Key result
Physiologically based pharmacokinetic models accurately predict drug-drug interactions between clarithromycin and DOACs.
Why the study?
This study was conducted to assess pharmacokinetic interactions between clarithromycin and four direct oral anticoagulants using physiologically based pharmacokinetic models to elucidate the influence of P-gp.
Can physiologically based pharmacokinetic models accurately predict drug-drug interactions between clarithromycin and direct oral anticoagulants?
Can physiologically based pharmacokinetic models accurately predict drug-drug interactions between clarithromycin and direct oral anticoagulants?
PBPK models successfully predicted P-glycoprotein-mediated drug-drug interactions between clarithromycin and DOACs, providing theoretical support for clinical dose adjustments.
Answers are grounded in this paper and cite their sources.
Supports PBPK modeling of clarithromycin-DOAC interactions; leaves open need for human validation before clinical use.
Objective: This study assessed the pharmacokinetic (PK) interactions between clarithromycin (a P-glycoprotein [P-gp] inhibitor) and four direct oral anticoagulants (DOACs) (P-gp substrates) using physiologically based PK (PBPK) models to elucidate the influence of P-gp in the interaction between them. Methods: PBPK models for clarithromycin, DABE–dabigatran (DAB), rivaroxaban, apixaban, and edoxaban were constructed using GastroPlus™ (version 9.9), based on physicochemical data and PK parameters from the literature. The models were optimized and validated in healthy subjects. We evaluated the predictive performance of the established model and further assessed the impact of P-gp on the PK of the four DOACs. Successfully validated models were then used to evaluate potential drug–drug interactions (DDIs) between clarithromycin and the DOACs. Results: The established PBPK models accurately described the PK of clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban. The predicted PK parameters (Cmax, Tmax, AUC0-t) were within 0.5–2 times the observed values. A sensitivity analysis of P-gp parameters indicated that an increase in P-gp expression was reduced by in vivo exposure to DOACs. The models demonstrated good predictive ability for DDIs between clarithromycin and the anticoagulants, and the ratio of the predicted values to the observed values of Cmax and the area under the curve (AUC) in the DDI state was within the range of 0.5–2. Conclusions: Comprehensive PBPK models for clarithromycin, DABE–DAB, rivaroxaban, apixaban, and edoxaban were developed, which can effectively predict DDIs mediated by P-gp’s function. These models provide theoretical support for clinical dose adjustments and serve as a foundation for future PBPK model development for DOACs under specific pathological conditions.
No takes yet. Share an insight, caveat, or question.
Yang et al. (2024) studied Drug-drug interactions. Clarithromycin and DOACs was evaluated on Pharmacokinetic parameters (Cmax, Tmax, AUC0-t) and DDI prediction. Physiologically based pharmacokinetic models accurately predicted drug-drug interactions between clarithromycin and DOACs, with predicted-to-observed Cmax and AUC ratios within 0.5-2.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: