Model-based optimization of a preparative ion-exchange step for antibody purification

Area of Science:

• Biochemical Engineering
• Separation Science
• Process Optimization

Background:

• Protein purification is critical in biopharmaceutical development.
• Ion-exchange chromatography is a key technique for separating proteins like immunoglobulin G (IgG).
• Optimizing purification processes balances yield, purity, and cost.

Purpose of the Study:

• To develop and validate a model-based approach for designing and optimizing ion-exchange chromatography for IgG purification.
• To maximize productivity and yield while ensuring 99% IgG purity.
• To analyze the influence of process parameters, such as bead diameter, on purification efficiency.

Main Methods:

• A three-step method involving model calibration, validation, and optimization.
• Utilizing a chromatographic model incorporating convective/dispersive flow, particle diffusion, and Langmuir adsorption kinetics with mobile phase modulators (MPM).
• Optimizing loading volume and elution gradient to maximize productivity and yield under purity constraints.

Main Results:

• The purification step is primarily governed by kinetics, even for large proteins like IgG.
• Achieved optimal conditions yielding 2.7 g IgG/(s m³) stationary phase productivity and 90% yield.
• Demonstrated that bead diameter significantly impacts productivity due to diffusion hindrance, with an optimal range of 15-35 micrometers.

Conclusions:

• Model-based optimization is effective for designing robust and efficient ion-exchange chromatography processes.
• The developed method provides insights into process robustness and parameter sensitivity.
• This approach can be applied to optimize the purification of other biomolecules.