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A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection

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 administrat...

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Autores principales: Dogra, Prashant, Schiavone, Carmine, Wang, Zhihui, Ruiz-Ramírez, Javier, Caserta, Sergio, Staquicini, Daniela I., Markosian, Christopher, Wang, Jin, Sostman, H. Dirk, Pasqualini, Renata, Arap, Wadih, Cristini, Vittorio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Clinical Investigation 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10371350/
https://www.ncbi.nlm.nih.gov/pubmed/37227783
http://dx.doi.org/10.1172/jci.insight.169860
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author Dogra, Prashant
Schiavone, Carmine
Wang, Zhihui
Ruiz-Ramírez, Javier
Caserta, Sergio
Staquicini, Daniela I.
Markosian, Christopher
Wang, Jin
Sostman, H. Dirk
Pasqualini, Renata
Arap, Wadih
Cristini, Vittorio
author_facet Dogra, Prashant
Schiavone, Carmine
Wang, Zhihui
Ruiz-Ramírez, Javier
Caserta, Sergio
Staquicini, Daniela I.
Markosian, Christopher
Wang, Jin
Sostman, H. Dirk
Pasqualini, Renata
Arap, Wadih
Cristini, Vittorio
author_sort Dogra, Prashant
collection PubMed
description 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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spelling pubmed-103713502023-07-27 A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection Dogra, Prashant Schiavone, Carmine Wang, Zhihui Ruiz-Ramírez, Javier Caserta, Sergio Staquicini, Daniela I. Markosian, Christopher Wang, Jin Sostman, H. Dirk Pasqualini, Renata Arap, Wadih Cristini, Vittorio JCI Insight Technical Advance 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. American Society for Clinical Investigation 2023-07-10 /pmc/articles/PMC10371350/ /pubmed/37227783 http://dx.doi.org/10.1172/jci.insight.169860 Text en © 2023 Dogra et al. https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Technical Advance
Dogra, Prashant
Schiavone, Carmine
Wang, Zhihui
Ruiz-Ramírez, Javier
Caserta, Sergio
Staquicini, Daniela I.
Markosian, Christopher
Wang, Jin
Sostman, H. Dirk
Pasqualini, Renata
Arap, Wadih
Cristini, Vittorio
A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
title A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
title_full A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
title_fullStr A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
title_full_unstemmed A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
title_short A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection
title_sort modeling-based approach to optimize covid-19 vaccine dosing schedules for improved protection
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10371350/
https://www.ncbi.nlm.nih.gov/pubmed/37227783
http://dx.doi.org/10.1172/jci.insight.169860
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