Related Experiment Video
Updated: May 17, 2025
Using Mouse Mammary Tumor Cells to Teach Core Biology Concepts: A Simple Lab Module
Published on: June 18, 2015
Drug Interactions With Tamoxifen and Treatment Effectiveness in Premenopausal Breast Cancer Patients: A Bayesian
Kirsten M Woolpert1,2, Deirdre P Cronin-Fenton1,2, Per Damkier3,4
1Department of Clinical Epidemiology, Aarhus University and Aarhus University Hospital, Aarhus, Denmark.
Purpose:
Tamoxifen is guideline treatment for premenopausal women with estrogen receptor-positive (ER+) breast cancer. Therapeutic efficacy relies partly on tamoxifen biotransformation by CYP2D6, CYP2C19, and CYP3A4 enzymes. We conducted a cohort study to evaluate whether concomitant prescription of drugs that inhibit these enzymes impacted breast cancer recurrence.
Methods:
We enrolled 4493 premenopausal women with stage I-III ER+ breast cancer (2002-2011) treated with tamoxifen. We defined time-varying CYP-inhibiting drug exposures as the proportion of overlapping days during the tamoxifen treatment period. We estimated associations of concomitant medication use with recurrence using: (1) Bayesian joint modeling (hazard ratio [HR] and 95% credible intervals [95% CrI]), (2) traditional Cox regression (HR and 95% confidence intervals [95% CI]).
Results:
During tamoxifen therapy, 13% of the cohort used strong CYP2D6 inhibitors, 31% weak CYP2D6 inhibitors, 37% CYP2C19 inhibitors, and 12% CYP3A4/5 inhibitors. Bayesian joint models showed that women with ≥ 50% overlap between tamoxifen and CYP2D6 inhibitors had increased recurrence risk compared with 0% overlap (HR: 1.24, 95% CrI: 0.96, 1.58). No recurrence association was seen for CYP2C19 inhibitors (≥ 50% vs. 0%, HR = 1.0, 95% CrI: 0.69, 1.40), but traditional Cox models yielded positive associations for CYP2C19 overlap (≥ 50% vs. 0%, HR = 1.45, 95% CI: 1.07, 1.96). With Bayesian joint models, we observed no association between ≥ 50% versus 0% overlap with CYP3A4/5 inhibitors (HR: 0.84, 95% CrI: 0.32, 1.93).
Conclusions:
With Bayesian joint modeling, we saw a slight increase in recurrence among CYP2D6-inhibitor users, but no increase among CYP2C19- or CYP3A4-inhibitor users. Results from Cox regression models were less plausible.
Insights
Tamoxifen efficacy for ER+ breast cancer may be affected by CYP2D6 inhibitors, potentially increasing recurrence risk. Other enzyme inhibitors showed no significant impact on recurrence in this study.
Area of Science:
- Pharmacogenomics
- Oncology
- Clinical Pharmacology
Background:
- Tamoxifen is a standard treatment for premenopausal women with estrogen receptor-positive (ER+) breast cancer.
- Tamoxifen's effectiveness depends on its metabolism by CYP2D6, CYP2C19, and CYP3A4 enzymes.
- Concomitant medications inhibiting these enzymes may alter tamoxifen's therapeutic effect.
Purpose of the Study:
- To investigate the impact of co-prescribed CYP enzyme-inhibiting drugs on breast cancer recurrence in premenopausal women treated with tamoxifen.
- To evaluate the association between drug-drug interactions affecting tamoxifen metabolism and breast cancer recurrence risk.
Main Methods:
- A cohort study of 4493 premenopausal women with ER+ breast cancer treated with tamoxifen between 2002-2011.
- Time-varying CYP inhibitor exposure was defined as the proportion of overlapping days with tamoxifen treatment.
- Associations were estimated using Bayesian joint modeling and traditional Cox regression.
Main Results:
- Bayesian models indicated a potential increase in recurrence risk with substantial CYP2D6 inhibitor use (HR: 1.24, 95% CrI: 0.96, 1.58).
- No significant recurrence association was observed for CYP2C19 or CYP3A4/5 inhibitors using Bayesian models.
- Cox regression models produced less reliable results, showing a positive association for CYP2C19 inhibitors.
Conclusions:
- Bayesian joint modeling suggests a slight increase in breast cancer recurrence associated with CYP2D6 inhibitor use during tamoxifen therapy.
- No significant association between CYP2C19 or CYP3A4/5 inhibitor use and recurrence was found using Bayesian methods.
- Bayesian joint modeling provided more plausible results compared to traditional Cox regression for evaluating drug-drug interactions in this context.
More Related Videos
19:44Enhancement of Apoptotic and Autophagic Induction by a Novel Synthetic C-1 Analogue of 7-deoxypancratistatin in Human Breast Adenocarcinoma and Neuroblastoma Cells with Tamoxifen
Published on: May 30, 2012
07:45Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
Related Concept Videos
01:21Cancer Survival Analysis
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:14Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
00:59Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
01:30Combined Effects of Drugs: Antagonism
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...