Model-based process development of continuous chromatography for antibody capture: A case study with twin-column...

Area of Science:

• Biotechnology
• Chemical Engineering
• Chromatography

Background:

• Continuous chromatography, specifically multi-column periodic counter-current chromatography (PCC), is crucial for efficient antibody capture.
• Experimental optimization of complex continuous processes is often time-consuming and resource-intensive.

Purpose of the Study:

• To develop and validate mathematical models for a twin-column continuous PCC system.
• To systematically evaluate the impact of key operating parameters on process performance.
• To propose a model-based design approach for optimizing continuous chromatography processes.

Main Methods:

• Establishment of mathematical models for a twin-column PCC system.
• Experimental validation of model predictions using breakthrough curves and process performance data.
• Systematic evaluation of residence time for interconnected feeding (RTc), breakthrough percentage control (s), and disconnected feeding time (tDC).

Main Results:

• The model accurately predicted experimental data under varying conditions.
• Productivity and resin capacity utilization exhibited three distinct phases relative to RTc.
• A defined working window for RTc and s was identified for process development.
• Optimal conditions yielded 12.8 g/L/h productivity and 91.9% capacity utilization for MabSelect SuRe resin.

Conclusions:

• Mathematical modeling provides an efficient strategy for optimizing continuous PCC processes.
• The model-based design approach enables determination of optimal operating conditions for enhanced productivity and capacity utilization.
• This methodology can streamline the development of continuous bioprocessing.