Impact of <i>CYP2D6</i>, <i>CYP3A5</i>, <i>CYP2C9</i> and <i>CYP2C19</i> polymorphisms on tamoxifen pharmacokinetics in Asian breast cancer patients
Bibliographic record
Abstract
WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Tamoxifen is metabolized to active metabolites, 4‐hydroxytamoxifen and endoxifen, by multiple cytochrome P450 (CYP) enzymes including CYP2D6, CYP3A4/5, CYP2C9/19, CYP1A2 and CYP2B6. • The steady‐state plasma concentrations of tamoxifen and its metabolites can be affected by variations in the activity of these enzymes. • Although CYP2D6*4 and *10 have been shown to influence the plasma concentration of endoxifen in Caucasian and Korean patients respectively, there is still a paucity of data on CYP2D6 pharmacogenetics in other Orientals such as Chinese, Malays and Indians. WHAT THIS STUDY ADDS • Pharmacogenetic analyses of a comprehensive panel of CYP2D6 polymorphisms (*2, *2A, *3, *4, *5, *6, *7, *8, *9, *10, *12, *14, *17, *29, *41 and *xN) were performed in three distinct Asian ethnic groups and breast cancer patients with CYP2D6*5 and *10 found to be highly prevalent. • Both CYP2D6*5 and *10 were significantly associated with lower endoxifen and higher N‐desmethyltamoxifen concentrations as well as a lower rate of metabolic conversion of N‐desmethyltamoxifen to endoxifen. • Polymorphisms present in CYP3A5, CYP2C9 and CYP2C19 were not found to be significantly associated with plasma concentrations of analytes suggesting that these enzymes may be playing minor roles in the metabolic pathway of tamoxifen compared with CYP2D6. AIM To investigate the impact of genetic polymorphisms in CYP2D6, CYP3A5, CYP2C9 and CYP2C19 on the pharmacokinetics of tamoxifen and its metabolites in Asian breast cancer patients. METHODS A total of 165 Asian breast cancer patients receiving 20 mg tamoxifen daily and 228 healthy Asian subjects (Chinese, Malay and Indian; n= 76 each) were recruited. The steady‐state plasma concentrations of tamoxifen and its metabolites were quantified using high‐performance liquid chromatography. The CYP2D6 polymorphisms were genotyped using the INFINITI™ CYP450 2D6I assay, while the polymorphisms in CYP3A5, CYP2C9 and CYP2C19 were determined via direct sequencing. RESULTS The polymorphisms, CYP2D6*5 and *10, were significantly associated with lower endoxifen and higher N‐desmethyltamoxifen (NDM) concentrations. Patients who were *1/*1 carriers exhibited 2.4‐ to 2.6‐fold higher endoxifen concentrations and 1.9‐ to 2.1‐fold lower NDM concentrations than either *10/*10 or *5/*10 carriers (P < 0.001). Similarly, the endoxifen concentrations were found to be 1.8‐ to 2.6‐times higher in *1/*5 or *1/*10 carriers compared with *10/*10 and *5/*10 carriers (P≤ 0.001). Similar relationships were observed between the CYP2D6 polymorphisms and metabolic ratios of tamoxifen and its metabolites. No significant associations were observed with regards to the polymorphisms in CYP3A5, CYP2C9 and CYP2C19. CONCLUSIONS The present study in Asian breast cancer patients showed that CYP2D6*5/*10 and *10/*10 genotypes are associated with significantly lower concentrations of the active metabolite of tamoxifen, endoxifen. Identifying such patients before the start of treatment may be useful in optimizing therapy with tamoxifen. The role of CYP3A5, CYP2C9 and CYP2C19 seem to be minor.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".