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The influence of genotype on warfarin maintenance dose predictions produced using a Bayesian dose individualization tool

机译:基因型对使用贝叶斯剂量个体化工具产生的华法林维持剂量预测的影响

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摘要

Background A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In the following study, we aimed to (1) determine if the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from two different clinical settings, (2) explore the influence of CYP2C9 and VKORC1 genotype on the predictive performance of the Bayesian dosing tool, and (3) determine if the prior population used to develop the kinetic-pharmacodynamic (KPD) model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions. Methods The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared to the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (e.g., EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates to published values. Results The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% CI]; 0.32 mg/day [0.14, 0.5]). The bias was only observed in patients requiring ≥7 mg/day. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, suggesting that the bias was not caused by different prior and posterior populations. Conclusions Maintenance doses for patients requiring ≥7 mg/day were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose–response relationship at higher warfarin doses
机译:背景技术发现先前建立的用于华法令的贝叶斯定量给药工具产生了有偏差的维持剂量预测。在接下来的研究中,我们旨在(1)确定先前观察到的华法林剂量偏倚预测是否可以在来自两个不同临床背景的新患者队列中复制,(2)探讨CYP2C9和VKORC1基因型对预测性能的影响(3)确定用于建立贝叶斯配药工具基础的动力学-药效学(KPD)模型的先前人群与受试(后)人群是否有足够的差异,以解释剂量预测的偏差。方法使用给药工具预测140例患者的华法林维持剂量,并将其与观察到的维持剂量进行比较。通过预测维持剂量以及已知参数会因遗传变异而改变的先前参数值(例如,VKORC1基因型的EC50)来评估基因型的影响。通过使用NONMEM,将支持贝叶斯工具的已发布的动力学-药效学模型拟合到观察到的数据,并将模型参数估计值与已发布的值进行比较,来评估先前的人群。结果贝叶斯工具在新患者队列中产生剂量预测为正偏差(平均预测误差[95%CI]; 0.32 mg / day [0.14,0.5])。仅在需要≥7 mg /天的患者中观察到偏倚。观察到的偏倚的方向和大小不受基因型的影响。先验模型很好地拟合了我们的数据,表明偏倚不是由不同的先验和后验群体引起的。结论对于需要≥7 mg /天的患者,维持剂量被高估了。偏倚既不是由于基因型的影响,也不是与先前和之后种群之间的差异有关。需要一种更机械的模型来捕获较高华法林剂量下的华法林剂量-反应关系

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