Observed tamoxifen drug interactions are dependent on both CYP2D6 phenotype and inhibitor potency
Bibliographic record
Abstract
OBJECTIVES: Tamoxifen is a prodrug that undergoes cytochrome P450(CYP)-mediated bioactivation to its active metabolite endoxifen, primarily due to CYP2D6. We aimed to investigate the clinical impact of CYP2D6 phenotype on the conversion of tamoxifen to endoxifen as well as the interplay of genetic variation and drug interactions. METHODS: Samples were analyzed from a cohort of 932 breast cancer patients on tamoxifen therapy. CYP2D6 phenotype, tamoxifen, endoxifen, 4-hydroxytamoxifen, and N-desmethyl tamoxifen plasma concentrations and antidepressant CYP2D6 inhibitor use were analyzed. KEY FINDINGS: There was a significant effect of CYP2D6 phenotype and CYP2D6 inhibitor use on endoxifen concentrations (pinteraction < 0.05). CYP2D6 inhibition was predictive of patients who attained plasma endoxifen concentrations below the 16 nM and 9 nM threshold. CYP2D6 poor metabolizers and CYP2D6 normal or intermediate metabolizers on strong CYP2D6 inhibitors had the largest proportion of patients below an endoxifen threshold of 16 or 9 nM. CONCLUSIONS: Patients on tamoxifen should avoid strong CYP2D6 inhibitors as their endoxifen concentrations are similar to CYP2D6 poor metabolizers. The utility of endoxifen concentrations and which threshold to consider in clinical practice remains unclear. Ultimately, the clinical impact of mild or moderate CYP2D6 inhibitors on CYP2D6 normal or intermediate metabolizer depends on the endoxifen threshold applied.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".