A Quantitative Clinical Pharmacology-Based Framework For Model-Informed Vaccine Development.

• Analogous to model-informed drug development (MIDD), we outline a clinical pharmacology-based framework termed as model-informed vaccine development (MIVD) for quantitative vaccine dose and dosing-regimen optimization. • MIVD approaches can optimize vaccine doses using the dose/exposure-response and immunogenicity-reactogenicity trade-off paradigms (analogous to therapeutic window determination for conventional drugs). • We illustrate possible advantages of an MIVD approach over conventional empirical approaches with hypothetical examples that mimic real-world scenarios arising during various phases of vaccine development. Historically, vaccine development and dose optimization have followed mostly empirical approaches without clinical pharmacology and model-informed approaches playing a major role, in contrast to conventional drug development. This is attributed to the complex cascade of immunobiological mechanisms associated with vaccines and a lack of quantitative frameworks for extracting dose-exposure-efficacy-toxicity relationships. However, the Covid-19 pandemic highlighted the lack of sufficient immunogenicity due to suboptimal vaccine dosing regimens and the need for well-designed, model-informed clinical trials which enhance the probability of selection of optimal vaccine dosing regimens. In this perspective, we attempt to develop a quantitative clinical pharmacology-based approach that integrates vaccine dose-efficacy-toxicity across various stages of vaccine development into a unified framework that we term as model-informed vaccine dose-optimization and development (MIVD). We highlight scenarios where the adoption of MIVD approaches may have a strategic advantage compared to conventional practices for vaccines. [ABSTRACT FROM AUTHOR]