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O. Rotkaja, J. Golushko and K. Kuprevics

Abstract

The chromatographic behavior of tegafur and its impurities on a naphthalene Cosmosil piNAP column under reversed-phase high-performance liquid chromatography conditions was examined. A good description of the retention was achieved through the application of statistical weights to the widely used quadratic relationships between the logarithm of the retention factor (log k) and the organic solvent concentration in the mobile phase. Optimum conditions for isocratic separation of the compounds were found with acetonitrile concentration of 10-30% in the mobile phase

Open access

O. Rotkaja, J. Golushko and P. Mekss

Abstract

Protein molecules present more complex analytical challenges than conventional low molecular weight pharmaceutical compounds, and thus need powerful analytical approaches for the entire pharmaceutical development and quality control process. We used high-performance liquid chromatography to investigate the reversed-phase chromatographic behavior of eleven proteins (albumin, carbonic anhydrase, cytohrome c, L-glutamic dehydrogenase, enolase, α-lactoglobulin, Lysozyme, myoglobin and ribonuclease. By using a water/organic solvent/trifluoroacetic acid system the influence of experimental parameters was examined, and chromatographic results from two C4- chain-length supports were found to be comparable. The model enables prediction of initial conditions from two experimental data points for different types of reversed-phase columns with water-acetonitrile-TFA, water-methanol-TFA, and water-2-propanol-TFA mobile phases

Open access

S. Vorslova, J. Golushko, S. Galushko and A. Viksna

Abstract

We report our experience with highly polar and charged analyte retention parameter prediction for a reversed-phase high-performance liquid chromatographic method. The solvatic retention model has been used to predict retention of phenylisothiocyanate derivatives of 25 natural amino acids under gradient elution conditions. Retention factors have been calculated from molecular parameters of analyte structures and from the column and eluent characteristics. A step-by-step method which includes the first guess prediction of initial conditions from structural formula and fine tuning of the retention model parameters using data from successive runs can substantially save method development time