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Prediction of warfarin dose: why, when and how?
Prediction models are the key to individualized drug therapy. Warfarin is a typical example of where
pharmacogenetics could help the individual patient by modeling the dose, based on clinical factors and
genetic variation in CYP2C9 and VKORC1. Clinical studies aiming to show whether pharmacogenetic
warfarin dose predictions are superior to conventional initiation of warfarin are now underway. This
review provides a broad view over the field of warfarin pharmacogenetics from basic knowledge about
the drug, how it is monitored, factors affecting dose requirement, prediction models in general and
different types of prediction models for warfarin dosing.
KEYWORDS: dosing algorithm n human CYP2C9 protein n human VKORC1 protein
n pharmacogenetics models n statistical regression ana­lysis n warfarin
Niclas Eriksson*
& Mia Wadelius1
Department of Medical Sciences,
Clinical Pharmacology, Uppsala
University, Uppsala University
Hospital, entrance 61, SE-751 85
Uppsala, Sweden
*Author for correspondence:
Uppsala Clinical Research Center,
Uppsala University, Dag
Hammarskjölds väg 50A, SE-751 85
Uppsala, Sweden
Tel.: +46 18 611 9516
Fax: +46 18 506 638
[email protected]
1
There is overwhelming evidence that the individual maintenance dose of warfarin can be predicted by pharmacogenetic dose models [1–4] . In
2007 the US FDA updated the label of warfarin
to encourage, but not require, pharmacogenetic
testing of patients initiating warfarin therapy [5] .
In 2010 dosing recommendations according to
genotypes of CYP2C9 and VKORC1 were added
to the label (Table 1) . Starting a patient’s treatment with an individually predicted warfarin
dose would, in theory, lower the risk of early
bleeding, which is highly related to the intensity of anticoagulation [6] . In spite of this, the
translation of pharmaco­genetic dose models into
clinical practice has been slow. Clinical studies
aiming to show whether pharmacogenetic warfarin dose predictions are superior to conventional
initiation of warfarin are now underway.
This review provides an overview of the field
of warfarin pharmacogenetics including basic
information about the drug and how the anticoagulation effect is monitored, common problems
encountered during treatment and the impact
of genetic factors. Special focus is put on warfarin dose prediction models and how they are
­developed and evaluated.
and X. It is administered as a racemic mixture
of (R) and (S)-enantiomers with the (S)-isomer
being three- to five-times more potent then the
(R)-form [8] .
Warfarin is mainly used for primary and secondary prevention of venous thrombo­embolism,
and for prevention of systemic embolism in
patients with prosthetic heart valves or atrial
fibrillation [6] . Studies on atrial fibrillation
patients show that the overall risk reduction
of stroke is approximately 70% compared with
placebo [6] .
Warfarin
Since the introduction of warfarin in the
early 1950s, its use has steadily increased. It
is now the most widely used anticoagulant
in the world with annual prescriptions equaling 0.5–1.5% of the population [7] . Warfarin
is a vitamin K-antagonist that targets the
vitamin K cycle, thereby inhibiting the vitamin K-dependent coagulation factors II, VII, IX
Monitoring of anticoagulation
One of the main problems with warfarin therapy is the large interindividual variation in the
dose needed to reach adequate levels of anti­
coagulation. Dose requirements vary more than
tenfold, ranging from <10 to >100 mg per week.
The anticoagulant effect therefore needs to be
monitored, especially during the initiation of
therapy. This is done by measuring the prothrombin time (PT) International Normalized
Ratio (INR), which is a measure of three of the
four vitamin K–dependent coagulation factors:
II, VII and X. The INR is determined by dividing the PT of a patient with the geometric mean
of PT for at least 20 healthy subjects of both
sexes with the same test system [9] . An INR of
1.0 is considered to be normal coagulation and
an INR of 2.0 means that the clotting time has
been doubled.
INR target ranges vary between countries
and indications, but the most common target range is 2.0–3.0. For patients with a high
risk of thrombosis, for example, patients with
10.2217/PGS.11.184 © 2012 Niclas Eriksson
Pharmacogenomics (2012) 13(4), 429–440
part of
ISSN 1462-2416
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Eriksson & Wadelius
Table 1. Ranges of recommended warfarin doses (mg/day) from
the US FDA drug label.
VKORC1
CYP2C9 (mg/day)
*1/*1
*1/*2
*1/*3
*2/*2
*2/*3
*3/*3
GG
5–7
5–7
3–4
3–4
3–4
0.5–2
AG
5–7
3–4
3–4
3–4
0.5–2
0.5–2
AA
3–4
3–4
0.5–2
0.5–2
0.5–2
0.5–2
Reproduced from the updated warfarin (Coumadin) product label.
mechanical heart valves, the target range can be
increased to 2.5–3.5 or even higher [10] . Patients
receiving warfarin have frequent INR checks
during the induction phase of therapy and once
the target range is achieved the INR is measured
once or twice a month.
A measure of how well a patient is anti­
coagulated during a specific time interval is the
time in treatment range (TTR). The standard way
to calculate TTR is by the Rosendaal method,
which uses linear interpolation to calculate the
percentage of time a patient is within treatment
range (usually INR 2.0–3.0) [11] . TTR is associated with the efficacy of warfarin treatment, and
a 10% increase in time spent outside therapeutic
range relates to an augmented risk of mortality, ischemic stroke and other thromboembolic
events [12,13] .
