Protein comparability assessments and potential applicability of high throughput biophysical methods and data visualization tools to compare physical stability profiles

Abstract

In this review, some of the challenges and opportunities encountered during protein comparability assessments are summarized with an emphasis on developing new analytical approaches to better monitor higher-order protein structures. Several case studies are presented using high throughput biophysical methods to collect protein physical stability data as function of temperature, agitation, ionic strength and/or solution pH. These large data sets were then used to construct empirical phase diagrams (EPDs), radar charts, and comparative signature diagrams (CSDs) for data visualization and structural comparisons between the different proteins. Protein samples with different sizes, post-translational modifications, and inherent stability are presented: acidic fibroblast growth factor (FGF-1) mutants, different glycoforms of an IgG1 mAb prepared by deglycosylation, as well as comparisons of different formulations of an IgG1 mAb and granulocyte colony stimulating factor (GCSF). Using this approach, differences in structural integrity and conformational stability profiles were detected under stress conditions that could not be resolved by using the same techniques under ambient conditions (i.e., no stress). Thus, an evaluation of conformational stability differences may serve as an effective surrogate to monitor differences in higher-order structure between protein samples. These case studies are discussed in the context of potential utility in protein comparability studies.

Keywords: biophysical; comparability; formulation; high throughput; monoclonal antibodies; protein; stability.

Figure 1

Summary of step-wise nature of studies performed in a typical comparability assessment of a protein based drug candidate. Reproduced from Federici et al. (2013) with permission from Elsevier.

Figure 2

Typical procedure for collection of biophysical stability data sets and construction of Empirical Phase Diagram (EPDs), Three-Index EPDs, and radar charts for data visualization and analysis.

Figure 3

Empirical Phase Diagrams (EPDs) for comparative analysis of conformational stability of FGF-1 and its mutant. The EPDs were developed for wildtype FGF-1 (A), wildtype FGF-1 with heparin (B) and K12V/C117V/P134V mutant of FGF-1 (no heparin) (C). Stability data as function of pH and temperature were collected from the following methods: intrinsic fluorescence intensity ratio at two wavelengths (I305/I330 nm), CD at 228 nm, static light scattering (SLS) and ANS fluorescence intensity. Reproduced from Alsenaidy et al. (2012) with permission from John Wiley and Sons.

Figure 4

Analysis of the conformational stability of IgG1 mAb glycoforms using Empirical phase diagrams (left panel) and Radar charts (right panel). The diagrams show conformation stability of fully glycosylated (control) (A), partially deglycosylated (B) and fully deglycosylated (C) IgG1 mAb. Stability data as function of pH and temperature were collected from the following methods: differential scanning calorimetry, differential scanning fluorimetry, ANS fluorescence intensity and static light scattering. Reproduced from Alsenaidy et al. (2013) with permission from John Wiley and Sons.

Figure 5

Comparative Signature Diagrams (CSDs) for different formulations of Granulocyte Colony Stimulating Factor (GCSF). Formulation GCSF 12 was used as the control condition. Each diagram (A–P) represents the comparison of a particular formulation with GCSF 12. Red, blue, and green colors represent differences in spectra of ANS fluorescence, intrinsic fluorescence and CD, respectively. The side bars represent differences in aggregation pattern. The solid outline indicates positive differences and dashed outline negative differences. Reproduced from Iyer et al. (2013) with permission from John Wiley and Sons.

Figure 6

Radar chart analysis of subvisible particles in IgG1 mAb solution after stirring and shaking stresses in the presence of varying amount of sodium chloride. The size and number (A) and morphology (B) of subvisible particles were measured by Micro Flow Imaging (MFI). Each axis in an individual radar chart represents particles of various size ranges (clockwise from top: 2–5, 5–10, 10–25, 25–50 and 50–70 μm). Each ring positioned from the center to periphery displays in (A) 10 fold increase in particle concentration from <10 (center) to >10⁶ (edge) particles/ml, and in (B) 0.1 increments in morphological parameters (aspect ratio or intensity/1000) from <0.35 (center) to >0.85 (edge). The outer polygons show mean values whereas inner polygons represent mean minus 1SD (standard deviation) in (A) and mean plus 1SD in (B). The light red shaded regions in (B) represent areas with insufficient number of particles to report results (less than 25). Reproduced from Kalonia et al. (2013) with permission from John Wiley and Sons.