Literature DB >> 26992151

Comparison of batch and continuous multi-column protein A capture processes by optimal design.

Daniel Baur1, Monica Angarita1, Thomas Müller-Späth1,2, Fabian Steinebach1, Massimo Morbidelli3.   

Abstract

Multi-column capture processes show several advantages compared to batch capture. It is however not evident how many columns one should use exactly. To investigate this issue, twin-column CaptureSMB, 3- and 4-column periodic counter-current chromatography (PCC) and single column batch capture are numerically optimized and compared in terms of process performance for capturing a monoclonal antibody using protein A chromatography. Optimization is carried out with respect to productivity and capacity utilization (amount of product loaded per cycle compared to the maximum amount possible), while keeping yield and purity constant. For a wide range of process parameters, all three multi-column processes show similar maximum capacity utilization and performed significantly better than batch. When maximizing productivity, the CaptureSMB process shows optimal performance, except at high feed titers, where batch chromatography can reach higher productivity values than the multi-column processes due to the complete decoupling of the loading and elution steps, albeit at a large cost in terms of capacity utilization. In terms of trade-off, i.e. how much the capacity utilization decreases with increasing productivity, CaptureSMB is optimal for low and high feed titers, whereas the 3-column process is optimal in an intermediate region. Using these findings, the most suitable process can be chosen for different production scenarios.
Copyright © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords:  Model based optimization; Monoclonal antibody; Periodic counter-current chromatography; Protein A affinity chromatography; Sequential capture

Mesh:

Substances:

Year:  2016        PMID: 26992151     DOI: 10.1002/biot.201500481

Source DB:  PubMed          Journal:  Biotechnol J        ISSN: 1860-6768            Impact factor:   4.677


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