Hepatic effects of aminoglutethimide: A model aromatic amine
Bibliographic record
Abstract
Primary aromatic amine drugs are structural alerts in drug development because of their association with a high incidence of idiosyncratic drug reactions (IDRs). If biomarkers could be found that predict IDR risk, it would have a major impact on drug development. Previous attempts to do this through screening of hepatic gene expression profiles in rodents treated with aromatic amine drugs found limited changes. Of the drugs studied, aminoglutethimide (AMG) induced the most changes, and this led to a more comprehensive study of its effects on the liver. Brown Norway rats treated with AMG for up to 14 days showed only a transient elevation of glutamate dehydrogenase. Pathway-specific PCR arrays found few AMG-induced gene changes associated with an immune response and, of these changes, the majority were involved with innate immunity such as Tlr2, Ticam2, CD14, and C3. AMG treatment also led to significant changes in the apoptosis and mitochondrial panel of genes. It was recently found that AMG does induce significant changes in the bone marrow of rats, and agranulocytosis is a common IDR caused by AMG. In contrast, liver injury is not a common IDR associated with AMG. Therefore, the liver may be able to effectively deal with AMG reactive metabolites, and changes observed in this study may be involved in adaptation. Myeloperoxidase is also known to be able to oxidize aromatic amines to reactive metabolites, and these observations suggest that metabolism outside of the liver may be important for the mechanism of aromatic amine-induced IDRs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".