Christina Schräpel (1,2), Lukas Kovar (1), Simeon Rüdesheim (1,2), Boian Ganchev (2), Patrick Kröner (2), Svitlana Igel (2), Thomas E. Mürdter (2), Matthias Schwab (2,3,4) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbrücken, Germany, (2) Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, (3) Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany, (4) Department of Pharmacy and Biochemistry, University Tübingen, Tübingen, Germany
Introduction: Polycystic ovary syndrome (PCOS) is one of the major causes of female infertility, which affects about 15-20% of couples worldwide. Clomiphene, a selective estrogen receptor modulator (SERM), is the first line therapy for the treatment of anovulation for more than 50 years. However, since clomiphene is metabolized primarily through CYP2D6 and CYP3A4, altered clomiphene exposure can occur due to CYP2D6 polymorphisms (drug-gene interaction (DGI)) or due to concomitant use of clomiphene with CYP2D6 and CYP3A4 inhibitors (drug-drug interaction (DDI)) [1]. This may be one of the reasons for the high rate of nonresponding patients (8-30%) [2]. To overcome this issue, physiologically based pharmacokinetic (PBPK) modelling can be applied as a valuable tool for quantifying DDI and DGI scenarios [3].
Objectives:
- Development and evaluation of a PBPK model of the enantiomer (E)-clomiphene (enclomiphene) after oral administration
- Subsequently, prediction of both DDI and DGI effects
Methods: A PBPK model of enclomiphene was built in PK-Sim® (Version 8.0) as part of the Open Systems Pharmacology Suite [4]. Data for model development were extracted from literature, including physicochemical parameters and plasma concentration-time profiles from various CYP2D6 phenotypes. Moreover, data from an internal multi-center pharmacogenetic panel study were used with plasma concentration-time profiles and urinary data from 20 healthy females, including 6 poor metabolizer (PM), 6 intermediate metabolizer (IM), 5 extensive metabolizer (EM) and 3 ultrarapid metabolizer (UM). For PBPK model development, 20 plasma profiles (109 patients) were split into an internal training (6 plasma profiles, 6 urinary profiles) and an external test (14 plasma profiles, 6 urinary profiles) dataset. When necessary, parameters were estimated based on the internal dataset. Model performance was evaluated by comparing observed plasma profiles from the external dataset with predicted profiles and by comparison of predicted to observed area under the plasma concentration-time curve (AUC) values and maximum plasma concentration (Cmax) values. Moreover, as quantitative measures of the descriptive and predictive model performance the geometric mean fold errors (GMFEs) of predicted and observed AUC and Cmax, as well as the mean relative deviations (MRDs) of all predicted compared to observed plasma concentrations were calculated.
Results: The PBPK model includes metabolism of enclomiphene through CYP2D6 and CYP3A4 as reported in literature [1]. The values for the corresponding Michaelis-Menten constants KM were obtained from literature. Separate kcat values were assigned to the different CYP2D6 phenotypes and estimated based on the internal dataset. In addition, enclomiphene is cleared by glomerular filtration. The final model was capable to describe and predict all plasma concentration-time profiles of the internal and external dataset with GMFE values for AUC of 1.31 and for Cmax of 1.52 for all enclomiphene plasma concentration-time profiles as well as an overall MRD value of 1.73. Additionally, based on CYP2D6 activity scores CYP2D6 poor metabolizer (PM), intermediate metabolizer (IM), extensive metabolizer (EM) and ultrarapid metabolizer (UM) profiles could be predicted successfully with mean DGI ratios (predicted AUC ratio PM, IM or UM to EM versus observed AUC ratio PM, IM or UM to EM) of 0.91 for PM, 0.87 for IM and 1.07 for UM. Furthermore, an effective coupling with an existing clarithromycin model [5] enabled the description of DDI effects of concomitant use of clarithromycin with clomiphene. The DDI mean ratio (predicted AUC ratio clomiphene + clarithromycin to clomiphene versus observed AUC ratio clomiphene + clarithromycin to clomiphene) was 0.89 and hence, within the common acceptance criterion of a twofold deviation.
Conclusions: The successfully developed whole-body PBPK model of enclomiphene was able to predict enclomiphene plasma profiles of different dosing regimens both within CYP2D6 DGI and CYP3A4 DDI scenarios. Overall, the model holds the potential to develop dosing recommendations for a more personalized medicine by reducing the rate of nonresponders.
Funding: Supported by the Robert Bosch Stiftung (Stuttgart, Germany) and GUIDE-IBD grant 031L0188D
References:
[1] Mürdter T et al.: Genetic polymorphism of cytochrome P450 2D6 determines oestrogen receptor activity of the major infertility drug clomiphene via its active metabolites. Hum Mol Genet (2012) 21(5):1145-1154.
[2] Kim MJ et al.: Effect of the CYP2D6*10 allele on the pharmacokinetics of clomiphene and its active metabolites. Arch Pharm Res (2018) 41:347-353.
[3] U.S. Food and Drug Administration. Clinical Drug Interaction Studies – Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendations. Draft Guidance for Industry (2017).
[4] Eissing T et al.: A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol (2011) 2:4.
[5] Moj D et al.: Clarithromycin, Midazolam, and Digoxin: Application of PBPK Modeling to Gain New Insights into Drug-Drug Interactions and Co-medication Regimens. AAPS J (2017) 19(1):298-312.
Reference: PAGE () Abstr 9524 [www.page-meeting.org/?abstract=9524]
Poster: Drug/Disease Modelling - Absorption & PBPK