In vitro metabolism and interaction of cilostazol with human hepatic cytochrome P450 isoforms
Bibliographic record
Abstract
1. Cilostazol (OPC-13013) undergoes extensive hepatic metabolism. The hydroxylation of the quinone moiety of cilostazol to OPC-13326 was the predominant route in all the liver preparations studies. The hydroxylation of the hexane moiety to OPC-13217 was the second most predominant route in vitro. 2. Ketoconazole (1 microM) was the most potent inhibitor of both quinone and hexane hydroxylation. Both the CYP2D6 inhibitor quinidine (0.1 microM) and the CYP2C19 inhibitor omeprazole (10 microM) failed to consistently inhibit metabolism of cilostazol via either of these two predominant routes. 3. Data obtained from a bank of pre-characterized human liver microsomes demonstrated a stronger correlation (r2=0.68, P < 0.01) between metabolism of cilostazol to OPC-13326 and metabolism of felodipine, a CYP3A probe, that with probes for any other isoform. Cimetidine demonstrated concentration-dependent competitive inhibition of the metabolism of cilostazol by both routes. 4. Kinetic data demonstrated a Km value of 101 microM for cilostazol, suggesting a relatively low affinity of cilostazol for CYP3A. While recombinant CYP1A2, CYP2D6 and CYP2C19 were also able to catalyze formation of specific cilostazol metabolites, they did not appear to contribute significantly to cilostazol metabolism in whole human liver microsomes.
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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) | 0.001 | 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) | 0.002 | 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".