pharmacogenetic studies of antiepileptic drugs

PHARMACOGENETIC STUDIES OF ANTIEPILEPTIC DRUGS

Thesis Submitted to

UNIVERSITY OF PUNE GANESHKHIND ROAD, PUNE-411007 MAHARASHTRA, INDIA

For the Award of the Degree of

DOCTOR OF PHILOSOPHY IN

BIOTECHNOLOGY (2013)

By

SANDEEP GROVER, M.Sc. (Regn. No.: IGIB/PME/Pune Univ/071, dated 14-10-2008)

Under the Supervision of

Dr. RITUSHREE KUKRETI (Regn. No.: BUTR/Sci/296-109, dated 05-11-2007)

Place of Research

CSIR-INSTITUTE OF GENOMICS & INTEGRATIVE BIOLOGY MALL ROAD, DELHI-110007 INDIA

CERTIFICATE OF THE GUIDE

Certified that the work incorporated in the thesis “Pharmacogenetic Studies of Antiepileptic Drugs” submitted by Mr. Sandeep Grover was carried out by him, under my supervision. It is declared that the material obtained from other sources has been duly acknowledged in the thesis.

Dr. Ritushree Kukreti Research Supervisor/ Guide University of Pune Pune, India (Regn. No.: BUTR/Sci/296-109, dated 05-11-2007) and Senior Scientist CSIR-Institute of Genomics and Integrative Biology Delhi, India

Date:

DECLARATION BY THE CANDIDATE

I declare that the matter embodied in this thesis entitled “Pharmacogenetic Studies of Antiepileptic Drugs” is the result of the investigations carried out by me for the degree of Doctor of Philosophy during the period from 14th October, 2008 to 6th March, 2013 under the supervision of Dr. Ritushree Kukreti. This work has not been submitted for the award of any other degree, diploma, associateship or membership of any University or other Institution of higher learning. I further declare that the material obtained from other sources has been duly acknowledged.

Sandeep Grover Regn. No.: IGIB/PME/Pune University/071, dated 14-10-2008 CSIR-Institute of Genomics and Integrative Biology Delhi, India & University of Pune Pune, India

Date:

Dedicated to the patients enrolled in the study and To my wife Shobha for her unwavering love and support in difficult times

ACKNOWLEDGEMENT I am forever indebted to my mentor, Dr. Ritushree Kukreti. Under her supervision, I have learned ways to approach a scientific objective, tackling it and transforming complexities to simpler explanations. She further guided me at every stage of my Ph.D., gave me space and freedom to help me evolve my skills and inculcated principles of honesty and belief in hard work. I am especially thankful to Prof. Samir K. Brahmachari and Prof. MandavilleGourie Devi for providing me an opportunity to work in the field of pharmacogenomics, for which I had always yearned for. It was their vision, unwavering support and facilitation of resources for this relatively new but challenging field of pharamacogenomics of neurological disorders that helped me in facing stiff competition and achieving objectives within a stipulated time. I would also like to acknowledge our key collaborators Dr. Kiran Bala and Dr. Sangeeta Sharma, actively engaged in day-to-day patient management including therapeutic intervention and guidance to patients and their family members. I always felt welcome in their departments and have benefited tremendously from their vast expertise and will always have my respect and admiration. Their crucial inputs in defining drug response phenotype helped us to gain universal acceptance of our novel hypothesis. I would also like to thank my thesis committee members, Dr. G. L. Sharma and Dr. A. Agrawal for their periodic critical judgment of my work and crucial suggestions that led to improvement in the clarity of my objectives. None of my achievements would have been possible without unwavering support of family. Their hours of wait for spending time with me and handling of dayto-day problems in my absence further lowered the immense pressure to finish unending tasks. I gratefully thank my university friends of my bachelors and masters degree who always found ways to interact with me when I failed to reach them. I am thankful to all my lab mates for maintaining a relaxed atmosphere and helping in nittygritty tasks.

(Sandeep Grover)

TABLE OF CONTENTS Page No. List of Tables ................................................................................................................... i-ii List of Figures ............................................................................................................... iii-v List of Abbreviations .................................................................................................. vi-viii Abstract ......................................................................................................................... ix-xi

Chapter 1: Introduction and Aims of the Study ............................................ 1-5 1.1. Introduction ............................................................................................................... 1 1.2. Aim and the objectives of the study.......................................................................... 4

Chapter 2: Review of Literature.................................................................... 6-42 2.1. Introduction ............................................................................................................... 6 2.2. Pharmacokinetics and therapeutic drug monitoring of antiepileptic drugs........................................................................................................................... 6 2.2.1. Absorption and Distribution .......................................................................11 2.2.2. Metabolism and Excretion ..........................................................................11 2.3. Pharmacodynamics and drug action of antiepileptic drugs....................................13 2.4. Genetic variants influencing disposition of antiepileptic drugs .............................15 2.4.1. Drug metabolizing enzymes .......................................................................15 2.4.1.1. Phase I drug metabolizing enzymes ...........................................15 2.4.1.1.1. CYP1A2 (cytochrome P450, family 1, subfamily A, polypeptide 2) .....................................16 2.4.1.1.2. CYP2C8 (cytochrome P450, family 2, subfamily C, polypeptide 8) .....................................16 2.4.1.1.3. CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) .....................................17 2.4.1.1.4. CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19) ...................................17 2.4.1.1.5. CYP3A4 (cytochrome P450, family 3, subfamily A, polypeptide 4) .....................................18 2.4.1.1.6. EPHX1 (epoxide hydrolase 1, microsomal (xenobiotic)) ................................................................ 18 2.4.1.2. Phase II drug metabolizing enzymes ..........................................19

Page No. 2.4.1.2.1. UGT1A1 (UDP glucuronosyltransferase 1 family, polypeptide A1) ........................................19 2.4.1.2.2. UGT2B7 (UDP glucuronosyltransferase 2 family, polypeptide B7).........................................19 2.4.1.3. Drug transporters .........................................................................20 2.4.1.3.1. ABCB1 (ATP-binding cassette, subfamily B (MDR/TAP), member 1) ..........................20 2.4.1.4. Drug targets .................................................................................21 2.4.1.4.1. SCN1A (sodium channel, voltage-gated, type I, alpha subunit) ................................................21 2.4.1.4.2. SCN1B (sodium channel, voltage-gated, type I, beta subunit) ..................................................22 2.4.1.4.3. SCN2A (sodium channel, voltage-gated, type II, alpha subunit) ...............................................22 2.5. Estrogens and drug response in the treatment of brain diseases ............................26 2.6. Genetic variants influencing disposition of estrogens ............................................26 2.6.1. Estrogen metabolizing enzymes .................................................................27 2.6.1.1. Phase I estrogen metabolizing enzymes .....................................27 2.6.1.1.1. CYP1A1 (cytochrome P450, family 1, subfamily A, polypeptide 1) .....................................27 2.6.1.1.2. CYP1A2 (cytochrome P450, family 1, subfamily A, polypeptide 2) .....................................29 2.6.1.1.3. CYP1B1 (cytochrome P450, family 1, subfamily B, polypeptide 1) .....................................30 2.6.1.1.4. CYP17A1 (cytochrome P450, family 17, subfamily A, polypeptide 1) .....................................31 2.6.1.1.5. CYP19A1 (cytochrome P450, family 19, subfamily A, polypeptide 1) .....................................31 2.6.1.2. Phase II Estrogen Metabolizing Enzymes ..................................33 2.6.1.2.1. COMT (catechol-O-methyltransferase) ...................33 2.6.1.2.2. SULT1A1 (sulfotransferase family, cytosolic, 1A, phenol-preferring, member 1) and SULT1E1 (sulfotransferase family, cytosolic, 1E, phenol-preferring, member 1) .................................................................35 2.6.1.3. Estrogen Receptors ......................................................................36 2.6.1.3.1. ESR1 (estrogen receptor 1) and ESR2 (estrogen receptor 2) .................................................36

Page No. 2.6.1.4. Estrogen transporters ...................................................................38 2.6.1.4.1. ABCB1 (ATP-binding cassette, subfamily B (CFTR/MRP), member 1), ABCC1 (ATP-binding cassette, subfamily C (CFTR/MRP), member 1) and ABCC2 (ATP-binding cassette, subfamily C (CFTR/MRP), member 2) ........................38

Chapter 3: Materials and Methods .............................................................. 43-61 3.1. Study participants and phenotyping ........................................................................43 3.2. Selection of genes and prioritization of genetic variants .......................................45 3.3. Genotyping of genetic variants ...............................................................................50 3.3.1. Genomic DNA isolation .............................................................................50 3.3.2. Genotyping of genetic markers unlinked to epilepsy ................................54 3.3.3. Genotyping of genetic markers related to response to antiepileptic drugs .......................................................................................55 3.3.3.1. Sequenom .................................................................................... 55 3.3.3.1.1. Assay design................................................................ 55 3.3.3.1.2. Genotyping protocol ................................................... 55 3.3.3.1.3. MALDI-TOF MS analysis ......................................... 56 3.3.3.2. SNaPshot ..................................................................................... 57 3.4. Genotype-phenotype association analysis ..............................................................58 3.4.1. Population stratification ..............................................................................58 3.4.2. Distribution of categorical and continuous variables.................................58 3.4.3. Interaction analysis .....................................................................................59 3.4.4. Power calculations ......................................................................................59 3.4.5. Meta-analysis ..............................................................................................59 3.4.5.1. Literature search and identification of relevant studies ............... 59 3.4.5.2. Inclusion and exclusion criteria ..................................................59 3.4.5.3. Data extraction ............................................................................60 3.4.5.4. Statistical analysis .......................................................................60

Chapter 4: Results and Discussion ............................................................. 62-117 4.1. Distribution of functional genetic variants involved in antiepileptic drug disposition .......................................................................................................62 4.1.1. Demographic characteristics at the time of enrollment .............................62

Page No. 4.1.2. Test for population stratification among patients and healthy controls ........................................................................................................ 62 4.1.3. Frequency distribution of genetic variants .................................................63 4.2. Association analysis of genetic variants with seizure control ................................68 4.2.1. Demographic and clinical characteristics at the end of the study duration ..............................................................................................68 4.2.2. Test for population stratification among patients with noseizure and recurrent-seizures ....................................................................71 4.2.3. Phenotype-genotype association analysis ..................................................72 4.2.3.1. Stage I: Functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets ...........................................................................................72 4.2.3.1.1. Frequency distribution of genetic variants...............72 4.2.3.1.2. Association analysis of genotypes with drug response ............................................................72 4.2.3.2. Stage II: Functional genetic variants from estrogen metabolizing enzymes and estrogen receptors ...........................74 4.2.3.2.1. Frequency distribution of genetic variants...............74 4.2.3.2.2. Association analysis of genotypes with drug response ............................................................75 4.2.3.3. Stage III: Genetic variants from estrogen transporters ...................81 4.2.3.3.1. Frequency of genetic variants ..................................81 4.2.3.3.2. Association analysis of genotypes with drug response ............................................................82 4.2.3.3.3. Association analysis of haplotypes and diplotypes with drug response..................................95 4.2.3.3.4. Meta-analysis of distribution of functional ABCC2 variants for predicting drug response ..........................................................105 4.3. Association analysis of genetic variants with dose and drug levels ....................108

Chapter 5: Summary and Conclusions ................................................... 118-121 Bibliography ............................................................................................... 122-144 Appendices.................................................................................................. 145-150 List of Publications .................................................................................... 151-152

LIST OF TABLES

Table No.

Titles

Page No.

Table 1.1:

Examples of recent Food and Drug Administration recommended pharmacogenetic tests based on genotyping of specific genetic variants, mentioned in the respective drug labels .............................................................................................................. 3

Table 2.1:

Pharmacokinetic characteristics of commonly administered first-line antiepileptic drugs .......................................................................... 8

Table 2.2:

Pharmacodynamic characteristics of commonly administered first line antiepileptic drugs ........................................................................14

Table 3.1:

List of candidate genes (n=13), their products and their known functions associated with pharmacogenetics of first-line antiepileptic drugs prioritized at stage I of the drug response study. ...........................................................................................................46

Table 3.2:

List of candidate genes (n=9), their products and their known functions associated with disposition of estrogens prioritized at stage II of the drug response study .............................................................47

Table 3.3:

Summary of genes (n=22) and genetic variants (n=135) studied at different stages (I, II and III) of the drug response study. ...........................................................................................................50

Table 3.4:

List of functional genetic variants (n=54) from genes (n=22) involved in disposition of first-line antiepileptic drugs as well as estrogens. ................................................................................................51

Table 4.1:

Demographic Characteristics of the patients enrolled in the study (n=400). .............................................................................................62

Table 4.2:

Test for population stratification among patients (n=400) and healthy controls (n=100). ............................................................................63

Table 4.3:

Distribution of alleles of clinically relevant polymorphisms for drug metabolizing enzymes, drug transporters and drug targets involved in disposition of first-line antiepileptic drugs in patients with epilepsy (n=400). ..................................................................64

Table 4.4:

Demographic and clinical characteristics of patients who had completed the study duration (n=216). ......................................................69

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Table No.

Titles

Page No.

Table 4.5:

Test for population stratification among patients with “noseizure” (n=128) and “recurrent-seizures” (n=88).....................................71

Table 4.6:

Distribution and association analysis of alleles and genotypes of genetic variants from genes encoding estrogen metabolizing enzymes and estrogen receptors in women with epilepsy (n=99) showing “no-seizure” (n=57) and “recurrent-seizures” (n=42). .........................................................................................................76

Table 4.7:

Distribution and association analysis of alleles and genotypes of genetic variants encoding estrogen transporters in women with epilepsy (n=99) showing “no-seizure”(n=57) and “recurrent-seizures” (n=42). .......................................................................86

Table 4.8:

Distribution and association analysis of haplotypic block variants of genes encoding estrogen transporters with seizure control in men with epilepsy (n=117) and women with epilepsy (n=99). ..........................................................................................96

Table 4.9:

Multivariate logistic regression analysis of ABCC2 gene promoter diplotype with drug response in women with epilepsy (n=99). ..........................................................................................98

Table 4.10: Haplotype distribution of functional polymorphic SNPs across ABCC2 gene, and their association with seizure control in men with epilepsy (n=117) and women with epilepsy (n=99). .......................103 Table 4.11: Association analysis of functional genetic variants from carbamazepine disposition pathway with dose (n=84) and drug levels (n=75) of carbamazepine in patients with epilepsy. ......................112 Table 4.12: Association analysis of functional genetic variants from phenytoin disposition pathway with dose (n=43) and drug levels (n=35) of phenytoin in patients with epilepsy. .............................115

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LIST OF FIGURES

Figure No.

Titles

Page No.

Figure 1.1: Representation of worldwide age-standardized disability adjusted life years for epilepsy by (per 100,000 inhabitants) ...................... 1 Figure 2.1:

List of Food and Administration approved antiepileptic drugs along with their year of marketing in the U.S................................. 7

Figure 2.2:

Ideal pharmacokinetic parameters of commonly administered first-line antiepileptic drugs. ..............................................10

Figure 2.3:

Schematic pathway of genes involved in disposition of firstline antiepileptic drugs. ............................................................................16

Figure 2.4:

Timeline of landmark studies in the field of epilepsy pharmacogenetics. ...................................................................................23

Figure 2.5:

An interaction network of variables that may influence efficacy and safety of antiepileptic drug therapy. ...................................25

Figure 2.6:

Schematic pathway of genes known to be involved in disposition of estrogens ...........................................................................28

Figure 2.7:

Timeline of landmark studies in the field of estrogen genetics .....................................................................................................40

Figure 2.8:

An interaction network of variables that may influence estrogen related susceptibility to brain diseases leading to altered drug response ...............................................................................41

Figure 3.1:

Study design and collection of phenotypic data .....................................44

Figure 3.2:

Graphical representation of prioritized ABC transporters and respective genetic variants. (A) ABCB1 (B) ABCC1 (C) ABCC2 .....................................................................................................49

Figure 4.1:

Predicted phenotypes and their distribution based on our current knowledge of pharmacokinetics and pharmacodynamics of antiepileptic drugs in North Indian patients with epilepsy (n=400). ...............................................................66

Figure 4.2:

Distribution of CYP2C9 allelic variants among 552 individuals representing 24 Indian sub-populations. ..............................67

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Figure No.

Titles

Page No.

Figure 4.3:

Distribution of seizure types in patients with epilepsy who had completed the study duration (n=216). ............................................70

Figure 4.4:

Distribution of drug response phenotype in patients with epilepsy prescribed different treatments who had completed the study duration (n=216). .....................................................................70

Figure 4.5:

Distribution of dosages and drug levels in patients with epilepsy on different treatments who had completed the study duration (n=216). ...........................................................................71

Figure 4.6:

Association analysis of functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets with seizure control in patients with epilepsy (n=216)...........................73

Figure 4.7:

Association analysis of functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets with seizure control in epilepsy patients treated with carbamazepine (n=99). ............................................................................73

Figure 4.8:

Association analysis of functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets with seizure control in epilepsy patients treated with phenytoin (n=55). ....................................................................................74

Figure 4.9:

Diagnostic performance of the genetic marker CYP1A1 rs2606345 for detecting patients with “no-seizure” in women with epilepsy (n=99). ...............................................................................79

Figure 4.10: Haplotype block structure of ABCB1 gene in patients with epilepsy (n=400). .....................................................................................83 Figure 4.11: Haplotype block structure of ABCC1 gene in patients with epilepsy (n=400). .....................................................................................84 Figure 4.12: Haplotype block structure of ABCC2 gene in patients with epilepsy (n=400). .....................................................................................85 Figure 4.13: Sub-group analysis showing odds ratio for no-seizures vs. recurrent seizures in patients with epilepsy for ABCC2 gene promoter diplotype...................................................................................99 Figure 4.14: Diagnostic performance of the genetic marker ABCC2 rs1885301 for detecting patients with “no-seizure” in women with epilepsy (n=99). ................................................................100

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Figure No.

Titles

Page No.

Figure 4.15: Logarithm of the P values for the multivariate genotypic association analysis of the genetic variants with seizure control in men and women with epilepsy. ............................................104 Figure 4.16: Study methodology for inclusion and exclusion of studies exploring role of genetic variants from ABCC2 in drug response in patients with epilepsy. ........................................................106 Figure 4.17: A Forest plot showing pooled odds of poor responders with respect to odd of good responders in the presence of ABCC2 c.-24CT+TT ...........................................................................................106 Figure 4.18: A Funnel plot showing publication bias among studies exploring odds of poor responders with respect to odd of good responders in the presence of ABCC2 c.-24CT+TT ....................107 Figure 4.19: A bar graph showing relationship between daily maintenance dose and serum drug levels in all the patients with epilepsy on carbamazepine (n=75). ..............................................109 Figure 4.20: A bar graph showing relationship between daily maintenance dose and drug response in all the patients with epilepsy on carbamazepine (n=84).......................................................110 Figure 4.21: A bar graph showing relationship between serum drug levels and drug response in all the patients with epilepsy on carbamazepine (n=75). ..........................................................................110 Figure 5.1:

Diagnostic performance of the combination of genetic markers CYP1A1 rs2606345 and ABCC2 rs1885301 for detecting patients with “no-seizure” in women with epilepsy (n=99). ....................................................................................................119

Figure 5.2:

Essential components of future pharmacogenetic studies. ...................121

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LIST OF ABBREVIATIONS

AA

Austro-Asiatic

ADR

Adverse drug reaction

AED

Antiepileptic drug

bp

base pair

C

Central

CBZ

Carbamazepine

Chr

Chromosome

CI

Confidence interval

CNS

Central nervous system

CPS

Complex partial seizures

CPS sec. gen.

Complex partial seizures with secondary generalization

ddNTP

Dideoxyribonucleotide triphosphate

DME

Drug metabolizing enzymes

DNA

Deoxyribonucleic acid

DR

Dravidian

E

Eastern

E1

Estrone

E2

Estradiol

E17βG

Estradiol 17β-glucuronide

EEG

Electroencephalogram

EM

Extensive metabolizers

FDA

US Food and Drug Administration

GABA

Gamma-aminobutyric acid

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GTCS

Generalized tonic-clonic seizures

GWAS

Genome wide association studies

HWE

Hardy-Weinberg Equilibrium

IE

Indo-European

IHBAS

Institute of human behavior and allied sciences

IM

Intermediate metabolizers

IP

Isolated population

Iplex

Increased plexing efficiency and flexibility for MassARRAY system

LD

Linkage disequilibrium

LP

Large population

MAF

Minor allele frequency

MALDI TOF –MS

Matrix assisted laser desorption ionization: mass spectrometry

MRI

Magnetic resonance imaging

MWE

Men with epilepsy

N

Northern

NaCl

Sodium chloride

NE

North-Eastern

NLB

Nucleic acid lysis buffer

OR

Odds ratio

PB

Phenobarbitone

PBS

Phosphate buffer saline

PCR

Polymerase chain reaction

PEG

Polyethylene glycol

PHT

Phenytoin

PM

Poor metabolizers

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PWE

Patients with epilepsy

RLB

Red blood cells lysis buffer

S

Southern

SAP

Shrimp alkaline phosphatase

SD

Standard deviation

SDS

Sodium dodecyl sulphate

SNP

Single nucleotide polymorphism

SP

Special population

SPS

Simple partial seizures

SPS sec. gen.

Simple partial seizures with secondary generalization

SPSS

Statistical package for social sciences

TB

Tibeto-Burman

TE

Tris-Ethylenediaminetetraacetic acid

UTR

Untranslated region

VPA

Valproate

W

Western

WWE

Women with epilepsy

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ABSTRACT

Over the last two decades, several allelic variants have been well categorized for their potential influence on antiepileptic drug (AED) metabolism, transport and action by various in vitro and in vivo studies. However, beginning this decade, studies across different ethnic groups have failed to replicate consistently clinical utility of these variants for predicting drug response phenotype. So far, majority of these pharmacogenetic studies have focused on exploring role of transporter variants on drug refractory patients maintained on multitherapy of old and new generation AEDs. One of the possible explanations for failure to discover reliable genetic markers could be dependence of drug response phenotype on interaction of multiple genetic factors from different pathways influencing neuronal excitability including AED disposition. Typically, genes involved in the disposition of AEDs fall into three main categories – drug-metabolizing enzymes (DME), drug transporters and drug targets. We explored distribution of 19 functional variants from 12 such genes in non-refractory pool of 400 patients with epilepsy administered firstline AEDs namely Phenytoin, Carbamazepine, Valproate and Phenobarbital. Exploration of the polymorphic functional genetic variants (MAF>0.05) on drug response phenotype in 216 patients with epilepsy administered monotherapy treatment failed to reveal any direct role of genes involved in drug disposition on seizure control in a one year follow-up study. We further scanned literature for other common reasons for seizure susceptibility that could influence drug response. We further hypothesized that interindividual difference in AED response may be mediated by genetic variants of genes encoding proteins involved in metabolism of sex-steroids. Based on in vitro and in vivo studies showing influence of genetic polymorphism on sex steroid disposition, we prioritized 22 functional variants spanning nine genes encoding estrogen metabolizing enzymes and receptors. Association analysis of the polymorphic variants in patients with epilepsy (PWE) helped us identify an intronic single nucleotide polymorphic (SNP) variant from CYP1A1 - rs2606345 (c.-27+606C>A) as a significant marker for

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drug response in 99 women with epilepsy (WWE) (- Pdom = 6.3 X 10- 3, OR = 0.31 (95% CI = 0.18 - 0.71); Pres = 1.0 X 10-4) after adjusting for age, body weight, age at onset of seizures, seizure type and treatment. The significance was retained after applying the Bonferroni corrections for multiple testing. Our study showed predominant distribution of wild type C allele in WWE showing complete control of seizures with CC showing a considerably specificity of 71.4%, but with a low sensitivity of 56.1%. The mutant AA genotype was further observed with extremely high positive predictive value and specificity of 100.0% for predicting poor drug response. The polymorphism has also been earlier reported to influence estradiol levels in healthy women from different ethnic groups and hence may influence seizure susceptibility resulting in differential drug response in women harboring the variant allele. However, lower sensitivity of the variant to detect drug response forced us to look for additional variants including those from estrogen transporters known to be over-expressed in brain-tissue of drug refractory epilepsy patients. Genotyping a total of 98 SNPs from estrogen transporters namely ABCB1, ABCC1 and ABCC2 helped us identify several genetic variants from ABCC2 as markers of drug response in WWE withstanding adjustments of covariates and corrections for multiple testing. Furthermore, none of the associated variants were observed to influence dose or drug levels. We also did not observe significant distribution of all the other covariates including age, body weight, age at onset of seizures, seizure type and epilepsy type, treatment, dose and drug levels when comparing women showing no-seizure and recurrent-seizures during the study duration. A haplotype block based approach helped us identify block B1 (rs1885301 (c.1549G>A) and rs2804402 (c.-1019A>G); r2=1.0) in the promoter region as the most significant region for predicting good response in WWE (Pdom = 8.3x10-5, ORdom= 3.5 (95% CI = 1.7-7.2). In the absence of any known functional role of block B1, we further checked for linkage of the block with known functional haplotypes and our results suggested that absence of seizures in women with epilepsy may be attributed to low expression of ABCC2. An interaction analysis of the earlier associated variant from CYP1A1 (rs2606345) and ABCC2 (rs1885301 or rs2804402) variant further helped us identify a stronger genetic model for predicting

x

complete seizure control in North Indian WWE with higher accuracy (OR = 7.89 (95% CI = 3.18-14.22); p = 2.1 X 10-4, sensitivity = 73.6%, specificity = 73.8%). In summary, in the absence of any direct role of associated genes in AED disposition, CYP1A1, involved in metabolism of estradiol and ABCC2, involved in transportation of estrogens provided significant interaction signals. These findings provide new insight into the mechanism of controlling seizures, possibly by modulation of estradiol reaching the brain tissue in women with epilepsy. It is hypothesized that alteration in these female sex steroid levels may modify drug response by altering seizure susceptibility. Furthermore, as we move into the next decade, there is also a need for increasing the sample size and development of replication cohort for the present study. Lastly, independent replication across different ethnic groups in well powered-studies needs to be demonstrated before concluding the clinical utility of the present findings.

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Chapter 1

Introduction and Aims of the Study

Chapter 1 Introduction and Aims of the study Chapter 1 Introduction and Aims of the study

RESEARCH HIGHLIGHTS Grover S and Kukreti R. Highlights from latest articles on pharmacogenetic studies in antiepileptic drugs. Pharmacogenomics 2012.

LETTER TO THE EDITOR Grover S and Kukreti R. HLA Allelic Variants and CarbamazepineInduced Hypersensitivity. Clin Pharmacol Ther. 2013.


1

Chapter 1


1.1. Introduction Epilepsy is one of the most prevalent chronic neurological syndromes affecting an estimated 50 million people worldwide contribution to 0.5% of global burden of disease (Figure 1.1) (Birbeck, 2012; WHO, 2006). This is in contrast with five million Indians afflicted with active epilepsy at any given time (Gourie-Devi et al., 2004; Radhakrishnan, 2009).

Figure 1.1: Representation of worldwide age-standardized disability adjusted life years for epilepsy by (per 100,000 inhabitants). One DALY can be thought of as one lost year of ‘healthy’ life and the burden of disease as a measurement of the gap between current health status and an ideal situation where everyone lives into old age free of disease and disability. The figure reports DALYs lost because of epilepsy to more than 200 in African population (per 100,000 persons), representing the highest epilepsy burden regions in the world. This is in contrast to South Asian and South American populations with DALY lost ranging from 100-200 (per 100,000 persons). On the other hand, most of the developed world had less than 100 DALYs lost (per 100,000 persons) representing the lowest epilepsy burden regions in the world prior to 2004. Source: http://commons.wikimedia.org/ wiki/File: Epilepsy_world_map_-_DALY_-_WHO2004.svg: Vector map from BlankMap-World6, Data from Death and DALY estimates for 2004 by cause for WHO Member States (Persons, all ages) (2009-11-12). Combined by Lokal_Profil.

A cardinal feature common to all forms of epilepsy syndromes is a condition characterized by recurrent unprovoked synchronous excitation of neurons (seizures) (Chang & Lowenstein, 2003; Ono & Galanopoulou, 2012). This excessive excitation may be initiated by simultaneous involvement of most or all parts of both the cerebral hemispheres (generalized seizures) or may involve only one cerebral hemisphere from its onset (partial/focal/localized seizures) (Duncan et al., 2006). In addition to region of onset of seizures and their propagation, a syndrome may be further categorized on the

1

Chapter 1


basis of impairment of consciousness/awareness, age of onset, progression of ictal events, EEG patterns (ictal and interictal), associated interictal signs and symptoms, postictal features; pathophysiological mechanisms, anatomic substrates, etiology, and mode of inheritance (Berg et al., 2010; Christensen & Sidenius, 2012; Engel, 2006). Despite considerable advancement in our understanding of syndrome types, many critical challenges remain in the AED treatment of epilepsy. For instance, relative frequencies of non-responders to AED treatment typically comprise approximately onethird of epilepsy population, despite adequate AED dosing (Gao et al., 2012; Glauser et al., 2006). Further, optimization of AED dosing could be challenging because of several possible reasons such as narrow therapeutic indices and non-linear pharmacokinetics of several AEDs (Birnbaum et al., 2012; Kerb et al., 2001). Determination of appropriate dosing for some patients could take several months, during which a patient may suffer from serious adverse drug reactions (ADRs) such as gingival hyperplasia (with phenytoin (PHT)), hypersensitivity or cutaneous reactions (with carbamazepine (CBZ)) (McCorry et al., 2004; Qiu et al., 2012). Drug response is multi-factorial, as demonstrated by its dependency upon several pharmacokinetic parameters (absorption, distribution, metabolism, excretion (ADME)), and pharmacodynamic parameters (binding affinity of drug with respective target sites) at different stages of drug disposition (Johannessen Landmark et al., 2012; Loscher et al., 2009). These parameters may have profound influence on the interindividual variability in clinical response to AED treatment. In addition to age, gender, seizure type and other confounding variables, genetic factors may also contribute significantly to this inter-individual variability in drug disposition parameters reflected in drug response and predisposition to ADR (Hung et al., 2012; Kesavan et al., 2010). Pharmacogenetics addresses this genetic heterogeneity in pharmacokinetics as well as pharmacodynamics characteristics of drugs. Recent developments and reports on several allelic variants have also led US Food and Drug Administration (FDA) to incorporate pharmacogenetic information into drug labelling of some of the commonly administered therapeutic agents (Table 1.1) (Daly, 2012; Ikediobi et al., 2009).

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Table 1.1: Examples of recent Food and Drug Administration recommended pharmacogenetic tests based on genotyping of specific genetic variants, mentioned in the respective drug labels. Drug name

Drug label

Carbamazepine

“Patients with ancestry in genetically at risk populations should be screened for HLAB*1502 prior to treatment initiation.”

Abacavir

“Prior to initiation of therapy with abacavir, screening for HLA-B*5701 is recommended.”

Warfarin

“Lower initiation dose should be considered for patients with certain genetic variants in CYP2C9 and VKORC1.”

Irinotecan

“A reduction in the starting dose should be considered for patients known to be homozygous for UGT1A1*28”

Azothioprine

“It is recommended that consideration be given to genotype for TPMT”

Source: http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm

Advances in the pharmacogenetics also support potential role for genetic variants in the treatment of epilepsy. Allelic variants of drug-metabolizing enzymes, including CYP2C9 and CYP2C19 have already been recognized as crucial factors in variability in pharmacokinetic profile of commonly administered AEDs (Allabi et al., 2005; Goto et al., 2007; Guo et al., 2012; Kesavan et al., 2010; Lee et al., 2007). Further, despite similarities in pharmacokinetic profiles, pharmacodynamic response may show considerable variability owing to differential transport of AEDs by ABC transporters across blood brain barrier and sensitivity of drug targets such as sodium channels (Qu et al., 2012; Szoeke et al., 2006). Most of the FDA approved recommendations involve monogenetic variation in drug metabolism and immune response (Bhathena & Spear, 2008; Daly, 2012). Furthermore, multivariate and polygenetic nature of drug response and underpowered studies still evades us from designing tailored medicines (Margineanu, 2012; Tate & Goldstein, 2004). In summary, pharmacogenetics is still in its infancy stage and necessitates large sample size collection, accurate phenotyping and an integrative approach incorporating information on gene-gene and gene-environment interaction (Marian, 2012; Wilke et al., 2005). However, recent advances in genotyping technologies, use of bioinformatics tools, development of statistical techniques for handling large data sets and multiple collaborations for achieving sufficient sample size may help us in future to interpret pharmacogenetic information and translate it to clinical application for personalized treatment (Goldstein et al., 2003; Nesbitt et al., 2012).

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1.2. Aim and the Objectives of the Study The overall aim of the thesis is to investigate role of pharmacogenetics in predicting inter-individual variability in drug response, steady-state drug levels and optimal dose for safe and efficacious treatment of epilepsy patients from north India. The specific objectives of the thesis are: Objective A To study distribution of pharmacogenetically relevant genetic markers from drug metabolizing enzymes (CYPs, EPHXs and UGTs), drug transporters (ABCs) and drug targets (SCNs) of first-line antiepileptic drugs in Indian population. Objective B To explore role of genetic variants from metabolizing enzymes, transporters and targets in influencing drug response or seizure control with first line antiepileptic drugs (AEDs) namely phenobarbital (PB), phenytoin (PHT), carbamazepine (CBZ), and valproate (VPA) in north Indian patients with epilepsy (PWE). We hypothesize that functional variants from drug metabolizing enzymes (such as cytochrome P450 family), drug transporters (such as ABC transporters) and drug targets (such as sodium channels) influence drug response in north Indian patients with epilepsy (Stage I of drug response study). We further hypothesize that functional variants from estrogen metabolizing enzymes (such as cytochrome P450 family) and estrogen receptors (ESR family) influence drug response in north Indian patients with epilepsy (Stage II of drug response study). And lastly we hypothesize that genetic variants from estrogen transporters known to be over-expressed at the blood brain barrier influence drug response in north Indian patients with epilepsy (Stage III of drug response study). Objective C To examine contribution of functional genetic variants from genes encoding products that are involved in disposition of first-line antiepileptic drugs namely phenytoin and

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carbamazepine on steady state drug levels and therapeutic dose requirement of respective antiepileptic drugs. We hypothesize that functional genetic variants from AED disposition pathway mainly cytochrome P450 family, ABC transporters and sodium channels influence steady state serum drug levels and therapeutic dose requirement in north Indian patients with epilepsy.

5

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Review of Literature

Chapter 2 Chapter 1 Introduction and Aims of the study

Review of Literature ORIGINAL ARTICLE Grover S et al. Genetic profile of patients with epilepsy on first-line antiepileptic drugs and potential directions for personalized treatment. Pharmacogenomics. 2010.

