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

Area of Science:

• Biopharmaceutical Development
• Vaccine Technology
• Analytical Chemistry

Background:

• Predictive modeling is crucial for vaccine stability, especially with limited long-term data.
• Accurate stability predictions require high-quality input, often demanding significant analytical resources.
• Developing robust predictive models is essential for accelerating vaccine development and ensuring product quality.

Purpose of the Study:

• To demonstrate a synergistic approach integrating high-throughput screening (HTS) techniques with Advanced Kinetics Modeling (AKM) for robust vaccine stability prediction.
• To develop and validate predictive stability models using accelerated stability data.
• To assess the efficiency and resource requirements of HTS assays as alternatives to traditional methods.

Main Methods:

• Integration of HTS techniques (UV-VIS, intrinsic fluorescence, DLS) with Advanced Kinetics Modeling (AKM).
• Application of AKM to a glycoconjugate vaccine targeting extra-intestinal pathogenic Escherichia coli (ExPEC).
• Utilizing size-exclusion high-performance liquid chromatography (SE-HPLC) for aggregation analysis.

Main Results:

• Kinetic models effectively described aggregation processes observed via SE-HPLC, DLS, UV-Vis absorbance (turbidity), and intrinsic fluorescence.
• AKM successfully elucidated key degradation pathways using accelerated stability data across multiple temperatures.
• HTS assay results demonstrated a strong correlation with SE-HPLC data.

Conclusions:

• AKM provides a robust framework for describing vaccine degradation pathways.
• HTS assays serve as an efficient alternative to traditional methods, requiring fewer resources and less time.
• The integrated HTS-AKM approach facilitates accurate and resource-efficient vaccine stability prediction.