Pharmacogenetic Characteristics of Substance Abuse ... · population of Arab descent and phenotypic and genotypic data were systematically compiled in a database for study purposes

Pharmacogenetic Characteristics of Substance Abuse

Patients Who Are Receiving Therapy for Their

Addiction

Laith Naser AL-Eitan

BSc (Genetic Engineering and Biotechnology)

MSc (Toxicology and Forensic Science)

This thesis is presented for the degree of

Doctor of Philosophy

Centre for Forensic Science

2012

i

DEDICATION

This thesis is dedicated with love and thanks to my special brother Fehan, and my

family

For being there every step of the way

For their encouragements and constant support

ii

DECLARATION

This thesis is submitted to The University of Western Australia in fulfilment of the

requirements for the Degree of Doctor of Philosophy.

This thesis has been composed by myself from results of my own work, except where

stated otherwise, and no part of it has been submitted for a degree at this, or at any other

university.

Laith Naser AL-Eitan

iii

PREFACE

This thesis is presented as a series of nine chapters. The introductory chapter provides

the basic for the work undertaken during the tenure of this study. This followed by

seven chapters presented as manuscripts of submitted or published papers including

reference lists in the format of the journal to which the manuscript is submitted.

Preceding each paper description of author contribution is provided. The final chapter

summarises the main features and findings of the work performed and establishes the

direction for future work.

iv

TABLE OF CONTENTS

DEDICATION ................................................................................................................ i

DECLARATION ........................................................................................................... ii

PREFACE ..................................................................................................................... iii

TABLE OF CONTENTS ............................................................................................ iv

ACKNOWLEDGMENTS .......................................................................................... viii

ABSTARCT ................................................................................................................... xi

LIST OF ABBREVIATION ..................................................................................... xvii

DEFINITIONS ............................................................................................................ xix

CHAPTER 1 .................................................................................................................... 1

A REVIEW OF PHARMACOGENETICS AND HUMAN MOLECULAR

GENETICS OF SUBSTANCE DEPENDENCE ......................................................... 1

Introduction .................................................................................................................. 3

Epidemiology of substance Dependence ...................................................................... 5

Substance Abuse in Jordan ........................................................................................... 6

Dependence Scales ....................................................................................................... 8

Substance Dependence ............................................................................................... 11

Nicotine ................................................................................................................... 11

Ethanol (Alcohol) .................................................................................................... 14

Opiates and Other Opioids ...................................................................................... 18

Benzodiazepines ..................................................................................................... 24

Amphetamines and Related Derivatives ................................................................. 28

Cocaine ................................................................................................................... 32

Cannabinoids .......................................................................................................... 37

Human Molecular Genetic and Pharmacogenetics Approaches towards Understanding

Substance Dependence ............................................................................................... 41

Human DNA ........................................................................................................... 41

Pharmacogenetics ................................................................................................... 45

Role of Pharmacogenetics in Personalized Medicine and Forensic Toxicology .... 46

Candidate Gene Approach in Studying Genetic Susceptibility to Substance

Dependence and Pharmacogenetics ........................................................................ 48

Opioid Related Genes ............................................................................................. 48

Monoaminergic Related Genes ............................................................................... 54

Conclusion, Scope and Outline of Thesis ................................................................... 59

References .................................................................................................................. 64

v

CHAPTER 2 .................................................................................................................. 94

CLINICAL CHARACTERISATION OF SUBSTANCE ABUSE PATIENTS

PRESENTING FOR TREATMENT AT A DRUG REHABILITATION CENTRE

IN JORDAN .................................................................................................................. 94

Abstract ....................................................................................................................... 98

Introduction ................................................................................................................. 99

Methodology ............................................................................................................. 102

Results ...................................................................................................................... 105

Discussion ................................................................................................................. 118

Acknowledgments .................................................................................................... 123

References ................................................................................................................ 124

CHAPTER 3 ................................................................................................................ 127

CHARACTERIZATION OF SLC6A4 GENE POLYMORPHISMS AND ITS

ASSOCIATION WITH DRUG DEPENDENCE IN A JORDANIAN ARAB

POPULATION ........................................................................................................... 127

Abstract ..................................................................................................................... 131

Introduction .............................................................................................................. 132

Material and Methods ............................................................................................... 134

Results ...................................................................................................................... 137

Discussion ................................................................................................................. 145

Conflict of Interest .................................................................................................... 150


References ................................................................................................................ 151

CHAPTER 4 ................................................................................................................ 157

PHARMACOGENETIC APPROACH TO TREATING DRUG DEPENDENCE:

SEROTONIN TRANSPORTER PROMOTER REGION (5-HTTLPR) AND

rs25531 MARKERS AS A TREATMENT PREDICTOR IN JORDANIAN

PATIENTS .................................................................................................................. 157

Abstract ..................................................................................................................... 162

Introduction .............................................................................................................. 163


Results ...................................................................................................................... 171

Discussion ................................................................................................................. 179

Conclusions and Future Outlook .............................................................................. 181

Conflict of Interest .................................................................................................... 183

Lists of Abbreviations .............................................................................................. 183

vi


References ................................................................................................................ 184

CHAPTER 5 ................................................................................................................ 189

SNP GENOTYPING FOR DRUG DEPENDENCE CANDIDATE GENES USING

A SEQUENOM MASSARRAY® IPLEX PLATFORM ........................................ 189

Abstract ..................................................................................................................... 193

Introduction .............................................................................................................. 194


Results ...................................................................................................................... 200

Discussion ................................................................................................................. 211


References ................................................................................................................ 214

CHAPTER 6 ................................................................................................................ 217

CUSTOM GENOTYPING FOR SUBSTANCE ADDICTION SUSCEPTIBILITY

GENES IN JORDANIAN OF ARAB DESCENT ................................................... 217

Abstract ..................................................................................................................... 221

Background ............................................................................................................... 222


Results ...................................................................................................................... 229

Discussion ................................................................................................................. 237

Conclusion ................................................................................................................ 240

Competing of Interests ............................................................................................. 241

Authors’ Contributions ............................................................................................. 241


References ................................................................................................................ 242

CHAPTER 7 ................................................................................................................ 248

POLYMORPHISMS IN THE µ-OPIOID RECEPTOR GENE IN JORDANIAN

ARABS WITH OPIATE DRUG DEPENDENCE .................................................. 248

Abstract ..................................................................................................................... 252

Introduction .............................................................................................................. 253


Results ...................................................................................................................... 256

Conclusion ................................................................................................................ 256

References ................................................................................................................ 257

vii

CHAPTER 8 ................................................................................................................ 263

ΜU-OPIOID RECEPTOR (OPRM1) AS A PREDICTOR OF TREATMENT

OUTCOME IN OPIATE DEPENDENT INDIVIDUALS OF ARAB DESCENT

....................................................................................................................................... 263

Summary ................................................................................................................... 267

Introduction .............................................................................................................. 268


Results ...................................................................................................................... 278

Discussion ................................................................................................................. 288


Disclosure ................................................................................................................. 293

References ................................................................................................................ 294

CHAPTER 9 ................................................................................................................ 301

CONCLUSION AND FINAL REMARKS .............................................................. 301

Conclusion and Final Remarks ................................................................................. 302

References ................................................................................................................ 309

viii

ACKNOWLEDGMENTS

First and foremost I would like to thank God, the merciful and the passionate, for

providing me with the opportunity to step up in the world of science and academic

writing. You have given me the power to believe in myself and pursue my dreams. I

could never have done this without the faith I have in you, the Almighty.

The work presented in this thesis was undertaken at the Centre for Forensic Science

(CFS), The University of Western Australia (UWA). I consider myself lucky that I was

given the opportunity to study here. I have always been drawn to the notion of learning

something new, and I now have the opportunity to pursue knowledge as a career and I

have learnt during my time at CFS that Knowledge is painfully slow but worthwhile.

What has also become clear to me is that knowledge cannot be pursued as a solo

endeavour and that it takes the support and effort of many to accomplish even a little. I

was very fortunate to have the support of a tremendous group of family, colleagues, and

friends. Without them, I would not have made it through my candidature.

I am grateful to Jordan University of Science and Technology, Jordan, which enabled

and supported me to undertake my studies at UWA. This study would not have been

possible without the generous support of my Jordanian supervisors and collaborators.

I would also like to thank my two supervisors: Associate Professor Guan Tay at the

CFS, UWA and Winthrop Professor Gary Hulse at the School of Psychiatry and

Clinical Neurosciences at UWA who opened my eyes to the world of pharmacogenetics

and for their patience, encouragement and guidance. They were generous in their

supervision and helped me overcome many challenges. I am also grateful for their

efforts in reviewing the draft of this thesis and thankful for their comments and

constructive criticism.

To my coordinating supervisor Winthrop Professor Ian Dadour, I thank you for

welcoming me into CFS and your ongoing honesty, encouragement and support for all

aspect of my research. Special thanks to Sasha Voss and Steve Iaschi from the CFS

staff for their friendship and encouragement. I would also like to thank Dr. Kellie

Bennett at the School of Psychiatry and Clinical Neurosciences and Dr. Steve Su at the

School of Mathematics and Statistics, UWA for their statistical support.

ix

I also would like to extend my sincere thanks to Dr. Saied Jaradat and other staff at the

Genomics Research Group at the Princess Haya Biotechnology Center (PHBC), Jordan

University of Science and Technology, Jordan for warmly welcoming me into their

labs, for their technical assistance and collaboration.

I would like to thank Dr. Jamal Anani, Director of the National Center for

Rehabilitation of Addicts (NCRA) at the Jordanian Ministry of Health and Major

Mazen Magableh, Director of the Public Security Department’s (PSD) Drug

Rehabilitation Centre (DRC-PSD) for approving the work in the first instance. I would

like to extend my gratitude to numerous other individuals at the NCRA who helped in

conducting this study, including Dr. Abdullah Abuadas, Ms. Nahedah Al Labady, Ms.

Intesar Al Hassan, Mr. Ammar Al Shara and Mr. Jamal Alghaled. Gratitude is also

extended to the drug treatment personnel and clients, without whom I could not have

conducted this study.

A large part of this research was conducted in the laboratories of the Australia Institute

for Medical Research (WAIMR), Perth, Australia. Specifically, I would like to extend

my thanks to Professor Dieter Wildenauer and his research assistance at the

Neuropsychiatric Genetic Lab (NGL) for their collaboration and sharing your wealth of

experience and teaching me so much about the nature of research. Special thanks to Dr.

Mutiara Wildenauer and Ms Wenwen Qin at the WAIMR/NGL for their friendship,

valuable help and technical assistance. I appreciate all the technical help and support

from the Australian Genome Research Facility (AGRF; Melbourne and Perth Nodes,

Australia), in particular, Shane Herbert, David Hawkes and staff from the Perth and

Melbourne AGRF Nodes.

My thanks also go to Dr. Jo Edmondston, who interviewed me in the first week of my

study at UWA for her valuable support during my studies. Great appreciation also goes

to the Graduate Research School at UWA for providing me a travel award which

allowed me to present my work at the Human Genome Meeting, Genetics and

Genomics in Personalized Medicine, Sydney 2012, as well as the Convocation Office at

UWA for providing a travel award which allowed me to present a poster at the

International Congress on Personalized Medicine, in Florence, Italy 2012.

x

To my parents, Naser Al-Eitan and Seeta Al Abdullah, my brothers and sisters-in-law;

Fehan, Jafar, Qasem, Ghaith, and my sisters; Ghazieh, Radwah, Norah, Mariam and

Daulha - you have been a huge influence in my life. I have been very lucky to have

been able to pursue knowledge as a career and I would not have this chance without

you, who have always inspired me and encouraged me to further my education. Family

is the most important thing in life and it is a simple fact that, without the love and

support of my entire family, I would not have made it through my studies to reach

where I am today. I owe more to my mum than words could ever say; you have been a

major influence in my life. You have resounding strength of character, a brilliant mind,

a sense of humility and a caring heart capable of more love than most people will ever

know. You inspire me to be the very best I can be. Thank you from the bottom of my

heart.

The work described in this thesis was performed with approval from The University of

Western Australia's Human Research Ethics Committee (reference # RA/4/1/4344).

xi

ABSTRACT

This project- 7 studies was first proposed in 2009 with the aim of identifying genetic

markers within selected candidate genes that may influence susceptibility to substance

dependence and treatment response. To achieve this aim, a comparative study of

substance dependent patients and individuals with no lifetime history of substance abuse

was planned. Samples were obtained from consenting volunteers from a Jordanian

population of Arab descent and phenotypic and genotypic data were systematically

compiled in a database for study purposes. Prior to this project, clinical,

epidemiological and pharmacogenetic characteristics of substance dependent patients of

Arab descent had not yet been described. Significant advances in DNA technology,

particularly in the field of DNA arrays, provide the opportunity to study this population

for the first time.

Collection of the data for this study was made possible through the collaboration of four

institutions: The National Centre for Rehabilitation of Addicts (NCRA) at The

Jordanian Ministry of Health, the Drug Rehabilitation Centre at The Jordanian Public

Security Directorate, Jordan University of Science and Technology and The University

of Western Australia. Through assistance from the Jordanian Ministry of Health and

collaborators of this network, demographic and clinical data of substance dependent

patients (N = 220) were collected and collated in a database for analysis. Clinical

specimens were also collected for genetic association and pharmacogenetic studies. In

addition, 240 healthy males from an ethnically homogenous Jordanian Arab population

with no lifetime history of psychosis or mood disorders, or substance dependence

according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)

criteria were used as controls. Written informed consent was obtained from all subjects

(N = 460) included in the study.

The first study assessed the patterns and severity of substance abuse in a group of

patients presenting for treatment at the National Centre for Rehabilitation of Addicts

(NCRA) at Jordanian Ministry of Health, and the Drug Rehabilitation Centre at the

Jordanian Public Security Directorate (DRC-PSD). As epidemiological data on

substance abuse in Arab countries is scarce, the aim of the first study was to provide

epidemiological data to describe this substance abuse population. A total of 220

patients with substance abuse were interviewed at the drug rehabilitation centres. The

xii

interviews were carried out over a period of six months (December 2010 to June 2011)

using a structured baseline survey based on the 5th

edition of the Addiction Severity

Index. Of these 220 patients, 135 were interviewed during intake sessions and the

remaining 85 were interviewed upon admission to the treatment program. Opiates

(84%), specifically heroin, were the major substance used reported by most of the

patients. Other substances of abuse included: benzodiazepines (59%), cannabis (58%),

and alcohol (53%), amphetamines (14%), cocaine (3%) and inhalants (1%). The main

route of administration was chasing. Heroin abusers were at ongoing risk of switching

from chasing to injecting, suggesting this should be taken into consideration in

treatment programs.

The characteristics of the Arab population make them ideal for the study of complex,

polygenic and multifactorial disorders such as drug dependence. Drug dependence is a

pattern of repeated self-administration of a drug that can result in tolerance, withdrawal,

and compulsive drug-taking behaviour. It has been recently suggested that 5-HTTLPR

(LL/LS/SS) variants and a rs25531 (A/G) polymorphism in the serotonin transporter

gene (SLC6A4) may play a role in drug dependence. The second study in this project

involved genetic association analysis which aimed to: (1) identify allelic, haplotypic

and genotypic frequencies of the 5-HTTLPR variants and rs25531 polymorphisms for

SLC6A4 gene in the drug and non-drug dependent Jordanian Arab population; and (2)

determine if there is an association between these variants in a drug dependent

population from the same area. Jordanian male addicts of Arab descent meeting the

DSM-IV criteria for drug dependence (n = 192) and healthy male controls from an

ethnically homogenous Jordanian Arab population (n =230) were examined.

Genotyping was performed using a PCR- RFLP based method to genotype the 5-

HTTLPR variants and detect the A/G polymorphism at position rs25531. The bi-allelic

analysis revealed that the frequency of 5-HTTLPR (LL/LS/SS) genotypes were

statistically significant different between drug dependent individuals and controls (χ2

(2, N= 422), p value = 0.04). Drug dependent subjects had a higher frequency of the

“L” allele. However, using a triallelic approach, the estimated frequency of haplotypes

(SA, SG, LA, LG) and phased genotypes (LA /LA, LA /SA, LA /LG, SA /SA, SA /SG) did not show

significant association with drug dependence (χ2 (3, N= 422), p value = 0.53 and χ2 (4,

N= 422), p value = 0.06, respectively). This suggests a putative role for the 5-HTTLPR

for drug dependence in the Jordanian patients of Arab descent.

xiii

Recognition of the role of serotonergic systems in drug dependence, in particular

alterations in serotonin availability, levels and function have been shown to affect drug

consumption patterns, behavior and responses to treatment. The aim of the third study

in this project was to assess the influence of SLC6A4 gene polymorphisms (5-HTTLPR

and rs25531) on the clinical and biological outcomes of drug dependence in patients (N

= 192) of Arab descent undergoing an 8-week pharmacological and behavioural

inpatient treatment program. PCR-RFLP based genotyping of the 5-HTTLPR gene

variants (LL/LS/SS) and rs25531 marker polymorphisms (A/G) was performed for these

patients. Drug dependence diagnoses were made and clinical data were obtained.

Patients were then stratified according to receptor polymorphism using a biallelic

(Group A: LL versus Group B: SS and LS genotype) and a triallelic approach (Group A′:

LA/LA versus Group B′: non-LA/LA genotype). This study found that the biallelic 5-

HTTLPR genotype was associated with responsiveness to treatment in these drug

dependent patients but responsiveness to treatment was not affected by the triallelic

polymorphism. However, the biallelic LL or the triallelic LA/LA genotype of 5-HTTLPR

gene was found to be a genetic risk factor for these patients. Patients with this genotype

were at risk of relapse, had the earliest age of first use and onset of dependence and the

highest frequency of drug use.

In genetic association and pharmacogenetic studies, high throughput single nucleotide

polymorphism (SNP) genotyping technologies, such as the Sequenom MassARRAY®

platform (iPLEX GOLD) are often required. Sequenom technology is being used to

identify genes that involved in polygenic traits or complex diseases and drug response

for either genetic association or pharmacogenetics studies or their application in

personalized medicine and forensic toxicology. The fourth study in this project

describes SNP genotyping method, based on the commercially available Sequenom

MassARRAY® platform (iPLEX GOLD), to genotype 68 SNPs within nine drug

dependence candidate genes (DRD1, DRD2, DRD3, DRD4, DRD5, BDNF, SLC6A3,

COMT and OPRM1). Two multiplexes (36 SNPs in multiplex A and 32 SNPs in

multiplex B) were used to genotype 460 subjects of Arab descent (220 drug dependent

individuals and 240 controls without history of drug of abuse). The assay consisted of

an initial locus-specific PCR reaction, followed by single base extension using mass-

modified dideoxynucleotide terminators of an oligonucleotide primer annealing

immediately upstream of the polymorphic site of interest. Using MALDI-TOF mass

spectrometry, the distinct mass of the extended primer was used to identify the SNP

xiv

allele. The Sequenom MassARRAY® platform determined the genotypes for all 68

SNPs highly accurately with an average success rate of 96.6%. The average (±SD) rate

of genotype discrepancy across the 68 SNPs was only 0.04% (±0.00085%) in the whole

cohort. However, three SNPs in multiplex A (rs165599, rs3778156, rs2734838) and

one SNP in multiplex B (rs6347) failed to produce any quality results which lowered

the overall genotyping success rate. Overall, the results of this study showed that

Sequenom is a reliable, cost-effective and sensitive assay for simultaneously detecting

multiple SNPs in candidate genes in a routine clinical setting.

The fifth and sixth study in this project subsequently examined the relationship between

the 68 SNPs within the nine genes and the susceptibility to opiate and substance

dependence in Arab populations. They revealed nine new genetic associations

involving SNPs within two genes (DRD2, OPRM1). Six SNPs within DRD2 on

chromosome 11 were found to be most strongly associated with substance dependence

in the Jordanian Arabic sample. It has been suggested that DRD2, dopamine receptor

D2, plays an important role in dopamine secretion and the signal pathways of

dopaminergic reward and drug addiction (Xu et al., 2004). The strongest statistical

evidence for these new association signals in the present studies were from rs1799732

in the C/−C promoter and rs1125394 in A/G intron 1 regions of DRD2, with the overall

estimate of effects returning an odd ratio of 3.37 (χ2 (2, N = 460) = 21, p-value =

0.000026) and 1.78 (χ2 (2, N = 460) = 8, p-value = 0.001), respectively. Further work is

required to confirm these preliminary results in other sample sets.

Finally, the seventh study in this project investigated the genetic associations of a series

of markers spanning the coding sequence of OPRM1 gene with the responsiveness to

the biopsychosocial treatment in opiate dependent Arab population of Arab descent (N

= 183). These patients, all males, met the DSM-IV criteria for opiate dependence and

were undergoing a voluntary 8 week treatment program at a Jordanian Drug

Rehabilitation Centre. All patients were genotyped for 22 SNPs within this gene using

the Sequenom MassARRAY® system (iPLEX GOLD). Patients receiving the

biopsychosocial treatment showed that there was a significant difference in OPRM1

SNP genotyping distribution between good, moderate and poor responders to the

treatment at two sites: rs6912029 (G-172T) and rs12205732 (G-1510A). Specifically,

this study showed that OPRM1 GG-172 and GG-1510 genotypes are more frequent

among patients who are non-responders to biopsychosocial treatment. Further

xv

pharmacogenetic studies in a larger cohort of opiate dependent patients of Arab descent

are needed to confirm these findings and identify individuals with increased chance of

relapse.

Overall, this project is the first clinical, epidemiological and genetic study of substance

dependence in Jordanian population of Arab descent. These findings may provide

important insights into understanding drug addiction mechanisms in Middle Eastern

populations and managing or dictating the therapy used for each individual.

Comparative analysis with different ethnic groups could also assist in understanding the

mechanisms that causes the addiction. It is important to develop an understanding of

the relationship between ethnic specific allelic and genotypic patterns that leads to

disease, in an effort to control the spread of and manage the consequences of the

disease.

In conclusion, comparative genetics in medical science has been widely applied to

identify genetic factors that cause disease and treatment response. The results of this

project provide additional clinical, epidemiological and genetic knowledge that may be

useful in the context of further genetic and pharmacogenetics analyses to reduce the

severity of drug consumption and alcohol intake and improve drug abstinence. This

may lead to more accurate matching of individuals to different treatment options and

early identification of persons of high risk of relapse and therefore requiring more

intensive intervention. This project may also stimulate further genetic analyses of the

Arab population to better understand both their unique genetic background, the

aetiology of drug dependence that affects this group of individuals, and improve

treatment strategies according to the genetic variation of the patients. To strengthen the

claims made in this project, finer genetic and pharmacogenetic analyses in a larger

cohort of drug dependent patients from the Jordanian Arab population or from the

greater Middle East region is required.

The null hypothesis of this project was that the alleles and genes in the Jordanian Arab

population that predispose individuals to substance dependence and treatment response

are the same as those described for other populations previously studied. This study

goes part way to rejecting this hypothesis and suggests that novel genetic factors unique

to the Arabic population contribute to the etiology of substance dependence as well as

to the treatment response. The data collected from this project has defined a number of

xvi

Arabic genotypes associated with substance dependence and treatment response. These

genetic characteristics may have broader applications, for example in forensic molecular

toxicological analysis. The understandings of the role of genetic variations in drug

metabolism will provide additional information about an individual’s metabolic

capacity or a drug consumption pattern that may contribute to a better interpretation of

forensic toxicological results. In the future, this may help forensic toxicologists draw

the right conclusions from post-mortem toxicology results.

xvii

LIST OF ABBREVIATIONS

ASI The Addiction Severity Index

ASMR The Australian Medical Research Society

AGRF The Australian Genomics Research Facilities

BAT Breath Alcohol Test

BDNF Brain-derived neurotrophic factor

BLAST Basic Local Alignment Search Tool

COMT Catechol-O-methyltransferase

CNV Copy number variations

CFS Centre For Forensic

DNA Deoxyribonucleic Acid

ddNTPs Dideoxy nucleoside triphosphates

DRD1 Dopamine receptor D1





DSM-IV Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

DRS-PSD The Drug Rehabilitation Centre-Public Security Directorate

EDTA Ethylenediaminetetraacetic acid

EMEA The European Medicines Agency

F_A Minor allele frequency in affected individuals

F_U Minor allele frequency in unaffected individuals

HGM The Human Genome Meeting

5-HHT The serotonin transporter

5-HHTLPR The Serotonin-transporter-linked polymorphic region

HWE Hardy-Weinberg Equilibrium

ICD The International Classification of Diseases

M Mean

M_A Minor allele for whole cohort sample

MAF Minor allele frequency

MALDI-

TOF-MS

Matrix assisted laser desorption/ionisation time of flight mass spectrometry

MOR The μ-opioid receptors

NCBI The National Center for Biotechnology Information

xviii

NCRA The National Centre for Rehabilitation of Addicts

NGL The Neuropsychiatric Genetic Lab

OPRM1 Mu-opioid receptor gene

OR CMH Allelic odds ratio from the 2x2xK Cochran-Mantel-Haenszel’s test.

PCR The Polymerase chain reaction

PHBC The Princess Haya Biotechnology Centre

p value Probability Value

QC Quality Control

RFLP Restriction fragment length polymorphism

SAP Single base extension primers

SBE Shrimp alkaline phosphatase

SD Standard deviation

SERT Serotonin transporter

SLC6A3 Solute carrier family 6, member 3

SLC6A4 Solute carrier family 6, member 4

SNP Single Nucleotide Polymorphism

TBE Tris-Borate EDTA

UDS Urine drug Screen

UNODC The United Nations Office on Drugs and Crime

UNDCP The United Nations International Drug Control Program

UWA The University of Western Australia

3'-UTR The three prime untranslated region

5'-UTR The five prime untranslated region

WAIMR The Western Australian Institute for Medical Research

WHO The World Health Organization

χ2 Chi square

xix

DEFINITIONS

Allele Alternative form of a genetic locus; a single allele for each locus

is inherited from each parent (e.g., at a locus for eye colour the

allele might result in blue or brown eyes).

Candidate gene A gene believed to influence expression of complex phenotypes

due to known biological and/or physiological properties of its

products, or to its location near a region of association or linkage.

Gene The fundamental physical and functional unit of heredity. A gene

is an ordered sequence of nucleotides located in a particular

position on a particular chromosome that encodes a specific

functional product (i.e., a protein or RNA molecule).

Genetic Association

Study

A study aims to test whether single-locus alleles or genotype

frequencies differ between two groups of individuals (usually

diseased subjects and healthy controls).

Genetic polymorphism Difference in DNA sequence among individuals, groups, or

populations (e.g., genes for blue eyes versus brown eyes).

Genome The entire complement of genetic material in a chromosome set.

Genotyping call rate Proportion of samples or SNPs for which a specific allele SNP

can be reliably identified by a genotyping method.

Haplotype A way of denoting the collective genotype of a number of closely

linked loci on a chromosome.

HapMap Project Genome-wide database of patterns of common human genetic

sequence variation among multiple ancestral population samples.

Hardy Weinberg

Equilibrium

Population distribution of 2 alleles (with frequencies p and q)

such that the distribution is stable from generation to generation

and genotypes occur at frequencies of p2, 2pq, and q2 for the

major allele homozygote, heterozygote, and minor allele

homozygote, respectively under the assumption of natural

selection does not act on the alleles under consideration.

High-throughput

sequencing

A fast method of determining the order of bases in DNA.

xx

Minor allele frequency Proportion of the less common of 2 alleles in a population

(with 2 alleles carried by each person at each autosomal

locus) ranging from less than 1% to less than 50%.

Pharmacogenetics It is the study of interindividual variations in DNA sequence

related to drug response.

Phenotypes The total characteristics displayed by an organism under a

particular set of environmental factors, regardless of the

actual genotype of the organism.

Polymerase Chain

Reaction

A method for amplifying segments of DNA, by generating

multiple copies using DNA polymerase enzymes under

controlled conditions. As little as a single copy of the DNA

segment or gene can be cloned into millions of copies,

allowing detection using dyes and other visualization

techniques.

Population

stratification

A form of confounding in genetic association studies caused

by genetic differences between cases and controls unrelated to

disease but due to sampling them from populations of

different ancestries.

Single Nucleotide

Polymorphism

DNA sequence variations that occur when a single nucleotide

(A, T, C, or G) in the genome sequence is altered. Each

individual has many single nucleotide polymorphisms that

together create a unique DNA pattern for that person. SNPs

promise to significantly advance our ability to understand and

treat human disease.

Sequenom It is a manufacturer of DNA MassARRAY, based in San

Diego, California.

1

CHAPTER 1

A REVIEW OF PHARMACOGENETICS AND

HUMAN MOLECULAR GENETICS OF

SUBSTANCE DEPENDENCE

2

Chapter 1

A Review of Pharmacogenetics and Human Molecular

Genetics of Substance Dependence

This chapter is an introduction to a study that develops an understanding of the

environmental and genetic predisposition that gives rise to the collection of event

aetiologies resulting in substance dependence in the Middle Eastern population of Arab

descent. Although the focus of the study is on the Arab race, the work is the beginning of

a research effort to understand diseases that affect the entire Arab population. A

PubMed search for research articles containing the words “genetic of substance

dependence” finds a large body of work with almost 8,013 peer-reviewed articles.

Although substance dependence or addiction has been known for over 80 years,

approximately 75% of research in the field has been conducted since 1990, and over

50% of published research has appeared since 2000. There are three explanations for

such a recent, intensive effort to understand substance dependence: 1) substance

dependence is a severely devastating condition; 2) it is highly widespread in the general

population; and 3) a number of quality of life issues and financial considerations

regarding affected individuals, their family members and society, are involved.

This chapter will outline the definition of substance dependence following a uniform

pattern: the origin of substance, the pharmacology of the substance, the mechanisms of

substance dependence, and the toxicology and pharmacotherapy of the substance. The

epidemiological and genetic risk factors of substance dependence will also be explored.

This chapter will also highlight the implications of genetic research, with specific

emphasis on the molecular genetics and pharmacogenetics involved in addictions to

nicotine, alcohol, opiates and other opioids, amphetamine and related derivatives,

cocaine, benzodiazepines and cannabinoids, focusing primarily on genes that have been

associated with or show evidence of linkage to substance dependence.

3

I. INTRODUCTION

For thousands of years the use of alcohol and drugs for medical purposes has gone hand

in hand with their abuse as euphoric agents. Alcohol and drugs have been manufactured

since ancient times (Khalil et al., 2008). The earliest alcoholic beverages were thought

to be made of berries or honey, and winemaking may have originated in the wild grape

regions of the Middle East (Blum et al., 1969). Beer and wine were used for medicinal

purposes as early as 2000 BC, and the opium poppy has been cultivated and used by

Egyptian for pain relief since 3400 BC (Babor & Mandelson, 1986). Illicit drug intake

is considered by many societies as an antisocial or criminal behaviour (Fawzi, 1980;

Haddad et al., 2010). The extent, pattern and severity of substance abuse worldwide

differ from country to country, and within a country there are variations from area to

area (Hadidi, 2004); the uncontrolled use of alcohol and illicit drugs, leading to

substance dependence, is a significant problem in many countries (Jonsson et al., 2007).

The most commonly abused substances worldwide are alcohol (Jonsson et al., 2007),

opiates (Hulse et al., 1999; Zhou et al., 1999), cannabinoids (Tang et al., 1996), cocaine

(Robson et al., 1997) and amphetamines (Poulin et al., 1998).

Substance dependence is a chronic relapsing disease with complex aetiologies, and

significant comorbidities such as human immunodeficiency virus, hepatitis B and C

infections, depressive and anxiety disorders and other psychiatric illnesses, and major

negative socioeconomic consequences. Various molecular genetic studies have

estimated that genetic factors contribute to 40%–60% of a person’s vulnerability to

substance dependence, and environmental factors provide the remainder (Enoch &

Goldman, 1999; Goldman et al., 2005; Nestler & Landsman, 2001; Uhl, 2004).

However, there is also evidence for shared genetic vulnerability to two or more

substances such as opiates, alcohol, cannabis, sedatives and stimulants, which may

explain the finding that addicted patients are often dependent on more than one category

of substance (Enoch & Goldman, 1999; Goldman et al., 2005; Haile et al., 2008; Nestler

& Landsman, 2001; Uhl, 2004). The presence of unique and shared genetic factors for

substance dependence (Haile et al., 2008; Kreek et al., 2005; Tsuang et al., 1994) leads

to the hypothesis that there is an association between specific genetic polymorphisms

and increased risk of substance dependence; and that this genetic influence also extends

to treatment outcomes.

4

Currently, substitution therapies aimed at long-term maintenance with prescribed drugs

such as methadone, buprenorphine and naltrexone have proven the most effective

treatments for alcohol and opioid dependence (Johnson, 2011). When combined with

psychosocial services, these maintenance treatments are effective in reducing drug use,

dangerous behaviour and criminal activity, while improving the mental health of

patients (Ball & Ross, 1991; Johnson, 2011). There are no pharmacological treatments

for cannabis and cocaine dependencies; however, the majority of drug-dependent

individuals remain out of treatment, and those who remain in treatment exhibit only 60–

70% success rates (Ball & Ross, 1991). It remains an essential goal to further the

understanding of the factors underlying poor treatment outcomes and to assist in the

development of more individualised, optimised and ultimately more effective treatments

for opiate drug dependence.

The first major maintenance treatment for opiate dependants is methadone, a synthetic

μ-opioid receptor agonist used primarily for alcohol and opiate dependence. The second

major maintenance treatment is buprenorphine, a semi synthetic opioid that is used to

treat opiate addiction (Johnson, 2011). Pharmacogenetic studies have shown that both

methadone and buprenorphine display large interindividual variability in their

pharmacokinetics and contribute to high interindividual variability in response to

treatment (Creetol, 2006; Eap et al., 2002; Johnson, 2011). The third major maintenance

treatment is naltrexone, an opioid receptor antagonist used primarily in the management

of alcohol and opioid dependence. Meta-analyses of studies of naltrexone hydrochloride

medication have suggested that the effect size for response over placebo is in the small

to moderate range (Bouza et al., 2004; Kranzler & Van Kirk, 2001; Streeton & Whelan,

2001). For instance, in the Combined Pharmacotherapies and Behavioral Interventions

for Alcohol Dependence (COMBINE) Study, naltrexone reached prime efficacy when

used in the context of medical management (MM) (Anton et al., 2004). Based on these

studies, it is clear that not all individuals with alcohol dependence respond to

naltrexone.

This introduction will explain why drugs continue to be used and abused, why drug

abuse represents a significant social burden, and why we require effective treatments for

drug dependence. For each substance the following will be reviewed: its origin,

pharmacology, mechanisms of dependence, toxicology and pharmacotherapy. The

epidemiological and genetic risk factors of substance dependence will also be explored.

5

This review will also highlight the implications of genetic research, with specific

emphasis on the molecular genetics and pharmacogenetics of substance dependence,

focusing primarily on genes that have been associated with or show evidence of linkage

to, drug dependence. Background information on the effectiveness and the failures of

current drug substitution treatments, and the potential role of pharmacogenetics in

improving treatment individualisation, will be provided. Variants in genes encoding the

proteins involved in the metabolism or biotransformation of drugs of abuse, and of

treatment agents, are also reviewed.

II. Epidemiology of Substance Dependence and Addiction

The field of epidemiology involves investigation of the distribution and determinants of

health conditions in populations or population subgroups. According to the current

Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), substance

dependence is defined as a pattern of behavioural, physiologic and cognitive symptoms

that develop due to substance use or abuse, usually indicated by tolerance to the effects

of the substance and by withdrawal symptoms that develop when use of the substance is

terminated (APA, 1994). Three main factors contribute to the development of

dependence: environmental factors; drug-induced physiological effects such as the

effects on neurochemistry, neural networks, mRNA, peptide and protein levels; and

genetic factors (Johnson, 2011).

In general, the development of dependence involves transition from casual drug use to a

pattern of compulsive use; however, the majority of individuals who try substances with

addictive potential do not become dependent. Anthony et al. (1994) have reported that

23% of individuals, who use opiates at least once in their lifetime become dependent,

compared with 32% for tobacco, 17% for cocaine, 15% for alcohol and 13% for other

illicit drugs. Opiate dependence has the highest propensity for causing physical harm to

the user and societal harm through damage to family and social circles. The economic

costs of opiate dependence are also high, and include the costs of health care, social care

and crime (Ball et al., 1991; Bament et al., 2004; Hulse et al., 1999; Nutt et al., 2007).

Substance dependence is grouped with chronic psychiatric disorders. Individuals

suffering from addiction are exposed to a major threat to their health and social

standing, in both the short term and, equally important, the long term. According to the

World Health Organisation (WHO), 76.3 million people worldwide abuse or are

6

dependent on alcohol, and at least, 15.3 million persons have drug use disorders (WHO

2012). Alcohol contributes to traumatic outcomes that kill or disable at a relatively

young age, and therefore contribute far more to years of life lost to death and disability

than tobacco or illegal drugs (WHO 2012). The burden of drug abuse disorders,

measured in disability adjusted life years (DALYs), is not equally distributed among

countries: it is significantly higher in Europe and the Western Pacific than in Africa and

the Eastern Mediterranean (WHO 2004). Restrictive drug regulations and religious

influences are known to have an impact on specific drug abuse patterns (and might even

include behaviours such as compulsive gambling, which are also thought to rely on the

same reward mechanisms as dependence on legal and illegal substances) (Ball et al.,

1991; Bament et al., 2004; Hulse et al., 1999; Nutt et al., 2007).

