Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter...

Area of Science:

• Biochemical Engineering
• Chromatographic Separation Science
• Process Systems Engineering

Background:

• Mechanistic models are crucial for chromatographic process development and optimization.
• Hydrophobic interaction chromatography (HIC) mechanistic models lack effective parameter estimation, particularly for multi-component systems.
• Accurate parameter estimation is essential for reliable HIC process modeling and scale-up.

Purpose of the Study:

• To develop and validate a novel parameter estimation method (mPbP-HIC) for multi-component HIC systems.
• To improve the accuracy and applicability of HIC mechanistic models, especially under high loading conditions.
• To enhance the predictive capabilities of HIC models for biopharmaceutical process development.

Main Methods:

• Derived a parameter-by-parameter (mPbP-HIC) method to estimate six parameters of the Mollerup isotherm for HIC.
• Utilized linear regression (LR) and linear approximation (LA) for initial parameter estimation, followed by an inverse method (IM).
• Introduced a simplified linear approximation (SLA) for initial q_max estimation, combined with IM (SLA+IM) for improved calibration.

Main Results:

• The mPbP-HIC method accurately predicted protein elution at a 10 g/L loading.
• Initial LA step showed unsatisfactory fitting at higher loadings (12-14 g/L) due to experimental conditions and q_max error.
• The improved SLA+IM strategy significantly enhanced curve fitting, reduced estimation errors, and minimized error accumulation.

Conclusions:

• The SLA+IM strategy makes the improved mPbP-HIC method more rational and applicable for practical protein mixture separations.
• The enhanced HIC model demonstrates excellent predictive ability and reasonable extrapolation for process optimization.
• This approach accelerates HIC process development in downstream biopharmaceutical manufacturing.