Molecular mechanism of 17α-ethinylestradiol cytotoxicity in isolated rat hepatocytes
Bibliographic record
Abstract
17α-Ethinylestradiol (17-EE) is used in formulations of contraceptives and hormone replacement therapy because it is an estradiol derivative. However, it has been associated with an increase in the risk of liver cancers and injury. The carcinogenic properties of 17-EE are similar to that of other estrogens, but the molecular mechanism of liver injury is still unclear. It is important to identify any secondary toxic mechanisms that can be used to prevent or treat the toxicity. The LC50 of 17-EE toward isolated rat hepatocytes was determined to be 150 ± 8 μmol/L. Accelerated cytotoxicity mechanism screening (ACMS) techniques using isolated rat hepatocytes showed that CYP1A inhibitors decreased cytotoxicity, whereas tyrosinase increased toxicity; this suggests that the toxic mechanism involved is the oxidation of 17-EE. A hepatocyte inflammation model also increased 17-EE-induced mitochondrial toxicity, as well as the formation of ROS and H2O2. Cytotoxicity was increased when inhibitors of quinone reduction, catechol-O-methylation, glucuronidation, glutathione conjugation, and sulfation were co-incubated with 17-EE. The hepatocytes could be rescued with antioxidants and quinone trapping agents, thereby suggesting a role for quinoid moiety induced oxidative stress in 17-EE induced cytotoxicity. These mechanisms for 17-EE hepatotoxicity could provide a new perspective for the treating 17-EE-induced liver injury.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".