SUPPORTING EVIDENCE
1. Warfarin dose is correlated with CYP2C9 and VKORC1 genotypes
Our literature search revealed 87 studies that reported significant associations between
the required warfarin dose and CYP2C9 and/or VKORC1 genotypes (Supplemental
Table 6). Of these, 14 studies focused solely on CYP2C9 genotype while 10 studies
reported results for VKORC1 genotype only. The remainder of the studies investigated
dose associations with both genotypes.
1.1 VKORC1-1639
39 of 47 studies found that VKORC1 is the most important genetic factor influencing
variability in warfarin dosing. A meta-analysis based on 17 studies reported that
compared to -1639AA carriers, GA and GG carriers required a 52% and 102% higher
mean daily warfarin dose, respectively (Supplemental Table 2) [1].
Several other VKORC1 SNPs that are in linkage disequilibrium with VKORC1-1639 are
also associated with warfarin dose but these variants do not add any additional
information in a dose prediction model that already includes the -1639G>A variant [2,3].
In particular, the VKORC1 1173C>T (rs9934438) polymorphism, which is in strong
linkage with -1639G>A, has a similar effect on dose requirement [4]. A meta-analysis
based on 9 studies reported that patients carrying the 1173CT (intermediate sensitivity)
and 1173CC (low sensitivity) genotype required 44% and 97% higher warfarin doses
compared to the high sensitivity group (1173TT) (Supplemental Table 2) [5]. The -1639
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and 1173 SNPs are therefore used interchangeably to define patients as low, intermediate,
or high sensitivity [4].
1.1.1 Other VKORC1 variants
A study in African Americans reported that a rare SNP (rs17886199) not found in
Caucasians was associated with warfarin dose independent of -1639G>A [6]. A second
novel SNP (VKORC1-1891) was also predictive of warfarin dose in African Americans
but VKORC1-1173 remained the most significant genetic predictor [7]. This finding was
similarly replicated in a Sudanese population where three SNPs in VKORC1 (rs8050894,
rs7199949, rs7294) were independent predictors of dose but possessed less predictive
ability compared to VKORC1-1639 [8].
Multiple rare SNPs in VKORC1 are also associated with warfarin resistance [9-12]. The
definition of warfarin resistance varies between studies but is typically classified as
patients requiring abnormally high daily doses to maintain a therapeutic INR. One study
arbitrarily defined warfarin resistance as requiring more than 25 mg of warfarin a day,
which is substantially higher than the median 4 mg/day for the entire study cohort [10].
Due to the low frequency of these variants their predictive ability and clinical utility has
yet to be clearly defined.
A study that examined the frequency of the VKORC1 Asp36Tyr variant, which confers
warfarin resistance, in several ethnic populations concluded that this SNP is most
prevalent in northeast African and western parts of the middle east, while being virtually
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undetected in several other populations, including Caucasian, Indian, Han Chinese, and
African American [13]. When the effect of this SNP on warfarin dose requirements was
studied in a cohort of Egyptian patients, variant allele carriers required a significantly
higher dose than non-carriers. Furthermore, this SNP improved the warfarin dose
variability explained by a regression model [13]. A study by Watzka et al. (2011) also
identified this SNP in 6 of 626 patients with warfarin resistance [14]. Four of the patients
were of European descent, while the other two patients were of Turkish and AfricanAmerican origin.
1.2 CYP2C9*2 and CYP2C9*3
CYP2C9 is the second strongest genetic factor associated with warfarin dose. As with
VKORC1, the amount of variability explained by CYP2C9 is dependent on the population
studied due to differences in allele frequency (Table 4). According to a meta-analysis
based on 39 studies, carriers of one *2 variant require a 19.2% lower dose compared to
the normal activity allele (*1/*1) while carriers of the extreme sensitivity*3/*3 genotype
require an approximately 78.1% lower dose (Supplemental Table 1) [15].
1.2.1 Other CYP2C9 variants
In African Americans, additional CYP2C9 variants that occur at a higher frequency have
also been shown to influence warfarin dose variation. These include the*5, *6, *8 and
*11 variants [7,16-20]. When the *5, *8 and *11 variants were added to the existing
International Warfarin Pharmacogenomics Consortium (IWPC) dosing algorithm [21], the
amount of variability explained by CYP2C9 star variants in an African American cohort
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increased from 2.9% to 5.6% [7]. A similar result was found in a study in Sudanese
patients where inclusion of *5, *6, and *11 increased the amount of explained variability
in a dose prediction model [8]. A study in a South African black population reported that
the CYP2C9*8 variant was significantly associated with dose, while the *3, *5, *6, and
*11 variants were not associated due to the extremely low frequency of these alleles in
the patient cohort [22]. A study in 145 African Americans also reported a significant
association between dose and the *6 and *8 variants but the association was not
significant for the *11 variant [20]. Scott et al. (2009) reported that the *8 variant was the
most frequent CYP2C9 variant (0.047) in African Americans and that inclusion of
CYP2C9*8 alone could reclassify the predicted metabolic phenotypes of almost 10% of
African American patients [18]. These results imply that additional CYP2C9 variants are
important for warfarin dosing in both Africans and African Americans. Interestingly, a
study in Caucasian patients also found a significant difference in the required dose
between *1/*1 and *1/*11 genotypes [23].
The CYP2C9*12 variant was also recently identified as being associated with lower dose
requirement [24]. This SNP was identified in four Caucasian patients who required a
combined low mean warfarin daily dose and did not possess any of the other known
common CYP2C9 functional polymorphisms (*2, *3, *5, *6, *7, *8, *9, *11, *13) but
were heterozygous (CT) for the *12 variant. According to the 1000 Genomes Project,
this SNP has an extremely low minor allele frequency of 0.0018 [25]. Furthermore, an
intronic SNP in CYP2C9 (rs708950) was found to be predictive of warfarin dose
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independent of previous associations with VKORC1 and CYP2C9 variants in African
Americans and then replicated in a predominately Caucasian cohort [7,26].
1.3 Negative studies
Fifteen studies did not find a significant association between CYP2C9 and/or VKORC1
genotype and warfarin dose (Supplemental Table 7). Eleven studies investigated
specific subpopulations of patients, including African Americans, Han Chinese, Japanese,
Brazilians, Lebanese, Indonesians, Lithuanians, and three studies in children. The
remaining four studies investigated warfarin dose requirement in Caucasians, with one
study focusing on dose requirement during initial anticoagulation (Supplemental Table
7).
