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STUDY OF GENETIC AND PHARMACOKINETIC ISSUES
RELATED TO EFFICACY AND TOXICITY OF
WARFARIN IN PAKISTANI SUBJECTS
By
Dr. Aisha Qayyum
MBBS, MPhil
2009-NUST-TfrPhD-Pharma-22
Department of Pharmacology and Therapeutics
Army Medical College
National University of Sciences & Technology
Rawalpindi, Pakistan
2014
i
STUDY OF GENETIC AND PHARMACOKINETIC ISSUES
RELATED TO EFFICACY AND TOXICITY OF
WARFARIN IN PAKISTANI SUBJECTS
By
Dr. Aisha Qayyum
MBBS, MPhil
2009-NUST-TfrPhD-Pharma-22
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENT FOR THE DEGREE
OF
DOCTOR OF PHILOSOPHY
IN
PHARMACOLOGY AND THERAPEUTICS
Department of Pharmacology and Therapeutics
Army Medical College
National University of Sciences & Technology
Rawalpindi, Pakistan
2014
ii
Certified that the content and form of thesis titled “Study of genetic and
pharmacokinetic issues related to efficacy and toxicity of warfarin in Pakistani subjects”
submitted by Dr. Aisha Qayyum have been found satisfactory for the requirement of the PhD
degree.
Supervisor: _______________________
Brig® Muzammil Hasan Najmi
Co-Supervisor: ____________________
Maj Gen Abdul Khaliq Naveed
Member (1): ______________________
Brig Akbar Waheed
Member (2): ______________________
Prof Muhammad Nawaz
Member (3): ______________________
Dr Anwar-ul-Hasan Gilani
iii
TABLE OF CONTENTS
S. No.
Content
Page Number
1
Summary
1
2
Introduction
4
3
Review of Literature
8
4
Aim and Objectives
37
5
Subjects, Materials and Methods
38
6
Results
68
7
Discussion
113
8
Conclusion
137
9
Recommendations
138
10
References
139
11
Appendices
199
iv
LIST OF ABBREVIATIONS
Abbreviations
Used for
AFIC Armed Forces Institute of Cardiology
ANOVA
Analysis of Variance
BMI
Body Mass Index
bp
Base Pair
CREAM
Centre for Research in Experimental and Applied Medicine
COAG Clarification of Optimal Anticoagulation Through Genetics
CYP450
Cytochrome P450
CYP2C9
Cytochrome P450 2C9
EDTA
Ethylene Diamine Tetra-acetic Acid
ESR
Erythrocyte Sedimentation Rate
EU-PACT European Pharmacogenetics of Anticoagulant Therapy-Warfarin
FDA
Food and Drug Administration
FFP
Fresh Frozen Plasma
GGCX
γ-glutamyl Carboxylase
GIFT Genetics Informatics Trial of Warfarin to Prevent Venous Thrombosis
CV
Coefficient of Variation
ºC
Degree Centigrade
dH2O
Deionized Water
DNA Deoxyribonucleic Acid
v
Abbreviations
Used For
dNTPs
Deoxynucleotide Triphosphate
HPLC
High Performance Liquid Chromatography
HWE
Hardy-Weinberg Equilibrium
IBGE Institute of Biomedical and Genetic Engineering
ICH
International Conference on Harmonization
INR
International Normalized Ratio
LLOQ
Lower Limit of Quantitation
LOD
Limit of Detection
Mg2+
Magnesium Ions
MgCl2
Magnesium Chloride
min
Minute
ml
Mililiter
µl
Microliter
mM
Milimoles
ng
Nanogram
NICVD
National Institute of Cardiovascular Diseases
nm
Nanometer
NSAIDs
Non-steroidal Anti-inflammatory Drugs
OD
Optical Density
PCC
Prothrombin Complex Concentrate
PCR
Polymerase Chain Reaction
vi
Abbreviations
Used For
PCR-RFLP Polymerase Chain Reaction- Restriction Fragment Length Polymorphism
pH
Potential of Hydrogen
PT
Prothrombin Time
QC
Quality Control
rpm
Revolutions Per Minute
SD
Standard Deviation
SDS
Sodium Dodecyl Sulfate
sec
Seconds
SNP
Single Nucleotide Polymorphism
TBE Tris-Borate- Ethylene Diamine Tetra Acetic Acid
U/µl
Unit/microliter
USA
United States of America
US FDA
United States Food and Drug Administration
UVAS
University of Veterinary and Animal Sciences
VKOR
Vitamin K Epoxide Reductase
VKORC1 Vitamin K Epoxide Reductase Complex Subunit 1
VTE
Venous Thromboembolism
WARF
Wisconsin Alumni Research Foundation
w/v Weight/volume
vii
LIST OF TABLES
Table No.
TITLE
Page No.
2.1
Risk factors for thrombosis
10
2.2
Drug, food, and dietory supplement interaction with warfarin by level of supporting evidence and direction of interaction
25
2.3
Biological half-lives of vitamin K-dependent coagulation proteins
29
4.1
Agarose gel constituents with quantities
54
4.2
List of primers used for genotyping
56
4.3
PCR reagents with their concentrations used for PCR
57
4.4
Thermal profile for the PCR for VKORC1 alleles
58
4.5
Thermal profile for the PCR for CYP2C9 alleles
63
5.1
Demographic characteristics of study population
69
5.2
Regional distribution of study population
70
5.3
Indications for warfarin administration in study population
71
5.4
Correlation between demographic factors and warfarin dose
72
5.5
Allele and genotype frequency distributions of VKORC1 1173C>T polymorphism
80
5.6
Allele and genotype frequency distributions of VKORC1 -1639G>A polymorphism
83
5.7
Relationship of warfarin dose with VKORC1 genotypes
85
5.8
Allele and genotype frequency distributions of CYP2C9*2 polymorphism
90
5.9
Allele and genotype frequency distributions of CYP2C9*3 polymorphism
94
viii
Table No.
TITLE
Page No.
5.10
Relationship of warfarin dose with CYP2C9 genotypes
95
5.11
Pair-wise comparison of warfarin dose among CYP2C9*3 genotypes
98
5.12
Intra- and inter-day precision and accuracy of S- and R-warfarin analysis
105
5.13
Analytical stability of single enantiomer
109
5.14
Relationship of S/R warfarin ratio with CYP2C9 genotypes
110
5.15
Pair-wise comparison of S/R warfarin ratio among CYP2C9 genotypes
111
5.16
Multiple linear regression models with mean weekly warfarin dose as dependent variable
112
ix
LIST OF FIGURES
Figure No.
TITLE
Page No.
2.1
Coagulation cascade
11
2.2
Classification of anticoagulants
14
2.3
Chemical structure of warfarin with chiral centre
20
2.4
Mechanism of action of warfarin
28
5.1
Relationship between weight and warfarin dose
73
5.2
Relationship between height and warfarin dose
74
5.3
Relationship between BMI and warfarin dose
75
5.4
Relationship between age and warfarin dose
76
5.5
Representative gel illustrating PCR-RFLP products for VKORC1 1173C>T genotyping
78
5.6
DNA sequencing electropherogram of VKORC1 gene region containing position 1173bp in intron 1
79
5.7
Representative gel illustrating PCR-RFLP products for VKORC1 –1639G>A genotyping
81
5.8
DNA sequencing electropherogram of VKORC1 gene region containing position –1639bp in promoter region
82
5.9
Different VKORC1 – 1639G>A genotypes along with mean warfarin dose in respective groups
86
5.10
Different VKORC1 1173C>T genotypes along with mean warfarin dose in respective groups
87
5.11
Representative gel illustrating PCR-RFLP products for CYP2C9*2 genotyping
88
x
Figure No.
TITLE
Page No.
5.12
DNA sequencing electropherogram of CYP2C9*2 polymorphism
89
5.13
Representative gel illustrating PCR-RFLP products for CYP2C9*3 genotyping
92
5.14
DNA Sequencing Electropherogram of CYP2C9*3 polymorphism
93
5.15
Different CYP2C9*2 Genotypes along with mean warfarin dose in respective groups
96
5.16
Different CYP2C9*3 Genotypes along with mean warfarin dose in respective groups
97
5.17
Chromatogram of pure S-wafarin standard
100
5.18
Chromatogram of pure R-wafarin standard
101
5.19
Chromatogram obtained from plasma sample spiked with 200 ng/ml standard
102
5.20
Calibration curve for S-warfarin
103
5.21
Calibration curve for R-warfarin
104
xi
DEDICATED
To
My parents
My first teachers
Who have not been with me since long
But their prayers are
xii
ACKNOWLEDGEMENT
Foremost, I am thankful to Almighty Allah who I believe carried me through thick and
thin. I would have not been writing these lines without this belief.
I would like to acknowledge and extend my heartfelt gratitude to my supervisor Brig®
Muzammil Hasan Najmi for his trust in me and providing me guidance. I am thankful to Brig
Akbar Waheed, Head of Pharmacology dept, who spared me to carry out research activities.
Thanks are due to my colleagues in Pharmacology department, both junior and senior faculty
members who encouraged and helped me on many occasions especially Dr Nuzhat Usmani.
With a deep sense of gratitude, I wish my sincere thanks to Maj Gen Abdul Khaliq Naveed,
Professor Mohammad Nawaz and Professor Anwar-ul-Hasan Gilani for their guidance and
valuable suggestions that improved the quality of my research. There are many people who made
my work easier and offered help when needed. I wish to express my cordial appreciation to Brig
Qaiser Khan (AFIC), Brig Jalil Anwar (PNS Shifa), Dr Tariq Ashraf (NICVD) and Mr Aqeel (AM
College). The cooperation received from the staff of laboritories at Military Hospital and AFIC is
gratefully acknowledged.
I must send thanks to all the subjects who cooperated and provided me with the
experimental data that I regard vital for my research work. Special gratitude goes to Higher
Education Commission (HEC) of Pakistan for funding this project.
My final words go to my family. I would like to share this moment of accomplishment with
my brother Usman and sisters, Amna and Shazia who supported me and prayed for me. I have no
suitable words that can describe the everlasting love and support from my husband, Col® Amer
Mohsin who was always there to get me through the difficult times and cheer me up when the
journey became tough. Without his loving support and understanding, I would never have
xiii
completed this work. Lastly and most importantly, I owe so much to my loving sons, Zehaib and
Haseeb for their prayers for my work and at times they also helped me in performing different
chores. They are my real inspiration and love of life.
At the end, special gratitude to all those people who have been part of this project but I
failed to mention their names. I would like to thank for their direct and indirect support in
completing my work.
1
SUMMARY
Warfarin has been the most commonly prescribed oral anticoagulant for various
thromboembolic disorders. Different environmental, demographic and genetic factors
contribute to intra- and inter-individual variations in warfarin dose requirement leading to
inadequate or supratherapeutic anticoagulation which results in morbidity and mortality.
Polymorphism in CYP2C9 gene affecting S-warfarin metabolism results in variation in S-
warfarin levels and in turn warfarin dose. The presence of polymorphisms in VKORC1 gene
also leads to variance in warfarin dose. Diverse genotype frequency distribution of these
polymorphisms has been demonstrated among different populations. Such differences have
led to need for characterization of genotype frequency distribution in different populations for
construction of customized dosing algorithms to enhance the efficacy and reduce the toxicity
of warfarin therapy by administering accurate individualized dose. This study was carried out
in Pakistani population with same objective to evaluate the contribution of demographic and
genotypic elements to warfarin dose variability.
The clinical part of the study was carried out at Armed Forces Institute of Cardiology
Rawalpindi and National Institute of Cardiovascular Diseases Karachi whereas the analytical
part was carried out at Centre for Research in Experimental and Applied Medicine, Army
Medical College Rawalpindi, University of Veterinary and Animal Sciences Lahore and
Institute of Biomedical and Genetic Engineering Islamabad. Six hundred and seven stable
patients taking warfarin were enrolled after medical history, physical examination and
laboratory investigations. Demographic and clinical data were recorded in pre-structured
proforma. Single blood sample was collected from each individual after informed consent and
divided in respective tubes for various analyses. Genomic DNA was extracted and genotype
2
analysis for CYP2C9*3, CYP2C9*2, VKORC1 1173C>T and VKORC1 –1639G>A
polymorphisms was done by polymerase chain reaction-restriction fragment length
polymorphism (PCR-RFLP) assay. A number of samples were also analyzed by direct DNA
sequencing for validation of results. In 170 patients, a blood sample of 5 ml was used to
separate plasma for analysis of S- and R-warfarin levels. This enantiomeric analysis was
carried out by a modified and validated high performance liquid chromatography (HPLC)
method. Data was analyzed using SPSS version 20.
Increasing age of the patients was found to cause significant reduction in warfarin
dose requirement whereas gender, weight, height and BMI did not show any significant effect
in warfarin dose variance. CYP2C9*2 and CYP2C9*3 genotype frequency distribution in
Pakistani subjects was found to be different from other populations. The presence of
CYP2C9*2 variant allele did not demonstrate any significant effect on warfarin dose whereas
CYP2C9*3 resulted in significant reduction of warfarin dose. The presence of CYP2C9*3
variant allele led to significant increase in S-warfarin levels, thus resulting in raised S/R
warfarin ratio in these patients. Genotype frequency distribution of VKORC1 1173C>T and
VKORC1 –1639G>A were also different from rest of the world populations. Both of these
polymorphisms did not demonstrate significant effect on warfarin dose requirement. The
overall effect of demographic and genotypic factors studied was small but statistically
significant.
Thus, it can be inferred that there is a need to study the genotype frequency
distribution and their effect on warfarin dose variability among different populations as it may
result in substantial variation in response to warfarin. At the same time, a small proportion of
3
warfarin dose variation explained by factors studied in present work stresses the need for
exploration of more genetic and non-genetic factors in Pakistani population.
4
Chapter 1
INTRODUCTION
Thromboembolic disorders are among the major health problems. These disorders lead
to not only higher rates of morbidity and mortality but also there is substantial financial
burden on health care systems associated with these disorders. By administering effective
prophylaxis and treatment in such conditions, the incidence or recurrence of thromboembolic
events can be reduced to great extent. Anticoagulants are one of the main groups of drugs
used to prevent or treat venous thromboembolism (Lefebvre et al., 2012, Kishore, 2013).
Warfarin has been the most commonly used oral anticoagulant for treatment and
prophylaxis of various arterial and venous thromboembolic diseases. Management of warfarin
therapy is troublesome because of significant intra- and inter-individual variability, low
therapeutic index and highly variable pharmacokinetics. Many factors like age, sex, weight,
ethnicity, genetic factors, dietary intake, concurrent diseases and medications have been
reported to affect the warfarin dose requirements. Inadequate or supra-therapeutic
anticoagulation may result in substantial morbidity and mortality due to failure to prevent
thromboembolism or bleeding complications respectively. The fear of the complications often
results in avoidance by clinicians to prescribe warfarin to those who may benefit from it.
Warfarin exists in S– and R-isomers which differ in their potency and metabolism. The S-
warfarin is metabolized mainly by CYP2C9 and R-warfarin by CYP3A4, CYPIA2 and
CYPIA1. The S-enantiomer is around 3–5 times more potent than R-enantiomer and is mainly
responsible for warfarin’s anticoagulant effect, so variations in gene encoding CYP2C9
enzyme may affect the clinical outcome of warfarin therapy. The presence of CYP2C9*3 or
5
CYP2C9*2 variant alleles leading to slower clearance of S-warfarin is linked with a
significant reduction in warfarin dose and a higher risk for serious bleeding. Warfarin acts by
inhibiting vitamin K epoxide reductase (VKOR) enzyme which is required for recycling of
vitamin K, a crucial cofactor in the formation of the active clotting proteins X, IX, VII and II,
thus resulting in anticoagulation through decreased levels of active form of these clotting
factors. The gene encoding VKOR is vitamin K epoxide reductase complex subunit 1
(VKORC1). Two single nucleotide polymorphisms (SNPs) in VKORC1, –1639G>A and
1173C>T, have been found to be significantly affecting the warfarin dose requirement. For
both polymorphisms, the individuals with variant alleles require lower warfarin dose to
produce therapeutic response (Ageno et al., 2012, Lee and Klein, 2013).
With the expansion in knowledge of human genome, the world is heading towards
pharmacogenetics. A large portion of research has focused on screening of different SNPs and
their association with drug response. During last decade many polymorphisms in different
genes involved in warfarin pharmacokinetics and pharmacodynamics have been studied. The
reported studies have shown that VKORC1 and CYP2C9 gene variants alongwith clinical and
demographic variables explain around 50–60 percent variability in warfarin dose. As a result
of convincing reported studies in this regard, United States Food and Drug Administration
(US FDA) in 2010 added a table containing pharmacogenetic-guided dose recommendations
including common VKORC1 and CYP2C9 genotypes. Currently the results of major
prospective trials on warfarin pharmacogenetics are in pipeline which will further define the
pharmacogentics of warfarin therapy. Some commercial tests which are aimed to be cost-
effective and less time consuming, have been approved by US FDA and are now available for
6
genotyping of common CYP2C9 and VKORC1 polymorphisms for clinical use (Limdi, 2012,
Weeke and Roden, 2013).
To expedite the use of VKORC1 and CYP2C9 genotyping routinely in anticoagulation
practice, it is imperative to carry out the studies to characterize the allelic frequency in various
populations and to determine its effect on warfarin anticoagulant response. Most of the studies
have been conducted in Caucasians revealing strong relation of CYP2C9 and VKORC1
genotypic variations with warfarin dose requirement. A number of studies have been
conducted in different Asian countries. Studies have been carried out in some populations
which have shown racial differences in these allelic frequencies. With the help of results of
these studies, different populations have constructed customized warfarin dosing algorithms
taking into account their population’s demographic, clinical and genotypic data. The outcome
of pharmacogenetic-guided warfarin therapy is in the form of improved safety profile and
reduction in burden of frequent international normalized ratio (INR) measurements. A
comprehensive dosing model that is applicable regardless of ethnicity can be developed by
identifying the allelic frequency of common alleles in different ethnic populations. This may
further help in drafting international clinical guidelines for warfarin prescription applicable to
different populations (Jorgensen et al., 2012, Mahajan et al., 2013).
Pakistan, although being one of the populous countries of the world, has been under-
represented in the field of pharmacogenetics. This is because of the fact that there have been
very few studies conducted on local population. There is no population-based study providing
data regarding common CYP2C9 and VKORC1 genotypes frequency distribution status in
Pakistan and their contribution to warfarin anticoagulant response. This study has been
planned to report the frequency distribution of CYP2C9*3, CYP2C9*2, VKORC1 1173C>T
7
and VKORC1 –1639G>A in Pakistani population and to quantify their effect along with that
of demographic factors on warfarin dose. The effect of CYP2C9 genotypes on warfarin
enantiomers metabolism has also been studied.
The present study has generated the local data of effect of demographic factors like
age, gender, height and weight on warfarin dose requirement which would help to adjust the
patient’s dose according to these parameters to avoid any sub- or supra-therapeutic dosage
and subsequently thromboembolic or bleeding complication respectively. As cost-effective
commercial assays for genotyping of common VKORC1 and CYP2C9 SNPs are now
available in many clinical settings in western world and soon will be in Pakistan as well. The
locally generated genotypic frequency data will not only help in rational export and use of
these commercial kits according to population’s need, but when used, will provide data to
construct customized dosing algorithm for Pakistani population. This will help in further
tailoring of warfarin dose adjustments on the basis of both these demographic and genotypic
data. The genotypic data from one of the populous countries like Pakistan will fill the gap in
pharmacogenetic knowledge databases by providing results from this part of the world that
will faciltate drafting of meaningful international guidelines.
8
Chapter 2
REVIEW OF LITERATURE
2.1 THROMBOEMBOLISM
2.1.1 Hemostasis and Thrombosis
Balance between procoagulant and anticoagulant factors in vascular system maintains
fluid-state of blood in healthy vessels. The main components of this system are vascular wall
especially endothelium, platelets and coagulation cascade. A sequence of hemostatic events
occurs whenever there is any vascular injury to limit the blood loss. These events are
vasoconstriction, platelet plug formation and fibrin clot formation. After the bleeding is
controlled, the hemostatic system remodels the damaged vessel and limits the hemostatic plug
to restore blood flow (Mitchell, 2010, Craig et al., 2010, Campbell et al., 2011, Konkle, 2012,
Kishore, 2013).
Thrombosis is a localized clotting of blood within intact vessel and is a pathological
equivalent of hemostasis. Embolism is the fully or partially detached thrombus carried by
blood to distant body part. Thromboembolism involves the same components of hemostatic
system. Virchow’s triad describes three factors responsible for thrombosis which are injury to
endothelium, turbulence or stasis of blood flow and hyper-coagulability of blood. The
pathophysiology of venous thrombosis is somewhat different from arterial thrombosis. In
arterial thrombi, primary trigger is vascular abnormality like atherosclerosis and main role is
of platelets. Because of this reason arterial thrombi are rich in platelets and poor in fibrin
(white clot) with antiplatelet drugs playing an important role in preventing their formation. In
venous thrombi formation, changes in blood flow and hyper-coagulability play the major role.
Venous thrombi are rich in fibrin but poor in platelets (red clot) (Mackman, 2008, Mitchell,
9
2010, Manly et al., 2011, Campbell et al., 2011, Konkle, 2012, Freedom and Loscalzo, 2012,
Kishore, 2013).
2.1.2 Risk Factors and Outcomes
There are many factors and conditions related to increased hazard of thromboembolic
events. Some factors are responsible for arterial thrombosis and others predominantly lead to
venous thromboembolism whereas a few conditions are associated with both of them. Both
environmental and genetic factors are known to have risk of thrombo-embolism. Many of
these risk factors are modifiable and preventable with appropriate healthy lifestyle and
prophylactic medication. The risk factors have been summarized in Table 2.1 (Heit, 2005,
Lowe, 2008, Goldhaber, 2010, Mitchell, 2010, Konkle, 2012, Freedom and Loscalzo, 2012,
Creager and Loscalzo, 2012).
Thromboembolism leads to morbidity, serious long-term complications, impaired
quality of life, loss of functions and even death. Recurrence of thromboembolism is also
common especially if patients are not treated with appropriate medication. Arterial
thromboembolism can cause ischemia and infarction leading to functional loss of affected
tissue such as myocardial infarction and stroke. Venous thromboembolism is associated with
post-thrombotic syndrome, pulmonary hypertension and venous stasis syndrome (Heit, 2005,
Goldhaber, 2010, Mitchell, 2010, Lefebvre et al., 2012).
2.1.3 Venous Thromboembolism
Coagulation is the mainstay of venous thromboembolism (VTE). Coagulation pathway
comprises of clotting factors majority of which are synthesized by liver and are circulating in
blood as inactive zymogens. Coagulation involves the formation of fibrin by a cascade of
enzymatic reactions in which each reaction product, an activated clotting factor converts the
subsequent inactive zymogen into an activated protease (Figure 2.1).
