A Convenient Strategy for Studying Antibody Aggregation and Inhibition of Aggregation: Characterization and Simulation
<b>Background/Objectives:</b> Protein aggregation, particularly the aggregation of antibody-based drugs, has long been a significant challenge in downstream processes and formulation. While the inhibitory effects of excipients on aggregation have been extensively studied using early experimental characterization methods, complete formulation research requires significant amounts of antibodies and time, resulting in high research costs. <b>Methods:</b> This study proposed a quick and small-scale position-restrained simulation method which elucidated the mechanism of the reversible self-association (RSA) of antibodies and the influence of excipients on RSA under different conditions. We also validated the rationality of rapid and small-scale simulations through long-term (>1 μs) and large-scale (>1,000,000 atoms) simulations. <b>Results</b>: Through combing with simple stability characterization, the effects of different excipients on monomer residual content and the trend shown with concentration changes after thermal incubation were found to be similar to those observed in the simulations. Additionally, the formulation proposed by the simulations was validated using experimental characterization. <b>Conclusions</b>: Simulations and experiments revealed the mechanism and showed consistent trends, providing better understanding for aggregation research.
Authors
- Xuejun Cao (ORCID: https://orcid.org/0000-0001-9132-4614)
- Junfen Wan (ORCID: https://orcid.org/0000-0002-5472-0818)
- Xi Chen (ORCID: https://orcid.org/0000-0002-5166-0872)
- Yibo Guo (ORCID: https://orcid.org/0009-0005-1036-3811)
- Guining Fang
Institutions
- East China University of Science and Technology (CN)
Publication Details
- Journal
- Pharmaceutics
- Published
- 2025-04-19
- DOI
- https://doi.org/10.3390/pharmaceutics17040534
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00