Affinity precipitation using Z‐elastin‐like polypeptide‐functionalized E2 protein nanocages has been shown to be a promising alternative to Protein A chromatography for monoclonal antibody (mAb) purification. We have previously described a… Click to show full abstract
Affinity precipitation using Z‐elastin‐like polypeptide‐functionalized E2 protein nanocages has been shown to be a promising alternative to Protein A chromatography for monoclonal antibody (mAb) purification. We have previously described a high‐yielding, affinity precipitation process capable of rapidly capturing mAbs from cell culture through spontaneous, multivalent crosslinking into large aggregates. To challenge the capabilities of this technology, nanocage affinity precipitation was investigated using four industrial mAbs (mAbs A–D) and one Fc fusion protein (Fc A) with diverse molecular properties. A molar binding ratio of 3:1 Z:mAb was sufficient to precipitate >95% mAb in solution for all molecules evaluated at ambient temperature without added salt. The effect of solution pH on aggregation kinetics was studied using a simplified two‐step model to investigate the protein interactions that occur during mAb–nanocage crosslinking and to determine the optimal solution pH for precipitation. After centrifugation, the pelleted mAb–nanocage complex remained insoluble and was capable of being washed at pH ≥ 5 and eluted with at pH < 4 with >90% mAb recovery for all molecules. The four mAbs and one Fc fusion were purified from cell culture using optimal process conditions, and >94% yield and >97% monomer content were obtained. mAb A–D purification resulted in a 99.9% reduction in host cell protein and >99.99% reduction in DNA from the cell culture fluids. Nanocage affinity precipitation was equivalent to or exceeded expected Protein A chromatography performance. This study highlights the benefits of nanoparticle crosslinking for enhanced affinity capture and presents a robust platform that can be applied to any target mAb or Fc‐containing proteins with minimal optimization of process parameters.
               
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