In vitro characterization of the metabolism of haloperidol using recombinant cytochrome p450 enzymes and human liver microsomes.
Bibliographic record
Abstract
A systematic in vitro study was carried out to elucidate the enzymes responsible for the metabolism of haloperidol (HAL) using human liver microsomes and recombinant human cytochrome P450 isoenzymes. In the first series of experiments, recombinant cytochrome P450 (P450) isoenzymes were used to evaluate their catalytic involvement in the metabolic pathways of HAL. Recombinant CYP3A4, CYP3A5, and CYP1A1 were shown to be able to catalyze the metabolism of HAL to its pyridinium analog (HP(+)) and the oxidation of reduced HAL (RH) back to HAL; Recombinant CYP3A4, CYP3A5, CYP1A1, CYP2C19, CYP2C8, CYP2C9, and CYP2D6 were able to catalyze the dealkylation of HAL to 4-(4-chlorophenyl)-4-hydroxypiperidine (CPHP). CYP3A4 was capable of metabolizing HAL to its tetrahydropyridine analog 4-(4-chlorophenyl)-1-[4-(4-fluorophenyl)-4-oxobutyl]-1,2,3,6-tetrahydropyridine and metabolizing to CPHP; CYP3A4 and CYP3A5 were able to metabolize RH to its pyridinium analog (RHP(+)); CYP1A1, CYP1A2, and CYP3A4 were able to catalyze the oxidation of RHP(+) to HP(+). In the second series of experiments, the metabolic activities of human liver microsomes from 12 donors were correlated with catalytic activities of selective substrates of different P450 isoenzymes and immuno-reactivities toward different P450 isoenzymes. CYP3A4 activities were found to correlate to all the seven metabolic pathways of HAL mentioned above. This suggests a prominent role for CYP3A4 in the metabolism of HAL. Interestingly, it was found that recombinant CYP1A1 has the highest activity for oxidizing RHP(+) to HP(+). The activity of recombinant CYP1A1 was 50 times higher than CYP1A2 and 220 times higher than 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".