Identification of the cytochrome P450 enzymes involved in the metabolism of domperidone
Bibliographic record
Abstract
The objective was to identify the major cytochrome P450 enzyme(s) involved in the metabolism of domperidone. Experiments were performed using human liver microsomes (HLMs), recombinant human cytochrome P450 enzymes, cytochrome P450 chemical inhibitors and monoclonal antibodies directed against cytochrome P450 enzymes. Four metabolites were identified from incubations performed with HLMs and excellent correlations were observed between the formation of domperidone hydroxylated metabolites (M1, M3 and M4), N-desalkylated domperidone metabolite (M2) and enzymatic markers of CYP3A4/5 (r2 = 0.9427, 0.951, 0.9497 and 0.8304, respectively). Ketoconazole (1 microM) decreased the formation rate of M1, M2, M3 and M4 by 83, 78, 75 and 88%, respectively, whereas the effect of other inhibitors (quinidine, furafylline and sulfaphenazole) was minimal. Important decreases in the formation rate of M1 (68%), M2 (64%) and M3 (54%) were observed with anti-CYP3A4 antibodies. Formation of M1, M2 and M3 in HLMs exhibited Michaelis-Menten kinetics (Km: 166, 248 and 36 microM, respectively). Similar Km values were observed for M1, M2 and M3 when incubations were performed with recombinant human CYP3A4 (Km: 107, 273 and 34 microM, respectively). The data suggest that CYP3As are the major enzymes involved in the metabolism of domperidone.
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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".