REVIEW ARTICLE Grover S et al. Genetic variability in estrogen disposition: Potential clinical implications for neuropsychiatric disorders. Am J Med Genet B Neuropsychiatr Genet. 2010.

LETTER TO THE EDITOR Grover S and Kukreti R. HLA Allelic Variants and Carbamazepine-Induced Hypersensitivity. Clin Pharmacol Ther. 2013.

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2.1. Introduction Clinical efficacy of an AED is highly variable and is dependent upon intracellular neuronal AED concentration and sensitivity of its drug targets (Scott et al., 2012). Further, ADRs, which are common during treatment with AEDs, are also subjected to inter-individual variability in serum AED levels (Hertz et al., 2012; Johannessen Landmark et al., 2012). Traditionally, therapeutic drug monitoring (TDM) or measurement of drug concentration in body fluids has been one of the most reliable tools for judging possible causes underlying therapeutic failure and side effects. In recent times, pharmacogenetic studies have also shown considerable promises, in predicting side-effects specifically hypersensitivity to specific AED treatment, with relatively sparse studies also showing association of genetic variants with drug levels or toxicity related side effects (Chung & Hung, 2012). With the advent of high throughput technologies for measuring drug metabolites and genotyping millions of variants, the combinatorial approach of TDM and application of pharmacogenetic studies may prove to be a valuable asset for an early optimization of efficacious and safe treatment of epilepsy (Adaway & Keevil, 2012; Kim et al., 2011; Thorn et al., 2010). In addition to age, gender and seizure type, the genetic profile of an individual may have a profound influence on pharmacokinetic and pharmacodynamic parameters of drug disposition (Kesavan et al., 2010). In recent years, substantial studies have indicated role of genetic polymorphisms from genes encoding drug metabolizing enzymes (DMEs), drug transporters and drug targets in influencing clinical endpoints of AED treatment (seizure control, drug levels and therapeutic dose) (Cavalleri et al., 2011). There is an urgent need for incorporation of this pharmacogenetic information into future treatment guidelines for an efficacious and safe pharmacotherapy of seizure disorder (Depondt, 2006). However, we need to overcome considerable limitations including consistency in study designs, limited sample size and accountability of potential confounders, before realization of clinical utility of pharmacogenetic studies (Chan et al., 2011).

2.2. Pharmacokinetics and therapeutic drug monitoring of antiepileptic drugs Here we summarize pharmacokinetic and pharmacodynamic characteristics of first line AEDs and recent advances in AED pharmacogenetics that will help us to gauge the clinical relevance of screening genetic polymorphisms from candidate genes in the

6

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treatment of epilepsy. For last several decades, clinicians have relied heavily upon measurement of drug concentrations for accountability of multi-factorial variability of drug response. The clinical utility of TDM is multi-dimensional with various applications such as assessment of drug compliance, diagnosis of drug toxicity, and judging influence of potential confounders (age, gender, pregnancy and hepatic/renal disease) (Howard et al., 2011). AEDs administered for the treatment of seizure disorders typically fall into two main categories – first generation AEDs (traditional AEDs) and second generation AEDs (modern AEDs) (Figure 2.1). While, traditional AEDs including phenobarbital (PB), phenytoin (PHT), carbamazepine (CBZ), valproate (VPA) were discovered prior to 1980s by serendipity (animal model screening), modern AEDs mainly gabapentin (GBP), lamotrigine (LTG), topiramate (TPM), levetiracetam (LEV), oxcarbamazepine (OXC), and zonisamide (ZNS) were developed as a result of combinatorial approach of animal model screening, rational strategies based on pathophysiology of seizures, and use of computational resources (Chaudhry et al., 2010). First-generation antiepileptic drugs LuminalTM

DilantinTM

MesantoinTM

MysolinTM

ZarontinTM

ValiumTM

TegretolTM

KnlonopinTM

FriziumTM

DepakoteTM

1912

1938

1946

1954

1960

1963

1974

1975

1979

1983

Second-generation antiepileptic drugs FelbatolTM

NeurontinTM

LamictalTM

CerebyxTM

TopamaxTM

GabitrilTM

KeppraTM

TrileptalTM

ZonegranTM

LyricaTM

1993

1993

1994

1996

1996

1997

1999

2000

2000

2004

Figure 2.1: List of Food and Drug Administration approved antiepileptic drugs along with their year of marketing in the U.S. Data Source: http://www.fda.gov/Drugs/InformationOnDrugs/ ucm079750.htm.

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All AEDs can be further distinguished on the basis of several parameters for judging advantage of one over another in terms of cost-effectiveness of specific AEDs. These include cost of treatment, efficacy specific to seizure type/syndrome, tolerability, elimination half-life, potential for cross-interference with other drugs and oral contraceptives, and propensity to induce side-effects including impact on cognitive functioning and teratogenicity. Both the generations of AEDs have their own pros and cons. The old drugs seem to be similar in efficacy to newer drugs, but inferior in tolerability with side effects, and tendency for drug-drug interactions (Stephen & Brodie, 2011). However, owing to cost-effectiveness of old drugs, they are commonly used as first-line AEDs. On the other hand, newer ones serve as add-on or alternative therapy in patients with multiple seizure types or poorly controlled seizures (Johannessen Landmark et al., 2012; Stephen & Brodie, 2011). In addition to cheaper treatment with first-line AEDs, their respective chemical characteristics and tendency to interact amongst themselves and sex hormones (Table 2.1; Figure 2.2), and relative pharmacokinetic parameters may influence clinician’s choice of treatment. Based on their physiological significance, these pharmacokinetic parameters fall into four main categories – absorption, distribution, metabolism and excretion (ADME). Table 2.1: Pharmacokinetic characteristics of commonly administered first-line antiepileptic drugs. Generic name*

Phenobarbital

Phenytoin

Carbamazepine

Valproate

Trade name*

Luminal; Epilum

Dilantin

Tegretol

Depakene

Formula

C12 H12N203

C15H12N2O2

C15H12 N2O

C8H16O2

Molecular mass (g/mol)

232.23

252.26

236.27

144.21

5-ethyl-5-phenyl1,3-diazinane2,4,6-trione

5,5-di(phenyl)

IUPAC name

benzo[b][1]benzazepine-11carboxamide

2-propylpentanoic acid

Year of approval by FDA

1912

1939

1974

1978

Chemical structure

imidazolidine2,4-dione

8

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Generic name*

Phenobarbital

Phenytoin

Carbamazepine

Valproate

Drug transporters

ABCB1, ABCC2

ABCB1, ABCC2

ABCB1, ABCC2

ABCB1, ABCC2

Absorption

Linear

Non-linear

Linear

Linear

Phase ICYP2C19 (40%); CYP2C9, CYP2E1

Phase ICYP2C9 (90%); CYP2C19 (10%)

Phase IEPHX1 (major), CYP1A2, CYP2C8 CYP3A4, CYP3A5

Phase ICYP2C9, CYP2A6

Phase II-

Phase IIUGT1A1

Phase IIUGT2B7

Phase IIUGT2B7 (50%)

Site of biotransformation

Hepatocytes

Hepatocytes

Hepatocytes

Hepatocytes

Major metabolite/ Active metabolite

p-hydroxyphenobarbital;

5-(p-hydroxy phenyl)-5-phenyl hydantoin

Carbamazepine-10,11epoxide

3-keto-valproic acid; trans-2-ene valproic acid;

Kinetics*

Linear*

Non-linear*

Non-linear

Linear

Autoinduction*

↔

Non-significant

by CYP3A4

Non-significant

Enzyme inhibition

-

-

↔

mEPHX1; CYP2C9; UGTs

Enzyme induction

CYP2B6, CYP3A4

CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4

CYP3A4

-

Drug-drug interaction*

↑ metab. PHT, CBZ

↓ metab. PB

↑metab. PHT, VPA

↓ metab. PB, CBZ, PHT

Drug-hormone interaction*

↑ metab. of hormones

↔

↔

-

Drug metabolizing enzymes

* Key terms explained in detail below, ↔ non-significant, - data not available  Generic name (Official): When a drug is approved by the Food and Drug Administration (FDA), it is given a generic name by FDA.  Trade name (Brand or propriety name): When a drug is approved by the Food and Drug, the trade name is developed by the company that has requested approval for the drug and identifies it as the exclusive property of that company.  Linear and non-linear kinetics: When the dose of a drug is increased, plasma concentration at steady state will increase proportionately, drug is said to follow linear-pharmacokinetics (e.g. Phenobarbital and Valproate). However, for some drugs, the plasma drug concentration changes either more or less than would be expected from a change in the dose of the drug, drug is said to follow linear-pharmacokinetics that can cause problems when adjusting doses (Phenytoin and Carbamazepine).  Auto-induction: Clearance rate of Carbamazepine increases several folds within first few weeks of its administration, as carbamazepine is known to induce expression of its own metabolizing enzyme (CYP3A4), and hence facilitating faster clearance.  Drug-drug interaction: Change in activity of AED because of presence of another AED.  Drug-hormone interaction: Change in metabolism of sex hormones because of presence of AED.

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Phenobarbital

Phenytoin

Prescription pattern (%)

0

15

30

45

Recommended dosage (mg/d)

60

0

Volume of distribution (l/kg)

0

0.4

0.8

1.2

30

60

90

750

1500

2250

1.6 0

25

50

75

3000 0

25

3

6

9

50

75

100

Protein bound fraction (%)

100

0

Time to peak concentration Tm (h)

120 0

Valproate

Therapeutic range (mg/l)

Oral bioavailability (%)

Elimination half life t1/2 (h)

0

Carbamazepine

25

50

75

100

Renal elimination (%)

12

0

25

50

75

100

Figure 2.2: Ideal pharmacokinetic parameters of commonly administered first-line antiepileptic drugs. Antiepileptic drugs with superior or desired pharmacokinetic parameters have been included in dashed boxes and are discussed below.  Most commonly prescribed: Phenytoin and Carbamazepine (Low cost and high efficacy for several seizure types).  Recommended dose and Therapeutic range: An interval of dose or drug levels where majority of patients are expected to show an optimal response. Broad recommended dose and therapeutic range is desirable as in the case of Valproate (A drug with a broad therapeutic range with allow for an increase in dose without any risk of toxicity).  Volume of distribution (Vd): Volume of distribution is defined as a proportionality constant that describes the apparent volume into which the drug would be distributed in order to obtain the measured serum (or plasma) concentration. Low Volume of distribution is exhibited by Valproate (Lower the volume of distribution, greater the water solubility and renal excretion).

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 Oral Bioavailability: It is only a proportion of drug dosage that is readily absorbed across the gastro-intestinal tract, also known as oral bioavailability of that drug. High oral bioavailability (>80%) is desirable as in the case of Phenobarbital and Phenytoin (An ideal AED should have rapid and complete oral absorption).  Protein binding: Serum albumin and glycol-proteins are the major carrier proteins that serve to lower the fraction of AEDs that circulates freely in the blood and hence lowers the drug available for penetration of blood-brain barrier. Lower protein binding affinity is desirable and Phenobarbital and Carbamazepine exhibit lowest affinity.  Elimination half life (t1/2): It is the time taken by the drug to fall to half of its maximum concentration in the blood. Shorter elimination half life (ideally t1/2 12-24hrs) is desirable as in the case of Carbamazepine and Valproate (Allows once to twice daily administration, dosing interval~ ½ half life).  Time to peak concentration (Tm): It is the time taken by drug to achieve its maximum concentration in the blood. Low time to peak concentration is desirable as in the case of Phenytoin and Valproate (Lower the Tm, faster the drug action).  Major route via renal excretion: Valproate (indicative of low volume of distribution or low protein binding.

2.2.1. Absorption and Distribution It is only a proportion of drug dosage that is readily absorbed across the gastrointestinal tract, also known as oral bioavailability of that drug. This proportion of bioavailable AED depends upon its lipid solubility as most AEDs utilize a passive mode of transport, exception being PHT. Hence most AEDs are also expected to show linear absorption even at higher doses of drugs. Overall, an ideal AED must exhibit high degree of oral bioavailability and linear absorption (Anderson & Saneto, 2012). Serum albumin and glycol-proteins are the major carrier proteins that serve to lower the fraction of AEDs that circulates freely in the blood. It is the unbound (free) fraction of drug in the plasma that is available for penetration into the blood brain barrier (bioactive drug). All AEDs exhibit differential affinity to carrier proteins. Lower affinity of AEDs to such carrier proteins is desirable. Any renal or hepatic dysfunction may result in lowering of carrier proteins in the blood. Hence, any patient with hepatic or renal impairment should be closely monitored for any toxic effects of AEDs (Johannessen Landmark et al., 2012; Ruiz-Gimenez et al., 2010).

2.2.2. Metabolism and Excretion When a dose of an AED is administered, its serum concentration continues to rise, until the rate of drug clearance from the body exactly equals the rate of drug administered

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during a dosing interval (Steady state). Irrespective of AED type or its dosage, it takes about 3-5 elimination half-lives of a drug to achieve a steady state serum concentration. At times, AEDs may even influence their own metabolism (auto-induction or autoinhibition). In case of such drugs exhibiting auto-induction of their own metabolism, it may take a longer time period to attain steady state serum concentration (e.g. CBZ). After attaining steady state serum concentration, AEDs continues to exhibit clinically non-significant diurnal fluctuations in their serum concentrations between successive scheduled doses (once or twice daily). It varies between a maximum serum concentration (Peak levels) and a minimum serum concentration (Trough levels). Further, shorter the half-life elimination of a drug, greater the amount of variability in serum levels during the same duration. In clinical practice, it is always desirable to withdraw blood samples for TDM either before the next scheduled dose or 12 hours after the last dose (Trough levels). Further, an alteration in the dosage of AED during the course of treatment may or may not result in proportional changes in serum concentrations. For AEDs exhibiting zero-order kinetics, even a minimal change in dosage (although within the therapeutic range of a dose) could lead to toxicity as in the case of treatment with PHT (Krasowski & Penrod, 2012), necessitating lower increments of dose in patients. Also, any renal or hepatic dysfunction would be reflected in excessive serum levels of AEDs, necessitating lower dosage requirement in such patients (Ruiz-Gimenez et al., 2010). Hepatic and renal elimination pathways are available to an AED for excretion from the body. An AED may follow both the routes for its elimination although the proportion of AED following each route may vary from drug to drug (Aldaz et al., 2011). Further, an AED undergoes biotransformation into hydrophilic inactive metabolites in the hepatocytes before subsequent elimination. Most AEDs exhibit enzyme inducing and enzyme inhibiting properties. Those AEDs undergoing hepatic elimination, may decrease or increase concentration of concomitantly administered drugs including other AEDs by influencing expression or activity of corresponding DMEs in the hepatocytes (drug-drug interaction) (Bialer, 2012).

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2.3. Pharmacodynamics and drug action of antiepileptic drugs Pharmacodynamics is the interaction of drug with its cellular target to exert its biological function. Since the discovery of first AED in 1912 by serendipity, anticonvulsant characteristics of several chemical agents have been unearthed, with wide variety of drug targets and mode of actions (Bialer, 2012). In general, most of them exert their influence by either attenuating excitation or enhancing inhibition of neurons, preventing propagation of seizures across the cerebral hemispheres. Traditional or first-generation AEDs exert their principal effect through one of these mechanisms: (a) inhibition of voltage-dependent sodium channels mediated excitation of nerve impulses e.g. PHT, CBZ; (b) potentiating of gamma-aminobutyric acid (GABA)A-mediated postsynaptic inhibition of nerve impulses such as PB, VPA and (c) blockade of T-type calcium channels mediated spikes in the neurons. e.g. Ethosuximide (Table 2.2). On other hand, diverse chemical properties of new generation AEDs makes it difficult to find a common way of classifying new drugs on the mode of action (Malawska, 2005). For instance, Levetiracetam is known to bind to synaptic vesicle protein SV2A, which may interfere with the release of the neurotransmitter at the synaptic vesicle (Howard et al., 2011). First-generation AEDs are widely used and effective first-line anti-seizure agents, although in a small fraction of patients, their use is restricted by dose dependent ADRs including toxicity (Perucca & Gilliam, 2012). For instance, PHT is one of the most commonly administered first generation AEDs and is often associated with signs and symptoms of toxic reactions. Further, hypersensitivity reactions to several classes of drugs including first and second generation AEDs have been well documented. Such reactions are frequently characterized by cutaneous ADRs and are believed to be independent of mode of pharmacology and dose of AEDs. Clinical symptoms of patients presenting cutaneous ADRs show considerable heterogeneity in severity and may manifest itself in mild forms including maculopapular exanthema (ME) to severe forms such as toxic epidermal Necrolysis (TEN) (Wei et al., 2012).

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Table 2.2: Pharmacodynamic characteristics of commonly administered first line antiepileptic drugs. Drug action

Phenobarbital

Phenytoin

Drug targets

GABA-A receptor

Voltage gated sodium channels

Potentiates GABA inhibitory transmission and elevation of seizure threshold Decreases glutamate excitation

Carbamazepine Voltage gated sodium channels

Reduce rate at which Na+ channels recover from inactivation, thereby slowing firing

Reduce rate at which Na+ channels recover from inactivation, thereby slowing firing

Generalized tonicclonic seizures* Partial seizures* Status epilepticus* Neonatal seizures*

Generalized tonicclonic seizures Complex partial seizures

Generalized tonicclonic seizures Complex partial seizures

Common adverse drug reactions (tolerability)

Ataxia* Nystagmus* Hypnosis Sedation

Ataxia Nystagmus Diplopia* Gingival hyperplasia* Megaloblastic anaemia * Drowsiness

Pregnancy (teratogeniticy)

Neonatal coagulation defect *

Fetal hydantoin syndrome*

Mechanism of action

Seizures (indications)

Ataxia Diplopia Blurred vision Liver toxicity Steven Johnson syndrome* Toxic epidermal Necrosis*

Spina bifida

Valproate Blocks recovery in Na+ channels, Hyperpolarizes cell via an action on K+ channels and perhaps an action on GABA systems Generalized tonicclonic seizures Complex partial seizures Absence seizures*

Dyspepsia* Weight gain Hyperammonemia*

Neural tube defects * including Spina bifida Fetal valproate syndrome*

* Key terms explained in detail below  Absence seizures are generalized seizures characterized by brief episodes, usually lasting 3-20 seconds, of staring with impairment of awareness and responsiveness and effects children in the age group 4-14 years old.  Ataxia is characterized by poor coordination of voluntary muscles and unsteadiness due to the brain's failure to regulate the body's posture and regulate the strength and direction of limb movements.  Dyspepsia is characterized by upset stomach or indigestion.  Diplopia is the simultaneous perception of two images of a single object that may be displaced horizontally, vertically or diagonally with respect to each other.  Gingival hyperplasia is characterized by abnormal growth of gum tissue.  Hyperammonemia is a metabolic disorder characterized by abnormally high ammonia in the blood.  Fetal hydantoin syndrome: Pregnant women administered phenytoin may have intrauterine growth restriction with foetus showing microcephaly and may develop minor dysmorphic craniofacial features and limb defects including hypoplastic nails and distal phalanges. A smaller proportion may have growth problems and developmental delay, or mental retardation.  Fetal Valproate Syndrome is a congenital disorder characterized by distinctive facial appearance, a cluster of minor and major anomalies and central nervous system dysfunction.  Generalized seizures are seizures originating at some point within, and rapidly engaging, bilaterally distributed networks of neurons in both the hemispheres of brain.  Generalized tonic-clonic seizures are the most common type of generalized seizures that impair the consciousness and affect the entire brain region.  Megaloblastic anaemia is a type of anaemia characterized by very large red blood cells which causes the bone marrow to produce fewer cells, and sometimes the cells die earlier than the 120-day life expectancy. Instead of being round or disc-shaped, the red blood cells can be oval.

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 Neonatal coagulation defect is a coagulation defect in early neonatal period that may result in bleeding.  Neonatal seizures are the seizures that occur from birth to the end of the 28 days of life of full term infant.  Neural tube defects are the birth defects of brain and spinal cord when the neural tube fails to close completely.  Nystagmus is characterized by involuntary, rapid and repetitive eye movement and may be associated with reduced vision.  Partial seizures are the seizures that originate within neural networks limited to one hemisphere. They may be discretely localized or more widely distributed. Simple partial seizures are discretely localized partial seizures. Complex partial seizures are the widely distributed partial seizures.  Spina bifida is a birth defect characterized by incomplete development of spinal cord in which backbone and spinal cord fail to close before the birth.  Status epilepticus is characterized by impaired consciousness and persistent or repetitive seizures that may last 15 to 30 minutes.  Steven Johnson syndrome (SJS) and Toxic epidermal Necrosis (TEN) are forms of a lifethreatening skin conditions, in which cell death causes the epidermis to separate from the dermis.

2.4. Genetic variants influencing disposition of antiepileptic drugs The first line AEDs, although affordable and effective in control of seizures are associated with ADRs and large inter-individual variability in the appropriate dose, at which the patients respond favorably. This variability may partly be explained by functional consequences of genetic polymorphisms in the DMEs such as CYP450, mEH and UGTs, drug transporters namely ABC transporters and drug targets, including SCNs. DMEs, drug transporters and drug targets have been well characterized for most of the first line AEDs and several functional allelic variants from the genes encoding these proteins have been reported for their influence on seizure phenotype, dosing and serum drug levels. Here, we provide a comprehensive list of genes along with genes along with respective functional genetic variants with a special emphasis to currently known associations in epilepsy pharmacogenetics.

2.4.1. Drug metabolizing enzymes 2.4.1.1. Phase I Drug metabolizing enzymes Defect in metabolism of AEDs may be attributed to >10 mutated alleles of the genes expressing CYP450 and mEH DMEs (Figure 2.3) (Cavalleri et al., 2011).

15

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First-line Antiepileptic drugs


Phase I Drug metabolizing enzymes

Drug transporters

Drug targets

Phase II Drug metabolizing enzymes

CYP1A2 Phenobarbitone (PB) SCN1A

CYP2C8

ABCB1 UGT1A1 Phenytoin (PHT)

CYP2C9

SCN1B UGT2B7 ABCC2

Carbamazepine (CBZ) CYP2C19

SCN2A Valproate (VPA)

CYP3A4 GABRG2 EPHX1 Hepatocytes

Blood Brain Barrier

Neuronal membrane

Hepatocytes

Figure 2.3: Schematic pathway of genes involved in disposition of first-line antiepileptic drugs. Unknown/ unidentified.

2.4.1.1.1. CYP1A2 (cytochrome P450, family 1, subfamily A, polypeptide 2) Approximately 25% of CBZ is metabolized by aromatic hydroxylation, which is apparently mediated by CYP1A2 (Pearce et al., 2002; Thorn et al., 2011). Studies of genetic variations in CYP1A2 has identified an intronic single nucleotide polymorphism (SNP) in the 5’ UTR encoding CYP1A2*IF allele which has been reported to be associated with increased enzymatic inducibility in smokers (Sachse et al., 1999). Hence CYP1A2*1F, might increase rate of metabolism of CBZ in PWE in smokers. Owing to its minor contribution towards CBZ metabolism, the polymorphic variants from CYP1A2 might not be able to predict optimum CBZ dosage, on its own. 2.4.1.1.2. CYP2C8 (cytochrome P450, family 2, subfamily C, polypeptide 8) CYP2C8 plays a minor contribution in the conversion of CBZ to its active and one of the major metabolite, CBZ 10, 11-epoxide (Kerr et al., 1994; Thorn et al., 2011). CYP2C8*3 is one of the most extensively studied variant allele which has been demonstrated for its role in both significant decrease in 6-α hydroxylation of paclitaxel, an anticancer drug as well as response to treatment with neo-adjuvant paclitaxel (Hertz

16

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et al., 2012) (Dai et al., 2001). Based on the fact that CYP2C8 is a minor metabolizer of CBZ, the genetic basis for accurate assessment of CBZ drug levels in serum would require cumulative consideration of all the polymorphic genes involved in CBZ metabolism.

2.4.1.1.3. CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) CYP2C9 is a major CYP450 enzyme that is involved in up to 90% of metabolic clearance of PHT (Szoeke et al., 2006; Thorn et al., 2012; Veronese et al., 1991). CYP2C9*2 and CYP2C9*3 are the most common variants known to influence metabolism of its substrates. While CYP2C9*2 corresponds to Arg144Cys amino acid substitution, CYP2C9*3 corresponds to leu359Ile amino acid substitution. Weide et al. observed that PWE carrying at least one mutant allele required a 37% lower mean PHT dose than wild type individuals to attain therapeutic serum concentration, which could be attributed to reduced PHT hydroxylation by CYP2C9 in the mutants (van der Weide et al., 2001). Both population based study as well as case studies, have consistently demonstrated association of CYP2C9 *3/*3 as well a CYP2C9*1/*3 genotypes with higher PHT levels along with neurological toxicity (Depondt et al., 2011; Dorado et al., 2012; Kesavan et al., 2010; Ramasamy et al., 2007). Based on the catalytic activity of CYP2C9 and several clinical studies, individuals homozygous for wild type allele i.e.*1/*1 genotype were broadly classified as extensive metabolizers (EM), those who were heterozygous for mutant alleles i.e.*1/*2 or *1/*3 as intermediate metabolizers (IM) and those who were homozygous mutants i.e.*2/*2 or *3/*3 as poor metabolizers (PM). Variability in the frequencies of these metabolizer genotypes between different ethnic groups could have profound influence on population specific drug efficacy and safety profile.

2.4.1.1.4. CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19) CYP2C19 mediated oxidation plays a relatively minor role in the metabolism of PHT and PB (Szoeke et al., 2006; Yasumori et al., 1999). However, due to narrow therapeutic range of PHT, the role of CYP2C19 in metabolizing PHT needs to be monitored with caution (Thorn et al., 2012). Furthermore, genetic polymorphism in

17

Chapter 2


CYP2C19 has been known to contribute moderately to pharmacokinetic variability of PHT and PB (Chaudhry et al., 2010; Mamiya et al., 1998; Odani et al., 1997; Yukawa & Mamiya, 2006). CYP2C19*2 and CYP2C19*3 are the most extensively studied alleles, the presence of either of which results in the impaired enzymatic activity. While CYP2C19*2 is defined by a G681A transition that encodes a cryptic splice site, CYP2C19*3 is defined by a G636A transition that creates a premature stop codon. Similar to CYP2C9, based on the catalytic activity of CYP2C19, individuals were classified as EM, IM and PM taking into account functional effect of CYP2C19*2 and CYP2C19*3 respectively. A clear understanding of pharmacokinetic variability that could be attributed to CYP2C19 is dependent upon subsequent genotyping of all the remaining allelic variants of functional significance.

2.4.1.1.5. CYP3A4 (cytochrome P450, family 3, subfamily A, polypeptide 4) CYP3A4 is the most dominant enzyme that catalyzes 10, 11 epoxidation of CBZ, epoxide form being the major metabolite of CBZ metabolism (Pelkonen et al., 2001; Thorn et al., 2011; Yuki et al., 2012). A common “-392A>G” polymorphism in the promoter region of CYP3A4 has been shown to increase transcription in vitro (Amirimani et al., 2003). Another variant allele CYP3A4*16 resulting in Thr185Ser amino acid substitution has been associated with reduced hydroxylation of testosterone (Murayama et al., 2002).

2.4.1.1.6. EPHX1 (epoxide hydrolase 1, microsomal (xenobiotic)) mEH is an enzyme that catalyzes the conversion of active epoxide (CBZ-epoxide) metabolite of CBZ to inactive diol (CBZ-diol) metabolite (Thorn et al., 2011; Tybring et al., 1981). EPHX1 is expressed polymorphically and two most common allelic variants with T337C transition and A416G transition have been demonstrated to alter the enzymatic expression levels in vitro by several studies. While 337C (slow allele) which corresponds to Tyr113His substitution was shown to decrease enzymatic activity, on the other hand 416G (fast allele) which corresponds to His139Arg substitution was shown to increase enzymatic activity (Hassett et al., 1994; Maekawa et al., 2003). However, a reverse trend was observed in vivo studies by Nakijama et al. who

18

Chapter 2


demonstrated increased and decreased CBZ diol/CBZ epoxide ratio in CBZ administered PWE harboring 113His (337C) and 139Arg (416G) alleles respectively (Nakajima et al., 2005). Consistent with its role in CBZ metabolism, a recent study by Hung et al. demonstrated that the carriers of 113His (337C) tended to require higher CBZ dosage than non-carriers (Hung et al., 2012). In summary, supporting evidences on functional significance of allelic variants suggests that EPHX1 polymorphisms might be useful predictors for maintenance dose of CBZ. 2.4.1.2. Phase II Drug metabolizing enzymes UGTs are major phase II DMEs involved in the production of glucuronidated metabolites of first line AEDs (Figure 2.3) (Johannessen Landmark et al., 2012). 2.4.1.2.1. UGT1A1 (UDP glucuronosyltransferase 1 family, polypeptide A1) About 75% of PHT is mainly excreted in the urine as O-glucuronide conjugate of 5-(4’hydroxyphenyl)-5-phenylhydantoin (4’-HPPH) (Aliwarga et al., 2011; Nakajima et al., 2002). UGT1A1 is one of the many isoforms of UGT1As that has been shown to catalyze this reaction in human liver microsomes (Jones et al., 2012). UGT1A1*6 is one of the most common UGT1A1 coding variant characterized by G211A transition corresponding to codon 71 in exon 1 that changes glycine to arginine. It exhibits 30% of the wild-type activity and is associated with a defect in bile metabolism resulting in Gilbert’s and Crigler-Najjar syndrome and irinotecan related toxicity necessitating dose reduction in colorectal cancer patients (Okazaki et al., 2012) (Sai et al., 2004; Yamamoto et al., 1998). 2.4.1.2.2. UGT2B7 (UDP glucuronosyltransferase 2 family, polypeptide B7) UGT2B7 has been shown to play a role in glucuronidation of CBZ and VPA resulting in the formation of CBZ N-glucuronide and VPA glucuronide respectively, both being major metabolites for urinary excretion (Argikar & Remmel, 2009; Staines et al., 2004; Thorn et al., 2011). A C>T transversion at nucleotide 802 in the coding region of UGT2B7 which defines UGT2B7*2 that results in histidine to tyrosine substitution at residue 268 is a well-studied variant. Work carried out by Daly et al. showed that

19

Chapter 2


UGT2B7*2 was more common in the diclofenac hepatotoxicity patients as a result of increased reactive metabolites than in controls (Agundez et al., 2011; Daly et al., 2007). Furthermore, significantly higher morphine-3-glucuronide (M-3-G) and morphine-6glucuronide (M-6-G) concentrations were reported. in individuals with UGT2B7*2/*2 than UGTB2*1/*2 and UGT2B7*1/*1 (Daly et al., 2007). A recent multigenic study demonstrated role of UGT2B7*2 in combination with other variants, in influencing CBZ dose-concentration ratio and explained more than 50% of variance in the ratio (Hung et al., 2012). Although UGT2B7*2 is expected to influence glucuronidation rate of CBZ and VPA in different populations, its monogenetic significant impact on drug clearance in vivo is yet to be established. 2.4.1.3. Drug transporters These are transmembrane efflux transporters which translocate AED substrates across gastrointestinal tract and blood brain barrier, thus limiting both drug absorption and penetration of the drug substrate at its site of action in the brain (Figure 2.3) (Potschka, 2012). 2.4.1.3.1. ABCB1 (ATP-binding cassette, sub-family B (MDR/TAP), member 1) In vitro transport assays of first line AEDs in several studies have demonstrated PB, PHT, CBZ and PB are active substrates for human P-glycoprotein (Pgp) (Luna-Tortos et al., 2008; Potschka et al., 2001; Weiss et al., 2003; Zhang et al., 2012). For many years, inter-individual variability in expression and activity of Pgp has been attributed to combination of one or more variant alleles of ABCB1 - C1236T (rs1128503, Gly412Gly), G2677T/A (rs2032582, Ala893Ser/Thr) and C3435T (rs1045642, Ile1145Ile) in exon 13, 22 and 27 respectively(Cascorbi, 2011). Hoffmeyer et al. provided the first evidence for an association between a silent polymorphism, C3435T, and protein expression and function using digoxin as a substrate (Hoffmeyer et al., 2000). This critical observation was followed by a report by Kerb et al., which showed that high Pgp expressing 3435C allele was significantly overrepresented in healthy individuals with low PHT levels (Kerb et al., 2001). Another study showed a reduced expression of ∼5% compared with T allele as compared to C

20

Chapter 2


allele in peripheral blood mononuclear cells (Sterjev et al., 2012). The same study also reported that carriers of T allele also required higher maintenance dose of CBZ and influenced the therapeutic efficacy. Recent findings by Simon et al. showed that 2677T/A was significantly over represented in low dose group of PHT and CBZ implying a decreased ABCB1 expression and function resulting in a therapeutic serum concentration with low dose (Simon et al., 2007). The three alleles C1236T, G2677T/A and C3435T, which are also in strong linkage disequilibrium (LD) have been shown to predict treatment response in PWE by several reports (Hung et al., 2005; Kwan et al., 2007; Sayyah et al., 2011; Zimprich et al., 2004). However conflicting results have been observed by several studies attempting to associate C3435T with clinically defined phenotype for drug resistant epilepsy (Chen et al., 2007; Dericioglu et al., 2008; Dong et al., 2011; Kim et al., 2006; Kwan et al., 2007; Lakhan et al., 2009; Ozgon et al., 2008; Siddiqui et al., 2003; Sills et al., 2005; Szoeke et al., 2006; Tan et al., 2004). To further add to the conflicting results, a recent report published lack of association between C3435T and G2677T or their haplotype combinations on ABCB1 mRNA expression and Pgp content in brain tissue from patients with refractory epilepsy (Mosyagin et al., 2008). In conclusion, although physiological and functional relevance of ABCB1 genetic variants are still not well understood, information on clinical consequences of these SNPs on seizure phenotype might provide us vital clues to their functional significance. 2.4.1.4. Drug targets It is now well documented that alterations in the expression as well as binding site affinity in subunits of sodium channels (SCNs) for might render resistance to AEDs (Figure 2.3). 2.4.1.4.1. SCN1A (sodium channel, voltage-gated, type I, alpha subunit) PHT and CBZ have been well established to block voltage-dependent SCNs (Schwarz & Grigat, 1989). A recent work identified a significant association between an intronic polymorphism IVS5-91G>A in the SCN1A gene and maximum doses in regular usage of both CBZ and PHT in PWE (Tate et al., 2005; Tate et al., 2006). Most recently, the

21

Chapter 2


variant was also observed for its association with maintenance dose of CBZ (Hung et al., 2012). The polymorphism disrupts the consensus sequence of the 5’ splice donor site of a highly conserved alternative exon, and it significantly affects the proportions of the alternative transcripts in individuals with a history of epilepsy, resulting in an altered sensitivity for AEDs. A recent study replicated this finding by demonstrating a significant association between the SCN1A IVS5-91 AA genotype and CBZ-resistant epilepsy (Abe et al., 2008). However, other studies failed to observe any significant differences in CBZ dosages and different genotypic groups (Manna et al., 2011; Zimprich et al., 2008). Because of conflicting results reported in different populations, clinical validation for the role of this allelic variant in CBZ dosing is essential for elucidating its functional influence in our study population. 2.4.1.4.2. SCN1B (sodium channel, voltage-gated, type I, beta subunit) Mutations in genes encoding neuronal voltage-gated sodium channel beta 1 subunit (SCN1B) have been observed in disorders with altered membrane excitability (Wallace et al., 1998). A Cys121Trp (C121W) mutation disrupts a disulfide bridge that normally maintains an immunoglobulin-like fold in the β subunit extracellular domain of SCN1B (Barbieri et al., 2012). Co-expression of C121W mutant human β1subunit with a rat brain α subunit in xenopus laevis oocytes caused increased sodium current and neuronal hyper-excitability (Wallace et al., 1998) A subsequent study by Lucas et al .reported reduced channel sensitivity to PHT

in Chinese hamster ovary (CHO) cells co-

expressing human Na(v)1.3 SCNs and C121Wβ1 compared to cells co-expressing Na(v)1.3 and wild type β1 (Lucas et al., 2005). Further clinical evidence in support of genetic variants from SCN1B in resistance to treatment might provide directions for future research. 2.4.1.4.3. SCN2A (sodium channel, voltage-gated, type II, alpha subunit) Genetic polymorphisms from SCN2A may underlie the neuronal hyper-excitability that provokes seizures. In this direction, various variants of SCN2A have been identified in recent years as underlying cause for predisposition to wide spectrum of epilepsy phenotypes (Herlenius et al., 2007; Sugawara et al., 2001). However, literature-

22

Chapter 2


supporting role of these variants in influencing response to AEDs is very limited. Recently, a study by Kwan et al. demonstrated a significant association between an intronic SNP (IVS7-32A>G) and response to AEDs in Chinese PWE (Kwan et al., 2008). Both A allele and AA genotype were reported to be associated with nonresponsiveness in these patients. However so far, there is no experimental evidence to support the effect of the A allele on expression levels of SCN2A gene or altered sensitivity to AEDs. To date, pharmacogenetic studies in epilepsy have focused on candidate genes and have uncovered few inconsistent associations related to seizure control, serum drug levels as well as therapeutic dose requirement (Kumari et al. 2011). Based on all the pharmacogenetic studies discussed in this section, a brief timeline showing original studies with unique findings has also been depicted in the Figure 2.4.