The induction phase
There is no gold standard for how to initiate warfarin. The half-life of warfarin varies
greatly between individuals, and is on average
36–42 h [6] . This means that it takes more
than a week to reach a pharmacokinetic steady
state when starting on a maintenance dose.
Some apply a defensive strategy in patients
not requiring rapid anticoagulation by starting with low doses and raising them over time
with the guidance of frequent INR tests. This
method is safe with respect to over-anticoagulation, which is related to bleeding, but
patients requiring higher doses than average
will be under-anticoagulated for a large part
of the induction phase [10] . Loading doses are
therefore often used to reach steady state more
quickly. Commonly used initiation methods
include giving 5–10 mg on the first 2 days or
giving age-stratified initiation doses before
switching to maintenance doses [14,15] .
Adverse effects
The most common adverse effect of warfarin
is bleeding and the risk is highly related to the
intensity of anticoagulation [13] . The incidence
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Pharmacogenomics (2012) 13(4)
of major bleeding during warfarin treatment is,
according to the recent RE-LY trial, on average 3.36% per patient year, but varies between
studies depending on inclusion criteria and
quality of medical care in terms of INR control
[16] . Studies have demonstrated that increasing
the INR target range from 2.0–3.0 to 3.0–4.5
also increases the risk of clinically significant
bleeding [6] . An interesting observation from
these studies is that the mean increase in dose
per day is ≈1 mg when changing from the low
to the high target range.
If a patient is severely over-anticoagulated
with high INR levels or bleeding, treatment is
normally stopped and vitamin K is administered to reverse the effects of warfarin. In case of
severe bleeding there is also an option to infuse
fresh plasma or prothrombin concentrate [6] .
Dietary interactions
Vitamin K is the natural antidote to warfarin.
Vitamin K is found in food, and diet therefore imposes variability in warfarin response.
Normal intake of vitamin K is in the range of
60–200 µg/day. Most dark green vegetables such
as broccoli, Brussels sprouts and spinach contain high levels of vitamin K (>100 µg/2 dl), but
also other common foodstuffs contain a fairly
large amount of vitamin K [17] . It is estimated
that an increase in vitamin K intake of 100 µg
per day for 4 consecutive days lowers the INR
by 0.2 and vitamin K intake has therefore been
incorporated into some warfarin dose ­prediction
models [18,19] .
Studies on combining warfarin treatment
with a vitamin K supplement have been performed with the aim to reduce the variability
in drug response caused by a low or sporadic
dietary intake [20] . These studies show varying
results, but the overall conclusion is that vitamin K supplements do decrease the variation in
drug response caused by dietary intake. The current recommendation for patients on warfarin
treatment is to keep a constant intake of vitamin K through foodstuffs to minimize variation
in warfarin response.
Drug interactions
The two warfarin isomers are metabolized by
different pathways. The main enzyme involved
in the metabolic elimination of (S)-warfarin is
CYP2C9, while (R)-warfarin is eliminated by
CYP1A1/CYP1A2/CYP3A4 [5] . Patients starting or stopping drugs that are known inducers
or inhibitors of these enzymes should have extra
INR tests, and their dose of warfarin adjusted
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Prediction of warfarin dose: why, when & how?
accordingly. Examples of drugs that interact
with warfarin are amiodarone that inhibits the
clearance of warfarin thereby potentiating the
anticoagulant effect, and carbamazepine, phenytoin and rifampicin, which induce the metabolism of warfarin [1] . Drugs that inhibit clotting
and increase the risk of bleeding, like aspirin,
diclofenac and ibuprofen, are also regarded as
interacting drugs even though they have no
inhibiting or inducing effect on the elimination
of warfarin [6] .
Clinical & demographic effects
on dose
Although warfarin is almost entirely eliminated by metabolism, recent studies show
that patients with moderate and severe
renal impairment (estimated glomerular filtration rate: 30–59 ml/min/1.73 m 2 and
<30 ml/min/1.73 m2) require less warfarin than
those with no or mild kidney impairment [21,22] .
Among other factors that have effect on warfarin
dose are age, height, weight, ethnicity, smoking
and target INR value [2,4,23] .
Genetic effects
A genetic explanation to the large inter­individual variation in warfarin dose requirement was first sought with the candidate gene
approach and later with genome-wide association studies. In the 1990s candidate gene
studies focused on the enzyme CYP2C9, which
metabolizes the potent (S)-isomer of warfarin,
found an effect of CYP2C9 genetic variants on
warfarin dose [24] . It is now well-known that
patients with normal metabolism, that is, no
genetic variants of CYP2C9 (*1/*1), have an
estimated half-life of 30–37 h, while it is prolonged to 92–203 h in patients with severely
impaired metabolism (*3/*3) [25,26] . Some
years later the gene VKORC1 that codes for the
enzyme vitamin K epoxide reductase [27] , which
is the target of warfarin, was discovered [28–30] .
Initial studies showed that common variants of
VKORC1 make patients more sensitive to warfarin [31–33] . Recent research has also shown that
there are rare mutations in VKORC1 that cause
warfarin resistance [34] . The genes CYP2C9 and
VKORC1 have been confirmed as the major
genetic determinants of warfarin dose through
genome-wide studies [35,36] . In addition, a variant of the gene CYP4F2 has been shown to
have a small but significant effect on warfarin
dose [35,37,38] . The remaining genetic variance is
likely to be caused by multiple genetic variants
with a very small effect.
