High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes

Area of Science:

• Biopharmaceutical Process Development
• Chromatographic Separations
• Computational Modeling

Background:

• High-throughput (HT) experimentation and mechanistic modeling are key in chromatography but their integration in workflows is unclear.
• Developing efficient polishing steps for monoclonal antibodies (mAbs) is crucial for drug purity.

Purpose of the Study:

• To construct and validate a novel high-throughput in silico (HT-IS) workflow for chromatographic process development.
• To apply the HT-IS workflow for optimizing a Capto MMC polishing step for mAb aggregate removal.

Main Methods:

• Generated HT batch isotherm data and regressed parameters for the extended steric mass action (SMA) isotherm.
• Validated the model using the extended SMA isotherm and general rate model in CADET software.
• Optimized processes by minimizing an objective function based on key process metrics.

Main Results:

• The HT-IS workflow successfully developed a Capto MMC polishing step for mAb aggregate removal.
• Lab-scale evaluations confirmed high monomer yield (>85%) and effective aggregate removal.
• Process simulations identified elution buffer pH and resin ionic capacity as critical parameters.

Conclusions:

• The integrated HT-IS workflow significantly improves efficiency and speed in chromatographic process development.
• This approach enables rapid development and characterization of bioprocesses.
• The study highlights the importance of elution buffer pH and resin ionic capacity for process robustness.