Warfarin dose requirement and cytochrome P450 2C9 and Vitamin K epoxide reductase complex subunit 1-1639 genetic polymorphisms in Thai patients.
The oral anticoagulant warfarin has been the mainstay therapeutic drug for the treatment and prevention of thromboembolism in various cerebro- and cardio-vascular diseases. Warfarin inhibits Vitamin K epoxide reductase complex subunit 1 (VKORC1), a key enzyme in the Vitamin K recycling.  Inhibition of this enzyme results in inhibition of the synthesis of Vitamin K-dependent clotting factors, including factor II, VII, IX, and X.  Warfarin is usually available as a racemic mixture of S- and R-enantiomers. The S-enantiomer exhibits 3 to 5 times more anticoagulant activity than the R-enantiomer  and is principally metabolized by the cytochrome P450 2C9 isozyme (CYP2C9), whereas the CYP1A2, CYP2C8, CYP2C19, and CYP3A4  isoforms mainly transform the R-warfarin to relatively inactive metabolite which is later excreted from the body through the kidneys.
A major problem in the clinical use of warfarin is bleeding complications. Bleedings can vary and occur at any site from the gums while brushing or from visceral organs to fatal intracranial hemorrhages. As with other drugs with narrow therapeutic window, minute variations of warfarin plasma concentration may result in serious toxicity, and hence poor outcomes. Previous studies have identified several factors associated with clinical responses of warfarin. These include age, ethnicity, food, concurrent medications, and genetics. 
Among more important genetic characteristics known to affect warfarin dose requirement in the population are CYP2C9 and VKORC1 gene polymorphisms.  As mentioned above, CYP2C9 is involved in the metabolism of warfarin, whereas VKORC1 is the molecular target of the action of warfarin. Previous research has shown differences in polymorphic frequencies of these two genes, and this may be used to predict the optimal doses in different ethnic groups.  It has been demonstrated that CYP2C9*2 (430 C>T) and CYP2C9*3 (1075 A>C) polymorphisms reduce the ability of the enzyme to transform warfarin into inactive metabolites by 30% and 80%, respectively. CYP2C9*2 variant is found exclusively in European and African but Asian descents while CYP2C9*3 can be frequently found in all three populations.  People with CYP2C9*2 and CYP2C9*3 genotypes typically require lowered warfarin doses.  There are also many variants of VKORC1 genes. The VKORC1*1 (wild-type) is found exclusively in native African whereas VKORC1*2 (-1639 G>A) is present in 95% of Asians. VKORC1*3 (3730 G>A) and VKORC1*4 (698 C>T) are frequently found in European and African and are rarely found in Asians. [7,9] VKORC1*2 polymorphism is associated with the lower daily dose of warfarin compared with wild type. Interestingly, VKORC1*3 and VKORC1*4 are associated with higher daily dose than normal genotype. [9,10]
Previous studies have shown that CYP2C9 and VKORC1 polymorphisms could explain almost 50% of responsiveness of patients to warfarin therapy.  In 2007, the U.S. Food and Drug Administration had made a change in warfarin drug label recommending that CYP2C9 and VKORC1 genotypes may be useful in determining the optimal starting dose of warfarin for individual patients. 
This study was aimed to establish a relationship between warfarin weekly dose requirement and CYP2C9*3 and VKORC1-1639 G>A genetic polymorphisms commonly found in Southeast Asian in Thai patients.
SUBJECTS AND METHODS
This is an observational, retrospective study in outpatients who has been receiving warfarin with stable INR at Phaholpolpayuhasena Hospital, Kanchanaburi, Thailand. The investigation was approved by the IRB for medical research involving human subjects of the Hospital, and all the participants gave informed consent.
Study participants and sample size
A total of 91 patients who had a stable warfarin dose requirement for at least three consecutive clinic visits with a target INR of 2.0-3.0 were recruited to the study. All patients provided written consent as required by the institutional review board. Inclusion criteria were nonsmoking patients receiving warfarin for diverse thromboembolic disorders, including mitral or aortic valve replacement, rheumatic heart disease, atrial fibrillation, deep vein thrombosis, pulmonary embolism, embolic stroke, and cardiomyopathy. Exclusion criteria were made by attending physicians for patients who have had other medical conditions such as liver or renal diseases, thyroid disorders, and other malignant diseases, as well as those who were concurrently receiving medications capable of inducing or inhibiting hepatic microsomal enzymes.
