The development of computer-assisted approaches capable of accurate prediction of the retention behavior of analytes, leading to optimization of chromatographic performance, is a major goal for method development in chromatography. Statistically derived quantitative structure-retention relationships (QSRRs) represent a quite popular approach to retention prediction. A QSRR shows the relationships between chromatographic parameters, such as the retention as the dependent variable, and parameters describing analytes and columns for a given set of test molecules and columns.

Hydrophilic interaction chromatography (HILIC) using a polar sorbent in combination with a hydro-organic mobile phase, provides an approach for the effective separation and quantitative determination of small polar compounds. Recently, HILIC has been successfully applied for the analyses of a wide range of small polar compounds, including drugs, toxins, plant extracts, and other compounds important to food and pharmaceutical industries. The detailed retention mechanism applicable in HILIC is still under some discussion and for this reason, method development in HILIC is difficult.

This presentation will describe the development of retention prediction models for a variety of differently structured pharmaceutical compounds and commercially available stationary phases used in the HILIC mode. The column set comprised silica, amide, diol, zwitterionic and mixed surface modifications. The models are derived using the QSRR analysis based on molecular descriptors for the desired analytes, utilising multiple linear regressions (MLR) and artificial neural networks (ANN) as chemometric tools for the statistical treatment of the multivariate data. The derived models can be applied for method development for the separation of target pharmaceutical analytes.