Optimizing Column Length and Particle Size in Preparative Batch Chromatography Using Enantiomeric Separations of Omeprazole and Etiracetam as Models: Feasibility of Taguchi Empirical Optimization

• Published in: Chromatographia 2018, Vol.81 (6), p.851-860
• Main Authors: Samuelsson, Jörgen, Leśko, Marek, Enmark, Martin, Högblom, Joakim, Karlsson, Anders, Kaczmarski, Krzysztof
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Chemistry; Chemistry and Materials Science; Chromatography; Empirical analysis; Energiteknik; Energy Technology; Equilibrium–dispersive model; Etiracetam; Feasibility studies; Global optimization; Kemi; Laboratories; Laboratory Medicine; Mathematical models; Omeprazole; Optimization of productivity; Original; Particle size; Pharmacy; Predictions; Preparative chromatography; Pressure drop; Productivity; Proteomics; Separation; Taguchi methods; Taguchi optimization
• ISSN: 0009-5893; 1612-1112; 1612-1112
• DOI: 10.1007/s10337-018-3519-z

The overreaching purpose of this study is to evaluate new approaches for determining the optimal operational and column conditions in chromatography laboratories, i.e., how best to select a packing material of proper particle size and how to determine the proper length of the column bed after selecting particle size. As model compounds, we chose two chiral drugs for preparative separation: omeprazole and etiracetam. In each case, two maximum allowed pressure drops were assumed: 80 and 200 bar. The processes were numerically optimized (mechanistic modeling) with a general rate model using a global optimization method. The numerical predictions were experimentally verified at both analytical and pilot scales. The lower allowed pressure drop represents the use of standard equipment, while the higher allowed drop represents more modern equipment. For both compounds, maximum productivity was achieved using short columns packed with small-particle size packing materials. Increasing the allowed backpressure in the separation leads to an increased productivity and reduced solvent consumption. As advanced numerical calculations might not be available in the laboratory, we also investigated a statistically based approach, i.e., the Taguchi method (empirical modeling), for finding the optimal decision variables and compared it with advanced mechanistic modeling. The Taguchi method predicted that shorter columns packed with smaller particles would be preferred over longer columns packed with larger particles. We conclude that the simpler optimization tool, i.e., the Taguchi method, can be used to obtain “good enough” preparative separations, though for accurate processes, optimization, and to determine optimal operational conditions, classical numerical optimization is still necessary.