Cytochrome P450 2C9 and Vitamin K epoxide reductase complex subunit 1 genotyping
On arrival at the clinic visit, a blood sample (5 ml) was taken for CYP2C9 and VKORC1 genotyping. Patient demographics of sex, age, weight, and height, as well as indications and maintenance dose for warfarin therapy, additional medical problems, and concurrent medications, were also recorded during the clinic visit. Genomic DNA sample was extracted and purified from whole, fresh blood using Promega Wizard [R] genomic DNA purification kit (Madison, WI) following the manufacturer's protocol. VKORC1 and CYP2C9 genotypes were determined using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. The PCR reaction was carried out in a final volume of 100 [micro]l, containing 0.4 [micro]M of each primer, 0.2 mMdNTPs, 1.5 mM Mg[Cl.sub.2], 50 mMKCl, 10 mMTris-HCl (pH 8.0), and 2.5 unit of GoTaq [TM] DNA Polymerase (Promega, Madison, WI). RFLP primers for the CYP2C9*3 (NG_008385) were 5'-TGCACGAGGTCCAGAGGTAC-3', forward primer, and 5'-GGGACTTCGAAAACATGGAG-3', reverse primer. After purification with Wizard [R] PCR Preps DNA purification system (Promega, Madison, WI, USA), the 141 base pair (bp) PCR fragment was restriction digested with KpnI enzyme. Homozygous CYP2C9*3 alleles resulted in DNA fragment of 121 and 20 bp whereas heterozygous CYP2C9*3 yielded DNA fragment of 141, 121, and 20 bp. Forward and reverse primers for VKORC1-1639 A>G polymorphism (NG_011564.1) were 5'-GCCAGCAGGAGAGGGAAATA-3', and 5'-AGTTTGGACTACAGGTGC CT-3', respectively. The 290 bp fragment from VKORC1-1639 G allele created a MspI restriction site and, on MspI digestion resulted in 123 bp and 167 bp fragments.
For a description of age, weight, and warfarin dose, calculations of the mean and standard deviation were presented. Differences in baseline characteristics among genotypes were evaluated by analysis of variance or Kruskal--Wallis and the Mann--Whitney U-test or Chi-square test for parametric and nonparametric variables, respectively. Association between genetic factors and warfarin dose was based on Eta test, whereas associations between warfarin dose and polymorphisms were evaluated using Pearson's correlation test. Stepwise regression was used to identify factors contributing to warfarin dose requirement followed by linear regression model to develop a warfarin dosing algorithm. Multivariate linear regression adjusted for age, weight, sex was performed to investigate the influence of VKORC1 genotypes and CYP2C9 haplotypes on average daily dose of warfarin prescribed. Overlay scatter plots were also performed, comparing average daily dose of warfarin in this study with one of the other previous studies. All the analyses were performed according to the Statistical Package for Social Science (SPSS 11.5; SPSS Science, Chicago, IL). A significance level of 0.05 was used for all tests.
The study population consisted of Thai patients with stable control on warfarin therapy. Age, weight, sex distribution, indications for anticoagulation, and VKORC1 and CYP2C9 genotype frequencies of the study participants are summarized in Table 1. A total of 56 females and 35 males with average age and weight of 61-year-old and 58.51 kg, respectively, were included. The mean warfarin dose required to maintain a therapeutic INR of 2-3 was 24.88 mg/week. SNP frequency for participants with CYP2C9*1*1 (wild type) and CYP2C9*1*3 were 98.9% and 1.10% (1 subject), respectively. No homologous CYP2C9*3*3 individual was found. The VKORC1-1639 frequencies of subjects with GG, GA, and AA were 9.895 %, 32.97%, and 57.14%, respectively. Rheumatic heart disease and atrial fibrillation accounted for more than 50% of therapeutic indications for warfarin.
