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Record W3037446541 · doi:10.1007/s00204-020-02784-5

Chemical carcinogen safety testing: OECD expert group international consensus on the development of an integrated approach for the testing and assessment of chemical non-genotoxic carcinogens

2020· article· en· W3037446541 on OpenAlexaff
Miriam N. Jacobs, Annamaria Colacci, Raffaella Corvi, Monica Vaccari, M. Cecilia Aguila, Marco Corvaro, Nathalie Delrue, Daniel Desaulniers, Norman Ertych, Abigail Jacobs, Mirjam Luijten, Federica Madia, Akiyoshi Nishikawa, Kiyomi Ohmori, Martin Paparella, Anoop Sharma, Paule Vasseur

Bibliographic record

VenueArchives of Toxicology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsHealth Canada
FundersU.S. Environmental Protection Agency
KeywordsChemical safetyCarcinogenToxicologyEnvironmental healthChemistryRisk analysis (engineering)MedicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

While regulatory requirements for carcinogenicity testing of chemicals vary according to product sector and regulatory jurisdiction, the standard approach starts with a battery of genotoxicity tests (which include mutagenicity assays). If any of the in vivo genotoxicity tests are positive, a lifetime rodent cancer bioassay may be requested, but under most chemical regulations (except plant protection, biocides, pharmaceuticals), this is rare. The decision to conduct further testing based on genotoxicity test outcomes creates a regulatory gap for the identification of non-genotoxic carcinogens (NGTxC). With the objective of addressing this gap, in 2016, the Organization of Economic Cooperation and Development (OECD) established an expert group to develop an integrated approach to the testing and assessment (IATA) of NGTxC. Through that work, a definition of NGTxC in a regulatory context was agreed. Using the adverse outcome pathway (AOP) concept, various cancer models were developed, and overarching mechanisms and modes of action were identified. After further refining and structuring with respect to the common hallmarks of cancer and knowing that NGTxC act through a large variety of specific mechanisms, with cell proliferation commonly being a unifying element, it became evident that a panel of tests covering multiple biological traits will be needed to populate the IATA. Consequently, in addition to literature and database investigation, the OECD opened a call for relevant assays in 2018 to receive suggestions. Here, we report on the definition of NGTxC, on the development of the overarching NGTxC IATA, and on the development of ranking parameters to evaluate the assays. Ultimately the intent is to select the best scoring assays for integration in an NGTxC IATA to better identify carcinogens and reduce public health hazards.

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)0.097
metaresearch head score (Gemma)0.044
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0970.044
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0120.008
Science and technology studies0.0020.006
Scholarly communication0.0070.006
Open science0.0100.009
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0040.004

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 head0.048
GPT teacher head0.296
Teacher spread0.248 · 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

Citations125
Published2020
Admission routes1
Has abstractyes

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