Four studies investigated CYP2C9 genotype only. Two studies, conducted in Brazilian
patients and patients initiating anticoagulation, found a significant association with the *3
variant but not the *2 variant [27,28]. A study in African Americans did not find an
association with the *2 or *3 variants, which was most likely due to low allele frequency
as 92.3% of African patients were carriers of the *1/*1 genotype [29]. Finally, a study in
29 children on concomitant chemotherapy did not find a significant difference in dose
requirements between *1/*1 carriers and heterozygous *2 or *3 carriers [30].
Ten of the 11 studies that investigated both genotypes reported a significant association
between dose and VKORC1. A study in Japanese patients did not find an association
with CYP2C9*3 due to a low frequency of the *1/*1 genotype (8.6% with *1/*1 vs.
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91.4% with *1/*3) [31]. Furthermore, a study in Indonesian patients did not find an
association with the *3 variant due to a low number of patients carrying the *3 variant
and an absence of patients homozygous for *3 [32]. Three studies in children (n=34,
n=37, n=83) also did not find a significant difference in dose requirements between
homozygous *1/*1 and variant allele carriers [33-35]. The effect of genotype on warfarin
outcomes in children is discussed in greater detail in Supplemental Section 10.1.
Two studies, one of which was conducted in Lebanese patients and the other in African
Americans, found a significant association with VKORC1 and CYP2C9*3, but not
CYP2C9*2 [20,36]. A study in 557 Caucasian patients reported a significant association
between dose and the CYP2C9*2 variant but there was no significant difference in dose
between *1/*1 and *3 carriers [37]. Finally, the only study to not report a significant
association between dose and CYP2C9*2, *3, or VKORC1 (-1639G>A) variants was
conducted in a cohort of only 30 patients [38].
The sample size across studies with negative findings ranged from 29 to 557 patients.
Small sample size and low allele frequency in the populations studied may have
contributed to non-significant results. These studies highlight how additional variants in
CYP2C9 and VKORC1, or other genes not yet comprehensively studied, may contribute
to dose requirement in ethnic populations.
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2. Warfarin dose is correlated with CYP4F2 genotype
Twenty-two studies reported a significant association between warfarin dose and the
CYP4F2 V433M variant, with individuals carrying at least one copy of this variant
requiring a higher warfarin dose (Supplemental Table 6). Caldwell et al. (2008)
reported that carriers of the TT genotype required approximately 1 mg/day more warfarin
than carriers of the CC genotype [39]. When CYP4F2 was added to models that also
included CYP2C9, VKORC1, and clinical variables, the amount of dose variation
explained by CYP4F2 ranged from 1-11%, with the majority of studies reporting a value
<5%. The study that showed the greatest association between CYP4F2 and required dose
(11% variation explained) was conducted in Asians [40].
Two of the aforementioned studies were genome-wide association studies and found that
while the V433M variant was not significant in univariate analysis, this polymorphism
reached genome-wide significance in multiple regression analysis [41,42]. Two more
studies reported similar results, with CYP4F2 being significantly associated with dose
after controlling for clinical factors, CYP2C9 and VKORC1. One of these studies was
conducted in an admixed patient cohort (white, intermediate, and black Brazilians) and
found that only the TT genotype was associated with dose in a multiple regression model.
Furthermore, CYP4F2 explained only ~1% of the dose variation, which was negligible
with respect to the power of the algorithm to predict the stable warfarin dose [43]. The
second study was conducted in a Southern Brazilian population where CYP4F2 explained
<2.8% of dose variation [44]. Overall, CYP4F2 appears to influence the required dose but
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has less impact than CYP2C9 and VKORC1 in models developed to predict the required
dose.
In comparison, fourteen studies did not find a significant association between dose and
CYP4F2. Two studies conducted in specific ethnic populations (Han Chinese and
Indonesian), as well as a study in an admixed patient population, reported p-values >0.90
when comparing dose requirements between carriers and non-carriers of CYP4F2 V433M
[32,45,46]
. Two studies in African American patients also did not find an association
between dose and CYP4F2 [20,47]. This may be attributed to low frequency of the variant
allele, as the MAF was much lower in African American patients compared to European
and Hispanic-American patients who were analyzed in the same studies. Four studies,
conducted in Egyptian, Caucasian, Israeli, and elderly patients, reported a trend towards
higher dose requirements in individuals with the V433M polymorphism but these
associations were non-significant and remained non-significant in a multiple regression
model [48-51]. Finally, a study in Slovenian patients reported no significant difference in
dose across CYP4F2 genotypes, potentially due to the low number of TT homozygotes
(3%) [52].
Four studies in children also did not find a significant association between CYP4F2
genotype and dose. A study in 72 Caucasian children on either warfarin or
phenprocoumon reported that CYP4F2 explained only 0.17% of the dose variation
(P=0.94), with VKORC1 and CYP2C9 genotype also being non-significant contributors to
dose variation [53]. The other three studies were conducted in mainly Caucasian
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populations (n=120, n=100, n=83), with one study reporting a trend towards higher dose
requirements in variant allele carriers [35,54,55]
3. Stable warfarin dose is more accurately predicted by genotype-guided dosing
compared to standard dosing or clinical algorithms
By retrospectively combining clinical and genetic factors from patients with known
therapeutic doses, researchers have derived algorithms to predict the therapeutic dose for
a warfarin-naïve patient. A total of 12 algorithms were tested in nine adult study
populations, with some studies testing multiple algorithms in order to compare their
performance. Of note, only studies evaluating a previously derived algorithm were
considered for this guideline. All of the algorithms accounted for CYP2C9 genotype and
age, with 10 of the algorithms also including VKORC1 genotype. Other variables
accounted for in some algorithms included: amiodarone use, smoking, indication for
anticoagulation, INR target, statin therapy, gender, race, enzyme-inducing drugs, and
prior INR measurements. Ten of the algorithms were initially derived from predominantly Caucasian populations [2,21,39,56-62], one algorithm was derived from an Asian
population [63], and one algorithm was derived from an admixed patient cohort [64].
