Extending an In Vitro Panel for Estrogenicity Testing: The Added Value of Bioassays for Measuring Antiandrogenic Activities and Effects on Steroidogenesis
Bibliographic record
Abstract
In the present study, a previously established integrated testing strategy (ITS) for in vitro estrogenicity testing was extended with additional in vitro assays in order to broaden its sensitivity to different modes of action resulting in apparent estrogenicity, i.e., other than estrogen receptor (ER) binding. To this end, an extra set of 10 estrogenic compounds with modes of action in part different from ER binding, were tested in the previously defined ITS, consisting of a yeast estrogen reporter gene assay, an U2OS ERα CALUX reporter gene assay and a cell-free coregulator binding assay. Two androgen reporter gene assays and the enhanced H295R steroidogenesis assay were added to that previous defined ITS. These assays had added value, as several estrogenic model compounds also elicited clear and potent antiandrogenic properties and in addition also showed effects on steroidogenesis that might potentiate their apparent estrogenic effects in vivo. Adding these assays, examining mechanisms of action for estrogenicity apart from ERα binding, gives a more complete and comprehensive assessment of the ability of test compounds to interfere with endocrine signaling. It was concluded that the extended ITS will go beyond in vivo estrogenicity testing by the uterotrophic assay, thereby contributing to the 3R-principles.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".