Exploring and explaining variability in tamoxifen and endoxifen pharmacokinetics in breast cancer patients: A pooled analysis
Lena Klopp-Schulze (1), Markus Joerger (2), Stijn L. W. Koolen (3), Patrick Neven (4), Ron H.J. Mathijssen (3), Charlotte Kloft (1)
(1) Dept. of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universitaet Berlin, Germany, (2) Medical Oncology and Clinical Pharmacology, Dept. of Internal Medicine, Cantonal Hospital St. Gallen, Switzerland, (3) Dept. of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, Netherlands, (4) Vesalius Research Center – VIB, University Hospitals Leuven, KU Leuven-University of Leuven, Leuven, Belgium
Objectives: To improve tamoxifen treatment, it is crucial to better understand the complex pharmacokinetics (PK) of tamoxifen and its major metabolites, which is influenced by many internal (e.g. CYP polymorphisms) and external factors (e.g. drug-drug interactions). By combining data from different clinical studies we enriched the single database for analysis, i.e. increased the power to detect covariate relationships. This study aimed to explore and explain different levels of variability in tamoxifen and endoxifen PK.
Methods: Plasma concentration data of tamoxifen and metabolites from 468 breast cancer patients were pooled from six clinical studies: two large studies (Npatients=375) with sparse sampling (≤4 occasions, 1 sample/occasion) [1,2] and four smaller studies (Npatients=93) with rich sampling (≤3 occasions, ≤9 samples/occasion) [3–6]. A patient’s CYP2D6 phenotype was predicted from genotype according to CPIC guidelines [7]. A joint parent-metabolite model including key covariate relationships (based on prior knowledge) was developed. To account for different levels of variability (study, individual, occasion), several variability models were tested. Modelling activities were performed using NONMEM (v. 7.3).
Results: Large parts of the interindividual (IIV), inter-occasional variability (IOV) and inter-study variability (ISV) were explained by the investigated covariates: Drug-drug interactions (DDI) with the potent CYP3A4 inducer rifampicin (CLTAM increased by >500%) and the strong CYP2D6 inhibitors fluoxetine and paroxetine (CLTAM-ENDX reduced by >60%) had a tremendous impact on tamoxifen and endoxifen PK. For CYP2D6 poor, intermediate and ultra-rapid metabolisers (reference group: normal metabolisers), a change in CLTAM-ENDX of -65%, -45% and +75% was identified. However, unexplained variability remained in IIV and ISV (>25% CV).
Conclusions: By combining data from six studies influential factors on PK were identified and quantified, thereby substantially reducing ISV and IIV. However, unexpectedly high differences between the pooled studies were found, which could not be explained by the investigated covariates. Study design, bioanalytical methods or factors that had not been reported, such as adherence or tamoxifen formulation, might cause these differences [8]. To avoid subtherapeutic concentrations we need to identify and control these factors which will improve dose individualisation strategies such as model-based therapeutic drug monitoring.
References:
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[8] L. Klopp-Schulze, M. Joerger, S.G. Wicha, Z.P. Parra-Guillen, C. Kloft. Tamoxifen and endoxifen pharmacokinetics: Exploration of differences in model performance using simulations. PAGE 25: 5917 [www.page-meeting.org/?abstract=5917], (2016).
