid chromatography methods for chiral compounds.