Literature DB >> 30017887

How Well Do Low- and High-Concentration Protein Interactions Predict Solution Viscosities of Monoclonal Antibodies?

Mahlet A Woldeyes1, Wei Qi2, Vladimir I Razinkov2, Eric M Furst3, Christopher J Roberts4.   

Abstract

Protein-protein interactions (PPI) and solution viscosities were measured at low and high protein concentrations under a range of formulation conditions for 4 different monoclonal antibodies. Static light scattering was used to quantify the osmotic second virial coefficient (B22) and the zero-q limit static structure factor (Sq=0), versus protein concentration (c2) from low to high c2. Dynamic light scattering was used to measure the collective diffusion coefficient as a function of c2 and to determine the protein interaction parameter (kD). Static light scattering and dynamic light scattering were combined to determine the hydrodynamic factor (Hq=0), which accounts for changes in hydrodynamic PPI as a function of c2. The net PPI ranged from strongly repulsive to attractive interactions, via changes in buffer pH, ionic strength, and choice of monoclonal antibodies. Multiple-particle tracking microrheology and capillary viscometery were used to measure monoclonal antibodies solution viscosities under the same solution conditions. In most cases, even large and qualitative changes in PPI did not result in significant changes in protein solution viscosity. This highlights the complex nature of PPI and how they influence protein solution viscosity and raises questions as to the validity of using experimental PPI metrics such as kD or B22 as predictors of high viscosity.
Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Keywords:  dynamic light scattering; hydrodynamic factor; microrheology; monoclonal antibody; protein interactions; proteins; static light scattering; structure factor; viscosity

Mesh:

Substances:

Year:  2018        PMID: 30017887     DOI: 10.1016/j.xphs.2018.07.007

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  12 in total

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8.  Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

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