Immunostimulatory/Immunodynamic model of mRNA ‐1273 to guide pediatric vaccine dose selection
Year: 2024
Type: article
Abstract: COVID-19 vaccines, including mRNA-1273, have been rapidly developed and deployed. Establishing the optimal dose is crucial for developing a safe and effective vaccine. Modeling and simulation have the potential to play a key role in guiding the selection and development of the vaccine dose. In this context, we have developed an immunostimulatory/immunodynamic (IS/ID) model to quantitatively characterize the neutralizing antibody titers elicited by mRNA-1273 obtained from three clinical studies. The developed model was used to predict the optimal vaccine dose for future pediatric trials. A 25-μg primary vaccine series was predicted to meet non-inferiority criteria in young children (aged 2-5 years) and infants (aged 6-23 months). The geometric mean titers and geometric mean ratios for this dose level predicted using the IS/ID model a priori matched those observed in the pediatric clinical study. These findings demonstrate that IS/ID models represent a novel approach to guide data-driven clinical dose selection of vaccines.
DOI: 10.1002/psp4.13237
Authors: Vijay Ivaturi, Husain Attarwala, Weiping Deng, Baoyu Ding, Sabine Schnyder Ghamloush, Bethany Girard, Javid Iqbal, Saugandhika Minnikanti, Honghong Zhou, Jacqueline Miller, Rituparna Das
Institution: Manipal Academy of Higher Education
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,940
Funder: Moderna
OpenAlex ID: W4402933035