Evidence map›Paper›PMID 35632511›Full record

ArticleVaccines2022

Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity.

John Benest, Sophie Rhodes, Thomas G Evans, Richard G White

Abstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

4 authors.

John BenestDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.ORCID 0000-0003-0537-5437
Sophie RhodesDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.
Thomas G EvansVaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK.
Richard G WhiteDepartment of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK.ORCID 0000-0003-4410-6635

Funding

Defining drivers of TB transmission in the era of universal ART, and implications for finding the walking wellR01AI147321 · NIAID · LONDON SCH/HYGIENE & TROPICAL MEDICINE · PI GRANT, ALISON · 2019 to 2024
$2.3M
Bill & Melinda Gates Foundation OPP1084276Bill & Melinda Gates Foundation OPP1135288Biotechnology and Biological Sciences Research Council BB/M009513/1Economic and Social Research Council ES/P008011/1European & Developing Countries Clinical Trials Partnership RIA208D-2505BGates Foundation INV-001754Medical Research Council CCF17-7779 via SET BloomsburyNIAID NIH HHS R01 AI147321NIH HHS 1R01AI147321-01Wellcome TrustWellcome Trust 218261/Z/19/ZWorld Health Organization 2020/985800-0
6 · The paper itself

Abstract

Vaccination is a key tool to reduce global disease burden. Vaccine dose can affect vaccine efficacy and toxicity. Given the expense of developing vaccines, optimising vaccine dose is essential. Mathematical modelling has been suggested as an approach for optimising vaccine dose by quantitatively establishing the relationships between dose and efficacy/toxicity. In this work, we performed simulation studies to assess the performance of modelling approaches in determining optimal dose. We found that the ability of modelling approaches to determine optimal dose improved with trial size, particularly for studies with at least 30 trial participants, and that, generally, using a peaking or a weighted model-averaging-based dose-efficacy relationship was most effective in finding optimal dose. Most methods of trial dose selection were similarly effective for the purpose of determining optimal dose; however, including modelling to adapt doses during a trial may lead to more trial participants receiving a more optimal dose. Clinical trial dosing around the predicted optimal dose, rather than only at the predicted optimal dose, may improve final dose selection. This work suggests modelling can be used effectively for vaccine dose finding, prompting potential practical applications of these methods in accelerating effective vaccine development and saving lives.

Indexed as

adaptive designclinical trialscontinual modellingdose responsedosingmodelling

Identifiers

PMID35632511
PMCPMC9144167

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Registered trials

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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.