Model-Based Quantification of Impact of Genetic Polymorphisms and Co-Medications on Pharmacokinetics of Tamoxifen and Six Metabolites in Breast Cancer

Nonlinear Mixed-Effects Model of Z-Endoxifen Concentrations in Tamoxifen-Treated Patients from the CEPAM Cohort

Tamoxifen is widely used in patients with hormone receptor-positive breast cancer. The polymorphic enzyme CYP2D6 is primarily responsible for metabolic activation of tamoxifen, resulting in substantial interindividual variability of plasma concentrations of its most important metabolite, Z-endoxifen. The Z-endoxifen concentration thresholds below which tamoxifen treatment is less efficacious have been proposed but not validated, and prospective trials of individualized tamoxifen treatment to achieve Z-endoxifen concentration thresholds are considered infeasible. Therefore, we aim to validate the association between Z-endoxifen concentration and tamoxifen treatment outcomes, and identify a Z-endoxifen concentration threshold of tamoxifen efficacy, using pharmacometric modeling and simulation. As a first step, the CYP2D6 Endoxifen Percentage Activity Model (CEPAM) cohort was created by pooling data from 28 clinical studies (> 7,000 patients) with measured endoxifen plasma concentrations. After cleaning, data from 6,083 patients were used to develop a nonlinear mixed-effect (NLME) model for tamoxifen and Z-endoxifen pharmacokinetics that includes a conversion factor to allow inclusion of studies that measured total endoxifen but not Z-endoxifen. The final parent-metabolite NLME model confirmed the primary role of CYP2D6, and contributions from body weight, CYP2C9 phenotype, and co-medication with CYP2D6 inhibitors, on Z-endoxifen pharmacokinetics. Future work will use the model to simulate Z-endoxifen concentrations in patients receiving single agent tamoxifen treatment within large prospective clinical trials with long-term survival to identify the Z-endoxifen concentration threshold below which tamoxifen is less efficacious. Identification of this concentration threshold would allow personalized tamoxifen treatment to improve outcomes in patients with hormone receptor-positive breast cancer.

ESR1 and p53 interactome alteration defines mechanisms of tamoxifen response in luminal breast cancer

The canonical mechanism behind tamoxifen's therapeutic effect on estrogen receptor α/ESR1+ breast cancers is inhibition of ESR1-dependent estrogen signaling. Although ESR1+ tumors expressing wild-type p53 were reported to be more responsive to tamoxifen (Tam) therapy, p53 has not been factored into choice of this therapy and the mechanism underlying the role of p53 in Tam response remains unclear. In a window-of-opportunity trial on patients with newly diagnosed stage I-III ESR1+/HER2/wild-type p53 breast cancer who were randomized to arms with or without Tam prior to surgery, we reveal that the ESR1-p53 interaction in tumors was inhibited by Tam. This resulted in functional reactivation of p53 leading to transcriptional reprogramming that favors tumor-suppressive signaling, as well as downregulation of oncogenic pathways. These findings illustrating the convergence of ESR1 and p53 signaling during Tam therapy enrich mechanistic understanding of the impact of p53 on the response to Tam therapy.

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes

Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.

Value of therapeutic drug monitoring of endoxifen in Egyptian premenopausal patients with breast cancer given tamoxifen adjuvant therapy: A pilot study

BACKGROUND

The complex metabolic profile of tamoxifen anticancer drug and polymorphism in its metabolizing enzymes particularly CYP2D6 contribute to the high-observed inter-individual variability in its main active metabolite endoxifen. Therapeutic drug monitoring of endoxifen may play a key role in optimizing tamoxifen therapy, and control of both adverse effects and cancer recurrence. This pilot study aims to assess the clinical benefits of applying endoxifen measurement during tamoxifen therapy in patients with breast cancer.

METHODS

Adult premenopausal breast cancer patients ≥ 18 years who received tamoxifen at a fixed dose of 20 mg daily were included. The primary endpoint was to identify the inter-subject variability in serum concentration of the drug and its metabolites especially endoxifen, through fixation of the tamoxifen dose. The secondary endpoint was to check the correlation between endoxifen metabolite concentration and the development of tamoxifen's adverse effects and cancer recurrence.