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Developing & critically reviewing
prediction models
A prediction model is any algorithm relating to
a certain measure or measures of an outcome.
In the field of medicine, it is most commonly
the result of a regression model with estimated
co­efficients as weights of each variable. Although
the process of prediction modeling can differ
substantially between different projects/outcomes, there are still some general points to think
of. These points are highlighted under Box 1, and
are described in more detail here. Knowledge
of certain important steps in prediction modeling can both help in future projects and when
­reading literature including prediction models.
First of all, it is of high importance that
the data a prediction model is built on reflect
the population that it will be used on. For
instance, if we have a prediction model that
includes age, then the data used to build the
model should include the age interval of the
future populations where predictions will be
made. If a model has been built on data from a
cohort aged 20–40 years and is used to predict
in patients aged 60–80 years, there is a chance
that the co­efficient for age is wrong; the ageoutcome relationship might for instance be of
second order. This problem could arise if the
cohort used to build the model is from a clinical study with narrow inclusion criteria for some
key variables, while prediction in a more general
population is desired.
Think of different validation techniques
before any calculations are done. If there is no
external data set available and the data at hand
is fairly large, validation can be done by splitting the data into a training set and a validation
set. This, however, requires that no calculations/assumptions/modeling have been done on
the whole data. Data splitting could still give a
positive bias, that is, the performance will appear
Box 1. Key aspects when prediction modeling.
ƒƒ Is data representative for the population the model will be used on?
ƒƒ Validation of model; internal or external?
ƒƒ Missing data? Imputation?
ƒƒ Is the effect of continuous variables linear or should transformation be applied?
ƒƒ Read the literature; gather as much information as possible from previous
publications about important variables
ƒƒ Before discarding any variables from the model, estimate univariate estimates
(one estimate of variable effect against outcome at a time) and multiple estimates
(all variables included in the model)
ƒƒ If variable selection is necessary, decide which variables to drop by investigating
estimates of effects with confidence intervals, not only p-values. Do not
automatically discard variables with p > 0.05
ƒƒ Collaboration between statistical and medical expertise is essential
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Eriksson & Wadelius
better than in real life, because the validation
set is not entirely independent of the derivation set in terms of study wise definitions and
design. Unbiased measures of performance are
estimated by external validation on data totally
i­ndependent from the modeling process.
Depending on study type, missing values
could be a problem. A clinical study is most
likely to have a low number of missing values,
while data from registries often have more missing values. Missing values cause problems in the
ana­lysis, since patients with any missing value
are discarded if nothing else is done. Most serious is the case of informative missing, that is,
when the missing pattern has something to do
with the outcome being analyzed. Various imputation techniques can be use to fill in missing
values to reduce the amount of patients removed
from the ana­lysis.
If continuous predictors are used, ensure
that the relationship to the outcome is linear
by plotting Y against the predictor. If there is
evidence of a nonlinear effect, then apply some
kind of transformation (e.g., log or square root)
to attain linearity. In the worst case it is possible to categorize the variable, but think twice
before categorizing a continuous variable since
this means loss of information.
Utilizing previous knowledge about predictors for the outcome will firstly lower the risk
for spurious results by making it possible to drop
some variables from the ana­lysis before they are
analyzed against the outcome. Second, it will
help select variables for the modeling process.
As a first step in the modeling, estimating
univariate effects is a good way to gain knowledge about the data. The next step would be
to fit a full multiple model using all predictors. This model could be the final prediction
model if there is no need for variable selection.
However, the model is often improved, in terms
of simplicity and clinical utility, by dropping
some variables. For instance, do not keep a variable that, although significant, shows signs of
being nonimportant (estimate close to zero)
if it also costs money to produce in the clinic.
Another reason for dropping variables is that a
simple model is easier to interpret and use in
a clinical setting than a more complex model.
In the process of variable selection, do not
always obey the p ≤ 0.05 significance rule that
is inherited from clinical trials; point estimates
and CIs for each coefficient include more information. Why is a variable with p = 0.045 more
interesting than a variable with p = 0.055 or even
p = 0.15? The effect of all three variables could
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Pharmacogenomics (2012) 13(4)
actually be the same, but the precision of the
estimate could be different due to one variable
being infrequent in the cohort. As an example,
if a known interacting drug is used seldom in
your cohort and therefore has a low frequency,
the precision will be low. This gives large CIs
for the estimate, but the estimate itself may be
of the same magnitude as in previous studies.
In this case the p-value will probably be >0.05,
but given the previous knowledge the variable
should be kept in the model.
The most common performance measure
of linear regression models is the coefficient of
determination, R 2. It measures the percentage
(or proportion) of variability of the outcome that
is explained by the model. While the R 2 value
gives a point estimate for the predictive ability of
the model, examining scatter plots of observed
values versus predicted values gives more information about performance across the whole
range of outcome values.
If external validation of the final model is
used, and it includes or only consists of another
ethnicity, the estimates of performance might
be lower than expected. This could be due to
the ethnicities having different allele frequencies
of the genetic markers included in the model.
