Evidence map›Paper›PMID 42786507›Full record

ReviewLaboratory animal research2026

Rethinking non-human primate models for emerging viral infections after COVID-19.

Dong-Yeon Kim, Jung Joo Hong

Abstract readReview
In one paragraph

Review in Laboratory animal research, 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.

Dong-Yeon KimNational Primate Research Centre, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Cheongju, Chungcheongbuk, 28116, Republic of Korea.
Jung Joo HongNational Primate Research Centre, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Cheongju, Chungcheongbuk, 28116, Republic of Korea. hong75@kribb.re.kr.

Funding

Bio & Medical Technology Development Program of the National Research Foundation (NRF) RS-2022-NR067509Korea Research Institute of Bioscience and Biotechnology (KRIBB) Research Initiative Programs KGM 4572431
6 · The paper itself

Abstract

Non-human primates (NHPs) retain particular value in emerging viral infection research when questions require integrated analysis of systemic viral kinetics, protective immunity, tissue pathology, or longitudinal outcomes. However, their cost, limited availability, specialized husbandry, and ethical constraints preclude their routine use as default screening platforms. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a case study, we compare how rhesus macaques, cynomolgus macaques, African green monkeys, baboons, and marmosets have contributed to studies of pathogenesis, vaccine protection, therapeutic efficacy, immune memory, and post-acute outcomes. We emphasize that these species-question relationships cannot be transferred uncritically to future pathogens; model suitability must be re-established according to receptor usage, tissue tropism, disease phenotype, and experimental objective. AI-guided prediction, human organoids, and organ-on-chip systems can support this process by prioritizing variants and candidates, identifying tissue-specific mechanisms, and refining endpoints before NHP studies begin. We therefore propose a question-driven workflow in which computational and human-relevant platforms narrow the evidence gap before fit-for-purpose NHP validation. This strategy can improve interpretability, reproducibility, and alignment with the 3Rs while preserving NHP use for questions that require intact organism-level biology.

Indexed as

New approach methodologiesNon-human primatesSARS-CoV-2

Identifiers

PMID42786507
PMCPMC13602516

What Socratic holds

Textmetadata
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.