Adverse Outcome Pathways during Early Fish Development: A Conceptual Framework for Identification of Chemical Screening and Prioritization Strategies
Bibliographic record
Abstract
The fish early life-stage (FELS) test guideline (OECD 210 or OCSPP 850.1400) is the most frequently used bioassay for predicting chronic fish toxicity and supporting aquatic ecological risk assessments around the world. For each chemical, the FELS test requires a minimum of 360 fish and 1 to 3 months from test initiation to termination. Although valuable for predicting fish full life-cycle toxicity, FELS tests are labor and resource intensive and, due to an emphasis on apical endpoints, provide little to no information about chemical mode of action. Therefore, the development and implementation of alternative testing strategies for screening and prioritizing chemicals has the potential to reduce the cost and number of animals required for estimating FELS toxicity and, at the same time, provides insights into mechanisms of toxicity. Using three reference chemicals with well-established yet distinct adverse outcome pathways (AOPs) in early life stages of fish, we proposed FELS-specific AOPs as conceptual frameworks for identifying useful chemical screening and prioritization strategies. The reference chemicals selected as case studies were a cardiotoxic aryl hydrocarbon receptor agonist (2,3,7,8-tetrachlorodibenzo-p-dioxin), neurotoxic acetylcholinesterase inhibitor (chlorpyrifos), and narcotic surfactant (linear alkylbenzene sulfonate). Using qualitative descriptions for each chemical during early fish development, we developed generalized AOPs and, based on these examples, proposed a three-tiered testing strategy for screening and prioritizing chemicals for FELS testing. Linked with biologically based concentration-response models, a tiered testing strategy may help reduce the reliance on long-term and costly FELS tests required for assessing the hazard of thousands of chemicals currently in commerce.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".