Evidence map›Paper›PMID 40529181›Full record

ReviewComputational and structural biotechnology journal2025

Computational vaccine development against protozoa.

Omar Hashim, Isabelle Dimier-Poisson

Abstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Comparative 5' IsomiRome Analysis Uncovers Dysregulated 5' IsomiRs inComputational and structural biotechnology journal · 2026
    Article
  3. Review
  4. Article
  5. 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

2 authors.

Omar HashimLovaltech, Tours, France.
Isabelle Dimier-PoissonLovaltech, Tours, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protozoan parasites remain a major global health and economic burden, particularly in low- and middle-income countries. Conventional strategies such as chemotherapy and vector control face growing limitations due to resistance, toxicity, and implementation challenges. Vaccination represents a sustainable solution, but the complexity of protozoan life cycles and antigenic diversity has hindered vaccine development. Computational vaccinology offers innovative tools to overcome these barriers, combining immuno-informatics, reverse vaccinology, and artificial intelligence to accelerate the identification of immunogenic epitopes and streamline vaccine design. This review explores the current landscape of computational vaccine development against protozoa, highlighting advances in epitope prediction, population-specific vaccine design, and digital twin technologies. Applications include multivalent vaccines targeting conserved antigens across species, personalized formulations based on host immunogenetics, and the emerging use of protozoan vectors in cancer immunotherapy. Despite these promising avenues, significant challenges remain, particularly the need for robust experimental validation, improved delivery systems for short peptides, and greater acceptance of in silico methods by the broader scientific community. We argue that integrating computational tools with experimental immunology, high-throughput genomics, and translational research will be the key to developing safe, effective, and broadly accessible vaccines against protozoan infections. This convergence of disciplines has the potential to not only address neglected tropical diseases but also to establish new paradigms in precision vaccinology and immunotherapy.

Indexed as

Computational vaccinology1Immuno-informatics3Multi-epitope peptide vaccines5Protozoa2Vaccination4

Identifiers

PMID40529181
PMCPMC12172979

What Socratic 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 Socratic graph.