Mamiya et al. PB serum levels Kerb et al.

ABCB1, CYP2C9, CYP2C19 PHT plasma levels

CYP2C9

PHT serum levels

1997

1998

1999

2000

2001

Mamiya et al.

CYP2C9,CYP2C19 PHT serum levels

Ninomiya et al.

PB and PHT serum levels

Multidrug resistance

2002

2003

Tate et al.

Ozgon et al.

Maximum dose CBZ and PHT

CBZ resistance

2004

2005

2006

PHT dose

EPHX1, UGT2B7, ABCB1, ABCC2, SCN1A, SCN2A CBZ dose Hung et al.

UGT1A6, GRIN2B, UGT2B7

ABCB1

2007

2008

Nakajima et al.

Kwan et al.

haplotype CBZ serum levels

Drug resistance

EPHX1

CYP2C9

Hung et al.

ABCC2

SCN1A

Van der Weide et al.

CYP2C9 PHT intoxication

Ufer et al.

CYP2C19

CYP2C19

Odani et al.

Yukawa et al.

VPA dose

2009

2010

2011

2012

Chaudhry et al.

SCN2A

CYP2C9,CYP2C19 PHT maintenance dose

Siddiqui et al.

Tate et al.

Abe et al.

Murali et al.

Multidrug resistance

PHT serum levels at maintenance dose

CBZ resistance

PHT serum levels

ABCB1

SCN1A

SCN1A

NAT2

Figure 2.4: Timeline of landmark studies in the field of epilepsy pharmacogenetics.

It was in the late 90s, that the genetic basis for slow metabolic clearance of AEDs was first reported in carriers of DMEs mutations from CYP2C9 and CYP2C19 (Odani et al. 1997; Mamiya et al. 1998). These observations were followed by another study by Kerb et al. that showed role of genetic polymorphisms from drug transporter ABCB1 in addition to DMEs in improving the prediction of AED drug levels (Kerb et al. 2001). More recently, Tate et al. also showed that genetic polymorphism from a drug target SCN1A may also influence drug levels indirectly by influencing the dosing

23

Chapter 2


decision by clinicians (Tate et al. 2006). It was only in the beginning of the present century that pharmacogenetic studies began to emanate showing associations of DMEs with dose requirement of AEDs (Van der Weide et al. 2001). However, studies showing evidence for a direct role of genetic polymorphism in therapeutic efficacy were still lacking. It was only in 2003, that the first study was reported showing influence of genetic polymorphism from transporter ABCB1 on drug refractoriness in epilepsy patients (Siddiqui et al. 2003). Most recently, Ufer et al. also reported association of other AED transporter ABCC2 polymorphisms on drug response in PWE (Ufer et al. 2009, 2011). Most of these pharmacogenetic studies focused on single candidate gene until recent observations by Hung et al. (Hung et al. 2011, 2012). Hung et al. followed a multigenic approach and systematically screened functional genetic variants from DMEs, drug transporters and drug targets for influence on therapeutic dose requirement of AEDs. The study showed importance of these multiple genetic variants in accounting for high variance observed in variable dose prescribed to epilepsy patients. In summary, after reviewing the literature on pharmacogenetic studies in epilepsy, several genetic variants across genes covering DMEs, drug transporters and drug targets were identified for pharmacogenetic studies that may be of broad application for predicting therapeutic efficacy of first-line AEDs. Accountability of all these allelic variants might be important for predicting drug response and optimum dosage requirement specific for each individual. Such an approach may help us to investigate the impacts of interaction among polymorphisms in genes related to both pharmacokinetic and pharmacodynamic pathways in north Indian population. Adopting a multigenic approach in our population from North India for studying therapeutic response further gains importance because of failure to observe association of genetic variants from drug transporter ABCB1 in north and south Indian epilepsy populations (Lakhan et al. 2009, Vaheb et al. 2009). Using individualized phenotypic data related to ethnicity, demographics and environmental factors in epilepsy patients, pharmacogenetic studies could identify gene-gene and gene-environment interactions that are more likely to influence AED selection and efficacious and safe dose requirement in North Indian population (Figure 2.5). Further, if clinical application of the studied genetic variants is proven and validated, it will go a long way in accomplishing our goal for providing personalized

24

Chapter 2


medical treatment. In summary, it is our endeavor to harness region specific genetic variability and develop a unique database for providing tailored personalized medicine in years to come. Furthermore, our information on functional genetic variants offers a model opportunity for the application of pharmacogenetics into clinical practice by designing and conducting ‘genotype-phenotype’ association studies. To conclude, genotyping could help clinicians in choosing the right drug, the right dose and identify individuals susceptible for developing ADRs, an approach which would ultimately avoid delay in control of seizures and reduce the cost and duration of therapy.

Phenotypic variability Environmental variables

Genetic variability

Epilepsy type

Gender

Pharmacokinetics

Pharmacodynamics Seizure type

Drug abuse

Diet

Drug action

Absorption

Distribution

Drug transport

Metabolism

Elimination

Age at onset

Age

Body weight

Pregnancy

Comorbid condition

Physical characteristics

Disease specific variables

Socio-economic status

Concomitant therapy

Special conditions

Figure 2.5: An interaction network of variables that may influence efficacy and safety of antiepileptic drug therapy. Phenotypic variables that could influence drug response are enclosed in the yellow colored boxes and include epilepsy characteristics, environmental variables, physical characteristics of patients and special or miscellaneous circumstances known to influence seizure susceptibility or drug response. All these variables may exert their role by interfering with pharmacokinetics and pharmacodynamic characteristics of antiepileptic drugs. On other hand, genetic variables could directly influence expression or activity of proteins involved in pharmacokinetic and pharmacodynamic pathway of drug disposition, various stages of which have been enclosed in blue boxes.

25

Chapter 2


2.5. Estrogens and drug response in the treatment of brain diseases Gender specific effects of estrogen are frequently reflected in differential response and predisposition to ADRs to commonly prescribed medications for treatment of brain diseases. In addition, women might respond differently as per her hormonal status during menstrual cycle, pregnancy and postpartum period, perimenopausal transition and postmenopausal stage. Wong observed a high risk of lamotrigine related skin rash in women as compared to men diagnosed with Epilepsy (Wong et al., 1999). In addition, men with Epilepsy (MWE) are more prone to vigabatrin induced visual changes (Wild et al., 1999). Although women with parkinsonism being treated with levodopa showed a marked improvement in motor symptoms then men, they were more prone to drug induced dyskinesia (Arabia et al., 2002; Zappia & Quattrone, 2002). McGowan et. al. observed better response to acetylcholine esterase therapy for treatment of AD (MacGowan et al., 1998). Recently, a study by Dodick et al. (2008) observed that use of Eletriptan resulted in reduced incidence of headache recurrence in women aged 35 or above with a history of severe headache. Women are also known to respond better to selective serotonin uptake inhibitors (SSRI) (Berlanga & Flores-Ramos, 2006; Glassman et al., 1977; Khan et al., 2005; Young et al., 2009). In general, female schizophrenic patients respond faster to antipsychotics with greater improvement in overall clinical symptoms (Goldstein et al., 2002; Robinson et al., 1999; Salokangas, 1995; Usall et al., 2007). Overall, ADRs are more common to women than men possibly due to influence of female sex hormones on inducibility of DMEs. Moreover, estrogens are known to influence neurotransmission and might influence sensitivity of drug targets.

2.6. Genetic variants influencing disposition of estrogens Considerable evidence has emerged in recent years implicating genetic polymorphisms in estrogen metabolizing enzymes in contributing to the risk of hormone related diseases. However their role in altered disease susceptibility and altered drug response in brain diseases including epilepsy have been unexplored. Polymorphisms in the genes encoding phase I DMEs mainly cytochrome P450 enzymes and phase II DMEs including sulfo and catechol tranferases have been extensively studied in this regard. In addition, genetic

26

Chapter 2


variability in estrogen receptors could also alter the sensitivity of neuronal cells to estrogens. Further, corroborating role of genetic polymorphisms in modulating disease susceptibility also comes from several reports showing role of genetic variants in modulating, circulating estrogen levels. Based on the available literature, comprehensive schematic pathway of estrogen synthesis and degradation has been shown in Figure 2.6. Furthermore, in addition to their role in drug tansport, ABC transporters may also play a critical role in altered estrogen transport specifically by influencing clearance of estradiol (E2) glucuronide conjugates. Not only altered penetration of AEDs (xenobiotics), contribution of ABC efflux transporters to variable drug response may also result from imbalance in the levels of sex steroids (endobiotics) that reach the neuronal tissue. However, literature on the interaction of sex-steroids with transporters in the epileptic brain tissue, and its possible influence on drug response are lacking. 2.6.1. Estrogen metabolizing enzymes Over the last decade, complex network of enzymes involved in the estrogen metabolic pathway has been well characterized.

2.6.1.1. Phase I Estrogen metabolizing enzymes Phase I metabolism involves oxidation and reduction reactions that are primarily catalyzed by members of cytochrome P450 (CYP) superfamily of enzymes (Figure 2.6). Several genetic variants from genes encoding phase I estrogen metabolizing enzymes such as CYP1A1, CYP1A2, CYP1B1, CYP17A1, and CYP19A1 have been well studied with respect to estrogen metabolism and estrogen dependent disorders.

2.6.1.1.1. CYP1A1 (cytochrome P450, family 1, subfamily A, polypeptide 1) CYP1A1 is expressed predominantly in extrahepatic tissues including nervous tissue in the brain (McFayden et al., 1998). It is one of the major cytochrome P450 (CYP) isoforms involved in the hydroxylation of estrone (E1) and E2 into their respective CEs2-OH-E1 and 2-OH-E2, resulting in lowered estrogenicity (Lee et al., 2003). It also plays a minor role in the generation of 4- and 16α-hydroxylated derivatives of E1 and E2 (Badawi et al., 2001; Lee et al., 2003).

27

Chapter 2

Review of Literature Progestagens

16α-OH-progesterone CYP3A4

Progesterone

2β-OH-progesterone

(Oxidation)

Androgens

3βHSD-1/2 (Dehydrogenation) (Isomerization)

6β-OH-progesterone

CYP11A1

Cholesterol

Pregnenolone

(22-hydroxylation) (20-hydroxylation) (C20-22 bond cleavage)

CYP17A1

17α-OH-pregnenolone

CYP17A1

3βHSD-1/2

3βHSD-1/2

Progesterone

CYP17A1

17α-OH-progesterone

CYP21A2 (21α-Hydroxylation)

CYP11B2 (18-Hydroxylation)

Aldosterone

Mineralocorticoids

17β-HSD-1/5 (Reduction) CYP17A1

17β-HSD-2/4 (Dehydrogenation)

17β-HSD-5

3β-HSD-1/2

Androstenediol

CYP21A2

11-Deoxycortisol

CYP11B1 (11β-Hydroxylation)

Corticosterone

Androstenedione 17β-HSD-2

Testosterone

(C17-20 bond cleavage)

(17α-Hydroxylation)

11-Deoxycorticosterone

Dehydroepiandosterone 3β-HSD-1/2

(C17-20 bond cleavage)

(17α-Hydroxylation)

CYP19A1 (Aromatization)

CYP19A1 (Aromatization)

Estrogens 17β-HSD-1

CYP11B1

Estrone (E1)

Estradiol (E2) 17β-HSD -2/4

Cortisol CYP1A1 11β-HSD -2 CYP1A2 (Dehydrogenation) CYP1B1 CYP2C9 Cortisone CYP2C19 (Oxidation)

Glucocorticoids

2-OH-E1/E2

2-Me-E1/E2 & 2-OH-3-Me-E1/E2 4-Me-E1/E2

16α-OH-E1/E2

4-OH-E1/E2 SULT1A1 SULT1E1 (Sulfation)

COMT (Methylation)

CYP1A1 CYP1B1 CYP2C9 CYP2C19 CYP3A4 (Oxidation)

CYP1A1 CYP1A2 CYP1B1 CYP2C19 CYP3A4 (Oxidation)

11β-HSD -2 (Reduction)

UGT1A1 (Glucuronidation)

2-OH-E1/E2- S

2-OH-E1/E2-G

4-OH-E1/E2-S

4-OH-E1/E2-G

CYP1B1 (Oxidation)

CYP3A4

Estriol E1/E2-2,3-Q (E3) E1/E2-3,4-Q (Carcinogenic)

Inactive Secondary metabolites (excreted or act as reservoirs for active estrogens)

ESR1, ESR2 Estrogen receptors in the brain

Figure 2.6: Schematic pathway of genes known to be involved in disposition of estrogens.

28

Chapter 2


In recent years, SWAN (the Study of Women’s health Across Nations) group, engaged in a multiethnic longitudinal study has extensively studied genetic variants in CYP1A1 for predisposition towards estrogen related neuropsychiatric disorders. The group reported significant association of IVS1+606 (C/A) with depressive symptoms in premenopausal and perimenopausal women (Kravitz et al., 2006a). The study indicated CC and AC genotypes in Caucasians and CC genotype in African Americans as risk factors for showing depressive traits (Kravitz et al., 2006a). The ethnic variability in estrogen metabolism was further reflected in circulating serum E2 levels measured during the same study (Sowers et al., 2006c). Of all the ethnic groups studied, only Japanese women were associated with markedly lower E2 levels with CC genotype as compared to AC and AA genotype. Significantly lower E2 levels in Japanese women might be indicative of a higher catalytic efficiency of CYP1A1 with CC genotype. The Chinese women, on the other hand, showed an association of CC genotype with 2-OHE1 levels . Further, African American women with CC genotype had elevated 16α-OHE1 levels. However, so far, there has been no report of in vitro studies demonstrating the influence of IVS1 +606 (C/A) in altering enzymatic activity. In addition, few other genetic variants have been well characterized for their influence on enzymatic activity. An increase in E2 metabolism by several folds resulting in a lower free E2 index (Total E2: SHBG) and elevated mean urinary levels of estrogen metabolites have been observed in women with Thr461Asn variant (Napoli et al., 2005). In the same year, Kisselev et al. [2005] reported a higher catalytic efficiency of CYP1A1 with Ile462Val substitution for generation of 2-OH derivatives of estrogens in reconstituted CYP1A1 systems (Kisselev et al., 2005). Hence, both these genetic variants from CYP1A1 may confer differential vulnerability to diseases of CNS by modulating estrogen catabolism. 2.6.1.1.2. CYP1A2 (cytochrome P450, family 1, subfamily A, polypeptide 2) CYP1A2 plays a major role in the generation of hydroxylated derivatives of E1 and E2, mainly hydroxylated at II or IV carbon positions of the aromatic ring (Yamazaki et al., 1998). However, at higher estrogen concentration, other CYPs such as CYP2C19, CYP3A4 and to a lesser extent CYP2C9 might exert predominant influence in its metabolism (Cribb et al., 2006; Zhu & Lee, 2005).

29

Chapter 2


There has been paucity of literature on the role of genetic variants from CYP1A2 on estrogen metabolism. A report by Lurie et al. (2005) reported a significant association of -163C>A (CYP1A2*1F) polymorphism in the promoter region of CYP1A2 with lower E2 levels (Lurie et al., 2005). The study observed an association of CC genotype with lower serum E2 levels and AC genotype with lower urinary 2OHE1/16α-OHE1 during the luteal phase in premenopausal women. Hence, CYP1A2*IF may be a susceptible allele for neurotransmitter imbalance, exerting its influence through altered estrogen metabolism.

2.6.1.1.3. CYP1B1 (cytochrome P450, family 1, subfamily B, polypeptide 1) CYP1B1 is expressed primarily in the extrahepatic steroidogenic tissues including brain (Rieder et al., 1998). It plays an important role in the metabolism of estrogens, catalyzing the oxidation of E1 and E2 to their respective 2- and 4-hydroxy CEs and further to semiquinones and quinones (Belous et al., 2007; Hayes et al., 1996). In addition, CYP1B1 also contributes to estrogen toxicity by catalyzing 16α-hydroxylation of E2 to 16α-E2 having carcinogenic potency. Using a yeast expression system, Hayes et al. (1996) demonstrated that CYP1B1 exhibits a higher specific activity towards 4hydroxylation than 2-hydroxylation of 17β-E2. Furthermore Hanna et al. (2000) observed that genetic variants from CYP1B1 displays a higher fold increase in catalytic efficiency towards 4-hydroxylation reaction than 2-hydroxylation and 16αhydroxylation reactions, respectively (Hanna et al., 2000). Functional genetic variants from CYP1B1 have also been associated with variable estrogen levels in both premenopausal and postmenopausal women. Napoli et al. (2009)

in a study on postmenopausal women and Garcia-Closas et al. (2002) on

premenopausal women, independently reported a decline in the rate of estrogen catabolism in carriers of leu432Val variant, as indicated by decreased urinary E2 metabolites and increased serum E2 levels, respectively (Garcia-Closas et al., 2002; Napoli et al., 2009). De vivo et al. (2002), in contrast observed an increase in estrogen catabolism with leu432Val variant compared to wild type form (De Vivo et al., 2002). Significantly raised serum E2 levels were also observed with Asn453Ser polymorphism (Garcia-Closas et al., 2002). On the other hand, in vitro studies by Hanna et al. (2000)

30

Chapter 2


showed that these variants and Ala119Ser display a higher catalytic efficiency with a corresponding increase in 2-, 4- and 16α-hydroxylated forms of E2. Hence, both in vitro and in vivo studies have yielded conflicting results with leu432Val as well as Asn453Ser. Further, a study by Aklillu et al. (2002) demonstrated that neither of the missense mutations on its own could explain activity of enzyme, showing the role of haplotypic combinations of the genetic variants for better prediction of altered enzymatic activity (Aklillu et al., 2002).

2.6.1.1.4. CYP17A1 (cytochrome P450, family 17, subfamily A, polypeptide 1) CYP17A1 catalyzes conversion of Pregnenolone and Progesterone (Pg) to Dehydroepiandosterone (DHEA) and Androstenediol, respectively (Kristensen & Borresen-Dale, 2000; Zwain & Yen, 1999). Relatively, few functional variants from CYP17A1 gene have been studied for testing associations with steroid levels and altered disease vulnerability in women. Among them, a promoter polymorphism (-34T>C; A1>A2), which also generates a MspAI restriction enzyme recognition site, has shown an association with estrogen metabolism irrespective of menopausal status in women. Its significant association with elevated serum E2 and Pg levels were first reported by Feigelson et al. (1998) in premenopausal women (Feigelson et al., 1998). Later, role of this variant in influencing estrogen metabolism was also replicated in postmenopausal women with A2/A2 genotype resulting in raised E1 levels as compared to women with A1/A1 genotype (Haiman et al., 1999).

2.6.1.1.5. CYP19A1 (cytochrome P450, family 19, subfamily A, polypeptide 1) CYP19A1 (aromatase) catalyzes the final step in the biogenesis of estrogens by converting C19 androgens – androstenedione (A) and testosterone (T) into C18 estrogens - E1 and E2, respectively, with little modifications to follow in the downstream pathway of estrogen metabolism (Stoffel-Wagner et al., 1999). Being a rate limiting step in the synthesis of estrogens, expression and activity of CYP19A1 could play a major role in determining hormonal milieu in women (Mendelson et al., 1990). Consistent with its functional significance; several polymorphic variants have been reported for their association with altered steroid levels as well as estrogen

31

Chapter 2


dependent neuropsychiatric disorders. Recently, studies have demonstrated that the variants from brain aromatase gene may modify the risk of AD (Huang & Poduslo, 2006) and depressive symptoms (Kravitz et al., 2006a). In addition, role of these polymorphic variants might be of considerable significance in females with reports demonstrating a large reduction of aromatase levels in the brain of women AD patients (Yue et al., 2005). As CYPC19 catalyzes conversion of T into E2 and ‘A’ into E1, hence any alteration of T: E2 or A: E1 baseline levels might be indicative of its catalytic activity. For instance, an elevation in E2 levels or a fall in T or T: E2 levels could all be the consequence of higher enzymatic activity of CYPC19. A significantly lower T: E2 was reported by Sower’s et al., (2006b) in premenopausal or early perimenopausal African American women with TT genotype for IVS2+36415C>T (rs936306) variant. In the same study, author also observed markedly lower T levels in Japanese women with AA genotype as compared to AG genotype for IVS2-23584G>A (rs749292) polymorphism (Sowers et al., 2006b). TT genotype for rs936306 was further reported by Kravitz et al. (2006a) with a considerable increased risk for showing depressive symptoms in premenopausal or perimenopausal women (Kravitz et al., 2006a). The author also observed differences in cognitive functioning with the same variant in various ethnic populations. In a study by Somner et al. (2004), a synonymous exonic variant with G to A transition (rs700518) in postmenopausal women was significantly associated with higher serum E2 levels (Somner et al., 2004). On the contrary, another study reported reduced serum E2 levels and elevated serum T: E2 levels in postmenopausal women with T to C transversion for rs10046 present in 3’ untranslated region (Dunning et al., 2004). Similar results were also observed with silent [TCT] +/− polymorphism in the IVS4 of CYP19 gene by the same group. A study by Paynter et al. (2005) in postmenopausal women showed an increase in aromatase activity for several intronic allelic variants (rs4775936, rs11636639, rs767199) on the basis of serum E1, E2, ‘E1:A’ or E1:T levels (Paynter et al., 2005). A tetranucleotide repeat polymorphism (TTTA)n has also been extensively studied for its influence on hormonal milieu in women. A significant increase in E1: A was observed by Haiman et al. (2000) in women homozygous for 8 repeats of (TTTA) when compared with women with different

32

Chapter 2


number of repeats(Haiman et al., 2000). On the other hand, Tworoger et al. (2004) observed a decrease in E1 and E2 in women carrying (TTTA)8 in homozygous as well as heterozygous condition (Tworoger et al., 2004). The importance of this repeat polymorphism was further realized with a recent article reporting its association with AD in women having longer repeats (8-13) as compared to women homozygous for 7 repeats of the polymorphism (Butler et al., 2010). A significant association of AD was also observed with several other allelic variants including Insertion/Deletion polymorphism (TCT/-) and intronic variants (rs1065778, rs11575899, rs727479, rs767199) (Butler et al., 2010). In summary, genetic variants from CYP19 appear to play a major role in disease susceptibility with large number of polymorphisms showing notable associations with altered sex steroid levels in women.

2.6.1.2. Phase II Estrogen metabolizing enzymes Phase II metabolism involves conjugation of glucoronic, glutathione, methyl and sulphate moieties to estrogens and their metabolites (Figure 2.6). This makes them more hydrophilic as compared to their parent substrates and facilitates renal excretion. Several genetic variants in the genes encoding phase II enzymes mainly, COMT and SULT1A1 are known to influence estrogen metabolism, which might modulate predisposition to common neuropsychiatric disorders.

2.6.1.2.1. COMT (catechol-O-methyltransferase) Catechol-O-methyltransferase (COMT) is a ubiquitously expressed key phase II metabolizing enzyme involved in the inactivation of estrogen metabolites. After the conversion of E1 and E2 into 2- and 4- CEs by CYP1A1 and CYP1B1, COMT catalyzes O-methylation of these CEs into respective methoxy metabolites (Ball et al., 1972). The methoxy conjugates exhibit markedly reduced or no affinity for estrogen receptors as compared to their parent substrates and could act as temporary reservoirs for the release of active estrogens. Further, CEs can also be competitively oxidized by CYP1B1 and NADPH quinone oxidoreductase (NOQ1) to corresponding 3, 4- semiquinones and quinones, which may act as potent carcinogens by forming depurinated DNA adducts (Belous et al., 2007; Singh et al., 2009). Thus, COMT also plays a role of an intrinsic

33

Chapter 2


detoxificant agent by shifting the balance of estrogen metabolic pathway towards the generation of methylated derivatives. Estrogens are known to alter activity and expression of COMT by regulating its transcription, mediated via their binding to estrogen response elements in the COMT gene. Estrogen response elements, which are located in the proximal and distal promoter regions, further regulate the relative expression of two known isoforms of COMT – membrane bound, form (MB-COMT) and cytosolic or soluble isoform (SCOMT), previous isoform being expressed predominantly in the CNS (Hong et al., 1998; Tenhunen et al., 1994; Xie et al., 1999). Further, COMT could also influence neuronal activity by altering the degradation of catecholamines as dopamine and noradrenaline neurotransmitters are primarily inactivated by COMT (Hamilton et al., 2002). COMT, being a major inactivation enzyme for the metabolism of estrogens and neurotransmitters, could serve as a candidate gene for influencing estrogen levels and vulnerability to prevalent neuropsychiatric disorders. Female gender, characterized by higher estrogen levels with considerable variability might be at greater risk for predisposition to brain diseases. Further, some studies have highlighted lower COMT activity in women as compared to men, making women more vulnerable to diseases with a slight alteration in its activity [(Boudikova et al., 1990; Fahndrich et al., 1980). Several epidemiological studies have shown that alteration in COMT expression and activity could have a major impact on women’s mental health. In this regard, substantial evidence has emerged in last few years showing influence of functionally characterized genetic variants in altering, circulating E2 levels as well as prevalence of neuropsychiatric disorders in women. The most extensively studied functional polymorphism is valine to methionine substitution, corresponding to codon 158 in the MB-form. The methionine variant has been linked to a 40% reduction in the methylation activity of the enzyme as demonstrated by Chen et al. (2004) using postmortem human prefrontal cortex tissue (Chen et al., 2004). The functional effect of valine to methionine transition was also evident in significantly higher urinary levels of 16α-OH-E1 levels with Met/Met genotype as compared to Val/Val genotype in

34

Chapter 2


postmenopausal women from non-Hispanic white ethnicity (Tworoger et al., 2004). However, the study failed to observe any association with circulating E1 or E2. In another report by Worda et al. (2003), it was observed that postmenopausal women on exogenous E2 preparation, with Met allele in homozygous as well as heterozygous conditions, had significantly higher serum E2 levels as compared to wild type Val/Val genotype. So far, investigations of polymorphic variants from COMT gene with disease vulnerability in female gender have yielded mixed results with several studies failing to observe gender specificity (Worda et al., 2003). Few studies have observed association of intermediate phenotypes of anxiety mainly harm avoidance (Enoch et al., 2003), low extraversion and high neuroticism (Eley et al., 2003) (Stein et al., 2005); in women with low activity Met allele. In contrast, women with phobic anxiety (McGrath et al., 2004)and panic disorder (Rothe et al., 2006)showed significant over-representation of Val allele. Women specific influence of COMT gene variation has also been reported for other loci in the gene. Female gender with GG genotype for rs16559 displayed a significant association with schizophrenic symptoms in a case control study (Shifman et al., 2002). Another variant, IVS1+701A>G (rs737865) was significantly overrepresented in women showing low extraversion trait (Stein et al., 2005). In conclusion, due to the influence of COMT on nervous system via different pathways, it might be difficult to attribute gender specific associations with steroid levels and could be one main reason for inconclusive genetic associations with CNS disorders. 2.6.1.2.2. SULT1A1 (sulfotransferase family, cytosolic, 1A, phenol-preferring, member 1) and SULT1E1 (sulfotransferase family, cytosolic, 1E, phenolpreferring, member 1) Sulfotransferases (SULTs) are members of a superfamily of soluble cytosolic proteins that preferentially catalyze estrogen sulfonation through transfer of the sulfo group to nucleophilic sites of estrogens forming water-soluble and biologically inactive estrogen sulfates (Adjei & Weinshilboum, 2002). These conjugates are excreted into the bile or urine, resulting in reduced levels of estrogen exposure in the target tissues. SULT1A1 is considered as predominant type of SULT among SULT1E1, SULT1A1 and SULT2A1 due to its extensive tissue distribution, abundance, and broad substrate specificity for estrogens including CEs (Coughtrie, 2002).

35

Chapter 2


SULT gene, located on 16p12.1 is highly polymorphic with three commonly known allozymes (SULT1A1*1, SULT1A1*2 and SULT1A1*3) (Carlini et al., 2001; Raftogianis et al., 1999)Several recent studies have reported association of SULT1A1*2 allele, defined by Arg213His (638G>A) polymorphism, with a lower enzyme activity and reduced estrogen sulfation ability than the wild type variant (Adjei & Weinshilboum, 2002; Coughtrie, 2002; Nagar et al., 2006; Shatalova et al., 2005; Yang et al., 2005) demonstrated that women carrying ‘His’ allele show significantly decreased levels of plasma E1-S and DHEA-S. So far, none of the genetic variants in SULT have been characterized for their possible association with neuropsychiatric diseases. 2.6.1.3. Estrogen receptors Estrogens exert their action by binding to estrogen receptors, which are widely distributed throughout the human brain (Figure 2.6). These receptors are members of the nuclear receptor superfamily of ligand-activated transcription factors. 2.6.1.3.1. ESR1 (estrogen receptor 1) and ESR2 (estrogen receptor 2) Estrogen receptor proteins, ERα and ERβ are transcription factors encoded by estrogen receptor genes, ESR1 and ESR2, which exert their influence by binding to estrogen responsive elements (ERE) in the regulatory regions of multiple genes such as COMT, CYP19, APOE and HLA. Owing to their binding to numerous genes, these proteins could account for pleiotropic effects of estrogens in the nervous tissue by regulating transcription of their respective target genes. ERα and ERβ, being structurally and functionally distinct, it is the relative proportion of both the receptors that regulate estrogenicity in the brain in spatial as well as temporal dependent fashion. Consistent with their functional significance, several studies have demonstrated an alteration in expression levels of these receptor proteins in pathophysiology of neurological diseases with gender specificity observed in some studies. Common genetic variants including functionally

important

polymorphisms

have

schizophrenia, AD, PD and mood disorders.

been

implicated

in

migraine,

Intronic Pvu II (rs2234693), Xba I

(rs9340799) and variable number tandem repeat (VNTR) polymorphisms are the most extensively studied ERα genetic variations for their role in modulating disease risk, possibly by altering the expression level of estrogen receptors and serum E2 levels.

36

Chapter 2


Schuit et al. (2005) demonstrated a 22% reduction in E2 levels in postmenopausal women carrying PvuII-Xba1 haplotype (T-A) in homozygous condition as compared to non-carriers (Schuit et al., 2005). The author attributed the significant association to modulated expression of estrogen metabolizing enzyme, CYP19 or 17β HSD. This could be due to the influence of altered ESR1 transcription through E2 in homozygous carriers. Lower circulating E2 levels were even observed by Sower’s et al. (2006c) in African American women harboring ESR1 rs3798577 CC genotype and Japanese women with ESR2 rs1255998 GC genotype (Sowers et al., 2006a). Estrogens may also influence transcription of APOE, known to be a risk factor for predisposition to AD, thereby modulating synaptic sprouting and β amyloid metabolism in cholinergic neurons. Further, support for the interaction between estrogens and APOE also comes from a study by Porrello et al. (2006) in which carriers of ERα – T allele (PvuII) or ‘A’ allele (XbaI) in combination with APOE ε4 allele were at significantly increased the risk for developing sporadic AD in women as compared to individuals who had neither of the alleles (Porrello et al., 2006). Estrogens are known to influence prevalence of migraine in women; functional genetic variants in ESR1 might alter this prevalence by modulating E2 levels (Oterino et al., 2008). In a recent study in North Indian cohort of female patients, T allele and TT genotype of PvuII polymorphism were significantly over-represented in migraineurs (Joshi et al., 2010). In another study, carriers of 594A (rs2228480) allele were significantly associated with increased risk for developing Migraine with aura in women as compared to control group (Colson et al., 2004) . Several studies have suggested gender associated increased risk of cognitive impairment with genetic variants from ESR1 gene, specifically in elderly women. While Yaffe et al. (2002) observed increased likelihood of impaired cognition with PvuII as well as XbaI polymorphisms, a borderline association of XbaI with cognition in elderly postmenopausal women was observed by Olsen et al. (2006). Similar to ESR1, several genetic variants from ESR2 confer increased risk to neurological diseases in a gender dependent manner. A report with genetic analysis of ESR2 polymorphisms in AD patients and normal controls revealed significant allelic and genotypic associations with disease risk for women carrying IVS3-1880C>T (rs1271573) and IVS4+1231C>T (rs1256043), respectively (Pirskanen et al., 2005). In another study, G1082A polymorphism in heterozygous condition showed a strong

37

Chapter 2


association with susceptibility to anorexia nervosa in women (Eastwood et al., 2002). A study by Geng et al. (2007) indicated the role of shorter alleles of microsatellite repeats in the ESR2 gene in influencing age of onset of Major depressive disorder (MDD) in female adolescents(Geng et al., 2007). Role of genetic variants from estrogen receptors in diagnosis of MDD in women was also observed by Tsai et al. (2003) (Tsai et al., 2003). The author reported allelic as well as genotypic associations of PvuII polymorphism from ESR1 gene with susceptibility to MDD as compared to healthy controls. Significant alterations in cognitive functioning with rs9340799, rs22634693 and rs728524 were observed by SWAN group. However, these associations were not consistent across different ethnic groups in the same study (Kravitz et al., 2006b). Hence, it is evident, that genetic variants from estrogen receptors might alter vulnerability to neuropsychiatric symptoms particularly those associated with neurodegenerative disorders, possibly by modulating, binding affinity of estrogens to their respective receptors. Summing up, genetic polymorphisms from estrogen receptors might mask neuroprotective effect of estrogens.