Substance Abuse in Jordan

Addiction to alcohol and drugs is a significant concern for Jordan (The Hashemite

Kingdom of Jordan, 2010). Since its foundation in 1921, Jordan has been well aware of

the danger of drug use, issuing the first legislation concerning illicit substances in, 1962

(The Hashemite Kingdom of Jordan, 2010). The latest law regarding drug use was

issued in, 1988 (Narcotic and Psychotropic Substance Law, 1988). The Jordanian

government prohibits the import, export, transport, usage, production and possession of

illicit substances, and treats these activities as a crime except for medical and research

use (The Hashemite Kingdom of Jordan, 2010). If a drug-associated crime has

demonstrable links to international crime syndicates, the death penalty is applied

(Narcotic and Psychotropic Substance Law, 1988). Importantly, current legislation

protects those who voluntarily seek treatment for substance abuse from prosecution, and

treats all data relating to them as highly classified and confidential.

In Jordan, drug and alcohol use are considered unacceptable behaviours, from both

social and religious perspectives (Haddad et al., 2010), although the attitude of the

religious community has no influence on the prescribed criminal penalties (Hadidi et al.,

2009). Although Jordan is known for its conservative Islamic values, substance abuse is

increasing. Jordan has a young population, with 41% under the age of, 15 and 31% of

the population aged from, 15 to 29 years (The Hashemite Kingdom of Jordan, 2010;

WHO, 2010). An increase in nicotine use among young Jordanians has raised concern

about increases in the use of other drugs, as nicotine is considered to be the first step

towards illicit drugs (Haddad et al., 2010).

7

Epidemiological data in Arab countries on substance use, including use of alcohol,

opiates, cannabis, amphetamines and prescribed medicines (e.g. benzodiazepines), are

still scarce (Fawzi, 1980; The Hashemite Kingdom of Jordan, 2010), but two studies

have been undertaken to assess the nonmedical use of substances among young

Jordanians. The first was conducted by WHO in 2005. Of 2,471 students surveyed aged

18 to 25 years, 3% indicated they had used drugs. Clearly, students in Jordan are not

immune to drug use (United Nations Office on Drug and Crime (UNODC), The

Hashemite Kingdom of Jordan, 2010). The second study was conducted by the UNODC

in 2001 (The Hashemite Kingdom of Jordan, 2010). A total of 5,000 students (80%

from universities and 20% from community colleges) aged from 18 to 25 years were

interviewed with regard to their primary drug use. According to the results of this study,

tobacco (29%), sedatives (12%) and alcohol (12%) were the major substances abused

by the students. Other substances like stimulants (5%), cannabis products (3%) and

opiates, mainly heroin (1%), were used less frequently. A number of the students had

recently been imprisoned. Neither of these studies collected data about polysubstance

abuse, which is the simultaneous use of two or more substances to achieve a particular

effect (Johnson, 2011).

According to a report by UNODC (2010), there are indications that substance abuse in

Jordan is increasing, and is most prevalent among 25-year-old males (Haddad et al.,

2010). Studies conducted by the United Nations Office on Drugs and Crime (UNDCP)

with substance abuse patients in drug treatment centres indicate that up to 75% of

substance abusers are male. According to the UNODC report, using heroin by “chasing

the dragon” has increased significantly in recent years. “Chasing the dragon” refers to

inhaling the vapour from morphine, heroin, oxycodone or opium that has been heated

on a piece of foil (Gossop et al., 1991). The Jordanian Anti-Narcotic Department has

reported that the illicit substances of choice are alcohol (43%), pharmaceuticals such as

amphetamines, artane, kemadrin and benzodiazepines (28%), opiates such as heroin and

morphine (23%) and solvents such as acetone, thinner and toluene (7%) (UNODC, The

Hashemite Kingdom of Jordan, 2010).

Substance abuse is still primarily considered a social and legal problem, although there

is increasing recognition of the associated medical problems (Johnson, 2011). The

negative impacts of alcohol, heroin and other opiates, cocaine and amphetamines on

both patients and their families are well documented in international studies, but very

8

few studies have been conducted in this area in Jordan, where prior studies have focused

only on substance abuse in different age groups and none have addressed the severity of

problems commonly found among homecare substance abusers in Jordan. Nor have the

nature and severity of the problems reported by individuals voluntarily admitted into

substance abuse treatment programs in Jordan been examined in detail.

III. Dependence Scales

There are several dependence and addiction scales, such as the Diagnostic and

Statistical Manual of Mental Disorders (DSM), International Classification of Diseases

(ICD) and the Addiction Severity Index (ASI), available for the evaluation of drug

addiction (APA, 1994, McLellan et al., 1992, WHO, 1992). The American Psychiatric

Association has established the DSM, a manual designed to cover a wide range of

substances, and in the fourth edition (DSM-IV) both substance dependence and

substance withdrawal are included as disorders. According to DSM-IV (APA, 1994), a

diagnosis of substance dependence is met if at least three of the following criteria apply:

tolerance, as defined by an increased drug intake, characteristic withdrawal symptoms

after interruption of drug intake, increased or prolonged use, unsuccessful attempts to

cease or control use, significant time spent acquiring and using the drug, important

social, occupational, or recreational activities sacrificed because of substance abuse, and

persistent use despite clear evidence of related physical and psychological harm (Table

1).

WHO created the ICD as a guide to consistent clinical diagnosis of diseases worldwide.

It is a medical classification, coding diseases, signs and symptoms, abnormal findings,

complaints, social circumstances, and external causes of injury or disease. In the tenth

revision (ICD-10) substance addictions were included. According to the ICD, drug

addiction is a disorder that begins with the acute use of a drug and progresses to chronic

drug-seeking (WHO, 1992). The term “addiction” refers to the loss of control over drug

use despite unwanted consequences. Drug addiction develops in several stages, which

include initiation into drug use, intermittent to regular use, and, finally, addiction and

relapse. Features of addiction are the development of dependence on the drug, which

creates a physiological need for it if the individual is to function properly; the

development of tolerance, manifested as a need for larger doses of the drug to attain the

same effect; the development of withdrawal, symptoms that occur when the drug is

discontinued; and relapse, which occurs when, after a period of abstinence, the drug is

9

used again by the drug-dependent individual (WHO, 1992). ICD-10 lists six criteria for

the evaluation of substance addiction, three or more of which need to have been fulfilled

simultaneously at some time during the previous year. These criteria are presented in

Table, 1 (WHO, 1992).

McLellan et al. (1992) created the ASI, a one-hour, face-to-face semi-structured

interview that takes place when a patient is admitted for treatment (Fureman et al.,

1994; McLellan et al., 1992). The ASI is a reliable, valid instrument, freely available

and easily used to examine the status of substance abuse patients (McLellan et al., 1985;

Zanis et al., 1994, 1997). The baseline information assesses an individual’s status in

seven major life domains: alcohol and other drug use, psychiatric and medical status,

legal aspects, family, employment, and support; this information helps to determine the

patient’s level of stability and has also proven useful for understanding life events that

contribute to alcohol and drug dependency. It has been used for different purposes when

assessing substance abuse patients, such as developing treatment plans, matching

patients to the most suitable treatment options and identifying when to make referrals

(Fureman et al., 1994; McLellan et al., 1992). The ASI has been widely applied over the

past six years and has been used by researchers and clinicians internationally (McLellan

et al., 1992)

10

Table 1. Criteria for substance dependence diagnosis according to the ICD-10 (WHO 1992) and DSM-IV (APA 1994), individuals are classified

as dependent when they show three or more of the following symptoms within one year.

Substances ICD-10 DSM-IV

All Substances Three or more of the following six symptoms

occurring together for at least, 1 month, or if less

than 1 month, occurring together repeatedly within

a 12-month period:

1. Tolerance: need for significantly increased

amounts of alcohol to achieve intoxication or

desired effect or markedly diminished effect with

continued use of the same amount of alcohol.

2. A physiological withdrawal state of the

characteristic withdrawal syndrome for alcohol, or

use of alcohol (or closely related substance) to

relieve or avoid symptoms.

3. Difficulties in controlling drinking in terms of

onset, termination, or levels of use: drinking in

larger amounts or over a longer period than

intended; or a persistent desire or unsuccessful

efforts to reduce or control drinking.

4. Important alternative pleasures or interests given

up or reduced because of drinking; or a great deal of

time spent in activities necessary to obtain or use

alcohol or to recover from its effects.

5. Persisting with drinking despite clear evidence

and knowledge of harmful physical or

psychological consequences

6. A strong desire or sense of compulsion to drink.

A maladaptive pattern of substance use leading to clinically significant

impairment or distress as manifested by three (or more) of the following,

occurring at any time in the same, 12-month period:

1. Tolerance, as defined by either of the following:

a) A need for markedly increased amounts of the substance to achieve

intoxication or desired effect.

b) A markedly diminished effect with continued use of the same

amount of the substance.

2. Withdrawal, as manifested by either of the following:

a) The characteristic withdrawal syndrome for the substance

b) The same (or a closely related) substance taken to relieve or avoid

withdrawal symptoms.

3. The substance is often taken in larger amounts or over a longer period

than was intended.

4. There is a persistent desire or unsuccessful efforts to cut down or control

substance use.

5. A great deal of time is spent in activities necessary to obtain the substance

(e.g. visiting multiple doctors or driving long distances), to use the

substance (e.g. chain-smoking), or to recover from its effects.

6. Important social, occupational, or recreational activities are given up or

reduced because of substance use.

7. The substance use is continued despite knowledge of having a persistent

or recurrent physical or psychological problem that is likely to have been

caused or exacerbated by the substance (e.g. current cocaine use despite

recognition of cocaine-induced depression, or continued drinking despite

recognition that an ulcer was made worse by alcohol consumption).

11

IV. Substance Dependence

A. Nicotine

Nicotine is an alkaloid found in the nightshade family of plants (Solanaceae).

Biosynthesis of nicotine takes place in the roots and its accumulation occurs in the

leaves (Von Bibra BEF, 1995). Solanaceae leaves constitute approximately 0.6 to 3.0%

of the dry weight of tobacco and is present in the range of 2 to 7µg/kg of various edible

plants (Rodgman et al., 2009). In the past, nicotine was used as an antiherbivore

chemical and insecticide (Ujvary & Islvan, 1999). It acts as a stimulant substance in

mammals at low concentrations of about, 1 mg of absorbed nicotine, while as a clean

substance it is toxic, at concentrations of about 30 to 60 mg (Ujvary & Islvan, 1999).

According to the American Heart Association, the pharmacological and behavioural

characteristics of nicotine that determine nicotine dependence are similar to those

determining dependence to heroin and cocaine (Connolly et al., 2007).

Pharmacology of Nicotine

When cigarette smoke is inhaled, 25% of the nicotine dose is absorbed into the blood

stream. It takes only seven seconds for nicotine to reach the brain (Dar & Frenk, 1989;

Henningfield & Keenan, 1993). The affects in the central nervous system (CNS) are

immediate. The nicotine level in the plasma and the brain rises quickly, and causes a

rewarding effect (Dar & Frenk, 1989; Henningfield & Keenan, 1993). A considerable

part of the effect of nicotine is due to the increased release of neurotransmitters in the

CNS. It also increases the secretion of glucocorticoids and vasopressin, which have an

antidiuretic effect (Picciotto et al., 2008). It has positive and negative effects on the

CNS (Benowitz, 2008): the positive effects include improvement of cognitive functions

and attentiveness, decrease of anxiety, neuroprotection and analgesia; the negative

effects include hypothermia, ataxia, cramps, nausea, vomiting and development of

addiction.

The rewarding effect disappears quite quickly because the nicotinic receptors

desensitise and the nicotine metabolises (Benowitz, 2008; Picciotto et al., 2008). The

main metabolite is cotinine, which is metabolised in 24 hours (Benowitz, 2008).

Compared to the half-life of nicotine, which is 30 minutes, the difference is huge. The

amount of smoked cigarettes per day can be measured from cotinine levels in blood or

urine (Benowitz, 2008). Nicotine binds to nicotinic acetylcholine receptors (Picciotto et

al., 2008). These receptors are located in skeletal muscle tissue, medulla,

12

presynaptically throughout the central nervous system and postsynaptically in

autonomic nervous system. These nervous systems modulate the release of

neurotransmitters and ganglionic potentials (Role & Berg, 1996).

Nicotine Dependence

Nicotine acts on the brain to produce a number of effects (McKim, 1986). Specifically,

nicotine’s addictive nature has been found to act by activating the reward system in the

brain that regulates feelings of pleasure and euphoria (Balfour et al., 2000). Once

nicotine reaches the brain, it binds to nicotinic acetylcholine receptors located on

dopaminergic neurons in the ventral tegmental area, as well as to other neuronal cell

bodies (Dani & De Biasi, 2001; Mifsud et al., 1989). A single cigarette provides

sufficient nicotine to occupy approximately 90% of nicotinic acetylcholine receptors for

several hours. To date, 17 nicotinic acetylcholine receptor subunits have been identified

(α1–α10, β1–β4, γ, δ, and ε) (Brody et al., 2006; Millar, 2003). Nicotine acts as a

chemical with intense addictive qualities, and increases dopamine level by stimulating

nicotinic acetylcholine receptors in the brain. Dopamine is one of the key

neurotransmitters actively involved in the brain (Beuten et al., 2006). Brain imaging

research shows that nicotine causes an increase in the level of dopamine within the

rewards circuits in the brain; over time, however, the proportion of unbound nicotinic

acetylcholine receptors increases, causing nicotine withdrawal (Staley et al., 2006).

During the withdrawal period, neurotransmitter release including dopamine is reduced,

and smokers report symptoms such as negative moods, fatigue and mild cognitive

deficits (Leventhal et al., 2008; Munafo et al., 2008). Other neurotransmitters have been

shown to play a role in nicotine dependence, including norepinephrine, acetylcholine,

glutamate, serotonin, beta-endorphin and γ-aminobutyric acid (GABA) (Benowitz,

2008). The α4β2 nicotinic acetylcholine receptors on GABAergic interneurons

desensitise quickly, while α7 nicotinic acetylcholine receptors on glutamate neurons

desensitise more slowly (Davenport et al., 1990). The endogenous opioid system

contributes to these influences via release of beta-endorphin, which binds to the mu-

opioid receptors on GABAergic interneurons in the ventral tegmental area (Li et al.,

2003; Sullivan & Kendler 1999).

13

Toxicology of Nicotine

Various animal toxicology studies have indicated that the lethal dose (LD50) of nicotine

is 50 mg/Kg for rats and 3 mg/kg for mice (Lockhart et al., 1933; Okamoto, et al.,

1994), while for adult humans, 30 to 60 mg (0.5 to 1.0 mg/Kg) can be a lethal dosage

(Lockhart et al., 1933). Nicotine has a high toxicity in comparison with many other

alkaloids such as cocaine, which has an LD50 of 95.1 mg/Kg when administered to

mice (Lockhart et al., 1933). In extreme cases, high doses of nicotine can lead to

respiratory depression and increased secretion of saliva, increased blood pressure and

vomiting (Benowitz, 2008). Nicotine withdrawal, leads to a wide range of symptoms

including anxiety, dizziness, nausea, constipation, inability to concentrate, weight gain,

and sleep disturbances (Benowitz, 2008; Lerman et al., 2007; McKim, 1986). Toxic

effects of long-term nicotine use are found in the lung and heart (Kitamura, 1987;

McKim, 1986; Patel et al., 2008; Perrine, 1996; Seliskar & Rozman, 2007; Warner,

2005). The toxic effects of nicotine use on the lungs are due to the inhalation of smoke

rather than to the direct effect of the nicotine. Ash and tar are deposited in the lung and

pyrolytic compounds such as benzo[a]pyrine in the smoke, which is metabolised into a

carcinogenic compound by P-450 enzymes in the lung tissue (Perrine, 1996; Seliskar &

Rozman, 2007). Increased exposure to these toxic chemicals in the lungs can lead to

serious medical problems including emphysema and lung cancer (Patel et al., 2008;

Warner, 2005). Nicotine use is clearly linked to an increased risk of heart disease

(McKim,, 1986). The direct effects of nicotine on the heart and vascular system are

compounded by the effects of carbon monoxide and other pyrolytic compounds derived

from the accompanying smoke (McKim, 1986), causing a reduction in systematic

oxygen perfusion and further taxing the heart and brain. In addition, nicotine contributes

to the deposit of cholesterol on vascular walls, causing atherosclerosis (McKim, 1986).

This causes a reduction in blood perfusion and an increase in the circulatory pressure

(McKim, 1986).

Pharmacotherapies for Nicotine Dependence

A wide range of drugs have been tested as pharmacotheraputic for nicotine dependence

(Lerman et al., 2007), but few have proven more successful than placebos. Currently,

the best candidates for the pharmacotherapy of smoking are bupropion and nicotine

replacement (Wilkes, 2008). Bupropion is an atypical antidepressant and smoking

cessation aid. Its chemical name is β-Keto-3-chloro-N-tert-butylamphetamine; it is a

chemical derivative of substituted amphetamine (Wilkes, 2008). Its primary

14

pharmacological action is thought to be norepinephrine-dopamine reuptake inhibition. It

binds selectively to the dopamine transporter, but its behavioural effects have often been

attributed to its inhibition of norepinephrine reuptake. It also acts as a nicotinic

acetylcholine receptor antagonist (Lerman et al., 2007). Nicotine replacement is

provided either in chewing gum or on a transdermal patch. The benefit of nicotine

replacement therapy is that it prevents cravings in smokers while allowing them to

abstain from tobacco and avoid the harmful effects of smoking (Slotkin, 2008).

B. Ethanol (Alcohol)

Ethanol is derived from the fermentation of sugars in fruits, cereals and vegetables

(Gately, 2008). The details of the original discovery of fermented beverages have been

lost to time because of the unavailability of the written document. It is known that

fermented beverages existed as early as, 10,000 BC (Patrick, 1952). Aristotle is

considered the founding father of the scientific method, in the third century BC showing

that boiled wine lost its intoxicating character. He never took the next step of

condensing the ethanol (Gately, 2008); that was done by a Muslim chemist in the eighth

century.

Chemically, ethanol is a low-molecular weight hydrocarbon that is highly soluble in

water and lipids (Norberg et al., 2000). There are many applications for ethanol in the

daily life of every society: early versions of beer were used as food, and alcoholic

beverages such as beer and wine had medicinal properties (Patrick, 1952). Today, both

adults and adolescents abuse ethanol, resulting in significant medical and social

morbidity (Johnson, 2011).

Pharmacology of Ethanol

After oral administration, ethanol is rapidly absorbed into the blood, principally from

the lipid membranes of the stomach, small intestine and colon (Cooke et al., 1969;

Finch et al., 1974; Kalant, 1971). There are many factors such as food, ethanol

concentration and liquid volume that affect the gastric emptying rate and absorption of

ethanol, but once it reaches the small intestine, its absorption is rapid and complete

(Wilkinson et al., 1977). The metabolism of ethanol occurs mainly in the liver (Lieber,

1999). The first step of metabolism involves oxidation of ethanol to acetaldehyde.

Multiple enzymes are involved in the ethanol metabolism in the liver, including the

hydrogen peroxide-dependent peroxisome catalase system, the microsomal ethanol-

15

oxidising (MEOS) located in the endoplasmic reticulum and the hepatic enzymes such

as cytosol enzyme and alcohol dehydrogenase (ADH) (Lieber, 1999).

A secondary pathway of oxidative ethanol metabolism occurs in liver microsomal tissue

in the smooth endoplasmic reticulum (Lieber, 1999). Chronic consumption of ethanol

increases the capacity of the liver microsomal ethanol system, with a rise in several

cytochromes P-450, especially a nicotinamide adenine dinucleotide phosphate requiring

enzyme (CYP2E1), while the activity of ADH does not change with chronic ethanol

consumption (Lieber, 1999). The contribution of the MEOS to the oxidation of ethanol

increases as the BEC (blood ethanol concentration) increases. The activity of the MEOS

is significantly increased in chronic drinkers (Chesher et al., 1992; Lieber, 1999).

MEOS generates a reduced form of NAD+ (NADH) by oxidising nicotinamide-adenine

dinucleotide (NAD+) to yield the oxide form of NAD phosphate (NADP+). Oxidation

of ethanol by ADH causes an increase in the NADH/NAD+ ratio, which decreases the

oxidative capacity of the liver, creating an unfavourable state for oxidative metabolism

and limiting the rate of ADH activity. The activated MEOS in chronic alcoholics creates

excess NADP+, enhancing ADH activity in the liver (Lieber, 1999).

The next step in ethanol metabolism is the oxidation of acetaldehyde to acetate, yielding

more NADH (Wilkinson, 1980). The reaction is catalysed by various acetaldehyde

dehydrogenase isoenzymes. These isoenzymes are very efficient, having a Km (the

concentration of a substrate that yields one half maximal enzyme activities)

approximately 1000 times lower than that of the ADH for ethanol (Wilkinson, 1980).

The increased acetaldehyde levels funds in chronic alcoholics are a result of increased

production rather than of inadequate acetaldehyde dehydrogenase activity.

Acetaldehyde is thought to be a mediator of alcoholic liver toxicity, which decreases

cellular capacity to repair DNA, increases free radical mediated lipid peridoxication and

augments hepatic collagen synthesis (Wilkinson, 1980). Between 90 and 95% of

ingested ethanol is converted to acetaldehyde and acetate, and then metabolised further

to CO2 which ultimately is expired through the lungs. About 3–5% of a dose of ethanol

is eliminated unchanged in the urine, breath, or through the skin (Wilkinson, 1980).

Less than 2% of ethanol is metabolised non-oxidatively to ethyl glucuronide, ethyl

sulphate, phosphatidylethanol and fatty acid methyl esters (Wilkinson, 1980).

16

Ethanol (Alcohol) Dependence

According to WHO ICD-10 and DSM-IV-TR, alcohol dependence is characterised by a

cluster of physiological, behavioural and cognitive phenomena, in which the use of this

substance takes on a much higher priority for an individual than behaviours that once

had greater value, and a return to drinking after a period of abstinence is often

associated with the reappearance of the features of the syndrome (reinstatement) (WHO,

1994, APA, 1994). Criteria for diagnosis of alcohol dependence include a strong desire

or compulsion to drink alcohol despite knowledge or evidence of its harmful

consequences, difficulty in controlling drinking in terms of onset, termination or level

of its use, physiological withdrawal symptoms and development of tolerance (WHO,

1992, APA, 1994).

Current studies suggest several signal pathway mechanisms leading to the development

and maintenance of alcohol dependence (Harris, 2008; Wallner & Olsen, 2008). One

pathway involves the opioidergic and mesolimbic dopaminergic system, which seems to

cause alcohol craving and relapse by positively reinforcing the effects of alcohol

consumption, especially in the earlier stages of the disease (Harris, 2008; Wallner &

Olsen, 2008). A second pathway involves several components of the glutamatergic and

GABAergic system (Harris, 2008; Wallner & Olsen, 2008). This pathway seems to

induce alcohol craving and relapse due to a hyperglutamatergic state. A further pathway

seems to be a hypodopaminergic state. This pathway occurs especially during

withdrawal after chronic alcohol intake, and is associated with a state of dysphoria that

helps in the resumption of alcohol intake. Most patients with former alcohol dependence

are thought to retain a continuing vulnerability to relapse for years or even their lifetime

because of the neuroadaptive changes. The majority of relapses after abstinence occur

within a period of one year, and most within the first six months (Chesher et al., 1992).

Toxicology of Alcohol

From the medical perspective, ethanol is a selective CNS depressant in low doses, and a

generalised depressant in high doses. For example, ethanol consumption of less than

two standard drinks per day (≤∼30 g) appears to increase longevity (Bagnardi et al.,

2004), and the mechanism is probably related to a reduction in coronary heart disease

(Corrao et al., 2004); therefore, low or moderate levels of ethanol consumption are

thought to be healthful. On the other hand, chronic heavy ethanol consumption of five

standard drinks per day (∼75 g) or more for men, and four standard drinks per day

17

(∼60g) or more for women, or frequent binging, can cause severe health problems

resulting from the direct, toxic effects of ethanol on the liver, heart, brain, kidneys and

stomach. Indirectly, the replacement of calories from food by calories from ethanol

(malnutrition) can cause additional negative effects on these organ systems (Lieber,

1995). The serious abuse of ethanol may eventually result in alcoholism, which includes

very serious medical and behavioural difficulties (Lieber, 1995).

Pharmacotherapies for Alcohol dependence

According to the Food and Drug Administration, there are four approved medications

(Benzodiazepine, Disulfiram, Naltrexone and Acamprosate) for alcohol dependence. All

four are recommended only as adjuncts to psychosocial counselling in motivated

patients.

Benzodiazepine is a psychoactive drug whose core chemical structure is the fusion of

benzene and diazepine rings (Lindsay et al., 1998). This medication enhances the effect

of the neurotransmitter gamma-aminobutyric acid (GABA), which results in sedative,

hypnotic (sleep-inducing), anxiolytic (anti-anxiety), anticonvulsant, muscle relaxant and

amnesic actions (Gitlow et al., 2006; Kushner et al., 2000). These properties make

benzodiazepines useful in treating alcohol withdrawal symptoms such as anxiety,

insomnia, agitation, seizures and muscle spasms, and so they are useful in the

management of acute alcohol withdrawal (Gitlow et al., 2006).

Disulfiram is prepared by the oxidation of sodium diethyldithiocarbamate with iodine

(Wright & Moore, 1990). Disulfiram interferes with the process of alcohol metabolism

which causes an accumulation of toxic acetaldehyde as a by-product when alcohol is

broken down in the body (Wright & Moore, 1990). Excess amounts of this by-product

cause symptoms such as severe headache and nausea, and with higher amounts of

alcohol can lead to more dangerous toxic effects (Krampe et al., 2006).

Naltrexone is a non-selective opiate antagonist, which binds with receptors for

endogenous opioids and appears to modify some of the reinforcing effects of alcohol

and to reduce alcohol drinking or prevent alcohol-seeking behaviour (Krystal et al.,

2001, Soyka et al., 2008). Naltrexone-treated alcohol-dependent patients have been

reported to drink less frequently and in smaller quantities (Soyka et al., 2008). However,

meta-analyses of many studies of naltrexone hydrochloride medication have suggested

18

that the effective size for response over placebo is in the small to moderate range

(Bouza et al., 2004; Kranzler & Van Kirk, 2001; Srisurapanont & Jarusuraisin, 2005;

Streeton & Whelan, 2001). For instance, in the Combined Pharmacotherapies and

Behavioral Interventions for Alcohol Dependence (COMBINE) Study, naltrexone was

most efficacious when used in the context of medical management (Anton et al., 2006).

Based on these studies, it is clear that not all individuals with alcohol dependence

respond to naltrexone.

Finally, Acamprosate is a GABA agonist and functional glutamate antagonist, used as

an anti-craving substance in several EU countries for preventing relapses in abstinent

alcohol users (Mason et al., 2010; Rösner et al., 2010).

C. Opiates and Other Opioids

All opiates are extracted from the juice of the opium poppy, obtained from the seedpods

of the plant (Papaver Somniferum) (Brownstein, 1993). This plant and its product

opium was likely cultivated in the Mediterranean region by the Sumerians; it was

known as Hul Gil (the joy plant) around 3400 BC by ancient Egyptians and Greeks, and

later was used by the Romans (Brownstein, 1993). At least since opium cultivation,

opioids in various forms, both natural and synthetic, have been used and abused

(Brownstein, 1993). The term opioid refers to all compounds, natural or synthetic, that

exhibit morphine-like activity via opioid receptors (Brownstein, 1993). Today opioids

are categorised into three major groups based on their chemical structure (Chen et al.,

1993; Waldhoer et al., 2004; Bailey et al., 2005). The first category is phenanthrenes,

which are classical opioids processing the 4,5-epoxymorphine ring structure. These

include opiates such as morphine and heroin, as well as synthetic opioids like

buprenorphine and naloxone. The second category is piperidines and phenylpiperidines,

such as loperamide and fentanyl. The third category is diphenylheptylamines like

methadone. Other compounds such as pentazocine and tramadol do not fit any of these

categories but are agonists at opioid receptors and are therefore classified as opioids

(Bailey et al., 2005; Chen et al., 1993; Waldhoer et al., 2004).

19

Pharmacology of Opiates

The absorbed opioids (e.g. heroin) are rapidly cleared from the blood and deposited

primarily in the kidney, liver, brain, lung, spleen, skeletal muscles and placental tissue.

Heroin is metabolised primarily through the hepatic pathway with some form of

conjugation, dealkylation and oxidation (Aghajanian, 1978; Bailey & Connor, 2005;

Johnson & Fleming, 1989). Heroin (diacetylmorphine or diamorphine) is the drug most

often associated with illicit opioid use. It is a particularly effective euphoric agent due to

its rapid onset of action, and is most often taken intravenously. Heroin smoking and

intranasal administration are also common (Hemstapat et al., 2003; White & Irvine,

1999). It lipophilicity allows it to move rapidly across the blood brain barrier into the

CNS (Hemstapat et al., 2003; White & Irvine, 1999). It is also rapidly metabolised in

both blood and tissues, and has a relatively low mu-opioid receptor affinity compared to

its metabolites (Aderjan & Skopp, 1998; Boettcher et al., 2005; Kilpatrick & Smith,

2005; Klous et al., 2005; Sawynok, 1986). Heroin can essentially be considered a pro-

drug, with its major opioid active metabolites (6-monoacetylmorphine [6-MAM] and

morphine) being responsible for its euphoric effects (Aderjan & Skopp, 1998;

Kilpatrick & Smith, 2005).

The pharmacological effects of all opiate drugs are mediated via opioid receptors

(Dhawan et al., 1996; Hawkins et al., 1988; Kitchen et al., 1997; Mansour et al., 1987).

These G-protein coupled receptors are found in both pre- and post-synaptic membrane.

These receptors also located in various regions of the central nervous system (CNS) and

to a lesser extent in the periphery, and directly inhibit cell signalling by reducing both

neuronal excitability and neurotransmitter release. Various molecular pharmacological

studies have characterised these receptors as playing roles in antinociception, sedation

and drug rewards. Opioid receptors in the brain are responsible for the analgesia,

euphoria or dysphoria, sedation, respiratory and decreased gastrointestinal mortality

commonly associated with opioid compounds, while pre-synaptic opioid receptors on

primary afferents located in the dorsal horn of the spinal cord may also mediate

analgesia (Besse et al., 1990; Rang et al., 1999).

The opioid receptors belong to the G-protein-coupled receptor (GPCR) superfamily.

There are three different opioid receptor subtypes: mu (μ), kappa (κ), and delta (δ)

(Dhawan et al., 1996; Corbett et al., 2006). The mu-opioid receptor was the first opioid

binding site to be described, and provided the primary target of most opioid

20

therapeutics. The μ-opioid receptor (MOR) – the μ represents morphine – is primarily

involved in the regulation of pain. µ receptors are classified into µ1, μ2 and µ3

subclasses. µ1 receptors have a high affinity for morphine and are known to have

supraspinal analgesic proprieties; almost all analgesic properties of opiates are derived

from this subclass. µ2 and µ3 receptors have a lower affinity for opioids. In addition to

the clinically useful analgesic effects; activation of MOR leads to various other effects,

such as vomiting, nausea, hypothermia, constipation, respiratory suppression and death

(Pasternak, 1993). It has been hypothesised that the different effects of the drugs acting

through MOR might be mediated by the activation of different G-proteins.

Kappa (κ) opioid receptors bind to the peptide opioid dynorphins as a primary

endogenous ligand (Hasebe et al., 2004; Land et al., 2008). Kappa receptors are widely

distributed in the brain and spinal cord, and in pain neurons. Based on receptor binding

studies, three variants of the κ-opioid receptor designated κ1, κ2, and κ3 have been

characterised. However only one cDNA clone has been identified, hence these receptor

subtypes likely arise from interaction of one κ-opioid receptor protein with other

membrane associated proteins. κ-Opioid receptor activation by agonists is coupled to

the G protein, which subsequently increases phosphodiesterase activity.

Phosphodiesterases break down cAMP, producing an inhibitory effect in neurons. It has

long been understood that kappa-opioid receptor agonists are dysphoric. It is now

widely accepted that κ-opioid receptor (partial) agonists have hallucinogenic

("psychotomimetic") effects, and these effects are generally undesirable in medicinal

drugs and could have had frightening or disturbing effects in the tested humans. The

involvement of the kappa-opioid receptor in stress response has been elucidated.

Activation of the κ-opioid receptor appears to antagonise many of the effects of the μ

opioid receptor. Kappa ligands are also known for their characteristic diuretic effects,

because of their negative regulation of antidiuretic hormone (ADH). Kappa agonism is

neuroprotective against hypoxia and ischemia, which make kappa receptors a novel

therapeutic target (Hasebe et al., 2004; Land et al., 2008).

The delta (δ) opioid receptors (DOR) have enkephalins as their endogenous ligands.

Activation of delta receptors produces some analgesia, although less than that of µ-

opioid agonists. Many delta agonists may also cause seizures at high doses.

21

Evidence for whether delta agonists produce respiratory depression is mixed; one of

their most interesting qualities is their potential as a novel class of antidepressant drugs

(Broom et al., 2002).

Opiate Dependence

The American Psychological Association’s Diagnostic and Statistical Manual of Mental

Disorders (DSM-IV) defines substance dependence as three or more of the following

six characteristics or behaviours: Sense of compulsion to take the drug, difficulty in

controlling drug-taking behaviour in terms of the onset, termination or level of use,

withdrawal symptoms after interruption of drug intake, evidence of tolerance,

significant time acquiring and using the drug, and persistent drug use despite adverse

consequences (APA, 1994).

Regular administration of opiates leads to the development of tolerance to the drug

(Aghajanian, 1978; Bailey & Connor, 2005; Johnson & Fleming, 1989). Tolerance is

associated with the super activation of the receptor, which may be affected by the

degree of endocytosis caused by the opioid administered, and leads to a super activation

of cyclic AMP signalling. This pharmacodynamic tolerance reflects a decreased

response of the affected organs rather than a change in the metabolism of the substance,

and tolerance is hence not the same for all effects: there is, for instance, a distinct

reduction in euphoria and sedation (respiratory depression) and a slight reduction in

peripheral effects (miosis and constipation). When long-term use of opioids is stopped

suddenly, withdrawal symptoms are both physical and psychological, and are a reversal

of the effects of the opioid. They are perceived as unpleasant but not life-threatening.

The duration of withdrawal depends on the duration and amount of opioids that were

consumed; it usually lasts for several days.

Physical opioid dependence is a result of physiological changes caused by repeated

exposure to opioids (Aghajanian, 1978; Bailey & Connor, 2005; Johnson & Fleming,

1989). This is revealed by a withdrawal syndrome upon cessation of opioid use or the

administration of an opioid antagonist. Typical withdrawal symptoms are non-fatal, and

can include yawning, sneezing, watery eyes, muscle and bone ache, muscle cramps,

sleep disturbance, sweating, hot and cold flushes, lacrimation, rhinorrhoea, abdominal

pain, nausea, vomiting, diarrhoea, palpitations, elevated blood pressure, elevated pulse

and dilated pupils. Various studies suggest that most acute withdrawal symptoms can be

22

attributed to homeostatic changes in neurotransmitter systems such as noradrenaline,

dopamine, opioid peptides, serotonin and gamma-aminobutyric acid (GABA), as well as

brain stress system components including corticotrophin-releasing factors. However,

Hutchinson et al. (2007) have recently reported that glia may be implicated in the

development of opioid dependence and withdrawal in animals, by inhibiting opioid glial

activation. Many opioid addicts repeatedly experience withdrawal symptoms either

because of a lack of access to drugs or through detoxification attempts without medical

observation or treatment. In these circumstances, patients who relapse commonly take

the same dose that they took before the period of abstinence. Among these patients,

withdrawal symptoms generally peak around two to four days post-abstinence, with

obvious physical signs of withdrawal lasting up to seven days (Aghajanian, 1978;

Bailey & Connor, 2005; Johnson & Fleming, 1989).

While psychological manifestations of opioid withdrawal can largely revert to normal

within days or weeks of opioid cessation, acute withdrawal symptoms are typically

followed by a protracted withdrawal syndrome characterised by general feelings of

reduced wellbeing and periodic drug craving (Aghajanian, 1978; Bailey & Connor,

2005; Johnson & Fleming, 1989; Koob, 2005). These psychological symptoms include

anxiety and dysphoria, which often trigger renewed consumption and may be

manifested as continued drug craving following prolonged abstinence from opioids, and

a residual state of anxiety and dysphoria; they can be induced by stress or by specific

stimuli including certain environments, people or situations previously associated with

drug use. The mechanism behind drug craving and relapse following protracted

abstinence is still not clear; however, Koob et al. (2005) have proposed that in an

attempt to maintain a constant hedonic state during opioid challenge, normal brain

haemostasis is abandoned in favour of a new allostatic state. As result, there is a

persistent deviation from normal brain reward threshold regulation, which can reduce

the saliency of natural rewards. This complex psychological component of opioid

dependence is most responsible for relapse after protracted abstinence.

