Assess aggregation risk at higher protein concentrations with G22

Significance of the Topic

Protein aggregation driven by weak, nonspecific interactions poses a critical challenge during the development and manufacturing of biopharmaceuticals. As therapeutic proteins are often formulated at high concentrations (>10 mg/mL), short-range intermolecular forces become more pronounced, potentially compromising colloidal stability. Accurate early assessment of aggregation propensity under relevant concentration ranges is therefore essential for formulation optimization and ensuring drug safety and efficacy.

Objectives and Study Overview

This study demonstrates the application of the Kirkwood–Buff integral (G22) alongside established parameters—the diffusion interaction parameter (kD) and second virial coefficient (B22)—to evaluate colloidal stability across both dilute and concentrated protein solutions. The specific goals were to:

• Measure kD and B22 simultaneously in dilute regimes using the Uncle platform.
• Re-analyze the same dataset to calculate G22 values for higher protein concentrations, where crowding effects dominate.
• Compare colloidal interactions of alpha-chymotrypsinogen under different buffer and ionic strength conditions and assess a therapeutic monoclonal antibody (Adalimumab) at its commercial formulation concentration.

Methodology and Instrumentation

Alpha-chymotrypsinogen was dialyzed into sodium citrate buffer, formulated at 80 mg/mL in phosphate (pH 7) or acetate (pH 5) buffers, with and without 300 mM NaCl. Adalimumab was assessed at 100 mg/mL in its commercial buffer (pH 5.2). Samples were serially diluted to seven concentrations down to 14–20 mg/mL. Each sample (9 µL) was loaded in triplicate and analyzed on the Uncle platform using four dynamic light scattering acquisitions of 5 s each. Scattering intensities were converted to R90/K values and used to calculate G22 via the relation R90/K = MWapp·c + MW·G22·c². kD and B22 were also computed when the inequality |c·B22·MW| < 0.05 was satisfied.

Used Instrumentation

• Unchained Labs Uncle platform (fluorescence, SLS, DLS detection)
• Dynamic Light Scattering (DLS) module
• Static Light Scattering (SLS) module

Main Results and Discussion

Alpha-chymotrypsinogen in phosphate buffer showed positive R90/K slopes, indicating net-attractive interactions, with weaker attraction at 300 mM NaCl. Acetate buffer without salt yielded negative G22 at high concentrations, reflecting net-repulsive forces, whereas 300 mM NaCl reversed this trend. For Adalimumab, G22 was negative at high loading, confirming repulsive interactions under commercial formulation. B22 values were invalid at higher concentrations per the specified inequality, highlighting the need for G22 in crowded regimes.

Benefits and Practical Applications

Combining kD, B22, and G22 measurements in a single experiment accelerates formulation screening by providing comprehensive insights into protein self-interaction across concentration ranges. This multi-parameter approach enables formulation scientists to identify conditions that minimize aggregation risk, streamline candidate selection, and improve stability profiles of therapeutic proteins.

Future Trends and Opportunities

Advancements may include integrating G22 analysis with high-throughput microfluidic platforms for rapid screening, coupling computational modeling to predict interaction parameters, and employing machine learning to guide formulation design. Expanding the approach to novel modalities such as antibody–drug conjugates and fusion proteins could further enhance drug development efficiency.

Conclusion

The unification of kD, B22, and G22 measurements on the Uncle platform provides a robust framework for assessing protein colloidal stability from dilute to high concentrations. This methodology facilitates early detection of aggregation propensity under application-relevant conditions, supporting formulation optimization and de-risking biotherapeutic development.

Reference

• O’Brien CJ, Blanco MA, Costanzo JA et al. Modulating non-native aggregation and electrostatic protein–protein interactions with computationally designed single-point mutations. Protein Engineering, Design & Selection. 2016;29(6):231–243.
• Woldeyes MA, Rubio CC, Furst EM, Roberts CJ. Predicting protein interactions of concentrated globular protein solutions using colloidal models. Journal of Physical Chemistry B. 2017;121:4756–4767.