2.6.1.4. Estrogen transporters Dysregulation in expression of ABCs have long been known for its association with multidrug resistant phenotype due to altered penetration of drugs across biological membranes in epilepsy. However, drug resistance phenotype cannot be solely attributed to low permeability of AEDs since not all AEDs are substrates of ABC transporters. These conflicting results may have been due to multi-substrate specificity of ABC transporters for conjugated estrogens in addition to AEDs. It is now well established that altered levels of these sex steroids particularly in women may lead to seizure susceptibility and ABC transporters namely ABCB1, ABCC1 and ABCC2 are known to play a predominant role in transport of estrogens (Huang et al., 1998; Keppler et al., 1997).

2.6.1.4.1. ABCB1 (ATP-binding cassette, sub-family B (CFTR/MRP), member 1), ABCC1 (ATP-binding cassette, sub-family C (CFTR/MRP), member 1) and ABCC2 (ATP-binding cassette, sub-family C (CFTR/MRP), member 2)

Role of several genetic variants from estrogen transporters have been well characterized from ABCB1, ABCC1 and ABCC2 for altering their activity and expression. ABCB1 also

38

Chapter 2


being a well established AED transporter, genetic variants from ABCB1 have already been explain earlier in the section on “Genetic variants influencing disposition of antiepileptic drugs”. On the other hand, literature on role of transport of AEDs by ABCC1 is lacking. The major allele of rs504348 at the promoter region of the ABCC1 gene has been earlier shown to mediate lower ABCC1 promoter activity (Wang et al., 2005). Furthermore, PHT and CBZ are believed to be substrates of ABCC2 though experimental evidence is lacking (Loscher et al., 2009). Several functional genetic variants from ABCC2 have been well characterized for their influence on respective mRNA, protein expression or oral clearance of drug substrates (Choi et al., 2007; de Jong et al., 2007; Haenisch et al., 2007; Hirouchi et al., 2004; Kroetz, 2006; Laechelt et al., 2011; Meier et al., 2006; Naesens et al., 2006; Niemi et al., 2006; Rau et al., 2006). De Jong et al. was amongst the initial reports highlighting promoter variants as a part of six variant haplotype rs1885301−rs2804402−rs717620−rs2273697−rs127216177− rs3740066

(c.-1549G>A−c.-1019A>G−c.-24C>T−c.1249G>A−

c.3742-34T>C

−

c.3972C>T) causing a significant alteration in irinotecan related diarrhoea in Caucasian cancer patients, possibly by reduced hepatobiliary secretion of irinotecan (de Jong et al., 2007). Several other variants including c.-1019A>G (rs2804402), c.-24C>T (rs717620) and c.3972C>T (rs3740066) have also been observed for decreasing the clearance of drugs, especially anti-neoplastic drug Irrinotecan. However, it was only recently that direct contribution of ABCC2 haplotypic combinations on its expression or activity was demonstrated (Laechelt et al., 2011). Choi et al. showed that g.-1774delG and combined variation of c.-1549G>A (rs1885301) and c.-24A>G (rs717620) decrease ABCC2 promoter activity by 36 and 39%, respectively (Choi et al., 2007). More recently, Laechelt et al. demonstrated both decrease and increase in ABCC2 expression with different haplotypic combinations of c.-24A>G (rs717620), c.1249G>A (rs2273697) and c.3972C>T (rs3740066) variants. Other rare variants including c.1446C>G (rs113646094), c.2366C>G/T (rs56220353), c.3563T>A (rs17222723), c.4348G>C (rs56296335) and c.4544G>A (rs8187710) have all been shown to influence expression or activity of ABCC2 in different capacities by different studies over the last decade.” Concerning role of genetic variants from ABCC2 on drug response to AEDS, two independent reports in German (Caucasian) population were amongst the first

39

Chapter 2


studies which showed positive association of ABCC2 variants with drug response in PWE. In one report (Ufer et al., 2009), the author observed an over-representation of the promoter variant c.-24C>T in poor-responders. Another report (Ufer et al., 2011) observed an over-representation of non-synonymous c.1249G>A in good-responders in a distinct cohort of patients with childhood epilepsy. However, a recent report in another Caucasian cohort of Austrian PWE failed to observe the association of c.24C>T, c.1249G>A and c.3972C>T or their haplotypic combinations (Hilger et al., 2012). Most recently, associations of these variants were observed in the Chinese PWE (Qu et al., 2012). Another study in a different Chinese cohort had earlier contradicted the importance of ABCC2 variants in drug response (Kwan et al., 2011). Hence, it is evident, that genetic variants from estrogen transporters might alter efflux of estrogens across the biological membranes and may be responsible for gender specific drug response to AEDs possibly by modulating seizure frequency. To date, genetic studies related to polymorphism in genes encoding products of estrogen disposition pathway have focused on candidate genes and have uncovered few inconsistent associations related to disease susceptibility to brain disorders and differential metabolites levels of various pathway products. Based on all the genetic studies discussed in this section, a brief timeline showing original studies with unique findings have also been depicted in the Figure 2.7. Lurie et al.

CYP1A2

Tworoger et al.; Dunning et al.

CYP19A1

Schuit et al.

ESR1 Hanna et al.

et al. SULT1A1

COMT

CYP1B1

Feigelson et al.

Sparks

Worda et al.

Sowers et al.

1997

1998

1999

2000

Taioli et al.

CYP1A1

2001

2002

Adjei and Weinshilboum

SULT1E1

2003

2004

In vivo disposition

ESR2

CYP17A1

2005

2006

2007

Nagar et al.

Kisselev et al.

SULT1A1

CYP1A1

2008

2009

2010

In vitro disposition

Chen et al.

COMT

Figure 2.7: Timeline of landmark studies in the field of estrogen genetics.

40

2011

2012

Chapter 2


Estrogens have been traditionally viewed as female sex hormones secreted by ovaries which help in the development of secondary sex characters and regulation of reproductive life in females (Kane et al., 1969). Estrogens are also secreted in males, but in significantly lower quantities and may influence spermatogenesis (Luconi et al., 2002). However, in the last two decades, burgeoning number of articles has documented non-reproductive functional relevance of estrogens with emphasis upon their relationship with the central nervous system (CNS) (AC et al., 2006; Cosimo Melcangi & GarciaSegura, 2010; McEwen, 2002). Furthermore, it is now becoming increasingly evident that estrogens play a central role in maintaining health of a female brain (King, 2008). Their role in neuro-pathophysiology is further corroborated by several recent reports demonstrating local biosynthesis of sex steroids in neurons and an existence of complete machinery of estrogen metabolizing enzymes (Dutheil et al., 2008; Mellon & Deschepper, 1993). Besides their higher concentration, estrogens assume greater significance with fluctuating serum levels in a female’s life span, contributing to a wide array of functions. The surge or decline in estrogen levels can be attributed to change associated with the menstrual cycle (Farage et al., 2009), pregnancy (Venners et al., 2006) and ménopause (Burger et al., 2008; Nelson, 2008).

Sensitivity of Estrogen receptors (ESR1, ESR2)

SNPs

Age Body weight Menstrual cycle (Premenopausal)

Age at onset of epilepsy Seizure type

Pregnancy

Variability in estrogen levels

Perimenopausal and Postmenopausal stages

Hormonal replacement therapy

SNPs Expression and activity of Phase I estrogen metabolizing enzymes (e.g. CYPs)

Expression and activity of estrogen transporters (e.g. ABCs)

Expression and activity of Phase II estrogen metabolizing enzymes (e.g. UGTs, SULTs, COMT)

Figure 2.8: An interaction network of variables that may influence estrogen related susceptibility to brain diseases leading to altered drug response.

41

Chapter 2


However, these sudden changes might play a significant role in altering the homeostasis of the nervous system with increased vulnerability to neuropsychiatric disorders and decreased sensitivity to several pharmacological agents to treat the same. In addition, some women are more predisposed to these vulnerabilities with an early age of onset and increased severity of symptoms than others. Moreover, treatment of such patients with drugs and exogenous steroids has received mixed success (Riecher-Rossler & de Geyter, 2007). Variability in serum estrogen levels in different women may contribute to this differential predisposition, and response to medications. However, studies correlating serum E2 levels with disease susceptibility and severity of symptoms have yielded conflicting results. These discrepancies might be attributed to limitations imposed in measuring neuroactive steroid levels. Thus, studying genetic variations might be a better alternative to overcome this limitation as functional polymorphisms may have similar consequences irrespective of their sites of expression on the activity of proteins involved in estrogen metabolism, transport or action (Figure 2.8).

42

Chapter 3

Materials and Methods

Chapter 3 Materials and Methods

Chapter 1 Introduction and Aims of the study

ORIGINAL ARTICLE Grover S et al. Absence of a general association between ABCB1 genetic variants and response to antiepileptic drugs in epilepsy patients. Biochimie. 2010

ORIGINAL ARTICLE Grover S et al. Genetic polymorphisms in sex hormone metabolizing genes and drug response in women with epilepsy. Pharmacogenomics. 2010


43

Chapter 3


3.1. Study participants and phenotyping The study included 400 North Indian PWE attending the outpatient department of Neurology at Institute of Human Behavior and Allied Sciences (IHBAS), a tertiary care super-specialty hospital. All patients gave their written informed consent to participate in the study. The institution’s biomedical research ethics committee gave the approval before their enrollment. The following inclusion criteria were used for enrollment in the study: (i) patient’s age was above 5 years and (ii) receiving monotherapy of first line AEDs, i.e. PB, PHT, CBZ or VPA. The exclusion criteria were as follows: (i) the presence of gross neurological deficits such as mental retardation, motor deficit, and imaging abnormalities including tumors, tuberculoma, multiple neurocysticercosis, vascular malformations, and atrophic lesions, (ii) presence of sever hepatic, renal disorders or diabetes mellitus, (iii) a prior history of smoking or drug abuse, and (iv) pregnancy. A standardized questionnaire was administered to collect data on detailed clinical assessment including history, neurological examination, biochemical profile, daily dose, serum drug levels, and brain imaging records. Baseline clinical data were recorded at the time of enrollment and included age, body weight, age at onset of seizures, seizure type, epilepsy type and AED prescribed. In a 12-month prospective study, patients were assessed for seizure control and steady-state serum drug levels at two, four, eight, and 12 months respectively (Figure 3.1). In addition to measurement of drug levels, face-to-face interviews of patients and family members also ensured drug compliance. Patients showing side effects, non-compliance to treatment and patients moved to other or additional AEDs during duration of the study were excluded from the present study. Patients were classified for seizure types and treated by a neurologist as per the international standards. The seizures were classified as generalized tonic-clonic seizures (GTCS), simple partial with secondary generalization (SPS with sec. gen.), complex partial seizures with secondary generalization (CPS with sec. gen.) etc. The maintenance dose of a given drug was defined as dose, which had remained unchanged for successive visits in the 12-month period. To obtain the average steady state drug levels at maintenance dose, mean of drug level measurements over a period when consecutive doses were documented was used.

43

Chapter 3


Exclusion criteria • Gross neurological deficits • Motor deficits • Imaging abnormalities • Hepatic or renal disorder • Diabetes • Smoking • Pregnancy

Baseline evaluation • Disease/treatment history • Brain imaging • Biochemical tests

Enrollment of

A prospective cohort study 0

2

4

8

Timeline (months) epilepsy patients Inclusion criteria • Age above five years

• Treatment with monotherapy of first-line AEDs: PB, CBZ, PHT and VPA.

Daily Dose

Phenotypic data collection Serum drug levels Seizure frequency

Figure 3.1: Study design and collection of phenotypic data.

44

12

Chapter 3


CBZ, PHT, VPA and PB levels were measured at the department of neuropsychopharmacology, IHBAS, using cloned enzyme donor immunoassay (CEDIA®), on an autoanalyzer system (ECHO; ISE, Srl, Rome, Italy) (Sharma et al., 2008). The laboratory results were interpreted by a clinical pharmacologist. Subtherapeutic or supratherapeutic plasma levels were further verified as a part of quality assurance program. The inter-run assay precision for AEDs studied was 5% in either of the HapMap populations and r2C rs3740063T>C rs8187710G>A

rs72558202A>G rs3740065A>G

rs3740066C>T

rs3758395T>C rs17216177T>C

Exon 6

Exon 31

Exon 29 Exon 30

Exon 28

Exon 27

Exon 25 Exon 26

Exon 24

Exon 23

Exon 22

Exon 21

Exon 20

Exon 19

Exon 18

Exon 17

Exon 16

Exon 15

Exon 13 Exon 14

Exon 12

Exon 11

Exon 10

Exon 9

Exon 8

Exon 7

c v

Exon 27

rs7898096G>A rs17216345T>C rs72558200G>A rs72558201A>T rs8187692G>T rs17222723T>A

rs2002042C>T rs11442349T>delT rs3740071C>G

rs56199535C>T rs56220353C>G/T

r s3740072A>G

rs3740074T>C rs4148394A>C

rs2756114T>C

rs2073337A>G

rs2273697G>A rs113646094C>G

rs7080681G>A

rs2756109T>G

Exon 5

Exon 4

Exon 3

16043434

Exon 26

Exon 25

Exon 24

Exon 22 Exon 23

Exon 20 Exon 21

Exon 18 Exon 19

Exon 17

Exon 16

Exon 15

Exon 14

Exon 13

Exon 12

Exon 11

Exon 10

Exon 9

Exon 8

Exon 7

rs2804400C>T

Exon 2

5’

101542463 5’

rs212093G>A rs4148382G>A

rs212090T>A

rs8057331C>T

rs2299670A>G

rs13337489G>C

rs35529209G>A rs3887893G>A

rs11864374G>A

rs2239995G>A

rs3851713A>T

rs4148356G>A

rs45511401G>T

rs35621C>T

rs35597G>A

rs3765129C>T

rs8187852G>A rs35587T>C rs35592T>C rs60782127G>T

rs903880C>A

rs246240A>G rs924135A>T

rs2014800C>T rs41494447C>T rs4781712A>G

rs246220C>G rs119774G>A rs246217C>A

rs215049G>C

rs215106A>G

rs504348C>G

B

Exon 4 Exon 5 Exon 6

Exon 3

rs4148385C>A rs2180990C>G rs35191126G>delG rs4148389A>G

Exon 1

Exon 1

Exon 3 Exon 2

Exon 4

Exon 5

Exon 7 Exon 6

Exon 8

Exon 9

Exon 10

Exon 15 Exon 14 Exon 13 Exon 12 Exon 11

Exon 17 Exon 16

Exon 18

Exon 19

Exon 20

Exon 21

Exon 23 Exon 22

Exon 25 Exon 24

Exon 26

Exon 27

Exon 28

Exon 29

rs2188531 A>G rs2188530 T>C

rs7790722 A>T

rs10264856 G>A

rs4148731 C>T rs1978095 T>C

rs9282564 A>G

rs2520464 A>G rs1989830 C>T rs28381826 G>A rs9282565 C>A

rs2235023 G>A

rs10276036 C>T

rs1128503 T>C rs2229109 G>A

rs2235036 G>A

rs2235040 G>A rs2235039 G>A

rs2032582T/A>G rs2032581 A>G

rs2707944 C>G rs7779562 G>C

rs2235048 C>T rs1045642 T>C rs2229107 A>T

rs17064 A>T

A

Exon 2

Exon 1

-1774G>delG rs1885301G>A rs2804402A>G rs717620C>T rs4919395G>A rs2756104C>T rs927344T>A

Chapter 3 Materials and Methods

ABCB1 (ATP-binding cassette, sub-family B (MDR/TAP), member 1; NC_000007.13; 7q21.12; 209. 5 Kb

87133179 3’ 5’ 87342639

ABCC1 (ATP-binding cassette, sub-family C (CFTR/MRP), member 1); NC_000016.9;16p13.1; 193. 5 K b

c v

3’ 16236931

C ABCC2 (ATP-binding cassette, sub-family C (CFTR/MRP), member 2); NC_000010.10; 10q24; 69.2 Kb

3’ 101611662

Figure 3.2: Graphical representation of prioritized ABC transporters and respective genetic variants. (A) ABCB1 (B) ABCC1 (C) ABCC2.

Chapter 3


In summary, a total of 135 SNPs spanning 22 genes were covered at different STAGES (I, II, III) of the present study to explore the association of genetic variants with seizure control (Table 3.3). On the basis of functional significance of genetic variants, we selected a total of 54 genetic variants from 22 genes with a potential to influence

AED or estrogen disposition (Table 3.4). Further, we also prioritized nine randomly chosen autosomal markers (D10S548, D10S196, D10S1653, D11S937, D11S901, D13S218, D13S175, D20S115, and D20S107) unlinked to epilepsy, to test for genetic homogeneity in PWE. Table 3.3: Summary of genes (n=22) and genetic variants (n=135) studied at different stages (I, II and III) of the drug response study. Different stages of the present study

Functional category of prioritized genes

Number of prioritized genes

Stage I

Drug metabolizing enzymes Drug transporters Drug targets

12

19 (functional role)

Stage II

Estrogen metabolizing enzymes Estrogen receptors

9

22 (functional role)

Stage III

Estrogen transporters

3

17 (functional role) 81 (gene coverage)

Total

22 genes

Number of prioritized SNPs (selection criteria)

135 SNPs (54 with functional role)

3.3. Genotyping of genetic variants 3.3.1. Genomic DNA isolation Genomic DNA was isolated from the peripheral blood leukocytes using a modification of a salting out procedure (Miller et al., 1988). Approximately, 8-10 ml blood was drawn from patients as well as healthy controls and collected in vacuettes containing acid citrate dextrose (ACD) buffer. Red blood cell lysis buffer (RLB, 1X) was added to the blood in a falcon to make the final volume 50ml. The suspension was mixed by inverting the falcons several times until it became translucent and was further incubated for 20 minutes at room temperature. This was followed by centrifugation at 2500 rpm for 15 minutes at room temperature and the supernatant was discarded in sodium hypochlorite solution. Further, 15ml RLB was added to the pellet.

50

Chapter 3


Table 3.4: List of functional genetic variants (n=54) from genes (n=22) involved in disposition of first-line antiepileptic drugs as well as estrogens. Gene

Substrate

dbSNP id

Position

Alleles

Gene location (effect)

SNP function

Reference

Phase I Metabolizing enzymes CYP1A1

Estrogen

rs2606345

chr15: 75017176

c.-27+606C>A

Intron 1

↓ activity

(Sowers et al., 2006c)

rs1799814

chr15 : 75012987

c.1382C>A (*4)

Exon 7 (Thr461Asn)

↑ activity

(Napoli et al., 2005)

rs1048943

chr15: 75012985

c.1384A>G (*2C)

Exon 7 (Ile462Val)

↑ activity

(Kisselev et al., 2005)

CYP1A2

AED Estrogen

rs762551

chr15: 75041917

g.-163A>C (*1F)

Intron1 (5’ UTR)

↑ inducibility

(Lurie et al., 2005)

CYP1B1

Estrogen

rs1056836

chr02: 38298203

c.1294C>G (*3)

Exon3 (Leu432Val)

↑ activity

(Hanna et al., 2000)

rs1800440

chr02: 38298139

c.1358A>G (*4)

Exon 3 (Asn453Ser)

↑ activity

(Hanna et al., 2000)

rs11572080

chr10: 96827030

c.416G>A (*3)

Exon 3 (Arg139Lys)

↓ activity

(Dai et al., 2001)

rs10509681

chr10: 96798749

c.1196A>G (*3)

Exon 8 (Lys399Arg)

↓ activity

(Dai et al., 2001)

rs1799853

chr10: 96702047

c.430C>T (*2)

Exon 3 (Arg144Cys)

↓ activity

(van der Weide et al., 2001)

rs1057910

chr10: 96741053

c.1075A>C (*3)

Exon7 (Ile359Leu)

↓ activity

(van der Weide et al., 2001)

rs4244285

chr10: 96541616

c.681G>A (*2)

Exon 5 (Pro227Pro)

No activity

(Odani et al., 1997)

rs4986893

chr10: 96540410

c.636G>A (*3)

Exon 4 (Tyr 212 Ter)

No activity

(Odani et al., 1997)

rs2740574

chr07: 99382096

g.-392A>G (*1B)

5’ near gene (Promoter)

↑ expression

(Amirimani et al., 2003)

rs12721627

chr07: 99366093

c.554C>G (*16)

Exon 7 (Thr185Ser)

↓ activity

(Murayama et al., 2002)

CYP2C8

CYP2C9

AED

AED

CYP2C19

AED

CYP3A4

AED

CYP17A1

Estrogen

rs743572

chr10: 104597152

c.-34T>C (MspAI)

5’ UTR (Exon 1)

↑ activity

(Feigelson et al., 1998)

CYP19A1

Estrogen

rs936306

chr15: 51579598

c.-39+36415C>T

Intron 2

↑ activity

(Sowers et al., 2006b)

rs11636639

chr15: 51563092

c.-27983T>G

Intron 2

↑ activity

(Paynter et al., 2005)

rs767199

chr15: 51540387

c.-5278G>A

Intron 2 (5’ flanking)

↑ activity


rs4775936

chr15: 51536022

c.-913G>A

Intron 2

↑ activity


rs11575899

chr15: 51519949_50

c.451+26_451 +27insTCT

Intron 5

↓ activity

(Dunning et al., 2004)

rs10046

chr15: 51502986

c.19C>T

Exon 11 (5’ UTR)

↓ or ↑ activity

(Dunning et al., 2004; Paynter et al., 2005)

51

Chapter 3 Gene

Materials and Methods Substrate

dbSNP id

Position

Alleles


SNP function

Reference

Phase II Metabolizing enzymes COMT

Estrogen

rs4680

chr22: 19951271

c.472G>A

Exon 4 (Val158Met)

↓ activity

(Chen et al., 2004)

EPHX1

AED

rs1051740

chr01: 226019633

c. 337T>C

Exon 3 (Tyr113His)

↓ activity

(Hassett et al., 1994; Maekawa et al., 2003)

rs2234922

chr01: 226026406

Exon 4 (His139Arg)

↑ activity

(Hassett et al., 1994; Maekawa et al., 2003)

c.416A>G

SULT1A1

AED

rs9282861

chr16: 28617514

c.638G>A (*2)

Exon 5 (Arg213His)

↓ activity

(Nagar et al., 2006)

UGT1A1

AED

rs4148323

chr02: 234669144

c.211G>A (*6)

Exon1 (Gly71Arg)

↓ activity

(Sai et al., 2004; Yamamoto et al., 1998)

UGT2B7

AED

rs7439366

chr04: 69964338

c.801C>T (*2)

Exon2 (His268Tyr)

↑ activity

(Daly et al., 2007; Sawyer et al., 2003)

AED Estrogen

rs1128503

chr07: 87179601

c.1236T>C (*13)

Exon 13 (Gly412Gly)

↑ activity*

(Zimprich et al., 2004)

rs2032582

chr07: 87160618

c.2677T/A>G (*13)

Exon 22 (Ser/Thr893Ala)

↑ activity*

(Seo et al., 2006)

rs1045642

chr07: 87138645

c.3435T>C (*13)

Exon 27 (Ile1145Ile)

↑ expression ↑ activity

(Ozgon et al., 2008)

rs504348

chr16: 16043174

rs50438C>G

Near gene region

↓ activity

(Wang et al., 2005)

rs60782127

chr16: 16142079

c.1299G>T

Exon 10 (Arg433Ser)

↓ transport

(Letourneau et al., 2005)

rs35529209

chr16: 16205325

c.2965A>G

Exon22 (Thr989Ala)

↓ transport

(Letourneau et al., 2005)

g.-1774 G>deld

chr10: 101535688

g.-1774G>del

Near gene region

↓ activity

(Choi et al., 2007)

rs1885301

chr10: 101541053

c.-1549G>A

Near gene region

↓ activity*

(Choi et al., 2007)

rs2804402

chr10: 101541583

c.-1019A>G

Near gene region

↓ clearance

(Choi et al., 2007; de Jong et al., 2007)

rs717620

chr10: 101542578

c.-24C>T

5’ UTR

↓ promoter activity*

(Haenisch et al., 2007; Laechelt et al., 2011)

rs2273697

chr10: 101563815

c.1249G>A

Exon10 (Val417Ile)

↑ mRNA expression

(Laechelt et al., 2011)

rs113646094

chr10: 101564012

c.1446C>G

Exon10 (Thr482Thr)

↑ mRNA

(Niemi et al., 2006)

Transporters ABCB1

ABCC1

ABCC2

Estrogen

AED Estrogen

52

Chapter 3 Gene

Materials and Methods Substrate

dbSNP id

Position

Alleles


SNP function expression

Reference

rs56220353

chr10: 101578641

c.2366C>G/T

Exon18 (Ser789Cys/Phe)

↓acitivity ↓ expression

(Hirouchi et al., 2004)

rs17222723

chr10: 101595996

c.3563T>A

Exon25 (Val1188Glu)

↑ expression

(Meier et al., 2006)

rs3740066

chr10: 101604207

c.3972C>T

Exon28 (Ile1324Ile)

↑ expression*

(Laechelt et al., 2011)

rs56296335

chr10: 101610393

c.4348G>C

Exon31 (Ala1450Ser)

↓ acitivity ↓expression

(Hirouchi et al., 2004)

rs8187710

chr10: 101611294

c.4544G>A

Exon 32 (Cys1515Tyr)

↑ expression

(Meier et al., 2006)

rs9340799

chr06: 152163381

IVS1-351A>G (XbaI)

Intron 1

Influences estradiol levels

(Schuit et al., 2005)

rs3798577

chr 06: 152421130

c.1029C>T

Exon 1 (3’ UTR)


(Sowers et al., 2006a)

Targets/ receptors ESR1

Estrogen

ESR 2

Estrogen

rs1255998

chr14: 64693871

c.380C>G

Exon 9 (3’ UTR)


(Sowers et al., 2006a)

SCN1A

AED

rs3812718

chr02: 166909544

c.603-91A>G

Intron 5 (Splice site)

↓ sensitivity

(Abe et al., 2008; Tate et al., 2005; Tate et al., 2006)

SCN1B

AED

No dbSNP id

387C>G

Exon 3 (Cys121Trp)

↓ sensitivity

(Lucas et al., 2005)

SCN2A

AED

rs2304016

IVS7-32A>G

Intron 7

↓ sensitivity

(Kwan et al., 2008)

chr02: 166168503

*as a part of haplotype

53

Chapter 3


The suspension was mixed by inverting the tubes several times, followed by centrifugation at 1000 rpm for 10 minutes. The supernatant was discarded in sodium hypochlorite and the pellet was re-suspended in 12ml of nuclei lysis buffer (NLB), followed by addition of 0.8ml of 10% sodium dodecyl sulphate (SDS) and 50l proteinase K (20g/l). After incubation at 65ºC for 2 hours, the digested protein product was precipitated by the addition of 4ml of 6M sodium chloride (NaCl) solution. After centrifugation for 30 minutes at 3500 rpm at room temperature, the supernatant was transferred to a falcon. Absolute ethanol was now slowly added to the supernatant to spool the DNA. The spooled DNA was further washed with 70% ethanol twice, airdried, and dissolved in Tris-Ethylenediaminetetraacetic acid (TE) buffer. The quantity of the DNA was estimated using a spectrophotometer to determine the optical density at 260nm and 280nm. DNA quality was assessed using the 260nm/280nm ratio. The stock solution of the DNA was stored at -20ºC. The composition of buffers and reagents are provided in Appendix A.

3.3.2. Genotyping of genetic markers unlinked to epilepsy For genotyping of the prioritized microsatellite markers unlinked to epilepsy, specific primers labeled with fluorescent dyes 6-FAM or VIC were purchased from Applied Biosystems (ABI linkage mapping set, version 2.0, Perkin Elmer, USA). These markers were genotyped as per the manufacturer’s protocol. Briefly, polymerase chain reaction (PCR) was performed in a reaction volume of 2.5μl using 0.25μl 10X Taq polymerase buffer (10mM Tris, pH 9.0; 50mM KCl, 0.01% Gelatin), 0.15μl MgCl2 (25mM), 0.15μl primer mix (Linkage Mapping set version 2.0, Applied Biosystems), 0.25μl dNTPs (2mM), 0.025μl Taq polymerase enzyme (3U/μl), 0.675μl autoclaved milliQ water and 5ng genomic DNA. The PCR thermo-cycling conditions involved an initial denaturation step of 5min at 95ºC, followed by two short-cycle programs comprising of 10 and 25 cycles. The first 10 cycles of 30sec denaturation at 95ºC, 30sec annealing at 55ºC, 30sec extension at 72ºC; and 25 cycles of 30sec denaturation at 89ºC, 30sec annealing at 55cC, 30sec extension at 72ºC; were followed by a final extension at 72ºC for 10min. To these amplicons, 8.5 μl of 100% Hi-Di formamide and 0.3μl ROX size standard (Linkage Mapping set version 2.0, Applied Biosystems) were added, followed by snap-chill treatment, where the

54

Chapter 3


samples were denatured at 95ºC for 5min and then immediately kept in ice. The samples were then loaded on ABI 3100 Genetic Analyzer as per the manufacturer’s instructions, and were analyzed using the GeneScan Analysis software, version 3.7 and Genotyper software, version 3.7 (Applied Biosystems). 3.3.3. Genotyping of genetic markers related to response to antiepileptic drugs The prioritized genetic variants were primarily genotyped using the Sequenom MassARRAY iPLEX (Sequenom Inc., San Diego, USA) platform. The genotyping of variants which could not be standardized using Sequenom assays was performed using SNaPshot method (Applied Biosystems, Foster City, CA, USA). These methods are described below: 3.3.3.1. Sequenom The sequenom iPLEX assays (Increased Plexing Efficiency and Flexibility for MassARRAY System) enabled high throughput genotyping and analysis of SNPs through the application of matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The iPLEX method involves multiplex PCRs followed by single base primer extension reactions, resulting in allele-specific differences in mass between extension products which are analyzed by MALDI TOF MS. The method comprises of three basic steps described below: 3.3.3.1. 1. Assay design The SNP sequences (150-200 bp sequences flanking the SNP) were obtained from Ensembl Genome Browser (www.ensembl.org/) and MassARRAY Designer software (Sequenom Inc., San Diego, USA) was used for designing both the PCR and iPLEX single base extension primers for each SNP based on the sequences. The software provided the optimum plexing of all the SNPs. The primer details have been summarized in Appendix B. 3.3.3.1. 2. Genotyping protocol PCR amplification reactions were performed in 5 μl reaction volume using 0.625μl PCR buffer(10X), 0.325μl MgCl2 (25mM), 0.1μl dNTPs (25mM), 1μl primer mix

55

Chapter 3


(500nM), 0.1μl Hotstar Taq polymerase enzyme (5U/μl), 1.85μl nanopure water and 1μl genomic DNA (5-10ng/μl). The PCR thermo-cycling conditions involved an initial denaturation step of 5min at 95ºC, followed by 45 cycles of 20sec denaturation at 95ºC, 30sec annealing at primer-pair specific temperature, extension at 72ºC for 1min; and a final extension at 72ºC for 3min. As PCR amplification was carried out for multiplex reactions, PCR reagent concentrations and conditions were optimized for each plex. The amplicons were then subjected to SAP treatment to dephosphorylate unincorporated dNTPs, where 2μl SAP mix (0.17μl SAP buffer (10X), 0.3μl SAP enzyme (1U/μl), 1.53μl nanopure water) was mixed with 5μl amplicons and incubated at 37ºC for 20 minutes, followed by 5 minute incubation at 85ºC. The purified amplicons were then used to prepare the iPlex cocktail mix (0.2μl 10X iPLEX buffer, 0.2μl iPLEX termination mix, 0.804μl primer mix, 0.041μl iPLEX enzyme and 0.755μl nanopure water) for iPLEX primer extension according to the manufacturer’s protocol. (Sequenom Inc., San Diego, USA). iPLEX reactions were carried out using a 200-shortcycle program uses two cycling loops of 40 and 5 cycles as described below: 94º C for 30 seconds 94º C for 5 seconds 52º C for 5 seconds

5cycles 40cycles

80º C for 5 seconds 72º C for 3 minutes 4º C forever The iPLEX reaction products were then desalted using clean resin (Sequenom Inc., San Diego, USA) and dispensed onto a SpectroCHIP bioarray using a nanodispenser. The SpectroCHIP arrays are placed into the MALDI-TOF mass spectrometer and the mass correlating genotype is determined in real time. A SpectroCHIP is typically processed in 45-60 minutes. MassARRAY Workstation software was used to process and analyze iPLEX SpectroCHIP bioarrays. These steps were carried out by trained staff.

3.3.3.1. 3. MALDI-TOF MS analysis MassARRAY Typer software version 3.4 was utilized to visualize the SNP allele peaks (mass spectra data) and assess the data quality, assay success rate and genotype calls for

56

Chapter 3


each SNP. Every SNP was manually checked for quality by looking at the spectrum and cluster plots. The calling algorithm, Caller version 3.4 (Sequenom Inc., San Diego, USA) was used to create genotyping calls. The genotype calls are categorized as no calls, low mass homozygous, heterozygous, or high mass homozygous. No calls made by software in case of low probabilities of either allele were manually assigned after thorough inspection of all the SNP spectra. For SNPs with low genotype calls or improper cluster plots, genotyping was repeated using SNaPshot.

3.3.3.2. SNaPshot SNPs were genotyped using single base primer-extension reactions (SNaPshotTM ddNTP primer extension kit, Applied Biosystems). The PCRs were performed and the amplicons were purified by direct precipitation using polyethylene glycol (PEG)sodium acetate purification protocol. The purification process involved addition of PEG-sodium acetate solution twice the volume of the sample in 96 or 384-well PCR plates and centrifugation at 3200 rpm for 30 min at room temperature followed by inverting the plates on a pad of absorbent paper towels and a short spin of the plates in inverted position at 500rpm spinning. The pellets were washed twice with 70% ethanol and air-dried. The compositions of reagents for PEG purification are provided in Appendix A. SNaPshot reaction was set using ~50 ng of purified amplicon, 1μl of genotyping primer (2pm/μl), 0.5μl SNaPshot ready reaction mix, 0.8μl 5X dilution buffer (200mM Tris, pH 9.0; 5mM MgCl2) and milliQ water to make up the volume to 5μl . The thermo-cycling conditions followed for PCR were, 96ºC for 10sec, 55ºC for 5sec and 60ºC for 30sec for 30 cycles. The unincorporated ddNTPs in the SNaPshot reaction products were digested by incubating the samples with 0.25 units of calf intestinal alkaline phosphatase (CIP) at 37ºC for 1hr, followed by inactivation of CIP at 72ºC for 15min. 1μl of the SNaPshot products were mixed with 10μl of HiDi formamide and loaded on 3100 Genetic Analyser. Sequence analysis was carried out using Sequence analysis software, version 5.1 (Applied Biosystems, Foster City, CA, USA) and the homozygous and heterozygous alleles for each SNP were scored manually.

