BACKGROUND AND OBJECTIVES: The oral anticoagulant phenprocoumon, similar to other vitamin K antagonists, is characterized by pronounced interindividual variability in the doses needed to achieve the desired therapeutic effect. Previous studies assessed the effect of genetic and demographic covariates on empirical dose requirements of phenprocoumon to enable individualized dose prediction. The aim of the present study was to quantify major sources of interindividual variability separately on the pharmacokinetics and pharmacodynamics of phenprocoumon using a population pharmacokinetic-pharmacodynamic model. METHODS: A single steady-state blood sample was collected from 278 patients and assayed by liquid chromatography-tandem mass spectrometry for phenprocoumon and its metabolites. Genotyping was performed for variants of the cytochrome P450 (CYP) 2C9 (CYP2C9) and vitamin K epoxide reductase complex, subunit 1 (VKORC1) genes. Effects were quantified by international normalized ratio (INR). Data were analyzed simultaneously using NONMEM VII. RESULTS: The model confirmed CYP2C9 and VKORC1 variants as the major predictors of variability in phenprocoumon concentrations and effects, together with body weight, age, comedication with CYP3A modifiers (i.e. inhibitors or inducers) and presence of atrial fibrillation. These covariates explained 50.0 % of the observed variability in the model parameters. Phenprocoumon clearance fractions mediated per CYP2C9 allele were 13.4, 9.5 and 5.7 mL/h for the 1, 2 and 3 variants, respectively. An additional clearance fraction of 5.3 mL/h was independent of CYP2C9 activity. Homozygous VKORC1 wild-type carriers were estimated to have a 2.13-fold higher phenprocoumon exposure requirement than homozygous 1173 C>T carriers to achieve the same effect on INR. CONCLUSIONS: The model provides a deeper insight in the separate pharmacokinetic and pharmacodynamic parts of phenprocoumon action. Thus, it provides important information for individualized dose prediction, with the option to include further covariates not studied here with known effects on individual pharmacokinetic or pharmacodynamic processes.
BACKGROUND AND OBJECTIVES: The oral anticoagulant phenprocoumon, similar to other vitamin K antagonists, is characterized by pronounced interindividual variability in the doses needed to achieve the desired therapeutic effect. Previous studies assessed the effect of genetic and demographic covariates on empirical dose requirements of phenprocoumon to enable individualized dose prediction. The aim of the present study was to quantify major sources of interindividual variability separately on the pharmacokinetics and pharmacodynamics of phenprocoumon using a population pharmacokinetic-pharmacodynamic model. METHODS: A single steady-state blood sample was collected from 278 patients and assayed by liquid chromatography-tandem mass spectrometry for phenprocoumon and its metabolites. Genotyping was performed for variants of the cytochrome P450 (CYP) 2C9 (CYP2C9) and vitamin K epoxide reductase complex, subunit 1 (VKORC1) genes. Effects were quantified by international normalized ratio (INR). Data were analyzed simultaneously using NONMEM VII. RESULTS: The model confirmed CYP2C9 and VKORC1 variants as the major predictors of variability in phenprocoumon concentrations and effects, together with body weight, age, comedication with CYP3A modifiers (i.e. inhibitors or inducers) and presence of atrial fibrillation. These covariates explained 50.0 % of the observed variability in the model parameters. Phenprocoumon clearance fractions mediated per CYP2C9 allele were 13.4, 9.5 and 5.7 mL/h for the 1, 2 and 3 variants, respectively. An additional clearance fraction of 5.3 mL/h was independent of CYP2C9 activity. Homozygous VKORC1 wild-type carriers were estimated to have a 2.13-fold higher phenprocoumon exposure requirement than homozygous 1173 C>T carriers to achieve the same effect on INR. CONCLUSIONS: The model provides a deeper insight in the separate pharmacokinetic and pharmacodynamic parts of phenprocoumon action. Thus, it provides important information for individualized dose prediction, with the option to include further covariates not studied here with known effects on individual pharmacokinetic or pharmacodynamic processes.
Authors: Rianne M F van Schie; Judith A M Wessels; Saskia le Cessie; Anthonius de Boer; Tom Schalekamp; Felix J M van der Meer; Talitha I Verhoef; Erik van Meegen; Frits R Rosendaal; Anke-Hilse Maitland-van der Zee Journal: Eur Heart J Date: 2011-06-02 Impact factor: 29.983
Authors: Mike Ufer; Bernd Kammerer; Julia Kirchheiner; Anders Rane; Jan-Olof Svensson Journal: J Chromatogr B Analyt Technol Biomed Life Sci Date: 2004-10-05 Impact factor: 3.205
Authors: Eva Hummers-Pradier; Stephan Hess; Ibrahim M Adham; Thomas Papke; Burkert Pieske; Michael M Kochen Journal: Eur J Clin Pharmacol Date: 2003-05-01 Impact factor: 2.953
Authors: Mia Wadelius; Leslie Y Chen; Jonatan D Lindh; Niclas Eriksson; Mohammed J R Ghori; Suzannah Bumpstead; Lennart Holm; Ralph McGinnis; Anders Rane; Panos Deloukas Journal: Blood Date: 2008-06-23 Impact factor: 22.113