Alternatives to<i>in vivo</i>tests to detect endocrine disrupting chemicals (EDCs) in fish and amphibians – screening for estrogen, androgen and thyroid hormone disruption
Bibliographic record
Abstract
Endocrine disruption is considered a highly relevant hazard for environmental risk assessment of chemicals, plant protection products, biocides and pharmaceuticals. Therefore, screening tests with a focus on interference with estrogen, androgen, and thyroid hormone pathways in fish and amphibians have been developed. However, they use a large number of animals and short-term alternatives to animal tests would be advantageous. Therefore, the status of alternative assays for endocrine disruption in fish and frogs was assessed by a detailed literature analysis. The aim was to (i) determine the strengths and limitations of alternative assays and (ii) present conclusions regarding chemical specificity, sensitivity, and correlation with in vivo data. Data from 1995 to present were collected related to the detection/testing of estrogen-, androgen-, and thyroid-active chemicals in the following test systems: cell lines, primary cells, fish/frog embryos, yeast and cell-free systems. The review shows that the majority of alternative assays measure effects directly mediated by receptor binding or resulting from interference with hormone synthesis. Other mechanisms were rarely analysed. A database was established and used for a quantitative and comparative analysis. For example, a high correlation was observed between cell-free ligand binding and cell-based reporter cell assays, between fish and frog estrogenic data and between fish embryo tests and in vivo reproductive effects. It was concluded that there is a need for a more systematic study of the predictive capacity of alternative tests and ways to reduce inter- and intra-assay variability.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".