Sung-yoon Yang *, Lien Thi Ngo *, Sang Kyum Kim **, Hwi-yeol Yun**, Jung-woo Chae**; *Both of authors were contributed equally for this work as first-presenter **Both of authors were contributed equally for this work as correspondence
College of Pharmacy, Chungnam National University, Korea
Objectives: Rivaroxaban (direct oral anticoagulants, CYP3A4 substrate drug) and carbamazepine (anti-epileptic drug, potent CYP3A4 inducer) could be prescribed concomitantly to cerebrovascular disease patients. And it could induce CYP3A4-mediated DDI (drug-drug interaction), leading to potential adverse events including recurrent venous thrombosis 1–4. Physiologically based pharmacokinetic (PBPK) modeling and Population PK (PopPK) modeling have been extensively used to examine and predict the CYP-mediated DDI. The main purpose of this study is to predict potential DDI between rivaroxaban and carbamazepine using PBPK modeling and confirm the PBPK DDI simulation results by comparison with the results from the PopPK method in clinically untested treatment scenarios.
Methods: PBPK models for the rivaroxaban and carbamazepine were developed by implementing various parameters and evaluated based on clinical observed PK profiles. The final carbamazepine model was coupled to the developed rivaroxaban model to simulate DDI between rivaroxaban and carbamazepine. To evaluate the PBPK DDI prediction, a human-extrapolated PopPK model for DDI was developed based on the published preclinical DDI study using allometric scaling methods 5–8 9. Magnitude of drug interaction was assessed and compared by calculating the ratio of the relative change of the predicted AUC and Cmax (predicted DDI ratio) of rivaroxaban at a single dose of 20 mg on Day 7 in the presence of carbamazepine (450 mg b.i.d x 7 days; Day 1~Day 7) and the absence of carbamazepine based on the simulated results from the PBPK model and PopPK model.
Predicted DDI ratio = PKparameter_rivaroxaban in the presence of carbamazepine/PK parameter_rivaroxaban only
Results: Concerning the model evaluation of the PBPK model, geometric mean fold error (GMFE) values for PK parameters (AUC and Cmax) are 1.55 and 1.13 for rivaroxaban, and 1.40 and 1.22 for carbamazepine, respectively. The developed PBPK models showed accurate predictions of PK profiles for the oral administration route, showing good agreement with the observed PK profiles. PopPK model for estimating the effects of carbamazepine on PKs of rivaroxaban in rats was developed in our group 9. Using simple allometric scaling methods, the rivaroxaban concentration was simulated with the same scenario performed in PBPK modeling. For the assessment of the DDI, the predicted DDI ratio of AUC and Cmax are 0.46 and 0.59 for the PBPK model, and 0.48 and 0.59 for the PopPK model, respectively.
Conclusions: We successfully developed the comprehensive whole-body PBPK models of rivaroxaban and carbamazepine by incorporating various parameters which determine the drug disposition and drug interaction. DDI simulation results indicated that co-administration of carbamazepine with rivaroxaban leads to about two-fold decrease of AUC and Cmax of the rivaroxaban. It could induce the reduced treatment effects of rivaroxaban considering the paper that concomitant use of rifampicin with rivaroxaban leads to a 50% reduction of rivaroxaban AUC accompanied with the decreased pharmacodynamic effects 10. The present study could suggest a quantitative understanding of the carbamazepine effects on the pharmacokinetics of rivaroxaban. Further clinical trials of DDI would be needed to confirm our findings
References:
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Reference: PAGE 29 (2021) Abstr 9870 [www.page-meeting.org/?abstract=9870]
Poster: Drug/Disease Modelling - Absorption & PBPK