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Conference report WHO informal consultation on the draft WHO Guideline on the phasing out of animal tests for the quality control of biological products

2025· article· en· W4415103904 on OpenAlexfundno aff
Ian M. Feavers, Dianliang Lei, Catherine Milne, Ivana Knežević, Tiequn Zhou, Eunkyung Kim

Bibliographic record

VenueBiologicals · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Trade Organization Law
Canadian institutionsnot available
FundersHealth CanadaMinistry of Public HealthNational Institute of Food and Drug Safety EvaluationNational Centre for the Replacement, Refinement and Reduction of Animals in ResearchU.S. Food and Drug AdministrationNational Institutes for Food and Drug ControlNational Centre for the Replacement Refinement and Reduction of Animals in ResearchMinistry of Food and Drug SafetyAgence Nationale de Sécurité du Médicament et des Produits de SantéWorld Health OrganizationMerck
KeywordsGuidelineQuality (philosophy)Control (management)Product (mathematics)Quality controlProtocol (science)Animal testing

Abstract

fetched live from OpenAlex

Animal testing has long supported the development and quality control of biotherapeutics and vaccines by ensuring safety and efficacy. However, its variability and time-consuming nature can delay product availability. Advances in non-animal technologies, guided by the 3Rs principles, have led to more efficient and scientifically robust alternatives. Recognizing the limitations of animal assays, WHO encourages their replacement when scientifically justified and has drafted a Guideline on phasing out animal tests in biological product quality control. Following public consultation, an informal meeting at WHO Headquarters brought together regulators, industry representatives, and other stakeholders to review the draft. The Guideline was developed based on ECBS recommendations and a review of existing WHO documents. Participants proposed improvements, including a revised title, to better emphasize the scientific rationale for replacing animal-based tests used in quality control scheme. These updates aim to support finalization of the document for a second public consultation and ECBS adoption.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.046
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0100.005
Open science0.0040.006
Research integrity0.0250.016
Insufficient payload (model declined to judge)0.0460.021

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 head0.158
GPT teacher head0.420
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2025
Admission routes1
Has abstractyes

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