Changes in gene expression induced by aromatic amine drugs: Testing the danger hypothesis
Bibliographic record
Abstract
Virtually all drugs that contain a primary aromatic amine are associated with a high incidence of idiosyncratic drug reactions (IDRs), suggesting that this functional group has biological effects that may be used as biomarkers to predict IDR risk. Most IDRs exhibit evidence of immune involvement and the ability of aromatic amines to form reactive metabolites and redox cycle may be responsible for initiation of an immune response through induction of cell stress, as postulated by the Danger Hypothesis. If true, danger signals could be biomarkers of IDR risk. A previous attempt to test the Danger Hypothesis found that sulfamethoxazole (SMX), the only aromatic amine tested, was also the only drug not associated with an increase of cell stress genes in mice. To ensure that these observations were not species-specific, and to determine biomarkers of IDR risk common to aromatic amines, rats were treated with SMX and two other aromatic amine drugs, dapsone (DDS) and aminoglutethimide (AMG), and hepatic gene expression was determined using microarrays. As in mice, SMX induced minimal gene changes in the rat, and none indicated cell stress, whereas DDS and AMG induced several changes including up-regulation of enzymes such as aldo-keto reductase, glutathione-S-transferase, and aldehyde dehydrogenase, which may represent danger signals. Early insulin-induced hepatic gene (Eiih) was up-regulated by all three drugs. Some mRNA changes were observed in the Keap-1-Nrf2-ARE pathway; however, the pattern was significantly different for each drug. Overall, the most salient finding was that the changes in the liver were minimal, even though aromatic amines cause a high incidence of IDRs. The liver generates a large number of reactive species; however, the ability of aromatic amines to be bioactivated by cells of the immune system may be why they cause a high incidence of 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".