Risk-aware artificial intelligence for translational vaccine design

Area of Science:

• Vaccinology and Immunology
• Computational Biology and Bioinformatics

Background:

• Artificial intelligence (AI) has significantly advanced epitope and antigen discovery for vaccine development.
• However, predictive models alone are insufficient to determine a vaccine candidate's readiness for clinical use.

Purpose of the Study:

• To propose a comprehensive framework for evaluating and prioritizing vaccine candidates.
• To integrate multiple critical factors beyond simple prediction into the translational design process.

Main Methods:

• Review and synthesis of existing knowledge on vaccine development.
• Development of a risk-aware prioritization framework connecting immunogenicity, protection, and feasibility.

Main Results:

• The proposed framework systematically assesses candidates based on immunogenicity, protection, human leukocyte antigen (HLA) coverage, safety, immune correlates, model uncertainty, interpretability, and experimental feasibility.
• This approach enables informed decisions for advancing, deprioritizing, or validating candidates.

Conclusions:

• A structured, multi-factorial evaluation is crucial for efficient vaccine development.
• The proposed framework facilitates risk-aware decision-making, optimizing resource allocation and accelerating the path to effective vaccines.