23

Toxicology of Opiates

Various toxicological studies report that it is difficult to establish a median lethal dose

for heroin among regular users (Strandberg et al., 2006). Some individuals have

overdosed on as little as 1 mg/kg of heroin, while a median lethal dose for non-addicts

has been suggested as 1 to 5 mg/kg. There is no easily identifiable upper limit to the

amount of heroin that a heavily addicted individual can take. Research conducted

among opiate-dependent individuals described heroin being administered in doses of

1,600 to 1,800 mg, with no obvious adverse side effects (Strandberg et al., 2006). These

results are supported by studies in rats showing that 14 days of pre-treatment with

morphine or heroin reduces mortalities associated with subsequent morphine

administration. Consequently, long-term opiate abuse is typically associated with the

development of highly significant drug tolerance (Strandberg et al., 2006).

Neurological complications as result of heroin use include peripheral neuropathies,

nerve pressure palsies, hypoxic encephalopathy, seizures, rhabdomyolysis and

transverse myelopathies (Halloran et al., 2005). Spongiform leukoencephalopathy is a

relative complication of heroin use. During the period from 1984 to 2004, 70 cases with

this neurological complication were reported worldwide. The complication is typically

noticed among users who inhale fumes generated by heating heroin, “chasing the

dragon” (Hill et al., 2000). Symptoms include cognitive dysfunction, cerebellar ataxia,

dysarthria, and motor restlessness, with an estimated mortality rate of approximately

25%

Heroin-related death has been associated with the phenomenon of place tolerance,

revealed in the phenomenon of overdoses occurring as a result of using the drug in an

unfamiliar environment (Gerevich et al., 2005). When heroin is used outside the user’s

normal location, an environment-conditioned tolerance does not occur, and enhances the

effects of the drug. In response to this decrease in tolerance, the user increases the

typical dose of the drug. If extreme in self-dosing, the result can be a fatal overdose.

The greatest danger posed by an overdose is respiratory paralysis, which can lead to loss

of consciousness, coma and death (Aghajanian, 1978). Massive pupil constriction

(pinpoint pupils) is very noticeable. Naloxone, an opioid receptor antagonist, is applied

to patients with respiratory depression caused by opioid intoxication. It has no effect

when opioids are not present, but reverses the actions of opioids when they are present

in the patient’s body (Johnson, 2011).

24

Pharmacotherapies for Opiate Dependence

Currently, the three major maintenance pharmacotherapies for the treatment of opioid

dependence are methadone, buprenorphine and naltrexone. When combined with

psychosocial services, these maintenance treatments are effective in reducing opiate use,

dangerous behaviour and criminal activity, while improving the mental health of

patients (Ball & Ross, 1991; Johnson et al., 2011). However, the majority of opiate-

dependent individuals remain out of treatment, and those in treatment exhibit only 60–

70% success rates (Ball & Ross, 1991); it remains an essential goal to further the

understanding of the factors underlying poor treatment outcomes and assist in the

development of more individualised, optimised and ultimately more effective treatments

for opiate drug dependents.

The first major maintenance treatment is methadone, which is a synthetic μ-opioid

receptor agonist used primarily for alcohol and opiate dependence (Johnson, 2011). The

second major maintenance treatment is buprenorphine, a semi-synthetic opioid used to

treat opiate addiction (Johnson, 2011). Pharmacogenetic studies have shown that both

methadone and buprenorphine display considerable interindividual variability in their

pharmacokinetics and contribute to high interindividual variability in response to

treatment (Crettol et al., 2006; Eap et al., 2002; Johnson, 2011). The third major

maintenance treatment is naltrexone, an opioid receptor antagonist also used primarily

in the management of opioid dependence.

D. Benzodiazepines

Benzodiazepines are a class of psychoactive drugs with varying hypnotic, sedative,

anxiolytic, anticonvulsant and muscle relaxant properties, which are mediated by

slowing down the central nervous system (Lader, 1991). The first benzodiazepine is

chlordiazepoxide (Librium), whose core chemical structure is the fusion of a benzene

ring and a diazepine ring (Lader, 1991). This drug was discovered accidentally by Leo

Sternbach in 1955, and made available in 1960 by Hoffmann–La Roche, which has also

marketed diazepam (Valium) since 1963 (Lader, 1991).

Pharmacology of Benzodiazepines

Benzodiazepines are generally classified according to their pharmacokinetics,

specifically by their duration of actions. Short-acting benzodiazepines including

midazolam are primarily used for conscious sedation or the induction of anaesthesia,

25

and are available in commercially prepared solutions for intravenous administration

(Hobbs et al., 1996). These drugs generally have a half-life of minutes to a few hours.

Intermediate-acting benzodiazepines (e.g. alprazolam or lorazepam) are used orally for

anxiety and insomnia, and sometimes lorazepam is also available for parenteral

administration, primarily to reverse convulsions. Long-acting drugs such as diazepam

are generally taken orally (Hobbs et al., 1996).

Benzodiazepines are absorbed orally, and once in the circulation system they bind to

plasma proteins, with the extent of binding varying with lipid solubility from 70% for

alprazolam to 99% for diazepam (Hobbs et al., 1996). Redistribution can occur for

drugs with the highest lipid solubility. Benzodiazepines are extensively metabolised by

several hepatic microsomal systems. The most important aspect of the pharmacokinetics

of benzodiazepines is the formation of active metabolites. Although a few

benzodiazepines such as lorazepam are inactivated by the initial metabolic reaction,

most are converted to metabolites that have the same mechanism of action as the parent

compound. For some drugs, more than one biotransformation reaction is needed to

inactivate the drug, and often the subsequent reactions occur more slowly than the

initial reaction; consequently, the duration of action of most benzodiazepines has little

to do with its half-life in plasma. The hepatic enzymes responsible for the metabolism

of benzodiazepines are not induced by chronic benzodiazepine treatment.

Benzodiazepines produce a range of effects from depressing to stimulating the central

nervous system by modulating the GABAA receptor, the most prevalent inhibitory

receptor within the brain (Obata et al., 1988). The subsets of gamma-aminobutyric acid

(GABA) receptors that also bind to benzodiazepines are referred to as benzodiazepine

receptors (BzR) (Gaudreault et al., 1991; Nicholson & Balster, 2001). Some

benzodiazepines are full BzR agonists, producing anxiolytic and sedating properties.

Compounds that, in the absence of an agonist, have no apparent activity but

competitively inhibit the binding of agonists to the receptor are called BzR antagonists.

Ligands that decrease GABA function are termed benzodiazepine receptor inverse

agonists. Full inverse agonists have potent convulsant activities (Gaudreault et al., 1991;

Nicholson & Balster, 2001).

There has been interest in partial agonists for the BzR, with evidence that complete

tolerance may not occur with chronic use, and with partial agonists demonstrating

26

continued anxiolytic properties with reduced sedation, dependence, and withdrawal

problems. However, the anticonvulsant properties of benzodiazepines may be in part or

entirely due to binding to voltage-dependent sodium channels rather than to

benzodiazepine receptors. Sustained repetitive firing seems to be limited by

benzodiazepine’s effect of slowing the recovery of sodium channels from inactivation

(Nicholson & Balster, 2001). Benzodiazepine receptors also appear in a number of non-

nervous-system tissues and are mainly of the peripheral benzodiazepine receptor type

(Gaudreault et al., 1991; Nicholson & Balster, 2001). These peripheral benzodiazepine

receptors are not coupled or attached to GABAA receptors. They are found in various

tissues such as heart, liver, adrenal, and testis, and in lymphatic cells.

The major side effects which may occur from use of include drowsiness, dizziness,

upset stomach, headache, depression, impaired coordination changes in heart rate,

weakness, hangover effect, dreaming or nightmares, chest pain and vision changes, but

deaths resulting from pure benzodiazepine overdoses are rare (Gaudreault et al., 1991).

Long-term benzodiazepine usage, in general, leads to some form of tolerance and/or

drug dependence with the appearance of a withdrawal syndrome when the

benzodiazepines are stopped or the dose is reduced. Benzodiazepines are usually a

secondary drug of abuse, used mainly to augment the high received from another drug

or to offset its adverse effects (Gaudreault et al., 1991; Nicholson & Balster, 2001).

Benzodiazepines Dependence

According to DSM-IV, benzodiazepine dependence is a frequent complication of

regular benzodiazepine (e.g. diazepam) use for four weeks or longer. It is manifested by

tolerance to drug effects and occasional drug-seeking behaviour, and characterised by a

withdrawal syndrome on stoping treatment (APA, 1994).

Benzodiazepine dependence can be psychological, physical or both (Haddad et al.,

2004; O’Brien 2005; Uzun et al., 2010). Physical dependence occurs when a person

becomes tolerant to benzodiazepines as a result of both physiological tolerance and

withdrawal symptoms. Benzodiazepine withdrawal symptoms occur upon dosage

reduction or cessation, and can be categorised by severity. Minor symptoms include

increased anxiety, involuntary muscle twitches, tremor, progressive weakness,

dizziness, visual illusions, nausea, insomnia, weight loss and orthostatic hypotension.

Major symptoms include tonic-clonic seizures and psychosis resembling the delirium

27

tremens that occurs when alcohol use is discontinued. Several studies suggest that

withdrawal symptoms and the development of tolerance within benzodiazepine-

dependent individuals can be attributed to homeostatic changes or neuroadaptations in

neurotransmitter systems such as GABA, as well as in glutamate systems (Allison &

Pratt, 2003). These adaptations occur as a result of the body trying to overcome the

depressant effects of the drug on the central nervous system and restore homeostasis.

When benzodiazepines are stopped these neuroadaptations are unmasked, leading to

hyper-excitability of the nervous system and the appearance of withdrawal symptoms.

Psychological manifestations of benzodiazepine withdrawal largely revert to normal

within days or weeks of cessation (Haddad et al., 2004). Acute withdrawal symptoms

are typically followed by a protracted withdrawal syndrome characterised by general

feelings of reduced wellbeing and periodic drug craving. These psychological

symptoms include anxiety and dysphoria, which often trigger renewed consumption,

and may present as continued craving following prolonged abstinence from

benzodiazepines and a residual state of anxiety and dysphoria, or may be induced by

stress or specific stimuli including certain environments, people or situations previously

associated with drug use.

Toxicology of Benzodiazepines

Benzodiazepines are considered a relatively safe drug (Lader, 1991). Various

toxicological studies have reported that when benzodiazepine is administrated alone,

overdose does not result in life-threatening respiratory depression. Death as a result of

benzodiazepines is uncommon, but does occasionally happen (Gaudreault et al., 1991;

Owen & Tyrer, 1983); however high doses of benzodiazepines combined with alcohol,

barbiturates, opioids or tricyclic antidepressants are particularly dangerous, and may

lead to severe complications such as coma or death (Owen & Tyrer, 1983). For

example, ventilation can be dramatically decreased when benzodiazepines are

administered in combination with ethanol. The most common symptoms of overdose

include central nervous system (CNS) depression and intoxication, with impaired

balance, ataxia, and slurred speech. Severe symptoms include coma and respiratory

depression (Gaudreault et al., 1991).

28

Pharmacotherapies for Benzodiazepines Dependence

There are no agents approved by the Food and Drug Administration for the long-term

treatment of benzodiazepine dependence, but pharmacologic strategies are being

investigated to target achieving abstinence from benzodiazepines (Denis et al., 2006;

Gerra et al., 2002; Harris & Roache, 2000). Agents that have shown promising

treatment potential include the benzodiazepine antagonist flumazenil (Gerra et al., 2002)

and the anticonvulsants carbamazepine (Denis et al., 2006) and valproate (Harris &

Roache, 2000). Treatment of benzodiazepine abuse and dependence usually involves

detoxification followed by psychosocial intervention aimed at achieving long-term

abstinence. Individuals with benzodiazepine abuse and dependence often have other

substance dependence or use disorders plus psychiatric disorders, which should be

screened and diagnosed, and treated concurrently (Johnson, 2011).

E. Amphetamines and Related Derivatives

Amphetamine is the abbreviated term for alpha-methylphenethylamine approved by the

United States Adopted Name (USAN); alternatively it is called phenylisopropylamine.

It includes a number of structurally related derivatives such as methamphetamine,

feufluramine, phentermine and synthetic amphetamine analogues (Lock, 1984; Nichols,

1986; Nichols et al., 1986). Amphetamine and its related compounds are the prototype

of a class of noncatechol amine compounds that produce strong central nervous system

stimulation by increasing levels of norepinephrine, serotonin and dopamine in the brain

(Mendelson et al., 2006). They include prescription CNS drugs commonly used to treat

attention-deficit disorder (ADD) and attention-deficit hyperactivity disorder (ADHD).

The drug is used illegally by college and high-school students as a performance

enhancer, because of its ability to increase energy levels, concentration and motivation,

allowing students to study for extended periods.

Pharmacology of Amphetamines

Amphetamines are weak bases with a pka around 9.9, a low molecular weight, a low

protein binding (around 20%) and a high volume of distribution (de la Torre et al.,

2004). Both methamphetamine and amphetamine are readily absorbed from the

gastrointestinal tract and the nasal mucosa, and freely penetrate the blood brain barrier

(Cho & Melega, 2001; Harris et al., 2003). Both are mainly excreted unchanged in the

urine via the organic cation transport system (Cook et al., 1992), more rapidly in an

acidic environment (Oyler et al., 2002). Both amphetamine and methamphetamine are

29

also metabolised by Phase I (oxidation) and Phase II (conjugation) reactions. In Phase I

metabolism, both amphetamine and methamphetamine as substrates for the cytochrome

P450 systems undergo N-dealkylation and hydroxylation reactions. One of the

cytochrome P450 systems responsible for these reactions in humans is CYP2D6.

Around 7 to 10 per cent of the Caucasian population lack this enzyme and are designed

poor metabolisers in comparison to the rest of the population (extensive metabolisers);

therefore, the capacity to biotransform amphetamine and mediate the disposition and

action of these compounds depends on an individual’s phenotype (Kroemer &

Eichelbaum, 1995).

Methamphetamine and its metabolite “amphetamine” interact with amine systems in the

brain and the peripheral nervous system to produce a wide range of effects on

physiology and behaviour (Kuczenski & Segal, 1997; Rothman & Baumann, 2006),

produced by increasing the concentration of extracellular neurotransmitters such as

dopamine, noradrenaline and serotonin. Amphetamines inhibit the uptake of these

neurotransmitters into the presynaptic neuron by inhibiting the action of the relevant

transporter on the membranes of dopaminergic, serotonergic and adrenergic neurons

(Fleckenstein & Hanson, 2003; Rothman & Baumann, 2006). Amphetamines can also

reverse the action of the transporters to facilitate neurotransmitter movement across the

membrane and into the synaptic cleft (Kahlig & Galli, 2003). Amphetamine displaces

newly synthesised neurotransmitters from their respective vesicular libraries by acting

on the vesicular monoamine transporter, which causes an increase in the pool of

cytoplasmic transmitter release. Amphetamine can also inhibit the metabolism of these

neurotransmitters by inhibiting the action of monoamine oxidase (Seiden et al., 1993).

Both methamphetamine and amphetamine are potent releasers of noradrenaline and

dopamine, and relatively weak releasers of serotonin (Rothman & Bauman, 2006).

While methamphetamine and amphetamine are equally potent as dopamine and

noradrenaline releasers, methamphetamine is approximately 2.5 times more potent as a

serotonin releaser in comparison to amphetamine; but both drugs have relatively weak

serotonergic effects (Rothman & Bauman, 2002).

Amphetamines Dependence

Until relatively recently amphetamine use was not considered to be associated with

significant dependence (Morgan, 1981; Nir, 1980). Amphetamine dependence was first

measured by Gossop and associates in an Australian sample of heroin, cocaine and

30

amphetamine users (Gossop et al., 1995). This collaborative study resulted in the

development of the severity of dependence scale, useful in the measurement of different

drug dependencies including amphetamines (Cantwell & McBride, 1998; Topp &

Mattick, 1997). The diagnosis of amphetamine dependence is based on the presence of

three or more of the following within the past year: a sense of compulsion to take the

drug, difficulty in controlling drug-taking behaviour in terms of its onset, termination or

level of use, withdrawal symptoms after interruption of drug intake, evidence of

tolerance, significant time spent in activities to acquire and use the drug, and persistent

drug use despite adverse consequences (APA, 1994). These criteria do not include

physiological dependence as a requirement for a diagnosis of drug dependence.

Various genetic addiction studies suggest that the molecular mechanism of developing

amphetamine dependence involves different systems in the brain such as the

endogenous dopamine system, the neurone transporter system and the neurotrophine

system (Mendelson et al., 2006). Several molecular addiction studies suggest that

amphetamine is an inhibitor of monoamine transporters, especially dopamine, but it also

has minor effects on the serotonin and norepinephrine transporters (Mendelson et al.,

2006). Dopamine is moved via vesicular monoamine transporter-2 into synaptic vesicles

for storage and eventual release (Lovinger, 1991; Moore, 1977; Riddle et al., 2006;

Rothman & Baumann, 2006). Without vesicular transport, dopamine remains within the

cytoplasm where it is subject to degradation, including oxidation and reactive free

radicals which are potentially neurotoxic. Extracellular dopamine is moved back into

the presynapse via the dopamine transporter. Dopamine transporter inhibition or

blockade results in increased levels of extracellular dopamine. Amphetamines

specifically induce presynaptic dopamine release, block dopamine reuptake, inhibit

dopamine storage within presynaptic vesicles and block enzyme-catalysed dopamine

metabolism. Shortly after amphetamine administration, reversal of the dopamine

transporter results in non-vesicular dopamine efflux (Kuczenski & Segal, 1989; Sulzer

et al., 1995). Amphetamine also blocks the movement of dopamine into synaptic

vesicles. This causes an increase in dopamine release into the synapse, oxidises

dopamine and damages the free radical formation within the presynapse. Similarly,

these mechanisms happen to norepinephrine and to a lesser extent to serotonin

terminals. Amphetamine’s reinforcing and behavioural stimulant effects are associated

with enhanced dopaminergic activity, primarily within the mesolimbic dopamine

system (Del et al., 1999; Moore, 1977). Amphetamine acts to increase glutamate release

31

and dopaminergic activity in selected brain areas such as the nucleus accumbens,

striatum and prefrontal cortex (Del et al., 1999). These areas are implicated in reward

and dependence mechanisms.

In addition, like the effects of amphetamines on the dopamine transporter, amphetamine

can reverse the direction of serotonin movement via the serotonin transporter (Hilber et

al., 2005; Sulzer et al., 2005). Similarly, methamphetamine induces release of serotonin,

dopamine, and norepinephrine as well as blockading serotonin, dopamine and

norepinephrine transporters within the central nervous system, leading to increased

synaptic activities of these biogenic amines. Amphetamine dependence is, as a result, a

complex process that involves different systems in the brain such as the endogenous

dopamine system, the neurone transporter system and the neurotrophine system.

Toxicology of Amphetamines

Various toxicology studies have indicated that a toxic dose of amphetamine may be as

low as 25 mg/kg in some individuals who does not develop tolerance, while chronic

users may be able to survive more than 500 mg/kg (Angrist et al., 1987). Therapeutic

administration of amphetamines is usually oral. When used recreationally,

amphetamines are taken orally or nasally (snorted), smoked or injected intravenously

(Ellison & Dobies, 1984). Methamphetamine’s methyl group is lipid soluble and easily

transported across the blood brain barrier, and is relatively resistant to enzymatic

degradation catalysed by monoamine oxidase. For example, after 4 x 10 mg oral

administration, methamphetamine is initially detected in plasma samples within 15

minutes to 2 hours. Maximal plasma concentrations of 14.5 to 33.8μg/L are achieved

within 2 to 12 hours, and the drug remains measurable for 36 to 72 hours after

administration (Schepers et al., 2003). Methamphetamine has an elimination half-life of

9 to 15 hours, and is primarily excreted in the urine; the half-life varies with differences

in urinary pH. The toxic effects of amphetamine and related drugs can be very serious

and potentially lethal (Derlet et al., 1989). Short-term effects include an increase in

heart rate and blood pressure, a decrease in appetite, feelings of elation and self-

assuredness and a reduction of fatigue, while long-term use is associated with insomnia

and restlessness, significant weight loss, hallucinations and paranoid psychosis.

Amphetamine use can also include psychic dependence and tolerance (Connell, 1968).

Continuous high doses of amphetamine develop tolerance or dependence, and have been

32

associated with symptoms of paranoid psychosis such as hyperactivity, anxiety,

paranoid delusions and hallucinations.

Pharmacotherapies for Amphetamine Dependence

Few pharmacotherapies have been investigated by randomised clinical trials for the

treatment of amphetamine withdrawal and dependence; only four randomised clinical

controlled trials of pharmacotherapies for amphetamine dependence exist. Those drugs

that have been examined include the antidepressants desipramine and imipramine

(Tennant et al., 1986), the dopamine agonist antidepressant amineptine, the selective

serotonin reuptake inhibitor (Srisurapanont et al., 1999), fluoxetine (Batki et al., 1999)

and the calcium channel blocker, amlodipine (Batki et al., 2001). Of the four drugs

evaluated for treatment of amphetamine dependence and withdrawal in these studies,

the antidepressant desipramine, imipramine and fluoxetine showed limited promise;

while the dopamine agonist antidepressant amineptine was effective for some symptoms

such as depression, it is no longer available for use.

F- Cocaine

Cocaine is a derivative of the coca plant (Erythroxylon coca) native to the mountains of

South America (Calatayud & Gonzalez, 2003). In 1574, the Spanish physician Bautisha

Alfaro (1493–1588) published a description of the plant, its uses and effects. In 1862,

the German chemist Albert Neiman (1834–1861) isolated the active component and

called it cocaine (von Anrep, 2008). Since its synthesis from the coca plant in 1884,

cocaine has been medically used as an anaesthetic, anorectic and cardiovascular

stimulant (Freud, 1884). In recreational use, cocaine takes three common forms: cocaine

hydrochloride salt (powder), freebase and crack rocks, which can be smoked (Goldstein

et al., 2009).

Pharmacology of Cocaine

Cocaine in its purest form has a white. The salt form is weakly alkaline and is soluble in

water, while the freebase has a pka of 8.6, is a strong alkaline and is practically

insoluble in water (Eisenberg et al., 2008). Cocaine can be administered by mouth, nasal

insufflation, injection or inhalation; it is rapidly absorbed across nasal, trachea and

laryngeal epithelial membranes within minutes of its administration (Barnett et al.,

1981; Wilkinson, 1980). Peak plasma concentrations of 120ng/ml to 474ng/ml are

reached within 30–60 minutes of intranasal administration, and remain detectable up to

33

six hours after administration (Barnett et al., 1981). Cocaine is extensively metabolised,

primarily by esterase enzyme in the liver and plasma, with only 1% excreted unchanged

in the urine (Wilkinson, 1980). Liver esterase activity accounts for 30 to 50% of the

metabolism of cocaine to ecgonine methyl ester; another 30 to 40% is non-

enzymatically hydrolysed into benzoylecgonine (Jatlow, 1987). The elimination half-

lives of these major metabolites are 6 and 4 hours, respectively (Jatlow, 1987). These

metabolites can be detected in urine samples up to 60 hours after cocaine

administration. A small amount (1 to 5%) of cocaine is excreted unchanged in urine

within 8 hours of administration (Jatlow, 1987).

The pharmacodynamics of cocaine involve blocking neurotransmitter uptake by the

dopamine (DAT), serotonin (SERT) and norepinephrine (NET) transporters (Uhl et al.,

2002). Cocaine can also exert effects on ligand- and voltage-gated channels by blocking

these as well (Uhl et al., 2002). It non-competitively blocks the uptake of these

monoamines by binding to a site on the transporter that is different from the substrate

binding site (McElvain & Schenk, 1992). Acute administration of cocaine leads to an

increased level of serotonin in the medial prefrontal cortex and the hypothalamus (Yang

et al., 1992). In the dorsal raphe, acute cocaine suppresses the spontaneous activity of

serotonin neurons while decreasing the synthesis of serotonin in the striatum, nucleus

accumbens, and medial prefrontal cortex (Galloway, 1990).

Chronic doses of cocaine can increase the number of serotonin uptake sites in regions of

the prefrontal cortex and dorsal raphe. This in turn can alter the ability of cocaine to

inhibit the serotonergic activity in dorsal raphe neurons (Cunningham et al., 1992). A

study by Egan et al. (1994) has found changes of chronic cocaine treatment affect the

levels of serotonin (5-HT) and of one of its metabolites, 5-hydroxyindoleacetic acid (5-

HIAA) in the cortex during periods of withdrawal in rats. Repeated cocaine injections

produced a long-term decrease in 5-HT levels in frontal cortex regions. There was a

relationship between withdrawal time and 5-HT levels, with longer withdrawal times

producing a greater reduction in 5-HT neurotransmission, suggesting a progressive

reduction (Egan et al., 1994). When cocaine is injected into the ventral tegmental area

(VTA), dopamine, norepinephrine and serotonin increase in a concentration-dependent

fashion (Chen & Reith, 1994). Cocaine affects several anatomical areas in the brain,

including the ventral tegmental area, which is important for producing the reinforcing

properties of cocaine. The VTA receives inputs from the 5-HT system and is innervated

34

by norepinephrine (NE)-containing neurons. Repeated drug use leads to processes that

reverse the resulting effects of the drug. When an individual abstains from the drug, the

compensatory processes that have developed are still active and may be responsible for

a negative cognitive experience during withdrawal (Winstanley, 2007).

Cocaine Dependence

According to the Diagnostic and Statistical Manual of Mental Disorders, the diagnosis

of cocaine dependence is based on the same criteria used for other drugs of abuse (APA,

1994). These include the presence of three or more of the following within the past year:

sense of compulsion to take the drug, difficulty in controlling drug taking behaviour in

terms of its onset, termination or level of use, withdrawal symptoms after interruption of

drug intake, evidence of tolerance, significant time acquiring and using the drug, and

persistent drug use despite adverse consequences. These criteria do not include

physiological dependence for a diagnosis of drug dependence; although evidence of

physiological dependence may contribute to a more accurate diagnosis. Physiological

dependence is characterised by tolerance and the occurrence of specific withdrawal

symptoms when use is stopped. Symptoms of withdrawal include cocaine craving,

depressed mood, sleep disturbance, appetite disturbance and increased anxiety.

Development of cocaine dependence is speculated to be due to long-term effects of

repeated cocaine use on several areas of the brain, particularly those related to the

processing of reward-related information and executive control of behaviour (Everitt &

Robbins, 2005; Kalivas & Volkow, 2005; Nestler, 2005). Several addiction studies have

suggested that cocaine's intense addictive properties stem partially from its dopamine

active transporter (DAT)-blocking effects, in particular increasing the dopaminergic

transmission from ventral tegmental area neurons (Nestler, 2005; Shaham & Hope,

2005); however, studies on mice show that those with no dopamine transporters still

exhibit the rewarding effects of cocaine administration (Sora et al., 1998). Later work

has demonstrated that a combined dopamine active transporter/serotonin transporter

knockout eliminates the rewarding effects (Sora et al., 2001). The rewarding effects of

cocaine are influenced by circadian rhythms, possibly by involving a set of genes such

as dopamine receptor D2, dopamine transporter, serotonin transporter, norepinephrine

transporter and tryptophan hydroxylase genes (Yuferov et al., 2005). Increased levels of

synaptic dopamine and, thereby, dopamine receptor binding following cocaine

administration is a key mechanism through which cocaine develops reinforcing

35

properties (Cunningham et al., 1992; Egan et al., 1994). Whereas stimulation of

dopaminergic pathways may be sufficient to cause the reinforcing effects of cocaine,

dopamine transporter gene deletion studies have shown that this pathway is not essential

to the development of cocaine self-administration and reward effects. However, chronic

cocaine addiction is not solely due to cocaine reward. Chronic repeated use is needed to

produce cocaine-induced changes in brain reward centres and consequent chronic

dysphoria (Winstanley, 2007). Dysphoria magnifies the craving for cocaine because

cocaine reward rapidly reduced. This contributes to continued use and a self-

perpetuating, worsening condition, since those addicted usually cannot appreciate that

long-term effects are opposite to those occurring immediately after use.

Cocaine Toxicology

Cocaine does not appear to directly damage neurons except at very high doses

(Goldstein et al., 2009), but it does produce toxic effects on the central nervous system

(CNS) and other organs. N-methyl- D-aspartate (NMDA) receptors appear to be

involved in the behavioural toxic effects of cocaine (Robledo-Carmona et al., 2006;

Tazelaar et al., 1987; Virmani et al., 1988). Acute cocaine intoxication is characterised

by a “high” feeling, and by stimulation effects such as euphoria, increased pulse and

blood pressure, and psychomotor activation (APA, 1994). Other symptoms include

alertness, anger, anxiety, aggression, cognitive impairment, gregariousness, grandiosity,

hyperactivity, hypervigilance, impaired judgment, impaired social and occupational

functioning, interpersonal sensitivity, mood lability, restlessness, stereotyped and

repetitive behaviour, increased talkativeness and tension. With chronic intoxication

there also can be depressant effects such as social withdrawal, sadness, bradycardia,

decreased blood pressure and decreased psychomotor activity (Isabelle et al., 2007;

Roth et al., 1988). Both acute and chronic intoxication are associated with impaired

social and occupational functioning (Nestler, 2005, while severe intoxication is

associated with a number of medical complications including seizures, cardiac

arrhythmias, hyperpyrexia, and vasoconstriction leading to increased risk for

myocardial infarction and stroke (APA, 1994). Death may result from seizure, stroke,

heart attack or intracerebral haemorrhage. The literature reflects variations in the

reported lethal dose of cocaine in humans, from as little as 20 mg injected intravenously

to a mean of 500mg ingested orally to 1.4 g (Johnson, 2011).

36

Pharmacotherapies for Cocaine Dependence

There are no effective pharmacotherapeutic agents for the long-term treatment of

cocaine addiction (Lima et al., 2002). Tricyclic antidepressant and serotonin reuptake

inhibitors have been widely used in the treatment of cocaine addicts, but have not been

generally efficacious (Lima et al., 2002. while another pharmacotherapeutic,

“disulfiram”, used with minimal success in the treatment of alcohol dependence, has

been used for the treatment of cocaine dependence because of its ability to block the

conversion of dopamine to norepinephrine through inhibition of the enzyme dopamine

β-hydroxylase (Carroll et al., 1993; Higgins et al., 1993). Currently, this compound is

under study to determine whether it has any usefulness in the treatment of cocaine

dependence. Other therapeutics such as methylphenidate and other medications (e.g.

bromocriptine, pergolide and amantadine), which act as dopamine agonists, also show

limited efficacy in the treatment of cocaine addiction (Grabowski et al., 1997;

Handelsman et al., 1997; Khantzian et al., 1984; Malcolm et al., 2000; Shoptaw et al.,

2002). Finally, GABAA-GABAB Directed Drugs have been investigated to determine

their efficacy in treating cocaine dependence (Ling et al., 1998; Shoptaw et al., 2003). A

clinical trial using the GABAB agonist baclofen has shown reductions in cocaine

craving (Myrick et al., 2001). However, none of these medications for cocaine

dependence have achieved the success of the pharmacotherapeutics used for heroin

dependence, or even of those used to treat alcohol dependence.

37

F- Cannabinoids

Cannabinoids are considered to be the main biological constituents of the cannabis

plants (Cannabis Sativa L., Cannabis Indica and Cannabis Ruderalis) (Lewin, 1998;

McKim, 1986; von Bibra, 1995). These three plants are indigenous to Central Asia and

South Asia. Traditionally, cannabis has long been used for fiber, for seed and seed oil,

for medicinal purposes and as a recreational drug. The broader definition of

cannabinoids refers to a group of substances that are structurally related to

tetrahydrocannabinol (Δ 9 THC) or that bind to cannabinoid receptors (ElSohly, 2002).

Currently there are three general types of cannabinoids: phytocannabinoids occur

uniquely in the cannabis plant; endogenous cannabinoids are produced in the bodies of

humans and other animals; and synthetic cannabinoids are similar compounds produced

in a laboratory (Chait et al., 1988; Perrine, 1996).

Pharmacology of Cannabinoids

Raw cannabis contains 483 distinct chemical compounds (ElSohly, 2002). The genus

cannabis alone produces all 66 known chemicals that constitute the cannabinoids

(ElSohly, 2002). Cannabinoids are joined to an alkyl-substituted resorcinol (Perrine,

1996). Several are psychoactive, most notably Δ _9-tetrahydrocannabinol (Δ 9 THC). Δ

9 THC is regarded as the principal psychoactive constituent of cannabis, and can

produce discriminative stimulus effects in experienced cannabis users (Chait et al.,

1988).

Cannabinoids can be administered by smoking, vaporising, ingesting, applying a

transdermal patch, or injection (Crowley et al., 1998; Lambert & Fowler, 2005). Δ 9

THC is absorbed rapidly via cigarette, less quickly by ingestion, due to its first-pass

metabolism in the liver. Peak plasma levels of Δ 9 THC occur 7–8 minutes after the

initiation of smoking or intravenous infusion, and decline after 6–10 minutes (even with

continuing smoking) (Lambert & Fowler, 2005). By ingestion, Δ 9 THC reaches a peak

plasma level after 45 minutes and remains for 4–6 hours. Δ 9 THC is highly lipid

soluble, almost 100% protein bounded, being distributed between lipoprotein and

albumin (Lambert & Fowler, 2005). Once in the body, most cannabinoids are

metabolised in the liver, especially by cytochrome P450 mixed-function oxidases,

mainly CYP 2C9; thus, supplementing with CYP 2C9 inhibitors leads to extended

intoxication. Some is also stored in fat, in addition to being metabolised in the liver.

Some metabolites can be detected in the body after several weeks (Lambert & Fowler,

38

2005). The urinary excretion of Δ 9 THC metabolites is minor and takes from 3–8 days,

while biliary excretion via the faeces is the major route of elimination of unconjugated

cannabinoid metabolites.

Crowley et al. (1998) have reported that many cannabis-abusing patients have

experienced decreases in their physical responses due to chronic exposure (tolerance),

and experience numerous withdrawal symptoms when they discontinue use. The most

common symptoms reported are affective and behavioural in nature: current conduct

disorder, anxiety, appetite change, depressed mood, irritability, restlessness, attention-

deficit/hyperactivity disorder and craving. A subset of adolescents also experienced

physical symptoms like headache, nausea and sweating (Crowley et al., 1998).

Before the 1980s it was often speculated that cannabinoids produced their physiological

and behavioural effects via nonspecific interaction with cell membranes, instead of

interacting with specific membrane-bound receptors (Crowley et al., 1998). The

discovery of the first cannabinoid receptors in the 1980s helped to disprove this

hypothesis. There are currently two known types of cannabinoid receptors, termed CB1

and CB2. CB1 receptors are found primarily in the brain (Lambert & Fowler, 2005),

and in both male and female reproductive systems. CB1 receptors are essentially absent

from the medulla oblongata, the part of the brain stem that is responsible for respiratory

and cardiovascular functions; there is no risk of respiratory or cardiovascular failure as

there is with many other drugs. CB1 receptors appear to be responsible for the euphoric

and anticonvulsive effects of cannabis. CB2 receptors are almost exclusively found in

the immune system, with the greatest density in the spleen, and appear to be responsible

for the anti-inflammatory and possibly other therapeutic effects of cannabis (Crowley et

al., 1998; Lambert & Fowler, 2005).

Cannabinoids Dependence

According to the Diagnostic and Statistical Manual of Mental Disorders, cannabis

dependence can be defined by the same criteria used to define dependence on other

drugs of abuse (APA, 1994). These include the presence of three or more of the

following within the past year: sense of compulsion to take the drug, difficulty in

controlling drug taking behaviour in terms of its onset, termination or level of use,

withdrawal symptoms after interruption of drug intake, evidence of tolerance,

39

significant time spent in acquiring and using the drug, and persistent use despite adverse

consequences (APA, 1994).

Various molecular pharmacology studies have addressed the analysis of the molecular

changes underlying the pharmacological tolerance developed after prolonged exposure

to cannabinoids (Lichtman et al., 2005; Pertwee et al., 1991; Romero et al., 1997;

Rubino et al., 1994; Zhuang et al., 1998). These studies have provided robust evidence

that this pharmacological tolerance or dependence is mainly linked to changes in the

availability of the cannabinoid receptors. Specifically, the CB1 receptor subtype is

predominant in the CNS and involved in the psychoactive properties of plant-derived

cannabinoids. Dependence often develops alongside tolerance, and in some cases the

severity of physical dependence or of withdrawal symptoms is directly related to the

magnitude of tolerance. Therefore, chronic cannabis use leads to adaptive changes in

endocannabinoid signalling, and these changes contribute to the development of

cannabis physical dependence (Iversen, 2003; Oveido et al., 1993; Rodreguez et al.,

1991; Romero et al., 1997). However, various molecular studies strongly suggest that

cannabinoid tolerance is a consequence of region-dependent losses in cannabinoid CB1

receptor binding, mRNA expression and agonist-stimulated G protein activity, and also

of adaptive changes of endocannabinoid contents (Ashton et al., 2001; Romero et al.,

Rubino et al., 1994; 1997; Zhuang et al., 1998). This is a relevant issue for cannabinoid

consumption in humans, since there is no general agreement about whether cannabinoid

tolerance includes elements of physical dependence.

