Investigating the Mechanism of Trimethoprim-Induced Skin Rash and Liver Injury
Bibliographic record
Abstract
Trimethoprim (TMP)-induced skin rash and liver injury are likely to involve the formation of reactive metabolites. Analogous to nevirapine-induced skin rash, 1 possible reactive metabolite is the sulfate conjugate of α-hydroxyTMP, a metabolite of TMP. We synthesized this sulfate and found that it reacts with proteins in vitro. We produced a TMP-antiserum and found covalent binding of TMP in the liver of TMP-treated rats. However, we found that α-hydroxyTMP is not a substrate for human sulfotransferases, and we did not detect covalent binding in the skin of TMP-treated rats. Although less reactive than the sulfate, α-hydroxyTMP was found to covalently bind to liver and skin proteins in vitro. Even though there was covalent binding to liver proteins, TMP did not cause liver injury in rats or in our impaired immune tolerance mouse model that has been able to unmask the ability of other drugs to cause immune-mediated liver injury. This is likely because there was much less covalent binding of TMP in the livers of TMP-treated mice than TMP-treated rats. It is possible that some patients have a sulfotransferase that can produce the reactive benzylic sulfate; however, α-hydroxyTMP, itself, has sufficient reactivity to covalently bind to proteins in the skin and may be responsible for TMP-induced skin rash. Interspecies and interindividual differences in TMP metabolism may be 1 factor that determines the risk of TMP-induced skin rash. This study provides important data required to understand the mechanism of TMP-induced skin rash and drug-induced skin rash in general.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".