This is truly the case for warfarin (see section
on ‘Stable warfarin maintenance dose’).
For further in depth details about prediction
modeling, the authors recommend Regression
Modeling Strategies by Frank E Harrell Jr [39] .
Why predict warfarin dose?
The ultimate goal of pharmacogenetics is to
individualize therapy and warfarin is a good
example of where pharmacogenetics could help
tailor the dose from the beginning of treatment.
The narrow therapeutic index of warfarin and
high interindividual variation in dose needed
is a challenge in clinical care. Complications
from inappropriate warfarin dosing remain as
one of the most common reasons for emergency
room visits in the USA [7,40] . Unintentional over­
dosing of warfarin is the most common cause of
hospitalization due to adverse reactions in the
USA, and the second most common cause in
the UK [41,42] . There is evidence of a pronounced
risk of bleeding during the initiation phase
(1–3 months), which a genotype-guided dose
has the potential to reduce [6,43] . For instance, a
study by Wadelius et al. showed that the risk of
over anti­coagulation (INR >4) is highly related
to CYP2C9 and VKORC1 genotypes [4] . A small
prospective cohort study by Gong et al. indicated that the genotypic risk could be removed
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Prediction of warfarin dose: why, when & how?
by using a pharmacogenetics-guided initiation
dosing protocol [44] .
When to predict warfarin dose
In the following sections three different types
of prediction model are discussed. The first is
the prediction of stable warfarin maintenance
dose, which is the most studied outcome in
warfarin pharmacogenetics. However, maintenance dose prediction models do not tell us
how to initiate treatment in the best way, which
leads us to the second type of model: prediction
of starting doses. The third type of prediction
model is the dose refinement model that also
includes INR response after the initial doses.
For the different types of models examples from
the largest international studies are shown and
discussed.
„„
Stable warfarin maintenance dose
The first prediction models for stable warfarin
dose were estimated on relatively small populations mainly consisting of Caucasian patients
included from one area or clinic [23] . While
these models might predict the dose within
the same catchment area, they are probably not
portable to a more broad population from other
regions or countries [45] . To address the problem
of generalizability, the International Warfarin
Pharmacogenetics Consortium (IWPC) was
established with the primary aim to develop
an algorithm for warfarin dose prediction.
The IWPC includes 21 research groups from
nine countries that have contributed clinical and genetic data for 5700 patients who
were treated with warfarin [1] . The resulting
pharmaco­genetic model was shown to predict
patients requiring low doses (≤21 mg/week) or
high doses (≥49 mg/week) significantly better
than a clinical model. In the validation data the
pharmacogenetic algorithm explained 43% of
the variance in warfarin dose (R 2 = 43%), while
the clinical algorithm had an R 2 of 26%. The
IWPC model includes the variables VKORC1
(rs9923231), CYP2C9 (*2 and *3), age, height,
weight, race and the following interacting
drugs: enzyme inducers and amiodarone (Table 2)
[1] . Several studies have externally validated the
IWPC model together with other models, and
the overall conclusion is that the IWPC model
performs well; however, the sample size has been
small in these studies [46–48] . In 2010 the US
FDA updated their Coumadin label with a table
giving stable dosing recommendations per genotype of CYP2C9 and VKORC1 (Table 1) . A dose
nomogram for the IWPC model illustrating the
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effect of VKORC1, CYP2C9 and age on week
dose (mg) for a Caucasian patient with no interacting drugs, height 175 cm and weight 75 kg
is given in Figure 1. The FDA’s recommended
dosing recommendations from the Coumadin
label are shown for comparison (shaded areas).
A pharmacogenetic dosing algorithm has been
shown to be superior to the FDA table on dosing
recommendations [49] .
The SNPs commonly used in warfarin
maintenance dose prediction models are presented in Table 3 together with the estimates
of minor allele frequencies (MAF) from Scott
et al. and Limdi et al. [1,2,4,50,51,52] . Notable is
the difference in MAF between ethnicities,
with more genetic variation among individuals of European descent than in individuals of
African and Asian descent. For the VKORC1
SNP rs9923231 there is also a shift in minor
allele between Europeans, Asians and Africans
with the minor allele being A for Europeans
and Africans and G for Asians. The differences
in MAF affects how well a prediction model
containing this variable, in terms of R 2 , performs in the population [53] . The second study
by IWPC showed that the reason for the lower
performance of the IWPC algorithm in African
Americans and Asians (R 2 ≈ 24%) than in
Caucasians (R 2 = 40%) was mainly due to less
variation in the VKORC1 SNP rs9923231 in
African–Americans and Asians. By simulations
the IWPC showed that given the population
MAF, the R 2 for the VKORC1 SNP rs9923231
was in the range of what would be expected if
the genotype effect was the same across ethnicities [52] . The conclusion was that non-Caucasian
patients with variant alleles have similar benefit
of genetic-based dosing with the IWPC model,
even if the performance of the algorithm is lower
on a population level. In other words genetic
based dosing for a single patient will work as
well for African–Americans and Asians as it
does for Caucasians but on a population level
Caucasians, with most variation in VKORC1,
will benefit most from genetic dosing.
