Model based robustness analysis of an ion-exchange chromatography step

Area of Science:

• Biochemical Engineering
• Separation Science
• Computational Chemistry

Background:

• Chromatographic separation development often relies heavily on empirical data and general knowledge.
• Robustness analysis is crucial for ensuring consistent product quality in biopharmaceutical manufacturing.

Purpose of the Study:

• To present a model-based approach for ion-exchange chromatography process development and robustness analysis.
• To demonstrate that model calibration can be achieved using gradient elution experiments, avoiding single-component studies.

Main Methods:

• Utilized a kinetic dispersive model incorporating steric mass action for column performance.
• Calibrated the model using gradient elution experiments varying gradients, flow rates, pH, and column loads.
• Employed confidence intervals for model parameters in a worst-case scenario analysis for impurity removal.

Main Results:

• Model calibration was successful using multi-component gradient elution data, eliminating the need for single-component experiments.
• The model accurately predicted robustness analysis outcomes and facilitated the design of a robust pooling strategy.
• Confidence intervals effectively accounted for model error in worst-case impurity removal predictions.

Conclusions:

• A model-based strategy can significantly enhance process knowledge and robustness analysis in ion-exchange chromatography.
• This approach reduces experimental burden while ensuring product quality under varying process conditions.
• The integration of confidence intervals provides a reliable framework for worst-case scenario planning.