Cannabinoids Toxicology

Various studies have indicated that the toxic effects of cannabis are mediated through its

effects on neural systems (Rosencrantz, 1983). Animal studies suggest that

tetrahydrocannabinol (Δ 9 THC) has the highest direct toxicity in all animals. The cause

of death in experimental animals was almost always apnoea; or cardiac arrest if apnoea

was prevented. These studies indicated that the lethal dose (LD50) of Δ 9 THC when

administrated intravenously is 50 mg/kg in the rat but 130 mg/kg in the monkey

(Rosencrantz, 1983). There are no studies that investigate the lethal dose in humans for

obvious ethical reasons (Blum, 1984; Nahas, 1984); however, extrapolation from the

animal evidence suggests that the lethal human dose of THC is at least as high as and

probably higher than that observed in the monkey. The acute toxicity of cannabis and

the cannabinoid is very low (Jaffe, 1985; Tart, 1970). One minor toxic side effect of

40

taking cannabis is the short-term effect on the heart and vascular system, which causes a

significant increase in heart rate and a lowering of the blood pressure. The short-term

effects of cannabinoid use also include problems with memory and learning, distorted

perceptions, difficulty in thinking and problem solving and loss of coordination

(Thomas, 1993,. while the long-term effects include a decrease in the activity of the

immune system and an irritation of the throat and lungs, which can occur with regular

smoking and in the long run causes cancer (Rickert et al., 1982; Tashkin et al., 1991;

Wu et al., 1988). Long-term effects on behaviour include disruption of normal sleep

stages causing fatigue, disruption of the ability to recall words or narrative material and

attention disorders (Linszen & van Amelsvoort, 2007; Lynskey et al., 2004).

Pharmacotherapies for Cannabinoids Dependence

There is no pharmacotherapy for use in cannabinoid dependence. Currently, various

pharmaceutical clinical trials have reported that rimonabant “cannabinoid-1 receptor

antagonist” is a potential candidate drug for dependence treatment (Le Foll & Goldberg,

2005), but there has been no approval for its clinical use from the Food and Drug

Administration because of safety concerns. At present, the only treatments shown to be

effective for cannabinoid dependence are behavioural and psychosocial therapy,

motivational enhancement therapy, or some combination of these three (Budney et al.,

2007).

41

V. Human Molecular Genetic and Pharmacogenetics Approaches Towards

Understanding Substance Dependence

Human DNA

The human nuclear genome consists of 22 autosomal chromosome pairs and 1 sex

chromosome pair, XY or XX, with each chromosome containing a single

deoxyribonucleic acid (DNA) molecule (Alberts et al., 2002; Court, 2007; Guttmacher

& Collins, 2002; Saenger & Wolfram, 1984). Chemically, DNA is a long polymer made

from repeating units called nucleotides. There are two types of nucleotide bases, purines

and pyrimidines. Purine bases are adenine (A) and guanine (G). Pyrimidine bases are

cytosine (C) and thymine (T). Complementary DNA strands are linked through base-

pairing of pyrimidine with purine nucleotides (A with T; G with C) (Watson & Crick,

1953).

The basic functional unit of the genetic material is the gene (Alberts et al., 2002; Court,

2007). A gene is a portion of the genetic information that codes for the synthesis of

messenger ribonucleic acid (mRNA), which is used as a secondary template by

ribosomes to translate into proteins. The human genome consists of 25 to 40,000 genes

encoded in 3.2 billion nucleotides of DNA. It is speculated that any two human

genomes are nearly 99.9% identical. The nucleotide diversity between human

individuals is about 0.1%, because of the 11 million single nucleotide polymorphisms

(SNPs) estimated to occur in the human genome with allelic frequencies greater than

1%. Variability is also introduced by such processes as alternative splicing of mRNA

transcript and imprinting (Alberts et al., 2002; Guttmacher & Collins, 2002).

Generally, a gene is composed of promoters, exons, introns and 3’-untranslated

regulatory regions (3’-UTR) (Saenger & Wolfram, 1984). The promoter region

determines the expression. Exons dictate the actual amino acid sequence of the protein.

Introns are non-coding regions that are spliced away following transcription. 3’-UTR

may control protein translation and mRNA degradation. The genetic information

dictated by genes is represented by the nucleotide sequence. The entire human genome

contains 3x109 nucleotide base pairs with up to 30,000 protein coding genes. It has

been suggested that these protein coding genes account for less than 2% of the total

DNA sequence in humans, whereas the remaining intergenic sequence (commonly

referred to as ‘junk DNA’) is thought to be important for chromosomal structure,

42

genetic integrity and evolution (Alberts et al., 2002; Court, 2007; Guttmacher and

Collins, 2002; Saenger & Wolfram, 1984). In addition to the nuclear genome,

eukaryotic cells contain essential genetic information separate from the chromosomal

DNA, stored in the mitochondria (the mitochondrial genome). In humans, mitochondrial

DNA is small and circular, and does not contain introns. Mitochondrial DNA codes for

proteins of the respiratory chain, subunits of ATPase and NADH-dehydrogenase

complexes, and a number of transfer and ribosomal RNAs (Iborra et al., 2004; Sykes,

2003).

Generally, the form and source of genetic variability can be categorised into

chromosomal variations, alternative splicing, copy number variation,

insertions/deletions and single nucleotide polymorphisms. Chromosomal variations are

largest-scale genetic variations, involving an extensive number of genes and mostly

leading to a severe impact on physiology. One well-known example is Down’s

syndrome (trisomy 21) (Tyler & Edman, 2004). These variations have no place in

pharmacogenetic testing due to their severe impact, but are important for other medical

areas such as oncology and clinical genetics.

Alternative splicing is a process by which the exons of the RNA produced (Alberts et

al., 2002; Court, 2007; Guttmacher and Collins, 2002; Saenger & Wolfram, 1984).

During splicing, the introns are removed from the primary gene transcript (RNA) and

the exons are joined to create different mRNA. The resulting different mRNAs may be

translated into different protein isoforms (Graveley, 2001). As result, a single gene may

code for multiple proteins. Relative examples in psychiatry are alternative splicing of

the genes encoding for serotonin 2C and histamine H1 receptors (Dracheva et al., 2003;

Swan et al., 2006; Tohda et al., 2006). Although there have not been many studies

conducted in this area, alternative splicing may be of considerable pharmacogenetic

importance when it is controlled by genetic polymorphisms affecting the process of

alternative splicing (Bracco & Kearsey, 2003).

Copy number variation (CNV) involves entire gene deletions, inversions, insertions or

duplications (Court, 2007; Guttmacher & Collins, 2002; Levy et al., 2007; Redon et al.,

2006). The CYP2D6 gene is an example of such a genetic variation. It displays

duplications (≤13 copies) and deletions (0 copies) at various rates across different

43

ethnicities. This type of variation may lead to subjects being classified as ultra-rapid

(duplication) or poor (deletion) metabolisers of CYP2D6 substrates.

An insertions or deletions (indel) polymorphism involves the insertion or the deletion of

one or more nucleotide (Alberts et al., 2002; Court, 2007; Guttmacher and Collins,

2002; Saenger & Wolfram, 1984); these can be of substantial pharmacogenetic

importance. For example, a simple indel polymorphism involving the insertion or

deletion of one nucleotide within the protein function, particularly when leading to a

reading frameshift mutation, causes a significant alteration in protein sequence and

length. Special types of the indel variation are called microsatellite and minisatellite

polymorphisms. Microsatellites are repeats of sequence less than five base pairs in

length and may be present as mononucleotides (e.g. “C C C C C”), dinucleotides (e.g.

“CT CT CT CT CT”) or trinucleotides (e.g., “TTA TTA TTA TTA TTA”), and

polynucleotide repeats. Minisatellites involve longer blocks (≥ 5 base pairs).

Microsatellite and minisatellite polymorphisms are called collectively Variable Number

of Tandem Repeats (VNTR). Well-known examples of indel relevant to the field of

substance dependence pharmacogenetic are the -141C Ins/Del polymorphism of the

dopamine D2 (DRD2) gene (Xu et al., 2004) and a 43-base pair indel polymorphism in

the 5' regulatory region of the serotonin transporter (SLC6A4) gene (Li et al., 2002).

Single nucleotide polymorphisms (SNPs) are single base pair substitution mutations that

occur at an average frequency of 1 SNP for every 1000 base pairs of DNA sequence

(Barreiro et al., 2008; Court, 2007; Guttmacher & Collins, 2002; Varela & Amos,

2010). The majority of the observed sequence variations between individuals appear to

result from SNPs. The position of the SNP in relation to a gene may determine the

possible effects on the function of that particular gene. SNPs localised inside the protein

coding region of a gene (exonic SNPs) may or may not change protein structure. When

SNP causes the substitution of a single amino acid within the protein polypeptide, it is

called nonsynonymous (missense) SNP, while when exonic SNP does not affect the

amino acid sequence; it is called a synonymous (silent) SNP. Exonic SNPs may cause

not only in the substitution of a single amino acid, but also in the introduction of a stop

codon that prematurely terminates protein translation and creating a truncated and

potentially non-functional protein; these SNPs are called nonsense polymorphisms.

About 1% of all human SNPs affect the protein coding portion of the DNA sequence,

which results in the substitution of a single amino acid within the protein polypeptide

44

chain. Well-known SNPs from the field of pharmacogenetics of substance dependence

are Ser9Gly within the DRD3 gene (Comings et al., 1999; Li et al., 2002), Val66Met

within the BDNF gene (Chang et al., 2005; Itoh et al., 2005), Vall158Met within the

COMT gene (Horowitz et al., 2000) and Asn40Asp within the OPRM1gene (Hoehe et

al., 2000; Ide et al., 2004).

In addition to being found in exons, SNPs may also be present in any place outside the

protein coding regions. The non-exonic SNPs in 5’-untranslated regulatory regions

(UTR), intron-exon boundaries and 3’-UTR may play a major role in pharmacogenetics

(Alberts et al., 2002; Court, 2007; Guttmacher & Collins, 2002; Saenger & Wolfram,

1984).

The presence of non-exonic SNPs may result in a quantitative, but not a qualitative,

change in the expressed protein. SNPs located in the promoter or the 5’-UTR region of

a gene may increase or decrease gene transcription through changes in the DNA

sequence essential for binding the transcription factors that are responsible for

enhancement or repression of gene expression (Alberts et al., 2002; Court, 2007;

Guttmacher & Collins, 2002; Saenger & Wolfram, 1984). An example of a 5’-UTR

SNP is the G-172T polymorphism in the µ-opioid receptor gene (OPRM1).

SNPs that occur in the introns may affect splicing, which is an important translational

step in gene expression (Alberts et al., 2002; Court, 2007; Guttmacher & Collins, 2002;

Saenger & Wolfram, 1984). During splicing the introns (noncoding regions) are

removed from the primary mRNA transcript, and leaving behind a sequential chain of

joined exons that is used as a template for protein translation. The removal of intronic

mRNA is mediated by enzymes. For these enzymes to act, specific sequence elements

(e.g., splice donor and splice acceptor sites at the exon–intron boundaries, or splice

repressor and enhancer sites) are considered essential if mRNA splicing is to occur

properly

SNPs localised within any of the above-mentioned splicing-regulatory sequence

elements have the potential to alter the efficiency of splicing, and may cause severe

scenarios in which mRNA is produced lacking one or more exons, or retaining a portion

of the intron sequence. Furthermore, intronic splice SNPs may create novel splice

donor, acceptor, or regulatory sites that compete with the original splice sites to create

45

novel mRNA variants (Court, 2007; Guttmacher & Collins, 2002). Finally, there are

SNPs that are located in the 3’-UTRs of the mRNA. Although 3’-UTR regions are not

translated to protein, this region is an important regulatory region, and disruption of

their structural integrity (due to the presence of a SNP) may have effects on protein

formation rates if the balance between mRNA degradation, stability, and protein

translation efficiency is altered (Alberts et al., 2002; Court, 2007; Guttmacher &

Collins, 2002; Saenger & Wolfram, 1984).

Pharmacogenetics

Pharmacogenetics is a newly established branch of science that deals with heredity and

the effect of drugs, currently applied most in the pharmaceutical industry in preclinical

models to identify how genetic polymorphisms among individuals affect their capacity

to metabolise drugs (pharmacokinetics), and how drugs affect individuals

(pharmacodynamics) (Burezynski, 2009; Giacomini et al., 2007; Kalow et al., 2005;

Haile et al., 2009; Innocenti, 2005; Kreek et al., 2009; Shah, 2005). One of the most

significant applications of pharmacogenetics is to provide the foundation for scientists

to identify biological predictors of drug response, drug efficacy and drug-induced

adverse events, enabling clinicians to use this information to make the best treatment

decisions (Kreek, et al., 2009). A well-known example from 1937 was the major finding

of haemolysis caused by the antimalarial drug primaquine in some American soldiers in

the 1940s (Innocenti, 2005; Kalow et al., 2005), later shown to be due to a genetic

polymorphism that caused a glucose-6-phosphate dehydrogenase (G6PD) deficiency. In

1957, a paper published by Arno Motulsky (Innocenti, 2005; Kalow et al., 2005),

entitled “Drug Reactions, Enzymes, and biochemical”, first described the relationship

between genetic variations and responsiveness to drug therapy. In the following years,

many centres contributed new data, but all the data represented monogenic variation (.

differences between individuals caused by the mutation of single genes). In 1997,

Weber states cited in Innocenti (2005), there are 15 variable drug-metabolising

enzymes, 11 variable drug receptors and other variable proteins in humans that affect

drug actions (Innocenti, 2005). In 2001, Kalow counted 42 variable drug-metabolising

enzymes (Innocenti, 2005). The knowledge of different kinds of protein variants that

may affect drug responses continues to grow.

46

Role of Pharmacogenetics in Personalized Medicine and Forensic Toxicology

Currently, pharmacogenetics can be defined according to the European Medicines

Evaluation Agency as the study of variations in DNA sequences related to drug

response (European Medicines Agency (EMEA), 2007). After its introduction, scientists

and clinicians expected that the study of pharmacogenetics would provide a means of

identifying individuals who were less likely to have an adverse effect from a specific

drug (Haile et al., 2008, 2009; Kreek et al., 2005). They also hoped that the application

of pharmacogenetics would eventually lead to the realisation of concepts like

“personalised medicine” (Musshoff et al., 2010; Wong et al., 2010) and “forensic

molecular toxicology” (Musshoff et al., 2010; Sajantile et al., 2010). However, the

adaption of pharmacogenetics into routine clinical practice has been relatively slow and

tedious, with only a few exceptions such as testing for TPMT (thiopurine S-

methyltransferase) genetic variation in azathioprine/6-mercaptopurine therapy

(Grossman, 2007; Nebert et al., 2003; Pirazzoli & Recchia, 2004). Substance addiction

is a striking and non-exclusive example of a field where implementation of

pharmacogenetic testing into routine clinical practice has not been generally established,

despite tremendous efforts, as witnessed by the many literature reviews, meta-analyses

and guidelines (Haile et al., 2008, 2009; Kreek et al., 2005; Wong et al., 2010).

Nevertheless, pharmacogenetic research remains valuable to basic and clinical research,

since it may enhance our understanding of the mechanisms of drug action and explain,

at least partially, the variability in drug response.

In genetic association and pharmacogenetic studies, hundreds of SNPs representing

many different candidate genes are being genotyped using different molecular methods.

The studies are searching for genes associated with specific diseases, their

corresponding alleles and their links to treatment responsiveness (Jackson et al., 2000;

Tsuchihashi & Dracopoli, 2002). For example, various molecular genetic studies have

used mass spectrometry-based assays to search for genes associated with drug and

nicotine addiction and treatment (Jackson et al., 2000; Tsuchihashi & Dracopoli, 2002).

The results of these pharmacogenetics studies may be a useful tool, not only when

prescribing drugs to patients, but also when identifying the drug(s) involved in

intoxication cases (Jackson et al., 2000; Musshoff et al., 2010). An increased knowledge

of pharmacogenetics may contribute to safer use of drugs in the future (Haile et al.,

2008; Kreek et al., 2005).

47

In forensic toxicology, the use of SNP genotyping remains uncommon, but has the

potential to be highly relevant (Musshoff et al., 2010; Sajantile et al., 2010; Wong et al.,

2010). It is a challenge for the forensic toxicologist to draw conclusions from analytical

results in post-mortem toxicology (Zackrisson et al., 2010). An individual’s response to

drug and drug treatment varies because of genetic differences, which can cause adverse

reactions or even occasionally death (Musshoff et al., 2010; Sajantile et al., 2010; Wong

et al., 2010). Using high-throughput genotyping methods such as MALDI-TOF mass

spectrometry-based systems to obtain additional information about an individual’s

metabolic capacity may permit deeper interpretation of forensic toxicological results.

Various studies have investigated SNPs involved in drug transporters and receptors such

as dopamine transporter 1 (SLC6A3) and dopamine receptor 4 (DRD4), which can

explain some of the interindividual pharmacodynamics variability within individuals

(Haile et al., 2008; Kreek et al., 2005) and the effect of polymorphisms in morphine-

related gene sequences such as UDP-glucuronosyltransferase 2 B7 (UGT2B7) and mu

opioid receptor (OPRM1) genes (Haile et al., 2008; Kreek et al., 2005). Such research

may lead to an increased understanding of the importance of polymorphisms in drug

transporters, drug metabolising enzymes and receptors when adverse drug reactions and

fatal intoxication occur (Musshoff et al., 2010).

48

Candidate Gene Approach in Studying Genetic Susceptibility to Substance

Dependence and Pharmacogenetics

The basis for genetics studies on substance dependence is the evidence of heritability of

substance dependence, linked to the findings of biological studies regarding the

mechanisms involved in reward (Kreek et al., 2005; Haile et al., 2008; Wong et al.,

2010). Once a drug is administered, it is absorbed and distributed to its sites of action

where it interacts with targets such as receptors, and the enzymes undergo metabolism

before being excreted. The results of drug interaction with respective receptors, and the

downstream effects, are described as pharmacodynamic actions, whereas the absorption,

distribution and metabolism of a drug represent the pharmacokinetic aspects of drug

interaction. Proteins involved in pharmacokinetics and pharmacodynamics are known to

be variable in their effects; this variability can be under genetic control, and

consequently be determined in the DNA sequence coding for the protein.

Pharmacogenetics describes genetic variability, which has an impact on drug effects in

the human body, and associates this variability to the different outcomes of drug–body

interaction.

In contrast to pharmacogenomics and genome-wide association scans,

pharmacogenetics research involves a hypothesis-driven approach where one or more

candidate genes are selected on the basis of their relevance to the pharmacologic actions

(pharmacokinetics and pharmacodynamics) of the drug, and on the basis of their

relevance to the aetiology and pathogenesis of the phenotype investigated (Haile et al.,

2008; Kreek et al., 2005). In the following section, selected candidate genes that are of

possible relevance to substance dependencies and their treatments are discussed.

A. Opioid Related Genes

Opiate-dependent individuals have one of the highest levels of genetic variance,

compared with other illicit drug users (Tsuang et al., 1998). There are several gene

families implicated in drug addiction: one such family contains the opioid receptor gene

members (e.g. OPRM1). The endogenous opioid system is considered one of the most

important neurobehavioral signalling pathways implicated in drug use (Koob and

Simon, 2009), and consists of widely scattered neurons that produce three opioids: beta-

endorphin, the met- and leu-enkephalins, and the dynorphins. These opioids act as

neurotransmitters and neuromodulators at three major classes of opioid (μ, δ, κ)

receptors, and produce analgesia. The first endogenous ligands for opioid receptors

49

were identified in 1975 by Hughes et al. (1975), who isolated Leu- and Met-

enkephalins, which are five amino acid peptides with “opiate-like” or “opioid” activity.

Subsequently, two other endogenous opioid peptides were discovered: β-endorphin, and

dynorphin A (Bradbury et al., 1976; Cox et al., 1976; Goldstein et al., 1979; Li &

Chung, 1976).

Several studies have implicated the role of the products of opioid receptor gene variants

in mediating the behavioural and neurochemical properties of opiates such as heroin

(Gelernter et al., 1999, Kim et al., 2009). The endogenous opioid system may contribute

not only to the development of heroin dependence, but also to dependence on other

drugs of abuse such as alcohol, cannabis, cocaine and amphetamines (Goldman et al.,

2005; Haile et al., 2008, Koob & Simon, 2009). b/y rapidly activating the μ-opioid

receptors (MOR),some drugs of abuse can cause a euphoric effect, reinforcing or

rewarding the effects of the drug; this, constitutes a key psychomotor mechanism for the

development of addiction (Bond et al., 1998).

OPRM1 has been selected as a candidate for human genetic research of substance

dependence for many reasons. The MOR is the molecular target of the active metabolite

products of heroin (6-monoacetylemorphine and morphine), and is also a major target

for most opiate and opioid analgesic medications such as oxycodone, hydromorphone

and fentanyl, each which has a significant potential for dependence. Addiction to these

MOR directed agents is increasingly recognised as constituting a major addiction

problem. Therefore, the main focus of this study is the μ Opioid Receptor Gene

(OPRM1). Research into the genetics of opiate dependence has focused on the

opioidergic system, which is the primary target for opiates and, in particular, heroin

(Haile et al., 2008; Kreek et al., 2005). Figure 1 shows the opiate drug pathway

mechanisms where specific targets are highlighted. Specifically, Heroin is synthesised

from morphine, a derivative of the opium poppy (Johnson, 2011; Kreek & Vocci, 2002;

Kreek et al., 2005). Heroin is converted into morphine in vivo and activates the opioid

receptors (μ, δ, κ). This modulates several physiological processes such as pain, reward,

nociception, immune and gastric functions, and stress and treatment responses. The

opioid receptor µ1 (OPRM1) is thought to account for most of the opioidergic effects

(Kreek & Vocci, 2002; Kreek et al., 2005; Ravindranathan et al., 2009). OPRM1 is also

the primary site of action of endogenous ligands such as β-endorphin and enkephalin

(Beyer et al., 2004), µ-opioid receptor antagonists such as naltrexone (Volpicelli et al.,

50

1992), agonists such as methadone (Dole & Nyswander, 1992) and partial antagonists

such as buprenorphine (Kreek & Vocci, 2002). Haile et al. (2008) have reported that

opiates’ physiological and subjective effects are associated with the enhanced release of

β-endorphins (Haile et al., 2008).

The OPRM1 gene (6q24-q25; Gene ID: 4988) encodes the µ opioid receptor, which is

widely distributed in the brain (Benyhe et al., 1985, Chen et al., 1993; Delfs et al.,

1994). The OPRM1 receptor is a membrane of the G protein-coupled receptor family

(Beyer et al., 2004; Waldhoer et al., 2004) and over 300 OPRM1 sequence variants have

been identified to date (Hoehe et al., 2000; Ikeda et al., 2005). Most abundant among

the missenses variants is the A118G single nucleotide polymorphism (SNP) in exon 1 of

OPRM1 (Bergen et al., 1997). This polymorphism encodes an Asn40Asp amino acid

substitution that appears to be associated with changes in function. Bond et al. (1998)

have reported that β-endorphin has a higher binding affinity (threefold) at the Asp40

mutated receptor than at the receptor encoded by Asn40 (Befort et al., 2001; Bond et al.,

1998). In addition, β-endorphin activates G protein-coupled inwardly-rectifying

potassium channels (GIRKs) more in the presence of the Asp40 allele than the Asn40

allele (Befort et al., 2001; Bond et al., 1998), although other studies have reported that

the binding affinity or potency of β-endorphin for the variant receptor is no different

from the normal receptor (Befort et al., 2001; Beyer et al., 2004; Bond et al., 1998;

Zhang et al., 2005). A recent meta-analysis has reported that the OPRM1 Asn40Asp

does not appear to affect risk for drug dependence (Arias et al., 2006), but other studies

have indicated that it may influence response to opioid antagonist treatment for alcohol

dependence using naltrexone (Oroszi et al., 2009). The Asp40 allele frequency varies

considerably between different ethnic backgrounds (Haile et al., 2008): less than 5% in

African Americans (Gelernter et al., 1999), 16% in European Americans (Zhang et al.,

2006) and as much as 58% in those of Asian descent (Kim et al., 2009). The underlying

reasons for this have not been elucidated, but may be related to genetic architecture that

contributes to drug dependence mechanisms.

Various alcohol-l or drug dependence-related association studies have expanded their

investigations to include up to 20 SNPs spanning the coding sequence of the OPRM1

gene; all include the A118G (Asn40Asp) variants. For instance, Japanese subjects

meeting ICD-10 criteria for methamphetamine (MAP) dependence and controls were

genotyped for 20 SNPs including 10 SNPs in the 3'UTR region (Ide et al., 2004). The

51

study reported that A118G and other SNPs were not associated with MAP dependence

or psychosis, and the rs2075572 G-allele was only significantly associated with

increased risk for a diagnosis of MAP dependence or psychosis (p = 0.011). Ten SNPs

selected throughout OPRM1 were examined within a Chinese population to investigate

the relationship between the SNPs and heroin-induced subjective responses (Zhang et

al., 2007); the study reported that three SNPs in intron 1 were associated with an

increased risk of positive responses on first use of heroin and were likely to contribute

to further heroin consumption; A118G and rs2075572 were not associated with any

differences in heroin-induced subjective responses. Another association study of eight

SNPs within OPRM1 in alcohol-l, cocaine-, opioid- and polysubstance-dependent

European Americans (EA) and African Americans (AA) were genotyped (Luo et al.,

2003). The EA and AA study reported that C-2044A polymorphism was associated with

the combination of alcohol and opioid dependence in EA subjects, but not AA subjects.

Again, A118G was not associated with any of the substance-dependent phenotypes. A

genetic association study on the role of OPRM1 genetic variations in a large case-

control sample of alcohol- and drug- (cocaine and opioid) dependent European

Americans was conducted by Zhang et al. (2006). They typed thirteen SNPs

representing the major haplotypes observed in HapMap, all of which are included in the

present study. They found that seven SNPs (but not rs1799971 [A188G]) were

associated with alcohol, opioid and cocaine dependency. Zhang et al. (2006) found that

the frequency of the rs524731 A and rs648893 T alleles was significantly higher among

EA than AA subjects. Recent study have investigated different SNPs within both

ABCB1/MDR1 and OPRM1 gene suggested that the efflux transporter P-glycoprotein,

encoded by the ATP binding cassette B1 (ABCB1)/multiple drug resistance 1 (MDR1)

gene as a major determinant of opioid bioavailability (Campa et al. 2008). This study

was also found that pain relief variability was significantly associated with both

ABCB1/MDR1 and OPRM1 genes polymorphisms (Campa et al. 2008). Finally, a case-

control study of opiate and non-opiate dependent Jordanian Arabs was recently

conducted by Al-Eitan et al. (2012) to investigate the genetic association of twenty two

SNPs spanning the coding sequence of the OPRM1 locus with opiate dependence. The

study reported that three SNPs (rs6912029 [G-172T], rs12205732 [G-1510A] and

rs563649 [G-983A]) were associated with opiate dependence (Al-Eitan et al., 2012).

Various studies have provided varied and conflicting evidence of an association

between opioid receptor gene polymorphism and treatment response (Beyer et al., 2004;

52

Karhuvaara et al., 2007; Krystal et al., 2001; Zhang et al., 2005). For example, Oslin et

al. (2003) found that individuals with the Asp40 allele had significantly lower rates of

relapse and took longer to resume heavy drinking than those with the Asn40/Asn40

homozygous allele, in a sample of 130 EA alcoholics receiving naltrexone as treatment

(Oslin et al., 2003). A similar result was found in Korean alcoholics, with higher

therapeutic effects of naltrexone evident in individuals who had the Asp40 variant

genotype rather than the Asn40 genotype (Kim et al., 2009). Another study

investigating 10 SNPs within OPMR1 in a sample of 306 Caucasian subjects found that

Asn40Asp is predictive of a response to naltrexone treatment only; other OPRM1

genetic variants did not contribute substantially to interindividual variations in response

to this treatment (Oroszi et al., 2009).

Association studies have not been conclusive, with eleven showing evidence for

association and four rejecting association with substance dependence (Table 2). As in

most association studies targeting substance dependence, sample sizes were either small

or ethnically heterogeneous, two factors well known to adversely affect findings. In

addition, only a few polymorphisms were tested within each candidate gene, and those

polymorphisms might not present the complete variety of the genes and their related

proteins. Other opioid-related genes have also been targeted in association studies;

however, no conclusive results regarding the association of the studied SNPs with

substance dependence have been obtained (Table 2).

Figure 1. Opiate drug pathway mechanisms (Al-Eitan et al., 2012).

53

Table 2. Genetic association studies with substance dependence targeting opioid related genes

Gene Symbol NO. SNPs No. Cases/Control/ethnicity Type of Dependence Association References

OPRM1 42 172 AA/51 AA General substance Yes Hoehe et al., 2000

OPRM1 20 138 JA/213 JA Methamphetamine Ambiguous Ide et al., 2004

OPRM1 13 124 AA+318 EA /55 AA+179 EA General substance Yes Luo et al., 2003

OPRM1 10 336 CH/No Control Heroin Yes Zhang et al., 2007

OPRM1 8 382 EA/338 EA Alcohol + Opiate Yes Zhang et al., 2006

OPRM1 5 89 AA+ 124 EA/96 AA+100 EA Opiate Yes Crowley et al., 2003

OPRM1 5 145 CH/ 160 Heroin Yes Shi et al., 2002

OPRM1 2 139 SW/ 170 SW Heroin Yes Bart et al., 2004

OPRM1 2 200 CH/ 97 CH Heroin Yes Szeto et al., 2001

OPRM1 2 282 CH/ 258 CH Heroin No Li et al., 2000a

OPRM1 1 398 GE/ 365 GE General substance No Franke et al., 2001

OPRM1 1 113 EA, AA, HI/39 EA, AA, HI Opiate Ambiguous Bond et al., 1998

OPRM1 1 20 IN, 25 MA, 52 CH/ 117 IN, 131 MA, 156 CH Heroin Yes Tan et al., 2003

OPRM1 1 238 CA/217 CA Opiate No Crettol et al., 2008

OPRK1 1 145 EA, AA, HI/ 146 EA, AA, HI Opiate Ambiguous Yuferov et al., 2004

OPRD1 2 450 CH/ 304 CH Heroin No Xu et al., 2002

OPRD1 1 323 GE/ 173 GE Heroin + Alcohol No Franke et al., 1999

OPRD1 1 103 GE/ 115 GE Heroin Yes Mayer et al., 1997

OPRD1 1 238 CA/217 CA Opiate No Crettol et al., 2008

PENK 1 89 CA/ 132 CA Opiate Yes Comings et al., 1999

PDNY 1 236 GE/ 222 GE Heroin No Zimprich et al., 2000

*Population abbreviations: AA African American; CA Caucasian; EA European American; CH Chinese; GE German; HI Hispanic; IN Indian; JA Japanese;

MA Malay; SW Swedish.

54

B. Monoaminergic Related Genes

The monoaminergic neurotransmitter systems include the catecholaminergic and

serotonergic systems (see Figure 1). Research has indicated interactions between the

dopaminergic serotonergic and opioidergic neurotransmitter systems in reward, drug

dependence, and withdrawal (Kreek et al., 2005). In the monoaminergic system,

dopamine is thought to be the primary neurotransmitter involved in the mechanisms of

reward and reinforcement (Johnson, 2011; Spanagel & Weiss, 1999; Wong et al., 2000).

The function of dopamine is mediated by two classes of dopamine receptors termed D1-

like and D2-like families. The D1-like family (D1 and D5 dopamine receptors) mediate

a reduction in the drive to seek reinforcement effects, in contrast to the family of D2-

like receptors (including D2, D3, and D4 dopamine receptors) that mediate both reward

and reinforcement effects (Johnson, 2011; Kienast & Heinz, 2006; Wong et al., 2000).

The dopamine receptor gene family, which comprises DRD1 (MIM *126449), DRD2

(MIM *126450), DRD3 (MIM *126451), DRD4 (MIM * 126452) and DRD5 (MIM

*126453) is a prime candidate gene family for influencing substance abuse because it is

thought to play one of the most important roles in the neurobehavioral signalling

pathways implicated in substance addiction (Girault & Greengard, 2004; Wong et al.,

2000).

Several studies have indicated that the products of dopamine receptor gene variants

mediate the behavioural and neurochemical properties of opiates such as heroin (Kreek

et al., 2005; Wong et al., 2000). It has also been suggested that the endogenous

dopamine system may contribute to the development of dependence on other drugs of

abuse such as alcohol, cannabis, cocaine and amphetamines (Haile et al., 2008;

Hodgkinson et al., 2008, Koob et al., 2009). Various studies have shown that dopamine

receptors are involved in the reinforcement of drug use in addicted individuals (Wong et

al., 2000); other neurotransmitters also thought to play a role in reinforcement include

the dopamine active transporter (DAT; gene symbol SLC6A3, MIM *126455)

(Vandenbergh et al., 1992), neurotrophines such as brain-derived neurotrophic factor

(gene symbol BDNF, MIM *113505) (Altar et al., 1992; Barde et al., 1982; Grimm et

al., 2003) and enzyme systems such as catechol-O-methyltransferase (gene symbol

COMT, MIM *116790) (Matsumoto et al., 2003). All these genes are expressed within

the mesocorticolimbic dopamine system or associated structures such as the nucleus

accumbens, ventral tegmental area, amygdala, prefrontal cortex, hippocampus and

cerebral cortex (Haile et al., 2008; Hodgkinson et al., 2008).

55

Figure 2. The monoaminergic neurotransmitter systems (Kreek et al., 2005).

56

Human molecular genetic studies are also implicating the dopamine receptor gene

family in substance use disorders. The rs5326 SNP is located in the 5'-UTR of DRD1

gene and has been linked to heroin dependence in African Americans (Levran et al.,

2009). Although there are no similar confirmed associations between DRD2 gene and

substance addiction (Noble 2000; Wong et al., 2000), some variants within DRD2 gene

such as the rs1799732 SNP rs1799732 (C/-C, 5'-UTR) warrants further investigation as

these variants have a functional effect on gene expression (Arinami et al., 1997). The

DRD3 gene has been reported to be associated with substance abuse (Krebs et al., 1998)

and cocaine (Comings et al., 1999) and heroin abuse (Duaux et al., 1998) but others

have not reported association with abuse of either drug (Higuchi et al., 1996; Kotler et

al., 1999). The rs3758653 SNP located in the 5'-UTR of the DRD4 gene has been

reported to be associated with heroin dependence in 53 heroin Hungarian addicts

(Szilagyi et al., 2005). The DRD5 gene has not been the subject of many studies.

The dopamine active transporter (DAT, SLC6A3) is widely distributed throughout the

brain in areas of dopaminergic activity (Vandenbergh et al., 1992). The DA transporter

DAT1 mediates the active reuptake of DA from the synapse and is a principal regulator

of dopaminergic neurotransmission. It’s addictive effects are thought to be principally

mediated through blockage of DAT, resulting in a substantial increase in the

concentration of extracellular DA and stimulation of neurons in brain regions involved

in reward and reinforcement behavior (Ritz et al., 1987). Family and twin studies

suggest that DAT1 is a substantial genetic factor in the vulnerability of individuals to

cocaine dependence after exposure (Bierut et al., 1998; Kendler et al., 1998; Merikangas

et al., 1998). Therefore, polymorphic functional variants in the DAT gene may act to

modify susceptibility to substance abuse and dependence.

Brain-derived neurotrophic factor (BDNF) is a member of the nerve growth factor

family. This family is a group of structurally related secretory proteins widely

expressed in neurons and their target cells (Binder & Scharfman 2004). Induced by

cortical neurons, BDNF is required to support existing neurons in the brain and help in

the growth and differentiation of new neurons and synapses (Jones & Reichardt 1990;

Maisonpierre et al., 1991; Huang & Reichardt 2001). Studies in animals and humans

suggest that BDNF influences the dopaminergic and serotonergic functions that are

heavily linked to substance addiction (Horger et al., 1999; Lyons et al., 1999; Kernie et

al., 2000; Dluzen et al., 2001; Uhl et al., 2001). In mice, BDNF administration or

57

BDNF genetic knockouts have shown that this factor can alter drug preference or drug-

induced behavior. In humans, Uhl et al. (2001) used 1494 SNPs to scan for

vulnerability genes for polysubstance abuse. Using 1004 European American and

African American samples; they found that positive association markers flank the

BDNF gene and Val66Met at rs6265 position was associated with drug addiction

vulnerability (Uhl et al., 2001). Recently, various studies have shown that the

Val66Met substitution in the prodomain may affect intracellular trafficking and activity-

dependent secretion of BDNF (Egan et al., 2001; Chen et al., 2004). Overall these

animal and human studies indicate that BDNF may be involved in the mechanisms

underlying substance addiction (Chang et al., 2005).

Catechol-O-methyltransferase (COMT) is one of several enzymes that metabolises

catecholamine such as dopamine, epinephrine and norepinephrine and play a role in the

reinforcement mechanism (Haile et al., 2008). Nikoshkov et al. (2008) suggests that

heroin addicts with homozygous genotype at position rs4680 Met158/Met158 have a

significant up-regulation of COMT gene expression (Nikoshkov et al., 2008). In

contrast, heroin addicts with the heterozygous genotype (Vall158/Met158) or

homozygous genotype of Vall158 at this position show a down-regulation of COMT

gene expression. Goldman (2005) reported that the Val158 variant catabolizes

dopamine up to four times the rate of its methionine counterpart, resulting in significant

lower synaptic dopamine levels following neurotransmitter release. This ultimately

reduces dopaminergic stimulation of the post-synaptic neuron (Haile et al., 2008).

