A different approach to validating screening assays for developmental toxicity
Bibliographic record
Abstract
BACKGROUND: There continue to be many efforts around the world to develop assays that are shorter than the traditional embryofetal developmental toxicity assay, or use fewer or no mammals, or use less compound, or have all three attributes. Each assay developer needs to test the putative assay against a set of performance standards, which traditionally has involved testing the assays against a list of compounds that are generally recognized as "positive" or "negative" in vivo. However, developmental toxicity is highly conditional, being particularly dependent on magnitude (i.e. dose) and timing of exposure, which makes it difficult to develop lists of compounds neatly assigned as developmental toxicants or not. APPROACH: Here we offer an alternative approach for the evaluation of developmental toxicity assays based on exposures. Exposures are classified as "positive" or "negative" in a system, depending on the compound and the internal concentration. Although this linkage to "internal dose" departs from the recent approaches to validation, it fits well with widely accepted principles of developmental toxicology. CONCLUSIONS: This paper introduces this concept, discusses some of the benefits and drawbacks of such an approach, and lays out the steps we propose to implement it for the evaluation of developmental toxicity assays.
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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 | 0.030 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".