ArticleNPJ vaccines2018
Using vaccine Immunostimulation/Immunodynamic modelling methods to inform vaccine dose decision-making.
Article in NPJ vaccines, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Immunostimulatory and Immunodynamic Modeling Analysis to Determine a Plausible Starting Dose of mRNA-4359 for Use in First-In-Human Trials.CPT: pharmacometrics & systems pharmacology · 2026Trial
- Optimizing mouse models for mRNA vaccines: addressing dose translation challenges.Scientific reports · 2026Article
- Integration of Preclinical and Clinical Vaccine Safety and Immunogenicity Testing for Development of a Pediatric HIV Vaccine to Achieve Protective HIV Immunity Prior to Adolescence.Current HIV research · 2026Review
- A Multiscale Quantitative Systems Pharmacology Model for the Development and Optimization of mRNA Vaccines.CPT: pharmacometrics & systems pharmacology · 2025Article
- Immunostimulatory/Immunodynamic model of mRNA-1273 to guide pediatric vaccine dose selection.CPT: pharmacometrics & systems pharmacology · 2025Article
- Prediction of long-term humoral response induced by the two-dose heterologous Ad26.ZEBOV, MVA-BN-Filo vaccine against Ebola.NPJ vaccines · 2023Article
- Identifying COVID-19 optimal vaccine dose using mathematical immunostimulation/immunodynamic modelling.Vaccine · 2022Article
- Article
- Article
- Current and prospective computational approaches and challenges for developing COVID-19 vaccines.Advanced drug delivery reviews · 2021Review
- Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities.Molecular therapy : the journal of the American Society of Gene Therapy · 2021Review
- Article
- Uncovering drivers of dose-dependence and individual variation in malaria infection outcomes.PLoS computational biology · 2020Article
- Response Type and Host Species may be Sufficient to Predict Dose-Response Curve Shape for Adenoviral Vector Vaccines.Vaccines · 2020Article
- Review
- Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling.Journal of theoretical biology · 2019Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Unlike drug dose optimisation, mathematical modelling has not been applied to vaccine dose finding. We applied a novel Immunostimulation/Immunodynamic mathematical modelling framework to translate multi-dose TB vaccine immune responses from mice, to predict most immunogenic dose in humans. Data were previously collected on IFN-γ secreting CD4+ T cells over time for novel TB vaccines H56 and H1 adjuvanted with IC31 in mice (1 dose groups (0.1-1.5 and 15 μg H56 + IC31), 45 mice) and humans (1 dose (50 μg H56/H1 + IC31), 18 humans). A two-compartment mathematical model, describing the dynamics of the post-vaccination IFN-γ T cell response, was fitted to mouse and human data, separately, using nonlinear mixed effects methods. We used these fitted models and a vaccine dose allometric scaling assumption, to predict the most immunogenic human dose. Based on the changes in model parameters by mouse H56 + IC31 dose and by varying the H56 dose allometric scaling factor between mouse and humans, we established that, at a late time point (224 days) doses of 0.8-8 μg H56 + IC31 in humans may be the most immunogenic. A 0.8-8 μg of H-series TB vaccines in humans, may be as, or more, immunogenic, as larger doses. The Immunostimulation/Immunodynamic mathematical modelling framework is a novel, and potentially revolutionary tool, to predict most immunogenic vaccine doses, and accelerate vaccine development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.