Chemical reactivity and biological activity of dihydro-1,4-dithiin tetraoxides
Bibliographic record
Abstract
Certain dihydro-1,4-dithiin tetraoxides such as dimethipin, a commercial plant growth regulant, have been reported to exhibit highly selective biological activities depending on the type and number of substitutions on the α,β-unsaturated bond in the dithiin ring. Despite the abundant reports on this class of compounds, the study of chemical reactivity of the α,β-unsaturated bond in the dithiin ring has not been reported and the factors governing the biological selectivity of these compounds are still unknown. In this study, the reactivity of eight dithiin compounds substituted in varying degrees at the α,β-unsaturated bond towards biologically important nucleophilic groups at pH 7.4 were investigated using UV-vis, fluorescence, and 1H NMR spectroscopies. Their reactivity towards glutathione correlated strongly with their cell growth inhibitory activity and inhibition of DNA topoisomerase II, an enzyme containing critical sulfhydryl groups. On this basis, the mechanism by which these dithiins achieve the biological selectivity previously reported was proposed. Excellent correlations between glutathione reactivity and Taft’s polar substituent constants or electrostatic atomic charges of the dithiins were also demonstrated, suggesting that these descriptors might be useful for predicting the reactivity of other dithiins towards sulfhydryl nucleophiles.
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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 | 0.000 | 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".