RESULTS

Sixty patients were included in the study with a mean age of 38.4  ±  0.6 years (range: 26-50). The mean concentration of tamoxifen and endoxifen was 181  ±  9.6 ng/mL and 31.49 ng/mL, respectively. The inter-individual variability in concentrations for the drug and its active metabolite as estimated by the coefficient of variation percentage was in 41% and 31%, respectively. Cancer recurrence was observed in a group of patients (n  =  16) with an average endoxifen level of 24.48 ng/mL. Another group of patients (n  =  25) developed different tamoxifen adverse effects including hot flashes, vaginal bleeding, endometrial thickness, and ovarian cysts with the average endoxifen level of 38.61 ng/mL. The rest of the patients (n  =  19) who responded smoothly to the drug with no complications had an average endoxifen level of 31.37 ng/mL. Analysis of variance test showed a significant difference in endoxifen levels between the three groups (p  =  0.002).

CONCLUSION

The measurement of the endoxifen active metabolite of tamoxifen in breast cancer patients can help dose optimization in light of the observed wide inter-individual variability in drug fixed-dose related concentration of the metabolite. Monitoring of serum concentration of endoxifen can help to reveal, reduce and control tamoxifen's adverse effects and cancer recurrence.

Tamoxifen pharmacokinetics and pharmacodynamics in older patients with non-metastatic breast cancer

BACKGROUND

We aimed to study the pharmacokinetics and -dynamics of tamoxifen in older women with non-metastatic breast cancer.

METHODS

Data for this analysis were derived from the CYPTAM study (NTR1509) database. Patients were stratified by age (age groups < 65 and 65 and older). Steady-state trough concentrations were measured of tamoxifen, N-desmethyltamoxifen, 4-hydroxy-tamoxifen, and endoxifen. CYP2D6 and CYP3A4 phenotypes were assessed for all patients by genotyping. Multiple linear regression models were used to analyze tamoxifen and endoxifen variability. Outcome data included recurrence-free survival at time of tamoxifen discontinuation (RFSt) and overall survival (OS).

RESULTS

668 patients were included, 141 (21%) were 65 and older. Demographics and treatment duration were similar across age groups. Older patients had significantly higher concentrations of tamoxifen 129.4 ng/ml (SD 53.7) versus 112.2 ng/ml (SD 42.0) and endoxifen 12.1 ng/ml (SD 6.6) versus 10.7 ng/ml (SD 5.7, p all < 0.05), independently of CYP2D6 and CYP3A4 gene polymorphisms. Age independently explained 5% of the variability of tamoxifen (b = 0.95, p < 0.001, R² = 0.051) and 0.1% of the variability in endoxifen concentrations (b = 0.45, p = 0.12, R² = 0.007). Older patients had worse RFSt (5.8 versus 7.3 years, p = 0.01) and worse OS (7.8 years versus 8.7 years, p = 0.01). This was not related to differences in endoxifen concentration (HR 1.0, 95% CI 0.96-1.04, p = 0.84) or CYP polymorphisms.

CONCLUSION

Serum concentrations of tamoxifen and its demethylated metabolites are higher in older patients, independent of CYP2D6 or CYP3A4 gene polymorphisms. A higher bioavailability of tamoxifen in older patients may explain the observed differences. However, clinical relevance of these findings is limited and should not lead to a different tamoxifen dose in older patients.

Toward model-informed precision dosing for tamoxifen: A population-pharmacokinetic model with a continuous CYP2D6 activity scale

BACKGROUND

Tamoxifen is important in the adjuvant treatment of breast cancer. A plasma concentration of the active metabolite endoxifen of > 16 nM is associated with a lower risk of breast cancer-recurrence. Since inter-individual variability is high and > 20 % of patients do not reach endoxifen levels > 16 nM with the standard dose tamoxifen, therapeutic drug monitoring is advised. However, ideally, the correct tamoxifen dose should be known prior to start of therapy. Our aim is to develop a population pharmacokinetic (POP-PK) model incorporating a continuous CYP2D6 activity scale to support model informed precision dosing (MIPD) of tamoxifen to determine the optimal tamoxifen starting dose.

METHODS

Data from eight different clinical studies were pooled (539 patients, 3661 samples) and used to develop a POP-PK model. In this model, CYP2D6 activity per allele was estimated on a continuous scale. After inclusion of covariates, the model was subsequently validated using an independent external dataset (378 patients). Thereafter, dosing cut-off values for MIPD were determined.