Effects of demographic variables and Vitamin K epoxide reductase complex subunit 1 and cytochrome P450 2C9 polymorphisms on warfarin, weekly dosage
The mean weekly warfarin dosage was higher in patients with CYP2C9 homozygote wild-type (CYP2C9*1*1) genotype (24.96 [+ or -] 11.27 mg) than in patients with CYP2C9*1*3 genotypes (17.50 mg). The mean weekly dose of the patients with VKORC1-1639 AA genotype (19.97 [+ or -] 7.61 mg) was significantly lower than those of patients with VKORC1-1639 GA genotype (29.48 [+ or -] 11.50 mg) and GG genotype (37.89 [+ or -] 12.02 mg) (P = 0.000; P < 0.05). There was no statistically significant difference between mean weekly doses in patients with VKORC1-1639 GG and GA genotypes (P = 0.077; P > 0.05) [Table 2]. Age, weight, and body surface area (BSA) among patients with three VKORC1-1639 G>A genotypes were not significantly different.
The influences of both VKORC1 and CYP2C9 genetic polymorphisms were also investigated. VKORC1-1639 AA and CYP2C9*1*1 genotypes were present in most subjects, and these patients required weekly warfarin at almost half the dose of VKORC1-1639 GG/CYP2C9*1*1 patients who required the highest weekly drug [Table 3]. When the weekly dose of patients with VKORC1-1639 AA and CYP2C9*1*1 was taken as a reference, one G-allele of VKORC1-1639 contributed to an approximately 50% increase in warfarin, weekly dose (i.e. ~150% for GA, ~200% for GG, Table 3) in subjects with CYP2C9 wild-type. However, a patient with VKORC1-1639 GA genotype and one CYP2C9*3 allele in the study requires a further lower weekly dose of warfarin.
VKORC1-1639 G>A was also associated with weekly warfarin doses (Eta = 0.548). As expected, patient's age was inversely and significantly associated with weekly doses of warfarin (P = 0.000; P < 0.01, R = -0.423). Other parameters found to be proportionally associated with warfarin doses were patient's body weight (P = 0.005; P < 0.01, R = 0.292) and surface area (P = 0.005; P < 0.01, R = 0.289).
Regression analysis and warfarin dosing algorithm
Univariate analysis showed that significant cofactors influencing weekly warfarin dose requirements were VKORC1 diplotype status (P < 0.005), age (P = 0.000), weight (P = 0.007), BSA (P = 0.009), and body mass index (BMI) (P = 0.027) [Table 4]. Age and, specifically, VKORC1-1639 AA genotype had a negative influence whereas weight, BSA, BMI, and VKORC1-1639 GA and GG genotypes had a positive effect on warfarin dose requirements.
Due to skewed distribution, natural logarithm of collected data was used to model warfarin maintenance dose algorithm in multiple stepwise regression analysis. Moreover, due to the lower frequency of CYP2C9*3 allele, the accuracy of prediction for this group could not be determined. The multiple stepwise regression model including the variables age, VKORC1 AA genotype, and weight produced the best model for estimating warfarin maintenance dose with the largest [R.sup.2] value of 45.3% [Table 5]. Using data from all the samples, the model generated was dose (mg) = exp (3.648 - 0.457 x VKORC1 AA - 0.012 x age + 0.008 x weight) with the following keys: VKORC1 AA genotype: Input 1 for AA, 0 for AG or GG; input ages in years; and input weights in kilograms. Multivariate analysis of the algorithm revealed that the strongest influence to warfarin weekly requirement was VKORC1 AA genotype, followed by age and weight, respectively [Table 6].
The frequency of CYP2C9 and VKORC1 genotypes found in this study are consistent with other studies conducted in Thailand [Table 7] with the majority of patients having CYP2C9*1*1 and VKORC1-1639 AA genotypes. However, the VKORC1-1639 GG genotype frequency is somewhat higher in the present study. The basis for the difference is unknown but may be due to geographic variation from the results of other previous three studies in Bangkok and Chiang Mai. [12-14] However, VKORC1 genotypes among all studies is not statistically different using Chi-square method (P = 0.804; P > 0.05).
