Probabilistic pressure-flow operating space for chromatographic resins using mechanistic modeling.

Area of Science:

• Chemical Engineering
• Biopharmaceutical Manufacturing

Background:

• Pressure drop is a critical factor in chromatography column performance, influenced by column diameter and resin properties.
• Recent advancements in small-bead resins for bioprocessing necessitate renewed attention to pressure drop prediction.
• Traditional methods often require extensive large-scale data, limiting predictive capabilities.

Purpose of the Study:

• To develop and validate a mechanistic model for predicting pressure drop across chromatography columns at various scales.
• To enable accurate forecasting of pressure-flow behavior using minimal experimental data.
• To establish safe operating parameters for chromatography processes involving diverse resins.

Main Methods:

• A mechanistic model based on force balances was developed to predict pressure drop.
• Small-scale experiments were used for model calibration.
• Model predictions were validated against experimental data from Phenyl Sepharose 6 FF and seven other resins.
• Probabilistic operating spaces were determined based on pressure limits and process variability.

Main Results:

• The model demonstrated strong agreement between predicted and experimental pressure-flow data.
• Calibration with small-scale experiments allowed accurate prediction of large-scale performance.
• Safe operating ranges for bed height, fluid velocity, and viscosity were established for multiple resins.

Conclusions:

• The developed mechanistic model effectively predicts pressure drop across scales, facilitating resin selection.
• This approach allows for early identification of potential pressure-flow issues before process development.
• The model supports setting optimal flow rate ranges, even with changing solution viscosity during chromatographic sequences.