Warfarin dosing remains challenging due to substantial inter‐individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model‐informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP‐derived models for MIPD, offering a complementary approach to empirical model development.

中文翻译：

---

利用 QSP 模型进行 MIPD：华法林/INR 案例研究

由于个体间存在很大差异，华法林的剂量仍然具有挑战性，这可能导致标准剂量的治疗不安全或无效。模型知情的精确剂量（MIPD）可以帮助个体化华法林剂量，需要选择合适的模型。对于根据临床数据开发的模型，对研究设计和人群的依赖性引发了普遍性问题。定量系统药理学（QSP）模型有望提供更好的外推能力；然而，它们的复杂性和缺乏临床数据验证引发了 MIPD 的适用性问题。我们之前从凝血 QSP 模型导出了机械华法林/国际标准化比值 (INR) 模型。在本文中，我们使用外部数据集和开始华法林治疗的患者的 INR 数据，评估了 MIPD 背景下华法林/INR 模型的预测性能。我们以基于经验的参考模型为基准，评估了模型预测的准确性和精确度。此外，我们还评估了协变量的贡献，并在更具挑战性的门诊数据中分别评估了预测性能。尽管没有使用华法林起始数据进行校准，但华法林/INR 模型在各种测量中的表现与参考模型相当。包括CYP2C9和/或VKORC1即使在吸收了 4 天的 INR 数据后，基因型作为协变量也提高了华法林/INR 模型的预测质量。门诊患者 INR 表现出较高的无法解释的变异性，并且预测值略高于观察值，这表明从住院患者过渡到门诊患者时可能需要进行模型调整。总体而言，这项研究强调了 QSP 衍生模型对于 MIPD 的潜力，为经验模型开发提供了补充方法。