Weekly dose of warfarin in patients with VKORC1-1639 AA and CYP2C9*1*1 genotype in this study (0.345 [+ or -] 0.123 mg/kg/week) was statistically significantly lower than that of the most recent study (0.395 [+ or -] 0.124 mg/kg/week).  In addition, while the difference of coefficient of variation of weekly warfarin in patients with VKORC1-1639 GG and GA genotypes of the two studies was statistically significant, the data from groups of patients with VKORC1-1639 AA genotype were not [Table 8].
The equation from regression analysis in this present study is "weekly warfarin dose (mg) = exp (3.648 - 0.457 VKORC1 AA - 0.012 x age + 0.008 x weight)" with [R.sup.2] = 45.3%. We then input data of our patients and calculated the predicted weekly dose of warfarin using both our equation and formula from Sangviroon et al. which was "weekly warfarin dose (mg) = exp (1.846+ [0.412 x VKORC1AB] + [0.559 x VKORC1 BB] + [1.512 x CYP2C9*1*1] + [1.136 x CYP2C9*1*3] - [0.007 x age])," and compared with observed doses in the clinic. Figures 1 and 2 showed the distribution of observed doses versus predicted doses from the equation of this study and those of Sangviroon et al., respectively. The overlay scatter plots were then made as shown in Figures 3 and 4.
Paired t-test analyses of mean absolute different doses (P = 0.229) and mean square errors (P = 0.076) in an overlay scatter plot showed that distributions of all observed and predicted doses using both equations from this study and from Sangviroon et al. were not statistically different, P = 0.05 [Figures 3 and 4]. In the present study, we used three parameters which were VKORC1-1639 AA genotype, age, and weight, whereas Sangviroon et al. used five. This suggested that VKORC1 genotyping might be more important. In fact, it has been shown that 30% of the warfarin dose variance is explained by its target VKORC1 SNPs and a mere 12% by two nonsynonymous SNPs (*2, *3) of CYP2C9.  Therefore, the predicted doses of warfarin based on the equation from this study were coherent with those predicted Sangviroon et al. Although the predicted doses estimated either by the present study or by Sangviroon study were not different, the predicting factors used in the present study were fewer than those used in Sangviroon study. VKORC1-1639 AA, age, and weight were predicting factors employed by the present study, whereas VKORC1 AB haplotype, VKORC1 BB haplotype, CYP2C9*1/*1, CYP2C9*1/*3, and age were predicting factors employed by Sangviroon study. As a result, it should be seen and also noted that importance be given to VKORC1-1639 AA in the warfarin-treated Thai patient since the occurrence of this polymorphic genotype accounts for 50% in the Thai population. On the contrary, CYP2C9*1/*1 has been found to occur in approximately 90% for the Thai population. Consequently, warfarin dose reduction would require VKORC1-1639 AA as a major predictor and thus is needed as a priority for genotype measurement.
Limitation of the study
This present study may fall in short of controlling confounding factors such as nutrients or food that have Vitamin K or exhibit as Vitamin K agonists, compliance and the involved adverse drug reactions. Inclusion criteria did not have a criterion for weight as well. As these confounders are usually found in observational, retrospective study as in this present study and Sangviroon study, the proof of concept (i.e., the model equations of this present study and that of Sangviroon) by prospective clinical trials are needed to clarify their use in real clinical settings.
The present study demonstrated that the model equation to predict warfarin doses was relatively comparable to that of Sangviroon. However, fewer predicting and appropriate factors were found to give comparable predicted warfarin doses. This may be economically reasonable in terms of time and cost in clinical settings.
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
This study was supported by the Faculty of Pharmacy short-term research grant and student support fund from the Graduate School of Silpakorn University.
Financial support and sponsorship
Faculty of Pharmacy short-term research grant and student support fund from the Graduate School of Silpakorn University.
Conflicts of interest
There are no conflicts of interest.