10
Table 2.1: Risk factors for thrombosis
Venous
Venous and Arterial
Inherited
Protein C deficiency
Antithrombin deficiency
Protein S deficiency
Factor V Leiden
Acquired
Age
Long-haul travel
Immobilization
Previous thrombosis
Trauma
Infection
Major surgery
Pregnancy and pueperium
Hospitalization
Obesity
Smoking
Transvenous pacemaker
Atrial fibrillation
Inherited
Dysfibrinogenemia
Homocystinuria
Mixed (inherited and acquired)
Hyperhomocysteinemia
Acquired
Malignancy
Thrombotic thrombocytopenic purpura
Hormonal therapy
Hypertension
Heparin-induced thrombocytopenia
Diabetes
Hyperlipidemias
Myocardial Infarction
Disseminated intravascular coagulation
Prosthetic heart valve
Polycythemia Vera
Essential thrombocythemia
Antiphospholipid antibody syndrome
11
Figure 2.1: Coagulation cascade
XI
XIIa
XIa
IX IXa
Ca++
VII VIIa
X Xa X
VIII
Ca+
III Ca++
XIIa, XIa, Kallikrein
Prothrombin Ca++PIatelets Phospholipids
Thrombin
Thrombin
Fibrin (insoluble) (Clot or Thrombus)
Fibrin (soluble)Fibrinogen
Intrinsic system
Endothelial Damage
Exposure to Collagen
Extrinsic system
Tissue Damage
Tissue Factor
V
XIII
12
The coagulation cascade begins with injury to vascular wall which exposes tissue factor,
constitutively expressed in perivascular cells, to blood components. Coagulation occurs by
two pathways; it is initiated by tissue factor called extrinsic pathway and then amplified by
intrinsic pathway. In final step soluble fibrinogen is transformed into insoluble fibrin which is
the key component of thrombus formation (Craig et al., 2010, Mitchell, 2010, Mackman and
Becker, 2010, Campbell et al., 2011, Manly et al., 2011, Konkle, 2012, Freedom and
Loscalzo, 2012, Kishore, 2013).
2.1.4 Epidemiology of Thromoembolic Disorders
Thromboembolic disorders are among the major public health problems. Venous
thromboembolism is the third leading cause of death after myocardial infarction and stroke
(Mackman, 2008, Mackman and Becker, 2010). It occurs in around one million people
annually in United States of America (USA) alone, out of which 0.6 million are hospitalized
cases. Each year around 0.3 million deaths have been attributed to pulmonary embolism in
USA. The risk of VTE even among treated patients remains high with 6–18 percent occurring
within 1–2 years and 30 percent within 10 years (Heit et al., 2000, Heit, 2005, Lefebvre et al.,
2012). A study conducted in Europe has reported over one million VTE events or deaths per
anum in six European countries (Cohen et al., 2007).
There is substantial financial burden associated with VTE. Different studies have been
carried out to estimate the financial burden on healthcare systems and it has shown figures in
billions of dollars per year (Bullano et al., 2005, Mahan et al., 2011, Mahan et al., 2012,
Dobesh, 2009, Lefebvre et al., 2012). These drastic consequences in the form of higher
morbidity or mortality in affected patients and economic burden on healthcare system have
stressed the use of thrombo-prophylaxis. By ensuring that patients receive effective
prophylaxis and treatment for VTE for a sufficient period of time will reduce the incidence or
13
recurrence of VTE and its long-term complications (Bullano et al., 2005, Mackman, 2008,
Dobesh, 2009, Ruppert et al., 2010, Mahan et al., 2012, Chan and Shorr, 2012).
2.1.5 Management
Antithrombotic drugs are used for prevention and treatment of thromboembolism. The
groups of drugs used are antiplatelet drugs, anticoagulants and fibrinolytic agents. Due to
predominance of platelets, arterial thrombosis prophylaxis and treatment involves mainly
antiplatelets although anticoagulant drugs and fibrinolytics are also used in acute and high risk
patients. For venous thromboembolism where fibrin is main component, anticoagulants are
mainstay of prevention and treatment. In these patients, antiplatelets have limited role and
fibrinolytics are used in selected patients (Mackman, 2008, Mackman and Becker, 2010,
Weitz, 2012, Yeung et al., 2013, Kishore, 2013). .
2.2 ANTICOAGULATION THERAPY
Anticoagulants are the drugs that act on different components of coagulation cascade
and inhibit the coagulation and clot formation. An ideal anticoagulant should be efficacious
with selective targeting, has rapid onset of action with ease to administer, with no need for
regular laboratory monitoring and dose adjustments, with minimal adverse effects and
availability of specific antidote for reversal of over-dosage, with no food or drug interactions
and at the same time should be cost-effective. Presently there is no such anticoagulant that has
all of these characteristics. There are some anticoagulants that are established and in clinical
use for last several decades. Some newer anticoagulants are now recently being added to
clinical set up and some are in pipeline to be used for anticoagulation therapy. Anticoagulants
used in vivo are shown in Figure 2.2 (Eikelboom and Weitz, 2010, Campbell et al., 2011,
Ageno et al., 2012, Holbrook et al., 2012, Tsiara et al., 2011, Weitz and Gross 2012,
Ranganathan and Venkatesh, 2013, Yeung et al., 2013, Kishore, 2013).
14
bit coagulation by interacting with antithrombin.
assification of anticoagulants (Eikelboom and Weitz, 2010)
Anticoagulant
Oralon
Thrombin
Parenteral
Thrombin fXa
fXa fIXa Thrombin fXa Oth
d
nts
Bivalirudin Argatroban
Hirudin
Heparin* LMWH*
Fondaparinux* Vitamantago
nt
AVE5026* Idrabiotaparinux*
Otamixaban
RB006 Dabigatran etexilate
AZD0837
Rivaroxaban Apixaban Edoxaban Betrixaban
YM150 TAK442
15
Parentral anticoagulants cannot be used routinely for long-term treatment except in
patients either for emergency treatment or in which oral antcoagulants are not advisable.
Initiation of anticoagulation therapy with parentral heparin followed by oral warfarin
administration has long been standard regimen. Warfarin has been the most commonly
prescribed oral anticoagulant for last 60 years. Its mechanism of action, pharmacokinetics,
adverse effects, laboratory monitoring, food and drug interactions are now well-established. It
is cheap and cost-effective. Although warfarin use has its own limitations which have lead to
development of newer anticoagulants but the newer ones have their own drawbacks
(Eikelboom and Weitz, 2010, Maan et al., 2012, Garcia et al., 2010, Schulman, 2012,
Holbrook et al., 2012, Fox et al., 2012, Thomopoulos and Tsioufis, 2012, Ageno et al., 2012).
2.2.1 Newer Anticoagulants (NOACs)
Ximelagatran was the first oral direct thrombin inhibitor developed and clinical trials
supporting its efficacy started appearing in publications from year 2000 onwards. It was
withdrawn from market in 2006 due to hepatotoxicity after a brief use in clinical set up
(Eriksson et al., 2000, Schulman et al., 2003, Olsson, 2003, AstraZeneca, 2004, Gurewich,
2005, Fiessinger et al., 2005, EMA, 2006, Ho and Brighton, 2006, Testa et al., 2007, Ansell,
2005, Keisu and Andersson, 2010, Albers et al., 2005, Schulman, 2012). Dabigatran etexilate
was approved in 2010 as an orally given direct thrombin inhibitor for stroke prophylaxis in
patients with atrial fibrillation and VTE prophylaxis in orthopedic surgery patients in USA,
Europe and many other countries (FDA, 2010, Firriolo and Hupp, 2012, Beasley et al., 2011,
Ageno et al., 2012, Schulman, 2012, Maan et al., 2012, Eby, 2013). Rivaroxaban, an orally
given factor Xa inhibitor has recently been approved in 2011 for VTE prophylaxis in
orthopedic surgery patients, stroke prophylaxis in atrial fibrillation and for deep vein
thrombosis (DVT) in USA, Canada, Europe and many other countries (Firriolo and Hupp,
16
2012, Maan et al., 2012, Ageno et al., 2012, Schulman, 2012, FDA (b), 2012, Iyer et al.,
2012, Eby, 2013). Apixaban, an orally administered factor Xa inhibitor, has also been recently
approved in 2012 for stroke prophylaxis in patients with atrial fibrillation in USA and was
approved in 2011 for venous thromboembolism prophylaxis in orthopedic surgery patients in
Europe (Maan et al., 2012, FDA (a), 2012, Agrawal et al., 2012, Schulman, 2012, Eby, 2013).
Clinical studies have reported some superiority or non-inferiority of these agents in
comparison to warfarin in terms of efficacy and adverse effects. A meta-analysis of major
clinical trials has pointed out the factor of sponsoring of these supportive studies by industry
and the significance of margin of non-inferiority or superiority of these agents. It has been
suggested that results when applied to large population, a close vigilance should be there as
ximelagatran was withdrawn so early after its large scale use (Connolly et al., 2009, Fox et
al., 2012, Miller et al., 2012, Granger et al., 2011, Easton et al., 2012, Patel et al., 2011,
Hankey et al., 2012, Schulman et al., 2013, Capodanno et al., 2013).
There are some concerns regarding use of newer anticoagulants which have been
raised by many researchers around the world. There is no precise laboratory monitoring test to
measure their anticoagulant effect (Garcia et al., 2010, Tsiara et al., 2011, Stollberger and
Finsterer, 2012, Healey et al., 2012, Dogliotti et al., 2013, Iyer et al., 2012, Eby, 2013). It
remains to be determined whether these newer anticoagulants will be as effective in other
clinical thromboembolic disorders apart from their approved indications (Iyer et al., 2012,
Schulman, 2012, Coppens et al., 2012). Recently in 2012, US FDA issued a safety notice in
which use of dabigatran has been contraindicated in patients with mechanical heart valves
(Perera et al., 2013). Till now, no effective specific antidote is available to reverse the
overdosage or toxicity of these agents (Garcia et al., 2010, Campbell et al., 2011, Healey et
al., 2012, Iyer et al., 2012, Tsiara et al., 2011, Stollberger and Finsterer, 2012, Reynolds et
17
al., 2012, Eby, 2013). Some of them have been related to increase in risk of some toxic effects
such as Dabigatran has been associated with increase in risk of myocardial infarction, gastro-
intestinal bleeding and dyspeptic symptoms. Rivaroxaban has been associated with nausea,
anemia and increase in liver function enzymes (Schulman et al., 2009, Eikelboom et al., 2011,
Hohnloser et al., 2012, Reynolds et al., 2012, Campbell et al., 2011, Iyer et al., 2012,
Schulman, 2012, Dale et al., 2013). Because of shorter half life of newer anticoagulants (6–17
hours) as compared to warfarin (36–48 hours), a missed dose will make the patient more
vulnerable to thromboembolic event (Schulman, 2012). Use of newer drugs in patients with
renal or hepatic insufficiency has to be established (Garcia et al., 2010, Tsiara et al., 2011,
Campbell et al., 2011, Stollberger and Finsterer, 2012, Reiffel, 2012, Iyer et al., 2012,
Schulman, 2012, Eby, 2013). Their efficacy, adverse outcome and cost-effectiveness have yet
to be established for their long-term use in real-world set up (Campbell et al., 2011).
Although warfarin alone is much cheaper as compared to newer anticoagulants but
taking into account cost of laboratory monitoring and adverse effect management, these drugs
may be comparable to warfarin in cost-effectiveness. The real-world use cost still has to be
calculated when these drugs will be used for long-term therapy in large-scale population
(McCullagh et al., 2009, Campbell et al., 2011, Sorensen et al., 2011, Wolowacz et al., 2009,
Freeman et al., 2011, Tsiara et al., 2011, Schulman, 2012, Lee et al., 2012, Sanaei, 2012, Iyer
et al., 2012). Some studies have directly shown no superiority of these newer anticoagulants
over warfarin. Other newer anticoagulants are still under clinical trials (Bauersachs et al.,
2010, Sawaya et al., 2012, Schulman et al., 2009, Lakkireddy et al., 2012, Healey et al., 2012,
Flaker et al., 2012, Snipelisky et al., 2012, Patel et al., 2013). The use of dosing algorithms
based on clinical, demographic and genetic variables for precise warfarin dose administration
has lead to improved outcomes in limiting the adverse events and also less frequent laboratory
18
monitoring (Jorgensen et al., 2012, Anderson et al., 2012, Van Spall et al., 2012, Cini et al.,
2012, Daly, 2013). So till the time these newer anticoagulants would be able to demonstrate
adequate efficacy and safety in real-world clinical practice, warfarin will remain the drug of
choice for oral anticoagulation therapy.
2.3 WARFARIN
2.3.1 History
Discovery of warfarin started from cattle hemorrhagic disease in the first quarter of
last century leading to discovery of a rat poison which developed into one of the most
commonly prescribed anticoagulant. Sweet clover hay was being used as main feed for
livestock in northern areas of America and Canada. During 1920s, suddenly cattle started
dying of internal bleeding. Such incidences were more during wet climate when hay was
damp and infected by moulds like Penicillium nigracans and P. jensi. After a couple of years
of this incidece, a farmer from Wisconsin approached Dr Karl Paul Link at University of
Wisconsin in Madison to solve the mystery. Professor Link and his colleagues after six years
of work isolated and crystallized an active compound named Dicoumarol (3,3-methylene-
bis[4-hydroxycoumarin]) from spoiled hay. After a year, active dicoumarol was synthesized
in the laboratory. The work was sponsored by Wisconsin Alumni Research Foundation
(WARF), so the patent rights of Dicoumarol were granted to WARF in 1941 when it was
introduced as anticoagulant. In 1945, Professor Link recommended the use of dicoumarol as
rodenticide but it was not a very effective rat poison. In 1948, a more potent analogue of
dicoumarol was promoted as rodenticide and called as Warfarin after the name of the funding
organization (WARF). During 1950s, warfarin was introduced as clinically effective
anticoagulant under the brand name “Coumadin”. Afterwards many generic brands of
19
warfarin were introduced as till now it is the most commonly prescribed oral anticoagulant
(Last, 2002, Wardrop and Keeling, 2008).
2.3.2 Chemisty
Warfarin (4-hydroxy-3(3-oxo-1-phenylbutyl)coumarin) is a derivative of 4-
hydroxycoumarin (Figure 2.3). It occurs in two isomeric forms S- and R-warfarin due to
presence of an asymmetric carbon (Boppana et al., 2002, Malakova et al., 2009, Valliappan et
al., 2013). The presence of 4-hydroxycoumarin residue with the 3-position replaced by carbon
is required for the anticoagulant response of warfarin (Fitzpatrick and O’Kennedy, 2004).
Other coumarin derivatives like dicoumarol, acenocoumarol and phenprocoumon are less
commonly used anticoagulants (Langer and Ziemer, 2009, Puehringer et al., 2010, Cadamuro
et al., 2010, Daly, 2013).
2.3.3 Uses
Warfarin has been the most commonly prescribed anticoagulant since its approval in
1954 (Ansell et al., 2008, Kim et al., 2009, Frueh, 2012, Daly, 2013). In some parts of the
world data collection of patients on anticoagulation therapy has been done. Around one
million individuals in United Kingdom and more than two millions in USA are taking
warfarin. Each year more than 30,000 new patients are placed on warfarin therapy in USA
(Flockhart et al., 2008, Firrilo and Hupp, 2012, Poe et al., 2012). In USA number of patients
who were prescribed warfarin therapy rose 1.45-fold from 21.1 million to 30.6 million during
a period of 1998-2004 (Wysowski et al., 2007, Firrilo and Hupp, 2012). Around 180,000
Swedish patients are treated with warfarin comprising almost 1.8 percent of population
(Dimberg et al., 2012).
20
Figure 2.3: Chemical structure of warfarin with chiral centre denoted as *
21
Warfarin has been used for treatment and prophylaxis of many thromboembolic
conditions (Ansell et al., 2008, Keeling et al., 2011, Firrilo and Hupp, 2012). One of the
commonest uses of warfarin is atrial fibrillation which is associated with higher risk of
thromboembolism, stroke, heart failure and premature death (Ewen et al., 2012, Alli et al.,
2013, Carlsson et al., 2013). Many clinical trials have shown efficacy of warfarin therapy in
attenuating the risk of stroke in such patients (Friberg et al., 2012, Reardon et al., 2013, Lee et
al., 2013). The incidence of paroxysmal atrial fibrillation following coronary artery bypass
graft has also been reported to be reduced with use of warfarin (Hata et al., 2013).
Venous thrombembolism involving deep vein thrombosis and pulmonary embolism
has been reported to be occurring 1 in 1000 persons annually leading to serious morbidity and
mortality. Patients at higher risk of DVT and pulmonary embolism are prescribed warfarin
(Tullet et al., 2013, Liew and Douketis, 2013, Amin et al., 2013, Esponda and Tafur, 2013).
Not only in venous thrombembolism but it can also be used in arterial thrombembolism
(Anderson et al., 2013).
Warfarin reduces the risk of death, re-infarction and developing thromboembolic
conditions like stroke and systemic embolism after myocardial infarction. So in selected case
of myocardial infarction warfarin is used (HohnLoser et al., 2012, Fosbol et al., 2012,
Subherwal et al., 2012, Lopes et al., 2012). Patients with heart failure are at higher risk of
developing atrial fibrillation, stroke and thromboembolic events. Some studies have
advocated the use of warfarin in such patients (Homma et al., 2012, Eikelboom and Connolly,
2012, Hess et al., 2012, Liew and Douketis, 2013, Lin et al., 2013).
Individuals with valvular heart diseases are at higher risk of developing
thromboembolism. There is also risk of cardio-embolic stroke after prosthetic heart-valve
22
replacement as well, so patients with these valvular conditions are placed on long-term
anticoagulation therapy with warfarin (Merie et al., 2012, Bian et al., 2012).
The patients undergoing orthopedic surgical procedures like fixing hip fracture and
prosthetic total hip or knee joint replacement are at risk of thromboembolic events.
Anticoagulation therapy with warfarin is prescribed to such patients (Dager, 2012, Anderson
et al., 2012, Barnes et al., 2013, Nutescu et al., 2013). In some studies warfarin has been used
for prophylaxis of migraine but still more data is required for considering it for routine use in
these patients (Maggioni et al., 2012, Russo et al., 2013).
2.3.4 Adverse Effects
Use of Warfarin has been associated with adverse effects rather frequently. It has been
ranked among top ten drugs responsible for hospitalization due to adverse drug reactions in
USA. It was reported to cause 33 percent adverse drug-related hospitalizations between 2007
and 2009 in USA (Budnitz et al., 2007, Shehab et al., 2010, Budnitz et al., 2011, Classen et
al., 2010).
The most commonly encountered effect is increased bleeding tendency which is
extension of its anticoagulant pharmacological effect. Different factors have been associated
with greater risk for bleeding. Out of these, the common factors are advanced age, co-morbid
diseases like cancer, kidney or liver disease and concomitant use of antiplatelet agents like
aspirin. Most morbid outcome of bleeding is intra-cerebral hemorrhage which is associated
with increase in risk of mortality. Bleeding has also been reported from gastro-intestinal tract
and urogenital tract. Glomerular bleeding has been reported to be responsible for warfarin-
related nephropathy and hematuria. Minor bleeding involves subcutaneous hemorrhages and
nose-bleed (Ageno et al., 2012, Bergman et al., 2012, Lim and Campbell, 2013, Hanger et al.,
2013, Coppens et al., 2012, Gomes et al., 2013).
23
The careful selection of patients for warfarin administration and then careful
monitoring can reduce the incidence of bleeding. The anticoagulant response of warfarin
therapy is monitored by INR. Over-anticoagulation resulting in increased bleeding tendency is
manifested by increased INR (Garcia et al., 2012, Dowd et al., 2012). Strategies to reverse the
over-anticoagulation by warfarin are adopted according to the clinical condition of patient and
INR value. Common strategies involve discontinuation of warfarin and administration of
vitamin K (usually vitamin K1, phytonadione) either orally or parentrally according to the
patient’s condition. Other therapeutic options include administration of prothrombin complex
concentrate (PCC), fresh frozen plasma (FFP) and recombinant activated factor VII (Ageno et
al., 2012, Cabral et al., 2012, Holbrook et al., 2012, Meehan et al., 2013, Hall and Carson,
2012, Jones et al., 2013, Tran et al., 2013, Rashidi and Tahhan, 2013, Hanger et al., 2013,
Deloughery et al., 2013).
There are some non-hemorrhagic adverse effects of warfarin. Most important out of
them are skin necrosis and limb gangrene. Skin necrosis is attributed to thrombosis of
subcutaneous fat capillaries and venules. It is especially seen in patients taking drugs affecting
liver functions, those with hereditary or acquired factor V leiden mutation and protein S and C
deficiency (Ageno et al., 2012, Hostetler et al., 2012, Biscoe and Bedlow, 2013, Gaikwad (a)
et al., 2013). Other infrequent toxic effects are nausea, vomiting, diarrhea, purple toe
syndrome, alopecia, hypersensitivity rash, jaundice and hepatic dysfunction (Jones et al.,
1980, Black, 1994, Roberge et al., 2000, Talmadge and Spyropoulos, 2003, Bogart et al.,
2010, Jameson and Siri, 2010, Rindone et al., 2011, Nakamizo et al., 2010, Altikaya et al.,
2011, Mc and Swinson, 2012, Hsu et al., 2012).
Warfarin administered during pregnancy has been reported to cause fetal bone
abnormalities due to interference with carboxylation of Gla proteins synthesized in bones.
24
This leads to warfarin syndrome comprising of stippled epiphyses, hypoplastic nose, optic
atrophy, mental retardation and other skeletal abnormalities. Other adverse effects are in the
form of spontaneous abortion and premature birth (Gupta et al., 2010, Mehndiratta et al.,
2010, Dilli et al., 2011, Ageno et al., 2012).
2.3.5 Drug Interactions
Warfarin has demonstrted interactions with food and drugs through pharmacokinetic
and pharmacodynamic mechanisms. Warfarin product information provided by the
manufacturer and approved by US FDA has provided a list of more than 200 agents that may
interact with it (Coumadin, 2007). American College of Chest Physicians Evidence-Based
Clinical Practice Guidelines (9th ed) on oral anticoagulant therapy has given a table which
provides a detailed list of agents that potentiate, inhibit or have no effect on warfarin
anticoagulant response (Ageno et al., 2012). This data is based on the records of a systemic
review of available evidence compiled in 2005. These results evaluated warfarin drug
interactions according to the clinical severity, direction of interaction and quality of evidence.
The list is given in Table 2.2 (Holbrook et al., 2005).
2.3.6 Mechanism of Action
During normal coagulation cascade, factor X, IX, VII, II and protein S and C undergo
γ-carboxylation to become active. This reaction is catalyzed by enzyme gamma-glutamyl
carboxylase (GGCX) and requires vitamin K hydroquinone (reduced vitamin K) as cofactor.
For this reason these factors are referred as vitamin K dependent clotting factors. This
reaction adds a molecule of carbon dioxide to glutamic acid residue on these proteins leading
to formation of calcium binding γ-carboxyglutamic acid (γ-Glu) residue. Such γ-Glu residues
are required by these factors to bind with phospholipid surfaces for activation.
