Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/37900
Title: Factors affecting inter-individual variability in endoxifen concentrations in patients with breast cancer: results from the prospective TOTAM trial
Authors: Braal, C. Louwrens
Westenberg, Justin D.
Buijs, Sanne M.
ABRAMS, Steven 
Mulder, Tessa A. M.
van Schaik, Ron H. N.
Koolen, Stijn L. W.
Jager, Agnes
Mathijssen, Ron H. J.
Issue Date: 2022
Publisher: SPRINGER
Source: Breast cancer research and treatment (Print), 195 (1) , p. 65 -74
Abstract: 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.
Notes: Braal, CL (corresponding author), Erasmus MC Canc Inst, Dept Med Oncol, Dr Molewaterpl 40,POB 2040, NL-3015 CN Rotterdam, Netherlands.
c.braal@erasmusmc.nl
Keywords: Tamoxifen;Endoxifen;Early breast cancer;Predictive modeling;Therapeutic drug monitoring
Document URI: http://hdl.handle.net/1942/37900
ISSN: 0167-6806
e-ISSN: 1573-7217
DOI: 10.1007/s10549-022-06643-y
ISI #: 000826117600001
Rights: The Author(s) 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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