Evidence map›Paper›PMID 30273336›Full record

ArticlePLoS computational biology2018

Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design.

Andreas Handel, Yan Li, Brian McKay, Kasia A Pawelek, Veronika Zarnitsyna, Rustom Antia

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
3.9field-weighted citation impact, top 6% of its field
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

34 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
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  9. Article
  10. Quantitatively Mapping Immune Control during Influenza.Current opinion in systems biology · 2024
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  12. Review
  13. Article
  14. Utility of HumanJournal of virology · 2023
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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

6 authors at 3 institutions in 1 country.

Andreas HandelDepartment of Epidemiology and Biostatistics and Health Informatics Institute and Center for the Ecology of Infectious Diseases, University of Georgia, Athens, Georgia, United States of America.ORCID 0000-0002-4622-1146
Yan LiInstitute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America.
Brian McKayDepartment of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, United States of America.ORCID 0000-0003-3859-7634
Kasia A PawelekDepartment of Mathematics and Computational Science, University of South Carolina Beaufort, Bluffton, South Carolina, United States of America.
Veronika ZarnitsynaDepartment of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America.ORCID 0000-0003-3096-3695
Rustom AntiaDepartment of Biology, Emory University, Atlanta, Georgia, United States of America.ORCID 0000-0001-7991-614X
University of Georgia · USEmory University · USUniversity of South Carolina Beaufort · US

Funding

South Carolina IDeA Networks of Biomedical Research (SC INBRE V)P20GM103499 · NIGMS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI EDIE C GOLDSMITH · 2012 to 2026
$61.0M
Modeling the dynamics and evolution of immune responses to influenza virusesU19AI117891 · NIAID · EMORY UNIVERSITY · PI AHMED, RAFI, ANTIA, RUSTOM NOSHIR · 2015 to 2019
$8.4M
NIAID NIH HHS U19 AI117891NIGMS NIH HHS P20 GM103499
6 · The paper itself

Abstract

Vaccination is an effective method to protect against infectious diseases. An important consideration in any vaccine formulation is the inoculum dose, i.e., amount of antigen or live attenuated pathogen that is used. Higher levels generally lead to better stimulation of the immune response but might cause more severe side effects and allow for less population coverage in the presence of vaccine shortages. Determining the optimal amount of inoculum dose is an important component of rational vaccine design. A combination of mathematical models with experimental data can help determine the impact of the inoculum dose. We illustrate the concept of using data and models to inform inoculum dose determination for vaccines, wby fitting a mathematical model to data from influenza A virus (IAV) infection of mice and human parainfluenza virus (HPIV) infection of cotton rats at different inoculum doses. We use the model to map inoculum dose to the level of immune protection and morbidity and to explore how such a framework might be used to determine an optimal inoculum dose. We show how a framework that combines mathematical models with experimental data can be used to study the impact of inoculum dose on important outcomes such as immune protection and morbidity. Our findings illustrate that the impact of inoculum dose on immune protection and morbidity can depend on the specific pathogen and that both protection and morbidity do not necessarily increase monotonically with increasing inoculum dose. Once vaccine design goals are specified with required levels of protection and acceptable levels of morbidity, our proposed framework can help in the rational design of vaccines and determination of the optimal amount of inoculum.

Indexed as

Dose-Response Relationship, ImmunologicModels, ImmunologicalAnimalsComputational BiologyDrug DesignHost-Pathogen InteractionsHumansMiceRatsVaccinesViral LoadVaccines

Identifiers

PMID30273336
PMCPMC6181424
OpenAlexW2951083225

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.