Bayesian optimization and machine learning for vaccine formulation development
Lillian Li1, Sung-In Back1, Jian Ma1
1Vaccine CMC Development & Supply, Sanofi, Toronto, Ontario, Canada.
Plos One
|June 11, 2025
Summary
Machine learning, specifically Bayesian optimization, enhances viral vaccine formulation stability. This data-driven approach improves vaccine quality and aids scientists in making informed decisions for global health needs.
Area of Science:
- Vaccinology
- Biopharmaceutical Development
- Computational Biology
Background:
- Improving vaccine stability is crucial for global infectious disease prevention.
- Data science and artificial intelligence offer innovative solutions for vaccine development.
Purpose of the Study:
- To highlight machine learning applications in developing stable viral vaccine formulations.
- To demonstrate the utility of Bayesian optimization in optimizing vaccine critical quality attributes.
Main Methods:
- Applied Bayesian optimization to two viral vaccine formulation case studies.
- Monitored critical quality attributes: infectious titer loss (liquid) and glass transition temperature (freeze-dried).
- Utilized stepwise analysis, cross-validation, and test datasets for model evaluation.
Main Results:
- Achieved progressive improvements in model quality and prediction accuracy.
- Demonstrated high R² and low root mean square errors, indicating reliable model predictions.
- Gained insights into feature influence and non-linear responses through model analysis.
Conclusions:
- Bayesian optimization effectively enhances viral vaccine formulation development.
- This data-driven methodology supports scientists in making informed decisions for improved vaccine stability.
- Machine learning provides a powerful complementary tool for biopharmaceutical formulation.
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