25
Anti-infection Cardiovascular Analgesics,
Antiinflammatories, And Immunologies
CNS Drugs GI Drugs and
Food Herbal
Supplement Other D
Potentiation Cotrimoxazole Fluconazole Miconazole oral gel Erythromycin Miconazole vaginal suppository Ciprofloxacin Metronidazole Voriconazole Isoniazid
Propranolol Clofibrate Fenofibrate Amiodarone Propafenone Diltiazem Sulfinpyrazone (biphasic with later inhibition)
Piroxicam Phenylbutazone
Alcohol (if concomitant liver disease) Citalopram Entacapone Sertraline
Cimetidine Fish oil Mango Omeprazole
Quilinggao Boldo-fenugreek
Zileuton Anabolic stero
Azithromycin Itraconazole Ritonavir Clarithromycin Tetracycline Amoxicillin/clavulanate Levofloxacin
Aspirin Fluvastatin Quinidine Ropinirole Simvastatin
Celecoxib Aspirin Tramadol Acetaminophen Dextropropoxyphene Intreferon
Chloral hydrate Phenytoin (biphasic with later inhibition) Fluvoxamine Disulfiram
Grapefruit Lycium Barbarum L Danshen PC-SPES Don quai
Fluorouracil Paclitaxel GemcitabineTamoxifen Anabolic steroTolterodine Levamisole/fl
Ofloxacin Amoxicillin /tranexamic rinse Chloramphenicol Amoxicillin Gatifloxacin Nalidixic acid Miconazole topical gel Saquinavir Norfloxacin Terbinafine
Amiodarone-indueed Taxicosis Disopyramide Gemfibrozil Metolazone
Tolmetin Indomethacin Propoxyphene Celecoxib Rofecoxib Leflunomide Sulindac Topical salicylates
Felbamate Orlistat Methyl Salicylates Danshen
CyclophosphaFluorouracil Danazol IphosphamideMethotrexateTrastuzumab Daptomycin Acarbose
(Continued)
26
le 2.2: Drugs and foods interaction with warfarin by degree of supporting evidence and interaction direction*
a from Holbrook et al., 2005
Anti-infection
Cardiovascular
Analgesics, Antiinflammatories, And Immunologies
CNS Drugs
GI Drugs and Food
Herbal Supplement Other D
Cefazolin Cefamandole Sulfisoxazole
Heparin Bezafibrate
Levamisole Nabumetone Methylprednisolone
Fluoxetine/ Quetiapine Diazepam
Etoposide/carLevonorgestre
Inhibition Rifampin Griseofulvin Ribavarin Nafcillin
Cholestyramine Mesalamine Carbamazepine Barbiturates
Avocado High vitamin K content foods
Mercaptopurin
Dicloxacillin Ritonavir
Bosentan Azathioprine Chlordiazepoxide Sucralfate Soy milk
Ginseng Raloxifene HCChelation therMultivitamin Influenza vacc
Terbinafine Telmisartan Sulfasalazine Sushi containing seaweed
CyclosporineEtretinate Ubidecarenon
Dicloxacillin Nafcillin Cloxacillin Teicoplanin
Furosemide Propofol Green tea
27
During carboxylation, reduced form of vitamin K is oxidized into vitamin K 2, 3-epoxide
(oxidized vitamin K). Active or reduced form of vitamin K is regenerated from inactive or
oxidized form by action of enzyme vitamin K epoxide reductase (VKOR). Warfarin acts as
vitamin K antagonist. VKOR is the target molecule of warfarin action as it binds to VKOR
and inhibits its activity (Figure 2.4). This results in decreased regeneration of reduced form of
vitamin K leading to reduced activation of vitamin K dependent clotting factors ensuing in
anticoagulation. This mechanism of action explains the behavior of anticoagulant effect of
warfarin. Warfarin requires some time to manifest its effect because already active factors are
not affected by warfarin administration. These coagulation factors deplete according to their
half lives (Table 2.3) through endogenous catabolism and their replacement with
dysfunctional non-carboxylated factors is responsible for anticoagulant response. At start of
treatment with warfarin, there may be a transient prothrombotic effect because of reduction in
anticoagulant protein C levels which has short half life. But anticoagulant effect is dominant
in long run. Warfarin is given as racemic mixture of S- and R-warfarin but S-warfarin has
been found to be 3–5 times more potent anticoagulant than R-isomer. S- and R-warfarin are
responsible for 60–70 and 30–40 percent of anticoagulation response respectively (Weiss et
al., 1987, Lind et al., 1997, Wallin et al., 2002, Choonra et al., 1988, Wittowsky, 2005, Chu
et al., 2006, Oldenburg et al., 2008, Osinowale et al., 2009, Haines et al., 2006, Ageno et al.,
2012).
2.3.7 Dosage and Monitoring
Warfarin is usually administered with an initial dose of 5 mg daily. The dose is titrated
with repeated laboratory monitoring through prothrombin time (PT) and INR (Whitley et al.,
2007, Teh et al., 2011, Reynolds et al., 2007).
28
Figure 2.4: Mechanism of action of warfarin
Uncarboxylated Coagulation
Proteins
Glutamic Acid
Carboxylated Coagulation
Proteins
Carboxy-Glutamic Acid
Gamma-glutamyl carboxylase
Vitamin K Hydroquinone (Reduced Vitamin K)
Vitamin K 2, 3-epoxide (Oxidized Vitamin K)
Vitamin K epoxide reductase
Warfarin
O2
CO2
29
Table 2.3: Biological half-life of vitamin K-dependent coagulation factors (Wittkowsky,
2005)
Clotting Proteins
Half-life (hours)
Factor II
42-72
Factor X
27-48
Factor IX
21-30
Factor VII
4-6
Anticoagulant Proteins
Protein S
60
Protein C
9
30
PT measures the clotting factors II, V, X and fibrinogen and it is usually 12–15
seconds. INR is calculated as ratio of patient’s PT to that of control and normally its reference
range is 0.8–1.2. In most of the cases at risk of thromboembolism INR is kept within the
range of 2–3 but in some cases within the range of 2.5–3.5. Some studies have advocated
lower INR range of 1.5–2 to reduce the chances of bleeding. The INR below 1.5 is likely to
cause thromboembolism whereas above 3.5 has a risk of bleeding. Because of fear of these
complications warfarin has been underutilized. Now-a-days dosing algorithm based on
demographic variables along with common CYP2C9 and VKORC1 genotyping are in use
instead of hit and trial method in some parts of the world. Such individualized approach have
offered better outcome in warfarin response (Campbell et al., 2001, Shirazi, 2007, Lader et
al., 2012, Holbrook et al., 2012, Hirsh et al., 2003, Chappell et al., 2012, Keeling et al., 2011,
Lei et al., 2012).
2.3.8 Pharmacokinetics
Warfarin is given as a racemic preparation of R- and S-enantiomers. It is almost
completely absorbed by gastrointestinal tract on oral administration, with more than 95
percent bioavailability. Warfarin has an extensive plasma protein binding. Almost 99 percent
is bound to plasma albumin. The peak plasma concentration is achieved within 2–4 hours.
Two enantiomers not only differ in potency but also in their half-lives and metabolism. R-
warfarin has a half-life of 20–60 hours whereas it is 18–35 hours for S-enantiomer because of
its faster rate of clearance than R-enantiomer. The concentrations of two enantiomers differ in
plasma due to stereo-selective metabolism and difference in their half-lives. The steady-state
concentration of warfarin is reached after 4–5 days because of which heparin is administered
for first 3–4 days along with warfarin till levels of the latter are enough to produce
31
anticoagulation response (Ghoneim and Tawfik, 2004, Locatelli et al., 2005, Wittowsky,
2005, Malakova et al., 2009, Firriolo and Hupp, 2012, Jensen et al., 2012).
Metabolism of warfarin occurs mainly by hydroxylation and also involves oxidation
and reduction. Both enantiomers are metabolized by different cytochrome P450 (CYP450)
enzymes and are mainly converted to inactive 6-, 7-, 8- and 10-hydroxy metabolites. R-
warfarin is mainly metabolized by CYP1A2, CYP3A4, CYP2C19 and CYP1A1 whereas S-
warfarin mainly by CYP2C9. S-warfarin exists at only half the concentration of R-warfarin at
steady state because of rapid rate of metabolism than that of R-warfarin (Chan et al., 1994,
Kamisky and Zhang, 1997, Zhang et al., 2001, Yamazaki and Shimada, 1997, Locatelli et al.,
2005, Clapauch and Benchimol-Barbosa, 2012, Firriolo and Hupp, 2012, Maddison et al.,
2013, Shao and Jia, 2013). The inactive metabolites are mainly excreted through urine (Chan
et al., 1994, Ghoneim and Tawfik, 2004, Firriolo and Hupp, 2012).
2.3.9 Pharmacogenetics
Pharmacogenetics describes impact of genetic factors on drug pharmacokinetics and
pharmacodynamics. The concept of individualized medicine is now prevailing all over the
world to enhance the efficacy and safety of a drug. Warfarin is one of those drugs which have
been the candidate of this model. Both pharmacokinetic and pharmacodynamic aspects of
warfarin have been explored for their outcome influenced by genetic factors. Out of different
genes studied, two have been found to significantly affect the blood levels and anticoagulant
response of warfarin. One is CYP2C9 affecting metabolism of warfarin and in turn its blood
levels and the other is VKORC1 which alters the VKOR enzyme expression responsible for
warfarin action (Rioux, 2000, Elias and Topol, 2008, Ginsburg and Voora, 2010, Lesko and
Zineh, 2010, Jorgensen et al., 2012, Frueh, 2012, Limdi, 2012).
32
2.3.9.1 Metabolism of warfarin and CYP2C9 polymorphism
Two enantiomers of warfarin are metabolized by different CYP450 enzymes. R-
warfarin is mainly metabolized by CYP1A2, CYP3A4, CYP2C19 and CYP1A1 whereas S-
warfarin mainly by CYP2C9. As S-warfarin is more potent than R-warfarin, so changes in
blood levels of S-warfarin affects the anticoagulant response significantly (Kaminsky and
Zhang, 1997, Zhang et al., 2001, Clapauch and Benchimol-Barbosa, 2012, Jensen et al.,
2012,).
CYP2C9 enzyme metabolizes more than 16 percent of clinically used drugs including
S-warfarin. CYP2C9 have been found to be responsible for metabolism of many
therapeutically significant drugs like phenytoin, several non-steroidal anti-inflammatory drugs
(NSAIDs) and tolbutamide. This enzyme is encoded by CYP2C9 gene and consists of 490
amino acids. CYP2C9 enzyme protein has been purified from human liver and make up more
than 20 percent of total cytochrome P450 proteins in liver microsome (Kaminsky and Zhang,
1997, Miners and Birkett, 1998, Yamazaki et al., 1998, Si et al., 2004, Nakai et al., 2005).
Human CYP2C9 gene, sited on chromosome 10q24.2, is about 55-kbp long and
exhibits genetic polymorphism. The CYP2C9 gene has been found to contain at least 57
CYP2C9 variant alleles defined by the human CYP Allele Nomenclature Committee
(http://www.cypalleles.ki.se/cyp2c9.htm), but out of these CYP2C9*3 and CYP2C9*2 have
been well studied because of their significant effect on S-warfarin metabolism. The most
commonly present allele is CYP2C9*1 which is regarded as wild-type and produces CYP2C9
enzyme with normal activity. The CYP2C9*2 allele results from single nucleotide base
substitution from C to T at codon 430 located in exon 3. This single nucleotide polymorphism
(SNP) causes a change in amino acid residue from arginine to cysteine at codon position 144
(Arg144Cys) on the surface of CYP2C9 enzyme. The CYP2C9*3 allele results from single
33
nucleotide base substitution from A to C at codon 1075 located at exon 7. This SNP leads to a
change in amino acid residue from isoleucine to leucine at codon position 359 (Ile359Leu)
inside the CYP2C9 enzyme. The presence of these SNPs results in decrease in the activity of
CYP2C9 enzyme which is more with CYP2C9*3 than CYP2C9*2 (Rettie et al., 1992,
Goldstein and de Morais, 1994, Sullivan-klose et al., 1996, Yamazaki et al., 1998, Miners and
Birkett, 1998, Yin and Miyata, 2007, Kusama et al., 2009, Shin, 2012, Niinuma et al., 2013).
The presence of polymorphic alleles of CYP2C9 decreases the S-warfarin metabolic
rate resulting in increased levels of S-warfarin which in turn significantly affects the
anticoagulant response of warfarin. In such individuals lower doses of warfarin are required to
produce the therapeutic response without any bleeding risk. At the same time, R-warfarin
levels remain unaffected by CYP2C9 polymorphism. S-enantiomer exists at only half the
concentration that of R-warfarin at steady state. This S/R ratio of 0.5:1 is changed in
individuals possessing polymorphic alleles due to decreased rate of metabolism of S-warfarin.
The S/R ratio has been observed to increase even upto 4:1. At steady state, the S/R warfarin
ratio has been used to assess the activity of CYP2C9 enzyme. In patients carrying CYP2C9
*3/*3 genotype, S-warfarin clearance is decreased even upto 85 percent. Patients carrying
these alleles are at higher risk of bleeding after administration of usual dose of warfarin. So
genotyping of CYP2C9 especially for common alleles CYP2C9*3 and CYP2C9*2 before
administering warfarin reduces the chances of adverse complication (Steward et al., 1997,
Loebstein et al., 2001, Caraco et al., 2008, Scordo et al., 2002, Kusama et al., 2009, Higashi
et al., 2002, Ageno et al., 2012, Shin, 2012, Lane et al., 2012, Jorgensen et al., 2012).
2.3.9.2 Pharmacodynamics of warfarin and VKORC1 polymorphism
Warfarin results in anticoagulation by inhibiting VKOR enzyme. The gene coding for
VKOR is vitamin K epoxide reductase complex subunit 1 (VKORC1). Although VKOR
34
enzyme was purified in 1974 but the encoding gene was first cloned in 2004. VKORC1 is
present on human chromosome 16p11.2. It spans about 5 kbp and comprises of 3 exons and 2
introns. The encoded protein VKOR consists of 163 amino acids and has a mass of 18.2 kDa
(Li et al., 2004, D’Andrea et al., 2005, Rost et al., 2004).
Since VKORC1 breakthrough, existence of polymorphism in it has been extensively
studied. Several SNPs have been identified in VKORC1 as listed in Pharmacogenetics
Pharmacogenomics Knowledge Base (www.pharmgkb.org). But very few of them have been
found to be affecting the warfarin dose requirement or anticoagulation response. Out of these
SNPs, two have shown significant effect on warfarin anticoagulant response. First one is a
promoter polymorphism, VKORC1–1639G>A located 1639 bases upstream of the translation
start site of VKORC1. This polymorphism causes changes in VKORC1 transcription binding
site leading to decreased VKORC1 mRNA expression in human liver cells and decreased
VKOR protein synthesis. Patients possessing A allele require less doses of warfarin as
compared to those carrying G allele. Patients with genotype AA are at risk of bleeding if same
dose given as required by those possessing GG genotype (Yuan et al., 2005, Gage et al.,
2008, Oldenburg et al., 2007, Santos et al., 2013, Jorgensen et al., 2012).
The other SNP significantly affecting warfarin anticoagulant response is VKORC1
1173C>T. In this polymorphism, a C>T transition takes place at position 1173 in intron 1.
The carriers of TT genotype require less dose of warfarin as compared to those possessing CC
genotype to produce same anticoagulation response. Both VKORC1–1639G>A and VKORC1
1173C>T have been found to be in linkage disequilibrium (Yuan et al., 2005, D’Andrea et al.,
2005, Jorgensen et al., 2012, El Din et al., 2012, Daly, 2013).
35
2.3.10 Future Prospects
The reported studies have demonstrated that VKORC1 and CYP2C9 gene variants
together with demographic factors accounts for 50–60 percent variance in warfarin dose
requirement (Gage et al., 2008, Jorgensen et al., 2012, Wadelius et al., 2007, Valentin et al.,
2012). On the basis of impact of VKORC1 and CYP2C9 genotyping on pharmacokinetics and
pharmacodynamics of warfarin, FDA brought about a change in warfarin labeling in 2007 to
reflect the influence of polymorphism of these genes. As a result of convincing reported
studies in this regard, FDA in 2010 added a table containing pharmacogenetic-guided dose
recommendations including common VKORC1 and CYP2C9 genotypes
(http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/009218s108lbl.pdf). Since then
work is going on in different areas. Some commercial tests are now available for large-scale
genotyping of common CYP2C9 and VKORC1 genes which are aimed to be cost-effective
and less time consuming (Kim et al., 2009, King et al., 2008, Langley et al., 2009, Li et al.,
2011, Anderson et al., 2012, Bazan et al., 2012).
Different dosing algorithms have been constructed by researchers from data
comprising of demographic factors along with the frequencies of VKORC1 and CYP2C9
genotypes in different populations which are available online
(http://www.warfarindosing.org). The use of genotype-based dosing algorithms has shown
better results in terms of improved efficacy, less adverse effects like bleeding and less
frequent laboratory monitoring. They have provided with an overall better healthcare outcome
for patients using warfarin but it has been observed that they perform better in those
populations from where data was derived to construct them (Epstein et al., 2010, Suarez-
Kurtz 2011, Liu et al., 2012, Cini et al., 2012, Horne et al., 2010, Anderson et al., 2012,
Wang et al., 2012, Perera et al., 2013).
36
Understanding the importance of incorporating the genotyping data into clinical
practice, studies have been conducted in different populations to characterize the frequencies
of common VKORC1 and CYP2C9 genotypes and their impact on warfarin anticoagulant
response. Such studies have helped to construct the dosing algorithms. These algorithms are
especially effective for concerned populations as frequencies of these genotypes are different
in different populations. Most of these studies are in Caucasians (Valentin et al., 2012, Shuen
et al., 2012, Mazzaccara et al., 2013, Tatarunas et al., 2013, Sconce et al., 2005, Skov et al.,
2013). From Asian region, most studies are in Chinese and few are from other countries (Cho
et al., 2011, Tan et al., 2013, Wang et al., 2013, Saito et al., 2013, Rusdiana et al., 2013,
Bazan et al., 2013, Ozer et al., 2013). Those from neighboring countries are of small scale
and do not encompass all the important factors in one study (Namazi et al., 2010, Siddiqi et
al., 2010, Kianmehr et al., 2010, Pavani et al., 2012, Kumar et al., 2013, Shalia et al., 2012,
Gaikwad (b) et al., 2013). There is no comprehensive large-scale study reported to-date from
South Asia providing population-based frequency of common alleles of CYP2C9 and
VKORC1 genes along with demographic variables and warfarin enentiomers plasma levels.
To expedite the use of VKORC1 and CYP2C9 genotyping routinely in anticoagulation
practice, it is imperative to determine the allelic frequency in different populations. A
comprehensive dosing model that is applicable regardless of ethnicity can be developed by
identifying the allelic frequency of common alleles in different ethnic populations. This will
further help in drafting world-wide applicable clinical guidelines for warfarin prescription
leading to enhanced efficacy and safety of warfarin.
37
Chapter 3
AIM AND OBJECTIVES
The present study was planned to assess the impact of demographic parameters and
genotypic variations on pharmacodynamics and pharmacokinetics of warfarin in Pakistani
subjects by achieving following objectives.
1. To carry out CYP2C9 genotyping in Pakistani population for common
polymorphic alleles CYP2C9*3 and CYP2C9*2.
2. To assess the association of warfarin dose with CYP2C9 genotypes.
3. To measure S- and R-enantiomers of warfarin in plasma and to study the
association of plasma S/R ratio with CYP2C9 genotypes.
4. To carry out VKORC1 genotyping in Pakistani population for common alleles
VKORCI –1639G>A and VKORCI 1173C>T.
5. To assess the association of warfarin dose with VKORCI genotypes.
6. To assess the impact of demographic parameters on warfarin dose requirement.
7. To determine the contribution of polymorphism in VKORC1 and CYP2C9 along
with demographic factors to the anticoagulant response to warfarin.
38
Chapter 4
SUBJECTS, MATERIALS AND METHODS
4.1 SETTING OF THE STUDY
The clinical data collection and laboratory investigations were done at Armed Forces
Institute of Cardiology (AFIC) Rawalpindi and National Institute of Cardiovascular Diseases
(NICVD) Karachi. The analytical procedures were carried out at Centre for Research in
Experimental and Applied Medicine (CREAM), Army Medical College Rawalpindi in
collaboration with University of Veterinary and Animal Sciences (UVAS) Lahore and
Institute of Biomedical and Genetic Engineering (IBGE) Islamabad.
4.2 DURATION OF STUDY
The duration of study was two years.
4.3 STUDY PROTOCOL
The study was conducted in accordance with the current Good Clinical Practices
(FDA, 1996) and the Declaration of Helsinki (WMA, 2000). The study protocol was approved
by ethical committees of Centre for Research in Experimental and Applied Medicine, Army
Medical College and National Institute of Cardiovascular Diseases (Appendix I and II).
4.4 STUDY DESIGN
This was a cross-sectional analytical study.
4.5 SUBJECTS
Study subjects were adults of either sex who were taking warfarin for anticoagulation
therapy. Sample size was calculated by WHO Sample Size Calculator. There was no local
data regarding prevalence of target population. So the sample size was calculated by keeping
39
anticipated population proportion of 50 percent, 95% confidence interval and absolute
precision of 5 percent. The calculated sample size was 385 but for validating the reliability of
genotyping data and catering for missing analytical data sample size was increased. Six
hundred and seven stable patients fulfilling the criteria participated in the study. A stable
patient has been defined as the one whose warfarin dose had been constant for at least three
previous clinic visits over a minimum period of three months, and had an INR of the
prothrombin time (PT) within the range of 1.5–3.5 (Hirsh et al., 2001, Miao et al., 2007,
Wang et al., 2008, Yoshizawa et al., 2009). All participants were Pakistani citizens belonging
to different regions of Pakistan to provide representation from all areas. The number of
patients enrolled from different regions like Punjab, Khyber Pakhtunkhwa, Sindh,
Balochistan, Gilgit-Baltistan, Azad Jammu and Kashmir, were in accordance with the
population of that region. Their region was considered according to their place of birth.
Subjects were informed of the nature, significance and consequence of the study and the
investigational procedures. The informed consent was obtained on the consent proforma
attached as Appendix III and IV.
Each subject was evaluated with detailed medical history and physical examination
and laboratory tests in line with preclinical proforma attached as Appendix V. The
demographic and clinical data of individuals was collected at the clinical set up where
anticoagulant therapy was prescribed. Demographic data included age, gender, height and
weight of the patients. Clinical data incorporated dose and indication for warfarin therapy, any
co-morbid disease or other drug intake. Patients were recruited regardless of indications for
warfarin use. The hepatic and renal sufficiency was evaluated through baseline investigations
as mentioned in preclinical proforma (Appendix V). Those who were suffering from any co-
40
morbid disease or taking any concurrent medication or diet which would have affected
warfarin concentration in plasma or INR were not recruited for the study (Bodin et al., 2005,
Kamali et al., 2004, Aquilante et al., 2006).
4.5.1 Inclusion Criteria
Age ≥ 18 and ≤ 65
Both genders
Belonging to Pakistani regions
Taking warfarin for at least 3 months
Having INR ≥ 1.5 and ≤ 3.5
Non-smokers
4.5.2 Exclusion Criteria
Age below 18 and above 65 years
Not of Pakistani origin
Duration of warfarin therapy less than 3 months
Having INR<1.5 and >3.5
Smokers
Suffering from hepatic and renal disease
Suffering from any co-morbid disease interacting with warfarin
Those receiving concurrent therapy or diet known to interact with warfarin
41
4.6 DEMOGRAPHIC AND CLINICAL DATA
Quantitative data like age, height, weight, body mass index (BMI), dose of warfarin,
present and previous INR values were described as mean ± standard deviation (SD). The BMI
of each individual was calculated as:
BMI = Weight in kilograms/ (Height in meters)2
Qualtitative variables like gender, ethnicity and indications for warfarin use were
expressed as frequencies and percentages.
4.7 BLOOD SAMPLING
A blood sample of 5-10 ml was drawn from each recruited subject. Out of this sample,
2 ml of blood was collected in EDTA (ethylene diamine tetraacetic acid) containing tube and
stored at 4ºC for genotyping (Miao et al., 2007). Rest of the blood was distributed in
respective tubes for baseline investigations that included complete blood picture (blood CP),
ESR, liver function tests (Serum ALT and bilirubin), renal function tests (Serum creatinine
and urea), PT and INR. From one hundred and seventy patients selected randomly, an
additional 5 ml blood was drawn 12–16 hours after last administered warfarin dose in addition
to above mentioned sample. This 5 ml blood was transferred to heparinised tubes and
centrifuged immediately to separate plasma. Plasma was stored at –80 ºC till further analysis
on high performance liquid chromatographic (HPLC) system for estimation of S- and R-
warfarin levels in plasma (Unge et al., 1992, Jensen et al., 2012, Takahashi et al., 2006).
