Selectivity of Chiral Stationary Phases Based on Cellulose tris(3-chloro-4-methylphenylcarbamate) and Cellulose tris(3,5-dimethylphenylcarbamate)
Polysaccharide-based chiral stationary phases (CSP) are widely used due to their wide chiral recognition ability. Several cellulose and amylose derivatives are extremely effective in the separation of a wide range of compounds of interest in the pharmaceutical industry.1 This work demonstrates the different chiral recognition capabilities of CSPs based on cellulose tris(3-chloro-4-methylphenylcarbamate) and cellulose tris(3,5-dimethylphenylcarbamate). Over 180 racemates of pharmaceutical interest were analysed on these two phases in normal (NP), polar-organic (PO) and reversed phase (RP) separation modes. Numerous examples including important classes of drug compounds as well as statistical data prove that cellulose tris(3-chloro-4 methylphenylcarbamate) offers a good alternative to the commonly used cellulose tris(3,5-dimethylphenylcarbamate) in the separation of difficult racemic mixtures.
How feasible is it to integrate this Bayesian graph-theory methodology into existing commercial chromatography software workflows for semi-automated data curation in routine analytical laboratories?
Gabriel Vivó-Truyols outlines the benefits and challenges of combining graph theory with Bayesian statistics to automate peak assignment and alignment.
This week, Chromatography Online’s coverage spanned lipidomics-based early cancer detection, career paths in separation science, machine learning and uncertainty-driven approaches to LC method development, new EU wastewater rules for pharmaceutical micropollutants, automated Separation Quality Factor (SQF) evaluation in chromatography data systems, and solutions to GLP-1 peptide analysis challenges.
Gabriel Vivó-Truyols from the Spanish Council for Scientific Research, Madrid, Spain. discusses his work on using graph theory and Bayesian statistics for peak assignment and chromatographic alignment.
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