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

Shamin M. Saffian, Stephen B. Duffull, Rebecca L. Roberts, Robert C. Tait, Leanne Black, Kirstin A. Lund, Alison H. Thomson, Daniel F. B. Wright

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Abstract

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
Original languageEnglish
Pages (from-to)677-683
Number of pages7
JournalTherapeutic Drug Monitoring
Volume38
Issue number6
Early online date24 Sep 2016
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • warfarin monitoring
  • Bayesian analysis
  • dosing
  • maintenance doses
  • patient care
  • dose-response relationships

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