Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR

Author:

Falkenhagen Undine12ORCID,Cavallari Larisa H.3ORCID,Duarte Julio D.3ORCID,Kloft Charlotte4ORCID,Schmidt Stephan5ORCID,Huisinga Wilhelm2ORCID

Affiliation:

1. PharMetrX Graduate Research Training Program Berlin/Potsdam Germany

2. Institute of Mathematics, Mathematical Modelling and Systems Biology University of Potsdam Potsdam Germany

3. College of Pharmacy, Department of Pharmacotherapy and Translational Research University of Florida Gainesville Florida USA

4. Institute of Pharmacy, Department of Clinical Pharmacy and Biochemistry Freie Universität Berlin Berlin Germany

5. College of Pharmacy, Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology University of Florida Orlando Florida USA

Abstract

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.

Publisher

Wiley

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