Nine studies in adults that compared pharmacogenetic (PGx) dosing algorithms to
clinical dosing algorithms, which account for clinical and demographic factors, or
empirical dosing, which requires administering a standard dose to all patients, found that
a genotype-guided dosing algorithm more accurately predicted the stable warfarin dose
[20,38,65-71]
. In general, algorithms that included both CYP2C9 and VKORC1 performed
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better than algorithms that included CYP2C9 variants only [69]. Regarding ethnicity, one
study found that the International Warfarin Pharmacogenetics Consortium (IWPC)
model, which was mainly derived from European patients, was able to predict stable
warfarin doses in Japanese patients and was most useful in patients that required low or
high doses [65]. A study in 63 Egyptian patients also found that the required dose was
much more accurately predicted using a pharmacogenetic model compared to a clinical
model or fixed-dose approach [70]. Moreover, the non-pharmacogenetic models
significantly over-predicted the dose in a higher percentage of patients compared to the
pharmacogenetic models.
On the other hand, a study that compared the performance of five dosing algorithms in
Caucasians and African Americans found that three of the algorithms accounted for a
greater amount of dose variability in Caucasians compared to African Americans, and
that some pharmacogenetic dosing algorithms only performed marginally better than a
standard 5 mg dosing nomogram in African patients [69]. However, none of the
algorithms tested accounted for African-specific CYP2C9 variants. Similarly, when the
performance of the IWPC dosing algorithm was evaluated in European-Americans and
African-Americans, the mean absolute error (MAE) was lower in the European-American
patients compared to the African American patients (9.5 vs 13.8 mg/week) [20]. However,
the PGx algorithm had a lower MAE in both groups compared to dose prediction using a
clinical dosing table or a fixed-dose approach.
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One study in children also compared the accuracy of dose prediction using three
previously developed pediatric-specific pharmacogenetic dosing algorithms to a fixeddose approach (0.2 mg/kg). Compared to the PGx models, the fixed-dose approach was
much less accurate when determining the ideal dose and over-predicted the dose in the
majority of children [72]. The model developed by Biss et al. (2012) [54] showed the
highest dose prediction accuracy, predicting the ideal dose in 41% of patients, compared
to 35% for the other pediatric PGx models, and 33% using a fixed-dose approach.
4. Time to therapeutic/stable INR
4.1 Observational studies
Overall, twelve studies found a significant association between genotype and time to
therapeutic/stable INR. Specifically, nine studies found that both CYP2C9 and VKORC1
variants decreased the amount of time required to achieve an INR in or above the
therapeutic range [17,50,73-79]. Two studies investigated CYP2C9 genotype only and also
found a significant association [27,30], with one study reporting an association with the *3
variant only and not *2. Furthermore, one study reported a significant association with
time to therapeutic INR for VKORC1 only, with CYP2C9 genotype not showing a
significant association [80].
Three studies reported a significant association between CYP2C9*2 and *3 variants and
an increased time to stable INR. However, this association was not evident for VKORC1
genotype, as two studies did not find an association between time to stable INR and
VKORC1 [81,82], and one study did not include VKORC1 in the analysis [83].
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In contrast, a study in 293 Chinese patients did not find a significant association between
CYP2C9 variants and time to first therapeutic INR, which could also likely be attributed
to low variant allele frequency [84]. Furthermore, a study in 51 children did not find a
significant association between genotypes and time to first therapeutic INR [85]. Three
studies also did not find an association between genotypes and time to stable INR. A
study conducted in103 Brazilian patients did not find an association between CYP2C9
variants and time to stable INR [28]. However, this may have been due to pre-determined
scheduling for INRs and use of a specialized anticoagulation clinic, potentially biasing
the results. The same study also did not find a significant difference in dose requirement
between *2 carriers and *1/*1 carriers, suggesting a lower CYP2C9 variant allele
frequency in this population. Two additional studies did not find a significant difference
in time to stable anticoagulation between genotypes [37,86].
4.2 Intervention studies
Four prospective studies that compared standard dosing practices to genotype-guided
dosing found that the time (in days) required to achieve the first therapeutic INR was
significantly shorter in the genotype-guided group. Of these, three studies incorporated
both VKORC1 and CYP2C9 genotypes [87-89] while one study incorporated CYP2C9
genotype only [90]. An additional, prospective study implemented a PGx dosing
algorithm in all patients and found that there was no significant difference in time to first
therapeutic INR or time to stable anticoagulation between genotypes, suggesting that
differences in time to stability between VKORC1 and CYP2C9 genotype groups can
effectively be reduced with the use of PGx-guided dosing [91]. Finally, a study in 101
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patients found that the time required to achieve a stable warfarin maintenance dose was
significantly shorter in the genotype-guided dosing group compared to standard dosing
[92]
.
In contrast, two studies did not find a significant difference in time to first therapeutic
INR between dosing groups. One study conducted in 229 patients noted that these
findings might not reflect ‘real-world’ outcomes as dedicated anticoagulation teams
managed all patients [74]. Furthermore, a study in 2,370 patients found that the first
therapeutic INR was achieved earlier in the PGx-dosing group but that no significance
was achieved [62].
A study in 230 patients that compared a clinical-dosing algorithm to a PGx algorithm
containing CYP2C9, VKORC1, and CYP4F2 genotypes also did not find a significant
difference in the time required to achieve a stable INR [68]. Again, this could have been
due to ‘over-management’ as 80% of patients were started on warfarin as in-patients and
had INRs measured regularly during initiation. Furthermore, preliminary results from a
randomized study showed no difference in time to stable anticoagulation [93], while a
study in 61 Russian patients reported that patients in the PGx-based dosing group
required a significantly longer time to reach dose stabilization compared to patients in the
standard dosing group (P>0.05) [94].
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5. Time within the therapeutic INR range
5.1 Observational studies
Three retrospective studies investigated the effect of genotype on the amount of time
spent within the therapeutic range in patients who were dosed according to standard
practices. One study in 172 patients with a follow-up period of six months reported that
CYP2C9 variants were significantly associated with a longer time above the therapeutic
INR range. However, CYP2C9 was not associated with time below the therapeutic range
or with overall time within the therapeutic range. VKORC1 was not associated with
either outcome in this study [82]. A study in 61 pediatric patients with a mean follow-up
of 388 days found that neither CYP2C9 nor VKORC1 were associated with time above,
below, or within the therapeutic range [35]. Rather, the only variable associated with time
within the therapeutic range was target INR. Finally, a prospective study in 300 patients
reported a significant difference in time spent within the therapeutic range between
VKORC1 -1173C>T genotypes [95]; however, the percentage was highest in patients
homozygous for the TT variant, which is in contrast to what would be expected based on
the high sensitivity conferred by this variant. In the same study there was no significant
difference between CYP2C9 genotypes.
