Background Endoxifen is the most important active metabolite of tamoxifen. Several retrospective studies have suggested a minimal or threshold endoxifen systemic concentration of 14–16 nM is required for a lower recurrence rate. The aim of this study was to investigate the feasibility of reaching a predefined endoxifen level of ≥ 16 nM (5.97 ng/mL) over time using therapeutic drug monitoring (TDM). Methods This prospective open-label intervention study enrolled patients who started treatment with a standard dose of tamoxifen 20 mg once daily for early breast cancer. An outpatient visit was combined with a TDM sample at 3, 4.5, and 6 months after initiation of the tamoxifen treatment. The tamoxifen dose was escalated to a maximum of 40 mg if patients had an endoxifen concentration < 16 nM. The primary endpoint of the study was the percentage of patients with an endoxifen level ≥ 16 nM at 6 months after the start of therapy compared with historical data, in other words, 80% of patients with endoxifen levels ≥ 16 nM with standard therapy. Results In total, 145 patients were included. After 6 months, 89% of the patients had endoxifen levels ≥ 16 nM, compared with a literature-based 80% of patients with endoxifen levels ≥ 16 nM at baseline (95% confidence interval 82–94; P = 0.007). In patients with an affected CYP2D6 allele, it was not always feasible to reach the predefined endoxifen level of ≥ 16 nM. No increase in tamoxifen-related adverse events was reported after dose escalation. Conclusion This study demonstrated that it is feasible to increase the percentage of patients with endoxifen levels ≥ 16 nM using TDM. TDM is a safe strategy that offers the possibility of nearly halving the number of patients with endoxifen levels < 16 nM.

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.

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 modelling, we aimed to identify factors that determine the inter-individual variability. Aim is 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 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 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. However, the remaining unexplained inter-individual variability is still high and therefore model-informed tamoxifen dosing should be accompanied by therapeutic drug monitoring.

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