{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T10:39:03Z","timestamp":1772275143937,"version":"3.50.1"},"reference-count":31,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T00:00:00Z","timestamp":1713916800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clin Pharma and Therapeutics"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:p>Warfarin dosing remains challenging due to substantial inter\u2010individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model\u2010informed 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 <jats:italic>CYP2C9<\/jats:italic> and\/or <jats:italic>VKORC1<\/jats:italic> genotypes as covariates improved the prediction quality of the warfarin\/INR model, even after assimilating 4\u2009days 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\u2010derived models for MIPD, offering a complementary approach to empirical model development.<\/jats:p>","DOI":"10.1002\/cpt.3274","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T11:19:56Z","timestamp":1713957596000},"page":"795-806","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Leveraging QSP Models for MIPD: A Case Study for Warfarin\/INR"],"prefix":"10.1002","volume":"116","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0399-5886","authenticated-orcid":false,"given":"Undine","family":"Falkenhagen","sequence":"first","affiliation":[{"name":"PharMetrX Graduate Research Training Program  Berlin\/Potsdam Germany"},{"name":"Institute of Mathematics, Mathematical Modelling and Systems Biology University of Potsdam  Potsdam 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