57

Chapter 3


3.4. Genotype-phenotype association analysis 3.4.1. Population stratification Genotype frequencies of each of the microsatellite markers were compared between the patient and healthy control groups, and between good and poor responders using the Pearson 2 tests for association to test for population stratification. The p-value of the observed test statistic was estimated as the fraction of 10,000 simulated test statistics that exceeded the observed value using the STRAT program (Pritchard & Rosenberg, 1999). The sum of the test statistics for each locus was then computed with the number of degrees of freedom (df) being equal to the sum of the number of df of the individual loci as implemented in STRAT program. Hardy-Weinberg equilibrium (HWE) for the genotypic frequency distribution of polymorphic SNPs was tested in all cases and healthy controls using Pearson’s 2 test.

3.4.2. Distribution of categorical and continuous variables Comparison of the categorical and continuous phenotypic variables among “no-seizure” and “recurrent-seizures” was done using Pearson 2 test, and Mann-Whitney U test respectively. The distribution of allelic, genotypic, haplotypic and diplotypic frequencies were compared between “no seizure” and “recurrent seizures” groups using Pearson 2 test. Dose and drug levels were compared between genetic variants using non-parametric Mann-Whitney U test or the Kruskal-Waliis rank sum test. Genotypic associations were also calculated for SNPs separately under dominant or recessive genetic models. All the phenotypic and genotypic variables were also compared between “no-seizure” and “recurrent-seizure” groups in both males and females as a part of sub-group analysis. Odds ratio (OR) and confidence interval (95% CI) were also calculated for each marker and adjusted for other covariates including age, body weight, age at onset of seizures, seizure type, and treatment using binary logistic regression analysis module. All the P-values were corrected by highly conservative bonferroni method to rule out false positive associations. All the statistical analysis were performed using Statistical Package for Social Sciences (SPSS, version 16.0, SPSS Corporation, Chicago, Illinois, USA).

58

Chapter 3


Pairwise LD within the polymorphic markers across the selected genes were calculated with Haploview program (version 4.1) (Barrett et al., 2005). PHASE software (version 2.1), based on the Bayesian algorithm was used for reconstructing haplotypes from unphased genotype data. Parameter values of 100 iterations, a thinning interval of 10, and a burn-in value of 100 in the Markov chain Monte Carlo simulations were used for entry into PHASE software (Stephens et al., 2001). Genetic Power Calculator at http://pngu.mgh.harvard.edu/~purcell/gpc/cc2.html was used to determine power of the study (Purcell et al., 2003). 3.4.3. Interaction analysis A two-way interaction analysis between the most significant SNPs from different genes was conducted by identification of high risk and low risk genotypic combination markers. All the epilepsy patients with high risk genotypic markers were combined into one group and those with low risk genotypic markers into another group for association analysis with seizure control using the Pearson 2 test. 3.4.4. Power calculations Genetic Power Calculator at http://pngu.mgh.harvard.edu/~purcell/gpc/cc2.html was used to determine power of the study. 3.4.5. Meta-analysis 3.4.5.1. Literature search and identification of relevant studies A systematic search of the literature in Embase, Medline, Web of Science, and the Cochrane database of systematic reviews was performed for all the articles investigating association of ABCC2 polymorphisms with drug response in PWE. All the databases were searched up to 8th September 2012 using the following keywords: (ABCC2 OR MRP2 OR transporter) AND (polymorphism OR SNP OR allele OR genotype OR haplotype) AND (epilepsy OR seizure). 3.4.5.2. Inclusion and exclusion criteria Following were the inclusion criteria that were followed for assessing the eligibility of a study in the meta-analysis: (i) The study was on drug-response, (ii) The drug response

59

Chapter 3


phenotype was clearly described, (iii) The genotype frequency data was available. Following were the exclusion criteria: (i) The distribution of genotypic data was not in conformance with Hardy-Weinberg Equilibrium (HWE). 3.4.5.3. Data extraction First author, year of publication, population, phenotypic definition for drug response, genotyping method, allele, genotype and haplotype distributions in good and poor responders from the relevant studies were extracted and reviewed independently by two different investigators (Mr. Grover and Dr. Kukreti). The results were compared and discussed among the investigators.

3.4.5.4. Statistical analysis All the analyses were performed by the use of STATA 11.0 (Stata-Corp LP, College Station, TX, USA) and Review Manager 5.1 (Cochrane collaboration). A P valueA

Intron1 5’ UTR

Higher inducibility

A C

418 (56.0) 328 (44.0)

117 (59.1) 81 (40.9)

0.44

CYP2C8

rs11572080

*3

c.416G>A

Exon 3 Arg139Lys

Decreased activity

G A

688 (95.6) 32 (4.4)

175 (95.1) 9 (4.9)

0.79

rs10509681

*3

c.1196A>G

Exon 8 Lys399Arg

Decreased activity

A G

686 (95.5) 32 (4.5)

169 (94.9) 9 (5.1)

0.73

rs1799853

*2

c.430C>T

Exon 3 Arg144Cys

Decreased activity

C T

744 (100.0) 0 (0.0)

186 (100.0) 0 (0.0)

−

rs1057910

*3

c.1075A>C

Exon7 Ile359Leu

Decreased activity

A C

655 (90.7) 67 (9.3)

182 (91.9) 16 (8.1)

0.60

rs4244285

*2

c.681G>A

Abolished activity

G A

451 (62.8) 267 (37.2)

131 (66.8) 65 (33.2)

0.29

rs4986893

*3

c.636G>A

Exon 4 Tyr 212 Terb

Abolished activity

G A

723 (99.6) 3 (0.4)

185 (99.5) 1 (0.5)

0.82

rs2740574

*1B

g.-392A>G

5’ near gene Promoter region

Increased expression

A G

738 (98.1) 14 (1.9)

197 (98.5) 3 (1.5)

0.73

rs12721627

*16

c.554C>G

Exon 7 Thr185Ser

Decreased activity

C G

744 (100.0) 0 (0.0)

192 (100.0) 0 (0.0)

−

rs1051740

−

c. 337T>C

Exon 3 Tyr113His

Decreased activity

T C

473 (64.3) 263 (35.7)

131 (70.4) 55 (29.6)

0.11

rs2234922

−

c.416A>G

Exon 4 His139Arg

Increased activity

A

582 (78.2) 162 (21.8)

149 (74.5) 51 (25.5)

0.26

CYP2C9

CYP2C19

CYP3A4

EPHX1

Exon 5 Pro227Pro

64

G

Chapter 4

Gene

Results and Discussion

SNP id

Allele

Polymorphism

Location, protein effect

Functional Effect

Major allele Minor allele

PWE allele count (Allele freq.)

Healthy controls allele count (Allele frequ)

P value

Phase II DMEs UGT1A1

rs4148323

*6

c.211G>A

Exon1 Gly71Arg

Decreased activity

G A

732 (98.4) 12 (1.6)

191 (97.4) 5 (2.6)

0.38

UGT2B7

rs7439366

*2

c.801C>T

Exon2 His268Tyr

Increaased activity

C T

481 (64.1) 269 (35.9)

121 (60.5) 79 (39.5)

0.34

rs1128503

*13

c.1236C>T

Exon 13 Gly412Gly

Increased effluxa

T C

457 (60.8) 295 (39.2)

131 (65.5) 69 (34.5)

0.22

rs2032582

*13

c.2677G>T/A

Exon 22 Ala893Ser/Thr

Increased effux

T G A

480 (64.7) 236 (31.8) 26 (3.5)

126 (63.6) 64 (32.3) 8 (4.1)

0.92

rs1045642

*13

c.3435C>T

Exon 27 Ile1145Ile

Increased effux

T C

448 (62.6) 268 (37.4)

119 (64.0) 67 (36.0)

0.72

Drug transporters ABCB1

Drug targets SCN1A

rs3812718

IVS5-91G>A

Intron 5 Splice site

Decreased sensitivity

A G

388 (53.6) 336 (46.4)

94 (54.6) 78 (45.4)

0.80

SCN1B

No dbSNP id

387C>G

Exon 3 Cys121Trp


C G

742 (100.0) 0 (0.0)

188(100.0) 0 (0.0)

−

SCN2A

rs2304016

IVS7-32A>G

Intron 7


A G

733 (99.3) 5 (0.7)

193 (99.5) 1 (0.5)

a

Showed increased efflux when present in a haplotype combination ABCB1 3435C>T-2677G>T-1236C>T

65

0.80

Chapter 4


Furthermore, proportion of patients homozygous for UGTB7*2 were observed with a genotype frequency of 13.3%. On the other hand, we observed almost complete absence of another phase II DME variants UGT1A1*2 (1.6%) in our pool of patients. The frequency of ABCB1 transporter allelic variants which could influence disposition of any of the AEDs were considerable higher upto 68.2% for ABCB1 2677G>T/A variant. Among the all drug targets studied, SCN1A IVS5-91G>A variant was the polymorphic variant (53.6%) that could influence differential sensitivity to PHT and CBZ. This is in comparison to SNC2A IVS7-32A>G which was observed to be present in 99.3% of the population and may be in general responsible for poor efficacy in north Indian population. All the predicted phenotype and their distribution in North Indian PWE based on our current knowledge of pharmacogenomics of AED pharmacokinetics

Frequency of epilepsy patients

and pharmacodynamics have been shown in Figure 4.1.

100

100.0

90.9

90

Extensive metabolizer Intermediate metabolizer

96.8

81.5

Poor metabolizer

80

70 60

45.4

50

43.4

39.9

40

45.1 41.6

41.6

30

14.7

18.5

20

13.3

14.9

9.1

10

3.2

0

CYP2C8 CYP2C9 CYP2C19 CYP3A4 (*3) (*2,*3) (*2,*3) (*16)

EPHX1 UGT1A1 UGT2B7 (337T>C) (*6) (*2)

A. Drug metabolizing enzymes 100.0

Normal efflux Increased flux

90 80 70

100

68.2

62.6

60.8

60

50 40

39.2 31.8

99.3

90

Frequency of alleles

Frequency of alleles

100

37.4

30 20

70

60 50

53.6 46.4

40 30

20

10

10

0

0

ABCB1 ABCB1 ABCB1 (1236C>T) (2677G>T/A) (3435C>T)

Normal sensitivity Decreased sensitivity

80

0.7

SCN1A SCN1B SCN2A (IVS -91G>A) (387C>G) (IVS -32A>G)

B. Drug transporters

C. Drug targets

Figure 4.1: Predicted phenotypes and their distribution based on our current knowledge of pharmacokinetics and pharmacodynamics of antiepileptic drugs in North Indian patients with epilepsy (n=400).

66

Chapter 4


As a part of the Indian Genome Variation consortium effort, 552 healthy individuals drawn from 24 different Indian populations were further genotyped in the study to assess ethnic variability in the distribution of CYP2C9, CYP2C19, ABCB1 and SCN1A variants. Furthermore, of the 24 Indian sub-populations, 4.5-8.7% of individuals representing populations from north and west region displayed altered metabolizing status for CYP2C9 (Figure 4.2). However, rest of the 19 Indian subpopulations displayed extensive metabolizing effect with homozygosity for the wild type allele. Concerning distribution of CYP2C19 variants, all the Dravidian populations showed high proportion of PM (>25%). On the other hand ABCB1 variants and SCN1A IVS591G>A variants were observed to be highly polymorphic across all the Indian subpopulations.

Genotype frequency (%) 0– 1 1– 2 2-3 3-4 4-5 5-6 6-7 7-8 8-9

Figure 4.2: Distribution of CYP2C9 allelic variants among 552 individuals representing 24 Indian sub-populations. The figure depicts the frequency of CYP2C9*1/*3 mutant genotype representing the individuals with altered metabolism for CYP2C9 (Intermediate Metabolizers, IM) across Indian sub-continent. CYP2C9*1/*3 was the only mutant genotype detected in the Indian population.

67

Chapter 4

In


summary,

inter-ethnic

differences

were

elucidated

for

several

polymorphisms which might be responsible for differential serum drug levels and optimal dose requirement for efficacious treatment. Furthermore, in the study on 400 North Indian PWE, we present our preliminary objective signifying populationspecific allele frequencies of important SNPs known to be associated with differential drug levels and drug response to first line AEDs. In future, this comprehensive data could prove useful for designing and judging potential of pharmacogenetic studies. Whether functional consequences of these SNPs hold true for North Indian population will become clearer once genotypic dataset is further evaluated with respect to phenotypic data in the subsequent objectives of my work. Using phenotypic data related to seizure control and serum drug levels in epilepsy patients, the present study could identify genetic variants that are more likely to influence AED and dose requirement. If clinical application of the studied genetic variants is proven and validated, it will go a long way in accomplishing our goal for providing personalized medical treatment.

4.2. Association analysis of genetic variants with seizure control 4.2.1. Demographic and clinical characteristics at the end of the study duration After initial treatment, 184 (46%) patients failed to complete the 12-month follow-up or were prescribed other AEDs during the course of the study and were not eligible for assessing drug response. A total of 216 (54%) patients were available for assessing seizure control (of whom 99 were women) at the end of the study duration (Table 4.4). None had clinically relevant alterations in biochemical parameters suggestive of abnormal kidney or liver functioning. Further, none of the women patients included in the analysis were pregnant during the course of the study. The mean age and body weight of all the 216 patients were 22.7±9.9; 12-40 (age ± SD; range) yrs and 47.1±12.0; 15-82 (age ± SD; range) kg respectively. The distribution of mean age of onset of seizures was similar in both men and women.

68

Chapter 4


Table 4.4: Demographic and clinical characteristics of patients who had completed the study duration (n=216).

Phenotypic characteristics

No seizure (n=71)

Age (yrs.) Mean±SD 22.3±8.9 Body weight (kg) Mean±SD 50.5±13.0 Age at onset (yrs.) < 5 yrs 4 (5.7) 6 to 15 yrs 29 (41.4) 16-25 yrs 27 (38.6) > 25 yrs 10 (14.3) Seizure type* (n (%)) GTCS 42 (59.1) SPS 3 (4.2) SPS sec. gen. 13 (18.3) CPS 3 (4.2) CPS sec. gen. 8 (11.3) Unclassified 2 (2.8) Treatment** (n (%)) CBZ 14 (19.7) PHT 28 (39.4) VPA 19 (26.8) PB 10 (14.1) Maintenance dose (mg/d (n)) CBZ 622.7±262.1(11) PHT 244.4±67.0 (27) VPA 876.3±352.9(19) PB 99.0±42.5 (10) Drug levels at mantainence dose (µg/ml (n)) CBZ 10.50±2.72 (7) PHT 15.83±5.05 (20) VPA 95.35±19.72 (11) PB 23.64±12.70 (10)

Men (n=117) Recurrent seizures OR (95% CI) (n=46)

P

No seizure (n=57)

Women (n=99) Recurrent seizures OR (95% CI) (n=42)

P value

24.1±9.5

-

0.326

23.7±11.2

21.2±10.1

-

0.181

50.2±10.7

-

0.768

43.3±12.0

43.7±8.8

-

0.977

2 (4.3) 18 (39.1) 18 (39.1) 8 (17.4)

1.33 (0.18-15.29) 1.10 (0.48-2.53) 0.97 (0.42-2.25) 0.79 (0.25-2.53)

0.745 0.805 0.951 0.651

2 (3.5) 30 (52.6) 19 (33.3) 6 (10.5)

6 (14.3) 17 (40.5) 12 (28.5) 7 (16.7)

0.21 (0.02-1.32) 1.63 (0.67-3.96) 1.25 (0.48-3.29) 0.58 (0.15-2.25)

0.051 0.231 0.613 0.371

31 (67.4) 1 (2.2) 6 (13.0) 1 (2.2) 4 (8.7) 3 (6.5)

0.70 (0.29-1.62) 1.98 (0.15-106.48) 1.49 (0.47-5.19) 1.98 (0.15-106.48) 1.33 (0.33-6.42) 0.41 (0.03-3.80)

0.369 0.550 0.450 0.550 0.654 0.333

41 (71.9) 0 (0.0) 10 (17.5) 2 (3.5) 3 (5.3) 1 (1.7)

24 (57.1) 0 (0.0) 12 (28.6) 0 (0.0) 5 (11.9) 1 (2.4)

1.92 (0.76-4.85) 0.53 (0.18-1.54) 0.41 (0.06-2.28) 0.73 (0.009-58.85)

0.125 0.192 0.23 0.829

11 (23.9) 17 (37.0) 12 (26.1) 6 (13.0)

0.78 (0.29-2.13) 1.11 (0.48-2.57) 1.03 (0.41-2.65) 1.09 (0.32-3.95)

0.588 0.787 0.935 0.872

43 (75.4) 6 (10.5) 3 (5.3) 5 (8.7)

31 (73.8) 4 (9.5) 4 (9.5) 3 (7.1)

1.08 (0.39-2.98) 1.11 (0.24-5.76) 0.52 (0.07-3.33) 1.25 (0.22-8.51)

0.853 0.870 0.413 0.768

555.5±113.0 (9) 230.0±63.2 (10) 781.2±215.4 (8) 108.0±45.5 (5)

-

0.461 0.467 0.582 0.640

453.7±153.0 (41) 158.3±49.2 (6) 566.7±57.7 (3) 90.0±36.7 (5)

495.7±187.0 (23) 200.0±50.0 (3) 716.7±301.4 (3) 120±0.0 (2)

-

0.437 0.276 0.506 0.232

9.20±2.87 (9) 9.90±5.68 (9) 110.29±21.05 (8) 27.50±14.32 (4)

-

0.368 0.008 0.186 0.777

9.38±2.52 (38) 8.28±3.48 (4) 89.64±11.82 (2) 21.61±4.85 (4)

9.03±2.52 (21) 10.86±1.94 (3) 104.45±4.11 (3) 29.87±7.63 (2)

-

0.721 0.157 0.083 0.354

*GTCS - Generalized tonic clonic seizures; SPS-Complex partial seizures; SPS sec. gen.- Simple partial seizures with secondary generalization; CPS- Complex partial seizures; CPS sec. gen.-Complex partial seizures with secondary generalization, **PHT-Phenytoin; CBZ-Carbamazepine; VPA-Valproate; PB-Phenobarbitone

69

Chapter 4


The majority of patients were diagnosed with GTCS (63.9%) followed by SPS sec. gen. (19.0%) for both the genders (Figure 4.3). Approximately 59% of these patients showed complete control of seizures with men (60.7%) responding slightly better than women (57.6%). The seizure control profile was similar across all the treatment groups (Figure 4.4). The distribution of dosages and drug levels were in accordance with the reference ranges in the Indian population (Figure 4.5). The men were all balanced with regard to type of AED treatment with CBZ (21.3%) and PB (13.7%) being the least prescribed drugs. The women on the other hand were mostly prescribed CBZ (74.8%). Further, for both the genders, there was no significant difference in age, body weight, age at onset of seizures, seizure type and treatment in patients who had seizures and those who did not during the study. Simple partial with secondary generalization (19.4%)

Unclassified (3.0%) Generalized tonic-clonic (63.9%)

Complex partial (2.6%)

Complex partial with secondary generalization (9.6%) Simple partial (1.8%)

Figure 4.3: Distribution of seizure types in patients with epilepsy who had completed the study duration (n=216).

Number of epilepsy patients

60 50

No seizure

40

Recurrent seizures

30 20 10 0 Phenytoin (PHT) (n = 55)

Carbamazepine (CBZ) (n = 99)

Valproate (VPA) (n = 38)

Phenobarbitone (PB) (n =24)

Figure 4.4: Distribution of drug response phenotype in patients with epilepsy prescribed different treatments who had completed the study duration (n=216).

70


1500

150

1250

125

Serum drug levels at maintenance dose (mg/l)

Maintenance dose (mg/day)

Chapter 4

1000

750

500

250

0

100

75

50

25

0

PHT CBZ VPA PB

PHT CBZ VPA PB

Figure 4.5: Distribution of dosages and drug levels in patients with epilepsy on different treatments who had completed the study duration (n=216).

4.2.2. Test for population stratification among patients with no-seizure and recurrent-seizures Furthermore, we tested for population stratification by comparing genotypic distribution of nine microsatellite markers between no-seizure and recurrent-seizures group within the patient group. The results, showed that the test for population stratification was not significant in the sample sets analyzed (2 = 50.772, df = 46, p = 0.2911) (Table 4.5). Table 4.5: Test for population stratification among patients with “no-seizure” (n=128) and “recurrent-seizures” (n=88). No-seizure vs. Recurrent seizures S.No.

Genetic marker

1

2

df

Empirical P value

D10S548

7.14

3

0.067

2

D10S196

2.71

6

0.843

3

D10S1653

9.52

6

0.146

4

D11S937

11.61

9

0.235

5

D11S901

3.26

6

0.774

6

D13S218

5.13

4

0.273

7

D13S175

3.59

4

0.463

8

D20S115

0.61

3

0.894

9

D20S107

7.15

5

0.209

Global values

50.77

46

0.291

71

Chapter 4


4.2.3. Phenotype-genotype association analysis 4.2.3.1. Stage I: Functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets 4.2.3.1.1. Frequency distribution of genetic variants In the preliminary stage I analysis, out of 19 SNPs, a total of ten polymorphic functional SNPs were identified with MAF>0.05 in 400 PWE recruited for the study were also carried forward for genotype-phenotype association analysis in 216 PWE. These included CYP1A2*1F (rs762551), CYP2C9*3 (rs1057910), CYP2C19*2 (rs4244285), EPHX1 rs1051740, EPHX1 rs2234922, UGTB27*2 (rs7439366), ABCB1 rs1128503, ABCB1 rs2032582, ABCB1 rs1045642 and SCN1A rs3812718. In addition to these variants, CYP2C9*2 and CYP2C19*3 were also included in the association analysis, as these could contribute to altered AED metabolism with the other polymorphic variants of the respective genes. No significant deviations from HWE were observed in any of the polymorphic SNPs in 216 PWE (2 p-value ≥ 0.001).

4.2.3.1.2. Association analysis of genotypes with drug response We did not observe any significant difference in the distribution of the functional SNPs between the no-seizure and recurrent-seizure groups. (Figure 4.6). Furthermore, stratification of the patients by drug type in which patients treated with various drug types (CBZ, PHT, VPA and PB) were studied separately for the distribution of respective functional genetic variants failed to reveal any associations with drug response. The non-significant distribution of CYP1A2, EPHX1, UGT2B7, ABCB1 and SCN1A variants (known to influence CBZ disposition) in 99 patients treated with CBZ is shown in Figure 4.7. Similarly, non-significant distribution of CYP2C9, CYP2C19, ABCB1 and SCN1A variants (known to influence PHT disposition) in 55 patients treated with PHT is shown in Figure 4.8. In summary, we failed to find a significant genetic marker from DMEs, drug transporters and drug targets for drug response in PWE in the stage I of the study. The finding further necessitates exploring additional genetic markers influencing seizure frequency in North Indian epilepsy patients.

72

73

SCN1A rs3812718 (dom) SCN1A rs3812718 (rec)

ABCB1 rs1045642 (dom) ABCB1 rs1045642 (rec)



UGT2B7 rs7439366 (dom) UGT2B7 rs7439366 (rec)

EPHX1 rs2234922 (dom) EPHX1 rs2234922 (rec)


CYP1A2 rs762551 (dom) CYP1A2 rs762551 (rec)

Seizure type (GTCS) Age at onset (>15 yrs)

Body weight (>45 kg)

Gender (Male) Age (>20 yrs)

0.5 1.0

1.5

2.0

Univariate OR(95% CI)

2.5





UGT2B7 rs7439366 (dom) UGT2B7 rs7439366 (rec)



CYP2C19 *2,*3 (dom) CYP2C19 *2,*3 (rec)

CYP2C9 *2,*3 (dom)

CYP1A2 rs762551 (dom) CYP1A2 rs762551 (rec)

Treatment (CBZ)

Age at onset (>15 yrs)

Seizure type (GTCS)


Age (>20 yrs)

Gender (Male)

0 1

2

Univariate OR(95% CI) 3

Chapter 4 Results and Discussion

Figure 4.6: Association analysis of functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets with seizure in patients with epilepsy (n=216). All 216 control patients

Figure 4.7: Association analysis of functional genetic variants from drug metabolizing enzymes, on carbamazepine drug transporters and drug targets withPatients seizure control in epilepsy patients treated with carbamazepine (n=99).


4 2





CYP2C19 *2,*3 (dom) CYP2C19 *2,*3 (rec)

CYP2C9 *2,*3 (dom)

Age at onset (>15 yrs)

Seizure type (GTCS)


Age (>20 yrs)

Gender (Male)

0

Univariate OR(95% CI)

6

8

Chapter 4

Figure 4.8: Association analysis of functional genetic variants from drug metabolizing enzymes, drug transporters and drug targets with seizure control in epilepsy patients treated with phenytoin Patients on phenytoin (n=55).

4.2.3.2. Stage II: Functional genetic variants from estrogen metabolizing enzymes and estrogen receptors Failing to find association of genetic variants from DMEs, drug transporters and drug targets, we further scanned literature for other common reasons for seizure susceptibility that could influence drug response. We further hypothesized that inter-individual difference in AED response may be mediated by genetic variants of genes encoding proteins involved in metabolism of estrogens in women with epilepsy (WWE). Based on in-vitro and in vivo-studies showing influence of genetic polymorphism on sex steroid disposition, we further prioritized a total of 22 variants spanning nine genes encoding estrogen metabolizing enzymes (CYP1A1, CYP1A2, CYP1B1, CYP17A1, CYP19A1, SULT1A1, SULT1E1 and receptors (ESR1 and ESR2). 4.2.3.2.1. Frequency distribution of genetic variants In the stage II analysis, out of 22 SNPs, a total of 18 polymorphic functional SNPs were identified with MAF>0.05 in 400 PWE recruited for the study were also carried forward for genotype-phenotype association analysis in 216 PWE stratified by sex. These include

74

Chapter 4


estrogen metabolizing enzymes namely CYP1A1 rs2606345, CYP1A1 rs1048943, CYP1A2 rs762551 CYP1B1 rs1056836, CYP1B1 rs1800440, CYP17A1 rs743572, CYP19A1 rs936306, CYP9A1 rs11636639, CYP19A1 rs749292, CYP19A1 rs767199, CYP19A1 rs4775936, CYP19A1 rs700518, CYP19A1 rs11575899, CYP19A1 rs10046 and COMT rs4680. The polymorphic variants also included SNPs from estrogen receptors namely ESR1 rs9340799, ESR1 rs3798577 and ESR2 rs1255998. A similar allelic distribution was also observed in 100 ethnically matched healthy controls enrolled in the study. No significant deviations from HWE were observed in any of the polymorphic SNPs in MWE, WWE as well as healthy controls (2 p-value ≥ 0.001). 4.2.3.2.2. Association analysis of genotypes with drug response We observed strong association signals with drug response from CYP1A1 (rs2606345 Pdom = 6.3 X 10- 3, OR = 0.31 (95% CI = 0.18 - 0.71); Pres = 1.0 X 10-4) and ESR1 (Pres = 5.5 X 10-3, OR = 0.29 (95% CI = 0.12 - 0.71)) genes in WWE after adjusting for age, age at onset of seizures, seizure type and treatment (Table 4.6). On the other hand week association signals from COMT (rs4680 - Pdom = 1.2 x 10-2, OR = 0.29 (95% CI = 0.11 - 0.79)) and ESR2 (rs1255998 - Pdom = OR = 3.00 (95% CI = 1.03 - 8.71)) genes were also observed in MWE after adjusting for age, body weight, age at onset of seizures, seizure type and treatment. However, of all the genetic variants, CYP1A1 rs2605345 variant was the only polymorphism to retain significant association under recessive model (Pres = 1.0 X 10-4) in WWE after adjusting for Bonferroni corrections (a total of 68 tests were applied for each of the 18 polymorphic variant under dominant and recessive models in both MWE and WWE resulting in a threshold P value of 0.05/72 = 6.94 x 10-4). Our study showed predominant distribution of C alleles in WWE showing complete control of seizures with CC showing a considerably specificity of 71.4%, but with a low sensitivity of 56.1% for detecting good response in WWE (Figure 4.9). AA genotype was further observed with extremely high positive predictive value of 100.0% for predicting poor drug response in WWE with a specificity of 100%, but with a low sensitivity of 23.8%. We further failed to observe any significant differences in dose and drug levels between “no-seizure” and “recurrent-seizures” groups signifying absence of any correlation of therapeutic outcome with dosing and drug levels in WWE.

75

Chapter 4


Table 4.6: Distribution and association analysis of alleles and genotypes of genetic variants from genes encoding estrogen metabolizing enzymes and estrogen receptors in women with epilepsy (n=99) showing “no-seizure” (n=57) and “recurrent-seizures” (n=42). Women with epilepsy (n=99) Allele distribution S.No.

SNPs

Genotype distribution

Allele

No seizure n (%)

Recurrent seizures n (%)

Genotype

No seizure n (%)


Dominant model Recessive model OR (95% CI) (adjusted)*

P value (adjusted)*

CYP1A1 1

2

rs2606345

C

89 (78.1)

44 (52.4)

CC

32 (56.1)

12 (28.6)

(c.-27+606C>A)

A

25 (21.9)

40 (47.6)

CA

25 (43.9)

20 (47.6)

0.31 (0.13 - 0.73)

0.0063

AA

0 (0.0)

10 (23.8)

-

0.0001**

rs1048943

A

106 (93.0)

72 (85.7)

AA

50 (87.7)

32 (76.2)

(c.1384A>G (*2C))

G

8 (7.0)

12 (14.3)

AG

6 (10.5)

8 (19.0)

0.45 (0.16 - 1.30)

0.133

GG

1 (1.8)

2 (4.8)

0.36 (0.03 - 4.08)

0.388

CYP1A2 3

rs762551

A

59 (51.8)

44 (52.4)

AA

16 (28.1)

11 (26.2)

(g.-163A>C (*1F))

C

55 (48.2)

40 (47.6)

AC

27 (47.4)

22 (52.4)

0.91 (0.37 - 2.23)

0.836

CC

14 (24.6)

9 (21.4)

1.19 (0.46 - 3.09)

0.715

CYP1B1 4

5

rs1056836

C

88 (77.2)

69 (82.1)

CC

34 (59.6)

30 (71.4)

(c.1294C>G (*3))

G

26 (22.8)

15 (17.9)

CG

20 (35.1)

9 (21.4)

1.69 (0.72 - 3.97)

0.226

GG

3 (5.3)

3 (7.1)

0.72 (0.14 - 3.77)

0.698

rs1800440

A

95 (83.3)

66 (78.6)

AA

39 (68.4)

28 (66.7)

(c.1358A>G (*4))

G

19 (16.7)

18 (21.4)

AG

17 (29.8)

10 (23.8)

0.92 (0.39 - 2.16)

0.854

GG

1 (1.8)

4 (9.5)

0.17 (0.02 - 1.58)

0.081

TT

24 (42.1)

19 (45.2)

TC

26 (45.6)

18 (42.9)

1.14 (0.51 - 2.54)

0.756

CC

7 (12.3)

5 (11.9)

1.04 (0.31 - 3.52)

0.955

CYP17A1 6

rs743572

T

74 (64.9)

56 (66.7)

(c.-34T>C (MspAI))

C

40 (35.1)

28 (33.3)

76

Chapter 4

Results and Discussion Women with epilepsy (n=99) Allele distribution

S.No.

7

8

9

10

11

12

13

14

SNPs


Allele

No seizure n (%)


CYP19A1 rs936306 (c.-39+36415C>T)

C T

85 (74.6) 29 (25.4)

51 (60.7) 33 (39.3)

rs749292 (IVS2-23584G>A)

G A

80 (70.2) 34 (29.8)

64 (76.2) 20 (23.8)

rs11636639 (c.-27983T>G)

T G

82 (71.9) 32 (28.1)

64 (76.2) 20 (23.8)

rs767199 (c.-5278G>A)

G A

85 (74.6) 29 (25.4)

64 (76.2) 20 (23.8)

rs4775936 (c.-913G>A)

G A

82 (71.9) 32 (28.1)

58 (69.0) 26 (31.0)

rs700518 (c.240G>A)

G A

83 (72.8) 31 (27.2)

60 (71.4) 24 (28.6)

rs11575899 (c.451+26_451+27insTCT)

Del insTCT

79 (69.3) 35 (30.7)

62 (73.8) 22 (26.2)

rs10046 (c.19C>T)

C T

80 (70.2) 34 (29.8)

63 (75.0) 21 (25.0)

77

Genotype

No seizure n (%)


CC CT TT GG GA AA TT TG GG GG GA AA GG GA AA GG GA AA del.del del.insTCT insTCT.insTCT CC CT TT

31 (54.4) 23 (40.4) 3 (5.3) 28 (49.1) 24 (42.1) 5 (8.8) 30 (52.6) 22 (38.6) 5 (8.8) 29 (50.9) 27 (47.4) 1 (1.8) 29 (50.9) 24 (42.1) 4 (7.0) 29 (50.9) 25 (43.9) 3 (5.3) 23 (40.4) 33 (57.9) 1 (1.8) 27 (47.4) 26 (45.6) 4 (7.0)

16 (38.1) 19 (45.2) 7 (16.7) 26 (61.9) 12 (28.6) 4 (9.5) 26 (61.9) 12 (28.6) 4 (9.5) 25 (59.5) 14 (33.3) 3 (7.1) 19 (45.2) 20 (47.6) 3 (7.1) 19 (45.2) 22 (52.4) 1 (2.4) 25 (59.5) 12 (28.6) 5 (11.9) 23 (54.8) 17 (40.5) 2 (4.8)


P value (adjusted)*

0.52 (0.23 - 1.16) 0.28 (0.07 - 1.15)

0.109 0.063

1.68 (0.75 - 3.79) 0.91 (0.23 - 3.63)

0.207 0.898

1.46 (0.65 - 3.29) 0.91 (0.23 - 3.63)

0.358 0.898

1.42 (0.63 - 3.18) 0.23 (0.02 - 2.32)

0.393 0.178

0.80 (0.36 - 1.77) 0.98 (0.21 - 4.64)

0.579 0.981

0.80 (0.36 - 1.77) 2.28 (0.23 - 22.70)

0.579 0.472

2.17 (0.97 - 4.90) 0.13 (0.02 - 1.18)

0.059 0.055

1.35 (0.61 - 2.99) 1.51 (0.26 - 8.65)

0.467 0.642

Chapter 4


S.No.