5.2 Intervention studies
Five prospective studies (4 randomized, 1 non-randomized) reported that patients who
received genotype-guided dosing spent more time in the therapeutic range compared to
those who were dosed according to clinical algorithms or standard dosing protocols
[62,87,90,93,96]
. For one study, this difference was only significant when analyzing patients
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expected to require high warfarin doses [93]. Three studies included both CYP2C9 and
VKORC1 in the dosing algorithm, while one study investigated CYP2C9 only, and
another study included three relevant genotypes (CYP2C9, VKORC1, CYP4F2) in the
dosing algorithm. The increased amount of time spent within the therapeutic range
varied between studies, ranging from 7-21%. The length of follow-up also varied
between studies (19 days [93], 30 days [87], 50 days [96], and 90 days [62]). The fifth study
followed all patients for a minimum of 8 days until stabilization, resulting in a follow-up
time that was almost twice as long for patients in the standard-dosing group [90]. It is
possible that the longer follow-up time for the control group could bias the results in
favor of the intervention group; however, since fluctuations in INRs are reduced
following stabilization, it is unknown how much of an effect a difference in follow-up
time would have on the results.
Three of the aforementioned studies also investigated time to stable dose, with two
studies reporting a significant reduction in the amount of time required to reach a stable
dose in the genotype-guided group compared to the control group, which is consistent
with increased time spent in the therapeutic range [87,90]. The study that did not report a
significant difference in time to stable anticoagulation also did not find a significant
difference in time spent within the therapeutic range when considering all study
participants [93]. Finally, in a separate study where genotype-guided dosing was
implemented for all study participants, there was no significant difference in time within
the therapeutic range between genotype groups. Again, this suggests that differences in
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clinical outcomes between genotypes can be reduced when genotype-guided dosing is
implemented [91].
In contrast, three studies with a follow-up time ranging from 28-90 days did not find an
association between genotype-guided dosing and time spent in the therapeutic range
[56,88,97]
. However, all studies did observe a non-significant increase in the time spent in
the therapeutic range in the genotype-guided group compared to the control group. Two
of the studies were considered pilot studies (26 and 38 patients) and one study did not
include VKORC1 in the dosing algorithm. A systematic review concluded that there was
no statistically significant difference in time within the therapeutic INR range between
dosing groups when the results of 3 of the aforementioned studies were pooled [56,90,97].
However, there was strong heterogeneity between studies, including length of follow-up,
therapeutic INR range, and differences in study quality [98]. Most importantly, two of the
studies did not include VKORC1 genotype in the dosing algorithm.
6. Time to INR > 4 or incidence/frequency of INR > 4
6.1 Observational studies
Ten studies that investigated this end-point found a significant association between
CYP2C9 and/or VKORC1 genotype and an increased frequency of INR measurements ≥
4. Two studies analyzed CYP2C9 variants only [29,83], with one study concluding that
*1/*3 carriers were at a higher risk of an INR > 4 compared to *1/*2 carriers, with *1/*1
carriers being at the lowest risk [29]. One study that investigated VKORC1 only found that
Caucasian carriers of the AA genotype were at higher risk of INR ≥ 4, but that the
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VKORC1 genotype was not significantly associated with incidence of INR > 4 in African
Americans [99]. This result was replicated in a separate study that investigated frequency
of INR > 4 in both Caucasian and African populations [17]. When the results were
stratified by ethnicity, VKORC1 was significantly associated with frequency of INR > 4
in Caucasians only, with CYP2C9 genotype not significantly associated with frequency of
INR > 4 in either Caucasians or Africans. When African and Caucasian patients were
pooled, the possession of any variant (CYP2C9, VKORC1, CYP2C9 + VKORC1) was
significantly associated with frequency of INR > 4.
Two additional studies found a significant association between incidence of INR > 4 and
CYP2C9*2, CYP2C9*3, and VKORC1*2/*2 variants, but not with VKORC1*1/*2 [82,100].
Similarly, a study in Han Chinese patients reported a significantly increased risk of INR
> 4 for CYP2C9*3 carriers but there was no association with the VKORC1 sensitivity
genotype [101]. In contrast, a separate study found that AA and AG carriers were at higher
risk of INR>5 compared to GG carriers, while there was no significant difference in risk
between *2 or *3 carriers and *1/*1 carriers [37]. Another study that combined VKORC1
and CYP2C9 genotypes together found that carriers of either one or two sensitivity
variant alleles were at greater risk of INR>4 in the first 90 days of therapy [102]. Finally, a
prospective study that combined genotype-guided dosing and standard dosing cohorts
together found that the incidence of INR>4 was greater in multiple variant allele carriers,
especially in patients with a combination of VKORC1 and CYP2C9 variants [56].
However, since both cohorts were dosed according to different protocols this result may
not be accurate.
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In contrast, three studies did not find an association between genotypes and risk of overanticoagulation. One study was conducted in 51 children and did not find an association
between genotypes and number of INR>4 in either the first month or first three months of
therapy [85]. Two additional studies, conducted in Brazilian and Puerto Rican patients,
did not report a significant difference in number of INR>4 measurements across
VKORC1 or CYP2C9 genotypes [103,104].
Only 1 study investigated time to the first measurement of an INR>4, with carriers of
CYP2C9 and VKORC1 variants requiring significantly less time to reach an INR greater
than 4, representing a risk for over-anticoagulation [80].
6.2 Intervention studies
Preliminary results from a prospective randomized trial reported a lower incidence of
INR>4, as well as a lower amount of time spent at INR>4, for patients who received
genotype-guided dosing compared to patients who received standard dosing [93]. Doses
for patients in the intervention arm were determined using a pharmacogenetic dosing
algorithm that incorporated VKORC1, CYP2C9, and CYP4F2 genotypes. In addition, a
study that dosed every patient according to a PGx algorithm did not find a significant
difference in time to INR>4 between genotype groups, implying that differences in time
to INR>4 can be eliminated using genotype-guided dosing [91].
Three studies did not find a significant difference in the incidence of INR>4 between
dosing arms. The first study incorporated CYP2C9 only [105], while the second study
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investigated the overall incidence of adverse events, including bleeding and TE events,
making the actual difference in incidence of INR>4 between dosing arms unknown [106].
The third study, which had the largest patient population (n=2370), also did not find a
difference in the percentage of INR>4 between the two study arms (p=0.97)[62].
Similarly, one study did not find a significant difference in time to INR>4 between
dosing arms [68].
