Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling.

ABSTRACT

INTRODUCTION: Identifying optimal COVID-19 vaccine dose is essential for maximizing their impact. However, COVID-19 vaccine dose-finding has been an empirical process, limited by short development timeframes, and therefore potentially not thoroughly investigated. Mathematical IS/ID modelling is a novel method for predicting optimal vaccine dose which could inform future COVID-19 vaccine dose decision making. METHODS: Published clinical data on COVID-19 vaccine dose-response was identified and extracted. Mathematical models were calibrated to the dose-response data stratified by subpopulation, where possible to predict optimal dose. Predicted optimal doses were summarised across vaccine type and compared to chosen dose for the primary series of COVID-19 vaccines to identify vaccine doses that may benefit from re-evaluation. RESULTS: 30 clinical dose-response datasets in adults and elderly population were extracted for four vaccine types and optimal doses predicted using the models. Results suggest that, if re-assessed for dose, COVID-19 vaccines Ad26.cov, ChadOx1 n-Cov19, BNT162b2, Coronavac, and NVX-CoV2373 could benefit from increased dose in adults and mRNA-1273 and Coronavac, could benefit from increased and decreased dose for the elderly population, respectively. DISCUSSION: Future iterations of COVID-19 vaccines could benefit from re-evaluating dose to ensure most effective use of the vaccine and mathematical modelling can support this.