Accelerating chromatography process development: A hybrid approach to model hydrophobic interaction column based on...

Area of Science:

• Biotechnology
• Chemical Engineering
• Chromatography

Background:

• Developing hydrophobic interaction chromatography (HIC) processes for protein purification is typically time-consuming and resource-intensive in industrial settings.
• Optimization of HIC conditions for specific protein-resin systems requires extensive experimentation, posing challenges for commercial process development.

Purpose of the Study:

• To present a novel hybrid modeling framework for hydrophobic interaction chromatography (HIC) to streamline commercial chromatography process development.
• To reduce the time and resources required for optimizing protein purification processes.

Main Methods:

• Developed a hybrid model integrating mechanistic principles (fluid dynamics, mass transfer) with data-driven binding isotherms.
• Utilized high-throughput experimental platforms (slurry plates, micropipette tips) for automated characterization of binding isotherms.
• Investigated binding isotherms across a two-dimensional parameter space of pH and salt concentration for a monoclonal antibody.

Main Results:

• Validated the model's predictive accuracy by comparing simulated and experimental chromatograms for monoclonal antibodies (mAbs) and high molecular weight aggregates (HMWs).
• Achieved low mean squared errors: 0.45 mg/mL for all proteins, 3.66 mg/mL for target monomer, and 0.03 mg/mL for HMW species.

Conclusions:

• The proposed hybrid modeling methodology significantly enhances the predictive capability for HIC processes.
• This framework offers a more efficient and effective approach to protein purification process development in the biopharmaceutical industry.