RESULTS

A joint tamoxifen/endoxifen POP-PK model was developed describing the endoxifen formation rate. Using a continuous CYP2D6 activity scale, variability in predicting endoxifen levels was decreased by 37 % compared to using standard CYP2D6 genotype predicted phenotyping. After external validation and determination of dosing cut-off points, MIPD could reduce the proportion of patients with subtherapeutic endoxifen levels at from 22.1 % toward 4.8 %.

CONCLUSION

Implementing MIPD from the start of tamoxifen treatment with this POP-PK model can reduce the proportion of patients with subtherapeutic endoxifen levels at steady-state to less than 5 %.

Pharmacokinetics of Tamoxifen and Its Major Metabolites and the Effect of the African Ancestry Specific CYP2D6*17 Variant on the Formation of the Active Metabolite, Endoxifen

Tamoxifen (TAM) is widely used in the treatment of hormone receptor-positive breast cancer. TAM is metabolized into the active secondary metabolite endoxifen (ENDO), primarily by CYP2D6. We aimed to investigate the effects of an African-specific CYP2D6 variant allele, CYP2D6*17, on the pharmacokinetics (PK) of TAM and its active metabolites in 42 healthy black Zimbabweans. Subjects were grouped based on CYP2D6 genotypes as CYP2D6*1/*1 or *1/*2 or *2/*2 (CYP2D6*1 or *2), CYP2D6*1/*17 or 2*/*17, and CYP2D6*17/*17. PK parameters for TAM and three metabolites were determined. The pharmacokinetics of ENDO showed statistically significant differences among the three groups. The mean ENDO AUC_0-∞ in CYP2D6*17/*17 subjects was 452.01 (196.94) h·*ng/mL, and the AUC0-∞ in CYP2D6*1/*17 subjects was 889.74 h·ng/mL, which was 5-fold and 2.8-fold lower than in CYP2D6*1 or *2 subjects, respectively. Individuals who were heterozygous or homozygous for CYP2D6*17 alleles showed a 2- and 5-fold decrease in Cmax, respectively, compared to the CYP2D6*1 or *2 genotype. CYP2D6*17 gene carriers have significantly lower ENDO exposure levels than CYP2D6*1 or *2 gene carriers. Pharmacokinetic parameters of TAM and the two primary metabolites, N-desmethyl tamoxifen (NDT) and 4-hydroxy tamoxifen (4OHT), did not show any significant difference in the three genotype groups. The African-specific CYP2D6*17 variant had effects on ENDO exposure levels that could potentially have clinical implications for patients homozygous for this variant.

Population Pharmacokinetics of Z-Endoxifen in Patients With Advanced Solid Tumors

The purpose of this study was to develop and validate a population pharmacokinetic model for Z-endoxifen in patients with advanced solid tumors and to identify clinical variables that influence pharmacokinetic parameters. Z-endoxifen-HCl was administered orally once a day on a 28-day cycle (±3 days) over 11 dose levels ranging from 20 to 360 mg. A total of 1256 Z-endoxifen plasma concentration samples from 80 patients were analyzed using nonlinear mixed-effects modeling to develop a population pharmacokinetic model for Z-endoxifen. A 2-compartment model with oral depot and linear elimination adequately described the data. The estimated apparent total clearance, apparent central volume of distribution, and apparent peripheral volume of distribution were 4.89 L/h, 323 L, and 39.7 L, respectively, with weight-effect exponents of 0.75, 1, and 1, respectively. This model was used to explore the effects of clinical and demographic variables on Z-endoxifen pharmacokinetics. Weight, race on clearance, and aspartate aminotransferase on the absorption rate constant were identified as significant covariates in the final model. This novel population pharmacokinetic model provides insight regarding factors that may affect the pharmacokinetics of Z-endoxifen and may assist in the design of future clinical trials.

Factors affecting inter-individual variability in endoxifen concentrations in patients with breast cancer: results from the prospective TOTAM trial

INTRODUCTION

Endoxifen-the principal metabolite of tamoxifen-is subject to a high inter-individual variability in serum concentration. Numerous attempts have been made to explain this, but thus far only with limited success. By applying predictive modeling, we aimed to identify factors that determine the inter-individual variability. Our purpose was to develop a prediction model for endoxifen concentrations, as a strategy to individualize tamoxifen treatment by model-informed dosing in order to prevent subtherapeutic exposure (endoxifen < 16 nmol/L) and thus potential failure of therapy.

