Work

Predictive Stability of Aggregation in Glycoconjugate Vaccines Using Advanced Kinetics Modeling and High-Throughput Screening

Year: 2026

Type: article

Abstract: Background: Predictive modeling of vaccine stability is an essential tool for accelerating development and ensuring product quality, particularly when long-term data are limited. To ensure high-quality input for accurate stability predictions, it is often necessary to allocate substantial analytical resources. Methods: This study demonstrates that integrating high-throughput screening (HTS) techniques such as ultraviolet–visible spectrophotometry (UV-VIS), intrinsic fluorescence and dynamic light scattering (DLS) with Advanced Kinetics Modeling (AKM) results in a synergistic approach, facilitating the development of robust predictive stability models. Results: Here, we applied AKM to a glycoconjugate vaccine targeting extra-intestinal pathogenic Escherichia coli (ExPEC). The aggregation processes observed via size-exclusion high-performance liquid chromatography (SE-HPLC), DLS, turbidity by UV-Vis absorbance and local changes in tryptophan microenvironment captured by intrinsic fluorescence were effectively described by the developed kinetic models. Conclusions: Using accelerated stability data across multiple temperatures, AKM successfully described key degradation pathways. Furthermore, the HTS assay results showed strong correlation with SE-HPLC data, indicating that these assays provide an efficient alternative requiring minimal analytical resources, material, and time.

Source: Pharmaceutics

DOI: 10.3390/pharmaceutics18050564

Authors: Tao Ju Cui, Didier Clénet, Martinus A. H. Capelle, Milena Opacic

Institutions: Johnson & Johnson (Netherlands), Sanofi (France)

Language: English

Topic: Protein purification and stability

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,499