Maki Sasano1, Masako Ohno2, Yuya Fukuda3, Shinpei Nonen2, Sachiko Hirobe1,3, Shinichiro Maeda1,3, Yoshihiro Miwa4, Junya Yokoyama5, Hiroyuki Nakayama6, Shigeru Miyagawa5, Yoshiki Sawa5, Yasushi Fujio1,6, Makiko Maeda7,8. 1. Clinical Pharmacology and Therapeutics Project, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. 2. School of Pharmacy, Hyogo University of Health Sciences, 1-3-6, Minatojima, Chuo-ku, Kobe, Hyōgo, 650-8530, Japan. 3. Advanced Research of Medical and Pharmaceutical Sciences , Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. 4. Department of Pharmacy, Osaka University Hospital, 2-15 Yamadaoka, Suita, Osaka, 565-0871, Japan. 5. Department of Cardiovascular Surgery, Graduate School of Medicine, Osaka University, 2-15 Yamadaoka, Suita, Osaka, 565-0871, Japan. 6. Laboratory of Clinical Science and Biomedicine, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. 7. Clinical Pharmacology and Therapeutics Project, Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. makikom@phs.osaka-u.ac.jp. 8. Advanced Research of Medical and Pharmaceutical Sciences , Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka, 565-0871, Japan. makikom@phs.osaka-u.ac.jp.
Abstract
PURPOSE: Large inter-individual differences in warfarin maintenance dose are mostly due to the effect of genetic polymorphisms in multiple genes, including vitamin K epoxide reductase complex 1 (VKORC1), cytochromes P450 2C9 (CYP2C9), and cytochrome P450 4F2 (CYP4F2). Thus, several algorithms for predicting the warfarin dose based on pharmacogenomics data with clinical characteristics have been proposed. Although these algorithms consider these genetic polymorphisms, the formulas have different coefficient values that are critical in this context. In this study, we assessed the mutual validity among these algorithms by specifically considering racial differences. METHODS: Clinical data including actual warfarin dose (AWD) of 125 Japanese patients from our previous study (Eur J Clin Pharmacol 65(11):1097-1103, 2009) were used as registered data that provided patient characteristics, including age, sex, height, weight, and concomitant medications, as well as the genotypes of CYP2C9 and VKORC1. Genotyping for CYP4F2*3 was performed by the PCR method. Five algorithms that included these factors were selected from peer-reviewed articles. The selection covered four populations, Japanese, Chinese, Caucasian, and African-American, and the International Warfarin Pharmacogenetics Consortium (IWPC). RESULTS: For each algorithm, we calculated individual warfarin doses for 125 subjects and statistically evaluated its performance. The algorithm from the IWPC had the statistically highest correlation with the AWD. Importantly, the calculated warfarin dose (CWD) using the algorithm from African-Americans was less correlated with the AWD as compared to those using the other algorithms. The integration of CYP4F2 data into the algorithm did not improve the prediction accuracy. CONCLUSION: The racial difference is a critical factor for warfarin dose predictions based on pharmacogenomics.
PURPOSE: Large inter-individual differences in warfarin maintenance dose are mostly due to the effect of genetic polymorphisms in multiple genes, including vitamin K epoxide reductase complex 1 (VKORC1), cytochromes P450 2C9 (CYP2C9), and cytochrome P450 4F2 (CYP4F2). Thus, several algorithms for predicting the warfarin dose based on pharmacogenomics data with clinical characteristics have been proposed. Although these algorithms consider these genetic polymorphisms, the formulas have different coefficient values that are critical in this context. In this study, we assessed the mutual validity among these algorithms by specifically considering racial differences. METHODS: Clinical data including actual warfarin dose (AWD) of 125 Japanese patients from our previous study (Eur J Clin Pharmacol 65(11):1097-1103, 2009) were used as registered data that provided patient characteristics, including age, sex, height, weight, and concomitant medications, as well as the genotypes of CYP2C9 and VKORC1. Genotyping for CYP4F2*3 was performed by the PCR method. Five algorithms that included these factors were selected from peer-reviewed articles. The selection covered four populations, Japanese, Chinese, Caucasian, and African-American, and the International Warfarin Pharmacogenetics Consortium (IWPC). RESULTS: For each algorithm, we calculated individual warfarin doses for 125 subjects and statistically evaluated its performance. The algorithm from the IWPC had the statistically highest correlation with the AWD. Importantly, the calculated warfarin dose (CWD) using the algorithm from African-Americans was less correlated with the AWD as compared to those using the other algorithms. The integration of CYP4F2 data into the algorithm did not improve the prediction accuracy. CONCLUSION: The racial difference is a critical factor for warfarin dose predictions based on pharmacogenomics.
Authors: John F Carlquist; Benjamin D Horne; Joseph B Muhlestein; Donald L Lappé; Bryant M Whiting; Matthew J Kolek; Jessica L Clarke; Brent C James; Jeffrey L Anderson Journal: J Thromb Thrombolysis Date: 2006-12 Impact factor: 2.300
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Authors: Michael D Caldwell; Tarif Awad; Julie A Johnson; Brian F Gage; Mat Falkowski; Paul Gardina; Jason Hubbard; Yaron Turpaz; Taimour Y Langaee; Charles Eby; Cristi R King; Amy Brower; John R Schmelzer; Ingrid Glurich; Humberto J Vidaillet; Steven H Yale; Kai Qi Zhang; Richard L Berg; James K Burmester Journal: Blood Date: 2008-02-04 Impact factor: 22.113