Interactions Between Tamoxifen and Antidepressants via Cytochrome P450 2D6
Bibliographic record
Abstract
OBJECTIVE: Women taking tamoxifen for the treatment or prevention of recurrence of breast cancer are likely to take antidepressants either for a psychiatric disorder or for hot flashes. Recent evidence suggested that some antidepressants inhibit the metabolism of tamoxifen to its more active metabolites by the cytochrome P450 2D6 (CYP2D6) enzyme, thereby decreasing the anticancer effect. This article reviews the literature on the interactions between newer antidepressants and tamoxifen via CYP2D6 and offers treatment recommendations. DATA SOURCES: A literature search of clinical and nonclinical studies published prior to September 2008 was conducted on PubMed. We performed 3 different searches combining the terms tamoxifen and SSRIs; tamoxifen and CYP2D6 inhibitors; and antidepressant and breast cancer recurrence. A fourth search with CYP2D6 inhibition and the generic names of individual antidepressants was carried out. STUDY SELECTION: Seven clinical research articles were selected. Nonclinical research articles about antidepressants were included if they mentioned in vitro or in vivo inhibition of CYP2D6. DATA SYNTHESIS: There is consistent evidence that paroxetine and fluoxetine have a large effect on the metabolism of tamoxifen and should not be used. Indirect evidence indicates that bupropion may also have a large effect on the metabolism of tamoxifen. Venlafaxine has little or no effect on the metabolism of tamoxifen and may be considered the safest choice of antidepressants. Desvenlafaxine is not metabolized by the P450 system and may consequently be another option. Mirtazapine has not been extensively studied, but existing research suggests minimal effect on CYP2D6. The remaining commonly prescribed antidepressants have mild to moderate degrees of CYP2D6 inhibition. CONCLUSIONS: Clinicians treating patients with breast cancer should review the prescription profiles of their patients to evaluate the need for treatment modification. There are safe options for the treatment of depression and clinicians and patients should bear in mind the health risks of untreated depressive states.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".