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Record W2145560209 · doi:10.1186/bcr2747

Do selective serotonin receptor inhibitor antidepressants reduce tamoxifen's effectiveness and increase the risk of death from breast cancer?

2010· article· en· W2145560209 on OpenAlexaffabout
Kathleen I. Pritchard

Bibliographic record

VenueBreast Cancer Research · 2010
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsTamoxifenBreast cancerMedicineSurgical oncologyOncologyOestrogen receptorInternal medicineCancerAntidepressantPharmacology

Abstract

fetched live from OpenAlex

Tamoxifen has been shown over the past 30 years to be extremely effective in the treatment of estrogen receptor (ER)-positive breast cancer. The drug is widely used in the adjuvant setting where it reduces the risk of breast cancer recurrence by almost 40% and the risk of death from breast cancer by one-third [1]. It is now known that tamoxifen acts as a pro-drug. Its major active metabolites are N-desmethyl tamoxifen, which has a low affinity for ER, 4-hydroxy tamoxifen (4HT) and 4-hydroxy-N-desmethyl-tamoxifen (endoxifen). Endoxifen and 4HT have by far the highest affinity for ER and since endoxifen is produced in six to ten times the concentration of 4HT it is felt to be the most important metabolite. CYP2D6 is an enzyme of the cytochrome P450 family, subfamily D, found on chromosome 22. It catalyzes tamoxifen's metabolism and is encoded by a large polymorphic gene with more than 80 allelic mutations identified. Inherited variations alter the function of CYP2D6 and the geographic and ethnic distributions of these polymorphisms are varied. Drugs given concurrently can also alter the function of CYP2D6 by competing for its activity. The metabolic pathway of tamoxifen is shown in Figure ​Figure1.1. As can be seen, CYP2D6 catalyzes both tamoxifen's primary and secondary metabolism. Figure 1 Metabolic pathway of tamoxifen. CYP2D6 phenotypic expression can be divided into three groups: those with little or no enzyme activity (poor or intermediate metabolizers); those with normal enzyme activity (extensive metabolizers); and those with greatly increased enzyme activity (ultrarapid metabolizers). Several studies have shown that relapse free time and disease free survival as well as overall survival in women treated in the adjuvant setting with tamoxifen vary according to the presence of variants that produce ultrarapid, intermediate or low metabolism [2]. Results are, however, contradictory, with at least ten studies showing positive association between these genotypes and outcome and another eight showing no association [3]. Thus, evidence is contradictory as to how important CYP2D6 levels are to outcome with adjuvant tamoxifen therapy. The ideal study to confirm or refute the value of this association would be a randomized trial of tamoxifen versus no treatment as adjuvant therapy with these enzymes and endoxifen levels measured and correlated with the outcomes of recurrence and survival. In addition, a number of common drugs are known to be inhibitors of CYP2D6. Strong inhibitors include drugs such as chlorpromazine, fluoxetine, miconazole, paroxetine, quinidine and quinine whereas moderate inhibitors include cimetidine, diphenhydramine, haloperidol, ketoconazole, methadone, nicardipine and sertraline. Some selective serotonin receptor inhibitor (SSRI) antidepressants, such as venlafaxine (Effexor), are quite weak inhibitors of CYP2D6 activity. Kelly and colleagues [4] have recently shown in an observational population-based study from Ontario, Canada that women prescribed antidepressants, in particular paroxitene, concomitantly with tamoxifen adjuvant treatment had increasing breast cancer-related and/or all cause mortality whereas patients treated with the concomitant use of other antidepressants that are not such strong inhibitors, such as sertraline, fluvoxamine, fluoxetine and particularly venlafaxine, did not have this effect. Again, the interpretation of this study is limited by its observational design and the lack of measurement of endoxifen levels, which could help to draw the sort of direct conclusion one might like. In summary, tamoxifen pharmacogenetic studies in the past 20 years have detected a new active metabolite, endoxifen, which is likely most important in predicting outcome in relation to adjuvant therapy with tamoxifen. Several recent studies show a clear negative interaction between paroxitene and tamoxifen metabolism to endoxifen while other SSRIs such as venlafaxine do not appear to produce this effect. While it is clear that CYP2D6 plays an important role in tamoxifen metabolism and that drugs such as SSRIs can alter the phenotype, no consensus has been reached regarding the incorporation of CYP2D6 genotype testing in routine clinical practice, although an excellent recent review of this subject suggests that such testing may be useful [5]. Decisions to conduct genotype testing should still be individualized based on clinical indication and patient preference. Clinical trials to clarify this situation should be well designed, adequately powered prospective studies with strict inclusion criteria, genotype testing and endoxifen levels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.077
GPT teacher head0.467
Teacher spread0.390 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2010
Admission routes2
Has abstractyes

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