Evidence map›Paper›PMID 42450061›Full record

ReviewInternational journal of molecular sciences2026

Immune Mechanisms and Translational Study Design in Viral Vaccine Development.

Stephanie Lim, Byron Martina

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Stephanie LimArtemis Bioservices, Molengraasssingel 10, 2629 JD Delft, The Netherlands.
Byron MartinaArtemis Bioservices, Molengraasssingel 10, 2629 JD Delft, The Netherlands.ORCID 0009-0006-6127-2458

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Viral vaccine development requires both mechanistic understanding of protective immunity and translational study designs that connect preclinical data with human outcomes. Animal models remain important for early assessment of safety, immunogenicity and protective efficacy, but their predictive value depends on the question being asked, the pathophysiology of infection, the immune mechanisms expected to mediate protection, and the biomarkers chosen to bridge animal and human data. This review focuses on viral vaccines and examines innate and adaptive mechanisms of vaccine-induced protection, including B cell and antibody responses, Fc-mediated functions, Fc glycosylation, T cell memory and CD8+ cytotoxic responses. We discuss common reasons for clinical failure and show how preclinical endpoints can be classified as human-counterpart, surrogate or comparative/mechanistic readouts. Influenza and COVID-19 examples illustrate how different models can be combined across discovery, challenge, transmission and late-stage bridging studies. Emerging tools such as systems serology, omics, AI/ML and new approach methods can improve candidate prioritization, but their value depends on assay standardization, biological validation and cautious interpretation. A mechanism-driven model cascade, paired with human-relevant immunological readouts, can improve preclinical interpretation and reduce the risk of advancing candidates that are unlikely to succeed in clinical trials.

Indexed as

Translational Research, BiomedicalVaccine DevelopmentViral VaccinesAdaptive ImmunityAnimalsCOVID-19COVID-19 VaccinesHumansImmunity, InnateSARS-CoV-2COVID-19 VaccinesViral Vaccinesantibody effector functioncorrelates of protectionimmune mechanismsT cell immunitytranslational modelsviral vaccine development

Identifiers

PMID42450061
PMCPMC13360917

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.