4.8 ANALYSIS OF S- AND R-ENANTIOMERS OF WARFARIN IN
PLASMA
The measurement of plasma S- and R-warfarin was conducted in 170 patients out of
607 patients enrolled. The concentration of S- and R-warfarin was measured by a HPLC
42
method (Naidong and Lee, 1993). The method was modified and validated to make it further
simple and economical. The ratio of S- to R-warfarin was then calculated from the measured
S- and R-warfarin concentrations.
4.8.1 Analytical Method
Various HPLC methods have been described in literature for estimation of warfarin
enantiomers in human plasma and these have shown different sensitivities and limitations
(Locatelli et al., 2005, Boppana et al., 2002, Malakova et al., 2009). The method used as
described by Naidong and Lee was sensitive and reliable for accurate determination of
warfarin enantiomers in plasma (Naidong and Lee, 1993). The method was modified by using
fluorescence detector instead of UV detector making it more sensitive. Naproxen as internal
standard was not used as it is a commonly used non-steroidal anti-inflammatory drug and if
found in patients’ sample, can interfere with results. Various procedures were performed to
validate the modified method according to International Conference on Harmonization (ICH)
Guidelines on validation of analytical procedures (ICH, 2005).
4.8.2 Instrumentation
The High Performance Liquid Chromatography (HPLC) system by Agilent 1100
Series with autosampler and fluorescence detector was used. Chromatographic separation was
done on LiChroCART® 250-4 ChiraDex® (250x4 mm, 5 µm particle size) column along
with LiChroCART® 4-4 ChiraDex® (4x4 mm, 5 µm particle size) guard column provided by
Merck Darmstadt, Germany. The chromatograms were recorded on connected computer.
43
4.8.3 Chemicals and Reagents
The chemicals and solvents used in this study were of analytical and HPLC grade.
Acetonitrile, methanol and glacial acetic acid were bought from Merck Darmstadt, Germany.
Sulphuric acid was bought from Reanal Finechemical Pvt Ltd, Hungary. Diethyl ether was
purchased from LAB-SCAN Analytical Sciences, Thailand. Triethylamine was bought from
Fisher Scientific, USA. Racemic Warfarin (Rac-warfarin), S-warfarin and R-warfarin
standards were purchased from Sigma-Aldrich, USA.
4.8.4 Analytical and Chromatographic Conditions
The fluorescence detector was set at an excitation wavelength of 300 nm and an
emission wavelength of 390 nm. The mobile phase consisted of acetonitrile:glacial acetic
acid:triethylamine (1000:3:2.5, v/v/v). The mobile phase was pumped at a flow rate of 1
ml/min. All analyses were done at room temperature.
4.8.5 Preparation of Stock Solution and Standards
Separate stock solutions of 1 mg/ml concentration of Rac-warfarin, R-warfarin and S-
warfarin were prepared by dissolving appropriate amount in acetonitrile. Injection of pure S-
warfarin and R-warfarin were made at start of work to determine the elution order of
enantiomers. Rest of analyses were carried out by using Rac-warfarin. The eight working
standard solutions of Rac-warfarin were prepared to contain concentrations of 12.5, 25, 50,
100, 250, 500, 1000, 2500 ng/ml each of S- and R-warfarin enantiomers. All solutions were
stored in aluminum foil-wrapped bottles to avoid light exposure. The solutions were placed at
–20°C for storage.
44
4.8.6 Calibration Curve
Calibration standard curves were prepared by spiking 900 µl of blank or drug free
plasma with 100 µl of working standards to yield plasma standards containing final
concentrations of 12.5, 25, 50, 100, 250, 500, 1000, 2500 ng/ml each of S- and R-warfarin.
The curves were based on simple linear model relating the S- and R-warfarin enantiomers
concentration to the HPLC response. The standard curves were analyzed in triplicate.
4.8.7 Assay Validation Procedures
Assay validation was done by carrying out following steps.
4.8.7.1 Quality control samples
Three levels of quality control (QC) samples were prepared for method validation.
Quality control samples were run as replicates of blank plasma spiked with a low
concentration (40 ng/ml), a middle concentration (200 ng/ml) and a high concentration (1500
ng/ml) of both S- and R-warfarin enantiomers.
4.8.7.2 Identification and selectivity
The elution order of enantiomers was determined by injecting pure S- and R-warfarin
standards separately. The identification of two enantiomers in plasma sample spiked with
Rac-warfarin was made on the basis of retention times on chromatograms obtained from
plasma samples spiked with pure S- and R-warfarin standards separately. Selectivity of S- and
R-warfarin enantiomers was determined by spiking blank plasma with graded concentration
of Rac-warfarin and confirming from their retention times.
45
4.8.7.3 Sensitivity and limit of detection
Sensitivity of method is expressed by limit of detection (LOD) of analytical assay.
LOD is defined as the lowest concentration of the analyte which can be detected but not
necessarily quantitated with precision.
4.8.7.4 Linearity and limit of quantitation
Linearity was assessed by calibration curve constructed using 8 standard solution
concentrations covering the range of 12.5–2500 ng/ml. Standard curves were analyzed in
triplicate. The lower limit of quantitation (LLOQ) for both enantiomers was selected as the
lowest concentration of the respective standard curve. The lower limit of quantitation was the
lowest concentrations of R- and S-warfarin at which their peaks were identifiable and discrete
with suitable precision (coefficient of variation of less than 20 percent) and accuracy
(determined concentration being within 20 percent variation of the nominal concentration).
4.8.7.5 Accuracy and precision
The acceptance criterion for precision of analytical method as recommended by FDA
for each calculated standard concentration is a 15 percent coefficient of variation (CV) from
added concentration value except at the LLOQ for which coefficient of variation is 20
percent. The accuracy is determined as percentage recovery by the assay of known added
amount of analyte in the sample. The acceptance criterion for accuracy of the method is that
the mean measured concentration being within 80–120 percent of the nominal concentration.
The precision and accuracy of the plasma assay for warfarin enantiomers were determined by
running quality control samples for 3 days. The intra-day variability was tested with 6
replicates of each quality control concentration run on the same day whereas inter-day
46
variability was established by running 6 replicates of each quality control concentration for 3
consecutive days.
4.8.7.6 Analyte and system stability
The stability of analyte and the analytical system was assured by running quality
control samples daily at the beginning of each run throughout the period of analysis. Analyte
stability was also demonstrated by subjecting the quality control samples of three
concentrations to three freeze-thaw cycles. Benchtop stability was checked by placing
samples at room temperature and running them after 2 hours and then at 24 hours. To test the
stability of the stereoisomers against inter-conversion during analysis, samples containing
only one enantiomer were prepared and run immediately and then after 24 hours. In order to
assess recovery and extraction efficiency, the peak areas obtained with blank plasma sample
spiked with rac-warfarin were compared with the average peak areas attained by direct
injections of known amounts of rac-warfarin solutions in triplicate.
4.8.8 Plasma Sample Processing
Plasma sample of 1 ml was acidified by adding 700 µl of 1N sulphuric acid. After
mixing, 3 ml of diethyl ether was added to extract S- and R-warfarin. The organic layer
containing both enantiomers was separated and then evaporated under a stream of nitrogen till
dryness is achieved. The residual sample was reconstituted in 300 µl of acetonitrile and 40 µl
was injected on to the HPLC system.
4.8.9 Data Analysis
Data analysis was done using Microsoft Office Excel 2007. Mean ± SD, percentages
and coefficient of variation (CV) were calculated for different parameters of method
47
validation. Mean ± SD were calculated for concentrations of R- and S-warfarin enantiomers
in 170 subjects. The S/R ratio was determined from the measured S- and R-warfarin
concentrations.
4.9 GENOTYPING
4.9.1 Extraction of Genomic DNA from Whole Blood Samples
The genomic DNA from all of the samples was isolated mainly by standard organic
method which involved two principal organic chemicals chloroform and phenol (Sambrook
and Russell, 2001). The protocol was slightly modified as per requirement of the laboratory
working. DNA isolation kit was used (QIAamp DNA Mini, Qiagen Inc, USA) to extract
genomic DNA from some of the blood samples which were either less in quantity or
somewhat clotted.
4.9.1.1 DNA extraction through organic method
4.9.1.1.1 Composition of the solutions used in DNA extraction
I. Cell lysis buffer: This buffer is composed of three chemicals given below:
a) KHCO3 (Potassium Carbonate) – 1 gm/l
b) NH4Cl (Ammonium Chloride) – 8.29 gm/l
c) 0.5M EDTA (Ethylene Diamine Tetra Acetate) – 0.34 gm
The cell membranes are lysed by this buffer as a result of which the chromatin material of the
nucleated cells is exposed.
48
II. STE buffer: It provides a saline environment to newly exposed chromatin material. It
consists of:
a) 3M NaCl (Sodium Chloride) – 33.3 ml
b) 1M Tris-HCl buffer (pH-8.0) – 4 ml
c) 0.5M EDTA (pH-8.0) – 2 ml
The above reagents were mixed and final volume was made up to 1 litre with deionized water
(dH2O).
III. DNA dissolving buffer: It is used for dissolving DNA and consists of:
a) 10 mM Tris-HCl (pH-8)
b) 0.1 mM EDTA
IV. SDS solution: It is 10 percent solution of sodium dodecyl sulfate or sodium lauryl
sulfate helping in protein degrading and also enhancing the activity of proteinase K.
V. Chloroform-Isoamyl Alcohol: This solution is prepared in 24:1 v/v for 500 ml total
volume as:
a) Chloroform 480 ml (24 parts)
b) Isoamyl alcohol 20 ml (1 part)
4.9.1.1.2 Extraction method
The DNA extraction through organic method was carried out through following steps:
49
Day 1
1. Blood samples were transferred to 50 ml centrifuge tubes and 3X volume of cell
lysis buffer was added. These tubes were placed on ice for 30 min.
2. The samples were then centrifuged at 1200 rpm for 10 minutes at 4ºC in
refrigerated centrifuge (Eppendorf Refrigerated Centrifuge 5130).
3. The pellet obtained at the base of the tube was re-suspended after discarding the
supernatant.
4. If the pellet was still reddish in color then again 10 ml of cell lysis buffer was
added and the sample tubes were centrifuged at 1200 rpm at 4ºC for 10 minutes.
5. The supernatant was discarded and the pellet at the base of the tube was obtained.
6. To the pellet, 250 µl of 10 percent SDS and 4.75 ml of STE were added.
7. To the above sample mixture, 10 µl of proteinase K enzyme (20 mg/ml)
(Fermentas, Lithuania) was added and samples were then incubated at 55ºC for
overnight in shaking water bath (Orbit Shaker Bath, Lab-Line, USA).
Day 2
8. The samples were extracted with 5 ml (equal quantity) of equilibrated phenol (at
pH-8). Then they were agitated for 10 minutes and kept on ice for 10 minutes. The
samples were centrifuged at 3200 rpm for 30 minutes at 4ºC in refrigerated
centrifuge. The supernatant was removed with 1 ml micropipette tip into
separately labeled 15 ml centrifuge tubes.
50
9. To the above samples, 5 ml of chilled chloroform:isoamyl alcohol (24:1) was
added and agitated for 10 minutes. Samples were kept on ice for 10 minutes and
then spun at 3200 rpm for 30 minutes at 4ºC in refrigerated centrifuge. The
supernatant was removed with micropipette tip into the separate labeled 15 ml
centrifuge tubes.
10. To the separated supernatant, 500 µl of 10M ammonium acetate and 5 ml of
chilled Isopropanol were added and agitated until genomic DNA precipitates as
visible white threads. The samples were then kept overnight at –20ºC (or for 15
minutes at –70ºC).
Day 3
11. The overnight kept samples were then centrifuged at 3000 rpm for 60 minutes at
4ºC in refrigerated centrifuge. The supernatant was discarded and DNA pellet was
loosened by tapping the 15 ml centrifuge tube with fingers.
12. The loosened pellet was washed with 5 ml of chilled 70 percent ethanol and
centrifuged at 3200 rpm for 40 minutes at 4ºC in refrigerated centrifuge.
13. The DNA pellet was dried after discarding the supernatant.
14. The dried DNA pellet was dissolved in 10 mM TE buffer (the volume of buffer
added was according to the size of the pellet).
15. The complete dissolution of DNA in TE buffer was achieved by incubating the
sample tube in shaking water bath at 55ºC.
51
16. The DNA samples were then transferred to 1.5 ml eppendorf tubes and stored at
4ºC till further analysis.
4.9.1.2 DNA extraction through commercial kit
Genomic DNA from some blood samples was extracted by using DNA isolation kit
(QIAamp DNA Mini, Qiagen.Inc, USA), according to manufacturer’s protocol (Scibona
2012). A brief description of method is given here. A blood sample of 200 µl was added to a
1.5 ml microcentrifuge tube containing 20 µl of Qiagen Proteinase. Then 200 µl Buffer AL
was added to it, mixed and incubated at 56ºC for 10 min. To the sample 200 µl ethanol was
added and the mixture was applied to the capped QIAamp Spin Column (in a 2 ml collection
tube). The column was centrifuged at 8000 rpm for 1 min and then placed in another clean
collection tube. To this column, 500 µl Buffer AW1 was added and centrifuged at 8000 rpm
for 1 min. The column was again placed in another clean collection tube and 500 µl Buffer
AW2 was added to it. Then the column was centrifuged at 14000 rpm for 3 min and placed in
a 1.5 ml microcentrifuge tube. To the column, 200 µl Buffer AE was added, incubated at
room temperature for 1 min and then centrifuged at 8000 rpm for 1 min. DNA obtained in
microcentrifuge tube was stored at 4ºC till further analysis.
4.9.1.3 Assessment of quality and quantity of DNA
For this assessment, each sample was diluted 50 folds by adding 6 µl of DNA sample
to 294 µl of distilled water and quantified on UV Spectrophotometer (U-3210, Hitachi, Japan)
at 260 nm and 280 nm wavelength. Optical Density (OD) ratio for each sample was calculated
as:
OD=Absorbance at 260 nm / Absorbance at 280 nm
52
The ratio should lie within the range of 1.7-1.9 for good quality DNA.
The concentration of DNA samples was calculated as:
DNA concentration (µg/ml) =Absorbance at 260 nm × dilution factor ×correction factor
Value for dilution as per actual and value for correction factor is 50.
4.9.1.4 Working solution of DNA
Working solution containing 40 ng/µl of DNA was prepared from the stock DNA
solution by using the following formula:
C1V1=C2V2
4.9.2 Reconstitution of Primers
The lyophilized primers were reconstituted by adding calculated amount of dH2O to
make a stock solution of 100 nM of each primer. From these stock solutions, 20 nM working
solutions were prepared for each primer by mixing 20 µl stock solution and 80 µl of dH2O.
Polymerase chain reaction (PCR) amplifications were performed by using 20 nM working
primer solutions.
4.9.3 Agarose Gel Electrophoresis
The analysis of PCR products after PCR and restriction fragment length
polymorphism (RFLP) was carried out by using 2 and 3 percent agarose gel respectively
through electrophoresis. Following reagents used in the process of agarose gel electrophoresis
were prepared in the laboratory:
a. 10X Tris-Borate- Ethylene diamine tetra acetic acid (TBE) buffer
53
b. Agarose (Promega, USA) gel
4.9.3.1 Preparation of 10X TBE buffer stock
i. Tris-base (Promega, USA) = 107.8 gm/liter
ii. Boric acid (Promega, USA) = 55.02 gm/liter
iii. EDTA (Ethylene diamine tetra acetic acid) (Bio-Rad, USA) = 9.04 gm/liter
All chemicals were dissolved in 800 ml of deionized water using magnetic stirrer. Final
volume of 1000 ml was made in measuring cylinder and stirred to mix well.
4.9.3.2 Preparation of agarose gel
Three hundred milliliters of agarose gel solution is required for 20×20 cm agarose gel
pouring plate. The quantities of reagents for preparing 2 and 3 percent agarose gel are given in
Table 4.1. Agarose, 10X TBE and deionized water were mixed in a pyrex bottle and heated in
oven until agarose boiled to mix in liquid parts. Then 5 µl ethidium bromide (10 mg/ml) (Carl
Roth GmbH + Co. KG 76185 Karlsruhe) was added in the agarose gel solution and mixed
well. A 20×20 cm gel plate was set with combs at suitable distance and the gel solution was
poured in it. The gel was set in 30–45 minutes.
4.9.4 Genotyping of VKORC1 –1639G>A Polymorphism
Genotyping of the –1639G>A polymorphism in VKORC1 was done by polymerase
chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay.
54
Table 4.1: Agarose gel constituents with quantities
Reagents
2 percent agarose gel
3 percent agarose gel
Ion free fine agarose
6 gm
9 gm
10X TBE
30 ml
30 ml
Deionized water
270 ml
270 ml
Ethidium bromide (10 mg/ml)
5 µl
5 µl
55
4.9.4.1 Primers
The sequence of forward and reverse primers used, were obtained from a previously
reported study (Sconce et al., 2005). The sequences of both the primers have been shown in
Table 4.2. The sequences of these primers were confirmed through Primer-BLAST search
(http://blast.ncbi.nlm.nih.gov/Blast.cgi). These primers amplified the 290bp region including
the VKORCI –1639G>A in all PCR irrespective of G/A genotypes.
4.9.4.2 Polymerase chain reaction (PCR) analysis
The optimization of PCR with reproducible results was carried out. All the samples
were amplified for target DNA fragment spanning VKORC1 –1639G>A allelic region with
this optimized PCR. The PCR was carried out for each sample in a final volume of 20 µl
containing, 40 ng genomic DNA (2 µl), 2 µl 10X PCR buffer without Mg2+ (magnesium ions)
(Invitrogen, Brazil), 1 µl of 25 mM MgCl2 (Magnesium Chloride) (Invitrogen, Brazil), 0.2 µl
of 5 U/µl Taq DNA polymerase (Invitrogen, Brazil), 1 µl of 2 mM dNTPs (deoxynucleotide
triphosphate) (Invitrogen, Brazil), 1 µl of each 20 nM forward and reverse primers (e-oligos,
Gene Link Inc. USA). The final volume was adjusted to 20 µl with deionized water (dH2O).
The initial and final concentrations of the reagents used in PCR are given in Table 4.3. The
PCR was carried out in thermal cycler (Thermo Electron Corporation, Millford, USA). The
thermal stages with their temperatures and number of cycles are given in Table 4.4.
Out of each PCR sample, 5 µl of PCR product was mixed with 5 µl of 6X gel loading
buffer (Invitrogen, Brazil) and loaded on 2 percent w/v agarose gel. DNA ladder of 100bp
(Invitrogen, Brazil) was loaded as size reference for amplified DNA fragments in the first
well.
56
Table 4.2: List of primers used for genotyping
Single Nucleotide Polymorphism (SNP)
Forward Primer
Reverse Primer
VKORCI -1639G>A (rs9923231)
GCCAGCAGGAGAGGGAAATA
AGTTTGGACTACAGGTGCCT
VKORCI 1173C>T (rs9934438)
AGAGACTTACTTAAGGTCTA
TTCCAAGAAGCCACCTGGGC
CYP2C9*2 (rs1799853)
GGAGGATGGAAAACAGAGACTTA
TGAGCTAACAACCAGGACTCAT
CYP2C9*3 (rs1057910)
GCTGTGGTGCACGACGTCCAGAGATGC
ACACACACTGCCAGACACTAGG
57
Table 4.3: PCR reagents with their concentrations used for PCR
Serial
no.
Reagents with initial
concentration
Reagents with final
concentration
Quantity
used in µl
1 dH2O --- 11.8
2 10X PCR buffer 1X PCR buffer 2
3 25 mM MgCl2 1.25 mM MgCl2 1
4 2 mM dNTPs 0.1 mM dNTPs 1
5 5 U/µl Taq DNA polymerase 0.05 U Taq DNA
polymerase
0.2
6 20 nM Forward primer 1.0 nM 1
7 20 nM Reverse primer 1.0 nM 1
8 40 ng/µl Sample Genomic DNA 4 ng 2
--- Total reaction volume --- 20
58
Table 4.4: Thermal profile for the PCR for VKORC1 alleles
Thermal Stages
Temperature
Time
Number of
Cycles
Stage 1
Initial Denaturation
94ºC
5 minutes
1
Stage 2
Denaturation
94ºC
45 seconds
35
Annealing
55ºC
45 seconds
Extension
72ºC
45 seconds
Stage 3
Final Extension
72ºC
10 minutes
1
Storage
4ºC
59
Gel electrophoresis (Maxicell Electrophoretic Gel System, USA) was done by running the gel
in 1X TBE buffer at 200 volts using BioRad Power Pack 3000 (BioRad, USA). The gel was
run for 40 minutes. DNA bands obtained were visualized under UV illumination and
photographed with Syngene gel documentation system (GeneGenius, Syngene, UK).
4.9.4.3 Restriction fragment length polymorphism (RFLP) assay
The amplified DNA fragment harboring the SNP VKORC1 –1639G>A was digested
with Msp1 restriction enzyme (New England Biolabs Inc. UK). The digestion was carried out
by adding 1µl Msp1 enzyme (10 U/µl) and 3µl 10X digestion buffer to 10 µl of the PCR
product and adjusting the final volume to 30 µl with dH2O. The mixture was incubated at
37ºC for 16 hours. The RFLP products were checked on 3% w/v agarose gel. The agarose gel
electrophoresis was done at 200 volts for 40 minutes using Bio-Rad Power PAC 3000. The
size of the RFLP bands was depicted with DNA size reference ladder (Invitrogen, Brazil).
4.9.5 Genotyping of VKORC1 1173C>T Polymorphism
Genotyping of the 1173C>T polymorphism in VKORC1 was done by PCR-RFLP
assay.
4.9.5.1 Primers
The forward and reverse primer sequences has been designed by Professor Pieter H.
Reitsma (Reitsma et al., 2005) and were kindly provided on request. The sequences of both
primers have been shown in Table 4.2. The sequences of these primers were confirmed
through Primer-BLAST search (http://blast.ncbi.nlm.nih.gov/Blast.cgi). These primers
amplified the 200bp region spanning the VKORCI 1173C>T in all PCR irrespective of C/T
genotypes.
60
4.9.5.2 Polymerase chain reaction (PCR) analysis
The optimization of PCR with reproducible results was carried out. All the samples
were amplified with this optimized PCR for target DNA fragment spanning VKORC1
1173C>T allelic region. The PCR was carried out for each sample in a final volume of 20 µl
containing, 40 ng genomic DNA (2 µl), 2 µl 10X PCR buffer without Mg2+, 1 µl 25 mM
MgCl2, 0.2 µl of 5 U/µl Taq DNA polymerase, 1 µl of 2 mM dNTPs, 1 µl of each 20 nM
forward and reverse primers (e-oligos, Gene Link Inc. USA). The final volume was adjusted
to 20 µl with deionized water. The initial and final concentrations of the reagents used in PCR
are given in Table 4.3. The PCR was carried out in thermal cycler. The thermal stages with
their temperatures and number of cycles are given in Table 4.4.
From the PCR sample, 5 µl of PCR product was mixed with 5 µl of 6X gel loading
buffer and loaded on 2 percent w/v agarose gel. DNA size reference ladder was loaded in the
first well. Gel electrophoresis was done by running the gel for 40 minutes in 1X TBE buffer at
200 volts. DNA bands obtained were visualized under UV illumination and photographed
with Syngene gel documentation system.
4.9.5.3 Restriction fragment length polymorphism (RFLP) assay
The amplified DNA fragments harboring the alleles of VKORC1 1173C>T were
digested with Sty1 restriction enzyme at 37ºC for 16 hours in a final volume of 30 µl
containing 1 µl Sty1 enzyme (10 U/µl), 3 µl 10X digestion buffer, 10 µl of the PCR product
adjusted with dH2O to give final volume. The RFLP products were checked on 3% w/v
agarose gel. The agarose gel electrophoresis was done at 200 volts for 40 minutes. The size of
the RFLP bands was depicted with DNA size reference ladder.