SNPs


Allele

No seizure n (%)


rs4680

G

62 (54.4)

(c.472G>A)

A

52 (45.6)


P value (adjusted)*

Genotype

No seizure n (%)


53 (63.1)

GG

13 (22.8)

16 (38.1)

31 (36.9)

GA

36 (63.2)

21 (50.0)

2.08 (0.87 - 5.01)

0.099

AA

8 (14.0)

5 (11.9)

1.21 (0.37 - 4.00)

0.756

COMT 15

ESR1 16

17

rs9340799

A

80 (70.2)

64 (76.2)

AA

29 (50.9)

25 (59.5)

(IVS1-351A>G (XbaI))

G

34 (29.8)

20 (23.8)

AG

22 (38.6)

14 (33.3)

1.42 (0.63 - 3.18)

0.393

GG

6 (10.5)

3 (7.1)

1.53 (0.36 - 6.50)

0.563

rs3798577

C

62 (54.4)

32 (38.1)

CC

16 (28.1)

9 (21.4)

(c.1029C>T)

T

52 (45.6)

52 (61.9)

CT

30 (52.6)

14 (33.3)

0.70 (0.27 - 1.78)

0.452

TT

11 (19.3)

19 (45.2)

0.29 (0.12 - 0.71)

0.0055

ESR2 18

rs1255998

C

102 (89.5)

74 (88.1)

CC

45 (78.9)

32 (76.2)

(c.380C>G)

G

12 (10.5)

10 (11.9)

CG

12 (21.1)

10 (23.8)

0.85 (0.33 - 2.22)

0.744

GG

0 (0.0)

0 (0.0)

-

-

*OR and P-values (adjusted) adjusted for age, weight, age at onset of seizures, seizure type and treatment by multivariate logistic regression analysis. Total no. of genotype based tests - 72 (36 in men with epilepsy and 36 in women with epilepsy) **p-values A in 99 women with epilepsy

Measures of accuracy of the best genetic model Given patient with no seizures, what is the probability of genotype CC being detected?

Sensitivity – 56.1% Given patient with recurrent seizures, what is the probability of genotype CC being absent ?

Specificity – 71.4% OR – 3.20 (1.36-7.48) P-value – 6.3 x 10-3

Figure 4.9: Diagnostic performance of the genetic marker CYP1A1 rs2606345 for detecting patients with “no-seizure” in women with epilepsy (n=99).

Our preliminary finding suggests a genetic variant from CYP1A1 as one of the factors in determining drug response to first line AEDs. Since CYP1A1 is not involved in drug metabolism of any of the first line AEDs administered and we failed to observe its association with drug levels, hence its role in determining drug response by altering the serum concentration of active drug metabolites is ruled out. In summary, the female specific effect of this variant provides a strong evidence for involvement of female hormones in determining drug response in WWE, possibly by modulating seizure susceptibility. In females, E2 and Pg are the main reproductive steroids, which are also known to exhibit neuroactive properties with ability to modulate neuronal excitability. In addition, there is a considerable evidence in vitro as well as in vivo models that they play an important role in seizure pathophysiology (Scharfman & MacLusky, 2006). Furthermore CYP1A1 is one of the major metabolizing enzymes of E2 and exerts its role by reducing estrogenicity for maintaining hormonal balance in a female body. E2, being a proconvulsant, any factor that tends to raise its effective neuronal concentration might decrease threshold for neuronal excitability. Furthermore, increased risk for seizures might render drugs ineffective in such female individuals. We hypothesize that

79

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functional effect of associated variant may be attributed to decreased rate of E2 metabolism by CYP1A1 resulting in elevated neuroactive E2 levels. It is further hypothesized that abnormally high levels of neuroactive E2 in WWE might make them respond poorly to effective drug therapy. Several genetic variants have been identified in the CYP1A1 gene, which have been shown to be associated with E2 related diseases in women. Among them rs2606345 and rs1048943 (CYP1A1*2A) have been most extensively studied with indications of resultant altered enzymatic activity for E2. rs2606345C>A is an intronic SNP with a report of association of CC genotype with significantly reduced E2 serum levels compared to AC genotype in a Japanese population (Sowers et al., 2006c). E2 being a proconvulsant, reduced serum E2 levels are indicative of decreased seizure susceptibility which is consistent with our findings in which CC genotype was associated with excellent response (“no-seizure” group). Another functional variant, rs1048943A>G with Ile462Val amino acid substitution have been significantly elevated

reported with

hydroxylation activity resulting in reduced E2 serum levels

(Kisselev et al., 2005), however any association of G allele with excellent response was not observed in the present study. The basis for this event remains unclear. Furthermore, no significant association was observed when both the functional variants were contained in two SNP haplotype with rs2606345C-rs1048943G, both the alleles having been demonstrated independently with reduced serum E2 levels by earlier studies. We also checked for possible functional role of rs2606345 in influencing binding of transcription factor to its binding site that might alter promoter activity, which is responsible for regulation of CYP1A1 expression. Using “PupaSuite” tool, we observed that the associated variant was included in a 2.6kb conserved region as a part of untranslated portion of CYP1A1 gene. Further, the genomic sequence of predicted region also showed that it might be a binding site for Cartilage Oligomeric Matrix Protein 1 (COMP1) transcription factor that was also confirmed with “MATCH” tool. This finding could be indicative of allele specific effect of the associated variant on

80

Chapter 4


gene expression by affecting promoter activity. In summary, these results strongly implicates that variants from genes encoding sex hormone metabolizing enzymes might act as markers for predicting response to AED therapy in WWE. However, the low diagnostic performance of the associated marker variant further necessitates need to identify additional genetic variants from other genes.

4.2.3.3. Stage III: Genetic variants from estrogen transporters With extremely low sensitivity of the associated variant rs2606345 from CYP1A1 for detecting complete responders in WWE, we further explored the role of additional variants for role in drug response in WWE. We genotyped additional variants including those from estrogen transporters known to be over-expressed in brain-tissue of drug refractory epilepsy patients.

4.2.3.3.1. Frequency distribution of genetic variants A total of 98 SNPs including 15 functional variants spanning ABCB1, ABCC1 and ABCC2 were selected for the study. Out of 98 SNPs, a total of polymorphic 54 SNPs including seven functional variants were identified with MAF>0.05 in 400 PWE recruited for the study, and were carried forward for genotype-phenotype association analysis. A similar allelic distribution was also observed in 100 ethnically matched healthy controls enrolled in the study. No significant deviations from HWE were observed in any of the polymorphic SNPs in both epilepsy patients and healthy controls (2 p-value ≥ 0.001). For all the genes, haplotype structures were too diverse for association analysis with drug response and were examined by separating into blocks. We selected ten polymorphic variants for ABCB1 and 22 polymorphic variants for each of the ABCC1 and ABCC2 for inclusion in the haploblock structure of respective genes. Using the genotypes of 400 PWE, we defined the haploblock structure of SNPs within the regions of selected candidate genes. A total of 24 haplotypic variants (min. frequency>0.05) were identified across all the genes. LD map and block structure of the studied

81

Chapter 4


polymorphisms are presented in the (Figures 4.10-4.12). Regions of high LD were seen across all the genes. Most of the SNPs across ABCC1 gene showed very low pair-wise LDs. Block 1: rs35587-rs35592 (exon 9 to exon 10), Block 2: rs3851713-rs2239995 (intron 18) and Block 3: rs212090-rs212093 (exon 30-Near gene region) spanning 2, 7 and 3kb respectively were observed across the gene. On the other hand, majority of ABCB1 and ABCC2 loci were in high LD respectively. The ABCB1 loci could be demarcated into 2 distinct loci with a large block 2 spanning 40kb region (rs2520464rs10276036-rs1128503-rs2235040-rs2032582) from intron 5 to exon 22. The ABCC2 loci could be decomposed largely into three discrete blocks constituting 16 (72.7%) of the 22 SNPs. Of all the blocks, block 1(rs1885301-rs2804402) in the promoter region and block 2 (rs4919395- rs2756104- rs4148385-rs2180990-rs35191126) spanning intron 1 and intron 2 , separated apart by 1.5kb region, were smaller spanning 0.5 and 6 kb regions respectively. On the other hand, block 3 (rs2804400-rs2756109-rs2273697rs2073337-rs2756114-rs3740074-rs4148394-rs2002042- rs11442349) spanned from intron 3 to intron 19 covering 35.6 kb region.

4.2.3.3.2. Association analysis of genotypes with drug response Further analysis was performed on subsets of 400 PWE. As a first step, patients showing poor compliance or incomplete follow-up data on seizure control or those showing side effects or change in treatment during the study (n=164; 41.8%) from the overall PWE set of 400 individuals were removed from further analysis. As a second step, patients were differentiated on the basis of gender to investigate role of gender on drug response as both the genders showed on considerable differences in the type of medications prescribed. Distribution of all the 54 polymorphic SNPs was in conformance with HWE in both the genders for “no-seizure” as well as “recurrentseizures” groups (2 p-value ≥ 0.001). The allele and genotype frequencies found at each of these loci along with their respective association analysis are listed for women in Table 4.7.

82

Chapter 4


Figure 4.10: Haplotype block structure of ABCB1 gene in patients with epilepsy (n=400). The figures show the output of the Haploview linkage disequilibrium (LD) plot, where each square (with D’-values written within the box) represents a pairwise LD relationship between the two single nucleotide polymorphisms (SNPs). Red squares indicate statistically significant LD between the pair of SNP as measured by D’ up to a maximum of 1. White squares indicate pairwise D’-values less than 1 with no statistically significant evidence of LD. (Each unit of D’ shown in multiples of 100).

83

Chapter 4


Figure 4.11: Haplotype block structure of ABCC1 gene in patients with epilepsy (n=400). The figures show the output of the Haploview linkage disequilibrium (LD) plot, where each square (with D’-values written within the box) represents a pairwise LD relationship between the two single nucleotide polymorphisms (SNPs). Red squares indicate statistically significant LD between the pair of SNP as measured by D’ up to a maximum of 1. White squares indicate pairwise D’-values less than 1 with no statistically significant evidence of LD. (Each unit of D’ shown in multiples of 100).

84

Chapter 4


Figure 4.12: Haplotype block structure of ABCC2 gene in patients with epilepsy (n=400). The figures show the output of the Haploview linkage disequilibrium (LD) plot, where each square (with D’-values written within the box) represents a pairwise LD relationship between the two single nucleotide polymorphisms (SNPs). Red squares indicate statistically significant LD between the pair of SNP as measured by D’ up to a maximum of 1. White squares indicate pairwise D’-values less than 1 with no statistically significant evidence of LD. (Each unit of D’ shown in multiples of 100). Right Bottom of Haploview representation shows the haplotypic frequencies and strength of linkage between adjacent haplotype blocks.

85

Chapter 4


Table 4.7: Distribution and association analysis of alleles and genotypes of genetic variants encoding estrogen transporters in women with epilepsy (n=99) showing “no-seizure” (n=57) and “recurrent-seizures” (n=42). Women with epilepsy (n=99) Allele distribution S.No.


Genotype

No seizure (n=57) n (%)

Recurrent seizures (n=42) n (%)

75 (93.8)

AA

47 (90.4)

35 (87.5)

5 (4.8)

5 (6.3)

AG

5 (9.6)

5 (12.5)

GG

0 (0.0)

0 (0.0)

A

98 (96.1)

66 (94.3)

AA

47 (92.2)

31 (88.6)

T

4 (3.9)

4 (5.7)

AT

4 (7.8)

4 (11.4)

TT

0 (0.0)

0 (0.0)

SNPs Allele

No seizure n (%)


rs2188531

A

99 (95.2)

(c.-331+14474A>G)

G

rs7790772 (c.-331+49254A>T)

Dominant model OR (95% CI) (adjusted)*

P value (adjusted)*

0.81 (0.20-3.15)

0.763

0.69 (0.15-3.07)

0.628

0.64 (0.21-1.96)

0.442

0.56 (0.22-1.45)

0.237

0.48 (0.19-1.24)

0.132

0.51 (0.20-1.33)

0.173

ABCB1 1

2

3

4

5

6

rs1989830

C

72 (85.7)

47 (83.9)

CC

32 (76.2)

19 (67.9)

(c.287-6124C>T)

T

12 (14.3)

9 (16.1)

CT

8 (19.0)

9 (32.1)

TT

2 (4.8)

0 (0.0)

rs2520464

A

62 (54.4)

46 (54.8)

AA

19 (33.3)

9 (21.4)

(c.287-1547A>G)

G

52 (45.6)

38 (45.2)

AG

24 (42.1)

28 (66.7)

GG

14 (24.6)

5 (11.9)

rs10276036

C

68 (59.6)

45 (53.6)

CC

21 (36.8)

9 (21.4)

(c.1000-44C>T)

T

46 (40.4)

39 (46.4)

CT

26 (45.6)

27 (64.3)

TT

10 (17.5)

6 (14.3)

rs1128503

T

68 (59.6)

45 (53.6)

TT

20 (35.1)

9 (21.4)

(c.1236T>C)

C

46 (40.4)

39 (46.4)

TC

28 (49.1)

27 (64.3)

CC

9 (15.8)

6 (14.3)

86

Chapter 4


S.No.

7

8

9

10


Genotype



69 (82.1)

GG

45 (78.9)

28 (66.7)

12 (10.5)

15 (17.9)

GA

12 (21.1)

13 (31.0)

AA

0 (0.0)

1 (2.4)

T/A

76 (71.6)

52 (65.0)

TT/TA/AA

25 (47.1)

15 (37.5)

(c.2677T/A>G)

G

30 (28.4)

28 (35.0)

TG/GA

26 (49.0)

22 (55.0)

GG

2 (3.7)

3 (7.5)

rs1045642

T

72 (63.2)

49 (58.3)

TT

23 (40.4)

14 (33.3)

(c.3435T>C)

C

42 (36.8)

35 (41.7)

TC

26 (45.6)

21 (50.0)

CC

8 (14.0)

7 (16.7)

rs2235048

C

84 (73.7)

51 (60.7)

CC

32 (56.1)

15 (35.7)

(c.3489+80C>T)

T

30 (26.3)

33 (39.3)

CT

20 (35.1)

21 (50.0)

TT

5 (8.8)

6 (14.3)

SNPs Allele

No seizure n (%)


rs2235040

G

102 (89.5)

(c.2481+24G>A)

A

rs2032582


P value (adjusted)*

0.53 (0.20-1.35)

0.186

0.85 (0.34-2.07)

0.721

0.68 (0.28-1.63)

0.391

0.43 (0.18-1.02)

0.057

0.69 (0.21 -2.30)

0.553

0.90 (0.37 -2.18)

0.819

0.38 (0.14 -1.04)

0.060

ABCC1 11

12

13

rs504348

C

98 (92.5)

71 (88.8)

CC

45 (84.9)

32 (80.0)

(rs50438C>G)

G

8 (7.5)

9 (11.3)

CG

8 (15.1)

7 (17.5)

GG

0 (0.0)

1 (2.5)

rs215106

A

80 (76.9)

60 (76.9)

AA

30 (57.7)

22 (56.4)

(c.48+3886A>G)

G

24 (23.1)

18 (23.1)

AG

20 (38.5)

16 (41.0)

GG

2 (3.8)

1 (2.6)

rs215049

G

81 (84.4)

53 (71.6)

GG

35 (72.9)

20 (54.1)

(c.48+27112G>C)

C

15 (15.6)

21 (28.4)

GC

11 (22.9)

13 (35.1)

CC

2 (4.2)

4 (10.8)

87

Chapter 4


S.No.

14

15

16

17

18

19

20

SNPs


Genotype



69 (86.3)

CC

40 (75.5)

30 (75.0)

11 (13.8)

CG

13 (24.5)

9 (22.5)

GG

0 (0.0)

1 (2.5)

Allele

No seizure n (%)


rs246220

C

93 (87.7)

(c.49-19545C>G)

G

13 (12.3)

rs119774

G

93 (89.4)

71 (91.0)

GG

42 (80.8)

33 (84.6)

(c.49-14840G>A)

A

11 (10.6)

7 (9.0)

GA

9 (17.3)

5 (12.8)

AA

1 (1.9)

1 (2.6)

rs246217

C

92 (88.5)

67 (85.9)

CC

40 (76.9)

29 (74.4)

(c.49-11319C>A)

A

12 (11.5)

11 (14.1)

CA

12 (23.1)

9 (23.1)

AA

0 (0.0)

1 (2.6)

rs2014800

C

72 (67.9)

45 (57.7)

CC

23 (43.4)

13 (33.3)

(c.49-1707C>T)

T

34 (32.1)

33 (42.3)

CT

26 (49.1)

19 (48.7)

TT

4 (7.5)

7 (17.9)

rs4781712

A

74 (71.2)

48 (60.0)

AA

24 (46.2)

15 (37.5)

(c.226-401A>G)

G

30 (28.8)

32 (40.0)

AG

26 (50.0)

18 (45.0)

GG

2 (3.8)

7 (17.5)

rs246240

A

94 (88.7)

68 (89.5)

AA

41 (77.4)

31 (81.6)

(c.616-7942A>G)

G

12 (11.3)

8 (10.5)

AG

12 (22.6)

6 (15.8)

GG

0 (0.0)

1 (2.6)

rs924135

A

56 (66.7)

40 (54.1)

AA

18 (42.9)

11 (29.7)

(c.616-3507A>T)

T

28 (33.3)

34 (45.9)

AT

20 (47.6)

18 (48.6)

TT

4 (9.5)

8 (21.6)

88


P value (adjusted)*

0.90 (0.33 -2.46)

0.851

1.29 (0.40 -4.12)

0.662

0.89 (0.32 -2.45)

0.828

0.72 (0.28 -1.83)

0.497

0.84 (0.32 -2.21)

0.732

1.32 (0.42 -4.11)

0.628

0.72 (0.25 -2.06)

0.543

Chapter 4


S.No.

21

22

23

24

25

26

27

SNPs


Genotype



60 (76.9)

CC

39 (76.5)

22 (56.4)

18 (23.1)

CA

7 (13.7)

16 (41.0)

AA

5 (9.8)

1 (2.6)

TT

37 (64.9)

24 (57.1)

Allele

No seizure n (%)


rs903880

C

85 (83.3)

(c.809+54C>A)

A

17 (16.7)

rs35587

T

92 (80.7)

64 (76.2)

(c.1062T>C)

C

22 (19.3)

20 (23.8)

TC

18 (31.6)

16 (38.1)

CC

2 (3.5)

2 (4.8)

rs35592

T

91 (79.8)

64 (76.2)

TT

35 (61.4)

24 (57.1)

(c.1219-176T>C)

C

23 (20.2)

20 (23.8)

TC

21 (36.8)

16 (38.1)

CC

1 (1.8)

2 (4.8)

rs35597

G

56 (62.2)

38 (59.4)

GG

18 (40.0)

11 (34.4)

(c.1678-3979G>A)

A

34 (37.8)

26 (40.6)

GA

20 (44.4)

16 (50.0)

AA

7 (15.6)

5 (15.6)

rs35621

C

96 (90.6)

70 (87.5)

CC

43 (81.1)

30 (75.0)

(c.1913-1575C>T)

T

10 (9.4)

10 (12.5)

CT

10 (18.9)

10 (25.0)

TT

0 (0.0)

0 (0.0)

rs3851713

A

74 (64.9)

55 (65.5)

AA

23 (40.4)

20 (47.6)

(c.2644+428A>T)

T

40 (35.1)

29 (34.5)

AT

28 (49.1)

15 (35.7)

TT

6 (10.5)

7 (16.7)

rs2239995

G

80 (70.2)

56 (66.7)

GG

29 (50.9)

21 (50.0)

(c.2645-3919G>A)

A

34 (29.8)

28 (33.3)

GA

22 (38.6)

14 (33.3)

AA

6 (10.5)

7 (16.7)

89


P value (adjusted)*

0.40 (0.15 -1.04)

0.061

0.74 (0.32-1.72)

0.490

0.84 (0.36-1.96)

0.703

0.94 (0.35 -2.53)

0.908

0.64 (0.22 -1.81)

0.403

1.10 (0.46-2.63)

0.814

0.86 (0.36-2.05)

0.738

Chapter 4


S.No.

28

29

30

31

32


Genotype



49 (68.1)

GG

22 (42.3)

18 (50.0)

37 (35.6)

23 (31.9)

GA

23 (44.2)

13 (36.1)

AA

7 (13.5)

5 (13.9)

G

63 (59.4)

36 (46.2)

GG

18 (34.0)

8 (20.5)

(c.3079+62G>A)

A

43 (40.6)

42 (53.8)

GA

27 (50.9)

20 (51.3)

AA

8 (15.1)

11 (28.2)

rs2299670

A

59 (59.0)

47 (65.3)

AA

17 (34.0)

15 (41.7)

(c.3819+1090A>G)

G

41 (41.0)

25 (34.7)

AG

25 (50.0)

17 (47.2)

GG

8 (16.0)

4 (11.1)

rs212090

T

71 (62.3)

54 (64.3)

TT

19 (33.3)

18 (42.9)

(c.5462T>A)

A

43 (37.7)

30 (35.7)

TA

33 (57.9)

18 (42.9)

AA

5 (8.8)

6 (14.3)

rs212093

G

59 (51.8)

47 (56.0)

GG

12 (21.1)

14 (33.3)

(rs212093G>A)

A

55 (48.2)

37 (44.0)

GA

35 (61.4)

19 (45.2)

AA

10 (17.5)

9 (21.4)

SNPs Allele

No seizure n (%)


rs11864374

G

67 (64.4)

(c.2871+1155G>A)

A

rs3887893


P value (adjusted)*

1.06 (0.41 -2.71)

0.900

0.54 (0.19 -1.51)

0.243

1.81 (0.68-4.81)

0.229

1.45 (0.61-3.44)

0.393

1.75 (0.68-4.51)

0.240

5.64 (2.24-14.22)

0.00024**

5.64 (2.24-14.22)

0.00024**

ABCC2 33

34

rs1885301

G

60 (52.6)

67 (79.8)

GG

15 (26.3)

27 (64.3)

(c.-1549G>A)

A

54 (47.4)

17 (20.2)

GA

30 (52.6)

13 (31.0)

AA

12 (21.1)

2 (4.8)

rs2804402

A

60 (52.6)

67 (79.8)

AA

15 (26.3)

27 (64.3)

(c.-1019A>G)

G

54 (47.4)

17 (20.2)

AG

30 (52.6)

13 (31.0)

GG

12 (21.1)

2 (4.8)

90

Chapter 4


S.No.

35

36

37

38

39

40

41


Genotype



68 (89.5)

CC

26 (55.3)

30 (78.9)

23 (24.5)

8 (10.5)

CT

19 (40.4)

8 (21.1)

TT

2 (4.3)

0 (0.0)

G

60 (52.6)

67 (79.8)

GG

15 (26.3)

27 (64.3)

(c.33+329G>A)

A

54 (47.4)

17 (20.2)

GA

30 (52.6)

13 (31.0)

AA

12 (21.1)

2 (4.8)

rs2756104

C

61 (53.5)

69 (82.1)

CC

15 (26.3)

29 (69.0)

(c.34-339C>T)

T

53 (46.5)

15 (17.9)

CT

31 (54.4)

11 (26.2)

TT

11 (19.3)

2 (4.8)

rs4148385

C

63 (55.3)

65 (77.4)

CC

16 (28.1)

26 (61.9)

(c.207+3639C>A)

A

51 (44.7)

19 (22.6)

CA

31 (54.4)

13 (31.0)

AA

10 (17.5)

3 (7.1)

rs2180990

C

60 (52.6)

67 (79.8)

CC

15 (26.3)

27 (64.3)

(c.208-3017C>G)

G

54 (47.4)

17 (20.2)

CG

30 (52.6)

13 (31.0)

GG

12 (21.1)

2 (4.8)

SNPs Allele

No seizure n (%)


rs717620

C

71 (75.5)

(c.-24C>T)

T

rs4919395

rs35191126

G

60 (52.6)

67 (79.8)

GG

15 (26.3)

27 (64.3)

(c.208-2458_2082457G>del)

Del

54 (47.4)

17 (20.2)

G.del

30 (52.6)

13 (31.0)

del.del

12 (21.1)

2 (4.8)

rs4148389

A

50 (50.0)

62 (79.5)

AA

10 (20.0)

24 (61.5)

(c.208-2080A>G)

G

50 (50.0)

16 (20.5)

AG

30 (60.0)

14 (35.9)

GG

10 (20.0)

1 (2.6)

91


P value (adjusted)*

3.07 (1.12 -8.37)

0.028

5.64 (2.24-14.22)

0.00024**

7.04 (2.71-18.29)

0.000062**

4.35 (1.78-10.61)

0.00125

5.64 (2.24-14.22)

0.00024**

5.64 (2.24-14.22)

0.00024**

7.57 (2.65 -21.65)

0.00015**

Chapter 4


S.No.

42

43

44

45

46

47

48

SNPs


Genotype



65 (77.4)

CC

15 (26.3)

26 (61.9)

19 (22.6)

CT

32 (56.1)

13 (31.0)

TT

10 (17.5)

3 (7.1)

TT

22 (38.6)

11 (26.2)

Allele

No seizure n (%)


rs2804400

C

62 (54.4)

(c.334-49C>T)

T

52 (45.6)

rs2756109

T

73 (64.0)

44 (52.4)

(c.868-218T>G)

G

41 (36.0)

40 (47.6)

TG

29 (50.9)

22 (52.4)

GG

6 (10.5)

9 (21.4)

rs2273697

G

94 (82.5)

52 (61.9)

GG

40 (70.2)

19 (45.2)

(c.1249G>A)

A

20 (17.5)

32 (38.1)

GA

14 (24.6)

14 (33.3)

AA

3 (5.3)

9 (21.4)

rs2073337

A

59 (51.8)

69 (82.1)

AA

14 (24.6)

29 (69.0)

(c.1668+148A>G)

G

55 (48.2)

15 (17.9)

AG

31 (54.4)

11 (26.2)

GG

12 (21.1)

2 (4.8)

rs2756114

T

58 (50.9)

67 (79.8)

TT

14 (24.6)

27 (64.3)

(c.1816-408T>C)

C

56 (49.1)

17 (20.2)

TC

30 (52.6)

13 (31.0)

CC

13 (22.8)

2 (4.8)

rs3740074

T

56 (49.1)

69 (82.1)

TT

12 (21.1)

29 (69.0)

(c.1967+169T>C)

C

58 (50.9)

15 (17.9)

TC

32 (56.1)

11 (26.2)

CC

13 (22.8)

2 (4.8)

rs4148394

A

91 (79.8)

63 (75.0)

AA

36 (63.2)

24 (57.1)

(c.1968-432A>C)

C

23 (20.2)

21 (25.0)

AC

19 (33.3)

15 (35.7)

CC

2 (3.5)

3 (7.1)

92


P value (adjusted)*

4.82 (1.96-11.85)

0.00062

0.50 (0.20-1.24)

0.133

0.34 (0.14-0.82)

0.016

7.34 (2.87-18.78)

0.000031**

6.31 (2.48-16.02)

0.00010**

8.96 (3.42-23.49)

0.0000082*

0.74 (0.31-1.77)

0.502

Chapter 4


S.No.

49

50

51

52

53

54

SNPs


Genotype



63 (75.0)

TT

35 (61.4)

24 (57.1)

21 (25.0)

TC

20 (35.1)

15 (35.7)

CC

2 (3.5)

3 (7.1)

Allele

No seizure n (%)


rs2002042

T

90 (78.9)

(c.2621-2133T>C)

C

24 (21.1)

rs11442349

T

62 (54.4)

67 (79.8)

TT

16 (28.1)

27 (64.3)

(c.2621-849_2621848T>del)

Del

52 (45.6)

17 (20.2)

T.del

30 (52.6)

13 (31.0)

del.del

11 (19.3)

2 (4.8)

rs3758395

T

81 (79.4)

61 (78.2)

TT

31 (60.8)

26 (66.7)

(c.3741+154T>C)

C

21 (20.6)

17 (21.8)

TC

19 (37.3)

9 (23.1)

CC

1 (2.0)

4 (10.3)

rs3740066

C

61 (57.5)

64 (82.1)

CC

18 (34.0)

26 (66.7)

(c.3972C>T)

T

45 (42.5)

14 (17.9)

CT

25 (47.2)

12 (30.8)

TT

10 (18.9)

1 (2.6)

rs3740065

A

81 (79.4)

59 (79.7)

AA

31 (60.8)

25 (67.6)

(c.4146+154A>G)

G

21 (20.6)

15 (20.3)

AG

19 (37.3)

9 (24.3)

GG

1 (2.0)

3 (8.1)

rs3740063

T

59 (59.0)

58 (82.9)

TT

18 (36.0)

24 (68.6)

(c.4508+170T>C)

C

41 (41.0)

12 (17.1)

TC

23 (46.0)

10 (28.6)

CC

9 (18.0)

1 (2.9)


P value (adjusted)*

0.80 (0.33-1.90)

0.609

5.04 (2.04-12.89)

0.00046

1.43 (0.57 -3.60)

0.448

4.33 (1.69 -11.05)

0.0022

1.43 (0.55 -3.72)

0.459

3.88 (1.46 -10.29)

0.0063

*OR and P-values (adjusted) adjusted for age, weight, age at onset of seizures, seizure type and treatment by multivariate logistic regression analysis. Total no. of genotype based tests - 108; Subgroup analysis in WWE on CBZ for ABCC2 SNPs (Data not shown here)- 22. **p-values A (rs1885301) and c.-1019A>G (rs2804402), both of which were in perfect LD with each other in WWE (p=2.4x10-3, OR=5.64 (2.24-14.22). In addition to these variants, we also observed significant association of 8 other SNPs, none of which has been reported for any functional activity including an intronic SNP - rs3740074 for which most significant distribution was

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observed with p-value of 1.5x10-5 under a dominant genotypic model. For this variant SNP, women with “no-seizure” were more likely to have variant genotypes (CC+CT) as compared to women with “recurrent-seizures” with an OR of 13.2 (95% CI=4.2045.50) after adjusting for age, body weight, treatment, seizure type, age at onset of epilepsy and. Treatment. With a response rate of 0.57 in the present study, genotype relative risk varying from 2.06 to 2.65 and MAF ranging from 0.35 to 0.37 for associated genetic variants (10 SNPs), post-hoc power analysis revealed that our study had a minimal power of 95.9% (for rs1885301 and rs2804402) and maximal power of 99.7% (for rs3740074) to detect association with drug response in WWE with a minimum significance level of 0.05 under a dominant model. Further, we correlated dose and drug levels of CBZ with drug response in these 74 WWE. We failed to observe any significant differences in dose and drug levels between “no-seizure” and “recurrent-seizures” groups signifying absence of any correlation of therapeutic outcome with dosing and drug levels in WWE maintained on CBZ monotherapy. In an attempt to achieve a homogeneous phenotype maintained on CBZ, we further analyzed seizure control in 46 WWE diagnosed with GTCS and observed a similar pattern in the association analysis. 4.2.3.3.3. Association analysis of haplotypes and diplotypes with drug response Haplotypes for each of the blocks were separately analyzed for “no-seizure” as well as “recurrent-seizures” groups in both the genders. The haplotype frequencies found at each of these blocks along with respective association analysis with drug response are listed in Table 4.8. None of the haplotypic variants across all the genes were associated with drug response in MWE. Consistent with the distribution of SNPs in ABCC2, haplotypic variants across all the three haplotype blocks across ABCC2 loci showed significant distribution in WWE. For both block 1 and block 2, two haplotypic variants were most frequently observed (>5%) with major alleles for all the SNPs present in one variant as opposed to minor alleles in the other.

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Table 4.8: Distribution and association analysis of haplotypic block variants of genes encoding estrogen transporters with seizure control in men with epilepsy (n=117) and women with epilepsy (n=99).

S.No.

H1 H2 H3 H4 H5

H6 H7 H8

H9 H10

H11 H12 H13

Haplotype blocks


Men with epilepsy (n=117) Recurrent seizures OR (95% CI) (n=46) n (%)

ABCB1 Block1: rs2520464-rs10276036-rs1128503-rs2235040-rs2032582 (Intron 5-Exon 22) ACTGT 83 (58.5) 54 (58.7) 0.98 (0.56-1.74) GCTGT 2 (1.4) 1 (1.1) 1.30 (0.06-77.51) GTCAG 17 (12.0) 17 (18.5) 0.60 (0.27-1.33) GTCGG 26 (18.3) 12 (13.0) 1.49 (0.67-3.44) GTCGT 7 (4.9) 5 (5.4) 0.90 (0.23-3.72) a Others 7 (4.9) 3 (3.3) Block2: rs1045642-rs2235048 (Exon 27 to Intron 27) CC 0 (0.0) 3 (3.3) CT 48 (33.8) 36 (39.1) TC 90 (63.4) 52 (56.5) Othersa 4 (2.8) 1 (1.1) ABCC1 Block1: rs35587-rs35592 (Exon 9 to Exon 10) CC 17 (12.0) 14 (15.2) TT 123 (86.6) 76 (82.6) Othersa 2 (1.4) 2 (2.2) Block2: rs3851713-rs2239995 (Intron 18) AG 94 (66.2) 59 (64.1) TA 45 (31.7) 31 (33.7) TG 2 (1.4) 2 (2.2) a Others 1 (0.7) 0 (0.0)

P-value

Women with epilepsy (n=99) Recurrent No seizure seizures (n=57) OR (95% CI) (n=42) n (%) n (%)

P value

0.970 0.830 0.167 0.286 0.864

58 (50.9) 8 (7.0) 10 (8.8) 19 (16.7) 13 (11.4) 6 (5.3)

44 (52.4) 1 (1.2) 14 (16.7) 14 (16.7) 9 (10.7) 2 (2.4)

0.94 (0.51-1.72) 6.26 (0.80-281.19) 0.48 (0.18-1.24) 1.00 (0.44-2.31) 1.07 (0.39-3.00)

0.834 0.051 0.092 1.000 0.878

0.79 (0.44-1.42) 1.33 (0.75-2.35)

0.406 0.294

10 (8.8) 28 (24.6) 74 (64.9) 2 (1.8)

0 (0.0) 33 (39.3) 51 (60.7) 0 (0.0)

0.50 (0.26-0.97) 1.19 (0.64-2.23)

0.026 0.545

0.75 (0.33-1.76) 1.36 (0.61-2.98)

0.474 0.400

21 (18.4) 90 (79.0) 3 (2.6)

19 (22.6) 63 (75.0) 2 (2.4)

0.77 (0.36-1.65) 1.25 (0.60-2.56)

0.467 0.512

1.09 (0.60-1.96) 0.91 (0.50-1.66) 0.64 (0.04-9.03)

0.745 0.749 0.659

73 (64.0) 33 (29.0) 7 (6.1) 1 (0.9)

55 (65.5) 28 (33.3) 1 (1.2) 0 (0.0)

0.93 (0.49-1.76) 0.81 (0.42-1.56) 5.42 (0.67-247.57)

0.834 0.508 0.080

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S.No.