„„
Starting doses
The time to reach steady-state concentrations
of a drug depends on how quickly it is cleared
from the system. If you initiate a drug with
the maintenance dose, it takes approximately
five-times the half-life of the drug to reach
steady state. Compared with a person without
CYP2C9 variant alleles, the half-life of the more
potent enantiomer (S)-warfarin is doubled in
a *2/*2 person and increased three- to six-fold
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Eriksson & Wadelius
Table 2. Three different prediction models for warfarin dosing.
Study
Type of
model
Algorithm
Ref.
Klein et al.,
IWPC
Maintenance
dose
√Dose (mg/week) = 5.6044 – 0.2614 × age (in decades) + 0.0087 × height (cm) + 0.0128 × weight
(kg) - 0.8677 × VKORC1 A/G - 1.6974 × VKORC1 A/A - 0.4854 × VKORC1 genotype unknown 0.5211 × CYP2C9 *1/*2 - 0.9357 × CYP2C9 *1/*3 - 1.0616 × CYP2C9 *2/*2 - 1.9206 × CYP2C9
*2/*3 - 2.3312 × CYP2C9 *3/*3 - 0.2188 × CYP2C9 genotype unknown - 0.1092 × Asian race 0.2760 × Black or African–American - 0.1032 × missing or mixed race + 1.1816 × enzyme-inducer
status - 0.5503 × amiodarone status
The result is √dose (mg/week) and should be squared to calculate the weekly warfarin dose in mg
Age in decades should be entered as 1 for 10–19 years, 2 for 20–29 years, 3 for 30–39 years
Enzyme-inducer status = 1 if patient taking carbamazepine, phenytoin, rifampin, or rifampicin,
otherwise 0
For VKORC1, CYP2C9, race and amiodarone status enter 1 if present, otherwise 0. For example, if the
patient is VKORC1 A/G then the coefficient for VKORC1 is -0.8677 x 1
Avery et al.
Initial doses
R
V
1
- (1 + e- k* x + - k*2x) W
S
- e- k* x)
(1
S
W
x = MD *
S
W
1 + 2 * e- k* x + e- k*2x
S
W
3
3
T
X
[1]
[25]
Where MD is the calculated maintenance dose per day in mg. Could be calculated by the IWPC model
above (note that the IWPC model gives weekly dose, divide by seven to get the daily dose)
k is the elimination rate constant for the CYP2C9 genotypes: *1/*1 = 0.0189 h-1, *1/*2 = 0.0158 h-1,
*1/*3 = 0.0132 h-1, *2/*2 = 0.0130 h-1, *2/*3 = 0.009 h-1 and *3/*3 = 0.0075 h-1
t is the warfarin-dosing interval, use 24 (24 h)
The initial doses on days one to three are then calculated as: day one = MD + x, day two = MD +
2x/3 and day three = MD + x/3
Lenzini et al., Dose revision
IWDRC
on days four
or five
Dose (mg/week) = EXP (3.10894 - 0.00767 × age - 0.51611 × ln[INR] - 0.23032 × VKORC1-1639 G>A
- 0.14745 × CYP2C9*2 - 0.3077 × CYP2C9*3 + 0.24597 × BSA + 0.26729 × target INR - 0.09644 ×
African origin - 0.2059 × stroke - 0.11216 × diabetes - 0.1035 × amiodarone use - 0.19275 ×
fluvastatin use + 0.0169 × dose- 2 + 0.02018 × dose- 3 + 0.01065 × dose- 4)
Where INR is the INR on days four or five
VKORC1-1639 G>A should be entered as 0 for homozygous G/G, 1 for heterozygous and 2 for
homozygous A/A
CYP2C9*2 and CYP2C9*3 should be entered as 0 if absent, 1 if heterozygous and 2 if homozygous
Dose - 2, - 3 and - 4 refers to dose given 2, 3 and 4 days before INR is measured
BSA is calculated according to the formula by Dubois and Dubois where
BSA = (weight [kg] 0.425 × height [cm] 0.725)/139.2
For African origin, stroke, diabetes, amiodarone use and fluvastatin use, enter 1 if present,
otherwise 0
[56]
BSA: Body surface area; INR: International Normalized Ratio; IWDRC: International Warfarin Dose Refinement Collaboration; IWPC: International Warfarin
Pharmacogenetics Consortium; MD: Maintainance dose.
in a *3/*3 person. Hence,�����������������������
����������������������
if treatment is initiated with the predicted maintenance dose, the
time to reach steady state is prolonged two- to
six-fold in patients with only CYP2C9 variant alleles. Recent work by Hamberg et al. has
described these pharmacokinetic differences
in more detail [54,55] . The time to reach steady
state can be optimized by combining a stable
dose prediction algorithm with information
on the estimated clearance of CYP2C9 genotypes [25] . This individualized pharmacogenetics-based warfarin initiation dose regimen is
especially beneficial in patients with CYP2C9
variant alleles. A table of pharmaco­g enetic
starting doses calculated using the formulas by
Avery et al. (see formula in Table 2 ) is given in
Table 4 for an example patient with six different
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Pharmacogenomics (2012) 13(4)
combinations of CYP2C9 and VKORC1 [25] .