Therefore, due to the role of COMT in prefrontal dopamine degradation, the Val158Met

polymorphism is thought to be associated with increased risk of substance addiction by

modulating dopamine signaling in the frontal lobes.

In summary, similarly to the association studies targeting the opioid related genes,

inconsistent results of monoaminergic related genes with substance dependence have

been obtained in different studies (Table 3). There are many confounding factors such

as sample sizes, ethnicity, phenotype characterization might be held responsible for

obtaining conflicting results. Relevant monoaminergic genes analysed previously

include genes for dopamine receptors (DRD1, DRD2, DRD3, DRD4 and DRD5)

dopamine transporter (SLC6A3), serotonin transporter (SLC6A4), brain-derived

neurotrophic factor (BDNF) and the catechol-O-methyltransferase (COMT) are

summarised in Table 3.

58

Table 3. Genetic association studies with substance dependence targeting monoaminergic related genes

Gene Symbol NO. SNPs No. Cases/Control/ethnicity Type of Dependence Association References

DRD1 5 202 AA/ 167 AA Heroin Yes Levran et al., 2009 DRD1 5 238 CA/ 217 CA General Substance No Crettol et al., 2008

DRD1 2 134FR/96FR Alcohol Yes Batel et al., 2008

DRD2 10 486 CH + 471 GE/ 313 CH + 192 GE Heroin Yes Xu et al., 2004 DRD2 3 344 CH/ 104 CH Heroin Yes Li et al., 2002

DRD2 2 88 AA/ 140 AA General Substance No Berrettini et al., 1996

DRD2 1 420 CH (only cases) Heroin Yes Li et al., 2006

DRD2 1 84 CA (only cases) Alcohol + Nicotine Yes Connor et al., 2007

DRD3 1 121 CH/ 104 CH Heroin No Li et al., 2002 DRD3 1 47 CA/ 305CA Cocaine No Comings et al., 1999

DRD4 2 121 CH/ 154 CH Heroin NO Li et al., 2000c DRD4 1 53 HU/ 362 HU Heroin Yes Szilagyi et al., 2005

DRD4 1 396 GE/ 419 GE Opiate NO Franke et al., 2000

DRD5 1 420 CH (only cases) Heroin No Li et al., 2006 DRD5 1 680 CH (only cases) Nicotine Yes Wei et al., 2012

SLC6A3 1 420 CH (only cases) Heroin No Li et al., 2006 SLC6A3 208 IT/ 250 IT Heroin Ambiguous Gerra et al., 2005

SLC6A4 1 53 HU/ 362 HU Heroin No Szilagyi et al., 2005 SLC6A4 1 344 CH/ 104 CH Heroin No Li et al., 2002

SLC6A4 1 63 CH/72 CH Heroin Yes Tan et al., 1999

BDNF 2 202 JP/ 189 JP Methamphetamine No Itoh et al., 2005 BDNF 1 200 TA (Heroin) + 103 TA (amphetamine)/122 TA Heroin Yes Cheng et al., 2005

COMT 1 101 IS/, 126 IS Heroin Yes Horowitz et al., 2000

*Population abbreviations: AA African American; CA Caucasian; CH Chinese; FR French; GE German; HU Hungarian; IT Italian; IS Israeli; JA Japanese;

TA Taiwanese.

59

VI. Conclusion, Scope and Outline of Thesis

The present thesis provides clinical epidemiology and describes a substance use

population undergoing treatment in Jordan. It considers several genetic and

pharmacogenetic aspects of substance dependence in a population of Arab descent. The

clinical, epidemiological and pharmacogenetic characteristics of this ethnic group have

not previously been described in the scientific literature; there is an urgent need for

epidemiological and genetic studies that relate to the population of the Jordan and other

Arab nations. Without progress in these areas, there is no hope for proper treatment

strategies for substance-dependent individuals.

The aim of this study is first to study the pattern and severity of substance abuse in a

group of patients presenting for treatment at a Jordanian drug rehabilitation treatment

program. The second aim is to identify genetic markers within selected candidate genes

that are associated with increased risk of substance dependence and to responsiveness to

treatment. There is a wide range of expected outcomes from this study:

1) Medical and genetic applications: The study of the patterns and severity of substance

abuse in these individuals will allow researchers to assess clinical and epidemiological

features that may contribute to the development of the substance abuse treatment among

populations of Arab countries. The genetic aspect, the study of DNA from this ethnic

group, will provide additional genetic knowledge that may be used to establish a

pharmacogenetic treatment approach to reduce the severity of drug consumption and

alcohol intake and improve drug abstinence. This study will also stimulate further

genetic analyses of the Arab population to understand better both their unique genetic

background and the aetiology of drug dependence that affects this particular group of

individuals, and improve treatment strategies according to the genetic variation

identified in the patients. This may lead to more accurate matching of individuals to

different treatment options, including pharmacotherapy, and identify at an early stage

persons at high risk of relapse who require more intensive intervention.

The identification of polymorphisms that are associated with diseases in different ethnic

backgrounds will allow researchers to determine if the same gene variations are

associated with the aetiology of drug dependence, and, if so, the strength of the

association. Comparative analysis of different ethnic groups may also assist in

understanding the mechanisms that causes addiction.

60

2) Forensic toxicology application: Understanding the function of genetic variations in

drug metabolism will provide information about individual metabolic capacities, or may

reveal a drug consumption pattern that will contribute to a more rigorous interpretation

of forensic toxicological results. This will help the forensic toxicologist to draw useful

conclusions from the results of post-mortem toxicology. Various studies have

investigated SNPs involved in drug transporters and receptors such as dopamine

transporter 1 (SLC6A3) and dopamine receptor 4 (DRD4), as they can explain some of

the interindividual pharmacodynamics variability within individuals (Haile et al., 2008;

Kreek et al., 2005). Other studies have investigated the effect of polymorphisms in

morphine-related gene sequences such as UDP-glucuronosyltransferase 2 B7 (UGT2B7)

and mu opioid receptor (OPRM1) genes. The combination of this research may lead to

an increased understanding of the importance of polymorphisms in drug transporters,

drug metabolising enzymes and receptors for investigations of adverse drug reactions

and fatal intoxication.

In order to achieve these goals, collaboration between the National Centre for

Rehabilitation of Addicts (NCRA) at The Jordanian Ministry of Health, the Drug

Rehabilitation Centre at The Jordanian Public Security Directorate, the Jordan

University of Science and Technology and The University of Western Australia has

been established. Written informed consent was obtained from all subjects (N = 460) in

the study. The study was subject to and conducted in compliance with the National

Statement on Ethical Conduct in Human Research, Australia (2007), the Australian

Medical Association Code of Ethics (2006) and World Medical Association Declaration

of Helsinki (World Medical Association, 2008). Ethical approval to conduct this

research was granted by the University of Western Australia’s Human Research Ethics

Committee (Ref No. RA/4/1/4344).

The study considered a clinical population (n =220) of unrelated Jordanian Arab males

who met the criteria of the Diagnostic and Statistical Manual of Mental Disorders

(DSM-IV) (American Psychiatric Association, 1994) for substance dependence. Of

them, 84% were dependent on opioids, 14% on amphetamines and 4% on alcohol. The

majority of these patients (92%) had a nicotine co-dependence. Cannabis abuse was

common (58%) and 53% were also alcoholics. Each participant, who was undergoing a

voluntary eight-week treatment program at one of two Jordanian Drug Rehabilitation

Centres, gave signed informed consent before participating in the study. For each

61

patient, demographic data (date of birth, gender, nationality, marital status, children and

occupation) was provided, along with clinical data, by the administering officer of the

rehabilitation centre. Information on the substance abused (type of substance, cause of

addiction, initial date of addiction, starting amount, last taken amount, route of

administration, withdrawal periods, hospitalisation due to substance abuse, smoking

status and blood relatives with histories of substance abuse), reaction (withdrawal

symptoms and signs), and clinical history (cardiac disease, hepatic disease, overdose

and toxicity, surgery and accident) were also provided. All data were coded and no

specific individual was identified. In addition, healthy controls (n = 240) from an

ethnically homogenous Jordanian Arab population with no lifetime history of psychosis

or mood disorders, alcohol or heroin dependence according to DSM-IV criteria were

recruited. Blood samples were taken for genotyping, and DNA samples extracted

according to standard molecular protocols. A flow chart of the project chapters

described in this thesis is shown in Figure 1.

Chapter 2 provides an epidemiology and describes a substance use population

undergoing treatment in Jordan. The Addiction Severity Index (ASI) was used to assess

the pattern and severity of substance abuse as voluntarily reported by patients

undergoing therapy at two Jordanian drug rehabilitation centres. This chapter also

investigates the nature and extent of transitions in route of heroin administration among

these patients.

Chapter 3 describes the distribution of allele, haplotype and genotype frequencies of

the serotonin transporter gene SLC6A4 polymorphisms (5-HTTLPR and rs25531) in

drug and non-drug dependent Jordanian Arabs. It also examines the genetic association

of these variants in a drug-dependent population from the same area. The identification

of polymorphisms that are associated with diseases in different ethnic backgrounds will

allow researchers to determine if the same gene variations are associated with the

aetiology of drug dependence, and, if so, the strength of the association.

Chapter 4 examines the influence of SLC6A4 gene polymorphisms (5-HTTLPR and

rs25531) on clinical and biological measures of outcomes in a sample of Arab drug

dependent patients (N = 192) undergoing pharmacological and behavioural treatment.

This study was specifically interested in investigating whether the sample

characteristics differ among the serotonergic polymorphisms (5-HTTPLR and rs25531),

62

and if allelic variations within the SLC6A4 gene are associated with dependence

variables, drug consumption, psychiatric symptoms and responsiveness to treatment.

Chapter 5 describes the use of a Sequenom MassARRAY® platform (iPLEX GOLD)

method to genotype 68 single nucleotide polymorphisms (SNPs) within nine drug-

dependence candidate genes (DRD1, DRD2, DRD3, DRD4, DRD5, OPRM1, SLC6A3,

BDNF and COMT). This chapter also investigates the distribution of minor allele

frequencies (MAFs) in drug- and non-drug-dependent Jordanian Arab populations.

Chapter 6 identifies gene(s) and mechanisms associated with substance addiction in

patients of Arab descent within the Jordanian population, examining 220 Jordanian

Arab individuals with substance addiction and 240 matched controls. the chapter

presents the results of the study of 49 SNPs within eight candidate genes, the dopamine

receptors DRD1, DRD2, DRD2, DRD3 and DRD5, and solute carrier family 6, member

3 (SLC6A3) or dopamine active transporter (DAT), brain-derived neurotrophic factor

(BDNF) and catechol-O-Methyltransferase (COMT), for their association with

substance addiction.

Chapter 7 describes and characterises genetic variations within the µ-Opioid receptor

(OPRM1) gene that may be associated with susceptibility to opiate drug dependence,

among the study participants and a control sample.

Chapter 8 investigates whether the genetic polymorphisms within the OPRM1 gene in

opiate dependent patients of Arab descent are associated with opiate consumption, a

range of dependence and clinical variable, and psychiatric symptoms. It identifies

genetic factors associated with responsiveness to the biopsychosocial treatment offered

to opiate drug-dependent patients who receive naltrexone as a maintenance treatment.

Opiate-dependent patients (N = 183) were genotyped using the Sequenom

MassARRAY® system (iPLEX GOLD). This may lead to more accurate matching of

individuals to different treatment options, including pharmacotherapy, and identify at an

early stage persons at high risk for relapse who therefore require more intensive

intervention.

Chapter 9 summarises these studies and discusses the current challenges facing

pharmacogenetics and complex disease association mapping in general, and offers

suggestions for future research.

63

Figure 3. Flow chart showing the work described in this thesis

64

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CHAPTER 2

CLINICAL CHARACTERISATION OF

SUBSTANCE ABUSE PATIENTS PRESENTING

FOR TREATMENT AT A DRUG

REHABILITATION CENTRE IN JORDAN

This Chapter was submitted to the Journal of Ethnicity in Substance Abuse.

95

Chapter 2

Clinical Characterisation of Substance Abuse Patients

Presenting for Treatment at a Jordanian

Rehabilitation Centre

Chapter 2 is presented as a manuscript submitted to the Journal of Ethnicity in

Substance Abuse. Collection of the data for this study was made possible through the

collaboration of four institutions: The National Centre for Rehabilitation of Addicts

(NCRA) at The Jordanian Ministry of Health, the Drug Rehabilitation Centre at The

Jordanian Public Security Directorate, Jordan University of Science and Technology

and The University of Western Australia.

Epidemiological data on substance abuse in Arab countries are very scarce and few

reports are available. Recently, a survey of substance abuse and dependence in Jordan

was conducted (Source: The Hashemite Kingdom of Jordan, 2010). More than 5000

university and community college students aged 18 to 25 were interviewed with regard

to their patterns of substance abuse. The major substances abused by the students

were: tobacco (29%), sedatives (12%) and alcohol (12%). Other substances like

stimulants (5%), cannabis products (3%) and opiates including heroin (1%) were

abused much less frequently. The previous reports have focused only on substance

abuse in different age groups; none have addressed the severity of problems commonly

found among substance abusers in Jordan. In addition, the nature and severity of the

problems reported by individuals voluntarily admitted into substance abuse treatment

programs in Jordan have not been examined in detail.

This chapter describes the clinical characterisation of substance abuse patients

undergoing treatment at Jordanian drug rehabilitation centres. Extensive clinical data

were obtained.

For 220 male substance abuse patients of Jordanian Arab origin, attending two

rehabilitation centres from December 2010 to June 2011. Demographic data

(including their date of birth, nationality, marital status, number of children and

96

occupation) was also made available for epidemiological studies. Information on

substance abuse (type of substance, causes of addiction, initial date of addiction,

starting amount, last taken amount, route of administration, withdrawal periods,

hospitalisation due to substance abuse and smoking status), reactions (withdrawal

symptoms and signs), clinical history (cardiac disease, hepatic disease, overdose and

toxicity, surgery and accident) were also provided.

The data presented throughout this chapter describes the patterns and severity of

substance abuse of these patients. This study will allow researchers to assess clinical

and epidemiological features that may contribute to the development of the substance

abuse treatment among populations of Arab countries.

This manuscript was prepared by Laith AL-Eitan with support from the co-authors

listed. The samples were collected by local healthcare workers and data compiled by

AL-Eitan. Dr Jamal Anani, a psychiatrist; from NCRA, contributed as a clinical

psychiatrist and provided guidance. Dr Kellie Bennett, a behavioural psychologist at

University of Western Australia’s School of Psychiatry and Clinical Neurosciences who

carried out the statistical analysis and provided valuable comments and feedbacks. Dr

Tay and Dr Hulse assisted with the design of the study and proof read the manuscript.

97

Clinical Characterisation of Substance Abuse Patients Presenting for Treatment at

a Jordanian Rehabilitation Centre

Laith N. AL-Eitan1

, Kellie S. Bennett2, Jamal Y. Anani

3, Guan K. Tay

1, Gary K. Hulse 4,2

1 Centre for Forensic Science, The University of Western Australia, Crawley, WA

6009, Australia 2 School of Psychiatry and Clinical Neurosciences, Queen Elizabeth II Medical

Centre, The University of Western Australia, Crawley, WA 6009, Australia 3 The National Centre for Rehabilitation of Addicts, The Jordanian Ministry of

Health, Amman 22110, Jordan 4 Unit for Research and Education in Alcohol and Drugs, Queen Elizabeth II

Medical Centre, The University of Western Australia, Crawley, WA 6009,

Australia

Abbreviated title: Substance Abuse Patients in Jordan

Keywords: Drugs, Heroin, Alcohol, Substance Abuse, the Addiction Severity

Index, Jordan, Arab

Publication number LA09-001 of the Centre for Forensic Science at The University of

Western Australia

Corresponding Author:

Laith N. AL-Eitan


The University of Western Australia

35 Stirling Highway, Crawley WA 6009, AUSTRALIA

Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285

Email: [email protected]

[email protected]

mailto:[email protected]

98

ABSTRACT

The purpose of this study was to provide epidemiology and to describe a substance use

population of Arab descent undergoing treatment in Jordan. Patients (N= 220) with

substance abuse were interviewed using a structured baseline survey based on the

Addiction Severity Index at two Jordanian drug rehabilitation centres. In the 30 days

prior to admission into the treatment program, the patients reported: medical problems

(40%), employment problems (40%), illegal activities (15%), serious conflicts with

their families (85%), serious conflicts with other people (45%), and one or more

psychiatric symptoms (55%). The primary substance of dependence was opiates (63%)

and other substances (37%). These findings suggest that opiates, specifically heroin,

were the major substance used by most of the patients, representing 84% of cases.

Other substances included: stimulants, barbiturates, hallucinogens and inhalants. The

main route of administration was chasing. Heroin abusers were at ongoing risk of

switching from chasing to injecting, which should be taken into consideration in

treatment programs.

99

INTRODUCTION

Worldwide, the most commonly abused substances are alcohol (Jonsson et al., 2007),

opiates (Hulse et al., 1999, Zhou et al., 1999), cannabinoids (Tang et al., 1996), cocaine

(Robson et al., 1997) and amphetamines (Poulin et al., 1998). Alcohol and drugs have

been manufactured since ancient times (Khalil et al., 2008). The earliest alcoholic

beverages were likely made from berries or honey and winemaking may have originated

in the wild grape regions of the Middle East (Blum et al., 1969). Beer and wine were

used for medicinal purposes as early as 2000 B.C. (Babor and Mandelson, 1986).

Today, illicit drug intake is considered by many societies as an antisocial or criminal

behaviour (Fawzi, 1980, Haddad et al., 2010). The extent, pattern and severity of

substance abuse worldwide differ from country to country and within a country there

are variations from area to area (Hadidi, 2004). However, the uncontrollable use of

alcohol and illicit drugs remains a significant problem in many countries (Jonsson et al.,

2007)

Addiction to alcohol and drugs is a significant concern for Jordan (Source: The

Hashemite Kingdom of Jordan, 2010). Since the foundation of the Jordanian Kingdom

in 1921, Jordan has been well aware of the danger of drug use, as testified by issuing the

first Jordanian legislation concerning illicit substances in 1962 (Source: The Hashemite

Kingdom of Jordan, 2010). The latest law regarding drug use was issued in 1988

(Narcotic and Psychotropic Substance Law, 1988). Importantly, current legislation

protects those who voluntarily seek treatment for substance abuse from prosecution and

treats all data related to them as highly classified and confidential. The Jordanian

government also prohibits the import, export, transport, usage, production and

possession of illicit substances and treats these activities as a crime by law unless it is

for medical and research use (Source: The Hashemite Kingdom of Jordan, 2010). If a

drug associated crime has demonstrable links to international crime syndicates, the

death penalty is applied (Narcotic and Psychotropic Substance Law, 1988).

Jordan has a young population, with 41% under the age of 15 and 31% of the population

aged from 15 to 29 years (Source: The Hashemite Kingdom of Jordan, 2010; World

Health Organization, 2010). With an increase in nicotine use among young Jordanians,

there is concern about increases in the use of other drug as nicotine is considered to be

the catalyst to more illicit drugs (Haddad et al., 2010). Epidemiological data in Arab

100

countries on substance use including use of alcohol, opiates, cannabis, amphetamines

and prescribed medicines (e.g. benzodiazepines) are still scarce (Fawzi, 1980, Source:

The Hashemite Kingdom of Jordan, 2010). Two studies have been undertaken to assess

the nonmedical use of substances among young Jordanians. The first study was

undertaken by the World Health Organization (WHO) in 2005. Of 2,471 students

surveyed aged 18 to 25 years, 3% indicated they had used drugs. Clearly, students in

Jordan are not immune to drug use (Source: UNODC, The Hashemite Kingdom of

Jordan, 2010). The second study was conducted in 2001 (Source: UNODC, The

Hashemite Kingdom of Jordan, 2010). A total of 5,000 students (80% from universities

and 20% from community colleges) aged from 18 to 25 years were interviewed with

regard to their primary drug use. According to the results of this study, tobacco (29%),

sedatives (12%) and alcohol (12%) were the major substances abused by the students.

The students abused other substances like stimulants (5%), cannabis products (3%) and

opiate mainly heroin (1%) less frequently. A number of the students surveyed in the

study had recently been imprisoned. The study found that stimulants, opiates, sedatives,

volatile substances and alcohol were the most frequently abused substances within the

prison population, with 7.4% from the prisoner population samples of substance abusers

having used opiates intravenously. Neither of these studies collected data about

polysubstance abuse, which is the simultaneous use of two or more substances in

combination to achieve a particular effect (Johnson, 2011).

According to a report of UNODC (2010), there are indications that substance abuse in

Jordan is increasing, and is most problematic among 25 year old males (Haddad et al.,

2010). Studies conducted by the United Nations Office on Drugs and Crime (UNDCP)

with substance abuse patients in drug treatment centres indicated that up to 75% of

substance abusers are males. According to the UNODC report, using heroin by

“chasing the dragon” has increased significantly in recent years. Chasing the dragon

refers to inhaling the vapour from heated morphine, heroin, oxycodone or opium that

has been placed on a piece of foil (Gossop et al., 1991). The Jordanian Anti-Narcotic

Department reported that the illicit substances of choice are alcohol (43%),

pharmaceuticals such as amphetamines, artane, kemadrin and benzodiazepines (28%),

opiates such as heroin and morphine (23%) and solvents such as acetone, thinner and

toluene (7%) (Source: UNODC, The Hashemite Kingdom of Jordan, 2010).

101

Substance abuse is still primarily considered a social and legal problem, although there

is increasing recognition of the medical problems linked to substance abuse (Johnson,

2011). The negative impacts of alcohol, heroin and other opiates, cocaine and

amphetamines on both patients and their families are well documented. However, very

few studies have been conducted in this area in Jordan. While, prior studies in Jordan

have focused only on substance abuse in different age groups, none have addressed the

severity of problems commonly found among the homecare substance abusers in

Jordan. In addition, the nature and severity of the problems reported by individuals

voluntarily admitted into substance abuse treatment programs in Jordan have not been

examined in detail.

The present study aimed to assess the pattern and severity of substance abuse as

voluntarily reported by patients undergoing therapy at drug rehabilitation centres in

Jordan. This study also aimed to investigate the nature and extent of transitions in route

of heroin administration among these patients. An assessment tool based on the

Addiction Severity Index (ASI) was developed and used to assess the nature and

severity of these problems. As the first study of its kind in Jordan, its scope was limited

to the clinical characterisation of substance abuse patients presenting for treatment and

their short-term outcome after treatment, in the hope of highlighting relevant issues for

subsequent studies and in depth consideration.

102

Methodology

Subjects and Data Source

A total of 220 patients diagnosed with drug and alcohol dependence who met the

Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria (Association

American Psychiatric, 1994) were interviewed at the National Centre for Rehabilitation

of Addicts (NCRA) at Jordanian Ministry of Health, and the Drug Rehabilitation Centre

at the Jordanian Public Security Directorate (DRC-PSD) over a period of 6 months from

December 2010 to June 2011. NCRA and DRC-PSD provide services to both patients

with psychiatric disorders at the psychiatric unit and those with substance addiction

problems at the addiction unit. The addiction unit at NCRA has 46 beds for inpatients

and DRC-PSD has 40 beds for inpatients. Both have outpatient clinics providing

services on a limited basis, 3 days a week. Each addiction unit treats, on average, 20 to

30 patients every week (new and follow-up patients). All 220 assessment interviews

were completed within the first week of intake; 135 were completed during the intake

session and 85 were completed during the first week of treatment. All patients who

agreed to be part of this study were recruited regardless of their primary drug of abuse.

This study was conducted according to the provisions of the Australian Medical

Association Code of Ethics (2006) and World Medical Association Declaration of

Helsinki (World Medical Association, 2008). The study was also subject to, and in

compliance with, the National Statement on Ethical Conduct in Human Research,

Australia (2007). Ethical approval to conduct this research was granted by Human

Research Ethics Committee of the University of Western Australia (UWA) (Ref No.

RA/4/1/4344). This study was also approved by the Human Ethics Committee of the

Jordanian Ministry of Health (Ref No. Development/Trainees/535) and the Institutional

Review Board/Human Research Ethics Committee of the Jordan University of Science

and Technology (Ref No. RA/16/1/2010). Each participant gave signed informed

consent, before being allowed to participate in the study.

The NCRA and DRC-PSD serves most of the Jordanian substance abuse population as

patients access these two addiction units from all over the country. The patients are

predominantly middle and lower class, while upper-class patients are more likely to go

to private facilities. Some patients admit themselves to the units or are admitted by

family members. Most, however, are referred to the clinic by psychiatrists from other

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outpatient clinics. At the outpatient clinic, a psychiatrist evaluates the patients and

determines if they can be served on an outpatient basis or if they need inpatient service.

The Jordanian Government pays for the treatment of a majority of inpatients.

Instrument and Measures

The data collection tool was based on the Addiction Severity Index criteria (ASI, 5th

edition) (McLellan et al., 1992). The ASI is an instrument which has been in wide use

for clinical and research purposes to evaluate patients presenting for substance abuse

treatment. It was designed to provide information on patients prior, during and after

treatment for substance use related problems, and for information on patients’ status and

treatment outcome changes.

The ASI is a 1 hour face-to-face semi-structured interview that takes place when a

patient is admitted for treatment (McLellan et al., 1992, Fureman et al., 1994). The ASI

is a reliable, valid instrument, which is freely available and can be easily used to

examine substance abuse patients’ status (McLellan et al., 1985, Zanis et al., 1994,

Zanis, et al., 1997). The baseline information ASI assesses an individual status in 7

major life domains: alcohol and other drug use, psychiatric and medical status, legal

aspect, family, employment and support. This information helps to determine the

patient’s level of stability. It has also proven useful for understanding life events that

contribute to alcohol and drug dependency. It has been used for different purposes in

assessing substance abuse patients such as developing treatment plans, matching patient

to better treatment options and identifying when to make referrals (McLellan et al.,

1992, Fureman et al., 1994, Carise, et al., 2002). The ASI has been widely applied over

the past 6 years (McLellan et al., 1992). The ASI has been used by researchers and

clinicians internationally (McLellan et al., 1992, Carise, et al., 2002).

Clinical (Phenotype) Data

A semi-structured baseline interview was developed based on the ASI criteria. This

interview was used to collect demographic and clinical data. All interviewers were

given training on its administration. For each participant, demographic data (date of

birth, gender, nationality, marital status, children and occupation) was also provided.

Clinical data was provided by the administering officer of the Rehabilitation Centres.

Information on the substance abused (type of substance, cause of addiction, initial date

of addiction, starting amount, last taken amount, route of administration, withdrawal

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periods, hospitalisation due to substance abuse, smoking status and blood relatives with

history of substance abuse), reaction (withdrawal symptoms and signs), and clinical

history (cardiac disease, hepatic disease, overdose and toxicity, surgery and accident)

were also provided. All data was coded and no specific individual was identified.

Statistical Analysis

Epidemiological and clinical data were entered into an Excel (Microsoft Corporation)

database for descriptive statistics (mean (M), standard deviations (SD), percentage (%)

and frequencies). SPSS (Statistical Package for the Social Sciences) version 19.0 was

used to conduct all other analyses (e.g. Student t-test, Chi-Square Test of Contingencies

and One-Way ANOVA). The p value for each phenotype studied was calculated using

Dunnett's Multiple Comparison Test. Bonferroni corrections were applied for each

statistical test involving multiple comparisons.

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RESULTS

Main Characteristics of the Sample

General Information

The studied sample included 220 patients presenting with substance abuse. All were

males of Arab descent (Table 1). Ninety-five precent were Muslim and the remaining

5% were Christian. The mean age was 32.7 (SD = 8.4) with youngest being 18 years

old and the oldest 58 years old. Figure 1 shows the distribution of substance abuse

patients by age, with approximately 77% of patients aged between 20 and 40 years.

Seventy-two precent of the total patients reported that they had been in a controlled

environment in the previous month; 18% in jail, 54% in residential drug/alcohol

treatment, and the remainder being in some other type of controlled environment such

as a residential psychiatric or medical treatment centre (Table 2).

Medical Status

Forty precent of the patients reported that they were hospitalised at least once in their

lifetime for medical problems and 33% had chronic medical problems. Of the last

medical diagnosis, 20.5% reported having cardiac diseases, 3.6% reported hepatitis C

virus infection, 45% had drug overdose or alcohol poisoning and 61% had had surgery.

Fifteen precent of the patients were regularly taking prescribed medication for physical

problems and 40% reported medical problems in the previous 30 days.

Employment and Support Status

Nineteen precent of the patients reported that they have a university degree and 81%

had an average of 10 years of education. Thirty percent were in full time employment

and 23% had either traditional or technical part time jobs (Table 1). An additional 11%

were students, 16% were unemployed, 2% were retired, and 18% spent the majority of

the previous 2 years in a controlled environment such as jail or another institution. In

addition to the last occupation, 24% reported working in the government sector as

administrative workers, 26% reported working in technician occupations and 10%

reported being small business owners. Almost 5% had not been paid for their

employment and 40% had employment problems in the previous 30 days before

presenting for treatment (Table 2).

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Substance Use

Patients self-reporting of their last (30 days) substance use included: frequent use of

nicotine (92%), opiates (84%), benzodiazepines (59%), cannabis (58%), and alcohol

(53%). An additional 17% of patients reported using stimulant drugs (amphetamines

and cocaine). Sixty percent reported polysubstance use including alcohol (Table 1).

Table 3 shows the primary drug problem and the main route of administration. The

primary drugs of abuse among patients were heroin (35%), other opiates (28%), alcohol

(10%), benzodiazepines (9%) and cannabis (7%). Amphetamine and hallucinogens and

polysubstance use were each reported by approximately 3% of the patients. Cocaine,

barbiturate and inhalant use was reported by 1% of the patients. The route of

administration for heroin users was predominantly “chasing the dragon”. The route for

alcohol, other opiates, benzodiazepines, barbiturate, amphetamine and hallucinogens

was mainly oral (98%, 93%, 85%, 89% and 65%, respectively). The main route of

administration of cannabis was smoking (95%), cocaine was nasal (73%) and inhalants

was inhalation (100%).

Figure 2 illustrates the main factors leading to substance abuse, individually or

combined. The main factors were peer pressure (41%), curiosity (14%), family

negligence and broken families (13%), easy accessibility to drugs (6%), ignorance of

the nature of the drug (5%) and medical error factors (5%). The other factors leading to

substance abuse were masking mental problems (4%) and coping with emotional

problems (2%).

Average age of patient first drug use was 18.7 (SD = 10.1) years. The mean age of

patients developing dependence was 20.3 (SD =10.9) years. The mean of years regular

drug use and frequency of drug use in the previous 30 days were 3 (SD = 1.5) and 7.6

(SD = 6.6), respectively. In their lifespan, 46% of patients reported either drug

overdose or toxicity, 53% had received prior treatment for an alcohol problem and 84%

reported receiving prior treatment for drug problems. Twenty four percent reported

blood relatives with history of substance abuse (Table 1).

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Legal Status

At the time of the interview, 18% of the patients had been jailed in the previous 30 days

and 3% of the patients were referred to treatment by the Jordanian criminal justice

system. Fifteen precent of patients reported that they had engaged in criminal activities

or illegal gain. For example, drug abuse, possession or trading. Overall 5% had been

imprisoned at some stage of their life (Table 2).

Family and Social Relationship

Regarding family and social relationships, approximately 62% of patients were married,

35% reported that they had never married and 3% were separated. In the 3 years prior

to the interview, the majority of the patients (70%) reported living with their family,

13% reported living with their sexual partner and children, 7% with their sexual partner

only, 9% lived alone, and 1% had no stable living arrangements. In the previous 30

days prior to the interview, 84% reported serious conflicts with their family members,

and 45% reported serious conflicts with others. In their lifetime, 30% of the patients

reported that they had been emotionally abused and 5% reported physical abuse.

Psychiatric Status

Seven percent of patients reported a history of inpatient psychiatric treatment and 11%

of outpatient psychiatric treatment (Table 1). In the previous 30 days, 75% of patients

reported that they had suffered one or more psychiatric problem, with 95% reporting

depressive symptoms, 98% anxiety symptoms, 98% sleep problems, 92% impulsive

symptoms, 80% agitation, 97% restlessness, 25% thoughts of suicide, and 42% suicide

attempts. Only 5% of the patients reported that they had been prescribed medication for

psychiatric problems in the previous 30 days (Table 2).

Comparison of Characteristics of Younger, Middle-aged and Older patients

The patients were divided into three groups (Table 4a and Table 4b). The younger group

comprised patients aged 28 years or less (34%). The middle group comprised patients

aged between 28 and 36 years (39%). The older group comprised patients aged over 36

years (27%). One-way ANOVAs (continuous data) and Chi-Square Test of

Contingencies (nominal data) were used to compare these groups with regard to their

demographic data, nicotine status, primary drug dependence, dependence variables and

psychiatric and hospitalization status. When considering the primary drug problem

(Table 4a), more of the older group abused alcohol and opiates, however these were not

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significantly higher. The middle-age group reported a higher percentage of

benzodiazepines compared to the younger and older groups as their primary drug,

however again this was not a significant difference. The younger group had alcohol,

opiates, benzodiazepines and cannabis as their primary drugs.

There was no significant differences between groups in regard to nicotine use or age at

which they start smoking (F (2, 217) = 1.417, p = 0.245). There was no significant

difference in quantity of nicotine consumed per day (F (2, 217) = 2.385, p = 0.094).

There were statistically significant differences between the group means for first drug

use, and dependence (F (2, 217) = 5.435, p = 0.005) and (F (2, 217) =5.837, p = 0.003).

The older group had significantly more mean of years regular drug use (F (2, 217) =

16.370, p < 0.001) (Table 4a).

The older group had significantly higher levels of mental problems and divorce than the

younger and middle-aged groups. In addition, the older group had more children than

other groups (χ2 (2, N= 220) =32.664, p < 0.05). The younger group were more

educated than both the middle-age group and the older group; however this was not a

significant difference. There was no difference in regard to the blood relatives with

history of substance abuse between the three groups (χ2 (2, N = 220) = 0.621, p =

0.733) (Table 4a).

The older and middle-aged groups had higher prevalence reported levels of depressive

illness, agitation, restlessness, euphoria and diminution of attention than the younger

group (Table 4b). The older and middle-aged groups also had higher levels of

hospitalization for drug treatments, detoxification, rehabilitation, counselling and self-

help for substance abuse (Table 4b).

Transition in Patterns of Heroin Administration

Current Route

Twenty seven percent of the patients reported using injection as their current route of

heroin administration, and 66% reported chasing as their current route. Some patients

(7%) used both routes. When patients were asked which was the main way they

administrated heroin, of those using both routes, 4% patients reported mainly using by

injection, 3% patients reported mainly chasing. On this basis, 31% subjects of patients

were classified as injectors, 69% were classified as chasers. Subsequent analyses

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included the 220 patients that could be classified according to a predominant route of

abuse. The patients were divided into two additional groups (Table 5). The first group

(current injector) comprised patients who were injectors at the time of the interview.

The second group (current chaser) comprised patients who were chasers at the time of

the interview. Subsequently, both groups were subdivided into stable groups and

transition groups. The stable group included patients who had not made any transitions

in route. The transition group included patients who made transition in their route of

administration. A student t-test was used to compare the stable and transition groups

within each group (current injector and current chaser) with regards to their dependence

variables and drug consumption. Chi-Square tests were used to compare these groups

in regards to their overdose toxicity and hepatitis status.

Current Injector

Stable injectors (who had not made any transitions in route) were older and commenced

heroin/opiate use at a younger age (M = 21.2 years, SD = 5.8). They had also been

using heroin for longer with an average of 12.2 years (SD = 7.3) of regular heroin use

than injectors who made transitions with average age 20.7 years (SD = 9.5) and average

of years use 9.8 (SD = 6.8). However, there is no significant different between stable

injectors and transition group within the current injectors in regards to the means of first

use, age of drug dependence onset, regular heroin use, frequency of heroin use,

beginning and last taken amount (grams per day) and the percentage of overdose

toxicity and hepatitis (Table 5).

Current Chaser

The stable chasers group was older than the transition group with a statistical significant

difference between mean ages of first heroin use (t (218) = 7.457, p < 0.001), age when

they develop dependence (t (218) = 7.150, p < 0.001), and the frequency of heroin use (t

(218) = 6.160, p < 0.001) (Table 5). Six percent of the Jordanian current chaser

population reported hepatitis C virus infectious, 65% had drug overdose or alcohol

poisoning according to the data obtained from their medical records. Stable chasers

were less likely to have had an overdose toxicity (χ2 (2, N = 220) = 19.006, p < 0.001)

and hepatitis (χ2 (2, N = 220) = 1.349, p = 0.206) than the transition group within

current chasers. There was no statistical significant relationship between the mean

amount used at beginning and last taken amount of heroin between the stable group and

transition group (Table 5).

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Table 1. Clinical data for 220 Jordanian National substance abuse patients.

