Leveraging a Previously Published Population Pharmacokinetic Model to Predict Rivaroxaban Exposure in Real-World Patients.

Population pharmacokinetic (PK)/pharmacodynamic models are commonly used to inform drug dosing; however, often real-world patients are not well represented in the clinical trial population. We sought to determine how well dosing recommended in the rivaroxaban drug label results in exposure for real-world patients within a reference area under the concentration-time curve (AUC) range. To accomplish this, we assessed the utility of a prior published rivaroxaban population PK model to predict exposure in real-world patients. We used the model to predict rivaroxaban exposure for 230 real-world patients using 3 methods: (1) using patient phenotype information only, (2) using individual post hoc estimates of clearance from the prior model based on single PK samples of rivaroxaban collected at steady state without refitting of the prior model, and (3) using individual post hoc estimates of clearance from the prior model based on PK samples of rivaroxaban collected at steady state after refitting of the prior model. We compared the results across 3 software packages (NONMEM, Phoenix NLME, and Monolix). We found that while the average patient-assigned dosing per the drug label will likely result in the AUC falling within the reference range, AUC for most individual patients will be outside the reference range. When comparing post hoc estimates, the average pairwise percentage differences were all <10% when comparing the software packages, but individual pairwise estimates varied as much as 50%. This study demonstrates the use of a prior published rivaroxaban population PK model to predict exposure in real-world patients.

Duke Scholars

Author Daniel Gonzalez Medicine, Clinical Pharmacology