Identification of enzymes responsible for rifalazil metabolism in human liver microsomes
Bibliographic record
Abstract
1. The major metabolites of rifalazil in human are 25-deacetyl-rifalazil and 32-hydroxy-rifalazil. Biotransformation to these metabolites in pooled human liver microsomes, cytosol and supernatant 9000g (S9) fractions was studied, and the enzymes responsible for rifalazil metabolism were identified using inhibitors of esterases and cytochromes P450 (CYP). 2. The 25-deacetylation and 32-hydroxylation of rifalazil occurred in incubations with microsomes or S9 but not with cytosol, indicating that both the enzymes responsible for rifalazil metabolism were microsomal. Km and Vmax of the rifalazil-25-deacetylation in microsomes were 6.5 microM and 11.9 pmol/min/mg with NADPH, and 2.6 microM and 6.0 pmol/min/mg without NADPH, indicating that, although rifalazil-25-deacetylation did not require NADPH, NADPH activated it. Rifalazil-32-hydroxylation was NADPH dependent, and its Km and Vmax were 3.3 microM and 11.0 pmol/min/mg respectively. 3. Rifalazil-25-deacetylation in microsomes was completely inhibited by diisopropyl fluorophosphate, diethyl p-nitrophenyl phosphate and eserine, but not by p-chloromercuribenzoate or 5,5'-dithio-bis(2-nitrobenzoic acid), indicating that the enzyme responsible for the rifalazil-25-deacetylation is a B-esterase. 4. Rifalazil-32-hydroxylation in microsomes was completely inhibited by CYP3A4-specific inhibitors (fluconazole, ketoconazole, miconazole, troleandomycin) and drugs metabolized by CYP3A4 such as cyclosporin A and clarithromycin, indicating that the enzyme responsible for the rifalazil-32-hydroxylation is CYP3A4.
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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) | 0.001 | 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 | 0.002 |
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".