Population pharmacokinetic of tamoxifen and its metabolites in breast cancer patients
João Paulo Ximenez (1, 2); Salvatore D’Agate (2) Maria Paula Marques Pereira (1); Jurandyr Moreira de Andrade (1); Vera Lucia Lanchote (1); Oscar Della Pasqua (2)
(1) University of São Paulo, Ribeirão Preto, Brazil; (2) University College London, London, UK
Objectives: Tamoxifen is considered a pro-drug of its active metabolite endoxifen. The major metabolic enzymes involved in endoxifen formation are CYP2D6 and CYP3A, whose activity variability influences endoxifen exposure and may influence clinical outcome [1]. In this context, the aim of this study was to develop a population pharmacokinetic model for tamoxifen and its metabolites, and subsequently use it to explore opportunities for treatment personalization.
Methods: 40 breast cancer patients were recruited into the clinical study. Tamoxifen, endoxifen, 4-OH-tamoxifen and N-desmethyl-tamoxifen plasma concentrations were sampled at steady-state, during a 24h interval. PK modelling was performed using NONMEM 7.3. One and two-compartment models with first-order absorption and elimination were evaluated based on previous publications. Additional compartments were appended to the model to allow characterization of the different metabolites. Covariates factors included in the analysis were: age, body weight, hormonal stage, CYP activity in vivo activity and genotype. Selection of the best hierarchical model was based on standard model diagnostic criteria[2]. The inter-individual variability in PK parameters was estimated using an exponential model and the residual variability was described by a proportional model. The selection of covariates was performed through a forward selection and backward elimination method. Final model performance was assessed by bootstrapping, visual predictive checks (VPC) and posterior predictive checks (PPC).
Results: The PK of tamoxifen and its metabolites was best described by a four compartment model. The Vd of TAM, 4OHT and NDMT were assumed to be the same in order to prevent parameter identifiability issues. Ka, CL20, V2, CL50 and V5 were fixed based on published results. Inter-individual variability (IIV) was identified on Ka, K23, K24, K35, K45 and CL50. A proportional residual model error was used to describe residual variability.
Conclusions: Although previous models have been developed for tamoxifen, our study will be the first to describe tamoxifen metabolism in vivo, including information about CYP2D6 and CYP3A genotypes and phenotypes. Simulations suggest that efficacious endoxifen levels (>6 ng/mL) are achieved after a 20 mg/kg dose of tamoxifen. Hormonal status appear to influence endoxifen formation rate. The proposed parameterization allows the possibility to discriminate the contribution of different moieties and explore dosing algorithms taking into account covariate factors.
References:
[1] Province MA, Goetz MP, Brauch H, Flockhart DA, Hebert JM, Whaley R, et al. CYP2D6 genotype and adjuvant tamoxifen: meta-analysis of heterogeneous study populations. Clin Pharmacol Ther. 2014 Feb;95(2):216–27.
[2] Ter Heine R, Binkhorst L, de Graan a JM, de Bruijn P, Beijnen JH, Mathijssen RHJ, et al. Population pharmacokinetic modeling to assess the impact of CYP2D6 and CYP3A metabolic phenotypes on the pharmacokinetics of tamoxifen and endoxifen. Br J Clin Pharmacol. 2014 Apr 2;1–30.