Product-related impurities pose a significant challenge for the development of monoclonal antibody biotherapeutics not only because of the associated immunogenicity risks, but also because of the difficulty of developing a downstream purification process to purify the product from its product-related impurities due to their similar biophysical properties. In the case of monoclonal antibodies, the product-related impurities or product-variants are usually comprised of fragments, charged variants, or proteins aggregates. Considering the different groups of variants present, an optimal downstream purification process would not only demonstrate high separability between the product and product-related impurities, but also high orthogonality in that each step in the sequence will remove different groups of impurities. Unlike separability, however, orthogonality has been seldom quantified and only understood heuristically, where resins that operate in different modes of interaction such as ion exchange (IEX) and hydrophobic interaction (HIC) were believed to be orthogonal. In this study, three different analytical techniques for quantifying aggregate, charged variant, and fragments were utilized to obtain product-related impurity retention data from linear gradient resin screens over multiple resins and pH conditions. A method for quantifying orthogonality was then developed and integrated into an in-silico tool to rapidly process the linear gradient resin screening and analytical data to generate a rank of all possible resin sequences based on orthogonal separation of product-related impurities. Finally, suggested processes were evaluated and refined at the bench scale for further improvements to the process.

In-silico mediated rapid process development of an orthogonally selective downstream polishing sequence for the purification of monoclonal antibodies from product-related impurities

ACS Fall 2021

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