The diffusion interaction parameter (kD) as an indicator of colloidal and thermal stability

Significance of the Topic

Protein stability is critical for the development of biotherapeutic molecules because it impacts formulation, efficacy, and manufacturability.

Objectives and Study Overview

This study employs high-throughput dynamic light scattering to assess both thermal and colloidal stability of a monoclonal antibody across varying pH and concentration conditions. It aims to measure the diffusion interaction parameter kD, aggregation onset temperature Tonset, and particle size distributions.

Methodology

The monoclonal antibody was prepared in 50 mM bis-tris-propane buffer at pH values of 6.5, 7.5, 8.5, and 9.5 and diluted to six concentrations between 0.47 mg/mL and 15 mg/mL. Samples (20 µL) were loaded into 384-well plates, sealed under paraffin oil, centrifuged, and subjected to temperature ramps from 25 °C to 85 °C at 0.1 °C/min with five sequential acquisitions per condition. Diffusion coefficients were obtained via autocorrelation analysis, and kD was derived from the linear fit of Dt versus concentration.

Instrumentation

• DynaPro Plate Reader (Wyatt Technology) supporting 96/384/1536-well plates with temperature control from 4 °C to 85 °C
• 384-well microtiter plates (Aurora)
• DYNAMICS software and Microsoft Excel for data analysis

Main Results and Discussion

At 25 °C, negative kD values at all pH levels indicate net attractive protein–protein interactions, stronger at pH 8.5. Thermal scans revealed a primary unfolding and aggregation transition near 55–57 °C at pH 8.5, producing large irreversible aggregates (up to 800 nm). At pH 9.5, the antibody forms smaller, reversible oligomers (15–22 nm) above 62 °C. Notably, kD becomes more negative several degrees before any detectable increase in hydrodynamic radius, indicating that unfolding exposes surface regions that enhance colloidal attraction. A second unfolding transition around 70–75 °C was observed with concentration-dependent aggregate growth.

Benefits and Practical Applications

• Rapid ranking of formulations in early development phases
• Simultaneous evaluation of thermal, colloidal, and aggregation behavior
• Reduced sample consumption and experimental time
• Insight into unfolding-driven colloidal interactions

Future Trends and Potential Applications

Future work may integrate kD measurements with viscosity and chemical stability assays, extend the approach to other biomolecular classes and high-concentration formulations, and leverage machine learning for predictive formulation screening under multi-stress conditions.

Conclusion

High-throughput DLS measurement of the diffusion interaction parameter kD provides a sensitive, early indicator of colloidal and thermal stability and captures unfolding-induced interactions before visible aggregation, thereby accelerating formulation optimization.

References

1. Jocks T, Roessner D. Performing Automated Dynamic Light Scattering Using Plate Reader Technology. Int Pharm Ind. 2009;2:22–25.
2. Some D, Hitchner E. Characterizing protein–protein interactions via static light scattering: Nonspecific interactions. ResGate. 2016.
3. Kuehner DE, et al. Interactions of lysozyme in concentrated electrolyte solutions from dynamic light-scattering measurements. Biophys J. 1997;73:3211–3224.
4. Teraoka I. Polymer Solutions: An Introduction to Physical Properties. Wiley; 2002.
5. Roberts D, et al. The Role of Electrostatics in Protein–Protein Interactions of a Monoclonal Antibody. Mol Pharm. 2014;11:2475–2489.
6. He F, et al. High-throughput assessment of thermal and colloidal stability parameters for monoclonal antibody formulations. J Pharm Sci. 2011;100:5126–5141.
7. Lehermayr C, Mahler HC, Mäder K, Fischer S. Assessment of net charge and protein–protein interactions of different monoclonal antibodies. J Pharm Sci. 2011;100:2551–2562.
8. Saito S, et al. Effects of ionic strength and sugars on the aggregation propensity of monoclonal antibodies. Pharm Res. 2013;30:1263–1280.
9. Menzen T, Friess W. Temperature-ramped studies on the aggregation, unfolding, and interaction of a therapeutic monoclonal antibody. J Pharm Sci. 2014;103:445–455.
10. Zidar M, Šušterič A, Ravnik M, Kuzman D. High-throughput prediction approach for monoclonal antibody aggregation at high concentration. Pharm Res. 2017;34:1831–1839.
11. Esfandiary R, Parupudi A, Casas-Finet J, Gadre D, Sathish H. Mechanism of reversible self-association of a monoclonal antibody: role of electrostatic and hydrophobic interactions. J Pharm Sci. 2015;104:577–586.
12. He F, et al. High-throughput dynamic light scattering method for measuring viscosity of concentrated protein solutions. Anal Biochem. 2010;399:141–143.