Category Subcategory Value (n) (% or (M ± SD)a)

Demographic

data

Gender Male 220 100.0%

Female 0 0.0%

Age

18-20 12 5.5%

21-39 165 75.0%

+40 43 19.5%

Marital status

Married 136 61.8%

Divorced 6 2.7%

Never Married 78 35.5%

Religion Muslim 209 95.0%

Christian 11 5.0%

Education status Educated 41 19.0%

Uneducated 179 81.0%

Employment pattern Full time 67 30.0%

Part time 133 70.0%

Drug/alcohol

problem

Current drug abuse

Nicotine 203 92.0%

Opiates 185 84.0%

Cannabis 128 58.0%

Alcohol 117 53.0%

Amphetamine 31 14.0%

Cocaine 7 3.0%

Dependence

AWFUDb 220 18.7 ± 10.1

AWDDc 220 20.3 ± 10.9

YRDUd 220 7.6 ± 6.6

FDUe 220 3 ± 1.5

Drug overdose 100 45.5%

History of drug use 53 24.0%

Previous

treatment

Substance treatment Alcohol 117 53.0%

Drugs 185 84.0%

Psychiatric treatment Inpatient 15 7.0%

Outpatient 20 9.0% a. Mean (M) data are provided with ± Standard Deviation (SD).

b. AWFUD: Age when first used drug,

c. AWDD: Age when developed dependence,

d. YRDU: Years regular drug use,

e. FDU: Frequency of drug use in last 4 weeks.

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Table 2. Problems and symptoms of drug dependent patients in 30 days prior to

admission.

Category Subcategory Percentage (%)

In a controlled environment Jail 18.0%

Treatment 54.0%

Problems

Medical problems 40.0%

Employment problems 40.0%

Illegal activities 15.0%

Conflicts Family conflicts 84.5%

Conflicts with other people 45.0%

Psychiatric symptoms

Depression 95.5%

Anxiety 98.6%

Sleep disturbance 98.6%

Impulsive 92.5%

Agitation 80.9%

Restlessness 97.3%

Serious thoughts of suicide 25.0%

Suicide attempts 42.0%

Prescribed medication for any

psychological problem

5.0%

Past history

Chronic medical problem 33.0%

Taking prescribed medication regularly 15.0%

Cardiac disease 20.5%

Hepatic disease 3.6%

Surgery 61.0%

Accident 40.9%

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Figure 1. Frequency of substance abuse patients (N = 220) by age group. Approximately 3% (n = 7) of substance abuse patients were

under the age 20, about 77% (n = 170) of the patients were aged between 20 and 39 years and more than 19% (n = 43) of the

patients were aged 40 years or older.

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Table 3. Primary drug dependence and main route of administration.

Primary Drug Percentage (%) Main Route of Administration Percentage (%)

Alcohol 10% Oral 98%

Heroin 35% Intravenous, Chasing 45%, 55%

Other opiates 28% Oral 93%

Benzodiazepines 9% Oral 85%

Barbiturate 1% Oral 89%

Amphetamine 3% Oral 100%

Cocaine 1% Nasal 73%

Cannabis 7% Smoking 95%

Hallucinogens 3% Oral 65%

Inhalants 1% Inhalation 100%

More than one drug 3%

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Figure 2. Factors leading to substance abuse: peer pressure (41%), curiosity (14%), neglect within the family or broken families (13%),

recreational use (10%), easy accessibility to drug (6%), prescription drugs or ignorance of the nature of the drug substance (5%),

medical errors (5%), masking mental problems (4%) and emotional problems (2%).

115

Table 4a. Differences in demographics, primary drug dependence, nicotine status and dependence between age groups presenting for drug

rehabilitation treatment.

Category Subcategory 28y and younger

(34%)(% or (M ± SD)a)

Between 28y and 36y

(39%)(% or (M ± SD)a)

Over 36y (27%)

(% or (M ± SD)a)

f-value/χ2

Demographics

Marital (single, married, divorced) (64%, 35%, 1%) (28%, 69%, 3%) (15%, 81%, 4%) 41.112*

Education 28% 17% 16% 4.308

Employment 35% 35% 49% 3.836

Children 32% 63% 76% 32.664*

Family with history of drug abuse 26% 21% 26% 0.621

Nicotine status

Nicotine 88% 77% 83% 3.282

Age when start smoking 13.3 ± 5.72 12.1 ± 7.36 14.0 ± 7.15 1.417

Number of cigarettes per day 26.0 ± 16.57 26.0 ± 20.56 32.1 ± 21.95 2.385

Primary Drug

Dependence

Alcohol 45% 71% 77% 17.468*

Opiate 81% 81% 85% 0.436

Cocaine 3% 3% 3% 0.001

Benzodiazepines 60% 66% 50% 4.211

Amphetamine 20% 12% 8% 4.765*

Cannabis 62% 60% 44% 5.412

Dependence

Age when first use drug 16.4 ± 8.29 18.1± 9.03 21.7 ± 11.86 5.435*

Age of drug dependence onset 17.7 ± 8.84 19.9 ± 9.99 23.6 ± 12.70 5.837*

Years regular drug use 4.3 ± 4.26 8.8 ± 7.42 9.7 ± 6.29 16.370*

Frequency of drug use per a week 2.9 ± 1.56 3.0 ± 1.56 3.2 ± 1.43 0.530

a. Mean (M) data are provided with ± Standard Deviation (SD); * p <0.05 (Bonferroni-adjusted).

116

Table 4b. Differences in psychiatric and hospitalization status between age groups presenting for drug rehabilitation treatment.

Category Subcategory 28y and younger (34%)

(% or (M ± SD)a)

Between 28y and 36y (39%)

(% or (M ± SD)a)

Over 36y (27%)

(% or (M ± SD)a)

f-value/

χ2

Psychiatric

Impulsive disorder 91% 93% 95% 0.919

Family disruption 85% 90% 78% 3.984

Suicide attempts 32% 53% 40% 6.283*

Depressive illness 5% 11% 24% 11.825*

Anxiety 97% 100% 98% 1.982

Agitation 63% 90% 89% 21.873*

Euphoria 42% 71% 69% 16.127*

Diminution of attention 44% 72% 74% 18.277*

Hospitalization

Number of drug treatments 1.8 ± 0.98 2.6 ± 1.45 3.4 ± 2.57 14.954*

Number of detoxification treatments 1.7 ± 1.01 2.6 ± 1.46 3.4 ± 2.60 15.343*

Number of rehabilitation treatments 2.0 ± 1.34 3.0 ± 2.01 3.5 ± 2.56 11.353*

Number of counselling sessions 8.8 ± 7.99 15.5 ± 11.14 17.5 ± 12.43 13.339*

Number of self-help groups 8.7 ± 8.04 15.4 ± 11.21 17.3 ± 12.57 12.905*


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Table 5. Differences between stable groups and transitions groups for patients who (a) were heroin injectors at time of interview (b) were heroin

chasers at time of interview

Current Injectors Current Chasers

Stable group

((M ± SD)a or %)

Transition group

((M ± SD)a or %)

t-value

or χ2

Stable group

((M ± SD)a or %)

Transition group

((M ± SD)a or %)

t-value

or χ2

Mean age first use heroin 21.2 ± 5.8 20.7 ± 9.5 0.232 23.1 ± 5.9 14.0 ± 11.5 7.457*

Mean Age when developed dependence 23.5 ± 6.4 22.4 ± 10.3 0.536 24.9 ± 6.3 15.4 ± 12.6 7.150*

Mean years regular heroin use 12.2 ± 7.3 9.8 ± 6.8 1.344 7.7 ± 5.2 7.5 ± 7.8 0.227

Mean of frequency of heroin use 3.6 ± 0.9 3.4 ± 1.2 0.959 3.6 ± 0.8 2.4 ± 1.8 6.160*

Beginning taken amount (grams per

day) 1.2 ± 3.8 0.5 ± 0.4 1.005 0.5 ± 0.3 0.6 ± 0.3 -0.516

Last taken amount (grams per day) 1.3 ± 0.8 1.3 ± 0.8 0.030 3.7 ± 1.1 1.1 ± 0.7 1.792

Overdose or toxicity 46% 20% 3.240* 24% 41% 19.006*

Hepatitis (C virus infectious) 3% 3% 0.188 2% 4% 1.349


118

DISCUSSION

This report is the first published clinical and epidemiological study of drug dependence

in Jordan that uses a structured baseline interview based on the Addiction Severity

Index (ASI) criteria. The ASI was valuable in structuring the clinical and

epidemiological data and providing information about the severity, patterns and types of

drug dependence, the primary drugs of abuse, their main routes of administration, and

the nature of the problems presented by patients admitted into treatment in Jordan. The

patients who participated in this study were recruited from the National Centre for

Rehabilitation of Addicts (NCRA) at Jordanian Ministry of Health, and the Drug

Rehabilitation Centre at the Jordanian Public Security Directorate (DRC-PSD) over a

period of 6 months (December 2010 to June 2011).

The sample pool collected may not be entirely representative of all patients who need

treatment for their substance problems in Jordan, but this first study was conceived to

provide an insight into trends towards designing more comprehensive studies. The

mean age of our sample was 32.7 years, all patients were males and 95% were Muslim.

In Arab culture, males have more freedom than females as they are able to leave the

home more freely, go out late and interact with many more individuals (Source:

UNODC, The Hashemite Kingdom of Jordan, 2010, Haddad et al., 2010). In addition,

drug and alcohol use is culturally more acceptable for males than females (Fawzi et al.,

1980). Substance use is still considered as unacceptable behaviour or a moral failure by

the majority of Jordanians rather than a medical problem (Source: The Hashemite

Kingdom of Jordan, 2010, Haddad et al., 2010). This may help to protect the Jordanian

people from using drugs or alcohol. Nighty-two per cent of Jordanians are Muslims,

6% Christians and 2% other religions (Source: The Hashemite Kingdom of Jordan,

2010), which may explain the lower number of participants of other denominations in

this study.

The last medical records reported that 33% of the substance abuse patients had chronic

medical problems such as cardiac diseases, surgery, overdose toxicity and infection with

hepatitis C. However, if laboratory investigations were undertaken, a higher number of

hepatitis or other chronic medical diseases may have been observed as the main route of

transmission 45% (n = 99) of these agents (n = 99, 45%) were injecting drugs and

possibly sharing needles.

119

Education, employment and support were problematic issues for the substance abuse

patients. Only 19% of the patients reported being educated. Thirty percent reported

full-time employment in the previous 3 years. Other patients reported a wide range of

employment situations, ranging from being unemployed to having major professional

works. However, 70% of substance abuse patients reported part time employment in

the past 3 years. Although these patients reported employment in skill or semiskilled

jobs, many of them might not have worked for several months.

Opiates including heroin, were the primary drug of dependence for substance abuse

patients (62%). About 58% had used cannabis and 53% of the patients abused alcohol

abusers. However, few patients reported cannabis or alcohol as their primary drug of

dependence. Most substance abuse patients in Jordan do not consider cannabis as a

drug that can lead to addiction. They use it as a recreational drug and can stop it

whenever they want (Source: UNODC, The Hashemite Kingdom of Jordan, 2010).

Other primary drugs of dependence include alcohol (10%), benzodiazepines (9%). All

other drugs including amphetamine, cocaine, hallucinogens were the primary drug of

dependence for only 11% of substance abuse patients. The low rate of alcohol abuse as

a primary drug of dependence is not unexpected because alcohol is prohibited among

Muslims and drinking is a culturally unacceptable behaviour in Jordan (Source: The

Hashemite Kingdom of Jordan, 2010, Haddad et al., 2010). In addition, other

stimulants drugs like cocaine, amphetamines and barbiturate are not widely known or

available in Jordan (Fawzi, 1980, Suleiman et al., 2003, Haddad et al., 2010, Source:

UNODC, The Hashemite Kingdom of Jordan, 2010).

The main five factors that were reported to lead to substance abuse were: peer pressure,

curiosity, family negligence and broken families, recreational use and ignoring the

nature of the drug substance and being addicted to prescription drugs. It is not

surprising that peer pressure is the main factor with high percentage leading to the

substance abuse and also a factor in turning people into drug dependence. Despite what

most people think, peer pressure can happen at any age. Adults can be pressured to fit

into a new social class; teenagers are pressured daily by their peers on their appearance,

alcohol, sexual preference and activity and drug use (Haddad et al., 2010).

Experimentation and curiosity are the first factors that draw many people into trying

drugs. Some drugs, for example heroin are likely to cause dependence just from

experimentation (Robson and Bruce, 1997, Johnson, 2011). Prescription drug

120

dependence can occur from treating pain in order to get relief (Albsoul-Younes et al.,

2010). People become addicted to prescription drugs when they use more than the

recommended dosage, take it more frequently than recommended and continue using

the drug after their initial medical condition clears up (Albsoul-Younes et al., 2010).

The substance abuse patients in this study reported a relatively high percentage of

serious family conflicts, conflicts with other people and illegal activities in their life.

This high rate of conflicts with family and other people could be due to the social

stigma and the unacceptable behaviours associated with substance abuse in Jordan

(Haddad et al., 2010, The Hashemite Kingdom of Jordan, 2010). In fact, this stigma

and the unacceptable behaviour are not only creating a feeling of shame to the users but

also to their extended families (Haddad et al., 2010). As a result, these issues could be a

major source of family conflict.

Older patients in this study preferred to use opiates and alcohol and had more medical

problems, compared with younger and middle-aged groups. While, the younger group

preferred to use stimulants (e.g. amphetamines and cocaine), benzodiazepines, cannabis

and nicotine. The younger group was highly likely to shift from first use to dependence

development in a shorter period than middle-aged and older groups. The older and

younger groups were less likely to have jobs. The middle-aged group had more serious

thoughts about suicide than other groups. The middle-aged and older groups had higher

levels of depression, anxiety, agitation, euphoria and diminution of attention. The older

group had higher levels of hospitalization for drug treatment, detoxification,

rehabilitation, counselling and self-help.

This study showed that opiate users (e.g. heroin) had clear preference for specific routes

of drug administration. The identification of their drug use not only in terms of specific

drugs but also in terms of specific routes. The two predominant routes of administration

were chasing the dragon and injection. Only 7% of our sample did not have a single

predominant route of administration. Although some subjects reported having used

heroin by snorting, this was not common and was never reported as a current

predominant route by any user (Griffiths, et al. 1994). However, a report published by

Des Jarlais et al. (1992) stated that predominant heroin snorters do exist elsewhere. For

example in New York, snorting and injection were the two predominant routes of heroin

administration (Des Jarlais et al., 1992).

121

The results of this study highlighted that heroin users were highly likely to make the

transition from heroin chasing to injecting because they were more likely to be over-

represented among our samples containing predominantly heroin injectors. In addition,

heroin injectors were found to be more severely dependent than heroin chasers.

Therefore, they may be less likely to approach services offering treatment to the needs

of dependent users. Although only two main routes of heroin administration were

described, more than two different groups of heroin users who first use the drug by

injection and who continued to inject (stable injectors) were identified. The remaining

groups included the patients who moved from chasing to injecting; and those who had

previously been injectors and moved to chasing. When compared to current chasers

who had made a transition to injecting, stable chasers were less involved with heroin-

using subculture, they had more social contact with non-users, and they were much less

likely to have friends who were heroin injectors

The stable injectors group (who had not made any transitions in route) within current

injectors were younger when they started using heroin and they used heroin regularly

for longer periods. There was also a high percentage of overdose toxicity (46%) within

the stable group compared to the injection-transition group (current injectors who had

previously used by a different route). There were differences between stable and

transitions groups in terms of their overdose toxicity and hepatitis. The transition group

(who had made a transition in route from chasing the dragon to injection) within the

current chasers were more likely to be hepatitis positive (4%) or had overdose toxicity

(41%). While the stable-chaser group (heroin users for whom chasing the dragon had

always been their preferred route) were at low risk (2% hepatitis and 24% overdose

toxicity). The stable chasers group was older than the transition group with statistical

significance in regard to the mean age of first heroin use and developing dependence

and the frequency of heroin use.

This study showed that the ASI is a valuable tool in structuring a baseline form for

collecting clinical and epidemiological data and assessing the pattern and severity of

drug and alcohol problems among the Jordanian samples. In addition, this study also

reported an analysis of the likelihood over time of heroin chasers moving to injecting

and differences in sample characteristics between those heroin users who had made

transition and those who have not. Injecting was a major problem for heroin abusers

that needs to be addressed in treatment programs. Further epidemiological studies are

122

needed to assess the pattern and severity of these problems in the entire country and

evaluate effectiveness of the current treatment program and how well these programs

match patients’ treatment options.

123

ACKNOWLEDGMENTS

Publication number LA09-001 of the Centre for Forensic Science at the University of

Western Australia. Ethics approval was obtained from the Jordanian Ministry of Health

committee. Funding for this project was provided by the Centre for Forensic Science at

the University of Western Australia. We would like to extend our gratitude to

numerous individuals who helped in conducting this study, including Dr Abdullah

Abuadas, Ms Nahedah Al Labady, Ms Intesar Al Hassan, Ammar Al Shara and Mr

Jamal Alghaled at the National Centre for Rehabilitation of Addicts at the Jordanian

Ministry of Health. Gratitude is also extended to the drug treatment personnel and

clients, without whom we could not have conducted this study.

124

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127

CHAPTER 3

CHARACTERIZATION OF SLC6A4 GENE

POLYMORPHISMS AND ITS ASSOCIATION

WITH DRUG DEPENDENCE IN A JORDANIAN

ARAB POPULATION

This chapter was published in the Toxicology and Industrial Health.

128

Chapter 3

Characterization of Serotonin Transporter Gene

(SLC6A4) Polymorphisms and its Association with

Drug Dependence in a Jordanian Arab Population

Chapter 3 describes the distribution of Allele, haplotype and genotype frequencies of

SLC6A4 polymorphisms (5-HTTLPR and rs25531) in drug and non-drug dependent

Jordanian Arab population. The study described in our manuscript also advances our

understanding that the “LL” genotype of 5-HTTLPR gene has a higher serotonin

transporter function than other genotypes (Lesch et al (Science. 1996; 274:1527-1531),

Greenberg et al (American Journal of Medical Genetics. 1999; 88:83-87) and Williams

et al (Neuropsychopharmacology 2003; 28:532-541)), resulting in increased serotonin

uptake and a reduced level of intra-synaptic serotonin. Sellers et al (Trends in

Pharmacological Science. 1992; 13:69-75) suggested that pharmacologically induced

increases in the serotonin transmission cause a reduction in drug dependence and drug

self-administration in human. Therefore, it is possible that subjects with the “LL”

genotype are more susceptible to drug dependence.

This study also presented both bi-allelic and tri-allelic approaches for genotyping the 5-

HTTLPR and rs25531 markers based on Wendland et al’s suggestion. Recently,

Wendland et al (Molecular Psychiatry. 2006; 11(3):224-226) suggested that another

functional allelic variant rs25331, A/G located in the long form of the 5-HTTLPR

insertion site should be analysed along with the 5-HTTLPR alleles. This A/G

polymorphism may have functional significance influencing transcription activity of the

SLC6A4 gene.

The identification of polymorphisms that are associated with diseases in different ethnic

backgrounds will allow researchers to assess if the same gene variations are associated

with the aetiology of drug dependence and strength of the association. Moreover,

Comparative analysis with different ethnic groups could assist in understanding the

mechanisms that causes addiction. The comparative analysis revealed the genotype

129

frequencies of 5-HTTLPR (LL/LS/SS) in the Arab population to be approximately

similar to that previously reported for Italian and Israeli populations.


listed. All laboratory work was carried by AL-Eitan at the Neuropsychiatric Genetics

Laboratory at Western Australian Institute for Medical Research (WAIMR) under the

guidance of Dr Wildenauer. Ms Qin optimised the initial PCR conditions for the

SLC6A4 markers and Dr Mutiara Wildenauer provided her technical support. Patient

samples selection and DNA extraction were done through a collaboration link with Dr

Jaradat at Princess Haya Biotechnology Centre (PHBC). Dr Tay, Dr Wildenauer and

Dr Hulse guided me through the study from designing the study to proof reading the

manuscripts. All the co-authors have proof read the manuscript.

130

Characterization of Serotonin Transporter Gene (SLC6A4) Polymorphisms and its

Association with Drug Dependence in a Jordanian Arab Population

Laith N. AL-Eitan1, Saied A. Jaradat 2 , Wenwen Qin 4,3 , Diah Mutiara B. Wildenauer 4,3 ,

Dieter D.B. Wildenauer 5,4,2 , Gary K. Hulse 6,4, Guan K. Tay 1


6009, Australia 2 Princess Haya Biotechnology Center, Jordan University of Science and

Technology, Irbid 22110, Jordan 3 Western Australian Institute for Medical Research, Centre for Medical

Research, The University of Western Australia, Perth, Western Australia,

Australia 4 School of Psychiatry and Clinical Neurosciences, Queen Elizabeth II Medical

Centre, The University of Western Australia, Crawley, WA 6009, Australia 5 Centre for Clinical Research in Neuropsychiatry, Graylands Hospital, Mount

Claremont, Western Australia, Australia 6 Unit for Research and Education in Alcohol and Drugs, Queen Elizabeth II


Australia

Abbreviated title: SLC6A4 Polymorphisms and Drug Dependency

Keywords: SLC6A4, 5-HTTLPR, Polymorphisms, Drug Dependence, Jordan,

Arab


Western Australia


Laith N. AL-Eitan




Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285


[email protected]


131

ABSTRACT

Drug dependence is a pattern of repeated self-administration of a drug that can result in

tolerance, withdrawal, and compulsive drug-taking behaviour. It has been recently

suggested that 5-HTTLPR (LL/LS/SS) variants and rs25531 (A/G) polymorphism in the

serotonin transporter gene (SLC6A4) may play a role in drug dependence. The current

study aimed to: (1) identify allelic, haplotypic and genotypic frequencies of the 5-

HTTLPR variants and rs25531 polymorphisms of SLC6A4 gene in drug and non-drug

dependent Jordanian Arab population; and (2) determine if there is an association of

these variants in a drug dependent population from the same area. Jordanian male

addicts of Arab descent (n = 192) meeting the DSM-IV criteria for drug dependence and

230 healthy male controls from an ethnically homogenous Jordanian Arab population

were examined. Genotyping was performed using the PCR- RFLP based method to

genotype the 5-HTTLPR variants and detect the A/G polymorphism at position rs25531.

The bi-allelic analysis revealed that the frequency of 5-HTTLPR (LL/LS/SS) genotypes

were statistically significant different between drug dependent individuals and controls

(χ2 (2, N= 422), p value = 0.04). Drug dependent subjects had a higher frequency of

“L” allele. However, using the triallelic approach, the estimated frequency of haplotypes

(SA, SG, LA, LG) and phased genotypes (LA /LA, LA /SA, LA /LG, SA /SA, SA /SG) did not show

significant association with drug dependence (χ2 (3, N= 422), p = 0.53 and χ2 (4, N=

422), p = 0.06, respectively). This study suggests a putative role of the 5-HTTLPR for

drug dependence in the Jordanian Nationals of Arab ancestry.

132

INTRODUCTION

The Human serotonin transporter (SETR) is a monoamine transporter protein, encoded

by a single gene (SLC6A4, solute carrier family 6, member 4) located on the long arm of

chromosome 17 (17q11.2). It consists of 13 to14 exons, spanning approximately 35 kb.

The protein includes 12 to13 membrane-spanning domains (Lesch et al., 1994). A

repeat length polymorphism (5-HTTLPR) in the promoter of this gene has been shown

to affect the rate of serotonin uptake and may play a role in drug dependence and other

chronic neurological diseases (Lesch et al., 1994, 1996 and 1999). For more than a

decade, research into psychiatric, behavioural genetics and drug addiction has focused

on the 5-HTTLPR insertion/deletion in the regulatory region of SLC6A4 gene

(Stoltenberg et al., 2000). A recent search on PubMed for the term “5-HTTLPR” shows

that this polymorphism has been studied extensively in over 900 behavioural,

psychiatric, pharmacogenetic and other medical genetic papers over the past 10 years.

In humans, transcriptional activity of this gene is modulated by a 20 to 23 base pair

repeat polymorphism in the SLC6A4 promoter (5-HTTLPR) located upstream of the

transcription start site (Lesch et al., 1994 and 1999). Alleles of the 5-HTTPLR promoter

have either a short (S) or long (L) copy of an imperfect repeat. The short or “S” allele

with 14 repeats was shown to have lower transcriptional activity than the long or “L“

allele with 16 repeats (Lesch et al., 1994, 1996 and 1999). Little et al. (1993) suggested

that chronic treatment with cocaine in vitro causes enhanced surface expression of the

serotonin transporter in cells, while post-mortem studies have revealed an up-regulation

of 5-HTTLPR by cocaine. Various functional assays studied suggest that the “S” allele

has a lower transcription rate. The “S” allele was found to be associated with alcohol

dependence, anti-social behaviour and suicide. However, it has only a modest effect on

complex behaviour such as anxiety, though only by a 0.106 standard deviations

increment as measured on personality scales (Goldman, Oroszi and Ducci, 2005). Heils

et al. (1997) and Heinz et al. (2007) reported that “S” allele decreased transcription and

decreased serotonin reuptake in vitro. Bleich et al. (2007) reported that the “L” allele is

associated with a predisposition to lowered level response to alcohol, which is in turn

associated with the onset of alcoholism.

In Caucasian Italians, the SS genotype was associated with heroin dependence and

aggression within the heroin-dependent group (Gerra et al., 2004). However, the same

133

genotype was not associated with cocaine dependence in African-Americans or Chinese

heroin addicts (Li et al 2002; Mannelli et al., 2006). A meta-analysis of 1,400 subjects

of different ethnicities from 15 studies reported reduced response to antidepressant

therapy in “S” allele carriers (Serretti et al., 2006). Similarly, a more recent meta-

analysis found evidence that 5-HTTLPR with the “S” allele was more often associated

with the development of alcohol problems (Feinn et al., 2005). However, Mchugh et al.

(2010) stated that there is recent evidence suggesting that the “S” allele has a small

effect, but a direct relationship to the development of Alcohol dependence.

Recently, it has been discovered that another functional allelic variant, the single

nucleotide polymorphism (SNP) rs25531 (A/G) located in the long form of the 5-

HTTLPR insertion site, may have functional significance influencing transcription

activity of the serotonin transporter gene (e.g., Heils et al., 1996; Nakamura et al., 2000;

Hu et al., 2005; Parsey et al., 2006). It has been noted that the “S” and “L” alleles of

the 5-HTTLPR in the presence of either the A or G allele of rs25531 (SA/SG) and (LA/LG)

respectively predict a reduced expression of the SLC6A4 gene (e.g. Lesch et al., 1994;

Heils et al., 1996; Hu et al., 2005; Wendland et al., 2006). In contrast, the LA variant

predicts higher expression levels of this gene (Heils et al., 1996; Praschak- et al., 2007).

Hu et al. (2006) suggest that in tests of association the LG allele should be analysed

along with the “S” alleles. Hitherto, a bi-allelic analysis approach has been generally

used to genotype the 5-HTTLPR insertion/deletion and distinguish a short allelic variant

(“S” allele) from a long one (“L” allele). A tri-allelic approach, subdividing S and L

alleles (SA, SG, LA, LG) has been preponderantly focused upon.

This study focused on 5-HTTLPR (LL/LS/SS) polymorphisms and the rs25531 marker.

Both polymorphisms are located upstream of the transcription start of the SLC6A4

promoter (5-HTTLPR) site (Figure 1). The frequency of the SLC6A4 gene variants has

never been studied in the Jordanian Arab population. Jordan contains an ethnically

homogenous Arab population. In this study, an allele frequency analysis for SLC6A4

variants, 5-HTTLPR (LL/LS/SS) and rs25531 in a Jordanian Arab Population and in drug

dependents from the same area was carried out. The genotypic frequencies of these

variants were compared to those in different world populations. The association of 5-

HTTLPR variants and rs25531, A/G with drug dependence with the presence of genetic

variation within the 5-HTTLPR gene was investigated to determine if there is an

increased frequency in drug dependent individuals from this population.

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MATERIALS AND METHODS

Subjects

Written informed consent was obtained from all subjects included in the study. The

study was subject to and in compliance with the National Statement on Ethical Conduct

in Human Research, Australia (2007). This study was also conducted according to the

provisions of the Australian Medical Association Code of Ethics (2006) and World

Medical Association Declaration of Helsinki (World Medical Association, 2008).

Ethical approval to conduct this research was granted by the University of Western

Australia’s Human Research Ethics Committee (Ref No. RA/4/1/4344). The study

group consisted of 220 unrelated Jordanian Arab addicted males meeting the Diagnostic

and Statistical Manual of Mental Disorders (DSM-IV) criteria (American Psychiatric

Association, 1994) for drug dependence and entering a voluntary 8 week treatment

program at Jordanian Drug Rehabilitation Centers. The interviews were conducted by a

psychiatrist blinded to the subjects’ SLC6A4 status. In addition, 240 healthy males from

an ethnically homogenous Jordanian Arab population were used as controls. The two

groups were matched on the basis of age, sex and ethnic origin. The mean ages (±SD)

of the patients and controls were 32.70 (± 8.4) and 31.5 (± 5.6) years, respectively.

Genomic DNA

After blood was drawn into EDTA tubes (5ml), genomic DNA was extracted using the

Gentra Puregene® Blood Kit (Qiagen, Valencia, CA, USA) according to the

manufacturer’s instructions. Briefly, 300µl of whole blood from each sample was

mixed with 200µl of lysis buffer to break the cell membrane and to release the DNA.

The procedure also included the addition of Proteinase K following the manufactures

protocol for whole blood. Proteinase K (20mg/µl, 50µl/sample) was added to the cell

lysis buffer and samples were incubated at 37°C for 2 hours. Then, white cells were

centrifuged for 2 minutes at 2000 rpm to pellet the white blood cells. The supernatant

was discarded, followed by white cell lysis. Protein precipitation solution was added,

and lysis solution was spun to remove proteins. The DNA was then precipitated with

the use of 100% isopropanol to remove the residual amounts of protein, washed with the

use of 70% cold ethanol, and resuspended in Tris-EDTA. DNA quantity (ng/µl) and

purity (A260/280) were also verified using the Nano-Drop ND-1000 UV-Vis

Spectrophotometer (NanoDrop Technology, Wilmington, DE) and subsequently

adjusted to approximately 100ng/µL. Purified DNA was stored at -80°C before use.

135

5-HTTLPR and rs25531 PCR assay

The 5-HTTLPR insertion polymorphisms and the SNP at rs25531 were analysed by bi-

allelic and tri-allelic approaches according to the protocol previously reported by

Wendland et al. (2006). Briefly, the 43 bp insertion/deletion (ins/del, 5-HTTLPR)

polymorphism in the 5'-regulatory region of the gene was determined based on the

predicted size of the PCR product for each by specific designed primers for 5-HTTLPR

polymorphism. Table 1 summarizes the primer sequences, annealing temperatures and

amplicon restriction fragment lengths for 5-HTTLPR polymorphisms and rs25531

marker. The 5-HTTLPR PCR reaction mixture (20 μl) included 50 ng of DNA, 2 μl of

each primer, 0.4 μl of each deoxyribonucleotide triphosphate (dNTP), 0.1 μl of FastStart

Taq (Roche Applied Science, Indianapolis, IN, USA), 2 μl of 10× PCR buffer (600 mM

Tris-HCl, pH 8.3; 250 mM KCl; 1% Triton X100; 100 mM β-mercaptoenthanol), 4 μl

of 5× Q solution and 7.8 μl of 1 x Milli-Q water. PCR was performed using a MJ

Research Thermal Cycler “PTC-200 DNA Engine” (Bio-Rad Laboratories, Hercules,

CA, USA), with a single hot start step at 95°C for 5 min to release the FastStart Taq, A

total of 40x cycles were used, each consisting of 30 sec denaturation at 95°C, a 30 sec

annealing step at 67°C, and an extension step at 72°C for 1 min. A final extension step

of 72°C for 10 min completed the cycle. The amplified fragments were separated onto

a 3% Ultrapure agarose gel (Invitrogen, Carlsbad, CA, USA) in Tris-Borate EDTA

(TBE) on a horizontal model 96 gel electrophoresis sub-cell (Bio-Rad Laboratories,

Hercules, CA, USA), and bands were visualized by ethidium bromide (Sigma-Aldrich,

St Louis, MO, USA) staining and ultraviolet illumination. Images of the gels were also

captured in the Molecular Imager ChemiDoc™ XRS system (Bio-Rad Laboratories,

Hercules, CA, USA) using a range of lighting, screen and filter combinations.

The allele of the “rs25531” SNP was determined by restriction fragment length

polymorphism–polymerase chain reaction (RFLP-PCR) based method (see figure 2).

Basically, 15 μl of PCR product as described above was digested by adding 0.5 μl of

Msp1 (20U/ml) (New England Biolabs, Beverly, Massachusetts, USA), in a 20 μl

reaction assay containing 15 μl of PCR product, 0.5 μl of Msp1, 2 μl of 10 x NEBuffer 4

and 2.5 μl of 1 x Milli-Q water and then incubated at 37 °C for 6 hours. Finally, 5 μl of

undigested PCR product and 15 μl of digested product were loaded onto a 3% agarose

gel containing 10 μg/ml of ethidium bromide, run for 2.5 h at 80 V in TBE and the

fragments visualized using the Molecular Imager ChemiDoc™ XRS system(Bio-Rad

Laboratories, Hercules, CA, USA). Positive and negative controls were included in

136

each run. Genotypes were evaluated by investigators who were blind to the status of the

subject and any discrepancies were resolved by test replication.

Genotype and Statistical Analysis

The PCR assays were designed to detect the presence and absence of the insertion or

deletion characteristic of each 5-HTTLPR (LL/LS/SS) variants. The PCR-RFLP assay

was also designed to identify the rs25531 (A/G) in the presence of 5-HTTLPR variants.

The genotype possibilities (see Table 1) with presence of A/G SNP are shown in Figure

2 (panel ‘b’) by subdividing the 5-HTTLPR alleles into SA, SG, LA, LG.

The gene counting method was used to calculate the allele frequencies for the 5-

HTTLPR variants and rs25531 marker (Table 2) (Ceppellini et al., 1955; Gelernter et al.,

1999). The genotype frequency of each allele and haplotypes was calculated using the

Hardy-Weinberg equilibrium equation p² + 2pq + q² = 1 (Guo and Thompson, 1992).

Haplotype and phased genotypes frequencies for the 5-HTTLPR variants with A/G SNP

were also obtained using the gene allele counting method.

Data was entered in Excel (Microsoft Corporation) for the calculation of allele,

haplotype and genotype frequencies Hardy-Weinberg equilibrium (HWE) was tested to

determine if the population was fulfilling the HWE at each variant locus. It was

assessed in the observed genotype distribution with a Chi squared test. Allelic,

haplotypic and genotypic association p values were determined using a Chi squared test

between cases and controls. A web-based calculator was used to compute p values

(Preacher, 2001). A p value <0.05 was considered to be statistically significant.

137

RESULTS

The study involved 220 Jordanian Arab individuals with drug dependence and 240

matched controls. All drug dependent individuals and healthy controls were males. The

average age (±SD) was 32.70 (± 8.4) and 31.5 (± 5.6) years, respectively. No drug

dependent individual or control had any psychiatric diseases according to the DSM-IV

criteria assessment. There were no significant differences found between individuals

with drug dependence and controls with regard to age and sex. The mean age (±SD) of

onset for developing drug dependence was 20.3 (± 11).

Using the bi-allelic analysis, the 5-HTTLPR PCR assay results are shown in Figure 3. A

small band corresponds to the (“S” allele), while the large band represents the allele

containing the 43 base pairs insertion (“L” allele). Homozygotes for a 5-HTTLPR

insertion show one band, while the genotype with heterozygotes having both bands. For

example, lane 1 in Figure 3a shows the 5-HTTLPR assay result for an individual

homozygous for the “S” allele, which yields a band of 469-base-pair (denoted SS). In

contrast, the single, 512 band visible containing the 43 bp insertion in lane 2

corresponds to an individual homozygous for 5-HTTLPR (denoted LL). Lane 3 shows

result for an individual who is heterozygous for the 5-HTTLPR promoter region

(denoted LS).

Table 1 and Figure. 3b show accurate and comprehensive genotyping can easily be

conducted by triallelic analysis of amplicon and restriction fragment sizes between 66

bp and 512bp. As Figure 3b shows the RFLP-PCR assay results for genotyping the

rs25531 (A/G) in the presence 5-HTTLPR polymorphisms for twelve subjects including

the positive and negative controls.

As a result of the interaction between the rs25331 and 5-HTTLPR variants, genotype

possibilities are shown in Figure. 3b; in which, three bands with 340bp, 297bp and

166bp indicate the subject to be homozygous (lanes 3) for markers interaction (LA

haplotype). Lane 4, 5 and 12 with two bands (297bp, 166bp) indicate a homozygous

subject for the SA haplotype. While, Lanes 1, 9 and 10 show results for individuals who

are heterozygous with three visible bands, 340bp, 297bp, 166bp for both haplotypes (LA,

SA) and insertion element.

