TY - JOUR
T1 - A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
AU - Dogra, Prashant
AU - Schiavone, Carmine
AU - Wang, Zhihui
AU - Ruiz-Ramírez, Javier
AU - Caserta, Sergio
AU - Staquicini, Daniela I.
AU - Markosian, Christopher
AU - Wang, Jin
AU - Dirk Sostman, H.
AU - Pasqualini, Renata
AU - Arap, Wadih
AU - Cristini, Vittorio
N1 - Publisher Copyright: Copyright: © 2023, Dogra et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.
PY - 2023
Y1 - 2023
N2 - While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.
AB - While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.
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U2 - 10.1172/jci.insight.169860
DO - 10.1172/jci.insight.169860
M3 - Article
C2 - 37227783
SN - 2379-3708
VL - 8
JO - JCI Insight
JF - JCI Insight
IS - 13
M1 - e169860
ER -