A regressive approach to the design of continuous capture process with multi-column chromatography for monoclonal antibodies

Although empirical methods have been introduced in the process development of continuous chromatography, the common approach to optimize a multi-column continuous capture chromatography (periodic counter-current chromatography, PCCC) process heavily relies on numerical model simulations and the number of experiments. In addition, different multi-column settings in PCCC add more design variables in process development. In this study, we have developed a rational method for designing PCCC processes based on iterative calculations by mechanistic model-based simulations. Breakthrough curves of a monoclonal antibody were measured at different residence times for three protein A resins of different particle sizes and capacities to obtain the parameters needed for the simulation. Numerical calculations were performed for the protein sample concentration in the range of 1.5 to 4 g/L. Regression curves were developed to describe the relative process performances compared with batch operation, including the resin capacity utilization and the buffer consumption. Another linear correlation was established between breakthrough cut-off (BT%) and a modified group composed of residence time, mass transfer coefficient, and particle size. By normalizing BT% with binding capacity and switching time, the linear regression curves were established for the three protein A resins, which are useful for the design and optimization of PCCC to reduce the process development time. (c) 2021 Published by Elsevier B.V.