Model-based design of peptide chromatographic purification processes

Area of Science:

• Biochemical Engineering
• Separation Science
• Process Optimization

Background:

• Complex chromatographic purification of polypeptide mixtures is crucial in industrial settings.
• Accurate modeling is essential for optimizing these processes and ensuring product quality.
• Existing methods may not fully capture the nuances of peptide elution and impurity behavior.

Purpose of the Study:

• To develop and apply a generalizable model-based procedure for optimizing polypeptide crude mixture purification.
• To demonstrate the benefits of modeling in complex industrial chromatographic processes.
• To identify optimal batch process conditions for polypeptide purification.

Main Methods:

• Modeling the target peptide elution profile using a two-site adsorption equilibrium isotherm with two inflection points.
• Accounting for the variation of isotherm parameters with modifier concentration.
• Obtaining adsorption isotherm parameters via an inverse method.
• Approximating impurity elution by lumping them into pseudo-impurities and regressing their parameters.
• Model calibration and validation against experimental data.
• Performing Pareto optimization for batch process selection.

Main Results:

• Successfully modeled the target peptide elution profile and impurity behavior.
• Validated the model's predictive capability with experimental data.
• Identified optimal parameters for the batch purification process through Pareto optimization.
• Demonstrated the significant benefits of a model-based approach for industrial chromatography.

Conclusions:

• The presented model-based procedure offers a robust framework for optimizing polypeptide purification.
• Modeling is a powerful tool for enhancing the efficiency and effectiveness of industrial chromatographic processes.
• Pareto optimization effectively guides the selection of optimal batch process conditions.