Assessing the Mechanism of Fluoxetine-Mediated CYP2D6 Inhibition
Bibliographic record
Abstract
Fluoxetine is still one of the most widely used antidepressants in the world. The drug is extensively metabolized by several cytochrome P450 (CYP450) enzymes and subjected to a myriad of CYP450-mediated drug interactions. In a multidrug regimen, preemptive mitigation of drug-drug interactions requires knowledge of fluoxetine actions on these CYP450 enzymes. The major metabolic pathway of fluoxetine leading to the formation of its active metabolite, norfluoxetine, is mediated by CYP2D6. Fluoxetine and norfluoxetine are strong affinity substrates of CYP2D6 and can inhibit, potentially through various mechanisms, the metabolism of other sensitive CYP2D6 substrates. Remarkably, fluoxetine-mediated CYP2D6 inhibition subsides long after fluoxetine first passes through the liver and even remains long after the discontinuation of the drug. Herein, we review pharmacokinetic and pharmacogenetic information to help us understand the mechanisms underlying the prolonged inhibition of CYP2D6 following fluoxetine administration. We propose that long-term inhibition of CYP2D6 is likely a result of competitive inhibition. This is due to strong affinity binding of fluoxetine and norfluoxetine to the enzyme and unbound fluoxetine and norfluoxetine levels circulating in the blood for a long period of time because of their long elimination half-life. Additionally, we describe that fluoxetine is a CYP2C9 substrate and a mechanism-based inhibitor of CYP2C19.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".