On-line PAT based monitoring and control of resin aging in protein A chromatography for COGs reduction.

Resin aging is a common occurrence in chromatographic processes and generally influenced by factors such as cleaning procedure and composition of the feed stream. Two major events occur along with protein fouling, one is the loss of protein A ligand and the other is non-specific, irreversible interactions of foulants with resin particles. Both these are responsible for resin aging. As a result, the performance of the resin suffers a fall, and this can be quantified through indicators like reduction in dynamic binding capacity, increased column pressure, or peak broadening. The number of reuse cycles of a resin has a major influence on the cost per batch. This is even more significant in the case of protein A resin, which is the primary cost driver for downstream processing. In this work, we first identify chromatogram characteristics that correlate to resin aging. Next, we propose a data monitoring-based tool for prediction of resin aging. Principal component analysis of the UV data of Mab 1 showed a deviation at 120th cycle and an out of specification at around 149th cycle, corroborating with yield decline. Batch level modelling could deliver a predictable trend for resin aging and was demonstrated for two different Mabs (Mab1 and Mab2). The results demonstrate that significant resin aging can be detected 20-25 cycles prior to observable yield decline. A control strategy has been suggested such that once the deviation has been detected, additional resin cleaning is triggered. Overall, a 50-100 Protein A cycle enhancement in resin lifespan could be achieved.