61
4.9.6 Genotyping of CYP2C9 Polymorphism
4.9.6.1 Primers
The forward and reverse primers for CYP2C9*2 and CYP2C9*3 allelic variants being
used were reported in a previous study (Moridani et al., 2006). The sequences of both pairs of
primers have been shown in Table 4.2. The sequences of these primers were confirmed
through Primer-BLAST search (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The primers for
CYP2C9*2 amplified the 396bp region including the CYP2C9*2 in all PCR irrespective of
C/T genotypes. The primers for CYP2C9*3 amplified the 298bp region including the
CYP2C9*3 in all PCR irrespective of A/C genotypes.
4.9.6.2 Polymerase chain reaction (PCR) analysis
The optimization of PCR with reproducible results was carried out for both
CYP2C9*2 and CYP2C9*3 separately. All the samples were amplified for target DNA
fragments spanning CYP2C9*2 and CYP2C9*3 allelic regions with separately optimized
single PCR reaction. For CYP2C9*2, PCR was carried out for each sample in a final volume
of 20 µl containing, 40 ng genomic DNA (2 µl), 2 µl 10X PCR buffer without Mg2+, 1 µl 25
mM MgCl2, 0.2 µl of 5 U/µl Taq DNA polymerase, 1 µl of 2 mM dNTPs, 1 µl of each 20 nM
forward and reverse primers for CYP2C9*2 (e-oligos, Gene Link Inc. USA). The final
volume was adjusted to 20 µl with dH2O. The initial and final concentrations of the reagents
used in PCR are given in Table 4.3. The PCR was carried out in thermal cycler. The thermal
stages with their temperatures and number of cycles are given in Table 4.5.
For CYP2C9*3, same constituents as for CYP2C9*2 with similar concentrations and
amount of PCR mixture, were used except the primer set was different. The PCR for
62
CYP2C9*3 was carried out in thermal cycler. The thermal stages with their temperatures and
number of cycles are given in Table 4.5.
Out of each PCR sample for both CYP2C9 polymorphisms, 5 µl of PCR product was
mixed with 5 µl of 6X gel loading buffer and loaded on 2 percent w/v agarose gel. DNA size
reference ladder was loaded as for amplified DNA fragments in the first well. Gel
electrophoresis was done by running the gel for 40 minutes in 1X TBE buffer at 200 volts.
DNA bands obtained were visualized under UV illumination and photographed with Syngene
gel documentation system.
4.9.6.3 Restriction fragment length polymorphism (RFLP) assay
The amplified DNA fragment spanning the SNP CYP2C9*2 was digested with AVAII
restriction enzyme. The digestion was carried out by adding 1µl AVAII enzyme (10 U/µl) and
3µl 10X digestion buffer to 10 µl of the PCR product and adjusting the final volume to 30 µl
with dH2O. The mixture was incubated at 37ºC for 16 hours. The RFLP products were
checked on 3 percent w/v agarose gel. The agarose gel electrophoresis was done at 200 volts
for 40 minutes using Bio-Rad Power PAC 3000. The size of the RFLP bands was depicted
with DNA size reference ladder.
The amplified DNA fragments surrounding the CYP2C9*3 polymorphism were
digested with Nsi1 restriction enzyme at 37ºC for 16 hours in a final volume of 30 µl
containing 1 µl Nsi1 enzyme (10 U/µl), 3 µl 10X digestion buffer, 10 µl of the PCR product
adjusted with dH2O to give final volume. The RFLP products were checked on 3 percent w/v
agarose gel. The agarose gel electrophoresis was done at 200 volts for 40 minutes. The size of
the RFLP bands was depicted with DNA size reference ladder.
63
Table 4.5: Thermal profile for the PCR for CYP2C9 alleles
Thermal Stages
Temperature
Time
Number of
Cycles
Stage 1
Initial Denaturation
94ºC
5 minutes
1
Stage 2
Denaturation
94ºC
45 seconds
35
Annealing
60ºC
45 seconds
Extension
72ºC
45 seconds
Stage 3
Final Extension
72ºC
10 minutes
1
Storage
4ºC
64
4.9.7 DNA Sequencing
In order to validate the PCR-RFLP genotypes of study participants; direct sequencing
of one hundred samples was done through automated capillary sequencing method. The four
SNPs, VKORC1 –1639G>A, VKORC1 1173C>T, CYP2C9*3 and CYP2C9*2 were
amplified using the same primers as described above (Table 4.2). The PCR product was
purified through the protocol given below.
4.9.7.1 PCR product purification
To 25 µl PCR product, 2.5 µl of 10 M ammonium acetate was added first and then 50
µl chilled absolute ethanol was added. This product was mixed and kept at –4ºC for 20 min.
The mixture was then centrifuged at 14000 rpm for 10 minutes and supernatant was
discarded. The resultant pellet was re-suspended in 100 µl of 70 percent chilled ethanol. This
solution was then centrifuged at 14000 rpm for 10 minutes and supernatant was discarded.
The pellet obtained was dried and re-suspended in 15 µl deionized water.
4.9.7.2 Sequencing reaction
The purified PCR product harboring the SNP was subjected to Sanger sequencing
reaction. The sequencing reaction was done by using forward primer of each target region
having SNP in it. The reaction was carried out in the presence of 3 µl of purified PCR
product, 1 µl of 20 nM of forward primer, 4 µl of BigDye Terminator (Applied Biosystem®,
Life Technologies, USA) and final volume was adjusted to 10 µl using dH2O. This reaction
was subjected to a thermal cycle of 95ºC for 1 min and then 25 cycles each of 95ºC for 15 sec
and 55ºC for 4 min. After the completion of sequencing reaction the product was purified by
sequencing reaction purification protocol.
65
4.9.7.3 Sequencing reaction product purification
The sequencing reaction product was transferred to a 0.5 ml eppendrof tube and 2.5 µl
of 125 mM EDTA was added to it. Thereafter 30 µl of absolute ethanol was added and
vortexing was done to mix it well. The mixture was then kept at 4ºC for 15 min and
afterwards centrifuged at 14000 rpm for 20 min. The supernatant was discarded and
remaining pellet was vortexed after addition of 100 µl of 70 percent chilled ethanol. The
mixture was spun at 14000 rpm for 10 min and the supernatant was removed. The pellet was
dried at 55ºC in oven.
4.9.7.4 Denaturing the purified sequencing reaction product
To the purified dry sequencing reaction product 10 µl of HiDi Formamide (Applied
Biosystem®, Life Technologies, USA) was added and denatured at 95ºC for 5 min and
immediately cooled down to 4ºC at ice.
4.9.7.5 Sequencing
The purified sequencing reaction product was loaded to ABI Genetic Analyzer 3130
(Applied Biosystem®, Life Technologies, USA) for sequencing. The sequencing results were
read and the SNP genotypes were validated.
4.9.8 Data Analysis
The genotypes and allele frequencies were estimated from the observed numbers of
each specific allele and 95 percent confidence interval was calculated. The expected Hardy-
Weinberg (H-W) frequencies for the alleles and genotypes were calculated by using Hardy-
Weinberg Equilibrium equation:
66
p² + 2pq + q² = 1
Where ‘p’ and ‘q’ represent the frequencies of alleles and are calculated as below:
Genetic data for deviation from Hardy-Weinberg equilibrium was tested using chi-square test.
A p-value of less than 0.05 was taken as statistically significant.
4.10 STATISTICAL ANALYSIS
Data was analyzed using SPSS version 20.0 (IBM Corporation, USA). Descriptive
statistics was used to describe the data. Mean and standard deviation (SD) were calculated for
quantitative variables like age, weight, height, body mass index (BMI) and dose. Frequency
and percentages were calculated for qualitative variables like gender and genotypes. The
genotypes and allele frequencies were estimated from the observed numbers of each specific
allele and 95 percent confidence interval was calculated. Genetic data for deviation from
Hardy-Weinberg equilibrium was tested using chi-square test. Pearson correlation coefficient
was calculated to study the relationship of dose with age, weight, height and BMI.
Independent sample’s t-test was applied to study the relationship between gender and dose of
warfarin. Analysis of variance (ANOVA) was applied to compare S/R ratio and warfarin dose
among different CYP2C9 genotypes. ANOVA was also applied for comparison of different
VKORC1 genotypes with warfarin dose requirement. A p-value of less than 0.05 was taken as
statistically significant. ANOVA was followed by Post-hoc Tukey’s test for pair-wise
comparison if ANOVA gave p-value of less than 0.05. Multiple linear regression analysis was
No. of that allele in study group
Total number of alleles
Allele Frequency =
67
carried out to study the overall influence of genetic and demographic factors on warfarin dose
requirement.
68
Chapter 5
RESULTS
5.1 DEMOGRAPHIC AND CLINICAL DATA
A total of six hundred and seven (607) stable patients fulfilling the eligibility criteria,
participated in the study. There were 297 (48.9 percent) male and 310 (51.1 percent) female
subjects. The mean and range of age, body weight, height and body mass index (BMI) of all
patients are summarized in Table 5.1.
The total number of patients belonging to different provinces and regions of Pakistan
and their percentage values are given in Table 5.2. Patients recruited in this study had INR
values within range of 1.5–3.5 (mean 2.3±0.8). Mean daily dose of warfarin calculated in 607
patients was 5.62±1.98 mg with the range of 0.36–15 mg whereas mean weekly dose was
39.36±13.8 mg with the range of 2.5–105 mg. Patients were prescribed warfarin for different
disorders having risk of thromboembolism. The indications for warfarin therapy and number
of patients suffering from these are summarized in Table 5.3.
5.1.1 Correlation between Demographic Variables and Warfarin Dose
Correlation between demographic factors age, gender, weight, height, BMI and
warfarin dose was determined and the results are summarized in Table 5.4. There was a weak
positive correlation of weight (Figure 5.1) and height (Figure 5.2) with warfarin dose which
was statistically not significant as shown by the p-value of more than 0.05 for these variables
(Table 5.4). There was insignificant correlation of BMI with warfarin dose (Figure 5.3).
However, there was statistically significant negative correlation between age and warfarin
dose as given in Table 5.4 and depicted in Figure 5.4. Gender was shown to have no
statistically significant effect on warfarin dose as p-value was more than 0.05 (p=0.284).
69
Table 5.1: Demographic characteristics of study population undergoing wafarin therapy
Serial
No. Demographic Variable Mean±SD Range
1
Age (years) 37.93±12.23 18–65
2
Weight (Kg) 60.95±13.43 29–113
3
Height (cm) 163.46±9.41 124–201
4
BMI (Kg/m2) 22.8±4.7 12.7–39.54
70
Table 5.2: Regional distribution of study population undergoing warfarin therapy
Serial
No.
Province / Region
Number of Patients
Percentage of
Patients (%)
1
Punjab
306
50.4
2
Sindh
139
22.9
3
Khyber Pakhtunkhwa
106
17.5
4
Balochistan
28
4.6
5
Gilgit Baltistan
6
1.0
6
Azad Jammu & Kashmir
22
3.6
71
Table 5.3: Indications for warfarin administration in patients undergoing warfarin therapy
Serial
No
Indications
Number of Patients (%)
1 Mitral Valve Replacement 340 (56)
2 Aortic Valve Replacement 108 (17.8)
3 Double Valve Replacement 91 (15)
4 Mitral Stenosis 18 (3)
5 Atrial Fibrillation 14 (2.3)
6 Coronary Artery Bypass Grafting 10 (1.6)
7 Mitral Regurgitation 4 (0.7)
8 Left Ventricular Clot 3 (0.5)
9 Cerebrovascular Accident 3 (0.5)
10 Aortic Regurgitation 2 (0.3)
11 Ventricular Septal Defect Closure 2 (0.3)
12 Aortic Stenosis 2 (0.3)
13 Triple Valve Disease 2 (0.3)
14 Percutaneous Transvenous Mitral
Commissurotomy (PTMC) 2 (0.3)
15 Dilated Cardiomyopathy 1 (0.2)
16 Atrial Septal Defect Closure 1 (0.2)
17 Deep Vein Thrombosis 1 (0.2)
18 APLA Syndrome (Anti Phospholipid
Antibody Syndrome) 1 (0.2)
19 Patent Ductus Arteriosus 1 (0.2)
20 Rheumatic Heart Disease 1 (0.2)
TOTAL 607 (100)
72
Table 5.4: Correlation between demographic factors and warfarin dose in patients undergoing
warfarin therapy
Variable
Warfarin Dose (mg/week)
Correlation Coefficient (r)
p-value
Age
0.091
0.026*
Weight
0.019
0.648
Height
0.06
0.137
BMI
0.005
0.896
* Significant
73
Wei
ght
in K
g
Figure 5.1: Relationship between weight and warfarin dose in patients undergoing warfarin
therapy
r = 0.019
p-value = 0.648
74
Hei
ght
in c
m
Figure 5.2: Relationship between height and warfarin dose in patients undergoing warfarin
therapy
r = 0.06
p-value = 0.137
75
BM
I in
Kg/
m2
Figure 5.3: Relationship between BMI and warfarin dose in patients undergoing warfarin
therapy
r = 0.005
p-value = 0.896
76
Age
in y
ears
Figure 5.4: Relationship between age and warfarin dose in patients undergoing warfarin
therapy
r = –0.091
p-value = 0.026
77
5.2 GENOTYPING DATA
Blood samples of 607 were analyzed for genotyping. Out of these, 516 samples gave
successful results for VKORC1 1173C>T polymorphism, 527 for VKORC1 –1639G>A
polymorphism, 509 for CYP2C9*2 and 527 for CYP2C9*3 polymorphism.
5.2.1 VKORC1 1173C>T Polymorphism
The amplified PCR product was resolved at 200bp. The PCR fragment containing the
C allele was digested into two fragments of 144bp and 56bp. The T allele was not digested
and resolved at 200bp. Results are shown as gel electrophoresis image (Figure 5.5) and DNA
sequencing electropherogram (Figure 5.6)
A total of 516 samples gave successful results for VKORC1 1173C>T polymorphism.
The allele and genotype frequency distribution along with expected Hardy-Weinberg (H-W)
frequencies for VKORC1 1173C>T polymorphism are given in Table 5.5. A p-value of more
than 0.05 implies that observed frequencies did not deviate from H-W equilibrium.
5.2.2 VKORC1 -1639G>A Polymorphism
The amplified PCR product was resolved at 290bp. The PCR fragment containing the
G allele was digested into two fragments of 124bp and 166bp. The A allele was not digested
and resolved at 290bp. The results obtained on gel electrophoresis are shown in Figure 5.7.
Different genotypes confirmed by DNA sequencing represented as DNA sequencing
electropherogram (Figure 5.8).
A total of 527 samples gave successful results for VKORC1 –1639G>A
polymorphism. The allele and genotype frequency distribution along with expected Hardy-
Weinberg (H-W) frequencies for VKORC1 –1639G>A polymorphism are given in Table 5.6.
78
10bp 56bp 144bp 200bp
Figure 5.5: Representative gel illustrating PCR-RFLP products for VKORC1 1173C>T
genotyping. Lane 1: 10bp ladder; Rest of the lanes: each represents a single
individual’s genotype. The 56bp, 144bp and 200bp bands are indicated by
arrows.
79
Figure 5.6: DNA sequencing electropherogram of VKORC1 gene region containing position
1173bp in intron 1. The top sequence represents a wild-type sequence (C), the
middle sequence a heterozygous (C>T) and the bottom sequence characterizes
variant allele (T)
C>T
C
T
80
Table 5.5: Allele and genotype frequency distributions of VKORC1 1173C>T polymorphism
Parameters
Number of
subjects (n)
Observed Frequency
(%)
95%
Confidence Interval
Expected H-W Frequency
(%)
p-value
Genotypes
516
-
-
-
0.582
CC
115
22.3
18.71–25.89
25
CT
285
55.2
50.91–59.49
50
TT
116
22.5
18.9–26.1
25
Alleles
C
515
49.9
45.59–54.21
T
517
50.1
45.79–54.41
81
25bp 124bp 166bp 290bp
Figure 5.7: Representative gel illustrating PCR-RFLP products for VKORC1 –1639G>A
genotyping. Lane 1: 25bp ladder; Rest of the lanes: each represents a single
individual’s genotype. The 124bp, 166bp and 290bp bands are indicated by
arrows.
82
Figure 5.8: DNA sequencing electropherogram of VKORC1 gene region containing position
–1639bp in promoter region. The top sequence represents a wild-type sequence
(G), the middle sequence a heterozygous (G>A) and the bottom sequence
characterizes variant allele (A)
G>A
G
A
83
Table 5.6: Allele and genotype frequency distributions of VKORC1 –1639G>A
polymorphism
Parameters
Number of
subjects n
Observed Frequency
(%)
95%
Confidence Interval
Expected
H-W Frequency
(%)
p-value
Genotypes
527
-
-
-
0.0003
GG
88
16.7
13.52–19.88
26.8
GA
370
70.2
66.3–74.1
50
AA
69
13.1
10.22–15.98
23.2
Alleles
G
546
52
47.73–56.27
A
508
48
43.73–52.27
84
A p-value of less than 0.05 shows that observed frequencies deviated significantly from H-W
equilibrium.
5.2.3 Relatioship between VKORC1 Genotypes and Warfarin Dose
The effect of VKORC1 –1639G>A and VKORC1 1173C>T polymorphism on
warfarin dose was determined. The comparison of different VKORC1 –1639G>A genotype
frequencies alongwith mean warfarin dose for that genotype has been summarized in Table
5.7 and shown in figure (Figure 5.9). There was no statistically significant effect of different
genotypes on warfarin dose requirement (p-value >0.05). The association of different
VKORC1 1173C>T genotypes with warfarin dose has been presented in Table 5.7 and shown
in figure (Figure 5.10). There was no statistically significant effect of different genotypes on
warfarin dose requirement (p-value >0.05).
5.2.4 CYP2C9 Polymorphism
The genotype distribution of two SNPs in CYP2C9 gene (CYP2C9*2 and CYP2C9*3)
were analyzed. For CYP2C9*2, the amplified PCR product was resolved at 396bp. The PCR
fragment containing the C allele was digested into two fragments of 223bp and 173bp. The T
allele was not digested and resolved at 396bp. The results depicted as gel electrophoresis
image (Figure 5.11) and DNA sequencing electropherogram (Figure 5.12). A total of 509
samples gave successful results for CYP2C9*2 polymorphism. The allele and genotype
frequency distribution along with expected H-W frequencies for CYP2C9*2 polymorphism
are given in Table 5.8. A p-value of more than 0.05 implies that observed frequencies did not
deviate significantly from H-W equilibrium.
85
Table 5.7: Relationship of warfarin dose with VKORC1 genotypes
Genotypes
Number of
Subjects
n (%)
Warfarin Dose
(mg/week)
Mean±SD
Warfarin Dose
(mg/day)
Mean±SD
p-value
VKORC1 –1639G>A
527 (100)
-
-
GG
88 (16.7)
38.84±14.68
5.55±2.1
0.984NS
GA
370 (70.2)
38.88±13.3
5.55±1.9
AA
69 (13.1)
38.57±9.6
5.51±1.37
VKORC1 1173C>T
516 (100)
CC
115 (22.3)
38.57±13.36
5.51±1.91
0.946NS
CT
285 (55.2)
38.47±12.67
5.49±1.81
TT
116 (22.5)
38.94±13.0
5.56±1.86
NS Non Significant
86
Figure 5.9: Different VKORC1 – 1639G>A genotypes along with mean warfarin dose
in respective groups
0
10
20
30
40
50
60
70
80
GG GA AA
38.84±14.68 mg/wk
38.88±13.3 mg/wk
38.57±9.6 mg/wk
Genotype Group
Population (%)
87
Figure 5.10: Different VKORC1 1173C>T genotypes along with mean warfarin dose
in respective groups
0
10
20
30
40
50
60
CC CT TT
38.57±13.36 mg/wk
38.47±12.67 mg/wk
38.94±13.0 mg/wk
Genotype Group
Population (%)
88
100bp 173bp 223bp 396bp
Figure 5.11: Representative gel illustrating PCR-RFLP products for CYP2C9*2 genotyping.
Lane 1: 100bp ladder; Rest of the lanes: each represents a single individual’s
genotype. The 173bp, 223bp and 396bp bands are indicated by arrows.
89
Figure 5.12: DNA sequencing electropherogram of CYP2C9*2 polymorphism. The top
sequence represents a wild-type sequence (C), the middle sequence a
heterozygous (C>T) and the bottom sequence characterizes variant allele (T)
C
C>T
T
90
Table 5.8: Allele and genotype frequency distributions of CYP2C9*2 polymorphism
Parameters
Number of
subjects n
Observed Frequency
(%)
95%
Confidence Interval
Expected H-W Frequency
(%)
p-value
Genotypes
509
-
-
-
0.066
CC (CYP2C9*1/*1)
470
92.3
89.98–94.62
91.4
CT
(CYP2C9*1/*2)
33
6.5
4.36–8.64
8.4
TT
(CYP2C9*2/*2)
6
1.2
0.25–2.15
0.2
Alleles
C
973
95.6
93.82–97.38
T
45
4.4
2.62–6.18
91
For CYP2C9*3, the amplified PCR product was resolved at 298bp. The PCR fragment
containing the A allele was digested into two fragments of 274bp and 24bp. The C allele was
not digested and resolved at 298bp. The results are illustrated as gel electrophoresis image
(Figure 5.13) and DNA sequencing electropherogram (Figure 5.14). A total of 527 samples
gave successful results for CYP2C9*3 polymorphism. The allele and genotype frequency
distribution along with expected H-W frequencies for CYP2C9*3 polymorphism are given in
Table 5.9. A p-value of more than 0.05 implies that observed frequencies did not deviate
significantly from H-W equilibrium.
5.2.5 Relatioship between CYP2C9 Genotypes and Warfarin Dose
The effect of CYP2C9*2 and CYP2C9*3 polymorphism on warfarin dose was
determined. The comparison of different CYP2C9*2 genotypes with warfarin dose has been
shown in figure (Figure 5.15) and the data has been summarized in Table 5.10. There was no
statistically significant effect of different CYP2C9*2 genotypes on warfarin dose requirement
(p-value >0.05). The association of different CYP2C9*3 genotypes with warfarin dose has
been shown in figure (Figure 5.16) and the data has been given in Table 5.10. There was
statistically significant effect of different CYP2C9*3 genotypes on warfarin dose requirement
(p-value <0.05). On the basis of pair-wise comparison of different CYP2C9*3 genotypes
(Table 5.11), it has been inferred that subjects possessing homozygous polymorphic
CYP2C9*3/*3 genotype required lesser warfarin dose as compared to homozygous wild-type
CYP2C9*1/*1 genotype (AA) as the difference in dose requirement was statistically
significant (p-value <0.05).
92
10bp 24bp 274bp 298bp
Figure 5.13: Representative gel illustrating PCR-RFLP products for CYP2C9*3 genotyping.
Lane 1: 10bp ladder; Rest of the lanes: each represents a single individual’s
genotype. The 24bp, 274bp and 298bp bands are indicated by arrows.
