162 Keynote: Adverse Outcome Pathways – a Framework for Designing Novel Approach Methods for Safety Assessment
Bibliographic record
Abstract
Abstract The fast evolving technologies, the search for new chemicals and synthesis of substitutes for the existing chemicals, are posing unprecedented challenges on regulatory agencies that have mainly relied on toxicity testing in laboratory animals to ensure safety of humans and the environment from potential exposure to these substances. In response, a new and well-intended trend to reduce, refine and replace animal testing that are both time and resource intensive with animal alternatives called the New Approach Methodologies (NAMs) that are faster, cheaper, sensitive, and mechanisms-based, has been introduced and widely embraced by the scientific community. However, strategies that help identify and prioritize fit-for-purpose NAMs schemes involving test models, toxicity endpoints, and specific assays for the hazard assessment, as well as strategies that enable validation of their scientific relevance for the regulatory acceptance are lagging behind. As a consequence, the regulatory acceptance of NAMs in decision making is far from reality. This presentation will summarise the current status of NAMs in regulatory toxicology and the role of Adverse Outcome Pathways (AOPs) in the design and development of NAMs, with a specific focus on nanotoxicology.
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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 | 0.032 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".