pH is one of most important process parameters in protein purification processes. pH modulates biophysical interactions between target protein, impurities, buffer components, chromatography ligands, membranes, etc. throughout the series of unit operations that comprise the downstream process. The appropriate choice of pH is also often critical for in-process product stability. As such, control of pH is central to the development of robust downstream manufacturing processes of biopharmaceuticals. In this work, theoretical models are developed to predict pH titration curves during pH adjustments in downstream processing. Contributions to buffering capacity from both buffer components and protein residues are incorporated in the model, and results are compared to experimental trends. For protein residues, the impact of utilizing sequence-based versus structure-based pKa values is also evaluated. The application of this modeling approach is demonstrated for two steps – a low pH virus inactivation step (of a mAb platform process) and a post-UFDF dilution step (before further processing to final formulation). In both cases, application of the model facilitates the identification of failure modes that can manifest because of unexpected pH variations in large scale processing. Finally, based on process knowledge gained from the models, process scale-up recommendations are made to enable and ensure robust technology transfer of unit operations where pH titrations are performed at large scale. The approach presented here emphasizes how process development can be achieved within the quality-by-design (QbD) paradigm by the application of in silico tools during early- and late-phase process development, allowing increased process understanding and an early evaluation of process robustness to be subsequently complemented by experimental studies.

Modeling the predictive robustness of pH titrations in downstream manufacturing processes of biopharmaceuticals

ACS Spring 2022

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