The metabolism of amiodarone by various CYP isoenzymes of human and rat, and the inhibitory influence of ketoconazole
Bibliographic record
Abstract
PURPOSE: To evaluate the metabolism of amiodarone (AM) to desethylamiodarone (DEA) by selected human and rat cytochrome P450, and the inhibitory effect of ketoconazole (KTZ). METHODS: Some important CYP isoenzymes (rat CYP1A1, 1A2, 2C6, 2C11, 2D1, 2D2, and 3A1 and human CYP1A1, 1A2, 2D6 and 3A4) were spiked with various concentrations of AM to determine the relative kinetic parameters for formation of DEA in the presence and absence of various concentrations of KTZ. RESULTS: The formation of DEA was observed when AM was exposed to each of the CYP tested, although the rates were varied. Human CYP1A1 followed by 3A4 had the highest intrinsic clearance (CLint) for DEA formation whereas in rat, CYP2D1 followed by CYP2C11 had the highest CLint. Human and rat CYP1A2 seemed to have the lowest CLint. At high concentrations of AM and KTZ, near those expected in vivo, significant inhibition of all isoforms except for rat CYP1A2 was observed. At lower concentration ranges of both drugs, the inhibitory constant was determined. At these levels, KTZ was found to potently inhibit human CYP1A1 and 3A4 and rat 2D2 and 1A1. CONCLUSION: Human CYP1A1 and 3A4 and rat CYP2D1 and 2C11 were most efficient in converting AM to DEA. For DEA formation, the in vivo administration of KTZ could inhibit other CYP isoforms besides CYP3A in human and rat.
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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 | 0.001 |
| 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".