Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach

Area of Science:

• Vaccinology
• Quantitative Systems Pharmacology
• Translational Science

Background:

• The COVID-19 pandemic highlighted the need for advanced quantitative tools to accelerate the development of safe and effective SARS-CoV-2 vaccines.
• Existing methods require improvement for rapid vaccine development.

Purpose of the Study:

• To develop and apply a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical data for vaccine efficacy prediction.
• To establish a translational framework for early decision-making in vaccine development.

Main Methods:

• A systematic literature review identified relevant studies in rhesus macaques (RM) and humans.
• Developed an RM MBMA model linking serum neutralizing (SN) titres to viral load (VL).
• Translated the RM model to predict clinical efficacy and integrated clinical data to create three predictive models.

Main Results:

• RM data alone provided reasonable predictions of clinical efficacy.
• The predicted SN titre for 50% efficacy was consistently ~21% of the mean human convalescent titre.
• Models accurately predicted efficacies for BBV152 and CoronaVac and against the delta variant.

Conclusions:

• The developed MBMA models effectively predict protection against SARS-CoV-2.
• This framework enables early Go/No-Go decisions and study design optimization using immunogenicity data.
• The approach is valuable for developing novel SARS-CoV-2 vaccines and potentially for future pandemic preparedness.