EURL ECVAM Genotoxicity and Carcinogenicity Database of Substances Eliciting Positive and Negative Results in the Ames Test
Bibliographic record
Abstract
The bacterial reverse mutation test (Ames test) is the most commonly used genotoxicity test; it is a primary component of the chemical safety assessment data required by regulatory agencies worldwide. Within the current accepted in vitro genotoxicity test battery, it is considered capable of revealing DNA reactivity, and identifying substances that can produce gene mutations via different mechanisms. Evaluating the predictivity of the Ames test for in vivo genotoxicity and carcinogenicity, when considered alone or in association with mammalian cell assays for chromosome damage and/or gene mutations, is an important endeavour. A consolidated EURL ECVAM Genotoxicity and Carcinogenicity Database, which includes substances that elicited a positive response in the Ames test in >700 substances was initially published and constitutes to date a collection of data that serves as a reference for a number of regulatory activities in the area of genotoxicity testing. Consequently, we considered it important to update the database to include additional substances and data. This update is under finalisation. We also considered it critical to expand the database to include substances that fail to elicit a positive response in the Ames test, i.e., Ames negative substances. A curated collection of 211 Ames negative substances was, therefore put together. The databases include a summary of complementary data available for other genotoxicity endpoints in vitro and in vivo, plus available carcinogenicity data. Together with the description of the databases a descriptive analysis of the data will also be presented. This also includes a representation of the chemical space formed by the Ames negative database with respect to other substances (e.g. REACH registered substances, approved drugs, pesticides, etc.) and a description of the organic functional groups found in the database.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.014 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".