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Towards an improved prediction of concentrated antibody solution viscosity using the Huggins coefficient
Aisling Roche1, Lorenzo Gentiluomo2, Nicole Sibanda3
1Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, School of Chemical Engineering and Analytical Science, Manchester M1 7DN, UK; Currently at: National Institute for Biological Standards and Control, South Mimms, Potters Bar, Herts EN6 3QG, UK.
Abstract:
The viscosity of a monoclonal antibody solution must be monitored and controlled as it can adversely affect product processing, packaging and administration. Engineering low viscosity mAb formulations is challenging as prohibitive amounts of material are required for concentrated solution analysis, and it is difficult to predict viscosity from parameters obtained through low-volume, high-throughput measurements such as the interaction parameter, kD, and the second osmotic virial coefficient, B22. As a measure encompassing the effect of intermolecular interactions on dilute solution viscosity, the Huggins coefficient, kh, is a promising candidate as a parameter measureable at low concentrations, but indicative of concentrated solution viscosity. In this study, a differential viscometry technique is developed to measure the intrinsic viscosity, [η], and the Huggins coefficient, kh, of protein solutions. To understand the effect of colloidal protein-protein interactions on the viscosity of concentrated protein formulations, the viscometric parameters are compared to kD and B22 of two mAbs, tuning the contributions of repulsive and attractive forces to the net protein-protein interaction by adjusting solution pH and ionic strength. We find a strong correlation between the concentrated protein solution viscosity and the kh but this was not observed for the kD or the b22, which have been previously used as indicators of high concentration viscosity. Trends observed in [η] and kh values as a function of pH and ionic strength are rationalised in terms of protein-protein interactions.
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