Importance of Azo‐Hydrazo Tautomerization in the Oxidative Degradation of Procarbazine by Cytochrome P450: Computational Insights
Bibliographic record
Abstract
Abstract This work provides a comprehensive computational study on the oxidative degradation of prodrug procarbazine as a symmetrically disubstituted hydrazine (SDSH) by the active species of cytochrome P450 enzymes, compound I (Cpd I). Two model compounds, R‐CH 2 ‐NH‐NH‐CH 3 (R= Me and Ph), were selected for this study and all possible enzymatic and non‐enzymatic phases of their oxidative degradation were simulated. Procarbazine activation has three enzymatic processes. Dehydrogenation is the first step which leads to the release of azo compound. This step is either spontaneous (R=Me) or has very low barrier high (R=Ph). Azo system has another tautomer, hydrazo, which despite its more stability is not considered hitherto. Second enzymatic phase is the production of azoxy compound from either azo or hydrazo compound. The calculations revealed that the transition state of hydrazo oxidation to form azoxy compound (4.09/8.23 kcal.mol −1 for Me/Ph substituents), is almost half of the N2‐azo oxidation. In final enzymatic step the azoxy converts to hydroxyl‐azoxy compound. The transition state barrier of the third enzymatic phase is also lower for the hydrazo tautomer in comparison to the azo tautomer (6.55 vs. 19.39 kcal.mol −1 for R=Ph). The more stability and lower barrier energy showed very high importance of hydrazo tautomer in the catabolism of SDSH derivatives. The hydroxyl‐azoxy is not stable and undergoes decomposition to generate the metabolites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".