Identification and Quantitation of Cannabinoid Solute-Column Interactions Using the Hydrophobic-Subtraction Model to Predict Separation in High-Performance Liquid Chromatography

Abstract

The Hydrophobic-Subtraction Model (HSM) has been widely used for identification of similar and orthogonal chromatography columns since its inception in 2002 but only had limited uses as a predictive tool. Recent refinement of the model has improved the prediction accuracy to a level that offers promise for use in high-performance liquid chromatography (HPLC) separation development. The HSM quantitatively describes the parameters affecting solute retention on HPLC columns including hydrophobic, steric, hydrogen-bond, cation exchange, and dipole interactions. To demonstrate the process, 16 cannabinoid compounds were chosen as the test solutes due to increasing interest and applications. The HSM was used to identify and quantify the various cannabinoid solute retention parameters based on actual retention of each solute on a set of calibration columns with previously quantified column retention parameters. The resulting solute parameters were used to predict relative retention of each cannabinoid on over 550 columns in the HSM column database. A sorting system was then developed to rank the columns. Overall, this Thesis demonstrates the application of the HSM to cannabinoid compounds starting with identification of the best columns for the separation of these solutes.