93
Figure 5.14: DNA sequencing electropherogram of CYP2C9*3 polymorphism. The top
sequence represents a wild-type sequence (A), the middle sequence a
heterozygous (A>C) and the bottom sequence characterizes variant allele (C)
A
A>C
C
94
Table 5.9: Allele and genotype frequency distributions of CYP2C9*3 polymorphism
Parameters
Number of
subjects (n)
Observed Frequency
(%)
95%
Confidence Interval
Expected H-W
Frequency (%)
p-value
Genotypes
527
-
-
-
0.47
AA (CYP2C9*1/*1)
325
61.7
57.55–65.85
59.3
AC
(CYP2C9*1/*3)
163
30.9
26.95–34.85
35.4
CC
(CYP2C9*3/*3)
39
7.4
5.17–9.63
5.3
Alleles
A
813
77.1
73.51–80.69
C
241
22.9
19.31–26.49
95
Table 5.10: Relationship of warfarin dose with CYP2C9 genotypes
Genotypes
Number of
Subjects
n (%)
Warfarin Dose
(mg/week)
Mean±SD
Warfarin Dose
(mg/day)
Mean±SD
p-value
CYP2C*2
509 (100)
0.173NS
CC (CYP2C9*1/*1)
470 (92.3)
38.84±13.52
5.55±1.93
CT
(CYP2C9*1/*2)
33 (6.5)
38.56±11.56
5.51±1.65
TT
(CYP2C9*2/*2)
6 (1.2)
28.55±7.43
4.08±1.06
CYP2C*3
527 (100)
0.012*
AA (CYP2C9*1/*1)
325 (61.7)
40.53±13.53
5.79±1.93
AC
(CYP2C9*1/*3)
163 (30.9)
37.64±13.69
5.38±1.96
CC
(CYP2C9*3/*3)
39 (7.4)
35.02±14.35
5.0±2.05
* Significant
NS Non Significant
96
Figure 5.15: Different CYP2C9*2 Genotypes along with mean warfarin dose in respective
groups
0
10
20
30
40
50
60
70
80
90
100
CC CT TT
38.84±13.52 mg/wk
38.56±11.56 mg/wk
28.55±7.4 mg/wk
Genotype Group
Population (%)
97
Figure 5.16: Different CYP2C9*3 Genotypes along with mean warfarin dose in respective
groups
0
10
20
30
40
50
60
70
AA AC CC
37.64±13.69 mg/wk
40.53±13.53 mg/wk
35.02±14.35 mg/wk
Genotype Group
Population (%)
98
Table 5.11: Pair-wise comparison of warfarin dose among CYP2C9*3 genotypes
CYP2C9*3 Genotypes
p-value
AA
(CYP2C9*1/*1)
AC
0.071NS
CC
0.046*
AC
(CYP2C9*1/*3)
AA
0.071NS
CC
0.528NS
CC
(CYP2C9*3/*3)
AA
0.046*
AC
0.528NS
* Significant
NS Non Significant
99
5.3 DATA ANALYSIS OF R- AND S-ENANTIOMERS IN PLASMA
Blood samples of 170 patients were analyzed for S- and R-warfarin concentrations in
plasma. The HPLC method used was modified and validated according to ICH guidelines
(ICH, 2005).
5.3.1 Method Validation
The elution order of enantiomers was determined by injecting pure S- and R-warfarin
standards separately as shown in Figure 5.17 and 5.18 respectively. The retention time for S-
warfarin was 4.8±0.16 minutes and for R-warfarin was 5.7±0.13 minutes, making them well
resolved (Figure 5.19).
Standard calibration curves established to quantify both warfarin enantiomers
independently were linear over a concentration range of 12.5–2500 ng/ml for each enantiomer
(Figure 5.20 and 5.21). The equation of calibration using least square regression method was
y= 0.150x+5.799, R2 =0.999 for S-warfarin and y= 0.153x+4.273, R2 =0.999 for R-warfarin.
The limit of detection (LOD) was 6 ng/ml for both enantiomers. For both enantiomers, LLOQ
was 12.5 ng/ml. The coefficient of variation (CV%) and accuracy are given in Table 5.12. The
method was shown to be precise and accurate. Accuracy along with intra-day and inter-day
variability for quantitation of individual enantiomer is summarized in Table 5.12. The
coefficient of variation for intra-day variability ranged between 0.8–6.5 percent for S-
enantiomer and 1–7.3 percent for R-enantiomer. The coefficient of variation for inter-day
variability ranged between 4.9–9.3 percent for S-enantiomer and 4.5–11 percent for R-
enantiomer. The percent accuracy was in the range of 92–104 percent and 93–107 percent for
S- and R-warfarin respectively.
100
Figure 5.17: Chromatogram of pure S-wafarin standard
101
Figure 5.18: Chromatogram of pure R-wafarin standard
102
Figure 5.19: Chromatogram obtained from plasma sample spiked with 200 ng/ml
standard of racemic warfarin
103
Figure 5.20: Calibration curve for S-warfarin
104
Figure 5.21: Calibration curve for R-warfarin
105
le 5.12: Intra- and inter-day precision and accuracy of S- and R-warfarin analysis
ay
tion
S-warfarin R-warfarin
Conc.
Added
ng/ml
Conc. Found
ng/ml
Mean±SD
Coefficient
of Variation
(CV %)
Accuracy
(%)
Conc.
Added
ng/ml
Conc. Found
ng/ml Mean±SD
Coefficient of
Variation
(CV %)
Ac
OQ 12.5 13±1.75 13.4 104 12.5 13.4±1.97 14.6
day
ay
40 40.4±2.6 6.5 101 40 42.7±3.1 7.3
200 201±1.6 0.8 101 200 190.4±6.5 3.4
1500 1549±14 0.9 103 1500 1560±15 1
day
ay
40 36.6±3.4 9.3 92 40 37.5±4.3 11
200 192.3±9.3 4.9 96 200 185.3±8.3 4.5
1500 1419±99 7 95 1500 1404±117 8.3
ze
w
lity
40 39.9±1.5 3.7 99.6 40 43.8 ±0.8 1.7 1
200 217±0.9 0.4 108.7 200 222.8±1.4 0.6
1500 1511.5±22 1.5 100.8 1500 1580±25.5 1.6
-top
lity
40 40.3±0.9 2.2 100.7 40 43.4±1.4 3.2
200 216.6±1.5 0.7 108 200 222.5±1.9 0.9
1500 1506±26.7 1.8 100.4 1500 1575.7±36 2.3
106
Both S- and R- warfarin enantiomers were found to be stable after three freeze-thaw
cycles and at room temperature even after 24 hours. The data is summarized in Table 5.12.
The results have shown no effect on stability of analyte as CV% ranged between 0.4–3.7
percent for S-warfarin and 0.6–3.2 percent for R-warfarin.
The mean recovery of warfarin enantiomers from plasma assessed at three different
concentrations ranged from 86 to 103.8 percent. The recoveries of S-warfarin at 40, 200 and
1500 ng/ml were 94, 95 and 103.8 percent respectively and those of R-warfarin were 95, 86
and 103 percent respectively.
There was no enantiomer inter-conversion either during extraction or chromatography
as none of the unspiked enantiomer was detected on fresh analysis as well as upon re-injection
after 24 hours (Table 5.13).
5.3.2 S- and R-Warfarin Levels in Plasma
The S- and R-warfarin plasma levels along with their S/R ratios for individual subjects
have been attached as appendix VII. The mean steady-state plasma concentration from
analysis of 170 samples was 1025±658 ng/ml (range 28–3713 ng/ml) for S-warfarin and
1979±1189 ng/ml (range 41–7310 ng/ml) for R-warfarin. Mean plasma S/R ratio for warfarin
was 0.599±0.513 calculated from S- and R-warfarin plasma concentrations. The range of
plasma S/R ratio for warfarin was from 0.125 to 4.898.
5.3.3 Relationship of S/R Warfarin Ratio with CYP2C9 Genotypes
The concentration of S-warfarin was significantly different among different CYP2C9
genotypes as shown by p-value of less than 0.05 (p =0.018) whereas there was no effect of
CYP2C9 genotypes on plasma concentration of R-warfarin (p =0.134). The effect of different
107
CYP2C9*2 and CYP2C9*3 genotypes on S/R warfarin ratio was determined. The results are
summarized in Table 5.14. There was statistically significant effect of different genotypes on
S/R warfarin ratio (p=0.000). Because of high standard deviation (SD) in S/R warfarin ratio
data, normality of data was checked by One-sample Kolmogorov Smirnov test. A p-value of
more than 0.05 for S/R warfarin ratio among different genotypes confirmed the normality of
data. For pair-wise comparison with Post Hoc Tukey’s test, CYP2C9*2/*2 was not included
as there was only one subject in this group which cannot undergo statistical comparison. On
the basis of pair-wise comparison of different CYP2C9 genotypes, it has been inferred that
subjects possessing homozygous polymorphic CYP2C9*3/*3 genotype possessed
significantly higher S/R ratio as compared to rest of the CYP2C9 genotypes (p=0.00).
Heterozygous polymorphic genotypes including CYP2C9*1/*3, *2/*3, *3/*3 were having
significant higher S/R warfarin ratio as compared to homozygous wild-type CYP2C9*1/*1
genotype. The results are summarized in Table 5.15.
5.4 EFFECT OF DEMOGRAPHIC FACTORS AND GENOTYPES ON
WARFARIN DOSE
Multiple linear regression was performed on data from 402 subjects who had complete
demographic data as well as all 4 SNPs analyzed. The regression modeling revealed that both
demographic and genetic factors were independent variables of warfarin dose requirement.
The results are summarized in Table 5.16. The model 1 including all demographic factors
illustrated that 3.8 percent (R square =0.038) variability in warfarin dose was attributed to
these factors and it was statistically significant (p =0.009). Different genotypes were
incorporated in model 2. The total variation in warfarin dose explained by genetic factors was
3.8 percent (R square =0.038) and was statistically significant (p =0.004). The regression
108
model 3 including both demographic variables and genotypes produced a better fit for
estimation of warfarin dose variation than the model containing either demographic features
or genotypes alone, yielding the largest R square value as shown in table 5.16. Demographic
variables together with genotypes explained 8.1 percent (R square =0.081) variance in
warfarin dose and it was statistically significant (p =0.000).
109
Table 5.13: Analytical stability of single enantiomer
Time (hour)
S-warfarin Spiked Plasma
(1000 ng/ml)
R-warfarin Spiked Plasma
(1000 ng/ml)
S-warfarin
ng/mL
R-warfarin
ng/mL
S-warfarin
ng/mL
R-warfarin
ng/mL
0
888.9
ND*
ND*
935.1
24
850.4
ND*
ND*
888.6
*ND – Not Detected
110
Table 5.14: Relationship of S/R warfarin ratio with CYP2C9 genotypes
CYP2C9 Genotypes
Number of Subjects
n (%)
S/R Warfarin Ratio
Mean±SD
p-value
CYP2C9*1/*1
103 (60.6)
0.46±0.13
0.000*
CYP2C9*1/*2
4 (2.4)
0.6±0.09
CYP2C9*1/*3
54 (31.8)
0.65±0.34
CYP2C9*2/*2
1 (0.6)
1.02
CYP2C9*2/*3
3 (1.8)
1.02±0.42
CYP2C9*3/*3
5 (2.9)
2.75±1.59
* Significant
111
Table 5.15: Pair-wise comparison of S/R warfarin ratio among CYP2C9 genotypes
CYP2C9 Genotypes
p-value
CYP2C9*1/*1 CYP2C9*1/*2 0.915NS
CYP2C9*1/*3 0.008*
CYP2C9*2/*3 0.038*
CYP2C9*3/*3 0.000*
CYP2C9*1/*2 CYP2C9*1/*1 0.915NS
CYP2C9*1/*3 0.999NS
CYP2C9*2/*3 0.484NS
CYP2C9*3/*3 0.000*
CYP2C9*1/*3 CYP2C9*1/*1 0.008*
CYP2C9*1/*2 0.999NS
CYP2C9*2/*3 0.341NS
CYP2C9*3/*3 0.000*
CYP2C9*2/*3 CYP2C9*1/*1 0.038*
CYP2C9*1/*2 0.484NS
CYP2C9*1/*3 0.341NS
CYP2C9*3/*3 0.000*
CYP2C9*3/*3 CYP2C9*1/*1 0.000*
CYP2C9*1/*2 0.000*
CYP2C9*1/*3 0.000*
CYP2C9*2/*3 0.000*
* Significant
NS Non Significant
112
Table 5.16: Multiple linear regression models with mean weekly warfarin dose as
dependent variable
Model
Independent Variables
R Square (R2)
Adjusted R2
p-value
1 Age
Gender
Weight
Height
BMI
0.038
0.026
0.009*
2 VKORC1 –1639G>A
VKORC1 1173C>T
CYP2C9*2
CYP2C9*3
0.038
0.028
0.004*
3 Age
Gender
Weight
Height
BMI
VKORC1 –1639G>A
VKORC1 1173C>T
CYP2C9*2
CYP2C9*3
0.081
0.060
0.000*
*Significant
113
Chapter 6
DISCUSSION
The effect of different demographic factors like age, gender, height, weight and BMI
on warfarin therapy was studied. Age was found to be a significant factor affecting warfarin
dose. A significant negative correlation between age and warfarin dose indicated that with an
increase in age, lesser dose of the drug was required. Variations in warfarin sensitivity in
elderly patients have been attributed to many factors like reduced hepatic and renal functions,
co-morbid diseases and concurrent use of other drugs (Reynolds et al., 2007, Ansell et al.,
2008, Ageno et al., 2012, Maddula and Ansell 2013). Different studies have given some
postulates regarding less dose requirement in elderly. Some studies identified the
pharmacokinetic variables responsible for such effect like age related changes in warfarin
clearance whereas others have favored increased warfarin sensitivity to advancing age being
due to pharmacodynamic factors. A study done on population pharmacokinetics of warfarin
by Mungall et al. demonstrated that oral clearance of warfarin decreased by 1 percent with
each year over the age range of 20–70 years (Mungall et al., 1985). A recent study also
reported the decrease in metabolic drug clearance with advancing age responsible for lesser
warfarin dose requirement in elderly patients. The study predicted 20–40 percent lower drug
clearance in elderly as compared to younger adults (Polasek et al., 2013). A study comparing
the pharmacokinetic profile of warfarin in young (27–37 years) and older (62–89 years)
patients showed that although warfarin clearance was lower in older group but it was not
statistically significant (Chan et al., 1994). Another study not only reported the significant
negative correlation of old age with warfarin dose attributable to slower clearance of S-
warfarin but also gave a concept related to pharmacodynamics involving VKOR enzyme.
114
Their findings suggest that decrease in functional hepatic mass contributes to increase in
warfarin sensitivity which in turn may be due to reduction in content or activity of VKOR
enzyme (Wynne et al., 1995). Other studies have supported the concept of increased intrinsic
sensitivity of warfarin with advancing age either due to declining body functions or decreased
ability to metabolize warfarin (Gurwitz et al., 1992, Loebstein et al., 2001, Wadelius et al.,
2004, Garcia et al., 2005, Takahashi et al., 2006, Miura et al., 2009).
Most of the studies taking into account the effect of age on warfarin therapy have
reported similar results as this study. They have demonstrated an inverse relationship pointing
out that increasing age requires lesser dose to produce therapeutic anticoagulant response.
Majority of studies conducted in Caucasians have reported decrease in warfarin dose
requirement with increasing age (Kamali et al., 2004, Sconce et al., 2005, Garcia et al., 2005,
Limdi et al., 2008, Gage et al., 2008, Wadelius et al., 2009, Mazzaccara et al., 2013). A
recent study in USA has reported a significant negative correlation of age with warfarin dose
not only during initiation phase of therapy (r = 0.298, p <0.001) but also during long-term
therapy (r= 0.398, p <0.001) (Tatarunas et al., 2013). Some studies conducted on multiethnic
populations showed significant negative correlation of age with warfarin dose (Takahashi et
al., 2006, Tham et al., 2006, Nakai et al., 2007, Singh et al., 2011, Gan et al., 2011). A
number of studies conducted in other populations like Thai (Sangviroon et al., 2010),
Colombian (Isaza et al., 2010), Omani (Pathare et al., 2012), Hispanics (Cavallari et al.,
2011), Korean (Cho et al., 2011), Brazilian (Botton et al., 2011), African American (Momary
et al., 2007, Perera et al., 2013), Malaysian (The et al., 2012), Indonesian (Suriapranata et al.,
2011), Israeli (Muszkat et al., 2007), Turkish (Ozer et al., 2013) have demonstrated similar
results as in this study. Studies conducted in Egyptian population gave variable results. Two
115
studies reported the same effect of age on warfarin dose as shown by this study (Shahin et al.,
2011, Ekladious et al.,2013) whereas another study demonstrated a small variation (1.5%) in
dose requirement with age but it was statistically not significant (El Din et al., 2012). Same is
the case in Japanese. Some studies demonstrated significant effect of age on warfarin dose
requirement (Kimura et al., 2007, Yoshizawa et al., 2009, Miura et al., 2009) but one study
reported no significant effect (Obayashi et al., 2006). Not much of the studies have been
carried out in the neighboring countries except in China. Studies conducted in Chinese
population have reported the results similar to ours (Miao et al., 2007, Huang et al., 2009,
You et al., 2011, Lu et al., 2013) but one recent study did not show any significant correlation
of age with warfarin dose requirement (Wang et al., 2013). One study in the neighborhood is
from Iran (Namazi et al., 2010) and another from India (Pavani et al., 2012) which have
demonstrated the similar significant effect of age on warfarin dose. Age related dose
requirement of warfarin has not been reported earlier in the local population.
No significant effect of gender on warfarin dose requirement was observed in this
study. Most of the studies that have studied this effect have corroborated our findings
demonstrating no significant effect of gender on warfarin dose requirement. Studies
conducted in different populations like Turkish (Ozer et al., 2013), Egyptian (El Din et al.,
2012), Caucasian (Tabrizi et al., 2002, Wadelius et al., 2004), Japanese (Obayashi et al.,
2006, Saito et al., 2013), Indonesian (Suriapranata et al., 2011), Sudanese (Shrif et al., 2011),
Korean (Cho et al., 2011) have observed no significant effect of gender on warfarin dose
requirement. In the countries geographically close to Pakistan, the studies in China (Wang et
al., 2008, Wang et al., 2013) and Iran (Namazi et al., 2010) also reported similar results as
ours but a study conducted in India has shown increased dose requirement in males as
116
compared to females (Pavani et al. 2012). Some other studies have also demonstrated that
females required lesser dose as compared to male population (Garcia et al, 2005, Sconce et
al., 2005, Whitley et al., 2007, Wadelius et al., 2009). The possible explanation for this
difference may be due to the difference in body size, fat content and intrinsic drug clearance
between males and females (Garcia et al., 2005).
In the present study, results suggest that weight and height do not significantly affect
the warfarin dose. Some studies conducted in Caucasians did not observe any significant
effect of weight or height on wafarin dose (Tabrizi et al., 2002, Whitly et al., 2007) whereas
others demonstrated significant positive correlation of these factors with warfarin dose which
implied that with increase in weight or height, the warfarin dose requirement also increased
(Kamali et al., 2004, Sconce et al., 2005, Momary et al., 2007, Tatarunas et al., 2013). A
number of studies conducted in other populations like Malaysian (Teh et al., 2012), Turkish
(Ozer et al., 2013), Egyptian (Shahin et al., 2011, El Din et al., 2012) and Thai (Sangviroon et
al., 2010) reported results similar to this study demonstrating no significant effect of these
factors on warfarin dose requirement. Some studies in other populations like Japanese
(Takahashi et al., 2006, Saito et al., 2013), Brazilian (Botton et al., 2011) and Swedish
(Wadelius et al., 2005) have demonstrated a significant effect of weight or height on warfarin
dose. In Pakistan’s bordering countries, Chinese and Iranian studies have reported different
results. One Chinese study showed significant positive correlation of height and weight with
warfarin dose (Miao et al., 2007), while another gave positive association of only weight with
dose (Lu et al., 2013). A recent study conducted in Chinese demonstrated results similar to
ours (Wang et al., 2013) showing no effect of these factors on warfarin therapy. The study
117
conducted in Iran (Namazi et al., 2010) did not show any significant effect of weight and
height on warfarin dose requirement.
Body mass index (BMI) was not significantly correlated to warfarin dose requirement
in this study. As BMI is derived from weight and height of a person, most of the studies
reported in literature have taken weight and height into consideration instead of BMI. A few
studies have shown significant positive effect of BMI on warfarin dose resulting in increase in
dose with higher BMI (Botton et al., 2011, Wallace et al., 2013). Others have reported results
similar to present study demonstrating no significant effect of BMI on warfarin dose (Gurwitz
et al., 1992, Ageno et al., 2003, Whitley et al., 2007, Pavani et al., 2012).
Major guidelines on anticoagulation therapy with warfarin stressed only on
consideration of age out of all demographic variables to be taken into account while
prescribing specific dose because it is the only variable consistently showing association with
warfarin dose (Ansell et al., 2008, Keeling et al., 2011, Ageno et al., 2012). Our findings
support this practice in Pakistani population. Different measures can be taken to avoid
complications in elderly. One is to start with a lower dose of warfarin and use of age-adjusted
warfarin initiation protocol (Roberts et al., 2003). The other is use of point-of-care (POC)
instruments measuring PT-based INR in 2–3 minutes using a simple fingerstick, at home or
institutes. This will allow making dose adjustments according to the patient’s requirement as
elderly patients are frequently suffering from co-morbid diseases requiring multiple
medications which may interact with warfarin. This self management will cut down frequent
clinic visits and improve patient compliance but for such management, the patients or their
care-takers should be adequately trained (Fitzmaurice et al., 2001, Watzke et al., 2000).
118
When the combined effect of all the demographic factors like age, gender, weight,
height and BMI was calculated, they accounted for 3.8 percent of variability in warfarin dose
and it was found to be statistically significant. Different studies have been conducted to
quantify the effect of these factors on warfarin therapy. Many of them have reported
significant effect of one or more of these factors on warfarin dose. In a study by Whitley et
al., these factors accounted for 8 percent of variation in warfarin dose (Whitly et al., 2007) in
Caucasians. Other studies conducted in Caucasians attributed 12 percent (Carlquist et al.,
2006) and 14 percent (Tabrizi et al., 2002) of inter-individual warfarin dose variability to
demographic features. In a recent study in Italian population, age explained 8.5 percent and
gender 2 percent of variability in warfarin dose (Mazzaccara et al., 2013). In a study by
Herman et al., these factors explained 6.8 percent of dose variation in European population
(Herman et al., 2006). A study conducted in Indonesian population reported 5.9 percent
warfarin dose variation due to demographic factors (Suriapranata et al., 2011). In
geographically adjacent countries, a recent study in Chinese population reported 4.6 percent
variability attributed to demographic factors (Wang et al., 2013). Different proportions of
variability in warfarin dose requirement contributed by these factors indicate the diversity in
demographic and environmental features in different populations. This supports the initiative
for conducting such studies in different populations to generate local data and provide tailored
therapy for that particular population.
We next sought to assess the status of different genotypes affecting warfarin therapy.