METHODS

Tamoxifen pharmacokinetics with demographic and pharmacogenetic data of 303 participants of the prospective TOTAM study were used. The inter-individual variability in endoxifen was analyzed according to multiple regression techniques in combination with multiple imputations to adjust for missing data and bootstrapping to adjust for the over-optimism of parameter estimates used for internal model validation.

RESULTS

Key predictors of endoxifen concentration were CYP2D6 genotype, age and weight, explaining altogether an average-based optimism corrected 57% (95% CI 0.49-0.64) of the inter-individual variability. CYP2D6 genotype explained 54% of the variability. The remaining 3% could be explained by age and weight. Predictors of risk for subtherapeutic endoxifen (< 16 nmol/L) were CYP2D6 genotype and age. The model showed an optimism-corrected discrimination of 90% (95% CI 0.86-0.95) and sensitivity and specificity of 66% and 98%, respectively. Consecutively, there is a high probability of misclassifying patients with subtherapeutic endoxifen concentrations based on the prediction rule.

CONCLUSION

The inter-individual variability of endoxifen concentration could largely be explained by CYP2D6 genotype and for a small proportion by age and weight. The model showed a sensitivity and specificity of 66 and 98%, respectively, indicating a high probability of (misclassification) error for the patients with subtherapeutic endoxifen concentrations (< 16 nmol/L). The remaining unexplained inter-individual variability is still high and therefore model-informed tamoxifen dosing should be accompanied by therapeutic drug monitoring.

Comparison of adverse drug reactions between tamoxifen and toremifene in breast cancer patients with different CYP2D6 genotypes: a propensity-score matched cohort study

CYP2D6 gene polymorphism had a profound impact upon the effect of tamoxifen as adjuvant endocrine therapy in breast cancers. However, it had never been reported whether the adverse drug reactions vary by CYP2D6 metabolic status for patients treated with tamoxifen or toremifene. We conducted an retrospective study in breast cancer patients to investigate the impact of CYP2D6 metabolizers on liver dysfunction events, gynecological events, and dyslipidemia events. According to CYP2D6*10 (100C → T) genotype, the enrolled patients were further categorized into four cohorts (extensive metabolizers taking tamoxifen [EM + TAM], extensive metabolizers taking toremifene [EM + TOR], intermediate metabolizers taking tamoxifen [IM + TAM], intermediate metabolizers taking toremifene cohort [IM + TOR]). A total of 192 patients were included into the study, with a median follow-up time of 26.2 months. In EM + TAM cohort, the risks of liver dysfunction events (P = 0.004) and gynecological events (P = 0.004) were significantly higher compared with EM + TOR cohort. In IM + TAM cohort, the risks of liver dysfunction events (P = 0.14) and gynecological events (P = 0.99) were not significantly different from IM + TOR cohort. Significant decrease of total cholesterol was observed in EM + TAM cohort around 1 year after taking tamoxifen (P < 0.001). Significant interactions between CYP2D6 metabolic status and endocrine agents were observed in terms of liver dysfunction events (p-interaction = 0.007) and gynecological events (p-interaction = 0.026). These findings suggested that CYP2D6 gene polymorphism played a significant role in predicting liver dysfunction, gynecological diseases and lipid metabolism changes among patients taking tamoxifen or toremifene. This article is protected by copyright. All rights reserved.

Cytochrome P450 Enzymes and Drug Metabolism in Humans

Human cytochrome P450 (CYP) enzymes, as membrane-bound hemoproteins, play important roles in the detoxification of drugs, cellular metabolism, and homeostasis. In humans, almost 80% of oxidative metabolism and approximately 50% of the overall elimination of common clinical drugs can be attributed to one or more of the various CYPs, from the CYP families 1–3. In addition to the basic metabolic effects for elimination, CYPs are also capable of affecting drug responses by influencing drug action, safety, bioavailability, and drug resistance through metabolism, in both metabolic organs and local sites of action. Structures of CYPs have recently provided new insights into both understanding the mechanisms of drug metabolism and exploiting CYPs as drug targets. Genetic polymorphisms and epigenetic changes in CYP genes and environmental factors may be responsible for interethnic and interindividual variations in the therapeutic efficacy of drugs. In this review, we summarize and highlight the structural knowledge about CYPs and the major CYPs in drug metabolism. Additionally, genetic and epigenetic factors, as well as several intrinsic and extrinsic factors that contribute to interindividual variation in drug response are also reviewed, to reveal the multifarious and important roles of CYP-mediated metabolism and elimination in drug therapy.

