Standardisation and international adoption of defined approaches for skin sensitisation
Bibliographic record
Abstract
In the absence of stand-alone one-to-one replacements for existing animal tests, efforts were made to integrate data from in silico, in chemico and in vitro methods to ensure sufficient mechanistic coverage of the skin sensitisation Adverse Outcome Pathway (AOP) and generate predictions suitable for hazard identification and potency sub-categorisation. A number of defined approaches (DAs), using fixed data interpretation procedures (DIP) to integrate data from multiple non-animal information sources, were proposed and documented using a standard reporting template developed by the Organisation for Economic Co-operation and Development (OECD). Subsequent international activities focused on the extensive characterisation of three of these DAs with respect to the reference in vivo data, applicability domains, limitations, predictive performances and characterisations of the level of confidence associated with the predictions. The ultimate product of this project was an OECD Guideline that provides information equivalent to that provided by the animal studies and that can be used to satisfy countries’ regulatory data requirements for skin sensitisation. This Defined Approach Guideline was the first of its kind for the OECD, and provides an important precedent for regulatory adoption of human biology-relevant new approach methodologies with performances equivalent to, or better than, traditional animal tests. This mini review summarizes the principal features of the defined approaches described in OECD guideline 497.
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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 | 0.018 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".