BackgroundThe need for stationary phases with unique selectivity in reversed-phase liquid chromatography has been of utmost importance to chromatographers for advancing the analysis of complex samples. Macrocyclic glycopeptide based stationary phases have been widely used for chiral separations with different chromatographic modes such as normal phase, reversed phase, and supercritical fluid chromatography. Given the multimodal retention mechanisms namely π-π complex interaction, hydrogen bonding, dipole-dipole interaction, and strong Coulombic interactions by which analytes are separated using the macrocyclic glycopeptides, these stationary phases are expected to provide novel selectivity when used under the reversed phase for achiral separations. ResultsHerein, for the first time we have conducted a systematic study using the improved hydrophobic subtraction model (HSM) which incoporates dipole-dipole interactions to demonstrate the novel selectivity offered by four different macrocyclic glycopeptide based stationary phases, namely NicoShell, TeicoShell, TagShell, and VancoShell. A comparison of the HSM parameters for these columns has been made with 551 commercially available reversed phase stationary phases and the differences in the values point to the importance of adding these columns to the already existing arsenal. These stationary phases offer separations over a wide range of pH and show variability in selectivity depending on the pH of the mobile phase which make them versatile for method development in the reversed phase mode. Additionally, we have provided an actual example of a separation from an Amgen discovery project using the VancoShell column aided by computer-assisted modelling. SignificanceThis is the first report characterizing macrocyclic glycopeptides for achiral RPLC applications. The selectivity of these stationary phases were found to be unique when compared to other commercially available stationary phases thereby acting as their own class of columns. The unusual selectivity of the columns enabled separation of complex pharmaceutical samples.
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