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Burassakorn Subsuphan, Chatchai Chinpaisal (1), Manat Pongchaidecha (2), Wibun Phanthabordeekorn (3), Surawut Watana (1)
Departments of Pharmacy and (3) Surgery, Phaholpolpayuhasena Hospital, Kanchanaburi, Departments of (1) Pharmacology and Toxicology and (2) Pharmacy, Faculty of Pharmacy, Silpakorn University, Sanam Chandra Palace Campus, Muang, Nakhon Pathom, Thailand
Address for correspondence:
Assoc. Prof. Chatchai Chinpaisal, Department of Pharmacology and Toxicology, Faculty of Pharmacy, Silpakorn University, Sanam Chandra Palace Campus, Muang, Nakhon Pathom 73000, Thailand.
Table 1: Patient demographics Patient characteristics n (%) Age (mean [+ or -] SD) 60.98 [+ or -] 14.41 Mean weekly dose (range) 24.88 (7.50-56.00) Sex Female 56 (61.54) Male 35 (38.46) Body weight (mean [+ or -] SD) 58.51 [+ or -] 11.92 Indications for warfarin use Rheumatic heart disease 34 (37.36) Heart valve replacement 11 (12.09) Atrial fibrillation 25 (27.47) Deep vein thrombosis 8 (8.79) Others 13 (14.29) CYP2C9 genotype *1/*1 90 (98.90) *1/*3 1 (1.10) VKORC1-1639 genotype GG 9 (9.89) GA 30 (32.97) AA 52 (57.14) SD = Standard deviation, VKORC1 = Vitamin K epoxide reductase complex subunit 1, CYP2C9 = Cytochrome P450 2C9 Table 2: VKORC1 genotype of the patients in the study VKORC1-1639 genotype (mean [+ or -] SD) GG GA Age (years) (a) 54.67 [+ or -] 13.37 63.13 [+ or -] 14.13 Body weight (kg) (a) 58.33 [+ or -] 12.69 57.85 [+ or -] 10.98 BSA ([m.sup.2]) (a) 1.60 [+ or -] 0.21 1.61 [+ or -] 0.18 Weekly 37.89 [+ or -] 12.02 29.48 [+ or -] 11.50 dose (mg) (b) Sex (c) n (%) Female 6 16 Male 3 14 VKORC1-1639 genotype (mean [+ or -] SD) AA P Age (years) (a) 60.83 [+ or -] 14.67 0.304 Body weight (kg) (a) 58.92 [+ or -] 12.51 0.926 BSA ([m.sup.2]) (a) 1.61 [+ or -] 0.19 0.989 Weekly 19.97 [+ or -] 7.61 0.000 * dose (mg) (b) Sex (c) n (%) Female 34 0.528 Male 18 (a): One-Way ANOVA, (b): Kruskal-Wallis, (c): Chi-Square, * P < 0.05 VKORC1 = Vitamin K epoxide reductase complex subunit 1, SD = Standard deviation, BSA = Body surface area Table 3: Mean weekly warfarin dose of patients with VKORC1 and CYP2C9 genetic polymorphisms VKORC1 CYP2C9 n (91) Weekly -1639 dose (mg) GG *1/*1 9 37.89 [+ or -] 12.02 *1/*3 0 -- GA *1/*1 29 29.90 [+ or -] 11.48 *1/*3 2 17.50 AA *1/*1 52 19.97 [+ or -] 7.61 *1/*3 0 -- -- -- VKORC1 CYP2C9 Percentage Percentage -1639 of CV relative to reference GG *1/*1 32.20 200 *1/*3 -- -- GA *1/*1 38.39 150 *1/*3 -- 90 AA *1/*1 38.11 100 (reference) *1/*3 -- -- VKORC1 = Vitamin K epoxide reductase complex subunit 1, CYP2C9 = Cytochrome P450 2C9, CV = Coefficient of variation Table 4: Univariate analysis of cofactors influencing weekly warfarin dose Parameters [R.sup.2] Standardized P beta Sex 0.000 -0.022 0.833 Age 0.185 -0.430 0.000 * Body weight 0.079 