Theory and Practice of Industrial Applications of Chromatofocusing for Monoclonal Antibody Purification

Chromatofocusing is a special form of ion-exchange chromatography (IEX) that uses a self-generated pH gradient that travels through the column as a set of retained waves. The nonlinear adsorption behavior and displacement effect under mass overloaded conditions that take place in the method make its application particularly interesting for realistic, complex industrial purifications of monoclonal antibodies (mAbs), which are one of the most important types of biopharmaceuticals. This thesis investigated the use of both chromatofocusing and IEX using externally created gradients as the initial capture step in an overall purification process for antibody products. Both methods were found to be useful capture methods in many practical cases. This is especially true for chromatofocusing methods since only a stepwise change is needed to create complex gradient shapes. Results also indicate that chromatofocusing methods have more advantages over the other two IEX methods considered here for host cell proteins (HCPs) removal for the case of acidic to neutral antibody samples, although sometimes a low yield was observed. In addition to the use of chromatofocusing for the capture of mAbs, this study also investigated its use as a polishing method after a protein A capture step. To increase the loading capacity, chromatofocusing was extended from bind-and-elute mode to the overload-and-elute mode. To improve the purity of the product collected when using the latter mode, an auxiliary column was added after the main column. The results obtained indicate that chromatofocusing is useful for removing HCPs and aggregates simultaneously in a polishing step. Two approaches were used and compared for the design of a chromatofocusing pH gradient. One approach employed a mechanistic model based on the local-equilibrium assumption and the coherence conditions. The other approach employed a non-mechanistic model based on a machine-learning, linear regression algorithm. Results indicated that with a limited number of training experiments, which is the case considered here, the use of a mechanistic model for the design and prediction of chromatofocusing pH gradients is more reliable than using a linear-regression model. However, both approaches were found to be useful process development tools depending on the circumstances that apply.