In silico simulation study: A comparison of two population pharmacokinetic models of tamoxifen and its major metabolite endoxifen
Lena Klopp-Schulze (1), Markus Joerger (2), Zinnia Parra-Guillen (1), 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
Objectives: A high variability in the pharmacokinetics (PK) of tamoxifen and its metabolite endoxifen in estrogen receptor-positive breast cancer patients has been associated with differences in clinical efficacy and treatment-related toxicity. Therefore, optimising tamoxifen therapy by a personalised approach has been proposed [1]. The aim of this study was to compare the characteristics of two recently published PK models of tamoxifen and endoxifen and its ability to reflect observed data [3].
Methods: Concentration-time profiles of tamoxifen and endoxifen were simulated in Berkeley Madonna using the published PK models “Ter Heine model” [1] and “Dahmane model” [2]. Deterministic and stochastic simulations (each N=1000) of 20 mg QD p.o. tamoxifen for 6 month were performed using typical population PK estimates and clinically relevant covariates (CYP2D6, CYP3A4/5) on PK. The results were compared to steady-state concentrations reported in [3] and to a proposed threshold concentration associated with therapeutic success (CEndoxifen of 5.97 ng/mL) [4]. The percentage of virtual patients with endoxifen steady-state plasma concentrations above the therapeutic threshold was calculated for each population.
Results: The two evaluated models used different approaches to describe drug-metabolite concentrations. While Ter Heine et al. implemented a hypothetical liver compartment to describe the formation of endoxifen, Dahmane included two additional metabolite compartments accounting for two routes of endoxifen formation. When comparing the simulations of the virtual populations using typical covariates, the Dahmane model showed considerably higher steady-state concentrations of tamoxifen and endoxifen compared to the Ter Heine model. Hence, the probability of target attainment, i.e. as percentage of patients above the proposed threshold, was higher for the simulated typical patient profiles using the Dahmane model compared to the Ter Heine model. Additionally, the Dahmane model described the observed data better.
Conclusion: This simulation study of tamoxifen and endoxifen displayed substantial differences between the investigated PK models. Also for anticipated exposure-response, as indicated by the proposed threshold concentration, the two population PK models resulted in a profoundly different probability of target attainment. External validation with respect to the predictiveness of the PK models is currently ongoing and will eventually contribute to a more comprehensive understanding of the PK of tamoxifen and endoxifen.
References:
[1] Ter Heine R et al. Population pharmacokinetic modelling to assess the impact of
CYP2D6 and CYP3A metabolic phenotypes on the pharmacokinetics of tamoxifen and endoxifen. Brit J Clin Pharmacol (2014) 78(3): 572–86.
[2] Dahmane EBA. Tamoxifen pharmacokinetics and pharmacogenetics in endocrine sensitive breast cancer patients. Thèse de doctorat: Univ. Genève (2013) no. Sc. 4617.
[3] Mürdter TE et al. Activity levels of tamoxifen metabolites at the estrogen receptor and the impact of genetic polymorphisms of phase I and II enzymes on their concentration levels in plasma. Clin Pharmacol Ther (2011) 89(5): 708–17.
[4] Madlensky L et al. Tamoxifen metabolite concentrations, CYP2D6 genotype, and breast cancer outcomes. Clin Pharmacol Ther (2011) 89(5): 718–25.
