Changes in Gene Expression Induced by Carbamazepine and Phenytoin: Testing the Danger Hypothesis
Bibliographic record
Abstract
The aromatic anticonvulsants carbamazepine (CBZ) and phenytoin (PHN) are associated with a relatively high incidence of idiosyncratic drug reactions (IDRs). If biomarkers could be found that would predict the risk that a drug candidate would cause IDRs it would significantly decrease the risks associated with drug development. The IDRs associated with CBZ and PHN appear to be immune-mediated. The Danger Hypothesis posits that for something to induce an immune response, it must cause some type of cell damage that ultimately causes up-regulation of co-stimulatory molecules on antigen-presenting cells; without this, the response will be immune tolerance. If the Danger Hypothesis is correct, the ability of a drug or its reactive metabolite to induce cell damage or stress may be related to its risk of causing IDRs. In a parallel study reported elsewhere, we found that major metabolites of these two drugs: 3-OH-CBZ and 4-OH-PHN can be oxidized by peroxidases to phenoxyl free radicals, which could cause oxidative stress by redox cycling. In this study using mRNA microarrays, we found that CBZ and PHN treatment induced changes in mRNA expression in mice. Many of the changes were in genes related to Keap1-Nrf2-ARE signaling pathways and enzymes involved in responding to oxidant stressors and reactive metabolites such as glutathione transferase and heat shock proteins. The similar patterns of genes induced by these two drugs are consistent with the clinical observation that those two drugs exhibit cross-sensitivity. These findings are consistent with the induction of cell stress by CBZ and PHN, most likely due to reactive metabolites. Such changes may represent a danger signal and represent a biomarker of the potential that a drug will cause IDRs; however, different drugs likely cause cell stress by different mechanisms and, therefore, the biomarkers for other drugs would likely be different.
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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 | 0.001 |
| 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".