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Publications

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Epublish 12 citations

PubMed: 37227783 DOI: 10.1172/jci.insight.169860

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

JCI Insight 8(13)

Authors: Prashant Dogra, Carmine Schiavone, Zhihui Wang, Javier Ruiz-Ramírez, Sergio Caserta, Daniela I Staquicini, Christopher Markosian, Jin Wang, H Dirk Sostman, Renata Pasqualini, Wadih Arap, Vittorio Cristini

Affiliations

  • Mathematics in Medicine Program, Department of Medicine, Houston Methodist Research Institute, Houston, Texas, USA.
  • Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.
  • Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy.
  • Neal Cancer Center, Houston Methodist Research Institute, Houston, Texas, USA.
  • Centro de Ciencias de la Salud, Universidad Autónoma de Aguascalientes, Aguascalientes, Mexico.
  • CEINGE Advanced Biotechnologies, Naples, Italy.
  • Rutgers Cancer Institute of New Jersey, Newark, New Jersey, USA.
  • Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, New Jersey, USA.
  • Immunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute, Houston, Texas, USA. Department of Surgery, Weill Cornell Medical College, Cornell University, New York, New York, USA.
  • Weill Cornell Medicine, New York, New York, USA.
  • Houston Methodist Research Institute, Houston, Texas, USA.
  • Houston Methodist Academic Institute, Houston, Texas, USA.
  • Division of Hematology/Oncology, Department of Medicine, Rutgers New Jersey Medical School, Newark, New Jersey, USA.
  • Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
  • Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences, Weill Cornell Medicine, New York, New York, USA.

Abstract

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.

MeSH

Vaccines, Covid-19 Vaccines, Biomedical Research, Vaccination, Covid-19, Immunity, Pandemics, Infections, Breakthrough Infections

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