BACKGROUND: The pharmacokinetics and pharmacodynamics of warfarin are affected by polymorphisms in the genes coding for cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1). OBJECTIVE: The objective of this study was to develop a pharmacogenetic dosing algorithm for warfarin in Korean patients with atrial fibrillation and to compare it with the published pharmacogenetic dosing algorithms for accuracy to predict warfarin maintenance dose. METHODS: Clinical and genetic data from 130 Korean patients with atrial fibrillation (mean [SD] age: 66.2 [13.3] years; gender, male/female: 86/44; mean body weight: 66.6 [11.6] kg) were used to create a dosing algorithm, which was validated against an independent group of patients (n = 108; mean age: 67.4 [10.1] years; gender, male/female: 69/39; mean body weight: 66.0 [10.9] kg). Validation cohort data for the 12 previously published dosing algorithms incorporating CYP2C9 and VKORC1 genotype information were also applied. RESULTS: A multivariate regression model including the variables of age, VKORC1 and CYP2C9 genotype, body surface area, and statin status produced the best model for estimating the warfarin dose (R(2) = 0.62). Among the 12 algorithms that were compared, the predicted doses using algorithms derived from both the Swedish Warfarin Genetics (WARG) study and the Korean population study showed the best correlation with actual warfarin doses. Comparing the percentage of patients whose predicted dosages were within 20% of actual dosages, these algorithms showed similar overall performance. CONCLUSIONS: This study derived and validated a multivariate regression model for daily warfarin dose requirements in Korean patients with atrial fibrillation. As no algorithm could be considered the best for all dosing ranges, it may be important to consider the characteristics or limitations of each dosing algorithm and the nature of a population in choosing the most appropriate pharmacogenetic dosing.
BACKGROUND: The pharmacokinetics and pharmacodynamics of warfarin are affected by polymorphisms in the genes coding for cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1). OBJECTIVE: The objective of this study was to develop a pharmacogenetic dosing algorithm for warfarin in Korean patients with atrial fibrillation and to compare it with the published pharmacogenetic dosing algorithms for accuracy to predict warfarin maintenance dose. METHODS: Clinical and genetic data from 130 Korean patients with atrial fibrillation (mean [SD] age: 66.2 [13.3] years; gender, male/female: 86/44; mean body weight: 66.6 [11.6] kg) were used to create a dosing algorithm, which was validated against an independent group of patients (n = 108; mean age: 67.4 [10.1] years; gender, male/female: 69/39; mean body weight: 66.0 [10.9] kg). Validation cohort data for the 12 previously published dosing algorithms incorporating CYP2C9 and VKORC1 genotype information were also applied. RESULTS: A multivariate regression model including the variables of age, VKORC1 and CYP2C9 genotype, body surface area, and statin status produced the best model for estimating the warfarin dose (R(2) = 0.62). Among the 12 algorithms that were compared, the predicted doses using algorithms derived from both the Swedish Warfarin Genetics (WARG) study and the Korean population study showed the best correlation with actual warfarin doses. Comparing the percentage of patients whose predicted dosages were within 20% of actual dosages, these algorithms showed similar overall performance. CONCLUSIONS: This study derived and validated a multivariate regression model for daily warfarin dose requirements in Korean patients with atrial fibrillation. As no algorithm could be considered the best for all dosing ranges, it may be important to consider the characteristics or limitations of each dosing algorithm and the nature of a population in choosing the most appropriate pharmacogenetic dosing.
Authors: Marcus Fernando S Praxedes; Maria Auxiliadora P Martins; Aline O M Mourão; Karina B Gomes; Edna A Reis; Renan P Souza; Emílio Itamar F Campos; Daniel D Ribeiro; Manoel Otávio C Rocha Journal: Eur J Clin Pharmacol Date: 2019-11-12 Impact factor: 2.953
Authors: Erik Fung; Nikolaos A Patsopoulos; Steven M Belknap; Daniel J O'Rourke; John F Robb; Jeffrey L Anderson; Nicholas W Shworak; Jason H Moore Journal: Semin Thromb Hemost Date: 2012-10-06 Impact factor: 4.180
Authors: Talitha I Verhoef; William K Redekop; Ann K Daly; Rianne M F van Schie; Anthonius de Boer; Anke-Hilse Maitland-van der Zee Journal: Br J Clin Pharmacol Date: 2014-04 Impact factor: 4.335
Authors: Yun Kyung Park; Mi Ji Lee; Jae Ha Kim; Suk Jae Kim; June Soo Kim; Soo-Youn Lee; Oh Young Bang Journal: J Stroke Date: 2015-05-29 Impact factor: 6.967
Authors: Leiliane Rodrigues Marcatto; Luciana Sacilotto; Carolina Tosin Bueno; Mirella Facin; Celia Maria Cassaro Strunz; Francisco Carlos Costa Darrieux; Maurício Ibrahim Scanavacca; Jose Eduardo Krieger; Alexandre Costa Pereira; Paulo Caleb Junior Lima Santos Journal: BMC Cardiovasc Disord Date: 2016-11-17 Impact factor: 2.298
Authors: Justin B Kaye; Lauren E Schultz; Heidi E Steiner; Rick A Kittles; Larisa H Cavallari; Jason H Karnes Journal: Pharmacotherapy Date: 2017-09-06 Impact factor: 4.705
Authors: Sea Mi Park; Jong-Keuk Lee; Sa Il Chun; Hae In Lee; Sun U Kwon; Dong-Wha Kang; Jong S Kim Journal: J Stroke Date: 2013-05-31 Impact factor: 6.967