0.281 0.007 * Height 0.018 0.135 0.201 BSA 0.075 0.274 0.009 * BMI 0.054 0.232 0.027 * Indication Heart valve replacement 0.001 -0.036 0.738 Rheumatic heart disease 0.038 0.196 0.063 Atrial fibrillation 0.026 -0.160 0.129 Deep vein thrombosis 0.023 0.150 0.155 Others 0.003 -0.050 0.636 VKORC1-1639 genotype GG 0.121 0.347 0.001 * GA 0.087 0.295 0.005 * AA 0.239 -0.489 0.000 * * P < 0.05, VKORC1 = Vitamin K epoxide reductase complex subunit 1, BMI = Body mass index, BSA = Body surface area Table 5: Multiple stepwise regression analysis of CYP2C9, VKORC1 genotypes, and other cofactors Parameters Regression model P [R.sup.2] VKORC1 genotype Dose = Exp (3.648-0.445 0.000 * 23.1 VKORC1 AA VKORC1 genotype, Dose = Exp (4.206-0.450 0.000 * 41.6 age VKORC1AA-0.014 age) VKORC1 genotype, Dose = Exp (3.648-0.457 0.000 * 45.3 age, weight VKORC1 AA-0.012 age+0.008 weight) * P < 0.05, VKORC1 = Vitamin K epoxide reductase complex subunit 1, CYP2C9 = Cytochrome P450 2C9 Table 6: Multivariate analysis of the algorithm factors Parameter Coefficient SE Standardized beta P Constant 3.648 0.265 VKORC1-1639 AA -0.457 0.071 -0.502 0.000 * Age -0.012 0.003 -0.387 0.000 * Weight 0.008 0.003 0.211 0.000 * * P < 0.05, SE = Standard error, VKORC1 = Vitamin K epoxide reductase complex subunit 1 Table 7: CYP2C9 and VKORC1 polymorphism frequency in Thai population Genes Klamchuen Sangviroon Kuanprasert This et al. (%) et al. (%) (%) study (%) VKORC1-1639 GG 5.7 6.7 2.1 9.9 GA 31.1 31.5 34.3 33.0 AA 63.2 61.8 63.6 57.0 CYP2C9 *1/*1 92.5 95.5 95.0 98.9 *1/*3 7.5 3.4 5.0 1.1 *3/*3 -- 1.1 -- -- VKORC1 = Vitamin K epoxide reductase complex subunit 1, CYP2C9 = Cytochrome P450 2C9 Table 8: Warfarin dose comparison between this study and those from Sangviroon et al. VKORC1-1639 CYP2C9 Sangviroon et al. n (89) Weekly dose Percentage (mg/kg/week) of CV GG *1/*1 6 0.842 [+ or -] 0.403 47.86 *1/*3 0 *3/*3 0 GA *1/*1 27 0.552 [+ or -] 0.151 27.35 *1/*3 1 0.509 *3/*3 0 AA *1/*1 52 0.395 [+ or -] 0.124 31.39 *1/*3 2 0.173 [+ or -] 0.041 23.70 *3/*3 1 0.062 VKORC1-1639 CYP2C9 This study n (91) Weekly dose Percentage (mg/kg/week) of CV GG *1/*1 9 0.664 [+ or -] 0.210 31.63 *1/*3 0 *3/*3 0 GA *1/*1 29 0.517 [+ or -] 0.191 36.94 *1/*3 1 0.389 *3/*3 0 AA *1/*1 52 0.345 [+ or -] 0.123 35.65 *1/*3 0 *3/*3 0 VKORC1-1639 CYP2C9 P GG *1/*1 0.358 *1/*3 *3/*3 GA *1/*1 0.449 *1/*3 *3/*3 AA *1/*1 0.042 ** *1/*3 *3/*3 * P < 0.05, VKORC1 = Vitamin K epoxide reductase complex subunit 1, CV = Coefficient of variation, CYP2C9 = Cytochrome P450 2C9
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|Title Annotation:||Original Article|
|Author:||Subsuphan, Burassakorn; Chinpaisal, Chatchai; Pongchaidecha, Manat; Phanthabordeekorn, Wibun; Watana|
|Publication:||Archives of Pharmacy Practice|
|Date:||Jan 1, 2016|
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