The predicted maintenance doses are taken
from the dose nomogram in Figure 1. These predicted loading doses show that for a Caucasian
patient at the age of 60 years, height 175 cm,
weight 75 kg, with no interacting drugs and
the most common combination of genotypes
for Caucasians (CYP2C9 *1/*1 and VKORC1
rs9923231 A/G) the predicted maintenance
dose would be 4.71 mg per day and the loading
doses would be 7.6 mg on day one, 6.61 mg
on day two and 5.66 mg on day three. If a
patient with the same clinical and demographic
variables had impaired metabolism (CYP2C9
*3/*3) and required a low dose according to
VKORC1 (rs9923231 A/A) the predicted maintenance dose would be 1.00 mg and the loading
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Figure 1. A nomogram that shows the predicted doses for varying age and VKORC1/CYP2C9 genotypes using the International Warfarin Pharmacogenetics
Consortium model. Factors kept constant are ethnicity Caucasian, height 175 cm, weight 75 kg and no interacting drugs. Although the original International Warfarin
Pharmacogenetics Consortium model has age input as decades (1 for ages 10–19 years, 2 for ages 20–29 years) this figure was created by using age as a continuous variable by
dividing the age by 10 years. The shaded areas in the background correspond to dosing recommendations per genotype from the US FDA drug label (Table 1) .
Prediction model taken from Klein et al. [1] .
*2/*3
*2/*2
*1/*3
CYP2C9
*1/*1
0
10
20
20
*3/*3
40
60
Age (years)
80
100
FDA
*1/*3
*2/*2
*2/*3
*3/*3
FDA
*1/*1
*1/*2
30
*1/*1
40
50
www.futuremedicine.com
*1/*2
0
10
20
*3/*3
20
30
40
50
*1/*1
40
60
Age (years)
80
100
FDA
*2/*3
*3/*3
FDA
*1/*1
*1/*2
*2/*2
FDA
*1/*1
*3/*3
20
0
10
20
30
40
50
*3/*3
40
60
80
Age (years)
100
FDA
*3/*3
FDA
*1/*3
*2/*2
*2/*3
VKORC1 rs9923231:G/G
*1/*1
60
VKORC1 rs9923231:A/G
60
VKORC1 rs9923231:A/A
60
Predicted mean dose/week (mg)
How the prediction models could
be used
All three types of prediction models can be used
in all patients. If possible, use a model derived
in a population with similar characteristics to
the patients it will be used on, for example, age,
ethnicity and diet. Imagine a patient that is to
be treated with warfarin due to atrial fibrillation and additional risk factors for stroke with
a target INR of 2.5. The patient has the following characteristics; Caucasian, 60 years of
age, height 175 cm, weight 75 kg, no interacting
drugs and the genotypes CYP2C9 *3/*3 and
VKORC1 A/G. To predict the initial doses for
this patient first use a pharmacogenetic maintenance dose model, such as the IWPC model
Predicted mean dose/week (mg)
„„
Dose refinement
A dose refinement model is used after the first
doses of therapy and incorporates knowledge
about given doses and the INR response.
The International Warfarin Dose Refinement
Collaboration investigated if genetic information adds to dose prediction after initiating
treatment by deriving both pharmacogenetic
and clinical dose refinement models [56] . These
models that can be used on days four to five
were derived on 969 patients and internally validated in 20% of the data that were left out of
the modeling. In a final step the model parameters were refitted on the pooled derivation
and internal validation cohorts (n = 1213) and
evaluated in an external validation cohort (see
prediction model in Table 2 ). Results of internal
validation were R 2 ≈ 60% for the pharmacogenetic model on days four and five and for
the clinical model R 2 ≈ 44% on days four and
five. External validation gave somewhat lower
accuracy of prediction with R 2 ≈ 42% for the
pharmacogenetic model on days four and five
and for the clinical model R 2 ≈ 28% on days
four and five. The overall conclusion was that
the pharmacogenetic dose refinement model
significantly improved the R 2 by 12–17% compared with a clinical dose refinement model.
This indicates that knowledge of CYP2C9
and VKORC1 genotypes still adds information
4–5 days into warfarin therapy.
Predicted mean dose/week (mg)
doses would be 3.23 mg on day one, 2.49 mg on
day two and 1.74 mg on day three. This shows
that the loading doses are less than half and
the maintenance dose less than a quarter for a
patient with CYP2C9 *3/*3 and VKORC1 A/A
compared with a CYP2C9 *1/*1 and VKORC1
A/G patient.
FDA
*1/*1
*1/*2
Prediction of warfarin dose: why, when & how?
435
Review
Eriksson & Wadelius
Table 3. SNPs included in current prediction models.
Gene
SNP
Descent/ethnicity† n used to
estimate
MAF
MAF‡
(%)
Alleles
Effect
CYP2C9
rs1799853 (*2)
White§
3062
13.1
C>T
Asian§
1063
0.0
Black§
645
2.9
The T allele is associated with warfarin
clearance; patients require lower
warfarin doses
A>C
The C allele is associated with warfarin
clearance; patients require lower
warfarin doses
G>A
The A allele is associated with
decreased warfarin dose
C>T
The C allele is associated with
decreased warfarin dose
CYP2C9
rs1057910 (*3)
White
3062
7.1
§
1063
4.0
§
Black
645
1.8
White§
2426
38.0
Asian
883
91.2
643
10.8
Asian
VKORC1
rs9923231
§
§
Black
§
CYP4F2
rs2108622
Caucasian
600
34.2
Asian¶
600
30.5
African–American¶
600
11.7
¶
The descent/ethnicity is kept with the original wording from the cited papers.