Haplotype blocks


Men with epilepsy (n=117) Recurrent seizures OR (95% CI) (n=46) n (%)

P-value

Women with epilepsy (n=99) Recurrent No seizure seizures (n=57) OR (95% CI) (n=42) n (%) n (%)

P value

Block3: rs212090-rs212093 (Exon 30-Near gene region) H14

AA

47 (33.1)

25 (27.2)

1.32 (0.71-2.47)

0.337

43 (37.7)

30 (35.7)

1.09 (0.58-2.04)

0.772

H15

TA

7 (4.9)

10 (10.9)

0.42 (0.13-1.29)

0.087

12 (10.5)

7 (8.3)

1.29 (0.44-4.06)

0.604

H16

TG

87 (61.3)

57 (62.0)

0.97 (0.54-1.72)

0.915

59 (51.8)

47 (56.0)

0.84 (0.46-1.54)

0.558

1 (0.7)

0 (0.0)

0 (0.0)

0 (0.0)

Othersa ABCC2

Block1: rs1885301-rs2804402 (Upstream region) H17

AG

59 (41.5)

34 (37.0)

1.21 (0.68-2.15)

0.483

54 (47.4)

17 (20.2)

3.54 (1.78-7.22)

0.000083*

H18

GA

83 (58.5)

56 (60.9)

0.90 (0.51-1.59)

0.712

60 (52.6)

67 (79.8)

0.28 (0.13-0.56)

0.000083*

0 (0.0)

2 (2.1)

0 (0.0)

0 (0)

Othersa

Block2: rs4919395- rs2756104- rs4148385-rs2180990-rs35191126 (Intron1to Intron 2) H19

ATAGdel

56 (39.4)

33 (35.9)

1.12 (0.63-2.01)

0.672

50 (43.9)

15 (17.9)

3.59 (0.76-7.54)

0.00012*

H20

GCCCG

81 (57.0)

55 (59.8)

0.89 (0.50-1.57)

0.678

59 (51.8)

65 (77.4)

0.31 (0.15-0.61)

0.00023*

Othersa

5 (3.6)

4 (4.3)

5 (4.3)

4 (4.7)

Block3: rs2804400-rs2756109-rs2273697-rs2073337-rs2756114- rs3740074-rs4148394-rs2002042- rs11442349 (Intron 3 to Intron 19) H21

TTGGCCACdel

54 (38.0)

32 (34.8)

1.15 (0.64-2.06)

0.615

48 (42.1)

15 (17.9)

3.34 (1.64-7.03)

0.00029*

H22

CGGATTACT

14 (9.9)

11 (12.0)

0.80 (0.32-2.06)

0.611

14 (12.3)

16 (19.0)

0.59 (0.25-1.40)

0.189

H23

CGGATTCTT

21 (14.8)

14 (15.2)

0.55 (0.22-1.36)

0.153

23 (20.2)

20 (23.8)

0.80 (0.38-1.69)

0.539

H24

CTAATTACT

43 (30.3)

20 (21.7)

1.56 (0.81-3.05)

0.150

16 (14.0)

26 (30.9)

0.36 (0.16-0.77)

0.004*

Othersa

10 (7.0)

15 (16.3)

13 (11.4)

7 (8.4)

*p-values 5%. Among these variants, haplotypic variant H21 rs2804400T-rs2756109T-rs2273697Grs2073337G-rs2756114C-rs3740074C-rs4148394A-rs2002042C-rs11442349del was the most frequently observed marker and was also most significantly associated with drug response (OR=3.34, 1.64-7.03; p=2.9x10-4). We further analyzed distribution of most significant haplotypic block 1 containing functional variants with drug response in WWE when present in a diplotype combination. Diplotype analysis for block 1 containing functional variants from the promoter region revealed a high adjusted OR of 5.64 (95% CI=2.23-14.22; p=2.4x10-4) in women showing no seizures in the presence of H17/H17 and H17/H18 diplotype compared to those bearing H18/H18 diplotype (Table 4.9). Table 4.9: Multivariate logistic regression analysis of ABCC2 gene promoter diplotype with drug response in women with epilepsy (n=99). Variables

OR*

95% CI

P value

Diplotype**

5.64

(2.23-14.22)

0.00024

Age

0.62

(0.21-1.81)

0.391

Weight

2.19

(0.76-6.27)

0.144

Age at onset

1.17

(0.42-3.3`)

0.753

Type of seizures

1.86

(0.72-4.75)

0.193

Treatment

1.24

(0.44-3.45)

0.674

*OR=Odds ratio for Good response Coding scheme for different variables **Diplotype (H17/H17=1, H17/H18=1, H18/H18=0); Treatment (CBZ=1, others=0); Age (Above 20 yrs=1, others=0); Age at onset (Aove 15 yrs=1, others=0); Seizure type (GTCS=1, others=0); Weight (Above 45kg=1, others=0)

Similar trend was observed for WWE diagnosed with GTCS [n=65; OR=8.16 (2.3-28.6); p=0.001] and WWE on CBZ [n=74; OR=4.97 (1.7-14.3); p=0.0029] (Figure 4.13). As diplotype analysis is expected to reveal more relevant and detailed information, we analyzed its distribution in men as well as both the genders together. Expectedly, we failed to observe any significant distribution in both the analyses. Further, after inclusion

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of only those patients who were diagnosed with GTCS, the differences of diplotype frequency between no-seizure and recurrent-seizure groups remained non-significant. This trend of non-significance also applied to the combined groups of men and women treated with CBZ. Although a similar distribution was observed in men treated with CBZ, limited number of subjects with each genotype eluded us from interpreting it statistically.

All epilepsy patients 1.19

All (n=216)

2.14

3.82

1.28

2.78

On CBZ treatment (n=99)

1.13

2.74

On CBZ treatment and diagnosed with GTCS (n=55)

1.12

Diagnosed with GTCS (n=138)

p=0.010 6.04

p=0.0090

6.62

p=0.024

3.86

13.22

p=0.031

Women with epilepsy 2.23

All (n=99)

5.61

4.97

1.72

On CBZ treatment (n=74)

1.31

On CBZ treatment and diagnosed with GTCS (n=46)

p=0.00024 *

8.16

2.32

Diagnosed with GTCS (n=65)

14.13

5.22

28.63 14.36 20.86

p=0.0010 * p=0.0029 *

p=0.019

Men with epilepsy** All (n=117) Diagnosed with GTCS (n=73)

0.03

0.47

1.09 1.23

0.41

1.00

2.50

p=0.083 3.65

p=0.070

30.00

Odds ratio (95% CI)

Figure 4.13: Sub-group analysis showing odds ratio for no-seizures vs. recurrent seizures in patients with epilepsy for ABCC2 gene promoter diplotype. Odds ratio for odds of good response in patients harboring the “homozygous (-1549A)-(-1019G) haplotype and heterozygous for haplotypic combination of (-1549A)-(-1019G) and (-1549G)-(-1019A)” to odds of good response in patients homozygous for wild type (-1549G)-(-1019A) haplotype”.

In summary, we find that women show highly significant associations of haplotype blocks of considerable lengths across the ABCC2 with seizure control. Concerning the inclusion of several functionally important SNPs in the present study, the most important finding was significant association of promoter polymorphisms: c.1549G>A (rs1885301) and c.-1019A>G (rs2804402) either considered alone or in haplotype and diplotype combinations with seizure control in WWE. Owing to the

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perfect LD between both the markers, we further considered rs1885301 as a representative marker for evaluating the diagnostic performance of the region. Our study showed predominant distribution of A allele in WWE showing complete control of seizures with GG and GA collectively showing a considerably specificity of 64.2%, and a comparatively higher sensitivity of 73.6% for detecting good response in WWE (Figure 4.14). In addition, our data confirms regions of high LD and low haplotype diversity across ABCC2, and add more detailed information on the role of several functionally important genetic variants on AED response. Presence of phenotype (No seizure) Genotype and phenotype of interest for calculating diagnostic values

CC (+) CA+AA (–)

Presence of genotype (rs2606345 CC)

No seizure (+)

Recurrent seizures (–)

32 (TP)

12 (FP)

25 (FN)

30 (TN)

TP- true positive; FP- false positive TN- True negative; FN- False negative

Distribution of CYP1A1 rs2606345 C>A in 99 women with epilepsy

Measures of accuracy of the best genetic model Given patient with no seizures, what is the probability of genotype CC being detected?

Sensitivity – 56.1% Given patient with recurrent seizures, what is the probability of genotype CC being absent ?


Figure 4.14: Diagnostic performance of the genetic marker ABCC2 rs1885301 for detecting patients with “no-seizure” in women with epilepsy (n=99).

So far, most of the pharmacogenetic studies in epilepsy have focused on ABCB1 encoding PgP transporter. Numerous studies exploring role of genetic variability in ABCB1 on drug response across different ethnic groups have yielded conflicting results (Haerian et al., 2011). Further, studies exploring role of non-PgP transporters are very limited. In recent times, Ufer et al. demonstrated association of genetic polymorphism from ABCC2 with AED response in German PWE (Ufer et al., 2009; Ufer et al., 2011). However, other studies investigating role of genetic polymorphisms from genes encoding non-PgP efflux transporters mainly ABCC2, ABCG2, and ABCC5 on drug response have proved inconclusive (Kim et al., 2009; Kwan et al., 2011; Seo et al., 2008).

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These conflicting results may have been due to multi-substrate specificity of ABC transporters for conjugated estrogens in addition to AEDs. It is now well established that altered levels of these sex steroids particularly in women may lead to seizure susceptibility and ABC transporters are known to play a predominant role in transport of sex steroids (Herzog et al., 2011; Huang et al., 1998; Keppler et al., 1997; Verrotti et al., 2007). Hence, genetic variability in expression of these transporters may contribute to altered drug response possibly by modulation of transport of AEDs as well as sex steroids reaching the brain tissues. This hypothesis further gains strength from earlier findings during stage II of the study showing that genetic variability in genes encoding estrogenmetabolizing enzyme CYP1A1 may lead to altered drug response in WWE. In contrast, the role of these variants in influencing clearance of AEDs has not been well-studied which could be attributed to scanty reports on role of ABCC2 in transport of AEDs. Using ATPase assay and FACScan flow cytometer, Kim et al. in a recent article not only showed that CBZ as a substrate of ABCC2, but also suggested role of c.1249G>A (rs2273697) in altered transporter activity and predisposition to ADRs (Kim et al., 2010). Simultaneously, another report in the same year failed to show any role of ABCC2 on transport of AEDs using concentration equilibrium transport assay (Luna-Tortos et al., 2010). The conflicting reports on drug substrate studies are in contrast with a previous study showing over-expression of ABCC2 from resected brain tissues of patients with pharmacoresistant epilepsy (Dombrowski et al., 2001). However, subsequent studies examining expression of ABCC2 in the brain tissue have failed to detect its presence (Shawahna et al., 2011). High ABCC2 expression is seen in several tissues mainly liver, intestine and kidneys. So far, ABCC2 has also been shown for its role in influencing efflux of conjugated glucuronide metabolites of bilibrubin and estradiol (E217βG) from hepatocytes (Bellarosa et al., 2009; Keppler et al., 1997). Hence, ABCC2 may play an important role in maintaining systemic concentration of active metabolites of bilibrubin and E2 and may influence their respective associated physiological roles. Consistent with its role, several genetic variants from ABCC2 have been known for their role in Dubin Johnson syndrome due

101

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to impaired efflux of former metabolite. However, very little is known for their role in influencing efflux of E217βG and its possible role in influencing overall hormonal functioning in females. Adopting a block based approach may have lead to loss of information related to functionally important haplotypes in our population. Hence, as a next step, we focused our attention on influence of combination of all the five polymorphic functional SNPs namely c.-1549G>A, c.-1019A>G, c.-24C>T, c.1249G>A and c.3972C>T on drug response (Table 4.10). A total of five haplotypic variants each containing combinations of five markers were observed with more than 5% frequency namely AGCGC, AGCGT, AGTGT, GACAC and GACGC in either of phenotypic groups in MWE and WWE. Laechelt et al. recently demonstrated functional effects of combination of latter three SNPs (c.24C>T, c.1249G>A and c.3972C>T) in the haplotypes which forced us to characterize all the observed five-marker haplotypes on the basis of their influence on transport activity or expression of ABCC2 and analyzed their distribution in both the genders (Laechelt et al., 2011). To interpret the functional consequences of each of these variants we bifurcated these five-marker haplotypes into combinations of two (containing 1549G>A and c.-1019A>G) and three (c.-24C>T, c.1249G>A and c.3972C>T) marker haplotypes. Those five-marker haplotypes containing CGT and TGT combinations of last three markers were classified as low protein expressers and those containing CAC combinations were classified as high protein expressers, compared to wild type combination of CGC. Among all the commonly present haplotypes, we observed that latter three-marker haplotypes CGT and TGT combinations were always present along with initial two-marker haplotype AG. In addition, haplotype CAC was always present in combination with haplotype GA. However wild type haplotype CGC was equally distributed among AG as well as GA combination of initial two markers in the five-marker haplotypes. The distribution of three marker haplotypes in our population was in conformance with that reported by Ufer et al. in Caucasian epilepsy population (Figure 4.15).

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Table 4.10: Haplotype distribution of functional polymorphic SNPs across ABCC2 gene, and their association with seizure control in men with epilepsy (n=117) and women with epilepsy (n=99). Men with epilepsy (n=117) No.

Haplotype

Function a

No seizure n

Recurrent seizures n

No seizure n (%)


Women with epilepsy (n=99) OR (95% CI)

P-value

No seizure n

Recurrent seizures n

No seizure n (%)


OR (95% CI)

P value

rs1885301−rs2804402−rs717620−rs2273697−rs3740066 (c.-1549G>A−c.-1019A>G−c.-24C>T−c.1249G>A−c.3972C>T) H17

GACGC

37

28

Wild type H18

AGCGC

H19

AGCGT

H20

AGTGT

H21

GACAC Othersb

Low protein expressors/ efflux rate High protein expressors/ efflux rate

15

9

30

14

14

11

36

22

10

8

52 (39.4)

37 (44.0)

0.82 (0.45-1.49)

0.498

44 (33.3)

25 (29.8)

1.18 (0.62-2.23)

0.583

36 (27.3)

22 (26.2)

1.05 (0.54-2.07)

0.861

a

33

36

14

4

22

6

17

6

16

29

12

3

47 (46.1)

40 (49.4)

0.87 (0.46-1.63)

0.656

39 (38.2)

12 (14.8)

3.55 (1.63-8.10)

0.0004480

16 (15.7)

29 (35.8)

0.33 (0.15-0.70)

0.00169

Based on Laechelt et al. 2011 HaplotypesC

rs3740065A>G

rs3740066C>T

rs3758395T>C

rs2002042C>T rs11442349T>delT

rs3740074T>C rs4148394A>C

rs2073337A>G rs2756114T>C

rs2273697G>A

rs2756109T>G

rs2804400C>T

0

rs4148385C>A rs2180990C>G rs35191126G>delG rs4148389A>G

rs1885301G>A rs2804402A>G rs717620C>T rs4919395G>A rs2756104C>T

1

Exon 31 Exon 32

Exon 30

Exon 29

Exon 27 Exon 28

Exon 26

Exon 24

Exon 25

Exon 22 Exon 23

Exon 20 Exon 21

Exon 17

Exon 18 Exon 19

Exon 16

Exon 14

Exon 15

Exon 12 Exon 13

Exon 10

Exon 11

Exon 8 Exon 9

Exon 7

Exon 3 Exon 4 Exon 5 Exon 6

101611662

Exon 2

3’

101542463

Exon 1

5’

Figure 4.15: Logarithm of the P values for the multivariate genotypic association analysis of the genetic variants with seizure control in men and women with epilepsy. P-values have been adjusted for age, weight, age at onset of seizures, seizure type and treatment by multivariate logistic regression analysis. X-axis shows the physical representation of ABCC2 gene. Y-axis shows the (-logarithm) of the P values of association between genotypes and the seizure control in women with epilepsy assuming a dominant model. The significance threshold is calculated on the basis of number of statistical tests that were performed. SNPs across ABCB1, ABCC1 and ABCC2 were investigated for drug response in both the genders for seizure control. During the proces, a total of 130 statistical tests were performed leading to a Bonferroni correction of 0.05/ (130) = 0.00038. The -log of (0.00038) = 3.42 is represented by the upper line in the region.

104

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Similar to earlier results showing significant association of initial two-marker haplotypes, we observed significant distribution of five-marker haplotypes also in WWE. We observed an over-representation of low protein expresser haplotypes (AGCGT, AGTGT) in women showing completely controlled (OR=3.55(1.63-8.10), p=4.48x10-4) seizures. On other hand, high protein expresser haplotype GACAC was over-represented in women showing recurrent-seizures (OR=2.99(1.41-6.47), p=1.69 x 10-3). The distribution of five-marker haplotypes in responders and non-responders are in strong consent with the results in-vitro study by Laechelt et al. with respect to functional groupings of haplotypes. However, the distribution of initial two-marker haplotypes provide stronger prediction of drug response status in WWE, which could be attributed to presence of wild type combination of latter three marker haplotypes (CGC) in both the group of patients carrying AG as well as GA combination of initial two-markers in the five-marker haplotype. These results lay stress upon necessity for functional validation of five marker haplotype specifically those carrying wild type CGC combination of latter three SNPs. Nonetheless, our study strongly suggests that AG combination of initial two markers is associated with lower expression or activity of ABCC2 and vice-versa for the GA combination.

4.2.3.3.4. Meta-analysis of distribution of functional ABCC2 variants for predicting drug response Failing to find a common genetic marker for both the genders for predicting drug response in PWE, we further conducted a meta-analysis of the existing literature. A systematic literature search of database helped us identify a total of eight reports including the one reporting our findings as eligible studies, which included 1294 good responders and 1529 poor responders (Figure 4.16). Of all the commonly reported variants that included c.-24C>T or rs717620, c.1249G>A or rs2273697 (V417I) and c.3972C>T or rs3740066 (I1324I), we observed an overall significant association of high activity promoter variant c.-24C>T with poor drug response (TT+CT vs. CC: OR dom=1.38

(1.11-1.71), Pdom=0.004, I2 =3%; CT vs. CC: OR co-dom=1.28 (1.02-1.61), Pco-

dom=0.03,

I2 = 0%; T vs. C: OR all=1.34 (1.11-1.61), Pall=0.002, I2=35%) (Figure 4.17).

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Chapter 4


1056 records were retrieved 1. 2. 3. 4.

Embase (n=100) Medline (n=324) Web of science (n=107) Cochrane database of systematic reviews (n=8)

92 records excluded, due to 1. Duplicates across databases (n=87) 2. Conference abstracts (n=5)

Abstracts of 520 records were reviewed 159 records excluded, due to 1. Review articles (n=83) 2. Not a drug response study (n=71) 3. Non-ABC transporter polymorphism based drug response study (n=5) Full text of 90 records were reviewed

27 records excluded, due to 1. Not a ABCC2 polymorphism based drug response study (n = 27) 45 studies were included in meta-analysis

Figure 4.16: Study methodology for inclusion and exclusion of studies exploring role of genetic variants from ABCC2 in drug response in patients with epilepsy.

Study id

OR (95% CI) Weight in %

Asian Seo et al. (2008)

1.09 (0.67, 1.77)

22.01

Grover et al. (2012)

0.96 (0.51, 1.79)

14.25

Qu et al. (2012)

1.54 (1.08, 2.19)

35.09

Subtotal (I-squared = 11.8%, p = 0.322)

1.28 (0.99, 1.66)

71.35

Hilger et al. (2012)

1.08 (0.56, 2.10)

12.02

Ufer et al. (2011)

1.71 (0.73, 4.04)

6.09

Ufer et al. (2009)

2.11 (1.16, 3.85)

10.55

Subtotal (I-squared = 8.0%, p = 0.337)

1.60 (1.07, 2.37)

28.65

1.37 (1.11, 1.70)

100.00

. Caucasian

.

Overall (I-squared = 4.4%, p = 0.389)

.248 favours good response

1

4.04 favours poor response

Figure 4.17: A Forest plot showing pooled odds of poor responders with respect to odd of good responders in the presence of ABCC2 c.-24CT+TT.

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However, all the associations were lost after testing for multiple corrections. Tests for publication bias did not reveal any significant influence on the observed results (Figure 4.18). In summary, the results of our meta-analysis indirectly suggests possible role of the ABCC2 transporter at the blood brain barrier in altered drug response in PWE. Further studies are warranted in different ethnic groups to investigate the effects of other ABCC2 variants including rs1885301 and rs2804402 and perform stratified analysis on the basis of different phenotypic covariates including gender. Funnel plot with pseudo 95% confidence limits

Standard errot of log (odds ratio)

0.0

0.1

0.2

0.3

0.4 -0.5

0.0

0.5 log (odds ratio) Asian Lower CI Pooled

1.0

1.5

Caucasian Lower CI

Figure 4.18: A Funnel plot showing publication bias among studies exploring odds of poor responders with respect to odd of good responders in the presence of ABCC2 c.-24CT+TT.

Our results indicate that functionally relevant c.-1549G>A and c.-1019G>A in the promoter region are equally strong candidates for explaining response to AED due to altered ABCC2 promoter activity. Either of these variants could be directly causal to the drug response or it could be the combinatorial effect of both the variants. If only one of the variants were casual, than other displays significant association with the drug response phenotype because of strong LD between the variants. However, the significant overlap in the role of ABCC2 on transport of sex hormones and AEDs

107

Chapter 4


makes it difficult to assess biological basis of altered drug response. The overall largescale pattern of women specific association seen in this study is consistent with the literature on transport of E2 metabolites by ABCC2. Further, failure to detect differences in steady state serum AED levels as well as maintenance dose in WWE (data not shown) suggest role of functional variants on therapeutic outcome possibly by altered transport of E2 metabolite. It remains to be established clearly whether ABCC2 with consistent association of markers across its sequence, influence drug response by altering estrogenecity or permeability of CBZ, which was prescribed predominantly to women in the current study. Such demonstration will also require assessing gender specific ABCC2 expression studies in the brain tissue of epileptic and non-epileptic patients. Further, role of five marker haplotypic variants “1549G>A−c.-1019A>G−c.24C>T−c.1249G>A−c.3972C>T” in influencing permeability of sex hormones as well as specific AEDs needs to be investigated by in-vitro studies. In summary, our study suggests that ABCC2 may be a useful functional target for controlling seizures in WWE. In order to continue to make inroads into discovery of true markers of AED response, it is critical to capture all the AED as well as sex hormone transporters as a next logical step in the development and utilization of AEDs . We note that it is essential to systematically analyze genetic variants across all the genes encoding ABC transporters and understand role of underlying LD pattern on drug response in large epilepsy populations worldwide. Simply stated, pharmacogenomics of drug transporters may accelerate the ability of clinicians to enhance drug efficacy and safety.

4.3. Association analysis of genetic variants with dose and drug levels Out of 216 patients, 185 (85.6%) had data on maintenance dose. Of the 185 subjects with data for maintenance dose, drug levels were missing for 30. A total of 364 steady state samples were included in the analysis of serum drug levels. The mean for maintenance dose for CBZ, PHT, VPA and PB were 498.2±182.9 (n=84), 227.2±68.1 (n=46), 810.6±308.7 (n=33) and 100.9±38.8 (n=22) mg per day respectively. The mean

108

Chapter 4


for serum drug levels at maintenance dose for CBZ, PHT, VPA and PB were 9.4±2.4 (n=75), 13.1±5.7 (n=36), 101±19.2 (n=24), 24.6±11.1 (n=20). Men on average were prescribed slightly higher dose than women for all the drugs. A similar trend was observed for distribution of serum drug levels. Furthermore, we did not observe any significant correlation between dosing and drug levels irrespective of drug type. The correlation of CBZ dose and drug levels in 75 PWE have been shown in Figure 4.19.

Dose vs. drug levels in all the patients with epilepsy on carbamazepine (n=75) y = 0.0043x + 7.2143 R² = 0.1106

16

Serum drug levels (ug/ml)

14 12 10 8 6 4 2 0 0

200

400 600 800 Daily maintenance dose (mg)

1000

1200

Figure 4.19: A bar graph showing relationship between daily maintenance dose and serum drug levels in all the patients with epilepsy on carbamazepine (n=75).

Further examination of association between therepautic coutcome with dose and drug levels of respective drugs failed to reveal any influence of parameters of therapeutic drug monitoring on drug efficacy. The bar graph showing proportion of patients with no-seizures and dose, and drug levels of CBZ clearly demonstrates absence of any such relationship (Figures 4.20, 4.21).

109

Chapter 4


Proportion of patients with no-seizures

Dose vs. response in all the patients with epilepsy on carbamazepine (n=84) 100

80

60

40

20

0 150 (n=1)

200 (n=4)

300 (n=5)

350 400 500 600 750 (n=1) (n=29) (n=18) (n=16) (n=1)

800 (n=5)

1000 (n=3)

1100 (n=1)

Daily maintenance dose (mg)

Figure 4.20: A bar graph showing relationship between daily maintenance dose and drug response in all the patients with epilepsy on carbamazepine (n=84).

Proportion of patients with no-seizures

Drug levels vs. response in all the patietnts with epilepsy on carbamazepine (n=75) 80

60

40

20

0 Below therapeutic levels (< 6ug/ml) (n=8)

Therapeutic levels (6-12ug/ml) (n=56)

Above therapeutic levels (>12ug/ml) (n=11)

Serum drug levels (g/ml)

Figure 4.21: A bar graph showing relationship between serum drug levels and drug response in all the patients with epilepsy on carbamazepine (n=75).

110

Chapter 4


As a part of the secondary objective of the study, we further evaluated influence of functional genetic variants, known to influence AED disposition with dose and drug levels of most commonly administered first line AEDs namely CBZ and PHT in our pool of north Indian PWE who completed the one-year follow-up study. A total of 21 functional variants from 9 genes known to be involved in AED disposition that were prioritized at stage I of the drug response study were selected for the association analysis with dose and drug levels. In addition, 15 functional variants from ABCC1 and ABCC2 genes prioritized at stage III of the drug response study were also included in the analysis. Since phase I and phase II DME genes involved in disposition of PHT and CBZ different and several of the genetic variants were non-polymorphic (MAF17 mg/l) as compared to EMs (A

rs2606345C>A

GG CC

GA

AA

11 14 7 (21.1) (24.5) (12.3)

4 16 5 (7.0) (28.1) (8.7) 0 0 0 AA (0.0) (0.0) (0.0) No-seizure group CA

Genotype and phenotype of interest for calculating diagnostic values (Comparative % >1 of respective genotypes in no-seizure group with respect to recurrent seizure groups )

Measures of accuracy of the best genetic model Given patient with no seizures, what is the probability of combination marker genotypes being detected?

Sensitivity – 73.6%

rs2606345C>A

rs1885301G>A

CC

CA AA

GG

GA

AA

10 (23.8)

2 (4.7)

0 (0.0)

11 (26.2) 6 (14.3)

7 (16.7) 4 (9.5)

2 (4.7) 0 (0.0)

Given patient with recurrent seizures, what is the probability of genotype combination markers being absent ?


Recurrent-seizures group Distribution of combination genotypes of ABCC2 rs1885301 G>A and CYP1A1 rs2606345C>A in 99 women with epilepsy

Figure 5.1: Diagnostic performance of the combination of genetic markers CYP1A1 rs2606345 and ABCC2 rs1885301 for detecting patients with “no-seizure” in women with epilepsy (n=99).

In summary, we have elucidated for the first time, the role of genetic variants from estrogen metabolizing enzymes and estrogen transporters on drug response in WWE. Both the associated genetic variants from estrogen metabolizing enzyme and estrogen transporter are known to show functional influence directly or indirectly on expression or activity of respective genes. It is hypothesized that functionally relevant polymorphisms in genes encoding metabolizing enzymes and transport of these sex steroids influence drug response by predisposing WWE to seizure exacerbation. This might contribute to better understanding of role of reproductive steroids in seizure

119

Chapter 5

Summary and Conclusions

etiology, which is essential for development of modern therapeutic approaches for prevention or treatment of the disease. Despite of novel and highly significant findings reported in the present study, we must also recognize some of its strengths and limitations. The strengths include high proportion of patients with idiopathic epilepsies and monotherapy treatment. So far, most pharmacogenetic studies in the field of epilepsy have failed to exclude patients suffering from symptomatic epilepsies, which could be one of the major confounding variables in not only increasing the phenotypic heterogeneity but also adds to large inter-individual variability in therapeutic dosing regimen . Another serious limitation of pharmacogenetic studies has been inclusion of patients on polytherapy and thus ignoring the influence of dosing of one AED on the serum levels of another AED or vice versa in polytherapy patients. An attempt to address both these issues by excluding patients with symptomatic epilepsies and including monotherapy patients only adds to the strength of our study but has resulted in reduced sample size. In addition, an integrative approach with an accountability of dosing, drug levels and therapeutic outcome is lacking from the earlier studies. Single drug therapy in most of the enrolled patients offers an opportunity to evaluate seizure control for each drug with specific genetic variants. Perhaps, this would help us to understand why an individual has failed to respond to a particular drug on the basis of his genetic profile. However, at present lower sample size limits our ability to stratify patient population on the basis of medication type and other variables including age, gender and seizure type. It is the future endeavour of our lab to reach the desired number of patients in both the “noseizure” and “recurrent-seizures” groups with an accountability of all the potential confounders in the statistical analysis and interpretations In brief, the clinical findings presented in the study strongly suggest role of genetic variability on phenotypic manifestations of imbalances in the excitatory and inhibitory neurotransmission. If validated and replicated in populations from different ethnic backgrounds, these markers could aid in providing safe and efficacious treatment. Furthermore, genetic polymorphisms from estrogen pathway appear to modulate risk factors for showing recurrent seizures, despite on adequate AED treatment and may serve as useful drug targets for controlling seizures in WWE. Hence, such studies could further

120

Chapter 5

Summary and Conclusions

help in development of individualized pharmacogenetic therapies tailored according to the genetic background of the patients. Simultaneously, there is also an urgent need for conducting large-scale genetic epidemiological studies in a collaborative environment with consistency in study designs by various research groups by taking into account gene-gene and geneenvironment interactions and confounding factors including gender, age and disease etiology. The advent of high throughput genomic technologies (GWAS and exome sequencing) coupled with strong bioinformatics and statistical tools would further enhance the chances of discovering genetic markers or their combinations with high predictability for determining disease susceptibility, AED responsiveness and predisposition to side effects in epilepsy patients (Figure 5.2). Further, using an interdisciplinary approach including mRNA profiling and proteomics, such studies might be helpful for designing drugs targeting specific genes involved in AED disposition and action. In all, such a comprehensive integration of clinical evidence and methodological variability may reinvigorate reasons for optimism to the scientific community towards this naive field of “epilepsy pharmacogenomics”

Large sample size

Homogenous phenotypes

Linkage analysis (Family)

Extreme endophenotypes

Candidate genes

Univariate and multivariate regression

Disease association (Case control)

Localized genomic regions (Exome/deep sequencing)

Gene-gene interaction

Pharmacogenomics • Pharmacokinetics • Pharmacodynamics • Case studies

Gene-environment interaction Genome-wide

Functional validation

Individualized genetic prediction

Replication studies

New targets Novel drugs Gene therapy

Personalized treatment and prevention

Figure 5.2: Essential components of future pharmacogenetic studies.

121

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144

Appendices

Appendices

145

Appendices

Appendix A Compositions of buffers and reagents

Solutions for genomic DNA isolation RBC Lysis Buffer (10X): 8.20g ammonium chloride, 0.84g sodium bicarbonate and 0.37g EDTA were dissolved in autoclaved milli-Q water to a final volume of 100ml, and stored at 4C. Nuclei Lysis Buffer: 10mM Tris-HCl (pH 7.5), 400mM NaCl and 2mM Na2EDTA (pH 8.0) dissolved in milli-Q water to a final volume of 100ml, autoclaved and stored at room temperature. TE Buffer: 10mM Tris (pH 8.0), 1mM EDTA (pH 8.0) dissolved in milli-Q water to a final volume of 100ml, autoclaved and stored at 4C.

Solutions for polyethylene glycol (PEG)-sodium acetate purification PEG Solution: 13.3gm PEG (molecular weight 8000), 333μl MgCl2 (1M) and 10ml sodium acetate (pH 4.8) were dissolved in autoclaved milli-Q water to a final volume of 50ml and stored at 4C.