Novel models for the prediction of drug-gene interactions

INTRODUCTION

Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs.

AREAS COVERED

Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations.

EXPERT OPINION

Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

CYP3A4∗22 Genotyping in Clinical Practice: Ready for Implementation?

Cytochrome P450 3A4 (CYP3A4) is the most important drug metabolizing enzyme in the liver, responsible for the oxidative metabolism of ∼50% of clinically prescribed drugs. Therefore, genetic variation in CYP3A4 could potentially affect the pharmacokinetics, toxicity and clinical outcome of drug treatment. Thus far, pharmacogenetics for CYP3A4 has not received much attention. However, the recent discovery of the intron 6 single-nucleotide polymorphism (SNP) rs35599367C > T, encoding the CYP3A4^∗22 allele, led to several studies into the pharmacogenetic effect of CYP3A4^∗22 on different drugs. This allele has a relatively minor allele frequency of 3-5% and an effect on CYP3A4 enzymatic activity. Thus far, no review summarizing the data published on several drugs is available yet. This article therefore addresses the current knowledge on CYP3A4^∗22. This information may help in deciding if, and for which drugs, CYP3A4^∗22 genotype-based dosing could be helpful in improving drug therapy. CYP3A4^∗22 was shown to significantly influence the pharmacokinetics of several drugs, with currently being most thoroughly investigated tacrolimus, cyclosporine, and statins. Additional studies, focusing on toxicity and clinical outcome, are warranted to demonstrate clinical utility of CYP3A4^∗22 genotype-based dosing.

Estrogen-Like Effect of Mitotane Explained by Its Agonist Activity on Estrogen Receptor-α

Mitotane is the cornerstone of medical treatment of adrenocortical carcinoma. Estrogenic-like side effects frequently occur in patients, and previous studies explored the chemical nature of the interaction between estrogen receptor-α (ER-α) and toxic compounds, including the DDD derivatives. We used molecular docking and molecular dynamics (MD) simulations to explore the possible interaction between mitotane and the ER-α receptor and the induced conformational changes. The ER-α expressing MCF-7 cells were exposed to mitotane with/without tamoxifen, and the cell viability/proliferation was evaluated by MTT assay and direct count. The transient ER-α silencing was performed using two ER-α siRNA (50 nM) and verified by Western blot. MDA-MB-231 cells were used as a negative control. Mitotane showed a similar docking configuration to 17β-estradiol and bisphenol A (BPA) and a significant binding affinity to ER-α. MD simulations showed that mitotane preserves the active conformation of ER-α more than both BPA and Bisphenol C, classifying it as an agonist. Exposure of MCF-7 cells to mitotane led to the concentration-dependent increase of cell viability and proliferation, which was reduced in the presence of tamoxifen and nullified by the transient ER-α knock-down. Integrating bioinformatics approaches with cell biology and pharmacological methods, we demonstrated that mitotane directly binds and activates ER-α.

Effect of Genetic Variability in 20 Pharmacogenes on Concentrations of Tamoxifen and Its Metabolites

BACKGROUND

Tamoxifen, as a treatment of estrogen receptor positive (ER+) breast cancer, is a weak anti-estrogen that requires metabolic activation to form metabolites with higher anti-estrogenic activity. Endoxifen is the most-studied active tamoxifen metabolite, and endoxifen concentrations are highly associated with CYP2D6 activity. Associations of tamoxifen efficacy with measured or CYP2D6-predicted endoxifen concentrations have been inconclusive. Another active metabolite, 4-OHtam, and other, less active metabolites, Z-4'-endoxifen and Z-4'-OHtam, have also been reported to be associated with tamoxifen efficacy.

