Optimisation and robustness analysis of a hydrophobic interaction chromatography step

Area of Science:

• Biochemical Engineering
• Separation Science
• Process Chemistry

Background:

• Chromatography process development relies heavily on experimental data and general knowledge.
• Robustness analysis and optimization are crucial for industrial chromatography but often experimentally intensive.

Purpose of the Study:

• To present a model-based method for gaining process knowledge in hydrophobic interaction chromatography (HIC).
• To assist in robustness analysis and optimization of HIC separations, reducing experimental workload.
• To identify critical process parameters and optimal operating conditions.

Main Methods:

• Utilized a three-step methodology: factorial screening experiments, kinetic-dispersive model calibration, and model-based optimization.
• Calibrated the model using gradient elution and column load experiments.
• Applied the calibrated model for robustness analysis at optimal operating conditions.

Main Results:

• The model-based approach effectively identifies critical parameter variations in HIC.
• Optimal operating conditions for the chromatography column were determined using the calibrated model.
• Demonstrated a reduction in experimental effort for process development and robustness analysis.

Conclusions:

• A model-based strategy provides a robust framework for HIC process development and optimization.
• This method enhances efficiency by minimizing experimental work and guiding process improvements.
• Successfully applied to the separation of polyclonal IgG from BSA using HIC.