145

Appendices

Appendix B PCR and extension primer sequences for genotyping SNP (performed on a matrix-assisted laser desorption/ionization (MALDI)-TOF mass-spectrometer (MassArray system) employing the Spectrodesigner software (Sequenom, CA, USA) Gene

dbSNPid

Genomic location

Forward primer (FP) (5’→ 3’)

1

ABCB1

rs2188530

chr7:87332438

ACGTTGGATGGATTGAAAATTGATATTTACAG

ACGTTGGATGTTCGGTTATTACATAGACAC

ATCGTGAAATAATGAATTATGTCT

2

ABCB1

rs2188531

chr7:87328003

ACGTTGGATGGGTAAGATCACTAAGGCAGG

ACGTTGGATGTTTATTGAGTGCCTACTGTG

TGCCTACTGTGAGCCAAG

3

ABCB1

rs7790722

chr7:87293223

ACGTTGGATGGTGGAGACAGAGAATGAGAG

ACGTTGGATGAAATGAGGTATAAGGGTGGG

GAGCCAGTAGGATCATACAAAGAG

4

ABCB1

rs10264856

chr7:87262581

ACGTTGGATGGAGCGCAGGATTTGTATAAC

ACGTTGGATGGCAATCCATCCAAAATATTGT

GTAAATATTATGTGTTGTGGATTAAA

5

ABCB1

rs1978095

chr7:87251641

ACGTTGGATGATGGGTAAAGGGCATGAACG

ACGTTGGATGCATTAGTAATGAAAATGCAC

GGTTAGTAATGAAAATGCACATTTAT

6

ABCB1

rs4148731

chr7:87239329

ACGTTGGATGGTACCTTAACTTCTTTTCGAG

ACGTTGGATGTTCGAAGAGTGGGCACAAAC

GGAATTCAGAAATGTTCACTTCA

7

ABCB1

rs9282564

chr7:87229440

ACGTTGGATGACTCAAATCTCGCAACTATG

ACGTTGGATGGCAATGGAGGAGCAAAGAAG

AGAAGAACTTTTTTAAACTGAAC

8

ABCB1

rs9282565

chr7:87214875

ACGTTGGATGGTGATGTTTGACATCAGATC

ACGTTGGATGCTCATGATGCTGGTGTTTGG

GGAGAAATGACAGATATCTTTG

9

ABCB1

rs28381826

chr7:87214531

ACGTTGGATGGAAATCTCACAACAGATTCC

ACGTTGGATGGAGCCCTGTGAAGTCAGAAA

GGAACTTAATTAGAACCTTGAAT

10

ABCB1

rs1989830

chr7:87205663

ACGTTGGATGAGTGCCCAACCTGTTGTATC

ACGTTGGATGAGCTCTGTGAAACCATTTGC

CCATTTGCAAATTTTTTGAATAGG

11

ABCB1

rs2520464

chr7:87201086

ACGTTGGATGCTGAACTGTCTCTAAAACTGG

ACGTTGGATGTTGCAGTGCCTGGCACATTC

GGAATATGTCCAATAAGTATTCAGAC

12

ABCB1

rs2235023

chr7:87190452

ACGTTGGATGCTCCAAAATATTAGTTATGC

ACGTTGGATGCCCTTCAGATTGACAGTGTT

GTTTTCTTAAATAGCTTAATGGAT

13

ABCB1

rs10276036

chr7:87180198

ACGTTGGATGATGTTGCCTCGCCATTTTAA

ACGTTGGATGAAACCATCAGGCTACTGAGA

GAAGGCTACTGAGATAGTGA

14

ABCB1

rs2229109

chr7:87179809

ACGTTGGATGGTACCTTAACTTCTTTTCGAG

ACGTTGGATGTTCGAAGAGTGGGCACAAAC

GGAATTCAGAAATGTTCACTTCA

15

ABCB1

rs1128503

chr7:87179601

ACGTTGGATGTTTCTCACTCGTCCTGGTAG

ACGTTGGATGCACAGCCACTGTTTCCAACC

CTGGTAGATCTTGAAGGG

16

ABCB1

rs2235036

chr7:87175271

ACGTTGGATGGACCACCATTGTGATAGCTC

ACGTTGGATGCCACAATGACTCCATCATCG

CTCCATCATCGAAACCAG

17

ABCB1

rs2235039

chr7:87165854

ACGTTGGATGCTGAGAGTCTCATAAACAGC

ACGTTGGATGCCAGTGGTGTTTTTAGGGTC

GTCATCAAACCAACTCA

18

ABCB1

rs2235040

chr7:87165750

ACGTTGGATGATTAGTTTCATGCTGGGGTC

ACGTTGGATGATGCTGCTCAAGTTAAAGGG

CCCTTGCCTCCTTTCTACTGGT

19

ABCB1

rs2032581

chr7:87160810

ACGTTGGATGGGGTAATTACAGCAAGCCTG

ACGTTGGATGTAGAAGCATGAGTTGTGAAG

TTTGTTTTGTTTTGCAGGCT

20

ABCB1

rs2032582

chr7:87160618

ACGTTGGATGCATATTTAGTTTGACTCACC

ACGTTGGATGTGTTGTCTGGACAAGCACTG

TAGTGTGACTCACCTTCCCAG

21

ABCB1

rs7779562

chr7:87144816

ACGTTGGATGGAAGTGAAGCCAATTGTAAC

ACGTTGGATGTTTGAATGCTGCAATACTG

AGTGTATAGCTAACTCTCTC

22

ABCB1

rs2707944

chr7:87144641

ACGTTGGATGAGCTGGACCACTGTGCTCTT

ACGTTGGATGTTCAGGGACTGAGCCTGGAG

GAGCCTGGAGGTGAAGAAGG

23

ABCB1

rs2229107

chr7:87138659

ACGTTGGATGTTTGCTGCCCTCACAATCTC

ACGTTGGATGACTGCAGCATTGCTGAGAAC

CACAATCTCTTCCTGTG

24

ABCB1

rs1045642

chr7:87138645

ACGTTGGATGGCTGAGAACATTGCCTATGG

ACGTTGGATGTATGTTGGCCTCCTTTGCTG

GCCGCCTTTGCTGCCCTCAC

25

ABCB1

rs2235048

chr7:87138511

ACGTTGGATGGGTTGCTAATTTCTCTTCAC

ACGTTGGATGCAAATAAACAGCCTGGGAGC

GGAGTTTGATTTATAAGGGGCTGGT

S.No.

Reverse primer (RP) (5’→ 3’)

146

Extension primer (5’→ 3’)

Appendices Gene

dbSNPid

Genomic location


26

ABCB1

rs17064

chr7:87133470

ACGTTGGATGTCAAAGTTAAAAGCAAACAC

ACGTTGGATGTGAACTTGACTGAGGAAATG

AATGTTAAACAGATACCTCTTCA

27

ABCC1

rs504348

chr16:16043174

ACGTTGGATGCCGTTCACGTTATTTTCCCC

ACGTTGGATGACACACCCTGCGACCACTTT

GCGACCACTTTTCAAAT

28

ABCC1

rs215106

chr16:16047542

ACGTTGGATGTCTCAGAGGTGCACATCAAC

ACGTTGGATGAACTGCCACTGGAGAAGACC

GAAAGACCAGAAGGAAGC

29

ABCC1

rs215049

chr16:16070768

ACGTTGGATGATGACTTGGTGGTCAAGGGC

ACGTTGGATGACACAGCCTGGAAGTAGTAG

GGGAGAAGTAGTAGGACAGGGACT

30

ABCC1

rs246220

chr16:16082128

ACGTTGGATGAAATACCCGTCCTCAGCAAG

ACGTTGGATGGATGGTTTAGAAAGGAGAGG

GAAGACATGTGTGATGAAAAG

31

ABCC1

rs119774

chr16:16086833

ACGTTGGATGTTCTGCCTGGTATGTGCTTC

ACGTTGGATGCAGATGACATCCTAGCAGAC

TTGGAAGGGTCAGGAA

32

ABCC1

rs246217

chr16:16090354

ACGTTGGATGTCTCAACTGCCACTGATGGG

ACGTTGGATGAATTGGGAGTGGATGCTCAG

GGAACTTGTGGTACACAGTA

33

ABCC1

rs2014800

chr16:16099966

ACGTTGGATGAGTATGCCTAAAACAACAGC

ACGTTGGATGCAAAATAGGTTTCCCCACCC

CCCACAGTTTGGCCCATA

34

ABCC1

rs41494447

chr16:16101842

ACGTTGGATGGAAACCCCCCAGTGACTTAC

ACGTTGGATGTCTATCTCTCCCGACATGAC

CCTTTAGATGACACCTCTCAACAAAA

35

ABCC1

rs4781712

chr16:16103232

ACGTTGGATGCATTTGAGAATGGATGCACC

ACGTTGGATGCCAGTATTTACTGAGAGCCC

TCTCGCCCTCTACGGGTGGGATA

36

ABCC1

rs246240

chr16:16119024

ACGTTGGATGTGGTAAGCAACAGGGCAAAC

ACGTTGGATGACGGAGCCTAAATGTCCAGC

GTCCAGCAGTAAGAGAT

37

ABCC1

rs924135

chr16:16123459

ACGTTGGATGTGTATCTCTGAAGGACATGG

ACGTTGGATGTTGTGAGCCAAACTATTGCG

CCCATTGCGAAATAAAAAGTCATT

38

ABCC1

rs903880

chr16:16130514

ACGTTGGATGAAGCTAGTAACAGGCAGCAC

ACGTTGGATGTTTCCTTGTCCTCCAGGATG

TCACCTCCTTTCCACT

39

ABCC1

rs8187852

chr16:16139709

ACGTTGGATGACAGCAGCACGGTGTAGAAG

ACGTTGGATGTCCTTTGCAGGTTGCTCATC

GGGGCCTTCGTGTCATTCA

40

ABCC1

rs35587

chr16:16139714

ACGTTGGATGACGGTGTAGAAGTAGCCCTG

ACGTTGGATGCTTTGCTCCTTTGCAGGTTG

CCCTTTTGCTCATCAAGTTCGTGAA

41

ABCC1

rs35592

chr16:16141823

ACGTTGGATGGACGCCTGTGTCATCTCAAA

ACGTTGGATGCCTTGCCAACTGATGAGTTC

CAGCGCGGATAAGAA

42

ABCC1

rs60782127

chr16:16142079

ACGTTGGATGTTAATGTACGTGGCCAAGTC

ACGTTGGATGTCAGCCAGAAAATCCTCCAC

GAGGGCTGTGGACGCTCAGAG

43

ABCC1

rs3765129

chr16:16139714

ACGTTGGATGAAATAGCTGGTGATGTTGAG

ACGTTGGATGGTCTTTGCTCTTCATGTGGG

GGGGCGACCCTGGGATCA

44

ABCC1

rs35597

chr16:16158034

ACGTTGGATGTGTCCCCTAGGTGCCTTTTC

ACGTTGGATGGATTTTTAGGGAGTGGGCTG

GTCCATTCATTGGTTTTCCAC

45

ABCC1

rs35621

chr16:16168608

ACGTTGGATGTCCCAGTTTCCTCATTCCAC

ACGTTGGATGAGAGAGAGAGGAAGGTGCTG

TTTCCCTCAGCTCTTCTAC

46

ABCC1

rs45511401

chr16:16173232

ACGTTGGATGTCAGCATCACCTTCTCCATC

ACGTTGGATGTGAGAGCAGGGACGACTTTC

CCCAACGGCCACCAAAGCA

47

ABCC1

rs4148356

chr16:16177275

ACGTTGGATGATATGGTTCCTCCAGCTGAC

ACGTTGGATGGCAGGCCTGGATTCAGAATG

TGGATTCAGAATGATTCTCTCC

48

ABCC1

rs3851713

chr16:16184873

ACGTTGGATGTGAACCCAGACGTCACCAAC

ACGTTGGATGCGTGACACAAGAGATGTGAG

CCTTTTGAGGTGAAGCACGGAGTTAAGC

49

ABCC1

rs2239995

chr16:16192565

ACGTTGGATGTCTTGTGCAGTGTGCTTGTC

ACGTTGGATGAAGGGTCTGCTCCATTTGTG

TTTCCCCATCCCACA

50

ABCC1

rs11864374

chr16:16201885

ACGTTGGATGTGTCTGTTCCTGGCTTCTTC

ACGTTGGATGAGAGAGGACACCAGAGTCAC

CCCTCTGCCCCTTGTTA

51

ABCC1

rs3887893

chr16:16201885

ACGTTGGATGTGATTTGGGCCCCTGTTGTG

ACGTTGGATGAAACGCTGAGGACTCTAAGG

ACTCTAAGGATCCATTTCT

52

ABCC1

rs35529209

chr16:16205325

ACGTTGGATGGCTGAGCCAATAGTTGGAAG

ACGTTGGATGGACTCTTCATCTCCTTCCTC

TTTCATGTGTAACCATGTGTCC

53

ABCC1

rs13337489

chr16:16208683

ACGTTGGATGCAAAGAAGCTCATGGGTGAC

ACGTTGGATGTACTCCATGGCCGTGTCCATC

GATCTTGGCTTCCCGCT

54

ABCC1

rs2299670

chr16:16220858

ACGTTGGATGACAACAGCTGATCCAAGGTC

ACGTTGGATGGGCAACGCAACATCAAAGAC

AAAGACTGGAGGCAC

55

ABCC1

rs8057331

chr16:16230411

ACGTTGGATGCCAGTACTCGGATGAAGAAG

ACGTTGGATGTGGTCTAGCTTGTCAGGAAG

GGGCCAGCTCCAGGGAC

S.No.


147


Appendices Gene

dbSNPid

Genomic location


56

ABCC1

rs212090

chr16:16236004

ACGTTGGATGTCCAGGCTTTCCCTTTTTTC

ACGTTGGATGCTCCTTAATATTTACCCCAC

CATCAATCATGGTGGGA

57

ABCC1

rs212093

chr16:16237754

ACGTTGGATGTCACCAAGAACTGCGTGAGC

ACGTTGGATGGCGCTGCAGTAACAAATTCG

TTCTCCGTAAAATCTTCGGAAATGATC

58

ABCC1

rs4148382

chr16:16238494

ACGTTGGATGAGTGAAAAGCCAGTAAGGTC

ACGTTGGATGATCATTCAGGCATGAGCCAC

CACCATTAGAATAGGTAGTATCA

59

ABCC2

g.-1774G>del

chr10:101535688

ACGTTGGATGCTCTTAGTTCCACAGCTGAC

ACGTTGGATGCATATGGCAGTTTCTCCATC

TTCCATACCACTCACCAGAAGAGC

60

ABCC2

rs1885301

chr10:101541053

ACGTTGGATGCTATTGAGTTGTATGAGTTCC

ACGTTGGATGAAAGGCAGCATTCAGTGTGG

GATGAAGAGTTAATATCCACAA

61

ABCC2

rs2804402

chr10:101541583

ACGTTGGATGGGTAGCTCATGCCTGCAATC

ACGTTGGATGCTCAAACTCCAGGCTTCAAC

CAGGCTTCAACAATCCT

62

ABCC2

rs717620

chr10:101542578

ACGTTGGATGCCTGTTCCACTTTCTTTGATG

ACGTTGGATGAGCATGATTCCTGGACTGCG

CATTACTGGACTGCGTCTGGAAC

63

ABCC2

rs4919395

chr10:101542963

ACGTTGGATGCGTGTGAGGAAGGAGAATTG

ACGTTGGATGTCTCAGCAAGACTCAGTCAC

CGGATCTTCTCTTCCTTCTAC

64

ABCC2

rs2756104

chr10:101544026

ACGTTGGATGCCAGTGAGTTCAGTTAGTGC

ACGTTGGATGATGAAGAAGGACTGTTCCCC

GGCCCAGTTTAAAGGACAGAATA

65

ABCC2

rs927344

chr10:101544447

ACGTTGGATGTATACACGTGGAGAAGCTGC

ACGTTGGATGAGCAAACTGTTCTGGTGTGG

GGGGGCCAGGAGCCATAGG

66

ABCC2

rs4148385

chr10:101548177

ACGTTGGATGCAGCTCTGTATTCTGCCTTG

ACGTTGGATGGACTTCAGTCATATCCCACC

ATCCCACCCCCAAAT

67

ABCC2

rs2180990

chr10:101548974

ACGTTGGATGTCTCTCATCTTCCTTCCTCC

ACGTTGGATGGTGTAGTTTTACTCTGGGAC

TACGCTGGGACTAGTGGAAGAATTAGA

68

ABCC2

rs35191126

chr10:101549533:34

ACGTTGGATGAGAAAGAAAAGGAAAGGTGG

ACGTTGGATGAGCCTTAGCCATTTTTACAG

CATTTTTACAGTCCAGCTC

69

ABCC2

rs4148389

chr10:101549911

ACGTTGGATGGACTGAGTTAAGGATATGTG

ACGTTGGATGATGAGACCAAGGAAAGCCTC

CGCCGTTTGCACTGTGTATAAAAATT

70

ABCC2

rs2804400

chr10:101553259

ACGTTGGATGGGAGCTGGAGAGAAATTCAC

ACGTTGGATGTTCTCTGACATCCTTCTCCC

CCCTTCTCCCCTCAGTC

71

ABCC2

rs2756109

chr10:101558746

ACGTTGGATGCCGCTCCTAACTGATACTTG

ACGTTGGATGTACATGGGACTCTTACCAGC

CCAGGGGACTCTTACCAGCTTAGTT

72

ABCC2

rs7080681

chr10:101560169

ACGTTGGATGAGAGCCGCAGTGAATAAGAG

ACGTTGGATGTCTGGCAGATTGCTGATCTC

ATCCAATCCACAAATATGTGTCA

73

ABCC2

rs2273697

chr10:101563815

ACGTTGGATGAGGCATTGACCCTATCCAAC

ACGTTGGATGCATCCACAGACATCAGGTTC

CAGGTTCACTGTTTCTCCAA

74

ABCC2

rs113646094

chr10:101564012

ACGTTGGATGGGGTGACTTTTTCTTTACCTG

ACGTTGGATGGGGTGATGGTGCTTGTAATC

TAATGCGATACTGTCCAC

75

ABCC2

rs2073337

chr10:101567426

ACGTTGGATGTCTAGGCCTGACCGCAAGAT

ACGTTGGATGAAGGTGAAACTAGAGCTGGG

GTTAGGGTACAAAGGAGGAAAAG

76

ABCC2

rs2756114

chr10:101569483

ACGTTGGATGTCCTAACTAAGCCACTCCTG

ACGTTGGATGATCTGGCCTATGTGGAAGTG

GGCAGGGTCCCCCTGATGC

77

ABCC2

rs3740074

chr10:101571528

ACGTTGGATGGGAAAGCAGCATGGCAAATG

ACGTTGGATGTAAACCCCTGGAGAGATGAG

GCTGAAAGCAAAGGTT

78

ABCC2

rs4148394

chr10:101572343

ACGTTGGATGTTCCTTTGGACTAAGGACCC

ACGTTGGATGCAGCAAATGTTCCAGGTGAC

TAAATGTTAATCTAGTCCAATCCC

79

ABCC2

rs3740072

chr10:101577123

ACGTTGGATGAGTCCTGGATTCAGAATGGC

ACGTTGGATGACTTGCTGGTACCTCTTTTC

AACTCTGTTCCAAAAAGGATG

80

ABCC2

rs56199535

chr10:101578577

ACGTTGGATGATTTTGGTAGGTAGCTCTGG

ACGTTGGATGCAGGGTATAAATCTTAGTGGG

GTGGGGGTCAGAAGCAG

81

ABCC2

rs56220353

chr10:101578641

ACGTTGGATGGTTTTCCTACATGAGCATCC

ACGTTGGATGGCCAGAGCTACCTACCAAAA

CCGTCTAGATGACCCCCTGT

82

ABCC2

rs2002042

chr10:101587931

ACGTTGGATGGGCTTACGGGGATGTTTTGC

ACGTTGGATGGGATCTGAGTTTCTGGATTC

TCTGGATTCTGTTATAAACCA

83

ABCC2

rs11442349

chr10:101589215:16

ACGTTGGATGCCTCCAGATATCTACTCCTC

ACGTTGGATGCAGAGCTCTGAAAAAGTTGAC

AAAGTTGACTTTGACCATTTTT

84

ABCC2

rs3740071

chr10:101590120

ACGTTGGATGTTATGGAGGCTGCATCTTCG

ACGTTGGATGTGGCAGTGAAGAAGAAGACG

GGGCTGATATCCAGTGTGGAA

85

ABCC2

rs7898096

chr10:101593385

ACGTTGGATGAGGGATTTCTTTTCTCTGCC

ACGTTGGATGGAGTCTAACCTACCTTTCCC

CTATCAGAATGCAAATAACTATAC

S.No.


148


Appendices Gene

dbSNPid

Genomic location


86

ABCC2

rs17216345

chr10:101594274

ACGTTGGATGCACCATCATCGTCATTCCTC

ACGTTGGATGGAAGGAAGGATGACTTAGCC

CTCTACCTGAACAGATACATAAAT

87

ABCC2

rs72558200

chr10:101595882

ACGTTGGATGTTTATGTGTCTACCTCCCGC

ACGTTGGATGTCTCGCTGAAGTGAGAGTAG

CTGGTGACAGAGTCCAGA

88

ABCC2

rs72558201

chr10:101595950

ACGTTGGATGTTCAGAAATCGCTGCTGGTG

ACGTTGGATGCCCCAATCTACTCTCACTTC

CGTATCAGGTTTGCCAGTT

89

ABCC2

rs8187692

chr10:101595975

ACGTTGGATGTCTGGTTGGTGTCAATCCTC

ACGTTGGATGGACCGTATCAGGTTTGCCAG

CTTTGAGCACCAGCAGC

90

ABCC2

rs17222723

chr10:101595996

ACGTTGGATGCCAGCAGCGATTTCTGAAAC

ACGTTGGATGTGGAGGTGATCCAGGAAAAG

GAAGGGTTGGTGTCAATCCTC

91

ABCC2

rs3758395

chr10:101602004

ACGTTGGATGGAAGACTGAGGTTCAAACCC

ACGTTGGATGGGCATTCATGTCTACTTAGG

GAAGAGTAAGGAATACGTCC

92

ABCC2

rs17216177

chr10:101603522

ACGTTGGATGAGGGTTTGTGTGATCTACAG

ACGTTGGATGGGTTTGAGTGGTTGAGTTGG

GAGTTGGTTTCTGTGCC

93

ABCC2

rs3740066

chr10:101604207

ACGTTGGATGGTCCTCAGAGGGATCACTTG

ACGTTGGATGTGTTTGATCACAAGGCCTCC

CCTTCTCCATGCTACC

94

ABCC2

rs72558202

chr10:101605538

ACGTTGGATGTGCCCGAGTAAGTTCTAGAG

ACGTTGGATGTGCTTCCATTGGGCTCCAC

CCGAGAGAAGCTGACCATCATCCCCC

95

ABCC2

rs3740065

chr10:101605693

ACGTTGGATGGATACAGGCAGCCACAAATG

ACGTTGGATGTGAGCTAGTTCCCTAGGATG

CTCACTAGGATGGACACGTC

96

ABCC2

rs56296335

chr10:101610393

ACGTTGGATGATTTTGGTAGGTAGCTCTGG

ACGTTGGATGCAGGGTATAAATCTTAGTGGG

GTGGGGGTCAGAAGCAG

97

ABCC2

rs3740063

chr10:101610723

ACGTTGGATGGAGATGTCAAGTAATCTGGC

ACGTTGGATGAGTTCCCTTAACCTTGTTCG

CCTACCTTAACCTTGTTCGTTTTCATT

98

ABCC2

rs8187710

chr10:101611294

ACGTTGGATGTCAGGGTAATGGTCCTAGAC

ACGTTGGATGAAGGGTCCAGGGATTTGTAG

TCCTGTCTTCAGGGCTGCCG

99

COMT

rs4680

chr22: 19951271

ACGTTGGATGACCCAGCGGATGGTGGATTT

ACGTTGGATGTTTTCCAGGTCTGACAACGG

GCACACCTTGTCCTTCA

100

CYP171A1

rs743572

chr10: 104597152

ACGTTGGATGTAGAGTTGCCACAGCTCTTC

ACGTTGGATGTAGGGTAAGCAGCAAGAGAG

ACAGCTCTTCTACTCCAC

101

CYP19A1

rs936306

chr15: 51579598

ACGTTGGATGTTCAAGTGGGCTCTGTTTCC

ACGTTGGATGTGCGAACCTTGATACCTGTG

AAACCCTGTTTCCGGGCCAATTCCAA

102

CYP19A1

rs11636639

chr15: 51563092

ACGTTGGATGTGGAATCCCAGAGCCTAGTC

ACGTTGGATGCCAAGTCTTTAGACCGACTG

GACATAGCCTAGTCAGGCCTC

103

CYP19A1

rs749292

chr 15: 51558731

ACGTTGGATGTCTAGACGAAGGTGGACAAG

ACGTTGGATGGGGCCTGATAGAAATTGTGC

TTCAAACCTCGGAGTC

104

CYP19A1

rs767199

chr15: 51540387

ACGTTGGATGATCATGCCTGGTCCTTTGAG

ACGTTGGATGTGTCTTCAAGGGCAACACAG

CAGTCCATTCCCCAC

105

CYP19A1

rs4775936

chr15: 51536022

ACGTTGGATGACAGTCTGATGTGCTGGTTC

ACGTTGGATGGGGATTACAAAACCTGGCTG

TGGTTCTGCTGTGTTTTT

106

CYP19A1

rs700518

chr 15: 51529112

ACGTTGGATGTGTTTCCTCTCCAGAGATCC

ACGTTGGATGGCAGTGCCTGCAACTACTAC

GATACAGACTCGCATGAATTCTCCATA

107

CYP19A1

rs11575899

chr15: 51519949_50

ACGTTGGATGGGAAAACAACTCGACCCTTC

ACGTTGGATGAAAAGGCACATTCATAGAC

AGGTACTTAGTTAGCTACAATCTT

108

CYP19A1

rs10046

chr15: 51502986

ACGTTGGATGTGGAACACTAGAGAAGGCTG

ACGTTGGATGGACACTATTGGCAAGGATGG

GAGCGAAGGCTGGTCAGTACC

109

CYP1A1

rs2606345

chr15: 75017176

ACGTTGGATGTCTTTGTCCTTTGCTGGGAG

ACGTTGGATGGGTGAGTTTTTAGGGACTGG

CAGTTGGCAATCTGTCA

110

CYP1A1

rs1799814

chr15 :75012987

ACGTTGGATGTAGCCAGGAAGAGAAAGACC

ACGTTGGATGGGTGATTATCTTTGGCATGG

GGGATCCCAGCGGGCAATG

111

CYP1A1

rs1048943

chr15: 75012985

ACGTTGGATGGGATAGCCAGGAAGAGAAAG

ACGTTGGATGGTGATTATCTTTGGCATGGG

AGTGTATCGGTGAGACC

112

CYP1A2

rs762551

chr15: 75041917

ACGTTGGATGCCTTTCCAGCTCTCAGATTC

ACGTTGGATGCTAAGCTCCATCTACCATGC

AGCTACCATGCGTCCTG

113

CYP1B1

rs1056836

chr02: 38298203

ACGTTGGATGCTTGTCCAAGAATCGAGCTG

ACGTTGGATGCAACCAGTGGTCTGTGAATC

CCCTTCCGGGTTAGGCCACTTCA

114

CYP1B1

rs1800440

chr02: 38298139

ACGTTGGATGAAGTTCTTCGCCAATGCACC

ACGTTGGATGTCGATTCTTGGACAAGGACG

GAGGGCTGCTGGTCAGGTCCTTG

115

CYP2C8

rs11572080

chr10: 96827030

ACGTTGGATGGTTTCTCCCTCACAACCTTG

ACGTTGGATGCAGTGAGCTTCCTCTTGAAC

CACGGTCCTCAATGCTC

S.No.


149


Appendices Gene

dbSNPid

Genomic location


116

CYP2C8

rs10509681

chr10: 96798749

ACGTTGGATGTGGCATTACTGACTTCCGTG

ACGTTGGATGCTTATCTAGAAAGTGGCCAG

AGAGATATTTGGATTAGGAAATTCT

117

CYP2C9

rs1799853

chr10: 96702047

ACGTTGGATGTTCTCAACTCCTCCACAAGG

ACGTTGGATGATGACGCTGCGGAATTTTGG

GGAAGAGGAGCATTGAGGAC

118

CYP2C9

rs1057910

chr10: 96741053

ACGTTGGATGCTACACAGATGCTGTGGTGC

ACGTTGGATGATGTCACAGGTCACTGCATG

CTGGTGGGGAGAAGGTCAA

119

CYP2C9

rs4244285

chr10: 96541616

ACGTTGGATGCACTTTCCATAAAAGCAAGG

ACGTTGGATGGCAATAATTTTCCCACTATC

CCACTATCATTGATTATTTCCC

120

CYP2C9

rs4986893

chr10: 96540410

ACGTTGGATGAACATCAGGATTGTAAGCAC

ACGTTGGATGGACTGTAAGTGGTTTCTCAG

CCATCACTTGGCCTTACCTGGAT

121

CYP3A4

rs2740574

chr07: 99382096

ACGTTGGATGGAAAACATGGTGTTGTCTGTC

ACGTTGGATGGGGTGTGAGGTATATGGGAG

ATCTATTAAATCGCCTCTCTC

122

CYP3A4

rs12721627

chr07: 99366093

ACGTTGGATGATCTTTCTCCACTCAGCGTC

ACGTTGGATGAGAGAGTCGATGTTCACTCC

CACTCCAAATGATGTGCTA

123

EPHX1

rs1051740

chr01: 226019633

ACGTTGGATGTTGACTGGAAGAAGCAGGTG

ACGTTGGATGTGGCGTTTTGCAAACATACC

GGTCTTGAAGTGAGGGT

124

EPHX1

rs2234922

chr01: 226026406

ACGTTGGATGACTTCATCCACGTGAAGCCC

ACGTTGGATGAAAACTCGTAGAAAGAGCCG

GGGCAATCAGCAAGGGCTTCGGGGTA

125

ESR1

rs9340799

chr06: 152163381

ACGTTGGATGTTAGAGACCAATGCTCATCC

ACGTTGGATGTGAGTTCCAAATGTCCCAGC

AATGCTCATCCCAACTC

126

ESR1

rs3798577

chr:06 152421130

ACGTTGGATGGGCCCTGGTGTTGCATTTAG

ACGTTGGATGCCTCTTGTTCTCTAGTAGCC

CCCCTGGAGCTGAACAGTAC

127

ESR2

rs1255998

chr14: 64693871

ACGTTGGATGTTGTGCTTTGGCAGAGAAGG

ACGTTGGATGCAGTTCCTAACCTGCATCTG

GATTGGATTGTGTGGTCAGCTGTG

128

SCN1A

rs3812718

chr02: 166909544

ACGTTGGATGTAGGTACAAAGAGCCTATCC

ACGTTGGATGCGCACTTTCAGAGTCTTGAG

GGGTGTTTCGGTAATTCCAGGTAA

129

SCN1B

rs104894718

chr19: 35524558

ACGTTGGATGCACCAATGTCACCTACAACC

ACGTTGGATGTGTGCTCGTAGTTTTCGAAG

CAGGCGGTAGACGTG

130

SCN2A

rs2304016

chr02: 166168503

ACGTTGGATGGGGTGGCTGAAGTGTTTTAC

ACGTTGGATGGCCCCTCTAAAAAATAAAAG

GAGAAAGGAATAGAAAGAATCA

131

SULT1A1

rs9282861

chr16: 28617514

ACGTTGGATGGGGAGATTCAAAAGATCCTG

ACGTTGGATGTTGAACGACGTGTGCTGAAC

CCTGGAGTTTGTGGGGC

132

SULT1E1

rs11569705

chr04: 70723299

ACGTTGGATGAGTTTGAAGAAGTCCATGGG

ACGTTGGATGGGAACGCTTCCACATTATCC

GTCCATGGGATTCTAATGTATAAA

133

SULT1E1

rs34547148

chr04: 70723268

ACGTTGGATGCAAGATCATCTGGTCTTGCC

ACGTTGGATGAGTTTGAAGAAGTCCATGGG

GGTCTTGCCTGGAAC

134

UGT1A1

rs4148323

chr02: 234669144

ACGTTGGATGCTGACGCCTCGTTGTACATC

ACGTTGGATGCACATCCTCCCTTTGGAATG

GGGTCAAGGTGTAAAATGCTC

135

UGT2B7

rs7439366

chr04: 69964338

ACGTTGGATGGCTGACGTATGGCTTATTCG

ACGTTGGATGTGGAGTCCTCCAACAAAATC

TCATAAACATTTGGTAAGAGTGGAT

S.No.


150


List of Publications


151


Publications in support of this thesis (in the order of date of publication/communication): 1.

Grover S and Kukreti R. Sex steroids and seizure recurrence in women with epilepsy. Pharmacogenomics. 2009 Dec; 10(12):1895-6. Research Highlights.

2.

Grover S et al. Genetic profile of patients with epilepsy on first-line antiepileptic drugs and potential directions for personalized treatment. Pharmacogenomics. 2010 Jul; 11(7):927-41. Original article.

3.

Grover S et al. Absence of a general association between ABCB1 genetic variants and response to antiepileptic drugs in epilepsy patients. Biochimie. 2010 Sep; 92(9):1207-12. Original article.

4.

Grover S et al. Genetic polymorphisms in sex hormone metabolizing genes and drug response in women with epilepsy. Pharmacogenomics. 2010 Nov; 11(11):1525-34. Original article.

5.

Grover S et al. Genetic variability in estrogen disposition: Potential clinical implications for neuropsychiatric disorders. Am J Med Genet B Neuropsychiatr Genet. 2010 Dec 5; 153B(8):1391-410. Review article.

6.

Grover S et al. Challenges and recommendations for conducting epidemiological studies in the field of epilepsy pharmacogenetics. Indian J Hum Genet. 2011 Apr; 17(4):4-11. Review article.

7.

Grover S and Kukreti R. Highlights from latest articles on pharmacogenetic studies of antiepileptic drugs. Pharmacogenomics. 2012 Apr; 10(2):277-91. Research highlights.

8.

Grover S et al. Genetic association analysis of transporters identifies ABCC2 loci for seizure control in women with epilepsy on first-line antiepileptic drugs. Pharmacogenetics and Genomics. 2012 Jun; 22(6):447-65. Original article.

9.

Grover S and Kukreti R. Functional genetic polymorphisms from phase II drug metabolizing enzymes. CNS Neurosci Ther. 2012 Aug;18(8):705-6.; Letter to the editor.

10.

Grover S and Kukreti R. A systematic review and meta-analysis of the role of ABCC2 variants on drug response in patients with epilepsy. Epilepsia. 2013 May; 54(5):936-45. Original article.

11.

Grover S and Kukreti R. HLA Allelic Variants and Carbamazepine-Induced Hypersensitivity. Clin Pharmacol Ther. 2013 May;93(5):386-7. Letter to the editor.

12.

Grover S and Kukreti R. HLA alleles and hypersensitivity to carbamazepine an updated systematic review with meta-analysis. Pharmacogenetics and Genomics. Accepted. Original article.

151


13.

Kaur H et al. Concept of Pharmacogenomics and Future Considerations. CNS Neurosci Ther. 2013 Aug 7. doi: 10.1111/cns.12157. [Epub ahead of print]. Letter to the editor.

14.

Sharma S et al. Effect of Age, Gender, Dose and ethnicity on serum Phenytoin concentration in north Indian population. Communicated. Original article.

15.

Grover S et al. Clinical utility of SCN1AIVS5-91G>A variant in treatment of epilepsy – a genetic association study and an updated systematic review with meta-analysis. Manuscript in preparation. Original article.

16.

Grover S et al. Gene-gene interaction analysis of functional genetic variants with therapeutic drug response and seizure control in patients administered firstline antiepileptic drugs. Manuscript in preparation. Letter to the editor.

Other Publications (in the order of date of publication/communication): 1.

Indian Genome Variation Consortium Collaborators. Genetic landscape of the people of India: a canvas for disease gene exploration. J Genet. 2008 Apr; 87(1):3-20. Original article.

2.

Gupta M et al. Pharmacogenomics and treatment for dementia induced by Alzheimer's disease. Pharmacogenomics. 2008 Jul; 9(7):895-903. Review article.

3.

Gupta M et al. Genetic susceptibility to schizophrenia: role of dopaminergic pathway gene polymorphisms. Pharmacogenomics.2009 Feb; 10(2):277-91. Original article.

4.

Gupta M et al. Association studies of catechol-O-methyltransferase (COMT) gene with schizophrenia and response to antipsychotic treatment. Pharmacogenomics. 2009 Mar; 10(3):385-97. Original article.

5.

Talwar P et al. Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease pathogenesis. Communicated. Original article.

152