Weiner Daniel, Powell J Robert, Patterson J Herbert, Tyson Rachel, Gehi Anil, Moll Stephan, Konicki Robyn, Qaraghuli Farah Al, Campbell Kristen B, Kashuba Angela D M, Gonzalez Daniel
Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Division of Cardiology, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
J Clin Pharmacol. 2022 Dec;62(12):1518-1527. doi: 10.1002/jcph.2122. Epub 2022 Jul 25.
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.
群体药代动力学（PK）/药效学模型通常用于指导药物给药剂量；然而，在临床试验人群中往往不能很好地代表真实世界的患者。我们试图确定利伐沙班药品标签中推荐的给药剂量在浓度-时间曲线（AUC）范围内的参考区域内，能在多大程度上使真实世界的患者达到相应的暴露水平。为实现这一目标，我们评估了先前发表的利伐沙班群体PK模型预测真实世界患者暴露水平的效用。我们使用该模型通过三种方法预测230例真实世界患者的利伐沙班暴露水平：（1）仅使用患者表型信息；（2）基于在稳态下收集的利伐沙班单一样本PK数据，使用先前模型的个体事后清除率估计值，而不对先前模型进行重新拟合；（3）在对先前模型进行重新拟合后，基于在稳态下收集的利伐沙班PK样本，使用先前模型的个体事后清除率估计值。我们比较了三种软件包（NONMEM、Phoenix NLME和Monolix）的结果。我们发现，虽然根据药品标签为患者分配的平均给药剂量可能会使AUC落在参考范围内，但大多数个体患者的AUC将超出参考范围。在比较事后估计值时，比较软件包时平均成对百分比差异均<10%，但个体成对估计值差异高达50%。本研究证明了使用先前发表的确利伐沙班群体PK模型来预测真实世界患者的暴露水平。