7. Incidence of adverse events
7.1 Observational studies
Fourteen studies, including a systematic review, reported a significant association
between CYP2C9 and/or VKORC1 genotypes and incidence of bleeding events. Of these,
six studies investigated CYP2C9 variants only [28,83,107-110], one study investigated
VKORC1 only [111], and seven studies investigated associations with both genotypes. Of
these, three studies found an association with CYP2C9 variants only [82,101,112], while one
study reported a significantly higher risk of bleeding in VKORC1 AA carriers compared
to GG carriers, but no association with the VKORC1 AG or CYP2C9 genotype [37].
Another study reported a higher risk in combination VKORC1 GA/AA and CYP2C9*3
carriers only [113]. The sixth study reported a higher incidence of bleeding events in
variant allele carriers compared to wildtype carriers when CYP2C9 and VKORC1 variant
allele carriers were combined for analysis [103]. Finally, a study that compared allele
frequencies in patients who died of bleeding or related complications while taking
warfarin, to those in the general population matched for race, reported a significantly
higher frequency of the CYP2C9*2 allele compared to population frequencies. However,
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the CYP2C9*3 variant did not show a significant difference, while VKORC1 achieved
significance only when analyzing Hispanic whites and not in non-Hispanic whites [114].
One of the aforementioned studies found a significant association with the CYP2C9*3
variant compared to *1/*1 carriers, but not with the CYP2C9*2 variant [108].
Furthermore, one study did not analyze *2 due to low allele frequency in patients of
Asian ancestry [101].
In contrast, three studies did not find a significant association between genotype and
bleeding events. A study performed in Malaysian patients found that while there was a
trend towards a positive association between the CYP2C9*3 variant and bleeding events,
this association was non-significant [115]. However, no patients with the *2 variant or the
*3/*3 genotype were observed in this study and the number of patients with the *1/*3
genotype was very small (n = 13), suggesting a lack of sufficient statistical power. The
second study was conducted in 43 Lebanese patients and did not find a significant
association with either CYP2C9 or VKORC1 genotype [36]. Due to a small sample size
this study may have also been underpowered. Furthermore, numerous patients
experienced bleeds with therapeutic INRs, suggesting that additional factors, such as
concomitant medications, may have contributed to the bleeding episodes. Finally, a
study in 124 patients who were followed for six years did not find a significant difference
in bleeding events between VKORC1 genotypes. CYP2C9 genotype was not included for
analysis. Interestingly, the authors did report a significant association between VKORC1
and risk of arterial thrombosis, with AA carriers being at the highest risk [116].
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The sample sizes were variable across studies, ranging from 43 to 557 patients.
Unfortunately, the length of follow-up was not always indicated and was highly variable
depending on how long patients had been on warfarin therapy prior to study initiation.
Nine studies reported the length of follow-up as a maximum value, ranging from 1 month
to 40 years, with 5 studies using follow-up periods >4 years. Therefore, it is difficult to
determine whether length of follow-up had an impact on positive vs. negative findings.
7.2 Intervention studies
The definition of warfarin-induced adverse events varied between studies but most often
included minor bleeding, major bleeding, and thrombotic episodes. Overall, six studies
reported a lower incidence of adverse events in the intervention group compared to the
control group. The RCT with the largest patient population (504 intervention, 1866
controls) reported a significant increase in hemorrhagic events, thromboembolic events,
death, and any serious adverse events in parallel controls in comparison with genotypeguided patients (4.5% versus 9.4%; adjusted relative risk, 0.44) [62]. One randomized trial
that incorporated CYP2C9 genotype only found a significant reduction in minor bleeding
events, with no patients experiencing a thrombotic episode [90]. A third pilot (n=56)
randomized trial in geriatric patients >65 years of age also reported a lower rate of
bleeding complications in patients who received genotype-guided dosing (14%)
compared to standard dosing (39%) during the first three months of therapy [89]. The
remaining three studies were non-randomized and used dosing algorithms that
incorporated both CYP2C9 and VKORC1 variants. Of these, one study found a
significant reduction in adverse events when laboratory (INR>4) and clinical outcomes
Page 21
were grouped together [96], while another study reported zero incidence of adverse events
in the genotype-guided group compared to a 28% incidence of adverse events in a
historical control group [117]. However, this finding may be due to general differences in
the management of patients between groups. Finally, in a prospective study of 37
patients, the OR for incidence of hemorrhage in the standard dosing group was 8.727
compared to the genotype-guided group (P<0.020) [118].
On the other hand, seven prospective studies, six of which were randomized, did not find
a significant difference in adverse events between dosing arms. Six studies reported a
decrease in the number of adverse events in the genotype-guided group but these findings
were not significant [56,74,87,92,97,106]. In contrast, one study reported a non-significant
increase in the number of adverse events in the genotype-guided group [68]. A systematic
review based on three studies (all of which have been included in this discussion)
concluded that there was a trend towards less bleeding incidents in the genotype-guided
group but that these findings are not significant [119].
8. Effect of genotype on hospitalization rates of warfarin patients
Three studies have examined the effect of genotype-guided dosing on hospitalization
rates of warfarin patients. A prospective study that compared outpatients initiating
warfarin therapy who underwent genotyping to a historical control group matched for age
and sex observed a 28% reduction in hospitalization due to bleeding or thromboembolism
in patients who underwent genetic testing compared to controls that were not genotyped
[120]
. Furthermore, there was a 31% reduction in all-cause hospitalization rates in the
Page 22
genotyping group. In this study, genetic test results were provided to physicians but it
was not monitored whether physicians utilized the genetic information to adjust warfarin
dosing. A pilot trial in 56 patients >65 years of age also found that hospitalization for a
bleeding complication was lower in the genotype-guided dosing arm compared to
standard dosing (3% vs. 10%) during the first three months of therapy
[89]
. A third
prospective, observational study that investigated associations between genotype and
health resource utilization found that the OR for hospitalization due to adverse events in
patients with the VKORC1 AA genotype was 8.35 compared to other genotypes [121]. The
effect of CYP2C9 genotype on hospitalization was not reported.
9. Effect of including INR and prior doses in a dosing algorithm
A few studies have investigated the importance of genotype in determining warfarin
maintenance dose when INR and prior warfarin doses are included in a dose-prediction
algorithm.
Ferder et al.
[122]
reported that CYP2C9 and VKORC1 were significant
predictors of therapeutic dose at weekly intervals for the first 4 weeks, but that their
predictive ability diminished with each subsequent week. Instead, INR measurements
and prior dosing became increasingly important for predicting therapeutic dose. In a
prospective study, it was found that a dose refinement PGx algorithm was able to
improve warfarin-dosing accuracy after 4 days of therapy
[96]
.