‡
MAF is for the minor allele positioned to the right of the ‘>’ sign under the ‘Alleles’ column.
§
Estimates according to Limdi et al. [52].
¶
Estimates according to Scott et al. [51].
MAF: Minor allele frequency.
†
then use the formulas by Avery et al. to calculate the initial doses (Tables 2 & 4) [1,25] . This
patient would be predicted a maintenance dose
of 11 mg/week (1.6 mg/day) and initial doses
of 5.1, 3.9 and 2.7 mg on days one to three,
respectively. On day four the patient comes
back for an INR test and has an INR of 2.0.
At this point a dose revision model could be
used, such as the model by Lenzini et al. (Table 2)
which would predict a dose of 15.6 mg/week
(2.2 mg/day) [56] .
Preferably prediction models should be used
with the VKORC1 SNP they were derived for,
but due to the haplotype structure of VKORC1
it is possible to use other SNPs in linkage disequilibrium (LD) as surrogate markers [32] . Four
SNPs are in high LD (r2 > 0.9) in Caucasians
and Asians (rs9923231, rs2359612, rs9934438
and rs8050894) whereas the LD is lower in
people of African–American ancestry. This LD
information was used by the IWPC group when
rs9923231 was imputed, more information on
the imputation of rs9923231 is in the section S4
of the supplementary information to the IWPC
paper [1,101] .
All of the discussed prediction models have
been derived on datasets of adult patients. The
first bullet point under Box 1, “Is the data representative for the population the model will be
used on”, highlights that these models probably
would not work in children aged 0–18 years.
At the present time many research groups are
436
Pharmacogenomics (2012) 13(4)
evaluating genetic effects on warfarin dose in
children and a recent paper by Biss et al. showed
the current IWPC model constantly over estimates the warfarin dose in children by an average of 1.5 mg/day [57] . They however showed
that a similar proportion of the variation in
dose was explained by VKORC1 and CYP2C9
in c­hildren as in adult patients.
Randomized trials
The clinical benefit of pharmacogenetic dosing is still to be shown. At least five clinical
trials of pharmacogenetic dosing are underway
[102] . Three of the largest are the COAG and
the GIFT trials in the USA, and the European
EU-PACT trial [58–60] .
The COAG study is a two-armed, doubleblinded, randomized controlled trial that
compares genotype-based dosing with clinical guided dosing. Both treatment arms use a
baseline dose-initiation model [2] and a dose
refinement model after four or five days of warfarin therapy [56] . The aim is to include over
1200 patients that are expected to be on warfarin therapy for at least 3 months. The primary
outcome of the study is TTR within the first
4 weeks of therapy.
The GIFT trial is a 2 × 2 factorial-design,
randomized control trial. It has two aims where
the first aim is to compare pharmaco­genetic dosing with clinical dosing and the second aim is to
compare a target range of INR 2.0–3.0 with a
future science group
Prediction of warfarin dose: why, when & how?
lower target range of INR 1.5–2.1. Dosing will
be guided by the website WarfarinDosing.org
for a minimum of the first 11 days of treatment
[103] . The primary study outcome for aim one
is the composite of venous thromboembolism,
major hemorrhage, INR ≥ 4 or death and for
aim two the primary outcome is the composite
of nonfatal venous thromboembolism or death.
The plan is to include 1600 patients undergoing
elective total hip or knee replacement surgery.
The EU-PACT study is a two-armed, singleblinded, randomized controlled trial aiming
to recruit almost 3000 patients commencing anticoagulation therapy with warfarin,
acenocoumarol or phenprocoumon. Patients
in the intervention arm are dosed according
to a drug-specific dosing algorithm, which is
based on genetic information and clinical data.
For warfarin, three to four starting doses are
calculated by combining the IWPC pharmacogenetic prediction model with an estimated
accumulation index that is based on the clearance of warfarin in different CYP2C9 genotypes [1,25,55] . Thereafter a pharmacogenetic
dose revision algorithm is used [56] . Patients
in the control arm are dosed without information about genotype. The follow-up period
is 3 months. The primary aim of the study is
to show improved anti­coagulation therapy by
increased TTR ­during the first 3 months of
treatment.
New alternatives to warfarin
New alternatives to warfarin therapy have
recently been investigated. They include the
direct thrombin inhibitor dabigatran (RE-LY
study) [16] and the factor Xa inhibitors rivoxaban (ROCKET AF study) [61] and apixaban
(ARISTOTLE study) [62] . These new oral
Review
anticoagulants are proven noninferior or even
superior to warfarin; however, the TTR for the
warfarin arms of these three studies was low
with 64% in the RE-LY study, 55% in the
ROCKET study and 62% in the ARISTOTLE
study. The cost–effectiveness of dabigatran in
atrial fibrillation compared with warfarin was
studied in a recent paper by Shah et al. [63] . The
results were that for atrial fibrillation patients
dabigatran is more cost effective in patients
with poor INR control (TTR <57.1%) whereas
warfarin is more cost effective in patients with
excellent INR control (TTR >72.6). Another
aspect of these new drugs is that there is no
natural antidote, and agents to reverse the effect
are still under development [64] .
