Evidence map›Paper›PMID 30639297›Full record

ArticleJournal of theoretical biology2019

Dose finding for new vaccines: The role for immunostimulation/immunodynamic modelling.

Sophie J Rhodes, Gwenan M Knight, Denise E Kirschner, Richard G White, Thomas G Evans

Registry-linked trialAbstract readLetter
In one paragraph

Article in Journal of theoretical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04672395 (A Double-blind, Randomized, Controlled, Phase 2/3 Study to Evaluate the Efficacy, Immunogenicity, and Safety of CpG 1018/Alum-Adjuvanted Recombinant SARS-CoV-2 Trimeric S-protein Subunit Vaccine), which is not on this map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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.

NCT04672395 phase2 / phase3completednot on this mapstarted 2021, after this paper: background citation

A Double-blind, Randomized, Controlled, Phase 2/3 Study to Evaluate the Efficacy, Immunogenicity, and Safety of CpG 1018/Alum-Adjuvanted Recombinant SARS-CoV-2 Trimeric S-protein Subunit Vaccine (SCB-2019) for the Prevention of SARS-CoV-2- Mediated COVID-19 in Participants Aged 12 Years and Older

TypeinterventionalSponsorClover Biopharmaceuticals AUS PtyRan2021 to 2023Enrolled31,454ConditionsCOVID-19ArmsCpG 1018/Alum-adjuvanted SCB-2019 vaccine, Placebo, 0.9% saline, SCB-2019 vaccine, SCB-2019 vaccine for Placebo
3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Trial
  2. Article
  3. Development of a NovelVaccines · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Biodegradable scaffolds for enhancing vaccine delivery.Bioengineering & translational medicine · 2023
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Multiscale modeling meets machine learning: What can we learn?Archives of computational methods in engineering : state of the art reviews · 2021
    Article
  20. Personalized Cancer Vaccines: Clinical Landscape, Challenges, and Opportunities.Molecular therapy : the journal of the American Society of Gene Therapy · 2021
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sophie J RhodesTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK. Electronic address: sophie.rhodes@lshtm.ac.uk.
Gwenan M KnightTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK.
Denise E KirschnerUniversity of Michigan Medical School, Ann Arbor, MI, USA.
Richard G WhiteTB Modelling Group, CMMID, TB Centre, London School of Hygiene and Tropical Medicine, UK.
Thomas G EvansVaccitech, Oxford, UK.

Funding

Predicting protective T-cell responses in Tuberculosis using a systems biology approachR01AI123093 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FLYNN, JOANNE L., KIRSCHNER, DENISE E · 2016 to 2020
$3.6M
A Multi-scale systems pharmacology approach to TB therapyU01HL131072 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DARTOIS, VERONIQUE, FLYNN, JOANNE L. · 2016 to 2019
$2.9M
Medical Research Council MR/P002404/1NHLBI NIH HHS U01 HL131072NIAID NIH HHS R01 AI123093
6 · The paper itself

Abstract

Current methods to optimize vaccine dose are purely empirically based, whereas in the drug development field, dosing determinations use far more advanced quantitative methodology to accelerate decision-making. Applying these established methods in the field of vaccine development may reduce the currently large clinical trial sample sizes, long time frames, high costs, and ultimately have a better potential to save lives. We propose the field of immunostimulation/immunodynamic (IS/ID) modelling, which aims to translate mathematical frameworks used for drug dosing towards optimizing vaccine dose decision-making. Analogous to Pharmacokinetic/Pharmacodynamic (PK/PD) modelling, the mathematical description of drug distribution (PK) and effect (PD) in host, IS/ID modelling approaches apply mathematical models to describe the underlying mechanisms by which the immune response is stimulated by vaccination (IS) and the resulting measured immune response dynamics (ID). To move IS/ID modelling forward, existing datasets and further data on vaccine allometry and dose-dependent dynamics need to be generated and collate, requiring a collaborative environment with input from academia, industry, regulators, governmental and non-governmental agencies to share modelling expertise, and connect modellers to vaccine data.

Indexed as

Models, ImmunologicalAnimalsComputer SimulationDose-Response Relationship, DrugHumansImmunogenicity, VaccineReproducibility of ResultsVaccinationVaccinesVaccinesImmune responseMathematical modellingVaccine developmentVaccine dose optimization

Identifiers

PMID30639297
PMCPMC6860008

What OpenQuestion holds

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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.