Genotyping of CYP2C9 was done for two variants, CYP2C9*2 and CYP2C9*3. This study
has pointed to predominance of wild type genotype in CYP2C9*2 in Pakistani population
with only 6.5 percent possessing heterozygous (*1/*2) and 1.2 percent homozygous variant
119
genotype (*2/*2). Many of the studies have been carried out in Caucasians both in Europe and
America. In these studies the frequency of heterozygous variants for CYP2C9*2 were in the
range of 13–22 percent which points toward the substantial number of patients possessing
heterozygous variant genotype. Whereas in these studies, the frequency of homozygous
variant genotypes were in the range of 0.6–3 percent (Sconce et al., 2005, Moridani et al.,
2006, Klein et al.,2009, Pautas et al., 2010, Buzoianu et al., 2012, Cini et al., 2012,
Mazzaccara et al., 2013, Tatarunas et al., 2013). Pakistani population has much less number
of heterozygous variants for CYP2C9*2 polymorphism as compared to Caucasians whereas
prevalence of homozygous variant genotype is comparable to them. Some multiethnic studies
conducted in USA including European Americans and African Americans have demonstrated
interesting results. They have reported that African Americans possessed less frequency of
variant allele for CYP2C9*2 polymorphism as compared to their European counterparts
(Limdi et al., 2008, Schelleman et al., 2008). A recent review focusing on pharmacogenomics
of warfarin in Sub-Saharan people, African American and admixed Brazilians also have
pointed out the same observation of less prevalence of variant allele for CYP2C9*2
polymorphism in these populations as compared to Caucasians (Suarez-Kurtz and Botton,
2013). A number of studies have been carried out in different Asian populations. CYP2C9*2
polymorphism has been found to be absent in studies carried out in Japanese (Nakai et al.,
2005, Yoshizawa et al., 2009, Miyagata et al., 2011) and Chinese (Wang et al., 2008). So the
other studies have focused only on CYP2C9*3 genotypes analysis in Chinese (Tan et al.,
2013, Wang et al., 2013, Liang et al., 2013) and Japanese (Obayashi et al., 2006, Kimura et
al., 2007, Saito et al., 2013) populations to see their impact on warfarin therapy. The
CYP2C9*2 genotypes frequency studies in Korean (Kwon et al., 2011, Cho et al., 2011) and
Indonesian (Suriapranata et al., 2011, Rusdiana et al., 2013) populations have yielded similar
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results as Chinese and Japanese, so our results are different from Oriental population. Few
studies carried out in Egyptian population provided interesting results with diversity in
genotype frequencies within the same population. Heterozygous variant frequencies were in
the range of 4.5–19 percent and homozygous variants were in the range of 1–4.5 percent
(Hamdy et al., 2002, Shahin et al., 2011, Bazan et al., 2012, El Din et al., 2012). This points
out that within same population people belonging to different ethnic groups may present with
different prevalence of genotypes. Another example of such diversity has also been seen in
Jews belonging to different ethnic groups residing in USA with dissimilarity in CYP2C9*2
genotypes frequencies (Scott et al., 2008). Other studies carried out in Israeli population have
reported heterozygous variant frequencies in the range of 18–23 percent and homozygous
variants in the range of 0–2.1 percent (Muszkat et al., 2007, Caraco et al., 2008) which are
closer to Caucasians. The studies conducted in geographically bordering countries like Iran
and India, presented a diverse frequency data. In Iranian population, heterozygous variant
frequencies were found to be in the range of 10.5–41 percent and homozygous variants in the
range of 2–7.5 percent (Zand et al., 2007, Namazi et al., 2010, Kianmehr et al., 2010). These
results represent a higher prevalence of both heterozygous and homozygous genotypes as
compared to Pakistani population. Some recent studies have been reported from India to
assess the frequencies of CYP2C9 genotypes and their effect on warfarin therapy. CYP2C9*2
heterozygous variant were reported to be in the range of 1.2–9.6 percent whereas no
homozygous variants were found in these studies (Shalia et al., 2012, Pavani et al., 2012,
Nahar et al., 2013, Gaikwad(b) et al., 2013). The prevalence of heterozygous genotypes is
somewhat comparable to Pakistani population but that of homozygous genotypes are different
from our results but closer to Oriental population. A small scale study conducted locally in
120 Punjabi patients taking warfarin has reported the frequency of homozygous CYP2C9*2
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variants being 0.8 percent but no heterozygous variant was found (Siddiqi et al., 2010). As it
is seen in studies mentioned above in Egyptian and Israeli population, the ethnic groups
within a population can exhibit variation in frequency distribution. Same may be the case in
this study when compared to our results. As study population for present project comprised of
patients from all regions of Pakistan whereas that study only recruited patients from Punjab. If
we carry out analysis for separate regions or ethnic groups, we may also come across such
variations in frequency distribution of genotypes. Although warfarin dose was less in patients
with homozygous variant genotype in present study but there was statistically no significant
difference in dose requirement among different genotypes. As regard the effect of CYP2C9*2
variant allele on warfarin dose requirement is concerned, the results are not based on the
diversity of genotype frequency distribution but on the basis of the relative contribution of
this variant allele on warfarin anticoagulant response. Some of the studies have demonstrated
significant effect on warfarin dose which is reduced in patients possessing variant alleles
(Namazi et al., 2010, Cini et al., 2012, Santos et al., 2013, El Din et al., 2012, Mazzaccara et
al., 2013). Other studies have reported similar results as ours, where CYP2C9*2 variant
alleles did not show any significant effect on warfarin dose requirement (Kamali et al., 2004,
Muszkat et al., 2007, Wu et al., 2008, Lima et al., 2008, Pavani et al., 2012, Jorgensen et al.,
2012, Bazan et al., 2013, Perera et al., 2013). Even within the same population, different
studies presented different results, some exhibiting effect others not (Bazan et al., 2013, El
Din et al., 2012, Schwarz et al., 2008, Wu et al., 2008, Perera et al., 2013). This implies that
although CYP2C9*2 is a contributor to warfarin dose variance because it decreases CYP2C9
enzymatic activity to some extent (Kamali et al., 2004, Herman et al., 2005, Gong et al.,
2011), but if this effect is not that strong, at times it is weighed down or modified by other
genetic and non-genetic factors which act as more observable contributors.
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In present study the frequency of variant allele for CYP2C9*3 was notably higher than
CYP2C9*2. The frequency of heterozygous variant (*1/*3) for CYP2C9*3 was 30.9 percent
whereas 7.4 percent of study population possessed homozygous variant genotypes (*3/*3).
When we see the frequency data for CYP2C9*3 genotypes in different world populations,
again interesting diverse records are revealed. Majority of studies in Caucasians have
demonstrated frequency of heterozygous variant for CYP2C9*3 in the range of 8.5–18
percent whereas 0.4–4.6 percent possessed homozygous variant genotypes (Sconce et al.,
2005, Schalekamp et al., 2006, Klein et al., 2009, Pautas et al., 2010, Cini et al., 2012,
Buzoianu et al., 2012, Skov et al., 2013, Mazzaccara et al., 2013). Pakistani population
possessed higher frequencies for both heterozygous and homozygous variants as compared to
Caucasians. As seen with CYP2C9*2 genotypes, the variant allelic frequency for CYP2C9*3
was also found to be less in African American and African population (Schelleman et al.,
2008, Limdi et al., 2008, Suarez-Kurtz and Botton 2013). A number of studies have been
carried out in different Asian populations. In Chinese, the population frequencies for
heterozygous and homozygous CYP2C9*3 variants were found to be in the range of 6–10 and
0–0.7 percent respectively (Liang et al., 2012, Liu et al., 2012, Tan et al., 2013, Wang et al.,
2013) whereas in Japanese, these were 3.5–9.7 and 0–1 percent respectively (Mushiroda et
al., 2006, Kimura et al., 2007, Yoshizawa et al., 2009, Saito et al., 2013). The results indicate
a much lower prevalence of both heterozygous and homozygous CYP2C9*3 variant
genotypes among Oriental population as compared to Pakistani population. CYP2C9*3
heterozygous variants were reported to be in the range of 6.3–11.8 percent and homozygous
variants in the range of 0.5–6.3 percent in studies conducted in Egyptian population (Shahin
et al., 2011, Bazan et al., 2012, El Din et al., 2012, Bazan et al., 2013). Israeli population
studies have reported heterozygous variant frequencies in the range of 14.7–17 percent
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whereas no homozygous variants were found in these studies (Muszkat et al., 2007, Caraco et
al., 2008). Both these populations demonstrated results not matching exactly with any of the
reported frequency distribution in other populations and neither our results are comparable to
them. In closeby countries, one study in Iranian population has reported no variant allele for
CYP2C9*3 (Zand et al., 2007) whereas another study reported 9 percent subjects with
heterozygous variant genotype and no homozygous variants were found in this study (Namazi
et al., 2010). These figures are much lower than our genotype frequency distribution whereas
comparable to that of Oriental population. In Indian population recently conducted studies
have reported CYP2C9*3 heterozygous variant being in the range of 3.3–19 percent whereas
homozygous variants in the range of 0–3.7 percent (Shalia et al., 2012, Pavani et al., 2012,
Nahar et al., 2013, Gaikwad(b) et al., 2013). So frequency distribution of CYP2C9*3
genotypes in Indians is comparable to Caucasian but lower than Pakistani population in
present study. A small scale study conducted in 120 Punjabi Pakistani patients has reported
the frequency of heterozygous and homozygous CYP2C9*3 variants being 11.7 and 1.7
percent respectively (Siddiqi et al., 2010). These frequencies are much lower than our results.
The rationale is same as for CYP2C9*2 genotype frequencies, present study population
comprising all Pakistani regions whereas this reported study only recruited Punjabi patients.
In our study, warfarin dose requirement for patients with homozygous variant genotype was
significantly lower than homozygous wild type (*1/*1). But the dose requirement of
heterozygous variant genotypes was not significantly different from either of homozygous
wild type or homozygous variant genotypes. Apart from the fact that different genotype
frequency distribution reported in different populations, the effect of CYP2C9*3 variant allele
on wafarin dose was found to be significant in almost all the studies with the exception of few
(Li et al., 2006, Saito et al., 2013). Same significant effect was seen in patients in present
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study as well. The patients with variant allele achieved therapeutic anticoagulant response
with significantly less dose of warfarin as compared to those having wild-type genotype. This
implies that CYP2C9*3 variant allele is an effectual contributor to warfarin dose variability
by decreasing the CYP2C9 enzymatic activity leading to reduced S-warfarin clearance. This
aspect was further elucidated in present study by analyzing S- and R-warfarin levels in plasma
and their relationship with different CYP2C9 genotypes.
The effect produced by CYP2C9 genotypes on warfarin dose is because of their effect
on S-warfarin metabolism through their action on CYP2C9 enzymatic activity (Kamali et al.,
2004, Gong et al., 2011, Mahajan et al., 2013). In order to assess this effect we analyzed
warfarin enantiomers plasma levels. The HPLC method used for enantiomeric analysis
(Naidong and Lee, 1993) was modified to make it less time consuming, more sensitive and
economical. The earlier retention times for eluting peaks reduced the total analysis run time.
This resulted in less resources consumption making it economical. The use of fluorescence
detector made analysis more sensitive. The method was validated in light of ICH guidelines
(ICH, 2005). The results of different method validation parameters like accuracy, precision,
sensitivity, specificity, linearity, stability were well within the permissible limits.
In present study, the S-warfarin plasma levels were significantly affected by different
CYP2C9 genotypes whereas there was no significant effect on R-warfarin levels. This
supports the existing view that S-warfarin is metabolized by CYP2C9 enzyme whereas R-
warfarin is metabolized by other enzymes (Moyer et al., 2009, Mahajan et al., 2013).
Warfarin S/R enantiomeric ratio was found to be affected by CYP2C9 genotypes. Our data
displays large variability in S/R ratio among individuals due to possession of different
CYP2C9 genotypes by the patients. Some studies also have demonstrated such variations but
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they did not carry out CYP2C9 genotyping (McAleer et al., 1992, Chan et al., 1994, Locatelli
et al., 2005). In our study the S/R ratio was significantly affected by CYP2C9*3 variant allele
increasing this ratio to 2.75:1 for homozygous variant genotypes as compared to 0.46:1 seen
in subjects possessing wild-type genotype. CYP2C9*2 variant allele did not significantly
affected the S/R ratio, although it was raised to 1:1 for homozygous variant genotypes as
compared to 0.46:1 seen in subjects possessing wild-type genotype. This is in accordance with
reported data of effect of CYP2C9 genotypes on warfarin dose (Kamali et al., 2004, Herman
et al., 2005, Obayashi et al., 2006, Muszkat et al., 2007). As already discussed, CYP2C9*3
variant allele has demonstrated significant effect on warfarin dose whereas CYP2C9*2 did
not. This observation in present study supports the fact that CYP2C9 gene influences
CYP2C9 enzymatic activity in turn affecting S-warfarin levels and warfarin dose requirement.
The number of studies conducted to see the impact of CYP2C9 genotypes on warfarin
enantiomers concentration are much less than those studying their effect on warfarin dose.
Two USA-based studies conducted in Caucasians reported S/R ratio increased to 4:1 for
CYP2C9*3 variant allele as compared to 0.5:1 for wild-type genotype thus significantly
affecting warfarin enantiomeric levels (Steward et al., 1997, Henne et al., 1998). Another
study conducted in Caucasians in Slovenia observed S/R ratio increased to 1.43:1 with variant
genotype as compared to 0.45:1 in wild-type (Herman et al., 2005). Some studies were carried
out in Asian regions as well. A study conducted in Japanese showed warfarin S/R ratio to be
significantly different between CYP2C9*1/*3 and *1/*1 genotypes (0.52:1 vs 1.25:1)
(Obayashi et al., 2006). A recent Indonesian study has also reported warfarin S/R ratio to be
significantly different between CYP2C9*1/*3 and *1/*1 genotypes (Rusdiana et al., 2013). A
study conducted in Israeli population have also established significant difference in warfarin
S/R enantiomers ratio among different CYP2C9 genotypes with ratios of 0.35:1, 0.54:1 and
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1.02:1 for wild-type, heterozygous and homozygous variants respectively (Caraco et al.,
2008). To our best of knowledge, no such study till to-date is available in the neighboring
countries like Iran and India. This is the first study carried out in Pakistan demonstrating the
effect of CYP2C9 genotypes on warfarin enantiomers concentration.
We next assessed the frequencies of two SNPs in VKORC1 gene including VKORC1
–1639G>A and VKORC1 1173 and their impact on warfarin dose requirement. In present
study, the genotyping for VKORC1 –1639G>A polymorphism pointed to predominance of
heterozygous variant genotypes (GA) whereas homozygous wild-type (GG) and variant (AA)
genotypes accounting for 16.7 and 13.1 percent respectively. Majority of the studies have
been carried out in Caucasians in Europe and America. In these studies the frequency of
homozygous wild-type was in the range of 19–59.7 percent whereas heterozygous and
homozygous variants were in the range of 27–60 and 12–32.5 percent respectively (Zhu et al.,
2007, Klein et al., 2009, Buzoianu et al., 2012, Cini et al., 2012, Santos et al., 2013, Scibona
et al., 2012, Mazzaccara et al., 2013, Tatarunas et al., 2013). These studies identify the
diversity of genotypic frequencies even among Caucasians belonging to different countries
thus such intra-ethnic variability stresses the conduction of studies in different populations
separately. In comparison, our results for homozygous wild-type and variant genotypes are
closer to lower limits of reported data among Caucasians. But heterozygous variant genotype
frequency is higher than that for Caucasians. Few USA-based multiethnic studies including
Caucasians and African Americans have reported lesser frequency of variant allele for
VKORC1 –1639G>A in African population (Reider et al., 2005, Wu et al., 2008). Studies
carried out in Asian countries have revealed a very interesting diverse frequency status.
Chinese and Japanese both have very high prevalence of variant allele. Chinese studies
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reported the frequency of AA genotype to be in the range of 80.7–88 percent whereas GG and
GA genotypes were in the range of 0–1.3 and 11.5–15.7 percent respectively (Miao et al.,
2007, Liang et al., 2012, Tan et al., 2013, Wang et al., 2013). The studies in Japanese have
shown AA genotype frequency in the range of 83–86.6 percent whereas GG and GA
frequencies were 0–0.8 and 15–16 percent respectively (Kimura et al., 2007, Yoshizawa et
al., 2009, Saito et al., 2013). Our results are dissimilar from both Chinese and Japanese. Some
studies being conducted in Egyptian and Turkish populations have exhibited comparable
results which are similar to Caucasians. The frequency of GG, GA and AA genotypes were in
the range of 23.8–25.5, 51–56.6 and 18–25 percent respectively (Shahin et al., 2011, Bazan et
al., 2013, Ozer et al., 2013). A Malaysian study reported GG, GA and AA frequencies to be
1.6, 26.4 and 72 percent which is different from rest of the Asian studies described here (Teh
et al., 2012) but closer to the frequencies reported in Indonesians which were 4, 29.5, 66.5
percent respectively (Rusdiana et al., 2013). Our results are dissimilar to these Asian
populations. In geographically bordering countries, few studies have been carried out recently
which gave contrasting results. A study conducted in Iranian population has reported
frequencies of GG, GA and AA genotypes to be 15, 58 and 27 percent respectively (Namazi
et al., 2010). These frequencies are closer to Caucasians and somewhat comparable to ours as
well. A number of studies conducted recently in Indian population have demonstrated the
frequencies of homozygous wild-type in the range of 64–81 percent whereas heterozygous
and homozygous variants were of 17–34 and 0–2.1 percent respectively (Shalia et al., 2012,
Kumar et al., 2013, Gaikwad(b) et al., 2013, Nahar et al., 2013). The results in Indian
population are very much different from rest of the reported study populations including
Pakistani population. This data exhibit the fact that how much diversity exists in VKORC1 –
1639G>A polymorphism prevalence among different populations. Even the countries
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geographically so close to each other like China, India and Pakistan exhibiting so much
diversity in genotypes prevalence. This observation further strengthens the need for
generation of local frequency data.
Furthermore, we assessed the frequency of different VKORC1 1173C>T genotypes.
There was almost equal prevalence of wild-type and variant alleles. The frequency of
homozygous wild-type (CC) and variant (TT) genotypes in present study were 22.3 and 22.5
percent respectively with heterozygous variant (CT) genotype frequency of 55.2 percent. The
number of studies carried out to evaluate distribution of VKORC1 1173C>T polymorphism
are fewer than that for VKORC1 –1639G>A polymorphism. In Caucasians, the studies
reported VKORC1 1173C>T genotypes in the range of 33.7–43, 43.9–50.7 and 12.7–24
percent for CC, CT and TT genotypes respectively (D’Andrea et al., 2005, Carlquist et al.,
2006, Li et al., 2006, Botton et al., 2011, Mazzaccara et al., 2013, Skov et al., 2013). Our
results are comparable to Caucasians demonstrating somewhat lesser frequency for
homozygous wild-type and slightly more for heterozygous variant genotype in Pakistani
population. The USA-based studies including Caucasians and African Americans revealed
that people from African origin have much lower frequency of variant allele for VKORC1
1173C>T polymorphism (Limdi et al., 2008, Schelleman et al., 2008). A number of studies
were conducted in Asia. The studies conducted in Chinese and Japanese have demonstrated
predominance of variant allele. In Chinese the frequencies of CC, CT and TT genotypes were
1, 14 and 85 percent respectively in one study (Wang et al., 2008) whereas another study
reported these frequencies as 6, 25 and 69 percent respectively (You et al., 2011). The studies
conducted in Japanese described the frequencies of CC, CT and TT in the range of 0–1.2,
13.8–15.5 and 83.7–85 percent respectively (Obayashi et al., 2006, Kimura et al., 2007,
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Miyagata et al., 2011). A study in Malaysian population (Teh et al., 2012) reported the
frequencies of 1.7, 32.3 and 66 percent and another in Korean population (Cho et al., 2011)
the frequencies of 1.5, 23.1 75.4 percent for CC, CT and TT genotypes respectively. So these
four populations, also named as Oriental population, showed comparable results but they were
very different from Pakistani population. An Egyptian study demonstrated the CC, CT and TT
genotypes frequency to be 17.5, 20.5 and 62 percent respectively (El Din et al., 2012) which
is also dissimilar to ours. In the neighborhood, an Iranian study reported the frequencies of
34.5, 48.3 and 17.2 percent for CC, CT and TT genotypes respectively which are closer to
Caucasians (Kianmehr et al., 2010) but slightly different from Pakistani population. Two
recent studies in India have reported frequencies of 82.4, 16.7, 1 percent (Kumar et al., 2013)
and 75.9, 22.9, 1.6 percent (Shalia et al., 2012) for CC, CT and TT genotypes respectively.
These results are very much different from rest of the populations including ours, even their
trend is opposite to that of Oriental population.
There are some notable findings regarding VKORC1 genotyping and their effect on
warfarin anticoagulant therapy in present study. One is that there was no significant effect of
VKORC1 –1639G>A and VKORC1 1173C>T genotypes on warfarin dose requirement. The
other one was that no linkage disequilibrium (LD) between VKORC1 1173C>T and
VKORC1 –1639G>A genotypes was found in present study. A number of studies have shown
the presence of LD between VKORC1 –1639G>A and VKORC1 1173C>T genotypes (Wang
et al., 2008, Teh et al., 2012, Kumar, 2013). On the basis of this LD, some studies have
grouped different VKORC1 SNPs into certain haplotypes and further effect on warfarin dose
requirement was then studied on the basis of these haplotypes (Geisen et al., 2005, Schwarz et
al., 2008, Wu et al., 2008, Lee et al., 2009). But in our study we did not find LD existing
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between these two SNPs. It may be due to large number of patients with heterozygous variant
genotype (GA) which also made VKORC1 –1639G>A not following Hardy-Weinberg
equilibrium (HWE). It is suggested that existence of either too less or too many heterozygous
genotypes in data can cause deviation from HWE. We have excluded the genotyping
technique error by carrying out direct DNA sequencing as well. Pakistani population is
heterogeneous and this could lead to changes in genotype presentation and number. It can be
speculated that inbreeding may also be the reason behind this deviation (Lee et al., 2008, Sen
and Burmeister, 2008, Vine and Curtis, 2009). As we have conducted study on patients, so
this aspect can be further explored in healthy Pakistani population later on. A number of other
studies have also shown such deviation from HWE without affecting the results (Shikata et
al., 2004, Takahashi et al., 2006, Wu et al., 2008, Botton et al., 2011, Suriapranata et al.,
2011, Santos et al., 2013, Glurich et al., 2013). As far as the status of LD among these SNPs
is concerned, a recent Italian study also reported non-existence of LD between VKORC1
1173C>T and VKORC1 –1639G>A genotypes (Mazzaccara et al., 2013). A number of
studies have revealed that different VKORC1 SNPs group differently to form haplotypes
because of diverse LD relationship in different population and their distribution varies in
different populations (Reider et al., 2005, Geisen et al., 2005, Schelleman et al., 2008, Lee et
al., 2009, Perera et al., 2011, Kumar et al., 2013). In light of our results, it is imperative that
frequency and linkage disequilibrium studies for commonly reported VKORC1 SNPs (Reider
et al., 2005, Wadelius et al., 2007) ought to be carried out in different populations including
Pakistan to identify the haplotype structure in these particular populations.
The other noteworthy finding in present study is the effect of VKORC1 SNPs on
warfarin dose requirement. In Pakistani population, both VKORC1 –1639G>A and VKORC1
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1173C>T polymorphic alleles did not have any significant effect on warfarin dose. Majority
of studies have demonstrated that the presence of variant alleles in VKORC1 1173C>T and
VKORC1 –1639G>A polymorphisms results in significant reduction in warfarin dose
requirement (Botton et al., 2011, Santos et al., 2013, Bazan et al., 2013, Mazzaccara et al.,
2013, Skov et al., 2013). Few other studies also reported similar results as ours, in which
polymorphism did not show significant effect on warfarin anticoagulant response (Kianmehr
et al., 2010, Gan et al., 2011, Kwon et al., 2011). One aspect of this finding is that we have
only analyzed the effect of two of the commonly reported VKORC1 SNPs but there are many
more SNPs and mutations which have been recently studied in different populations and
found to be significantly affecting the warfarin dose (Reider et al., 2005, Wadelius et al.,
2007, Limdi et al., 2008, Ramirez et al., 2012, Pavani et al., 2012, Kumar et al., 2013). These
polymorphisms have not only been found to be responsible for increased sensitivity to the
warfarin anticoagulant response but some have been responsible for warfarin resistance which
means patients possessing variant allele require more dose as compared to wild-type genotype
(Cini et al., 2012, Jorgensen et al., 2012, Di Fusco et al., 2013, Tatarunas et al., 2013,
Mazzaccara et al., 2013, Czogalla et al., 2013). It has also been suggested that the presence of
VKORC1 Asp36Tyr polymorphic allele which leads to increased warfarin dose requirement
even over-rides the dose reducing effect of VKORC1 –1639A allele (Watzka et al., 2011,
Kurnik et al., 2012, Shuen et al., 2012). This implies that there may be other VKORC1 SNPs
prevalent in Pakistani population having more significant effect on warfarin dose requirement
as compared to these two studied SNPs. There may be a possibility of existence of currently
unidentified environmental factors or unknown genetic variants linked to VKORC1 genotype
expression which could influence vitamin K metabolic pathway. Another explanation comes
from a recent study which has pointed out the existence of mechanisms and factors other than
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vitamin K epoxide reductase playing significant role in recycling of vitamin K (Tie et al.,
2013). As these factors have not been studied in Pakistani population, there may be a
possibility of these factors playing significant role in this population.