Whereas the four, 340bp, 297bp, 174bp and 166bp products visible in lane 2 indicate an

individual heterozygous for the interaction haplotypes (LA, LG) with the presence of 5-

138

HTTLPR insertion and “A” and “G” alleles. For the interacted haplotypes (SA, SG),

yield three distinctive products 297bp, 166bp and 131bp. Thus, lanes 6, 7 and 8

indicate individuals who are heterozygous for both haplotypes with the absence of the

insertion element. Lane 11 represents an individual who has a long insertion repeat

more than 550bp.

One hundred and ninety two individuals with drug addiction and 230 healthy controls

were genotyped for the functional 5-HTTLPR variants and rs25331 (A/G)

polymorphism. Genotypic frequencies of the 5-HTTLPR and rs25331 in the control

population met HWE expectations. For 5-HTTLPR and rs25331 polymorphisms, the

number of individuals with each genotype, either homozygous for the absence of 43 bp

(SS), homozygous for the insertion (LL), or heterozygous (LS) were counted.

Frequencies were then established for each genotype by dividing the number individuals

with genotype by the total number of individuals in the population. The frequency of

observed allele, genotype, haplotype and phased genotypes frequencies in the Arab

population were determined for both markers (Table 2).

Accordingly, using the bi-allelic analysis, the distribution of 5-HTTLPR (LL/LS/SS)

genotypes was statistically significantly different between drug dependent individuals

and controls (χ2 (2, N= 422) = 6.24, p value = 0.04). However, the allele frequency

(“L” and “S”) was not (χ2 (1, N= 422) = 0.34, p value = 0.56) and similar results were

observed with allele and genotype frequencies of rs25331, A/G (χ2 (2, N= 422) = 0.10,

p value = 0.75) and (χ2 (1, N= 422) = 0.02, p value = 0.89) respectively.

Using the triallelic approach, the estimated frequency of haplotypes (SA, SG, LA, LG, and

others) did not show any significant association (χ2 (3, N= 422) = 2.21, p value = 0.53)

between the 43 bp insertion/deletion and the rs25331 and drug dependence. However,

phased genotypes (LA /LA, LA /SA, LA /LG, SA /SA, SA /SG and others) has a borderline

association with a p value = 0.06 (χ2 (4, N= 422) = 10.3).

Table 3 shows the comparison between the 5-HTTLPR genotype frequencies of the

insertion element in the Arab population for both subjects and controls and that of

previously studied population. For each of the three 5-HTTLPR (LL/LS/SS) variants, the

insertion frequencies in the Jordanian Arab population were approximately similar to

those in Italy and Israel in both cases and controls.

139

Figure 2. The solute carrier family 6 (SLC6A4) is located on the long arm of chromosome 17 (17q11.2), composed of 13 to14 exons, spanning

approximately 35 kb and includes 12 to13 membrane-spanning domains. Coding and non-coding exons are indicated by black and

white boxes, respectively. Locations of the genotyped polymorphisms (5-HTTLPR and rs25531, A/G) with forward primer (5-HTT_F)

and reverse primer (5-HTT_R) and alternative splicing are indicated.

140

Table 1. The primer sequences and the predicted product size of PCR amplified products of the 5-HTTLPR polymorphisms and rs25531 marker.

* The assay performed first amplifying 5-HTTLPR by PCR and then followed by single Msp1 digestion to genotype rs25531, A/G polymorphism.

** n/a: not applicable.

SLC6A4

polymorphic

loci

Primer name and oligonucleotide primer sequences Annealing

temperature

Amplicon and restriction fragment lengths*

Polymorphism PCR + Msp1 (see

Figure 3)

5-HTTLPR

5-HTT_F: 5'-GGC GTT GCC GCT CTG AAT GC-3'

5-HTT_R: 5'-GAG GGA CTG AGC TGG ACA ACC AC-3'

67°C

“S” allele by PCR = 469bp

“L” allele by PCR = 512bp

LA : 340bp

SA : 297bp

LG : 174bp + 166bp

SG : 166bp + 131bp

Super Long: > 550bp

SNP:

rs25531

(A/G)

n/a** n/a

A allele by Msp1 digest

= 340bp + 297bp (uncut)

G allele by Msp1 digest

= 174bp+166bp+ 131bp

141

Figure 3. Primers for amplifying 5-HTTLPR variants and restriction endonuclease Msp1 for detecting the rs25531 “A/G” polymorphism: This sequence was accessed through the National Centre for Biotechnology Information (NCBI) site with refseq DNA sequence:

NC_000017.10 (http://www.ncbi.nlm.nih.gov/), and annotated in this way at nucleotide sequence position of 3361 to 3961 on SLC6A4

gene to show the highly repetitive nature of this locus. The forward and reverse primers are shown in the red highlight, that yield

amplicons of 512 or 469 bp for the “L” or “S” alleles, respectively. The SNP, rs25531 (A/G) is shown as “R” in green highlight and the

second MspI site is highlighted in blue. The insertion/deletion (43 base pairs) is shown underlined in yellow highlight. For SNP (A/G)

analysis, the full length amplicons (from above) are incubated with the restriction enzyme MspI. The “G” allele which has the MspI

restriction site (CCGG) will yield a product of 174 bp, whereas the “A” allele, which lacks the restriction site does not. A second MspI

site 131 bp from the 3' end of the amplicon provides a positive control for the restriction reaction yielding cut products of 340 or 297 bp

for the “L” and “S” alleles, respectively.

http://www.ncbi.nlm.nih.gov/

142

Figure 4. Agarose gel electrophoresis of amplified genomic DNA from three individuals for the 5-HTTLPR (LL/LS/SS) variants (a: ‘5-

HTTLPR-PCR’) and from twelve subjects before and after (b: ‘PCR & Msp1’) restriction endonuclease digestion: (a) PCR “bi-

allelic approach” assays were designed to detect the presence and absence of insertion of “43 base pairs” 5-HTTLPR. The larger PCR

product size for 5-HTTLPR (LL) represent the presence of the insertion (referred to as “L” allele) and the smaller size (SS) represent the

absence of the insertion (“S” allele). For example, in the panel representing the amplification products for 5-HTTLPR, individuals who

are homozygous for the smaller “S” allele (469 basepair) product representing the insertion absence is shown in lane 1 (genotype: SS)

and the larger “L” allele (512 base pairs) product containing the insertion is shown in lane 2 (genotype: LL). An individual with the

heterozygous genotype (LS) is shown in lane 3. (b) RFLP-PCR “triallelic approach” assays were designed to genotype the rs25531

(A/G) in the presence of 5-HTTLPR variants, samples 11 and 12 represent positive and negative controls respectively. The genotype

possibilities ( see Table 1) with presence of A/G SNP are shown in the panel ‘b’ by subdividing the 5-HTTLPR alleles into SA, SG, LA, LG

and Superlong. Genotypes thus are: (1), (9) and (10) LA /SA, (2) LA /LG, (3) LA /LA. (4), (5) and (12) SA /SA, (6), (7) and (8) SA /SG and (11)

LA /Super long.

143

Table 2. Allele, haplotypes and phased genotypes distributions of the 5-HTTLPR and rs25531 polymorphisms in the SLC6A4 gene in Jordanian drug

dependents and controls.

Markers

Allele*/Haplotypes*/

Genotype** Drug Dependents (192) Controls (230)

Drug Dependents vs Controls

Chi-squared p value

5-HTTLPR

L 54.2% 52.2% 0.34 0.56

S 45.8% 47.8%

LL 32.8% 24.8%

6.24 0.04 LS 42.7% 54.8%

SS 24.5% 20.4%

rs25531

A 94.5% 95.1% 0.10 0.75

G 5.5% 4.9%

AA 89.6% 90.0%

0.02

0.89

AG 10.4% 10.0%

GG 0.0% 0.0%

Haplotypes (5-HTTLPR and

rs25531)

LA 49.0% 47.2%

2.21 0.53 SA

45.8% 47.8%

LG 3.1% 2.0%

SG

2.1% 3.0%

Phased genotype (5-HTTLPR

and rs25531)

LA/LA 26.6% 20.4%

10.30

0.06

LA/SA 38.5% 48.7%

LA/LG 6.3% 3.9%

SA/SA 24.5% 20.4%

SA/SG 4.2% 6.1%

Others

0.0% 0.4%

*Allelic, haplotypic and genotypic association p value, calculated using Pearson Chi-squared test for a 2 x 3 contingency table with 1 df for allelic, 2 df

for genotypic, 3 df for haplotypic and 4 df for phased genotypic comparisons; p value < 0.05 is significant, in bold.

144

Table 3. Frequency of the 5-HTTLPR variants in different populations compared with the Jordanian population. (p value < 0.05 is significant, in

bold).

Population

5-HTTLPR Genotype Frequencies%

p value References Cases Controls

LL LS SS LL LS SS

China 60 (56.0%) 38 (35.0%) 10 (9.0%) 113 (38.0%) 60 (31.0%) 19 (10.0%) 0.70 Li et al., 2002

Italy 27 (26.7%) 44 (43.6%) 30 (29.7%) 34 (33.7%) 51 (50.5%) 16 (15.8%) <0.001 Gerra et al., 2004

Israel 54 (29.0%) 84 (45.0%) 48 (26.0%) 53 (24.0%) 114 (53.0%) 50 (23.0%) 0.33 Kotler et al., 1999

Japan 21 (3.0%) 212 (30.6%) 459 (66.3%) 8 (3.0%) 83 (30.7%) 179 (66.3%) 0.998 Sachio et al., 2001

Jordan* 63 (32.8%) 82 (42.7%) 47 (24.5%) 56 (24.8%) 126 (54.8%) 47 (20.4%) 0.04 Present Study

Korea 29 (20.0%) 40 (27.6%) 29 (20.0%) 6 (3.0%) 65 (32.3%) 130 (64.7%) <0.001 Kweon et al., 2005

Spain 24 (20.4%) 59 (20.4%) 31 (27.4%) 124 (29.5%) 203 (48.3%) 93 (22.2%) 0.17 Saiz et al., 2009

USA** 60 (43.0%) 69 (37.0%) 57 (20.0%) 71 (43.0%) 64 (33.0%) 63 (24.0%) 0.60 Mannelli et al., 2006

*Arab Ancestry, ** African descent.

145

DISCUSSION

To the best of our knowledge, this report is the first genetic study on the serotonin

transporter (SLC6A4) gene in drug and non-drug dependent individuals of Arab descent.

We have investigated two functional polymorphisms (5-HTTLPR and rs25531) within

the SLC6A4 gene in control and drug dependent individuals matched for origin (Jordan)

and ethnicity (Arab). Study data indicates the 5-HTTLPR (LL/LS/SS) variants to be

associated with drug dependence in the Arabs males; the subjects with the “LL”

genotype having a significantly higher frequency of drug dependence than controls.

Several studies have reported positive association results between alcohol and drug

dependence (e.g. heroin and cocaine) and the 5-HTTLPR; however, other studies have

provided varied and conflicting results (e.g. Sander et al., 1997; Hammoumi et al.,

1999; Gorwood et al., 2000; Sachio et al., 2001; Preuss et al., 2001). Edenberg et al.

(1998) reported no linkage or association between the 5-HTTLPR and alcohol

dependence among family-based methods using a transmission disequilibrium test,

while others have reported an association (Lichtermann et al., 2000)

Similarly, some studies reported an association of the “S” allele with drug dependence

in different ethnic groups (Turker et al., 1998; Schuckit et al., 1999). For example, the

SS genotype was associated with heroin dependence and aggression within the heroin-

dependent group in Caucasian Italians (Gerra et al., 2004). Others have not reported an

association with heroin dependence in Chinese or cocaine dependence in African-

American (Kreek et al., 2005; Mannelli et al., 2006).

It is interesting that the “L” allele first found to be associated with drug dependence in

our samples, which is in contrast to previous researches that suggested an association of

the “S” allele with alcohol and drug dependence (e.g. Sander et al., 1997; Hammoumi

et al., 1999; Hallikainen et al., 1999; Gerra et al., 2004). There could be a reasonable

explanation for this disagreement. First, a weak or marginal significance of “S” allele

association has been reported in the previous studies. These studies were limited to

small sample size and focused on Caucasians race. For instance, Hammoumi et al.

(1999) stated that the significance of the genotype comparison was marginal with a p

value = 0.064, even though the allelic comparison had a p value of 0.008. This weak

association was in agreement with another study with a p value = 0.035 for genotype

comparison and a p value = 0.049 for allelic comparison, which has been conducted by

146

Sander et al. (1997). Therefore, the general application of previous studies for an

association of the “S” has limitations including the sample size and ethnicity.

Second, ethnic differences should be taken into consideration when explaining the

inconsistency between different study results. As for the studies using Asian subjects,

the reported frequencies of “S” allele in the controls were 80.8% by Kweon et al.

(2004), 81.7% by Sachio et al. (2001) and 83.2% by Ishiguro et al. (1999), which were

different from the results in the present study (Table 3). According to Willeit et al.

(2001), a conceivable explanation for the discrepancy results of the previous studies and

the current study is that the presence of a transcriptionally active gene element other

than the 5-HTTLPR, and the fact that there are more than two 5-HTTLPR alleles

possibly distributed unequally across various ethnic populations.

Third, the discrepancies have been noted in the relationship between the 5-HTTLPR

variants and the serotonin availability, the serotonin uptake and the central serotonin

activity in drug dependence. A recent study showed a significant adverse relationship

of serotonin availability with a lifetime of alcohol consumption. However, increases in

the serotonin uptake in the brain and platelets have been reported in alcoholics and their

offspring (Ernouf et al., 1993; Heinz et al., 1998). Stoltenberg et al. (2002) reported

that there is an association between the 5-HTTLPR “L” allele and the increased

serotonin platelet uptake. However, the 5-HTTLPR genotypes were similar with uptake

density of serotonin levels. Therefore, there is a need to assess whether the related

genetic factors such as the serotonin receptor, tryptophan hydroxylase and monoamine

oxidase play an essential role in the serotonin availability, levels and function.

Although the complexity of drug dependence and the differences in ethnicity have

influences on the 5-HTTLPR results, all the previous studies have confidence in the

hypothesis that the “LL” genotype has a higher serotonin transporter function than

others genotype (LL/SS), resulting in an increase of serotonin uptake and a reduction of

intra-synaptic serotonin level (e.g., Heils et al., 1996; Nobile et al., 1999; Kranzler et al.,

2002; Williams et al., 2003). Pharmacologically, the Serotonin transporter spans the

plasma membrane 12 times. These transporters appeared to be essential sites for agents

that treat drug dependence and psychiatric disorders by either stimulating serotonin

release, blocking serotonin uptake or by direct agonists (Sellers et al., 1992; Johnson et

al., 2011). As results, increases in serotonin transmission may cause a reduction in drug

147

dependence and drug self-administration in human beings. Consequently, individuals

with the “L” allele may be more vulnerable to drug dependence.

In research for the association between the drug dependence and the 5-HTTLPR

genotype using the bi-allelic analysis, this study showed that the drug dependent

individuals with genotype “LL” were higher than for the healthy individuals and this

was in agreement with proposed theory. Furthermore, there was a significant difference

in the genotypic distribution of 5-HTTLPR variants (LL/LS/SS) between the drug

dependent individuals and control group (χ2 (2, N= 422) = 6.24, p value = 0.04). Our

results do not support a direct role for the “S” allele of 5-HTTLPR in drug dependent

individuals of Arab descent. The frequency of “L” and “S” alleles was not significant

(χ2 (1, N= 422) = 0.34, p value = 0.56).

Gerra et al. (2004) reported that the “S” promoter polymorphism and SS genotype were

associated with an increased risk for heroin addiction, particularly when addictive

behaviour is in comorbidity with impulsive, aggressive and violent behaviour.

Hallikainen et al. (1999) also stated that the frequency of the “S” allele was significantly

higher in the alcoholic individuals who are associated with impulsivity and antisocial

behaviour. These studies were in agreement with the recent findings obtained by

Sander et al. (1998), Hallikainen et al. (1999) and Preuss et al. (2001). Their results

confirm that “S” allele and “SS” genotype contribute to an increased vulnerability for

aggressiveness and its behavioural consequences, including the higher likelihood of

illicit drug use and development of dependence.

However, Kreek et al. (2005) reported that the “SS” genotype was not associated with

cocaine dependence in African-American and heroin dependence in Chinese, while

another Chinese study failed to find association between the 5-HTTLPR and heroin

dependence (Li et al., 2002). These studies were in agreement with the results

published by Japanese scientists (Ishiguro et al., 1999; Sachio et al., 2001). Their

findings showed that the “SS” genotype frequencies were significantly lower in

alcoholics and the later study stated that there was no association between the 5-

HTTLPR and alcoholism.

Another theory was suggested by Wendland et al. (2006) explaining the effect of 5-

HTTLPR on SLC6A4 gene expression that could be due to a nearby rs25531

polymorphism in the promoter. The G allele of this polymorphism is located in a

consensus binding site for AP2, a family of transcription factors described as positive

148

and negative regulators of SLC6A4 gene expression. Data from Hu et al. (2006) and

Parsey et al. (2006) has shown that the “S” and “L” alleles of the 5-HTTLPR in the

presence of G allele of rs25531 was associated with a decreased expression of SLC6A4

mRNA compared to the A allele. Accordingly, Hu et al. (2006) suggest that in tests of

association the LG alleles should be analysed along with the “S” alleles.

In this study, no differences in rs25531 allelic frequencies between drug dependent

individuals and healthy controls were detected (χ2 (1, N= 422) = 0.10, p value = 0.75).

Similar results were observed with genotype frequencies of rs25331, A/G (χ2 (2, N=

422) = 0.02, p value = 0.89). In addition, using the tri-allelic approach, we have

observed that the frequency of the SG alleles and LG alleles haplotypes in both drug

dependent individuals and control were very low. Similarly, the estimated frequency of

SA, SG, LA, LG haplotypes did not show any significant association (χ2 (3, N= 422) = 2.21,

p value = 0.53). However, there was a marginal association between the phased

genotypes (LA /LA, LA /SA, LA /LG, SA /SA, SA /SG and others) and drug dependence (χ2 (4,

N= 422) = 10.3, p value = 0.06).

This divergence between the study results could be related to the past history, the

psychiatric status and the subgroups of drug dependent individuals with violent

behaviour. This might be due to a bias in classifying the subtype. There are some

possible confounding factors that should be taken into consideration when assessing the

patients with drug dependence such as the methods used for defining violent, antisocial

behaviour and the early onset of dependence. Several studies have based patients’

assessment on collecting the phenotypic data by only a self-report or interviewing the

patients by health professional workers, including this study.

Various studies showed a risk of false positive results due to population stratification.

However, a risk of false positive results was not found in this study because genotypic

frequencies of the 5-HTTLPR and rs25331 in the patients and control population met

HWE expectations. In addition, it is likely that there were some sample bias or

genotyping errors. However, our subjects were all from one geographic origin and were

100% native Jordanian of Arab ancestry, which is a population known to be genetically

homogenous. Regarding genotyping errors, these were minimized because all subjects

and controls were genotyped twice in order to avoid technical errors and genotyping

was conducted for both subjects and controls under the same conditions and during the

same period. Genotypes were also evaluated independently by investigators who were

149

blind to the status of the subject and any discrepancies were resolved by test replication.

Finally, only male individuals with drug dependence were genotyped. Therefore, the

generalisation of the results to all drug dependent individuals is limited.

In summary, the present study has identified a trend toward higher “LL” genotype

frequencies of the SLC6A4 gene in 192 drug dependent individuals and 230 healthy

controls. Based on the bi-allelic analysis of the 5-HTTLPR variants, the 5-HTTLPR

(LL/LS/SS) genotypes were found to be significantly associated with drug dependence.

This study also present the first data on SLC6A4 gene alleles in Jordanian male of Arab

descent addicted to drug. However, using the triallelic analysis of the interacted 5-

HTTLPR and rs25531 markers suggested that the phased genotypes (LA /LA, LA /SA, LA

/LG, SA /SA, SA /SG and others) had a marginal association with drug dependence with a p

value = 0.06. The data presented will be useful in the context of further genetic analysis

including Family-based association studies such as the haplotype relative risk design.

In addition, larger case-control samples are required to extensively investigate

genotypes variables among drug dependent individuals of Arab origin to understand

better both their unique genetic features and the aetiology of drug dependence that

affect this group of individuals.

150

CONFLICT OF INTEREST

All authors declare that they have no conflict of interest.

ACKNOLOGEMENTS

We would like to thank Doctor Jamal Anani Director the National Center for

Rehabilitation of Addicts (NCRA) at the Jordanian Ministry of Health and Director of

the Public Security Department’s (PSD) Drug Rehabilitation Centre Major Mazen

Magableh for approving the work carried out in the first instance. We also would like

to thank the Genomics Research Group at Princess Haya Biotechnology Center (PHBC)

for their technical support. We also thank Stephen Iaschi for his assistance during the

editing of this manuscript. Gratitude is extended to the drug treatment personal and

clients, without whom we could not have conducted this study.

151

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CHAPTER 4

Pharmacogenetic Approach to Treating Drug

Dependence: Serotonin Transporter Gene (SLC6A4)

Promoter Polymorphisms as Treatment Predictors in

Jordanian Arabs

This Chapter was accepted for publication in the Current Pharmacogenomics and

Personalized Medicine.

158

Chapter 4

Pharmacogenetic Approach to Treating Drug

Dependence: Serotonin Transporter Gene (SLC6A4)

Promoter Polymorphisms as Treatment Predictors in

Jordanian Arabs

The following chapter is presented as a manuscript for publication in Current

Pharmacogenomics and Personalized Medicine. Lesch (1996) and colleagues suggest

that deficits in central serotonin (5-HT) function are implicated in the expression of

drug dependence disorders. Among the various components of the 5-HT system, the

serotonin transporter (5-HTT) modulates the central effects of drugs. Drugs block the

uptake of both serotonin and dopamine by binding to the transporter specific for each

neurotransmitter (Ravna et al., 2003). Animal studies suggest that modification of 5-HT

transporter activity may significantly contribute to drug seeking behaviour (Burmeister

et al., 2003), drug self-administration (Arroyo et al., 2000) and reinforce drug use

(Sora et al., 2001; Rocha, 2003).

This chapter aims to identify genetic variation within the serotonin transporter (5-HTT)

SLC6A4 gene that could potentially influence the clinical and biological outcomes of

drug dependence in patients of Arab descent within the Jordanian population. This

chapter also investigates whether the allelic variations within the SLC6A4 gene are

associated with drug consumption and alcohol intake, psychiatric symptoms and

responsiveness to treatment.

Drug dependent patients were recruited for this study from the National Centre for

Rehabilitation of Addicts (NCRA) at the Jordanian Ministry of Health and the Drug

Rehabilitation Centre at the Jordanian Public Security Directorate (DRC-PSD),

between 2001 and 2011. A biopsychosocial treatment approach is offered to patients at

both the NCRA and the DRC-PSD. Treatment includes inpatient detoxification,

medication, psychological support and nutritional supplementation. Integration with

follow up is also involved in the treatment process.

159

The drug dependent patients participating in the study were stratified according to their

5-HTTLPR and rs25531 status using a biallelic as well as a triallelic approach. The

biallelic approach assumes that the LL genotype of the 5-HTTLPR gene has a higher

serotonin transporter function than others genotypes (Lesch et al., 1999; Greenberg et

al., 1999; and Williams et al., 2003). Biallelic analysis in this study shows 33% of the

patients were classified as LL genotype (Group A), and 77% of the patients as LS or SS

genotype (Group B). In contrast, lymphoblastoid cell lines derived from individuals

carrying one or two copies (LS or SS) of the S allele of the5-HTTLPR gene has reduced

SLC6A4 mRNA and serotonin uptake capacity (Weizman and Weizman, 2000).

The triallelic approach assumes that the S allele with either the G allele or A allele (SG

or SA) and the L allele with the G allele (LG) results in reduced expression of the

SLC6A4 gene (Heils et al., 1996; Nakamura et al., 2000; Hu et al., 2005; Parsey et al.,

2006). In contrast, L allele with A allele (LA) predicts higher expression of this gene

(Heils et al., 1996; Praschak et al., 2007). In this study, using the triallelic approach of

combining the 5-HTTLPR and rs25531 markers, 27% of the patients were homozygous

for LA/LA genotype (Group A′), while 73% had heterozygous LA or non-LA genotype

(Group B′).

Recognition of the role of serotonergic systems in drug dependence as well as their

interaction with the clinical outcomes of treatment has led to increased interest in the

modulating role of polymorphisms within 5-HT relevant genes. A number of studies

have been directed towards assessing the relationship between 5-HTTLPR and rs25531

polymorphisms and the expression of 5-HTT in drug addiction. However, Most of these

studies here recruited from European background with a primary diagnosis of alcohol

dependence (Mannelli et al., 2005). The influences of 5-HTTLPR status on various

clinical and biological drug dependence outcomes have not been studied in patients of

Arab descent.

This chapter shows that there is a significant difference in the age that drug use is first

observed according to the 5-HTTLPR genotype. Specifically, those who carry the L

allele were younger when they first used drugs. In the triallelic analysis (5-HTTLPR

and rs25531), the comparison across the phased genotypes showed a significantly

higher frequency of drug use (days/week) in patients carrying the LA /LA genotype. The

160

phased genotype comparison also revealed a significant higher number of

detoxification treatments for patients carrying the LA /LA and LA /SA genotypes.

These results are in agreement with previous studies (e.g. Lesch et al., 1999; Greenberg

et al., 1999; Kranzler et al., 2002 and Williams et al., 2003) which suggest that the

biallelic LL or the triallelic LA/LA genotype of 5-HTTLPR gene has a higher serotonin

transporter function than other genotypes, resulting in increased serotonin uptake and a

reduced level of intra-synaptic serotonin. Further analysis, in particular finer

pharmacogenetic analysis in a larger cohort of drug Jordanian Arab patients receiving

treatment for drug dependence is required.

This manuscript was prepared by Laith Al-Eitan with support from the co-author listed.

A previously published DNA genotyping data of 5-HTTLPR and rs25531in 192 drug

dependent patients of Arab descent were compiled and stratified by Al-Eitan. Patient

sample selection and clinical data collection was made possible by a collaboration link

with Dr Jaradat at Princess Haya Biotechnology Centre (PHBC). A part of the work

described in this chapter was presented by AL-Eitan as an oral presentation at The

Australian Society for Medical Research (ASMR) Symposium, 2-10 June, 2011, Western

Australia, Australia. Dr Tay and Dr Hulse guided me throughout the study from design

to proof reading the manuscripts.

161

Pharmacogenetic Approach to Treating Drug Dependence: Serotonin Transporter

Gene (SLC6A4) Promoter Polymorphisms as Treatment Predictors in Jordanian

Arabs

Laith N. AL-Eitan1, Saied A. Jaradat 2 , Gary K. Hulse 4,3

, Guan K. Tay 1


6009, Australia 2 Princess Haya Biotechnology Centre, Jordan University of Science and

Technology, Irbid 22110, Jordan 3 School of Psychiatry and Clinical Neurosciences, Queen Elizabeth II Medical

Centre, The University of Western Australia, Crawley, WA 6009, Australia 4 Unit for Research and Education in Alcohol and Drugs, Queen Elizabeth II


Australia

Abbreviated title: Pharmacogenetic Approach to Treating Drug Dependence

Keywords: 5-HTTPLR, Opiate Drug Dependence, Pharmacogenetics,

Treatment Response.

Publication number LA09-003 of the Centre for Forensic Science at The University of

Western Australia


Laith N. AL-Eitan




Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285


[email protected]


162

ABSTRACT

Alterations in serotonin availability, levels and function have been shown to affect drug

consumption patterns, behavior and responses to treatment. Despite the existence of

health burdens related to drug dependence in resource-limited settings outside the realm

of developed countries, there have been relatively few public health pharmacogenetics

studies focusing on drug dependence in developing countries outside Europe and North

America. This study examines the influence of SLC6A4 gene polymorphisms (5-

HTTLPR and rs25531) on clinical and biological outcomes of drug dependence in

patients of Arab descent in a sample from Jordan. PCR-RFLP based genotyping of the

5-HTTLPR gene variants (LL/LS/SS) and rs25531 marker polymorphisms (A/G) was

performed in 192 drug dependent patients of Arab descent. These patients were

undergoing an 8-week pharmacological and behavioural inpatient treatment program.

In treatment, end of treatment and follow-up outcome measures included changes in

drug consumption and alcohol intake, urine drug screen, alcohol breath test, days in

treatment, counselling and self-help group sessions and completion rate. Patients were

stratified according to receptor polymorphism using a bi-allelic (Group A: LL versus

Group B: SS and LS genotype) and a tri-allelic approach (Group A′: LA/LA versus Group

B′: non-LA/LA genotype). Bonferroni corrections were applied for multiple

comparisons. There were no significant differences between genotype subgroups

regarding severity of drug dependence, psychiatric status, past history, medications (p >

0.1). However, the frequency of drug use, reduction in heroin consumption and alcohol

intake and responsiveness to treatment were significantly different for the LL, LS and SS

genotypes (p < 0.05). In addition, assuming a dominant effect of S (Group A versus

Group B), a significant difference in the age of first drug use was observed (p < 0.05).

Notably, patients with the LL genotype were younger when they first used drugs.

Group A′ compared to Group B′ showed a significant difference in age of first use, drug

use frequency and detoxification with higher averages in patients who were carrying the

LA allele (p < 0.05). To the best of our knowledge, this is the first study of serotonin

transporter polymorphisms (5-HTTLPR and rs25531) and the clinical and biological

outcomes of drug dependence in patients of Arab origin. We conclude with a

discussion of these findings with a view to the emerging nascent field of “public health

pharmacogenetics”, and personalized medicine diagnostics for rational treatment of

drug dependence as seen through the lens of global postgenomics science.

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1. INTRODUCTION

While environmental factors are likely to contribute in varying degrees to drug

dependence, it is becoming increasingly clear that these are also likely to be influenced

by genetic factors. Twin and epidemiological studies have shown that genetic factors

account for 40% to 60% of the overall variations in drug dependence [1]. In particular,

changes in serotonin (5-HTT) availability, levels and function have been shown to affect

drug consumption patterns, behavior and responses to treatment in drug dependent

patients [1-5].

The SLC6A4 locus on chromosome 17q11.1–q12 encodes for 5-HTT [6]. As genetic

variations within the SLC6A4 gene (5-HTTLPR and rs25531) alter the function of

platelet 5-HTT, this might also be seen in neuronal cells [7]. In the brain, various

studies have shown that the mRNA of 5-HTT is found in the serotonergic cells in the

raphe nuclei [7-9]. This could be explained by the nature of the amino acids sequence

of human 5-HTT which is common to all tissues, including blood cells and neurons [10,

11].

Lesch et al. (1994 and 1999) have suggested that the transcriptional activity of the 5-

HTT is modulated by a 20 to 23 base pair repeat polymorphism in the SLC6A4 promoter

(5-HTTLPR) located upstream of the transcription start site [12, 13]. The 5-HTTLPR

promoter alleles in the transcriptional region are comprised of either a 44-base pair

insertion (long allele, L) or deletion (short allele, S) [2]. The L allele of this

polymorphism was associated in vitro with a higher transcription rate [14]. In contrast,

lymphoblastoid cell lines derived from individuals with one or two copies of the short

allele (LS or SS) have lower levels of 5-HTT mRNA and reduced 5-HTT uptake

capacity. Little et al. (1993) showed that 5-HTT binding and 5-HTT mRNA levels in

human brain tissue differ significantly by the polymorphism in the promoter region of

the 5-HTT genotype (LL/LS/SS) [15]. Hanna et al. (1998) have also shown that the 5-

HTTLPR gene variants influence whole blood 5-HT content [16].

Various functional assays have confirmed that the S allele is dominant and reduces

transcriptional efficiency [2, 17]. Heils et al. (1997) and Heinz et al. (2007) found that

the S allele is characterized by a decreased transcription rate and a lowered serotonin

reuptake invitro [18, 19]. Bleich et al. (2007) reported that the L allele predisposes

164

individual to lowered level response to alcohol, which is in turn associated with

increased risk of alcoholism [20]. Bleich et al. (2007) explained that the long allele

should be associated with higher serotonin transporter function, and therefore be

associated with lower serotonin levels due to increased re-uptake [20].

A single nucleotide polymorphism (SNP) rs25531 located in close proximity to the 5-

HTTLPR has been identified. This SNP is an A/G substitution that may modulate the

effect of 5-HTTLPR on transcriptional efficacy [2-5]. Hu et al. (2005) and Wendland et

al. (2006) suggested that the expression of the SLC6A4 gene is reduced when the S and

L alleles of the 5-HTTLPR are combined with the A or G allele of rs25531 (SA/SG and

LA/LG) [4, 21]. In contrast, the LA variant predicts higher expression levels of this gene

[2, 22]. It has been suggested that in tests of association, the LG allele should be

analysed along with the two S alleles [4]. Despite the fact that rs25531 is separate from

the linked polymorphic region of the 5-HHT gene and thus the two separate

polymorphisms are genotyped in strong linkage disequilibrium, the term triallelic 5-

HTTLPR has been used commonly to indicate that the genotyping of these three alleles

is being undertaken and analysed by others [21].

While previous studies have suggested that the expression of the serotonin transporter

may be influenced by genotype in case of alcohol [23], cocaine (Little et al., 1993) [15]

and heroin dependence [24], conflicting results have been reported in other studies [25-

29]. Using a transmission disequilibrium test, Edenberg et al. (1998) found no linkage

or association between 5-HTTLPR and alcohol dependence among family members

[30], while a study by Lichtermann et al. (2000) reported an association [31]. Similarly,

some studies reported an association of the S allele with drug dependence in different

ethnic groups such as Germans [32] and Caucasians [33]. For example, the SS genotype

was linked to heroin dependence and aggression within a heroin-dependent Italians [24]

and the S allele has been linked to alcoholism, anti-social behaviour and suicide [1].

Other studies have found no link between the S allele and heroin dependence in

Chinese, or cocaine dependence in African-Americans [34, 35].

Indeed, recognition of the importance of the pharmacogenetics of the serotonergic

system in drug dependence has seen an increasing in interest in the modulating role of

polymorphisms of 5-HTT relevant genes. There has been an increase in interest in

markers of serotonin activity and the outcome of drug dependence treatments [35]. The

165

relationship between two specific polymorphisms; 5-HTTLPR and rs25531; and the

expression of 5-HTT in drug addiction has been investigated in a number of studies

involving patients from a European background with a primary diagnosis of alcohol

dependence [35]. Despite the existence of similar public health problems related to

drug dependence in other populations, there are few studies focusing on drug

dependence in patients from non-European backgrounds and virtually no in-depth

studies in Arab populations have been conducted.

This study aimed to examine the influences of SLC6A4 gene polymorphisms (5-

HTTLPR and rs25531) on clinical and biological measures of outcome in a hitherto

understudied context: that is, in a sample of Arab drug dependent patients undergoing

pharmacological and behavioural treatment. These patients were stratified according to

their 5-HTTLPR and rs25531 status using a biallelic and triallelic approach. This study

was specifically interested in investigating whether the sample characteristics differed

by the serotonergic polymorphisms (5-HTTPLR and rs25531). Our study also

determined if allelic variations within the SLC6A4 gene are associated with dependence

variables, drug consumption, psychiatric symptoms and responsiveness to treatment.

We conclude with a discussion of the findings with a view to public health

pharmacogenetics, and personalized medicine diagnostics for rational treatment of drug

dependence.

166

2. MATERIAL AND METHODS

2.1. Patients and Study Protocol

Blood samples and clinical data collection were approved by the Jordanian Ministry of

Health (Ref No. Development/Trainees/535). The protocol, including the genetic

analysis, was approved by the Institutional Review Board of the Jordan University of

Science and Technology (Ref No. RA/16/1/2010) and the Human Research Ethics

Committee of The University of Western Australia (Ref No. RA/4/1/4344). This study

was also conducted according to the provisions of the Australian Medical Association

Code of Ethics (2006) and World Medical Association Declaration of Helsinki (World

Medical Association, 2008).

Patients for this study were recruited from the National Centre for Rehabilitation of

Addicts (NCRA) at Jordanian Ministry of Health and the Drug Rehabilitation Centre at

the Jordanian Public Security Directorate (DRC-PSD) between 2001 and 2011.

Inclusion criteria were being males of Arab origin (>18 years of ages), who met the

Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria for drug

dependence outlined by the American Psychiatric Association (1994) were screened

[36]. The sample collected for this study is not necessarily representative of all patients

who need treatment for their substance problems in Jordan, nor the Middle East, but this

first study was conceived to provide an insight into trends towards designing more

comprehensive studies, and reflects the patient population in our institutional clinical

catchment area [37-39].

Patients underwent a thorough medical and psychiatric examination to record

comorbidities, physical disorders and general medical conditions. Psychiatric diagnosis

was established using a structured baseline interview which was based on the Addiction

Severity Index (ASI) criteria [40]. The clinical diagnoses and structured clinical

interviews were conducted by independent psychiatric consultants.

To achieve a diagnostically and clinically homogenous sample, patients with axis-I

comorbidity (according to DSM-IV) other than drug dependence and abuse (e.g.

schizophrenia, major depression, bipolar I and II disorder, schizoaffective disorder, a

serious medical illness) or those receiving psychotropic medications were excluded.

Moreover, those patients with serious medical conditions (e.g. neuroendocrine,

167

metabolic or cardiovascular diseases, neurodegenerative disorders and epilepsy) were

also excluded. If patients used more than one substance, they were included only if all

of their dependencies were opiates.