In accordance, a
retrospective study reported that both the dose-adjusted INR on day 4 and
VKORC1/CYP2C9 genotypes were significantly associated with dose
[123]
, implying that
genetics remain an important dose predictor even after accounting for INR. Lenzini et al.
(2010) reported that a PGx dose-refinement algorithm was able to account for 63% of
Page 23
therapeutic warfarin variability after four or five doses, compared to 48% for a clinical
dose refinement algorithm [124]. Building upon this model, a study by Horne et al. (2012)
reported that the use of genetic factors for dose refinements 6-11 days after the initiation
of treatment significantly improved the prediction of maintenance dose compared to a
clinical algorithm that did not take into account genotypes
[125]
. Therefore, while the
greatest potential benefit of genotyping is likely in the early course of therapy [96], using a
clinical dosing algorithm for the first few doses followed by a PGx dose refinement
algorithm may improve dosing accuracy since the impact of slow metabolizing genotypes
on INR is delayed [126]. In contrast, three studies [96,127,128] found that if the INR from Day
3 or Day 4 at the initiation of therapy were included in a dose prediction model, VKORC1
was no longer a significant predictor of warfarin dose while CYP2C9 genotype remained
significantly associated, suggesting that information provided by VKORC1 regarding
warfarin sensitivity may be reflected by early INR values.
A novel pharmacogenetics-guided warfarin dosing protocol (WRAPID) that calculates
both loading and maintenance doses based on clinical variables, genetics, and prior INRs
was tested in a prospective cohort study [91]. After applying the WRAPID algorithm to all
study participants, there were negligible differences in time to first therapeutic INR, risk
of over-anticoagulation, time to stable anticoagulation, and time above or within the
therapeutic range when comparing VKORC1 and CYP2C9 genotype groups. This
suggests that genotypic differences in clinical outcomes can be eliminated when initiation
doses and prior INRs, as well as genetic and clinical factors are considered in a dosing
Page 24
algorithm may be beneficial for identifying patients who are particularly sensitive to
warfarin.
10. Testing in Specific Patient Populations
10.1 Children
There is fairly strong evidence for an association between dose and genotype in pediatric
patients (+++ evidence). Eight pharmacogenetic studies have investigated the role of
genetics in warfarin dosing and clinical outcomes in children. Of these, seven studies
found a significant association between dose requirement and genotype in children
35,54,55,129]
[26,33-
. One study that was conducted in the largest pediatric population (n=120)
found that VKORC1 and CYP2C9 explained 26.6% and 12.8% of dose variation,
respectively, which is comparable to values reported in adult populations
[54]
.
Furthermore, when height and indication for warfarin use were added to the dose model,
72.4% of the variation in warfarin dose was explained. A study in 100 pediatric patients
also found that homozygous AA carriers, as well as carriers of more than 1 CYP2C9
variant allele, required significantly lower doses than non-carriers [55]. Finally, a study on
77 children reported a significant difference in dose requirements across VKORC1
genotypes and between CYP2C9 *1/*1 and *1/*3 carriers, with these genotypes
accounting for 12.2% and 7.9% of dose variability, respectively [26].
Three additional studies in children did not find a significant association between dose
and CYP2C9 in univariate analyses but did report a significant difference in dose between
VKORC1 genotypes [33-35]. A study by Kato et al. (2011) in 48 Japanese pediatric patients
Page 25
also reported a significant difference in warfarin dose requirement between carriers and
non-carriers of the VKORC1-1639 variant
[129]
. CYP2C9 was excluded from all analyses
due to low variant allele frequency. Finally, a study by Ruud et al.
[30]
did not find a
significant difference in dose requirements between carriers of CYP2C9 low activity
variants and those with the *1/*1 normal activity genotype. This study was based on
very small sample size (n=29) and there was a significant difference in the number of
days needed to achieve target INR and the number of INR measurements above the
therapeutic range when comparing CYP2C9 low activity variants to normal activity
variants, suggesting that CYP2C9 variants represent a risk factor for over-anticoagulation
and potential bleeding complications in children.
As with most adult studies, CYP2C9 is less associated with dose (explains 0.4-12.8% of
dose variation) compared to VKORC1 (explains 3.7-2.6%). However, based on results
from three of the largest studies, there appears to be a significant association with
variants in both genes. More research is required to understand the contribution of
CYP2C9 to warfarin variability in children.
10.2 African Americans
Several studies have shown that pharmacogenetic dosing algorithms developed for
Caucasians do not perform equally well in African Americans, with clinical and genetic
factors (VKORC1-1639, CYP2C9*2 and *3) predicting only 25% of dose variation in
Africans compared to 47-56% of the variability in Caucasians
[2,21]
. In general, African
Americans require a higher warfarin maintenance dose compared to other ethnic
Page 26
populations
[130]
. As previously described, the CYP2C9*2 and *3 variants are much less
common in Africans but there are additional CYP2C9 gene variants, including
CYP2C9*5, *6, *8, and *11, that also result in reduced enzyme activity and are more
common in African Americans
[7,16-18]
.
A study in Sudanese patients found that
CYP2C9*2, *5, *6 and *11 variants were independent predictors of dose, while a study in
South Africans reported a significant association with *8, as well as two novel CYP2C9
SNPs (g.16179 and g.46028). Another study that included the CYP2C9*5, *6, *8 and
*11 variants observed a non-significant 17% reduction in warfarin dose in carriers of
CYP2C9 star variant alleles compared to non-carriers [17].
VKORC1 is also less predictive of dose in African Americans compared to Caucasians,
with only 4.2% of dose variation in African Americans explained by this SNP compared
to 22.5% in Caucasians
[3]
. This finding may be attributed to lower allele frequency, as
31-48% of Caucasians are carriers of this variant compared to only 3-15% of African
Americans
[4]
. One study reported that the R2 value for correlating dose with VKORC1-
1639 was 4% in African Americans and 11% in Caucasian patients. However, African
American patients possessing at least one copy of this high sensitivity variant still
required a significantly lower warfarin dose compared to those who did not possess any
copies [99].
A study by Perera et al.
[7]
reported that an ethnicity-specific model that included two
novel SNPs (VKORC1-8191 and CYP2C9-18786), as well as VKORC1-1173, CYP2C9
star variants, and clinical factors explained 40% of the variability in warfarin dose.
