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Record W4407175031 · doi:10.1111/imr.70001

Targeting Bottlenecks in Malaria Transmission: Antibody‐Epitope Descriptions Guide the Design of Next‐Generation Biomedical Interventions

2025· review· en· W4407175031 on OpenAlexafffund
Randy Yoo, Matthijs M. Jore, Jean‐Philippe Julien

Bibliographic record

VenueImmunological Reviews · 2025
Typereview
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthOpen Philanthropy ProjectBill and Melinda Gates Foundation
KeywordsMalariaBiologyEpitopeCircumsporozoite proteinPlasmodium falciparumVirologyComputational biologyAntibodyPlasmodium (life cycle)Transmission (telecommunications)ImmunologyComputer science

Abstract

fetched live from OpenAlex

Malaria continues to pose a significant burden to global health. Thus, a strong need exists for the development of a diverse panel of intervention strategies and modalities to combat malaria and achieve elimination and eradication goals. Deploying interventions that target bottlenecks in the transmission life cycle of the causative agent of malaria, Plasmodium parasites, is an attractive strategy. The development of highly potent antibody-based biologics, including vaccines, can be greatly facilitated by an in-depth molecular understanding of antibody-epitope interactions. Here, we provide an overview of structurally characterized antibodies targeting lead vaccine candidates expressed during the bottlenecks of the Plasmodium life cycle which include the pre-erythrocytic and sexual stages. The repeat region of the circumsporozoite protein (CSP), domain 1 of Pfs230 and domains 1 and 3 of Pfs48/45 are critical Plasmodium regions targeted by the most potent antibodies at the two bottlenecks of transmission, with other promising targets emerging and requiring further characterization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.248
GPT teacher head0.449
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

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