Computational Characterization of Protein A – Antibody Binding
thesisposted on 01.12.2018, 00:00 by Virginia LaneVirginia Lane
Protein A affinity chromatography is a key step in the downstream processing of monoclonal antibodies (mAbs). Because this step also accounts for over 50% of downstream process costs, improving its separation efficiency could have a significant impact on production costs, ultimately making mAb treatments cheaper and more widely available. Much research has previously been done on the large difference in binding affinity of antibodies and non-antibody proteins to protein A. However, we assert that it may be possible to exploit subtle differences in binding affinity between different antibody subspecies to the wild type protein A ligand, if properly understood. Previous adsorption experiments using media with wild-type protein A ligands have shown an unexpected binding and displacement phenomenon when polyclonal antibodies are in solution with monoclonal antibodies. This phenomenon has not been previously explained or examined in the literature, and we posit that it may have implications which could be exploited for future separations of antibody subspecies and variants. Here, we develop a computational model to simulate competitive antibody binding and displacement behavior on protein A media with wild-type ligands. Our computational results have successfully captured observed binding and displacement effects between competitive components at the single chromatography bead level. We have also developed a new isotherm to define the relationship between protein A- antibody complexation and pH. This provides a connection between antibody elution pH and binding strength that can be utilized in future simulations to predict relative antibody species binding strengths and, in turn, the performance of displacement-mode separations.
Degree TypeMaster's Thesis
- Master of Science (MS)