METHOD

Genotype for 20 pharmacogenes was determined by VeriDose^® Core Panel and VeriDose^®CYP2D6 CNV Panel, followed by translation to metabolic activity phenotype following standard activity scoring. Concentrations of tamoxifen and seven metabolites were measured by UPLC-MS/MS in serum samples collected from patients receiving 20 mg tamoxifen per day. Metabolic activity was tested for association with tamoxifen and its metabolites using linear regression with adjustment for upstream metabolites to identify genes associated with each step in the tamoxifen metabolism pathway.

RESULTS

A total of 187 patients with genetic and tamoxifen concentration data were included in the analysis. CYP2D6 was the primary gene associated with the tamoxifen metabolism pathway, especially the conversion of tamoxifen to endoxifen. CYP3A4 and CYP2C9 were also responsible for the metabolism of tamoxifen. CYP2C9 especially impacted the hydroxylation to 4-OHtam, and this involved the OATP1B1 (SLCO1B1) transporter.

CONCLUSION

Multiple genes are involved in tamoxifen metabolism and multi-gene panels could be useful to predict active metabolite concentrations and guide tamoxifen dosing.

Generating a Precision Endoxifen Prediction Algorithm to Advance Personalized Tamoxifen Treatment in Patients with Breast Cancer

Tamoxifen is an endocrine treatment for hormone receptor positive breast cancer. The effectiveness of tamoxifen may be compromised in patients with metabolic resistance, who have insufficient metabolic generation of the active metabolites endoxifen and 4-hydroxy-tamoxifen. This has been challenging to validate due to the lack of measured metabolite concentrations in tamoxifen clinical trials. CYP2D6 activity is the primary determinant of endoxifen concentration. Inconclusive results from studies investigating whether CYP2D6 genotype is associated with tamoxifen efficacy may be due to the imprecision in using CYP2D6 genotype as a surrogate of endoxifen concentration without incorporating the influence of other genetic and clinical variables. This review summarizes the evidence that active metabolite concentrations determine tamoxifen efficacy. We then introduce a novel approach to validate this relationship by generating a precision endoxifen prediction algorithm and comprehensively review the factors that must be incorporated into the algorithm, including genetics of CYP2D6 and other pharmacogenes. A precision endoxifen algorithm could be used to validate metabolic resistance in existing tamoxifen clinical trial cohorts and could then be used to select personalized tamoxifen doses to ensure all patients achieve adequate endoxifen concentrations and maximum benefit from tamoxifen treatment.

Clinical CYP2D6 Genotyping to Personalize Adjuvant Tamoxifen Treatment in ER-Positive Breast Cancer Patients: Current Status of a Controversy

Simple Summary

Tamoxifen is an important adjuvant endocrine therapy in estrogen receptor (ER)-positive breast cancer patients. It is mainly catalyzed by the enzyme CYP2D6 into the most active metabolite endoxifen. Genetic variation in the CYP2D6 gene influences endoxifen formation and thereby potentially therapy outcome. However, the association between CYP2D6 genotype and clinical outcome on tamoxifen is still under debate, as contradictory outcomes have been published. This review describes the latest insights in both CYP2D6 genotype and endoxifen concentrations, as well CYP2D6 genotype and clinical outcome, from 2018 to 2020.

Abstract

Tamoxifen is a major option for adjuvant endocrine treatment in estrogen receptor (ER) positive breast cancer patients. The conversion of the prodrug tamoxifen into the most active metabolite endoxifen is mainly catalyzed by the enzyme cytochrome P450 2D6 (CYP2D6). Genetic variation in the CYP2D6 gene leads to altered enzyme activity, which influences endoxifen formation and thereby potentially therapy outcome. The association between genetically compromised CYP2D6 activity and low endoxifen plasma concentrations is generally accepted, and it was shown that tamoxifen dose increments in compromised patients resulted in higher endoxifen concentrations. However, the correlation between CYP2D6 genotype and clinical outcome is still under debate. This has led to genotype-based tamoxifen dosing recommendations by the Clinical Pharmacogenetic Implementation Consortium (CPIC) in 2018, whereas in 2019, the European Society of Medical Oncology (ESMO) discouraged the use of CYP2D6 genotyping in clinical practice for tamoxifen therapy. This paper describes the latest developments on CYP2D6 genotyping in relation to endoxifen plasma concentrations and tamoxifen-related clinical outcome. Therefore, we focused on Pharmacogenetic publications from 2018 (CPIC publication) to 2021 in order to shed a light on the current status of this debate.