Process development and optimization of continuous capture with three-column periodic counter-current chromatography

Continuous capture with affinity chromatography is one of the most important units for continuous manufacturing of monoclonal antibody (mAb). Due to the complexity of three-column periodic counter-current chromatography (3C-PCC), three approaches (experimental, model-based, and simplified approaches) were studied for process development and optimization. The effects of residence time for interconnected load (RTC), breakthrough percentage of the first column for interconnected load (s) and feed protein concentration (c(0)) on productivity and capacity utilization were focused. The model-based approach was found superior to the experimental approach in process optimization and evaluation. Two phases of productivity were observed and the optimal RTC for the maximum productivity was located at the boundary of the two phases. The comprehensive effects of the operating parameters (RTC, s, and c(0)) were evaluated by the model-based approach, and the operation space was predicted. The best performance of 34.5 g/L/h productivity and 97.6% capacity utilization were attained for MabSelect SuRe LX resin under 5 g/L concentration at RTC = 2.8 min and s = 87.5%. Moreover, a simplified approach was suggested to obtain the optimal RTC for the maximum productivity. The results demonstrated that model-assisted tools are useful to determine the optimum conditions for 3C-PCC continuous capture with high productivity and capacity utilization.

Automated summary

Continuous capture with affinity chromatography is crucial for monoclonal antibody manufacturing. Three different approaches were studied for process optimization, with the model-based approach found to be superior. Optimal residence time for maximum productivity was identified at the boundary of two productivity phases. Comprehensive effects of operating parameters were evaluated, resulting in the prediction of an operation space. Achieving high productivity and capacity utilization was demonstrated using model-assisted tools in continuous capture.