Page 27
Furthermore, a GWAS in 546 African American patients found that a SNP upstream of
CYP2C18 (rs12777823) was associated with stable dose when conditioned on VKORC11639, CYP2C9*2, and *3
[131]
. This SNP was replicated in a separate African American
cohort and explained 5% of the dose variation. Due to large genetic heterogeneity
between Africans from different regions, the genetic associations described may not be
equally applicable to all African patients.
10.3 Asian populations
Compared to both Caucasian and African populations, the VKORC1-1639 variant is more
prevalent in Asian populations, with an allele frequency of 89%
[4]
. The high frequency
of this allele contributes to a lower average warfarin dose in Asian patients compared to
European patients
[12]
. When the IWPC algorithm, which incorporates VKORC1-1639,
CYP2C9 *2 and *3, was tested in a cohort of Japanese patients, the number of patients
allocated to low-dose or high-dose groups was significantly higher using the
pharmacogenetic algorithm compared to a clinical algorithm
[65]
. In this same study, the
R2 value for the IWPC algorithm was 28%, which is lower than the R2 of 39.5-44.9% for
Caucasian patients in the original IWPC study
[21]
.
Therefore, as with the African
populations, algorithms developed for predicting warfarin dose in Caucasians do not
perform as well in Asians. When smoking and drinking status were incorporated into the
IWPC algorithm, the R2 in Asians increased to 33%
[65]
, suggesting that the addition of
specific clinical variables to PGx algorithms may improve performance in different
ethnic groups. Significant genetic variation also exists between patients from different
regions of Asia and needs to be taken into consideration.
Page 28
11. Recommendations on pharmacogenetic testing in warfarin therapy from existing
Clinical Practice Guidelines
Six previously published guidelines provide recommendations on the use of genetic
testing in the management of warfarin therapy. Of these, only two recommend dosing
based on genotype
[132,133]
. Both guidelines do not recommend the use of genetic testing
in children (patients <20 years of age) as there is no evidence-based algorithm available
for this population.
On the other hand, four sets of guidelines published between 2008 and 2011 do not
recommend the use of genetic testing in warfarin management in all patient populations
[134-137]
. These guidelines come from groups such as the American College of Chest
Physicians and the British Journal of Hematology. The main reason cited is a lack of
randomized clinical trials showing the benefit of genotype-guided warfarin therapy.
Another reason is the impracticality of genetic testing in most centers due to long turnaround-times, hindering the use of genetics at the initiation of therapy when it is
considered to be most beneficial.
It is also believed that frequent monitoring and
incorporation of previous INRs and dosing information into algorithms can accurately
predict maintenance warfarin dose without genotyping.
12. Supplementary Methods
12.1 Systematic Literature Search
A comprehensive systematic search of the relevant English-language, published, peerreviewed literature was performed to identify available evidence on genetic testing for
Page 29
VKORC1 and CYP2C9 in the context of warfarin therapy. Embase from the period of
1980 to July 2011 (using the OVID interface) and MEDLINE from the period of 1948 to
July 2011 (using the OVID interface) were searched. Titles and abstracts of all records
retrieved were scanned for relevance to the guideline key questions. English language
original studies relevant to the guideline questions were selected for full-text review.
Editorials, notes, short surveys, and review articles were not included in the full-text
review. Conference abstracts were only included if they were published in or after 2009.
Monthly updates of the systematic literature search were performed until February 2012.
The last update of the literature search was performed in July 2013. In addition, articles
published after July 2013 that were of particular interest (ex. EU-PACT and COAG
trials) are also discussed but not included in the evidence summaries.
Systematic literature search strategy
Database: Embase <1980 to 2011 July 25>, Ovid MEDLINE(R) 1948 to Present with Daily Update
1. (cyp2c9 or vkorc1 or "cytochrome p450 2c9" or "vitamin k epoxide reductase complex").mp. [mp=ti, ab,
sh, hw, tn, ot, dm, mf, dv, kw, ps, rs, nm, an, ui]
2. (dosing or dosage* or dose* or bleed* or adverse event or adverse reaction or adverse effect).mp.
3. (pharmacogen* or genetic* or genom* or gene varia* or genotype* or polymorphism*).mp.
4. (warfarin or coumadin or anticoagula*).ab,ti.
5. 1 and 2 and 3 and 4
6. remove duplicates from 5
7.(“systemati 7. (prospective
(replace 6 with)
7. (cyp4f2
Above
7. 6 not
Articles
c review” or
or
= 6. (initiat* or
or
search in: review.mp taken
“metarandomized).mp. start* or naive or "cytochrom EBM
.
from
analysis” or
8. 6 and 7
first or clinical
e p450
Reviews - 8. limit 7
2011
“meta
9. 8 not
or standard).mp. 4f2").mp.
Cochrane to
warfarin
analysis”).mp review.mp.
7. (test* or
8. 6 and 7
Central
yr=”2008
guidelin
.
10. 9 not case
screen*).mp.
9. 8 not
Register
– current” e by
8. 6 and 7
report.mp.
8. 5 and 6 and 7
review.mp.
of
9. 8 not
Johnson
11. 10 not
9. remove
10. 9 not
Controlle
letter.mp.
et al.
letter.mp.
duplicates from
case
d Trials
10. limit 9
12. limit 11 to
8
report.mp.
<3rd
to english
English
10. 9 not case
11. 10 not
Quarter
language
language
report.mp.
letter.mp.
2011>
13. 12 not cost11. limit 10 to
1
effectiveness.mp english language
2
.
12. 11 not
3
review.mp.
4
13. 12 not
5
Page 30
letter.mp.
14. 13 not costeffectiveness.mp
.
Removed:
 Irrelevant articles (articles that studied association between pharmacogenetics and warfarin
response and/or dose requirements)
 Conference abstracts prior to 2009
 Editorials, short surveys, comments, reviews, letter, notes and studies with <10 patients (ex. case
studies)
12.2 Critical appraisal of evidence
Strength of scientific evidence was graded using an approach similar to scheme suggested
by the Grading of Recommendations Assessment, Development and Evaluation
(GRADE) working group[138] (Table 2). Strength of evidence was evaluated based on the
consistency of results, magnitude of the effect, as well as the number and quality of
studies conducted. Study quality assessment included the evaluation of limitations in the
study design, imprecision of effect estimates, and indirectness of evidence, as well as the
possibility of publication bias.
