Potential Involvement of Sulfotransferase in the Mechanism of Lamotrigine-induced Skin Rash
Bibliographic record
Abstract
The mechanism of drug-induced skin rash is not well understood. Circumstantial evidence suggests that the covalent binding of a reactive metabolite is involved in the mechanism of most idiosyncratic drug reactions. However, there is a limited quantity of drug metabolizing enzymes in the skin, except for sulfotransferases. It is possible that some drugs are metabolized to reactive sulfate metabolites that are responsible for skin rashes. For example, nevirapine-induced skin rash involves metabolism of nevirapine to 12-hydroxy-nevirapine, which is further metabolized by sulfotransferase in the skin to a reactive benzylic sulfate that covalently binds to proteins. The working hypothesis is that lamotrigine, valdecoxib, and sertraline skin rashes involve the formation of reactive sulfate in the skin. Lamotrigine- N -oxide, hydroxy-valdecoxib, and hydroxy-sertraline were tested as substrates with known human sulfotransferases. Hydroxy-valdecoxib and the benzylic alcohol metabolite of sertraline were not substrates for human sulfotransferases. Therefore, this pathway is presumably not involved in the mechanism by which they cause skin rashes. In contrast, lamotrigine- N -oxide is a substrate for several human sulfotransferases and the sulfate is chemically reactive. Furthermore, lamotrigine- N -sulfate not only alkylates proteins as we described previously but also forms the sulfate of tyrosine, suggesting another possible mechanism for protein modification. This study has further added to the understanding of the potential of the sulfotransferase pathways and protein sulfation to play a role in drug-induced skin rash.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".