Attractive protein–protein interactions (PPI) in concentrated monoclonal antibody (mAb) solutions may lead to reversible oligomers (clusters) that impact colloidal stability and viscosity. Herein, the PPI are tuned for two mAbs via the addition of arginine (Arg), NaCl, or ZnSO4 as characterized by the structure factor (S eff(q)) with small-angle X-ray scattering (SAXS). The SAXS data are fit with molecular dynamics simulations by placing a physically relevant short-range attractive interaction on selected beads in coarse-grained 12-bead models of the mAb shape. The optimized 12-bead models are then used to differentiate key microstructural properties, including center of mass radial distribution functions (g COM(r)), coordination numbers, and cluster size distributions (CSD). The addition of cosolutes results in more attractive S eff(q) relative to the no cosolute control for all systems tested, with the most attractive systems showing an upturn at low q. Only the All1 model with an attractive site in each Fab and Fc region (possessing Fab–Fab, Fab–Fc, and Fc–Fc interactions) can reproduce this upturn, and the corresponding CSDs show the presence of larger clusters compared to the control. In general, for models with similar net attractions, i.e., second osmotic virial coefficients, the size of the clusters increases as the attraction is concentrated on a smaller number of evenly distributed beads. The cluster size distributions from simulations are used to improve the understanding and prediction of experimental viscosities. The ability to discriminate between models with bead interactions at particular Fab and Fc bead sites from SAXS simulations, and to provide real-space properties (CSD and g COM(r)), will be of interest in engineering protein sequence and formulating protein solutions for weak PPI to minimize aggregation and viscosities.

The ability to design and formulate mAbs to minimize attractive interactions at high concentrations is important for protein processing, stability, and administration, particularly in subcutaneous delivery, where high viscosities are often challenging. The strength of protein−protein interactions (PPIs) of an IgG1 and IgG4 monoclonal antibody (mAb) from low to high concentration was determined by static light scattering (SLS) and used to understand viscosity data. The PPI were tuned using NaCl and five organic ionic co-solutes. The PPI strength was quantified by the normalized structure factor S(0)/S(0) HS and Kirkwood−Buff integral G 22 /G 22,HS (HS = hard sphere) determined from the SLS data and also by fits with (1) a spherical Yukawa potential and (2) an interacting hard sphere (IHS) model, which describes attraction in terms of hypothetical oligomers. The IHS model was better able to capture the scattering behavior of the more strongly interacting systems (mAb and/or co-solute) than the spherical Yukawa potential. For each descriptor of PPI, linear correlations were obtained between the viscosity at high concentration (200 mg/mL) and the interaction strengths evaluated both at low (20 mg/mL) and high concentrations (200 mg/mL) for a given mAb. However, the only parameter that provided a correlation across both mAbs was the oligomer mass ratio (m oligomer /m monomer+dimer ) from the IHS model, indicating the importance of self-association (in addition to the direct influence of the attractive PPI) on the viscosity.

A systematic understanding of intermolecular interactions is necessary for designing concentrated monoclonal and polyclonal antibody solutions with reduced viscosity and enhanced stability. Here, we determine the effects of pH and cosolute on the strength and geometry of short-range anisotropic protein-protein attractions for a polyclonal bovine IgG by comparing intensities [I(q)] obtained from small-angle X-ray scattering to those computed in molecular dynamics simulations with 12-bead models. As our model embodies key features of the protein shape, it can describe the experimental I(q) for solutions of 10-200 mg/mL protein with only a small (<1 k B T) variation in the model's well depth. At high concentration, small changes in the interaction potential produce large increases in clustering given the close interprotein spacing. Reducing the pH below the pI or adding NaCl weakens short-range anisotropic attractions but not enough to remove large reversible oligomers that raise viscosity. In contrast, for arginine added at pH 5.5, a uniform attraction model is sufficient to describe the I(q) that plateaus at low q. With primarily monomers and dimers, the viscosity is reduced relative to the other systems that have larger clusters as described with a model that includes the cluster size distribution.

Measurement and interpretation of self-diffusion of a highly concentrated mAb with different formulations in context of viscosity and protein self-interactions.

To understand and predict the viscosities of highly concentrated monoclonal antibody (mAb) solutions, it is important to characterize the protein–protein interactions (PPI) and how they influence the possible formation of protein clusters. Herein, the collective diffusion is measured by dynamic light scattering (DLS) for solutions of three different mAbs, each with various cosolutes to tune the PPI. The results are combined with measurements of static structure factor, S(0), and self-diffusion coefficient, D s , to understand the behavior of viscosity at high concentration. The small degree of variation in the hydrodynamic factor, H(0), and solvent protein friction, f sp c s , among systems with a wide range of viscosities suggests solvent–protein interactions have a small influence on viscosity. Measurements of net PPI such as S(0) and the diffusion interaction parameter, k D , are predictive of high concentration viscosity across cosolutes for a single mAb, but not for multiple mAbs. In contrast, properties that characterize the presence of clusters in solution, such as the polydispersity index from DLS, PDI, and the coefficient of protein–protein friction, f pp c p , exhibit stronger correlations to viscosity.

Polyelectrolyte solutions (PESs) recently have been proposed as high conductivity, high lithium transference number (t+) electrolytes where the majority of the ionic current is carried by the electrochemically active Li-ion....