Development of clinical practice recommendations
Clinical practice recommendations were developed during a two-day workshop with
participation of all guideline development group members using an informal consensus
process. Supporting evidence and draft recommendations as a basis for discussion were
presented by one member to the group, followed by discussion and revision of
recommendations according to group consensus.
Page 31
Each clinical practice recommendation was assigned one of three categories of strength,
based on the strength of available evidence, on which the recommendation was
formulated (Table 3). The strength of a recommendation was also modified according to
the balance between benefits and risks of genetic testing and genotype-guided treatment,
as well as the likelihood of variability in the individual values and preferences of patients.
A strong recommendation (level A) is considered a recommended therapeutic option that
is expected to be chosen by a majority of informed health care providers and patients,
whereas a moderate level (B) is given for a recommendation that is expected to require
individualized informed decision making by patients and health care providers, taking
into account the individual needs, values and preferences of each patient. A
recommendation of level C is considered an optional recommendation, e.g. for use of a
genetic test in a research context.
Review
In a first step, the draft guideline document was reviewed internally by all guideline
development group members. Secondly, the draft guideline was reviewed externally by
two independent content experts, who had not been part of the recommendation
development. Finally, a third review was performed by a group of members of the target
audience of the guideline. This third review step was aimed to ensure the clarity of the
presented context, as well as the ease of use of the guideline, as well as its applicability in
clinical practice.
Page 32
Test availability and cost
The availability of diagnostic genetic tests varies locally and was not exhaustively
assessed in a systematic search. For enquiries regarding local availability and cost of
genetic tests, local diagnostic laboratories (e.g. hospital-based molecular diagnostic or
immunogenetics laboratories) should be contacted. See Supplemental Table 4 for a
summary of testing laboratories and dose adjustment recommendations provided by the
respective laboratories.
An evaluation of the cost-effectiveness of genetic testing was not performed due to the
rapidly changing and locally varying costs of genetic testing.
Page 33
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Page 60
Supplemental Table 1. Effect of variant CYP2C9 genotypes on warfarin dose
requirements [15].
CYP2C9 Genotype Reduction in warfarin dose
requirement
*1/*1
Reference
*1/*2
19.6%
*1/*3
33.7%
*2/*2
36.0%
*2/*3
56.7%
*3/*3
78.1%
Page 61
Supplemental Table 2. Effect of variant VKORC1 genotype on warfarin dose
requirements [5].
Increase in Dose Requirement
VKORC1
High sensitivity
Intermediate
Low sensitivity
variant
sensitivity
-1639 G>A
Reference
52%
102%
+1173 T>C
Reference
44%
97%
Page 62
Supplemental Table 3. Recommended daily warfarin doses (mg/kg) to achieve a
therapeutic INR based on CYP2C9 and VKORC1 genotype using the warfarin product
insert approved by the United States Food and Drug Administration
VKORC1
Genotype
(-1639G>A)
CYP2C9
*1/*1
CYP2C9
*1/*2
CYP2C9
*1/*3
CYP2C9
*2/*2
CYP2C9
*2/*3
CYP2C9
*3/*3
GG (WT)
5-7
5-7
3-4
3-4
3-4
0.5-2
GA (*1/*2)
5-7
3-4
3-4
3-4
0.5-2
0.5-2
AA (*2/*2)
3-4
3-4
0.5-2
0.5-2
0.5-2
0.5-2
Page 63
Supplemental Table 4. Summary of commercially available genetic testing laboratories
(non-exhaustive list)
Genetic Testing
Laboratory
Test interpretation
ARUP Laboratories



Mayo Medical
Laboratories
Genelex

Molecular Diagnostics
Laboratories
Quest Diagnostics







Coumadin label insert (available on FDA website)
Warfarin dosing calculator (Sconce et al., 2005)
Dose revision algorithms after INR response is also
available (Lenzini et al., 2008)
Coumadin label insert
Coumadin label insert
WarfarinDosing.org
Sconce Algorithm (www.GeneMedRx.com)
Sconce Algorithm
WarfarinDosing.org
Genotyping results can be input into one of several
warfarin-dosing algorithms to calculate an initial warfarin
dose (ex. WarfarinDosing.org)
Coumadin label insert
Page 64
Supplemental Table 5. Pharmacogenetics-based initiation dose grid according to
VKORC1 and CYP2C9 genotype using the WRAPID dosing algorithm [91]
CYP2C9
VKORC1
*1/*1
*1/*2 or *1/*3
*2/*2, *2/*3, *3/*3
10 †
10 †
7.5 ‡
G/G
G/A
10 †
7.5 ‡
5‡
A/A
5‡
5‡
5‡
Loading doses are in mg
†Loading doses was adjusted to 7.5mg for patients with weight < 60kg.
‡ Loading doses was decreased by 2.5mg for patients with weight < 45kg.
Page 65
Supplemental Table 6. Evidence linking genotype with required dose.
Major Findings
CYP2C9 and/or VKORC1 genotype are associated with
warfarin dose
References
[1-4,6-8,15,16,21-23,27-29,31,32,36,44,50,58,76,77,7981,99,108,117,122,127,139-169] [20,3335,37,46,95,101,104,125,129,131,170-189]
CYP4F2 (V433M) genotype is associated with required
warfarin dose
[20,39-44,47,66,101,142,174,175,177,182-184,190-194]
Page 66
Supplemental Table 7. Summary of studies that did not find a significant association
between required dose and genotype
Population
Children
Caucasian
Sample
Size
29
30
CYP2C9*2
CYP2C9*3
No association
No association
No association
No association
Children
34
No association
No association
Children
37
No association
No association
Lebanese
43
No association
Han
Chinese
Lithuanian
73
Not studied
Significant
association
No association
83
No association
Not studied
Indonesian
85
Not studied
No association
Caucasian
(initiating
warfarin)
Caucasian
96
No association
Significant
association
93
No association
No association
Brazilian
103
No association
Japanese
104
Not studied
Significant
association
No association
African
American
African
American
Caucasian
145
No association
168
No association
557
Significant
association
Significant
association
No association
No association
VKORC1
(-1639)
Not studied
No
association
Significant
association
Significant
association
Significant
association
Significant
association
Significant
association
Significant
association
Not studied
Reference
Significant
association
Not studied
[155]
Significant
association
Significant
association
Not studied
[31]
Significant
association
[37]
[30]
[38]
[33]
[34]
[36]
[189]
[170]
[32]
[27]
[28]
[20]
[29]
Page 67