Chemistry Related to the Catalytic Cycle of the Antioxidant Ebselen
Bibliographic record
Abstract
The antioxidant drug ebselen has been widely studied in both laboratories and in clinical trials. The catalytic mechanism by which it destroys hydrogen peroxide via reduction with glutathione or other thiols is complex and has been the subject of considerable debate. During reinvestigations of several key steps, we found that the seleninamide that comprises the first oxidation product of ebselen underwent facile reversible methanolysis to an unstable seleninate ester and two dimeric products. In its reaction with benzyl alcohol, the seleninamide produced a benzyl ester that reacted readily by selenoxide elimination, with formation of benzaldehyde. Oxidation of ebselen seleninic acid did not afford a selenonium seleninate salt as previously observed with benzene seleninic acid, but instead generated a mixture of the seleninic and selenonic acids. Thiolysis of ebselen with benzyl thiol was faster than oxidation by ca. an order of magnitude and produced a stable selenenyl sulfide. When glutathione was employed, the product rapidly disproportionated to glutathione disulfide and ebselen diselenide. Oxidation of the S-benzyl selenenyl sulfide, or thiolysis of the seleninamide with benzyl thiol, afforded a transient thiolseleninate that also readily underwent selenoxide elimination. The S-benzyl derivative disproportionated readily when catalyzed by the simultaneous presence of both the thiol and triethylamine. The phenylthio analogue disproportionated when exposed to ambient or UV (360 nm) light by a proposed radical mechanism. These observations provide additional insight into several reactions and intermediates related to ebselen.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".