Evidence map›Paper›PMID 34205932›Full record

ReviewVaccines2021

Predictive Markers of Immunogenicity and Efficacy for Human Vaccines.

Matthieu Van Tilbeurgh, Katia Lemdani, Anne-Sophie Beignon, Catherine Chapon, Nicolas Tchitchek, Lina Cheraitia, Ernesto Marcos Lopez, Quentin Pascal, Roger Le Grand, Pauline Maisonnasse and 1 more

Abstract readReview
In one paragraph

Review in Vaccines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

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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

11 authors.

Matthieu Van TilbeurghImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.
Katia LemdaniImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.
Anne-Sophie BeignonImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.
Catherine ChaponImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.ORCID 0000-0002-3210-8468
Nicolas TchitchekUnité de Recherche i3, Inserm UMR-S 959, Bâtiment CERVI, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France.ORCID 0000-0003-3307-0446
Lina CheraitiaImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.
Ernesto Marcos LopezImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.
Quentin PascalImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.
Roger Le GrandImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.ORCID 0000-0002-4928-4484
Pauline MaisonnasseImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.ORCID 0000-0002-0555-207X
Caroline ManetImmunology of Viral Infections and Autoimmune Diseases (IMVA), IDMIT Department, Institut de Biologie François-Jacob (IBJF), University Paris-Sud-INSERM U1184, CEA, 92265 Fontenay-Aux-Roses, France.ORCID 0000-0002-7730-8818

Funding

Agence Nationale de la Recherche ANR-11-INBS-0008
6 · The paper itself

Abstract

Vaccines represent one of the major advances of modern medicine. Despite the many successes of vaccination, continuous efforts to design new vaccines are needed to fight "old" pandemics, such as tuberculosis and malaria, as well as emerging pathogens, such as Zika virus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Vaccination aims at reaching sterilizing immunity, however assessing vaccine efficacy is still challenging and underscores the need for a better understanding of immune protective responses. Identifying reliable predictive markers of immunogenicity can help to select and develop promising vaccine candidates during early preclinical studies and can lead to improved, personalized, vaccination strategies. A systems biology approach is increasingly being adopted to address these major challenges using multiple high-dimensional technologies combined with in silico models. Although the goal is to develop predictive models of vaccine efficacy in humans, applying this approach to animal models empowers basic and translational vaccine research. In this review, we provide an overview of vaccine immune signatures in preclinical models, as well as in target human populations. We also discuss high-throughput technologies used to probe vaccine-induced responses, along with data analysis and computational methodologies applied to the predictive modeling of vaccine efficacy.

Indexed as

high-throughput technologiesin vivo imagingmachine learningpreclinical modelspredictive biomarkerssystems immunologyunsupervised analysesvaccinesvaccine signatures

Identifiers

PMID34205932
PMCPMC8226531

What OpenQuestion holds

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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.