This project is the first large scale study in Pakistan to report the population-based
frequencies of CYP2C9*3, CYP2C9*2, VKORC1 1173C>T and VKORC1 –1639G>A
genotypes and their impact on warfarin dose requirement. From the above quoted
international studies as well as this study, it is evident that different genotypes have diverse
prevalence and effect in different populations and regions. Keeping in view these observations
every population has to have its own local frequency data. Even the population of
geographically close countries may not be having same genotype frequency distribution as
obvious from the results obtained from Pakistani population and those countries surrounding
our geographical boundary. It is also noticeable from the comparison of results from studies
conducted in different Asian countries that the word “Asian population” cannot be reflective
of one homogenous population. Different countries in Asia have different genotypes
frequency distribution so each region or country should be treated as an individual entity. This
observation has been confirmed by some multiethnic studies conducted in Asia including
subjects belonging to different Asian countries (Nakai et al., 2005, Lee et al., 2006, Nakai et
al., 2007, Lee et al., 2009, Gan et al., 2011). The results of present study and the other
reported studies in different parts of world in different ethnicities show how much genetic
diversity is encountered all-over the world. Although major part of human genome has been
found to be similar but still the small part is playing its part in this diversity. Different
evolutionary changes have taken their toll over the period of centuries that have lead to
population heterogeneity across the world. Many genetic studies have been carried out to
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know the origin of their population to trace their ancestory (Ni et al., 2013, Suarez-kurtz,
2011, Daar and Singer, 2005, Suarez-kurtz, 2005, Mansoor et al., 2004, Ayub et al., 2003).
We next assessed the extent of influence of analyzed genotypes alone and when
combined with demographic factors on warfarin dose variance. In present study, only 3.8
percent variance was explained by genotypic factors and it was found to be statistically
significant. Whereas the combined effect of demographic factors and genotypes on warfarin
dose requirement explained 8.1 percent of the dose variance and it was also statistically
significant. A number of studies have reported different degrees of warfarin dose variability
explained by these factors. The studies conducted in Caucasians have demonstrated 5–30
percent of dose variance being explained by genetic factors and 23.5–52 percent by combined
genetic and demographic factors (Wadelius et al., 2009, Pautas et al., 2010, Shrif et al., 2011,
Shuen et al., 2012, Mazzaccara et al., 2013, Tatarunas et al., 2013). Some studies carried out
in Chinese attributed 23–50 percent variability in warfarin dose towards genetic factors (Miao
et al., 2007, Liang et al., 2012, Wang et al., 2013, Tan et al., 2013) and one study reported
62.8 percent variance towards both genetic and demographic variables (Miao et al., 2007).
The contribution of genotypes in warfarin dose variability ranged between 11–13 percent in
Japanese (Obayashi et al., 2006, Kimura et al., 2007). In Malaysian (Teh et al., 2012) and
Korean (Cho et al., 2011) population, contributions of genotypic factors to variance
predictability were 27.6 and 20 percent respectively which improved to 36.5 and 59 percent
respectively with the addition of demographic factors. The studies conducted in Egyptians
reported 27.7 percent variance in dose explained by genotypes alone which increased to 34.5
percent when combined with clinical data (Bazan et al., 2013). A Turkish study reported 33.7
percent warfarin dose variability attributed to genotype data which improved to 36.5 percent
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when combined with demographic data (Ozer et al., 2013). In geographically adjoining
countries, only one study in Iranian population demonstrated 19.9 percent variance attributed
to genotypes and 41.3 percent when demographic data was also added in analysis (Namazi et
al., 2010). This comparison illustrates that genetic factors contribute to different extent to
warfarin dose variability in different populations. It is also obvious from the data that when
demographic variables and genotypic data are combined, the extent of dose variance
predictability improves.
On comparison of our results explaining warfarin dose variability with rest of the
reported studies, it is evident that our demographic and genotypic data does not explain a
substantial proportion of warfarin dose variance. There may be various aspects of this finding.
In relation to demographic data, firstly out of all demographic variables studied, only age was
the significant factor affecting warfarin dose requirement in present study. Secondly we
included only stable patients without any co-morbid diseases, hepatic or renal insufficiency or
taking interacting drugs affecting warfarin therapy. The differences in inclusion and exclusion
criteria for different reported studies such as target INR, addition of vitamin K status,
indications for warfarin therapy, smoking status and concurrent therapy may be contributing
to this variability in results. Such differences in drug variability prediction in different
populations including Pakistan thus identify the differences in nutritional habits and status,
cultural lifestyles and local environmental factors.
Regarding genetic data contribution to warfarin dose variance in present study,
CYP2C9 genotypes were found to have more effect as compared to VKORC1 genotypes
which is similar to some other studies (Sconce et al., 2005, D’Andrea et al., 2005,
Schalekamp et al., 2006). Only CYP2C9 variants were having significant effects thus
135
accounting for small contribution of genetic data on overall variance in warfarin dose in this
study. This implies that the studied SNPs in VKORC1 are not the significant contributor to
warfarin dose variability in Pakistani population. Other VKORC1 SNPs and their regulators
may have more contribution to warfarin dose variance in our population as already discussed
above. Another aspect supporting our finding is that there are more than 30 gene products
regulating warfarin pharmacokinetics or pharmacodynamics and genetic polymorphisms in
these genes affect warfarin anticoagulant response (Wadelius et al., 2007, Shahin et al., 2011,
Ramirez et al., 2012, Daly 2013, Scott et al., 2012, Shuen et al., 2012, Liang et al., 2013).
Polymorphisms in some of these genes have been studied in some populations. These studies
have reported substantial prevalence and significant effect on warfarin dose by these gene
products. These polymorphisms may be considered to be prevalent and substantial
contributors to warfarin dose variability in Pakistani population. Most extensively studied are
CYP4F2 polymorphisms which have been shown to significantly increase warfarin dose
requirements by affecting vitamin K1 metabolism and its incorporation into warfarin dosing
algorithm has increased the dose predictability (Danese et al., 2012, Nakamura et al., 2012,
Di Fusco et al., 2013, Tatarunas et al., 2013, Ozer et al., 2013). Some other CYP2C9 SNPs
have also recently been demonstrated to have significant effect on warfarin dose and their
incorporation especially in African population studies improved the outcome (Cavallari et al.,
2013, Perera et al., 2013, O’Brian et al., 2013, Suarez-Kurtz and Botton 2013).
Polymorphisms have been studied in various other components of vitamin K pathways such
as gamama-glutamyl carboxylase (GGCX) gene encoding an important enzyme GGCX
involved in clotting factors carboxylation (Huang et al., 2011, Lam and Cheung 2012, Liang
et al., 2013), epoxide hydrolase 1 (EPHX1) gene encoding EPHX1, a subunit of VKORC1
(Pautas et al., 2010, Lam and Cheung 2012, Ozer et al., 2013), calumenin (CALU) gene
136
encoding calumenin, a calcium binding protein (Voora et al., 2010, Glurich et al., 2013).
These genetic variants have demonstrated significant effect on warfarin anticoagulant
response. Other studied polymorphisms which also have exhibited significant effect on
warfarin therapy are in genes encoding apolipoprotein E (ApoE) (Kohnke et al., 2005, Lam
and Cheung, 2012), orosomucoid (ORM1) (Wang et al., 2013) and cytochrome P450
oxidoreductase (POR) (Zhang et al., 2011, Tan et al., 2013). Addition of these
polymorphisms in warfarin dose variability estimation has improved the outcome (Pautas et
al., 2010, Shahin et al., 2011, Buzoianu et al., 2012, Lam and Cheung 2012, Liang et al.,
2013). Although pharmacogenetics of a drug mainly focuses on the most relevant genes but
there may be unidentified genetic variants which may modify the phenotype expression of
that known candidate gene. Furthermore there are epigenetic markers such as microRNAs and
DNA methylation which regulate gene expression (Ni et al., 2013). Finally what is discovered
to-date in world-wide studies has explained only about 30–60 percent of the warfarin dose
variance, still a substantial portion remains to be explored. At the same time results of major
prospective trials on warfarin pharmacogenetics such as GIFT (Genetics Informatics Trial of
Warfarin to Prevent Venous Thrombosis), COAG (Clarification of Optimal Anticoagulation
Through Genetics), and EU-PACT (European Pharmacogenetics of Anticoagulant Therapy-
Warfarin) are in pipeline which will further define the pharmacogentics of warfarin therapy
(Rouse et al., 2013). It is obvious that present study analyzing the four common SNPs has
provided the baseline for carrying out further studies on other genetic and non-genetic factors
to explain the larger proportion of warfarin dose variance in Pakistani population.
137
Chapter 7
CONCLUSION
The study observing the effect of age, gender, height, body weight and BMI on the
warfarin dose shows that only age affects the dose significantly. The dose needs to be reduced
in elderly patients.
The frequency distribution of CYP2C9*3, CYP2C9*2, VKORC1 1173C>T and
VKORC1 –1639G>A genotypes in Pakistan are different from rest of the studied global
populations. The presence of CYP2C9*3 variant allele significantly increases the S-warfarin
plasma levels resulting in decreased warfarin dose requirement in these patients. The patients
possessing VKORC1 –1639, VKORC1 1173 and CYP2C9*2 variant alleles do not have
significant effect on warfarin dose.
A small proportion of warfarin dose variation explained by studied factors stresses the
need for exploration of more genetic and non-genetic factors in Pakistani population. On the
basis of our study results we can draft a warfarin dosing algorithm and guidelines applicable
to Pakistani population.
138
Chapter 8
RECOMMENDATIONS
Important aspects for future research are:
1. Development of customized warfarin dosing algorithm for Pakistani
population on the basis of demographic, clinical and genotypic data generated
locally from this study.
2. Clinical recommendations for genotyping of four studied SNPs to be weighed
on the basis of their clinical utility and cost-effectiveness.
3. Carrying out prospective studies during initiation phase of warfarin therapy
based on genotypes with reduced risk of bleeding complications taken as a
primary end point.
4. Evaluation of other demographic and clinical variables affecting warfarin dose
requirement.
5. Exploration of polymorphisms in other genes involved in warfarin
pharmacokinetics and pharmacodynamics.
6. Analysis of other VKORC1 SNPs to study the haplotypes structure in
Pakistani population
7. Genotyping analysis of studied four SNPs in healthy individuals.
8. Study of food and drug interactions in local environment by presently validated
HPLC method.
139
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APPENDIX I
200
APPENDIX II
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APPENDIX III
CONSENT PROFORMA
Study Number: __________
Title: Study of genetic and pharmacokinetic issues related to efficacy and toxicity of warfarin in Pakistani subjects
Principal Investigator: Dr Aisha Qayyum
Institute: Army Medical College, Rawalpindi
Background Information
Warfarin has been used for prophylaxis and treatment of various venous and arterial thromboembolic disorders. Patients with valvular heart diseases, prosthetic heart valves, atrial fibrillation, myocardial infarction, pulmonary embolism, deep vein thrombosis and recurrent strokes require anticoagulant therapy. Management of warfarin therapy is difficult because of significant intra- and inter-individual variability, low therapeutic index and highly variable pharmacokinetics. Warfarin dose requirement varies considerably among individuals and also in the same person at different time spans due to wide range of food and drug interactions. Many factors like age, sex, weight, ethnicity, genetic factors, dietary intake, concurrent diseases and medications have been reported to affect the warfarin dose requirements. Inadequate or supra-therapeutic anticoagulation may result in substantial morbidity and mortality due to failure to prevent thromboembolism or bleeding complications respectively. The fear of the complications often causes clinicians to avoid prescribing warfarin to patients who are likely to benefit from such therapy.
Purpose of Study
This is a research study and you are being asked to participate in this study. By this study the effect of demographic factors like age, sex, height, weight and common genetic polymorphisms in CYP2C9 and VKORC1 on warfarin dose requirements will be estimated. The results of this study will be used to calculate the appropriate dosing range and regimens not only for you but also for our Pakistani population. The results will also provide data from our part of world to be added into international databases related to such studies, so that they can include this information while making new guidelines for warfarin use.
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Procedure
In this study patients who are eligible and agree to participate will be enrolled after informed consent. Their medical history and physical examination will be carried out. Blood will be drawn for laboratory tests that will include blood CP, ESR, serum ALT, urea, creatinine and bilirubin. If you are eligible then a blood sample will be drawn at 12 hours after the routine dose of warfarin which will be used to measure S- and R-warfarin in plasma, INR and CYP2C9 / VKORC1 genotyping.
Possible Risks
As the stable patients will be enrolled who are already taking warfarin for at least 3 months and only a blood sample is required for the study so there is no possibility of any adverse event. If any adverse event during blood sample collection occurs then the medical aid will be provided by the principal investigator.
Possible Benefits
The participants will have their baseline investigations done free of cost. By this study they will have their plasma S/R-warfarin levels, INR and genotypes assessed which will help in providing them the right dose of warfarin without its adverse effects being faced. Their contribution will help other community members who are going to receive warfarin without the risk of bleeding or thromboembolic phenomenon.
Financial Consideration
There is no direct financial benefit for the participants of the study. The participants are also not supposed to give any fees to participate in this study. The baseline laboratory tests will be sponsored from study budget.
Right of Refusal to Participate
You are free to choose to participate in the study. You may refuse to participate without any loss of benefit which you are otherwise entitled to. Your refusal will not affect your status in future and you will receive the routine care and treatment which is considered best for you irrespective of your decision to participate in the study.
Access to Results of Study
Each participant can ask for his/her study results when the study is complete.
Confidentiality
We give you full surety that your identity in this study will be treated as confidential. The information provided by you will remain confidential and will not even be shared with your spouse, children or other family members without your permission. Nobody except
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investigators will have an access to it. You will be assigned a study number as a code to be used as reference during study. Your name or identity will also not be disclosed at any time. However the data may be seen by Ethical Review Committee and may be published in a journal and elsewhere without giving your name or disclosing your identity.
Legal Rights
By signing this form does not take away any legal rights in the case of negligence or other legal fault of anyone who is involved in the study.
Available Source of Information
If you have any further questions you may contact Principal Investigator (Dr Aisha Qayyum), Pharmacology department at Army Medical College at phone number 03005390800.
Authorization
I have read and understood this consent form, and I volunteer to participate in this research study. I understand that I will receive a copy of this form. I voluntarily choose to participate, but I understand that my consent does not take away any legal rights in the case of negligence or other legal fault of anyone who is involved in this study. I further understand that nothing in this consent form is intended to replace any applicable Federal, state or local laws.
Participant Name:
Date:
Participant Signature:
Date:
Signature of Person Obtaining Consent:
Date:
Principal Investigator Signature:
Date:
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APPENDIX IV
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206
207
APPENDIX V
PRECLINICAL & CLINICAL PROFORMA PERSONAL HISTORY
Study No: ______________ Date:______________
Name: _________________________________________________________________
Father’s / Husband’s Name: ________________________________________________
Age: ___________________ Sex: ______________
Region of Origin: _______________________ Ethnic Group: _________________
Profession:______________________________________________________________
Address:________________________________________________________________
_______________________________________________________________________
Smoking Status: _________________________________________________________
Interacting Food / Fruit: ___________________________________________________
MEDICAL HISTORY
Indication for warfarin: ____________________________________________________
Duration of warfarin therapy: _______________________________________________
Dose of warfarin : ________________________________________________________
Previous INR values: ______________________________________________________
Any co-morbid disease: ____________________________________________________
Other drugs in use: ________________________________________________________
________________________________________________________________________
PHYSICAL EXAMINATION
GENERAL PHYSICAL EXAMINATION Height: _______________Weight :___________________ Pallor:________________
Clubbing: ___________ Jaundice: _______________ Thyroid: _________________
Oedema: ______________
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CLINICAL EXAMINATION Central nervous system: Gasterointestinal system: Respiratory system: Cardiovascular system: LABORATORY INVESTIGATIONS Blood CP:
ESR:
Renal function tests:
Serum urea:
Serum creatinine:
LFT’s;
Serum ALT:
Serum Bilirubin:
Prothrombin time:
INR:
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Appendix VI
Individual Subjects Data for S- and R-warfarin Enantiomer Levels in Plasma and Their S/R Ratio
Subject Number
S-warfarin
ng/ml
R-warfarin ng/ml
S/R warfarin ratio
1 718.4615 1384.937 0.518768 2 1017.266 3110.197 0.327075 3 366.5359 870.6139 0.421008 4 824.7032 1554.208 0.530626 5 3226.43 2105.64 1.53228 6 1892.432 4481.942 0.422235 7 462.8174 1083.505 0.427149 8 1425.633 3354.989 0.424929 9 1187.917 1317.229 0.901831 10 1309.431 3455.249 0.378969 11 492.0339 1090.015 0.451401 12 1030.546 1034.676 0.996008 13 3439.577 7310.718 0.470484 14 1974.77 4252.775 0.464348 15 612.2198 2244.312 0.272787 16 843.2955 1978.036 0.42633 17 1048.475 2496.916 0.419908 18 1077.691 2551.604 0.422358 19 2381.808 5211.76 0.457007 20 1950.865 3635.588 0.536602 21 737.0538 2128.426 0.34629 22 832.6713 2194.833 0.379378 23 1534.531 3302.255 0.464692 24 483.4017 1511.89 0.319733 25 773.5744 2021.005 0.382767 26 342.6315 647.9577 0.528787 27 2521.914 2711.76 0.929992 28 282.8705 479.9889 0.589327 29 1081.011 1057.463 1.022269 30 952.1932 1688.322 0.563988 31 1433.601 3011.239 0.476083
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32 1225.766 2268.4 0.540366 33 1335.327 1191.577 1.120638 34 1468.129 3519.051 0.417195 35 501.994 621.265 0.808019 36 511.2902 1011.89 0.505282 37 886.4562 1874.52 0.472898 38 997.3459 3105.64 0.32114 39 274.9024 457.8535 0.600416 40 1037.851 3118.661 0.332787 41 341.3035 1461.76 0.233488 42 1869.192 3386.89 0.55189 43 252.99 642.0983 0.394005 44 882.4721 1640.145 0.538045 45 968.7935 2078.296 0.466148 46 1515.938 3428.557 0.442151 47 983.4017 1272.307 0.772928 48 1274.902 2798.348 0.455591 49 915.0086 3847.176 0.237839 50 1227.758 1712.411 0.716976 51 118.8599 275.5618 0.431337 52 1952.193 1360.197 1.435228 53 572.3792 948.7389 0.603305 54 826.0312 1534.025 0.538473 55 1830.679 3134.937 0.58396 56 1641.436 3303.557 0.496869 57 153.3884 709.1556 0.216297 58 1382.472 1091.968 1.266037 59 432.9369 486.4993 0.889902 60 1125.5 2341.317 0.480712 61 650.7324 711.1087 0.915096 62 1221.782 1833.505 0.666364 63 849.2716 2129.077 0.398892 64 610.8918 318.5306 1.917843 65 1249.67 362.8014 3.444501 66 313.415 1059.416 0.295838 67 527.8904 1347.176 0.39185 68 318.0631 496.265 0.640914 69 800.1348 1247.567 0.641356 70 96.94754 392.7493 0.246843
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71 185.261 1478.036 0.125343 72 742.3659 2524.26 0.294093 73 733.7337 1033.374 0.710037 74 979.4177 3229.989 0.303226 75 1221.782 3484.546 0.350629 76 601.5956 1166.838 0.515578 77 739.7098 2056.161 0.359753 78 227.0936 779.4681 0.291344 79 680.6129 1334.807 0.509896 80 889.1122 1252.124 0.710083 81 642.7643 557.4629 1.153017 82 1177.957 2685.067 0.438707 83 719.1255 873.8691 0.822921 84 877.824 2780.119 0.315751 85 543.8267 1679.859 0.323734 86 50.4668 41.18685 1.225313 87 1871.184 2270.354 0.824182 88 1960.825 2705.249 0.724822 89 1546.483 2871.916 0.538485 90 760.9582 2625.171 0.28987 91 187.253 603.6868 0.310182 92 277.5584 646.6556 0.429221 93 649.4044 1088.713 0.596488 94 446.2171 1235.197 0.361252 95 1540.507 2867.359 0.537256 96 1094.292 2490.406 0.439403 97 1219.125 2103.687 0.579519 98 1063.747 1482.593 0.717491 99 1787.519 2687.02 0.665242 100 227.0936 525.5618 0.432097 101 1535.859 2739.104 0.560716 102 2278.886 3051.604 0.746783 103 1289.511 2888.843 0.446376 104 1521.914 310.7181 4.898055 105 668.6607 2337.411 0.286069 106 940.241 2422.697 0.388097 107 514.6102 1118.01 0.460291 108 857.9037 1749.52 0.490365 109 320.0551 733.8952 0.436105
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110 1747.014 3396.005 0.514432 111 227.0936 1007.333 0.225441 112 27.89044 76.3431 0.36533 113 1043.827 1840.666 0.567092 114 1063.747 987.8014 1.076883 115 479.4177 851.0827 0.563303 116 565.739 1157.072 0.48894 117 1110.228 2524.26 0.439823 118 3713.149 5336.76 0.695769 119 240.3738 590.666 0.406954 120 658.7005 1743.01 0.37791 121 466.1375 1456.551 0.320028 122 346.6155 1658.374 0.209009 123 738.3818 1228.687 0.600952 124 804.7829 1384.937 0.581097 125 1488.714 3403.166 0.43745 126 2019.922 1761.89 1.146452 127 824.7032 1560.718 0.528413 128 1734.398 1423.999 1.217976 129 1123.508 3038.583 0.369747 130 1760.958 2888.843 0.609572 131 1022.409 1972.831 0.518245 132 1022.409 1972.831 0.518245 133 1714.477 3741.708 0.458207 134 1694.557 3429.208 0.494154 135 61.09097 76.3431 0.800216 136 944.2251 1866.708 0.505824 137 545.8187 1332.854 0.409511 138 1701.197 3285.979 0.517714 139 764.9422 2127.124 0.359613 140 1774.238 5004.729 0.354512 141 3181.941 3090.666 1.029532 142 233.7337 948.7389 0.246363 143 1734.398 2739.104 0.633199 144 54.45086 134.9368 0.403528 145 1189.909 2407.072 0.494339 146 1322.711 2211.76 0.598036 147 486.0578 160.9785 3.019395 148 1993.362 2726.083 0.731218
213
149 1747.678 2947.437 0.592948 150 1402.392 2778.166 0.504791 151 1322.711 1729.989 0.764578 152 207.1733 668.791 0.309773 153 1369.192 3455.249 0.396264 154 47.81076 154.4681 0.309519 155 532.5385 759.9368 0.700767 156 1335.991 2270.354 0.588451 157 1176.629 3761.239 0.31283 158 778.2224 1905.77 0.408351 159 1103.588 1560.718 0.707102 160 645.4203 2237.801 0.288417 161 612.2198 1560.718 0.392268 162 778.2224 1476.083 0.527221 163 499.338 1606.291 0.310864 164 2132.804 4705.249 0.453282 165 970.7855 2791.187 0.347804 166 977.4256 1573.739 0.621085 167 1143.428 1769.051 0.646351 168 784.8625 3045.093 0.257747 169 771.5823 2361.499 0.326734 170 1349.272 2472.176 0.545783
