Contribution of CYP2C9, CYP2A6, and CYP2B6 to Valproic Acid Metabolism in Hepatic Microsomes from Individuals with the CYP2C9*1/*1 Genotype
Bibliographic record
Abstract
The present study investigated the role of specific human cytochrome P450 (CYP) enzymes in the in vitro metabolism of valproic acid (VPA) by a complementary approach that used individual cDNA-expressed CYP enzymes, chemical inhibitors of specific CYP enzymes, CYP-specific inhibitory monoclonal antibodies (MAbs), individual human hepatic microsomes, and correlational analysis. cDNA-expressed CYP2C9*1, CYP2A6, and CYP2B6 were the most active catalysts of 4-ene-VPA, 4-OH-VPA, and 5-OH-VPA formation. The extent of 4-OH-VPA and 5-OH-VPA formation by CYP1A1, CYP1A2, CYP1B1, CYP2C8, CYP2C19, CYP2D6, CYP2E1, CYP4A11, CYP4F2, CYP4F3A, and CYP4F3B was only 1-8% of the levels by CYP2C9*1. CYP2A6 was the most active in catalyzing VPA 3-hydroxylation, whereas CYP1A1, CYP2B6, CYP4F2, and CYP4F3B were less active. Correlational analyses of VPA metabolism with CYP enzyme-selective activities suggested a potential role for hepatic microsomal CYP2A6 and CYP2C9. Chemical inhibition experiments with coumarin (CYP2A6 inhibitor), triethylenethiophosphoramide (CYP2B6 inhibitor), and sulfaphenazole (CYP2C9 inhibitor) and immunoinhibition experiments (including combinatorial analysis) with MAb-2A6, MAb-2B6, and MAb-2C9 indicated that the CYP2C9 inhibitors reduced the formation of 4-ene-VPA, 4-OH-VPA, and 5-OH-VPA by 75-80% in a panel of hepatic microsomes from donors with the CYP2C9*1/*1 genotype, whereas the CYP2A6 and CYP2B6 inhibitors had a small effect. Only the CYP2A6 inhibitors reduced VPA 3-hydroxylation (by approximately 50%). The extent of inhibition correlated with the catalytic capacity of these enzymes in each microsome sample. Overall, our novel findings indicate that in human hepatic microsomes, CYP2C9*1 is the predominant catalyst in the formation of 4-ene-VPA, 4-OH-VPA, and 5-OH-VPA, whereas CYP2A6 contributes partially to 3-OH-VPA formation.
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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 | 0.001 | 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".