Model-based process optimization for mAb chromatography

Abstract

Protein A affinity chromatography is an effective method for capturing and purification of monoclonal antibodies (mAbs), which are amongst the most important products in the biopharmaceutical industry. Being one of the most expensive steps of downstream purification, optimization of Protein A affinity chromatography towards higher productivity offers great potential for the reduction of production cost. Hence, this work presents the productivity optimization through four strategies of crude harvest loading in Protein A affinity chromatography. Loading strategies were optimized using a mathematical model and were compared on basis of their maximal productivities. It is theoretically shown, based on computational analysis, that the performance of existing classical batch processes can be optimized by implementing an improved loading step and fine tuning of process parameters without any additional investment in new or modified equipment, materials or energy. This approach offers an attractive alternative to existing capture steps and helps bridging a technological gap to new semi continuous processes that are still lacking sufficient reliability due to technical complexity. Increased productivity leads to lower amount of affinity resin demanded to process a given amount of crude harvest or to reduce the processing time. With a new loading strategy, less expensive affinity resins may also become an effective alternative. Amongst four different loading strategies, the loading using flow ramp was predicted by model as the most promising one and the mAb binding dynamic at changing loading velocity was tested experimentally on five different affinity resins to validate model predictions.