Hydrodynamic MW model rapidly assesses GLP-1/2 analog, insulin, and mAb oligomerization states in formulations

The formation of higher-order structures (HOS), including oligomers, in therapeutic protein and peptide formulations is a critical determinant of drug efficacy, safety, and stability. Current methods for assessing protein oligomerization can be time-consuming or invasive. A rapid, non-invasive technique is needed to monitor these structures, particularly for complex biologics like GLP-1/2 analogs, insulin analogs, and monoclonal antibodies (mAbs), where oligomerization directly impacts their pharmacological properties and shelf-life.

Researchers developed a simple hydrodynamic molecular weight (MWhd) model using dynamic light scattering (DLS). They measured translational diffusion coefficients (Ddls) for 1.3-660 kDa protein standards, correcting them to Dcorr using water diffusion data. This established a correlation: log(Dcorr) = -0.428 log MWhd - 5.412. The model was then applied to therapeutic protein formulations with monomeric MW ranging from 3.8 to 149 kDa, including glucagon-like peptide-1/2 analogs, insulin analogs, and monoclonal antibodies infliximab and bevacizumab, to assess their oligomerization states.

The MWhd values derived from the DLS model were consistently several-fold greater than the corresponding monomeric molecular weights for GLP-1/2 analogs, insulin analogs, and mAbs, strongly indicating varying degrees of oligomerization within these formulations. The observed oligomerization states were largely consistent with those previously reported in the literature. For insulin and mAb oligomers, pseudo-spherical diffusion coefficients (Ds) back-calculated from the oligomer MW agreed within 6% of experimental Dcorr values. However, the insulin dimer showed a larger initial discrepancy. > Incorporation of Perrin's anisotropic correction significantly improved accuracy, reducing the insulin dimer discrepancy from 9% to 5%. The model's exponent of 0.428 effectively accounts for protein anisotropy, enhancing its predictive power.