Future perspective
The dose of warfarin that is required for therapeutic INR levels is strongly related to SNPs in
CYP2C9 and VKORC1. Together with clinical
factors, these genetic markers explain a large
amount of the variability in dose. The variability in dose requirements between ethnicities is largely caused by different allele frequencies of the SNPs in VKORC1. Compared with
dosing according to a clinical algorithm, the
IWPC pharmacogenetic model performs better, especially in the high- and low-dose groups.
Drawbacks of current pharmacogenetic dosing
algorithms, such as the IWPC model, are that
they underestimate doses in patients with large
dose requirements. The cause of warfarin resistance, resulting in high warfarin doses, is not
yet fully understood; it could, for instance, be
another polymorphism, diet or nonadherence to
taking the medicine as prescribed.
To date, the reported prospective clinical
studies using prediction models for warfarin
Table 4. Pharmacogenetic loading doses according to Avery et al.
CYP2C9 VKORC1 IWPC†
IWPC
Loading doses‡
(dose/week in mg) (dose/day in mg) Day one
Day two Day three
(mg)
(mg)
(mg)
*1/*1
A/A
24
3.43
5.51
4.82
4.12
*3/*3
A/A
7
1.00
3.23
2.49
1.74
*1/*1
A/G
33
4.71
7.56
6.61
5.66
*3/*3
A/G
11
1.57
5.07
3.9
2.74
*1/*1
G/G
44
6.29
10.1
8.83
7.56
*3/*3
G/G
18
2.57
8.3
6.39
4.48
Approximate predictions are from the example nomogram in Figure 1 for a Caucasian patient with no interacting drugs,
age 60 years, height 175 cm and weight 75 kg [1].
‡
Prediction of loading doses were made using k (elimination rate constant) of 0.0189 for *1/*1 and 0.0075 for *3/*3 as in
Avery et al. [25].
IPWC: International Warfarin Pharmacogenetics Consortium.
†
future science group
www.futuremedicine.com
437
Review
Eriksson & Wadelius
dosing have been small. There is as yet no
conclusive evidence for the recommendation
to genotype CYP2C9 and VKORC1 when initiating a patient on warfarin [44,50] . New, well
designed, large studies are ongoing to provide clinical utility and validity for personalized warfarin therapy. These ongoing studies
have also been a catalyst for the development
of point of care systems for rapid genotyping.
Using blood, without DNA extraction, these
assays can successfully genotype one sample
with high accuracy in 2 h [65] .
The use of pharmacogenetic dosing of warfarin is dependent on the clinical utility of the
point of care testing, the availability and cost
of genotyping and the cost–effectiveness compared with new oral anticoagulants [63,66,67] .
Regardless of this, the future of pharmaco­
genetic warfarin dosing relies on the results of
the ongoing c­ linical trials.
Financial & competing interests disclosure
M Wadelius is supported by the Swedish Research Council
(Medicine 523-2008-5568), the Swedish Heart and Lung
Foundation, EU FP7 (HEALTH-F2-2009-223062), and
the Clinical Research Support (ALF) at Uppsala University.
The funding organizations played no role in the writing of
this review. The authors have no other relevant affiliations
or financial involvement with any organization or entity
with a financial interest in or financial conflict with the
subject matter or materials discussed in the ­manuscript
apart from those disclosed.
No writing assistance was utilized in the production of
this manuscript.
Executive summary
Warfarin
ƒƒ Warfarin is monitored by the International Normalized Ratio (INR), which is a measure of the clotting ability of blood. A level of 1 is
associated with normal coagulation and 2 indicates doubled coagulation time compared with normal.
ƒƒ The optimal effect for most patients is in the target range of INR 2.0–3.0 and the required dose to reach such levels varies between
<10 mg per week to >100 mg per week.
Adverse effects
ƒƒ The most common adverse effect of warfarin is bleeding and the risk is highly related to the intensity of anticoagulation.
Genetic effects
ƒƒ CYP2C9 is associated with the clearance of warfarin. Patients with normal metabolism (CYP2C9 *1/*1), have an estimated half-life of
30–37 h whereas those with severely impaired metabolism (CYP2C9 *3/*3) have an estimated half-life of 92–203 h.
ƒƒ VKORC1 codes for the enzyme vitamin K epoxide reductase, the target of warfarin.
Why predict warfarin dose?
ƒƒ Unintentional overdosing of warfarin remains as one of the most common causes of hospitalization due to adverse reactions in the
USA and the UK.
ƒƒ Genotype-guided dose has the potential to reduce the risk of bleeding during the initiation phase (1–3 months) of warfarin.
When to predict warfarin dose
ƒƒ Three different kinds of pharmacogenetic prediction models exist:
- Prediction of stable maintenance dose.
- Prediction of loading doses to be used during the initiation of warfarin.
- Dose revision models including the INR response on day 4 and later.
Randomized trials
ƒƒ Randomized trials incorporating pharmacogenetic dosing of warfarin have to date been too small to draw solid conclusion about the
value of genotyping before drug initiation.
ƒƒ At least three large trials are ongoing; the COAG and the GIFT trials in the USA, and the European EU-PACT trial.
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„„
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to the International Warfarin
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www.nejm.org/action/showSupplements?doi=
10.1056%2FNEJMoa0809329&viewType=P
opup&viewClass=Suppl
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www.clinicaltrials.gov
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Pharmacogenomics (2012) 13(4)
future science group