(167g) Design and Optimization of Preparative Chromatographic Processes for the Separation of Biomolecules | AIChE

(167g) Design and Optimization of Preparative Chromatographic Processes for the Separation of Biomolecules

Authors 

Tarafder, A. - Presenter, University of Tennessee


Biomolecules are being increasingly applied for therapeutic purposes, leading to higher demand on industrial production. One of the most challenging issues in the production of biomolecules is the separation of the target component (s). Chromatography plays an indispensable role in such separation, but often contributes to more than 50% of the production cost mainly because of the low productivity of the process units accompanied by low yield. This has strong consequences on the future growth of the biomolecular industries. The situation, however, can be made significantly better by employing model-based optimization studies to detect the most economic production routes.

Research on nonlinear chromatography, which represents the prep-scale or large-scale operations, during the last two decades, has added significant knowledge and confidence for accurately simulating separation performances. Developing a reliable first-principle-based simulation model for a particular chromatographic separation could be largely reduced down to estimating the adsorption isotherms and the transport properties of the component molecules.

The proposed talk will elaborate upon these issues taking example of developing modeling and optimization strategy for the separation of an industrial peptide with solvent gradient chromatography. First, a model for preparative solvent gradient chromatography will be described, which takes into account the changes of the Henry’s constant and the saturation capacities of the component molecules, with respect to the mobile phase composition. An optimization strategy, which ensures enough flexibility to evaluate an exhaustive array of linear and multi-linear solvent gradients, will then be described. It will be shown, based on the results from the optimization studies and subsequent experimental verifications, that through model-based optimization, solvent gradients which are hard to conceive intuitively can be developed and superior separation can be achieved.