Abstract
Analytical ultracentrifugation-sedimentation velocity (AUC-SV) is often used to quantify high molar mass species (HMMS) present in biopharmaceuticals. Although these species are often present in trace quantities, they have received significant attention due to their potential immunogenicity. Commonly, AUC-SV data is analyzed as a diffusion-corrected, sedimentation coefficient distribution, or c(s), using SEDFIT to numerically solve Lamm-type equations. SEDFIT also utilizes maximum entropy or Tikhonov-Phillips regularization to further allow the user to determine relevant sample information, including the number of species present, their sedimentation coefficients, and their relative abundance. However, this methodology has several, often unstated, limitations, which may impact the final analysis of protein therapeutics. These include regularization-specific effects, artificial "ripple peaks," and spurious shifts in the sedimentation coefficients. In this investigation, we experimentally verified that an explicit Bayesian approach, as implemented in SEDFIT, can largely correct for these effects. Clear guidelines on how to implement this technique and interpret the resulting data, especially for samples containing micro-heterogeneity (e.g., differential glycosylation), are also provided. In addition, we demonstrated how the Bayesian approach can be combined with F statistics to draw more accurate conclusions and rigorously exclude artifactual peaks. Numerous examples with an antibody and an antibody-drug conjugate were used to illustrate the strengths and drawbacks of each technique.
Highlights
The majority of purified commercial protein pharmaceutical preparations are accompanied by small quantities of product-related impurities, including aggregates of the protein product [1,2,3]
One of the fundamental difficulties in the algorithm to generate a sedimentation coefficient distribution, c(s), is that the process requires an inversion of the Fredholm integral
maximum entropy (ME) and TP regularization can address this issue to a certain extent, their application requires assumptions that are demonstrably false: ME assumes that the probability for all sedimentation coefficients is likely, and TP assumes that the solution with the least curvature is correct
Summary
The majority of purified commercial protein pharmaceutical preparations are accompanied by small quantities of product-related impurities, including aggregates of the protein product [1,2,3] The presence of these aggregates, referred to as high molar mass species (HMMS) or high molecular weight species (HMWS), has raised numerous safety concerns. There are significant difficulties in measuring and predicting various properties of the aggregates, including concentration, oligomerization state, and stability over a drug’s shelf life. These concerns are especially pronounced for monoclonal antibodies, which are often delivered in highconcentration formulations of >50 mg/ml [6]. The enhanced potential for self-association has resulted in the aggregate concentration becoming a critical quality parameter during antibody production, purification, and administration
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