Initially, 500 patients were screened. Of these, 350 fulfilled the chosen clinical criteria

for inclusion in the study. Among these, 120 of the patients could not finish the

treatment program for clinical reason and were excluded. From the remaining patients,

220 agreed to be enrolled after being given a complete description of the study and

providing their written informed consent. All patients were enrolled within the first

week of admission. A further 28 patients were then excluded from the final analysis

due to missing or insufficient data. In eight cases, the DNA quantity was not sufficient

for genotyping, while in the other 20 cases, 5-HTTLPR genotyping failed. In total,

complete data was obtained from 192 patients with opiate drug dependence.

2.2. Treatment Approach

The 192 participants received the same biopsychosocial treatment that was offered to all

patients in the NCRA and DRC-PSD programs. Treatment consisted of inpatient

detoxification involving Medications, psychological support, nutritional

supplementation and integration with follow up.

Benzodiazepine medication was used to reduce or relieve withdrawal symptoms in drug

dependent patients during detoxification. Benzodiazepines such as chlordiazepoxide

and diazepam are commonly prescribed for drug dependent patients such as alcoholics

and opiate addicts to ameliorate withdrawal symptoms such as tremors and anxiety [41].

In the NCRA and DRC-PSD program, chlordiazepoxide is prescribed for outpatients as

it has lower abuse potential. Valium (diazepam) is used for inpatients as it has a faster

action and a higher dose effect. All patients are given four doses every day at early

morning, mid-day, early evening and at bedtime and reviewed daily to assess

withdrawal symptoms. On admission, they are given four separate doses of 30mg. The

next day’s planned dosages are based on ongoing assessment of the patients’ symptoms

rather than the length of the course or the prescribed starting dose.

The patients recruited for the study were divided into three groups according to the

severity of their withdrawal symptoms. The first group of patients (lower-dose

responders) had the lowest severity of withdrawal symptoms and was medicated with

10mg diazepam four times a day. The second group of patients (high-dose responders)

168

had moderately severe withdrawal symptoms and was medicated with equal to or

greater than 20mg diazepam four times a day. The third group of patients (high-dose

non-responders) was non-responders to treatment with severe withdrawal symptoms and

medicated with equal to or greater than 30mg diazepam four times a day.

Four other oral medications were used to ameliorate other withdrawal symptoms:

Lofexidine, Loperamide, Metoclopramide and Ibuprofen. Lofexidine, an alpha-

adrenergic agonist was used to relieve the symptoms of withdrawal in patients

undergoing heroin or opiate detoxification. This medication was effective in reducing

the withdrawal symptoms such as chills, sweating, stomach cramps, diarrhoea, muscle

pain, runny nose and watering eyes. For these patients, 10 day course of lofexidine

treatment starting at 0.2mg two to four times a day and increasing 0.4mg until a

maximum of 2.4mg. Loperamide was used for treatment of diarrhea. All patients were

given an initial dose of 4mg after each loose stool up to a total of 16mg per day.

Metoclopramide was used for nausea and vomiting treatment. Patients were prescribed

10mg/day up to a maximum of 30mg/day. Ibuprofen was used to reduce fever and

headaches and treat muscle pains. Patients were given an initial dose of 0.4mg

Ibuprofen every 4 to 6 hours, increased to a maximum daily dose of 1.6mg according to

their response and tolerance to the drug. Supplementary vitamins (e.g. vitamin B and

thiamine) were also provided to patients who were malnourished.

For the patients who had successfully completed detoxification and had been drug free

for more than 7 days, the opiate antagonist, naltrexone, was used as an aid to prevent

relapse and promote abstinence. The drug was given at a starting dose of 25mg/day,

increased to 50mg three days a week. Sometimes, naltrexone was given to the drug free

patients 3 days a week (100mg on Monday and Wednesday and 150mg on Friday) to

aid compliance or to facilitate supervision. Naltrexone was also used for inpatients to

facilitate rapid detoxification over 5 to 7 days.

The NCRA and DRC-PSD treatment program also provide patients with psychological

support. Inpatients groups are given counselling sessions 3 times a week for a total of 8

weeks. Each session lasts for 2 hours. In addition, patients participate in 1 hour

individual counselling sessions per week over the 8 week treatment program. Patients

completing the intensive program are offered an aftercare program which includes one

individual counselling session per week for up to 6 months. The treatment approach is

169

problem oriented and focuses on achieving well defined goals. These objectives are

outlined in a treatment plan that is prepared at admission and is updated for each new

treatment program. Medical, behavioral, supportive and relapse prevention strategies

are drawn from different treatment models [42]. In this study, the treatment team was

kept blind to the genetic status of the patients.

2.3. Outcomes Measures

A semi-structured baseline interview was developed from the Addiction Severity Index

(ASI) criteria. These interviews were used to collect demographic and clinical data of

the 192 patients. The family history of substance abuse was also obtained. This

allowed the subset of drug dependent patients whose addiction may be influenced by

genetic factor to be identified [43]. Berrettini and Persico (1996) suggest that the

likelihood of detecting susceptibility genes is higher in these individuals. In treatment,

end-of-treatment and follow-up assessments were also conducted to provide valid

estimates of drug abstinence [44]. Retention and attrition from treatment were also

recorded.

The measures to assess outcome were number of negative urine and breath tests. Urine

drug screening (UDS) and breath alcohol tests (BAT) were performed for all patients at

the NCRA and DRC-PSD on the day of admission to the treatment program. Every

week during the treatment and 3 months after treatment completion, the patients were

randomly screened for drugs using The Multi Drug Test 10 Panel and tested for alcohol

using Breath Alcohol Detector (Jant Pharmacal Corporation, CA, USA). This test is a

one-step immunoassay for the detection of cannabis, cocaine, phencyclidine, opiates,

methamphetamine, methadone, amphetamine, barbiturates, benzodiazepines and

oxycodeine. The number of negative UDS and BAT for all tested substances was

adopted as a measure of substance abuse during treatment. Missed urines samples were

not taken into account for data analysis. If UDS and BAT results were positive for any

tested substance, a sample was considered positive. If UDS and BAT results were

negative for tested substance, a sample was considered negative.

The number of drug treatments and detoxification and rehabilitation outcomes were

recorded as this offered another estimate of treatment retention. Attendance was

recorded as duration from the date of admission to date of the last visit. The number of

treatment sessions attended by the patients including the total number of counselling

170

and self-help sessions was calculated. This measure was used to reflect participation in

the treatment process. The number of patients who dropped out treatment was also

recorded. Attrition was defined as patients who stopped attending the treatment

program within 7 days of admission. Attendance at aftercare treatment was also

recorded. These participants were described as patients completing treatment and

continuing with aftercare programs.

2.4. DNA Extraction and Genotyping

Genomic DNA was extracted from whole blood using a Gentra Puregene kit (Qiagen,

Valencia, CA, USA). Genotyping was performed by amplifying the 5-HTTLPR

variants (LL/LS/SS) and detect the A/G polymorphism at position rs25531 according to

the PCR-RFLP protocol of Wendland et al. (2006). Briefly, amplification of both

polymorphic sites was achieved using PCR. Subsequent digestion of the PCR product

with Msp1 (New England Biolabs, Beverly, Massachusetts, USA) and separation of

fragments using agarose gel electrophoresis allowed discrimination of SA, SG, LA and LG

alleles.

.

2.5. Statistical analysis

The allele frequencies and genotype distribution of the 5-HTTLPR variants and

rs25531were calculated for the drug dependent patients and χ2 tests were used to

compare the genotype frequency in the population as predicted by the Hardy Weinberg

Equilibrium (HWE). For further analysis, the patients were grouped according to the 5-

HTTLPR polymorphism. For the biallelic approach, S-allele carriers were included in

one subgroup, L/L homozygous carriers in the other. For the triallelic approach, the

LA/LA homozygous carriers were defined as one subgroup, the non-LA/LA carriers as the

other.

Continuous variables were analysed using ANOVA F-test, Kruskal-Wallis test, Welch

test and Student t-test. Categorical variables were analysed using Pearson χ2 tests,

student t-test and Fisher exact test. A p value < 0.05 was considered to be statistically

significant. All statistical analyses were performed using SPSS statistical package 19.0

(SPSS Corp., Chicago, Illinois, USA). Bonferroni corrections were applied for multiple

comparisons.

171

3. RESULTS

3.1. Sample Characteristics

A total of 192 Jordanian Arab drug dependent patients fulfilled the inclusion criteria and

consented to participate in the study. Their demographic and clinical characteristics are

summarized in Table 1.

All 192 patients were diagnosed with opiate dependence. The majority of these patients

were also co-dependent on nicotine (92%), cannabis (56%) and alcohol (53%). Life-

time stimulant abuse or dependence was recorded in 17% of the patients.

3.2. 5-HTTLPR and rs25531 Genotypes and Opiate Drug Dependent Patients

Genotype distributions were consistent with the Hardy-Weinberg equilibrium (HWE)

for both 5-HTTLPR variants (χ2 = 4.0, df = 4, p value = 0.053) and rs25531

polymorphisms (χ2 = 0.6 df = 4, p value = 0.447). The 5-HTTLPR allele frequencies

were L = 54.2% and S = 45.8%. The 5-HTTLPR genotype distributions were LL

(32.8%), LS (42.7%) and SS (24.5%). The rs25531 allele frequencies were A = 94.5%

and G = 5.5%. The rs25531 genotype distributions were AA (89.6%), AG (10.4%) and

GG (0.0%).

Using biallelic analysis, 63/192 patients were classified as LL genotype (Group A), and

patients with LS genotype (82/192) and SS genotype (47/192) (Group B) assuming a

dominant effect of S (Table 2). The triallelic approach of 5-HTTLPR revealed 51/192

patients with homozygous LA/LA genotype (Group A′), while 141/192 had heterozygous

LA or non-LA genotype (Group B′).

Three sets of comparisons were performed for admission, end of treatment and follow-

up measures. These included comparing LL, LS and SS genotypes, comparing Group A

with Group B genotypes and comparing L and S alleles based on the biallelic approach.

The analyses are summarized in Table 2. Using triallelic analysis, the three variables

mentioned above were compared across the combination of both 5-HTTLPR and

rs25531 genotypes (LA /LA vs LA /SA vs LA /LG vs SA /SA vs SA /SG). These also included

comparing Group A and Group B genotypes and comparing the combination both 5-

HTTLPR alleles and rs25531 alleles with assuming LA has a higher serotonin expression

(LA versus SA, SG and LG). The analyses are summarized in Table 3.

172

3.3. Baseline Differences across Genotypes

Using biallelic analysis (5-HTTLPR, LL/LS/SS), the frequency of drug use (days per

week) and treatment outcome (lower-dose responder, high-dose responder, high-dose

non-responder) were significantly different across the patients genotypes (p < 0.05)

(Table 2). In addition, using a dominant effect of S, a significant difference in age first

drug use (years) was observed for those with the LL genotype having the earliest age of

first drug use (p < 0.05). However, using either the biallelic analysis for genotypes and

alleles or the dominant effect of S, there were no differences in demographic variables,

nicotine status, number of hospitalisations (drug treatments, detoxification,

rehabilitation, counselling and self-help), psychiatric status (impulsive, suicide attempts,

depression and diminution) and past history (overdose, hepatitis) at admission or

measures of drug and alcohol use (UDS and ABT) at follow up after treatment.

In the triallelic analysis (5-HTTLPR and rs25531), the comparison across the phased

genotypes (LA /LA vs LA /SA vs LA /LG vs SA /SA vs SA /SG) revealed a significantly higher

frequency of drug use (days/week) in patients carrying the LA /LA phased genotype (p <

0.05). The phased genotypes comparison also showed a significant difference in

detoxification treatment (#) with higher means in patients who were carrying the LA /LA

and LA /SA genotypes (p < 0.05) (Table 3).

Assuming SG, SA and LG predict a reduced gene expression of the 5-HTT gene and LA

predicts a higher gene expression of this gene, the relationships between phased

genotypes (Group A′ versus Group B′) with drug related measures were explored (Table

3). There were no significant associations with the parameters of hospitalisation status

except detoxification treatment. There were also no significant differences between

both groups concerning the variables listed above excluding the age of first use, age of

onset and frequency of drug use with p values ranged from less than 0.05 to less than

0.01 (Table 3).

The triallelic analysis (Table 3) showed a significantly higher means of drug treatments,

detoxification and rehabilitation in case of combination alleles of 5-HTTLPR and

rs25531 (LA versus SA, SG and LG) (p ranged from < 0.05 to < 0.01).

Exploratory comparisons between the triallelic analysis (5-HTTLPR and rs25531) and

follow-up measures, psychiatric status, past history and treatment outcome found no

173

significant group differences, neither with the phased genotype nor with the

combination alleles (Table 3).

3.4. Change in Drug and Alcohol Consumption within Genotypes

Within patients repeated measures ANOVA F in the entire sample showed that both

heroin (mg) consumption and alcohol (ml) intake were significantly reduced over time

(F6.16, df = 2, p = 0.0025, F6.17, df = 2, p = 0.0028, respectively). After correction for

multiple comparisons, the heroin consumption and alcohol intake were decreased from

the baseline at admission to the follow up measures for each genotype (LL, LS, and SS)

with p values less than 0.05 in each case. However, no significant difference were

detected between genotypes in reduction in cannabis use (number of cigarettes) (F0.06, df

= 2, p = 0.98). The findings are summarized in Figure 1.

Multivariate analysis showed that heroin consumption, alcohol intake and cannabis

smoking were significantly different between genotypes over the treatment and follow-

up period. In particular, patients with LL genotype showed higher drug consumption for

drug of interest at dependence and at follow-up after treatment compared to LS and SS

genotypes with p values ranging from less than 0.05 to 0.001.

Using the triallelic analysis, the estimated marginal means of drug consumption showed

that there was a significant reduction of drug use over time after treatment within each

phased genotype (p value ranged from < 0.05 to < 0.001). However, the phased

genotypes did not differ with regard to drug consumption means from first use until

follow-up after treatment (p > 0.05). The findings are summarized in Figure 2.

174

Table 1. Demographic and clinical information for total of drug dependent patients and for each genotype

Category Subcategory Genotype (n = 192) LL (n = 63) LS (n =82) SS (n = 47)

Demographic

data

Gender Male 100% 33% 43% 25%

Age (years) 32.9 (8.4) 35.1 (8.4) 32.5 (8.3) 30.8 (7.9)

Marital status

Married 63% 73% 57% 57%

Divorced 2% 0% 5% 2%

Never Married 35% 27% 38% 41%

Education status Educated 20% 19% 23% 17%

Uneducated 80% 81% 77% 83%

Employment pattern Employed 40% 44% 63% 60%

Unemployed 60% 56% 37% 40%

Drug use

Current drug of abuse

Nicotine 91% 92% 91% 87%

Opiates 96% 98% 78% 100%

Cannabis 67% 57% 57% 55%

Alcohol 50% 57% 42% 57%

Amphetamine 15% 11% 14% 19%

Cocaine 3% 3% 5% 0%

Dependence

Age first use (years) 20.9 (8.2) 19.2 (9.7) 22.7 (7.3) 21.5 (5.5)

Age of onset (years) 22.7 (8.8) 21.0 (10.7) 24.4(7.8) 23.4 (5.5)

Duration (years) 8.5 (6.4) 7.8 (6.6) 9.8 (6.4) 8.2 (5.9)

Frequency (days/week) 3.4 (1.2) 3.0 (1.6) 2.9 (1.5) 3.0 (1.6)

Drug and alcohol test Positive admit UDS 43% 48% 40% 40%

Positive admit ABT 30% 27% 32% 32%

Drug Toxicity Overdose 45% 52% 44% 35%

History of drug use Blood relative 23% 21% 18% 34%

Values other than percentages represent means (SD)

175

Table 2. Comparison of the demographics, clinical variables and outcome measures across

genotypes and alleles

Variable

Genotypes

LL vs LS vs SS

Dominant Sc

LL vs LS + SS

Alleles

L vs S

Subject (n = 192) ANOVA F test/ χ2/Fisher exact test/Kruskal-Wallis test/Welch test

Demographic

Age 0.023d 0.013

d 0.007

d

Blood relative 0.110 0.715 0.130

Nicotine status

Age first smoke 0.209 0.165 0.625

Cigarettes per day 0.220 0.154 0.584

Dependence

Age first use (years) 0.236 0.037d 0.330

Age of onset (years) 0.391 0.056 0.402

Duration (years) 0.162 0.060 0.186

Frequency (days/week) 0.005d 0.133 0.933

Hospitalization (#)

Drug treatments 0.868 0.605 0.194

Detoxification 0.795 0.510 0.209

Rehabilitation 0.709 0.418 0.268

Counselling 0.831 0.586 0.534

Self-help 0.790 0.653 0.486

Follow-up

UDSa (1

st ) 0.553 0.196 0.329

UDSa (2

nd) 0.654 0.351 0.561

ABTb (1

st ) 0.832 0.355 1.000

ABTb (2

nd) 0.661 0.950 0.672

Psychiatric status

Impulsive 0.832 0.761 1.000

Suicide attempts 0.661 0.439 0.699

Depression 0.761 0.669 1.000

Diminution 0.485 0.841 0.307

Past History

Hepatic disease 0.164 0.878 0.330

Overdose toxicity 0.902 0.850 1.000

Outcome

Treatment responsee 0.030

d 0.616 0.438

a. UDS: Urine Drug Screens. b. ABT: Alcohol Breath Test. c. Patients with LL genotype: Group A, patients with either LS or SS: Group B. d. Statistically significant, p value < 0.05; all other comparisons not significant. Degree freedom (df)

for ANOVA equal 2. Bonferroni corrections were applied for multiple comparisons. e. Treatment response: Lower-dose responder, high-dose responder, high-dose non-responder.

176

Table 3. Comparison of the demographics, clinical variables and outcome measures across

phased genotypes and combination alleles (5-HTTLPR and rs25531)

Variable

Phased Genotypes

LA /LA vs LA /SA vs LA

/LG vs SA /SA vs SA /SG

Dominant LA/LA

LA/LA vs LA /SA + LA/LG

+ SA/SA + SA/SG

Combination Alleles

LA vs. LG+SG+SA

Subject (n = 192) ANOVA F test/ χ2/Fisher exact test/Kruskal-Wallis test/Welch test

Demographic

Age 0.063 0.005d 0.002

d

Blood relative 0.161 0.848 0.279

Nicotine status

Age first smoke 0.622 0.195 0.481

Cigarettes per day 0.260 0.080 0.294

Dependence

Age first drug use (years) 0.561 0.021d 0.089

Age of onset (years) 0.728 0.033d 0.117

Duration (years) 0.319 0.104 0.177

Frequency (days/week) 0.005d 0.018

d 0.324

Hospitalization (#)

Drug treatments 0.106 0.605 0.009d

Detoxification 0.043d 0.510 0.021

d

Rehabilitation 0.178 0.418 0.042d

Counselling 0.098 0.586 0.265

Self-help 0.114 0.653 0.295

Follow-up

UDSa (1

st ) 0.803 0.322 0.261

UDSa (2

nd) 0.726 0.188 0.197

ABTb (1

st ) 0.985 0.743 0.707

ABTb (2

nd) 0.965 0.723 0.839

Psychiatric status

Impulsive 0.469 0.748 0.999

Suicide attempts 0.734 0.621 0.574

Depression 0.999 0.999 0.775

Diminution 0.701 0.734 0.444

Past History

Hepatic disease 0.414 0.744 0.330

Overdose toxicity 0.685 0.684 0.999

Outcome

Treatment responsee 0.081 0.080 0.223

a. UDS: Urine Drug Screens. b. ABT: Alcohol Breath Test. c. Patients with LA /LA: Group A′, patients with LA /SA, LA /LG, SA /SA or SA /SG: Group B′. d. Statistically significant, p value < 0.05; all other comparisons not significant. Degree freedom (df)

for ANOVA equal 2. Bonferroni corrections were applied for multiple comparisons. e. Treatment response: Lower-dose responder, high-dose responder, high-dose non-responder.

177

Figure 1. Estimated marginal means of drug consumption (a. heroin (mg), b. alcohol (ml) and c. cannabis (#)) for each genotype of 5-HTTLPR

(LL/LS/SS) at first use, at admission and at after treatment. (a) Heroin consumption reduced significantly over time after treatment

within each genotype (ANOVA F = 6.16, df = 2, p = 0.0025). (b) Alcohol intake also reduced over time within each genotype (

ANOVA F = 6.17, df = 2, p = 0.0028). (c) no significant differences were detected with regard to cannabis consumption between

genotypes (ANOVA F = 0.06, df = 2, p = 0.98). Significant differences were also observed between patients genotype with LL group

showing higher drug (heroin and cannabis) consumption and lower alcohol intake at dependence and at follow up after treatment

compared to LS and SS genotypes (p ranged from <0.05 to <0.001).

178

Figure 2. Estimated marginal means of drug consumption (a. heroin (mg), b. alcohol (ml) and c. cannabis (#)) for each phased genotype (5-

HTTLPR and rs25531) at first use, at admission and after treatment. (a) Heroin consumption, (b) alcohol intake and (c) cannabis

smoking reduced significantly over time after treatment within each genotype (p ranged from <0.05 to <0.001). No significant

differences of drug consumption means were detected between genotypes over time (p > 0.05). P values were from a F-test.

179

4. DISCUSSION

To the best of our knowledge, this is the first study of serotonin transporter

polymorphisms (5-HTTLPR and rs25531) and the clinical and biological outcomes of

drug dependence in patients of Arab origin. This study was undertaken because of

recent advances in our understanding of the role of the serotonergic system in drug

dependence and the association between central serotonin (5-HT) and drug dependence

disorders. Among the various components of the 5-HT system, the serotonin transporter

(5-HTT) modulates central effects of drug abuse and thus drugs may block the uptake of

both serotonin and dopamine by binding to the transporter specific for each

neurotransmitter [44]. Various animal studies have shown that modification of 5-HT

transporter activity may significantly contribute to drug seeking behaviour, self-

administration of drugs and reinforcement of drug taking [45-48].

In the present study, genotyping of drug dependent patients was undertaken using two

genetic stratification approaches; biallelic analysis of the 5-HTTPLR locus and triallelic

analysis involving the additional typing of the rs25531 SNP to allow for subdivision of

the S and L alleles into LA and non-LA variants. Overall, statistical significance was

slightly higher in the triallelic model. This is not unexpected as according to functional

stratification, the triallelic distribution should be more significant. The most likely

reason for these differences in results is the variation in group-size; group A with LL

genotype: 63 versus group A′ with LA/LA genotype: 51 and group B with SS and LS

genotype: 129 versus group B′ with non-LA/LA genotype: 141; which might have

affected the statistical power of the comparison for the group A′ and group B′.

Previous studies in healthy volunteers have indicated that 5-HTTLPR variants may be

associated with drug dependence variables such as age of first use, age of onset of

dependence, duration of drug use and frequency of use [35]. Using the biallelic

approach, this study showed that the frequency of drug use was significantly different

for the LL, LS and SS genotypes (p value = 0.005) but no differences were observed for

the age of first use, age of dependence onset and duration. In contrast, a recent study

investigating the association of these variables in 141 drug dependent African

Americans found that age of onset and frequency of drug use varied across the LL, LS

and SS genotypes [35]. The present study also revealed that biallelic genotype

influences treatment responses, as the genotype frequencies were different in the lower-

180

dose responder, high-dose responder and high-dose non-responder groups. However,

we found no association of the 5-HTTLPR variants with hospitalization treatment

numbers, psychiatric status, or past history (including overdose toxicity and hepatitis).

Our study also revealed a significant impact of 5-HTTLPR on the severity of alcohol

and heroin consumption. The data suggests that the 5-HTTLPR polymorphism may be

associated with alcohol intake levels. Patients with the S allele had the highest alcohol

intake at admission and earlier onset of dependence. In addition, patients with the SS

genotype benefited the least from treatment in terms of reduction in alcohol intake

(ml/day) at the end of treatment and at follow up. This relationship between the

severity of alcohol intake and SS genotype observed prior to admission into treatment is

in agreement with other reports which have shown that susceptibility to alcohol

dependence may be linked to the 5-HTTLPR SS variant[25, 31, 33, 49-51].

Interestingly, in our study, patients with the LL and LS genotypes were able to alter their

drinking behavior in response to the combined pharmacological and behavioral

treatment program but the SS genotype was not. Lesch et al. (1996) suggests that the SS

genotype is associated with reduced 5-HTT expression [17]. It has been also argued that

individuals with such impairment are less likely to respond to treatment [17]. For

example, Berggren et al. (2001) found a link between serotonergic neurotransmission

impairment and poor responses to citalopram treatment in alcoholics [52]. From a

clinical perspective, this may suggest the 5-HTTLPR polymorphism could be used to

distinguish responders from non-responders in the treatment of alcoholism.

As expected, this study found a reduction in the severity of heroin use in response to a

combination of pharmacological and behavioral treatment. After treatment, the severity

of heroin use (mg/days) reduced significantly for each 5-HTTLPR genotype (ANOVA F

= 6.16, df = 2, p = 0.0025), even for the heroin abuse patients within the LL group

which had the highest heroin consumption at admission and follow up (p value < 0.05).

At follow up, there was a noticeable but statistically non-significant reduction in heroin

use among the SS and LS genotypes compared to the LL genotype. Interestingly, an

inverse correlation between heroin and alcohol use was observed for patients with the

SS genotype. While it is difficult to draw firm conclusions about the link between

reduction in heroin consumption and increase in alcohol intake among the patients

carrying the S allele, substitution of heroin with alcohol and vice versa is reported

commonly among substance abusers [41].

181

The triallelic analysis used in this study found that 5-HTTLPR is associated with patient

differences in drug use (days/week). Patients with the genotype coding for high 5-HTT

expression (LA/LA) used drugs more frequently than patients with a genotype coding for

low 5-HTT expression (LA/SA, LA/LG, SA/SA or SA/SG). Patients with the LA/LA and LA/SA

genotypes also spent more days undergoing detoxification (p value < 0.05).

Comparison of Group A′ (LA/LA) versus Group B′ (non-LA/LA) gave similar results

which are consistent with the assumption that patients with the SG, SA and LG genotype

have lower expression of the 5-HTT gene those patients with the LA genotype.

The results of the biallelic and triallelic analysis are in agreement with proposed theory

suggested by Kranzler et al. (2002) that the biallelic LL and triallelic LA/LA genotype of

the 5-HTTLPR gene has a higher serotonin transporter function than other genotypes,

resulting in an increase in serotonin uptake and a reduction in intra-synaptic serotonin

levels [53]. Sellers et al. (1992) also found that pharmacologically inducing increases

in 5-HT transmission (by either stimulating 5-HT release, blocking 5-HT uptake or

using direct agonists) can reduce drug use and dependence [54]. In combination, these

two studies suggest individuals with the L allele may be more susceptible to drug

dependence.

Interestingly, apart from the frequency of drug use and detoxification treatment, we did

not observe any differences in other variables between the phased genotype groups at

admission, end of treatment or follow-up. Triallelic analysis of LA versus SA, SG and LG

however, did show significant associations with drug treatment, detoxification and

rehabilitation (Table 3) and intake of heroin (mg/day), cannabis (cigarette/day) and

alcohol (ml/day) were significantly reduced over time after treatment for each phased

genotype (Figure 3). Overall the triallelic analysis showed that drug consumption did

not differ from the first drug use until the follow-up time after treatment.

CONCLUSIONS AND FUTURE OUTLOOK

The impacts of 5-HTTLPR genetic variation on various clinical and biological outcomes

have not been studied in drug dependent patients of Arab descent. Therefore, 192 drug

dependent patients have been examined in regard to these outcome measures. These

patients were stratified according to the 5-HTTLPR and rs25531 using a biallelic as well

as triallelic approach. This study found that the biallelic 5-HTTLPR genotype

associated with responsiveness to treatment within drug dependent patient undergoing

182

combined pharmacological and behavioral treatment. The responsiveness to treatment

was not affected by the triallelic polymorphism. However, the study reports that the

biallelic LL or the triallelic LA/LA genotype of 5-HTTLPR gene can be considered as a

putative risk factor among drug dependant patients. These patients having this genotype

were at risk of relapse, had the earliest age of first use and onset of dependence and the

highest frequency of drug use.

Genetic susceptibility to drug dependence as well as pharmacogenetics of the treatment

of such dependencies are expected to be complex and multi-factorial, involving more

than two variants in the promoter region of one gene [55]. Techniques such as the

Multivariate Analysis of Associations, which simultaneously examine the contribution

of multiple variants or genes is required for understanding the genetic makeup of

polygenic or multi-factorial disorders. Therefore, to develop novel pharmacotherapies

and to create new prevention and treatment programs, the roles of genes, their variants

and the environment in which they are expressed need to be further elucidated [34].

Future research could also be directed towards using a genome-wide association

analysis and including more specific cases with a wider set of phenotypes.

Overall the results of this study provides additional clinical knowledge that may be used

to establish a new pharmacogenetic approach to reduce the severity of drug

consumption and alcohol intake and improve drug abstinence based on variations in the

serotonin gene. This study should also stimulate further genetic analyses of the Arab

population to understand better both their population genetic background and the

etiology of drug dependence that affect this group of individuals and improve treatment

strategy according to the genetic variation of the patients. To strengthen the claims

made here, finer pharmacogenetic analysis in a larger cohort of drug dependent patients

from the Jordanian Arab population or from the greater Middle East region are required.

Finally, it is noteworthy that as genomics data-intensive life science applications are

being expanded globally with new initiatives such as the H3Africa Project, it becomes

all the more essential to build capacity for genomics research in resource-limited

countries beyond North America [56-58].

183

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

LISTS OF ABBREVIATIONS

SLC6A4 Solute Carrier Family 6, Member 4

5-HTTLPR The Serotonin-Transporter-Linked Polymorphic Region

LL/LS/SS LongLong/LongShort/ShortShort

UDS Urine Drug Screen

ABT Breath Alcohol Test

5-HHT The Serotonin Transporter

mRNA Messenger Ribonucleic Acid

SNP Single Nucleotide Polymorphism

NCRA The National Centre for Rehabilitation of Addicts

DRC-PSD The Drug Rehabilitation Centre-Public Security Directorate

DSM-IV Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

ASI The Addiction Severity of Index

DNA Deoxyribonucleic Acid

PCR Polymerase Chain Reaction

RFLP Restriction Fragment Length Polymorphism

HWE Hardy-Weinberg Equilibrium

SPSS Statistical Package for the Social Sciences

ACKNOWLEDGMENTS


Western Australia (UWA). We gratefully acknowledge the staff and clients of the drug

treatment clinic, without whom we could not have conducted this study. All authors

made a significant contribution to conception and design, acquisition of data, or analysis

and interpretation of data; drafting the article or revising it critically for important

intellectual content; and approved the final version to be published.

184

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189

CHAPTER 5

SNP GENOTYPING FOR DRUG DEPENDENCE

CANDIDATE GENES USING A SEQUENOM

MASSARRAY® IPLEX PLATFORM

This chapter was submitted to Journal of Biomolecular Techniques.

190

Chapter 5

SNP Genotyping for Drug Dependence Candidate Genes

Using a Sequenom MassARRAY® iPLEX Platform

This chapter describes the use of a Sequenom MassARRAY® iPLEX Platform method to

genotype 68 single nucleotide polymorphisms (SNPs) within nine drug dependence

candidate genes (DRD1, DRD2, DRD3, DRD4, DRD5, OPRM1, SLC6A3, BDNF and

COMT) and to investigate the distribution of minor allele frequencies (MAFs) in drug

and non-drug dependent Jordanian Arab populations. These genes are of particular

interest to drug and alcohol researchers as they could be involved in the mechanism of

drug dependence. They are also of interest to the general neuropsychiatric community

as they could be involved in other neurological and psychiatric disorders (Kreek et al.,

2005, Haile, Kosten and Kosten, 2008)

The method described in this chapter is based on the commercially available Sequenom

MassARRAY® platform (iPLEX GOLD). The Sequenom MassARRAY® iPLEX SNP

genotyping platform uses matrix assisted laser desorption/ionisation time of flight mass

spectrometry (MALDI-TOF MS) coupled with single-base extension PCR for multiplex

SNP detection. In this study, two multiplex genotyping assays for 460 subjects of Arab

descent (220 drug dependent individuals and 240 controls without a history of drug

abuse) were evaluated. The multiplex assays included 68 SNPs (multiplex A comprised

36 SNPs and multiplex B comprised 32 SNPs). Mass spectrometry, polymerase chain

reaction (PCR), and primer extension were used to determine each allele. PCR and

biochemical reactions were performed according to manufacturer’s instructions. Data

were acquired on a compact MALDI-TOF mass spectrometer and analysis was carried

out using a Typer 4 analyser (Sequenom).

Three SNPs in multiplex A and one SNP in multiplex B failed to produce any quality

results which affected the overall genotyping success rate. However, they assay was

cost-effective and sensitive and was able to simultaneously detect SNPs in nine drug

dependence candidate genes using as little as 5ng of DNA. Overall, this study shows,

191

Sequenom is a reliable high-throughput platform for detecting multiple SNPs

simultaneously in a routine genetic laboratory using very little starting material.

This manuscript was prepared by Laith Al-Eitan with support from the listed co-

authors. DNA extraction, SNP selection, experimental design and statistical analysis

were conducted by Al-Eitan with assistance from the listed co-authors. Sample

selection and DNA extraction were made possible through a collaboration link with Dr

Jaradat at Princess Haya Biotechnology Centre (PHBC). DNA samples were sent for

genotyping to the Australian Genome Research Facility (AGRF; Melbourne Node,

Melbourne, Australia). Dr Hulse and Dr Tay assisted in the design of the study.

192

SNP Genotyping for Drug Dependence Candidate Genes Using a Sequenom

MassARRAY® iPLEX Platform

Laith N. AL-Eitan , Saied A. Jaradat , Gary K. Hulse , Guan K. Tay

Centre for Forensic Science, The University of Western Australia, Crawley, WA

6009, Australia

Princess Haya Biotechnology Centre, Jordan University of Science and

Technology, Irbid 22110, Jordan

School of Psychiatry and Clinical Neurosciences, Queen Elizabeth II Medical



Australia

Abbreviated title: SNP Genotyping using a Sequenom MassARRAY® Platform

Keywords: SNPs, Genotyping, Sequenom, Drug Dependence


Western Australia


Laith N. AL-Eitan




Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285


[email protected]

1 2 4,3 1

1

2

3


193

ABSTRACT

We describe here use of a single nucleotide polymorphism (SNP) genotyping method,

based on the commercially available Sequenom MassARRAY® platform (iPLEX

GOLD), to genotype 68 SNPs within nine drug dependence candidate genes (DRD1,

DRD2, DRD3, DRD4, DRD5, BDNF, SLC6A3, COMT and OPRM1). Two multiplexes

(36 SNPs in multiplex A and 32 SNPs in multiplex B) were designed to genotype 460

subjects of Arab descent (220 drug dependent individuals and 240 controls without

history of drug of abuse). The assay consisted of an initial locus-specific PCR reaction,

followed by single base extension using mass-modified dideoxynucleotide terminators

of an oligonucleotide primer annealing immediately upstream of the polymorphic site of

interest. Using MALDI-TOF mass spectrometry, the distinct mass of the extended

primer was used to identify the SNP allele. The Sequenom MassARRAY® platform

determined the genotypes for all 68 SNPs highly accurately with an average success rate

of 96.6%. The average (±SD) rate of genotype discrepancy across the 68 SNPs was

only 0.04% (±0.00085%) in the whole cohort. However, three SNPs in multiplex A

(rs165599, rs3778156, rs2734838) and one SNP (rs6347) in multiplex B failed to

produce any quality results which lowered the overall genotyping success rate. Overall,

the results of this study indicate that Sequenom is a reliable, cost-effective and sensitive

assay for simultaneously detecting multiple SNPs in candidate genes in a routine

clinical setting.

194

INTRODUCTION

Single nucleotide polymorphisms (SNPs) are stable genetic variants within a DNA

sequence, which may alter the biological function of proteins and influence the

likelihood of developing a particular disease.1,2,3

There are many different SNP

genotyping methods available today, consisting of various allele discrimination and

signal detection methods.4-7

Many of these methods have been developed into

commercial products involving 384-well formats and automation including the

Sequenom MassARRAY® platform (iPLEX GOLD).7-8

SNP multiplex analysis using

the Sequenom MassARRAY® platform has the capacity to generate large quantities of

genetic information.9 The resulting information can be used for genetic linkage

studies10

, fine mapping11

and SNP studies12

.

Sequenom (San Diego, California, USA) has developed the MassARRAY system to

overcome some of the limitations of traditional genotyping approaches such as

restriction fragment length polymorphism (RFLP) and SNP analysis.13

The Sequenom

MassARRAY® technology is designed to rapidly distinguish genotypes with a high

level of precision and sensitivity.13,14,15

Using Matrix Assisted Laser

Desorption/Ionization-Time-of-Flight (MALDI-TOF) mass spectrometry, the

MassARRAY system directly measures target DNA associated with SNPs and other

forms of genetic variation. By combining MassEXTEND primer extension chemistries

with high-density SpectroCHIP arrays, the MassARRAY system offers high-throughput

SNP analysis. Use of this instrument reportedly allows for unambiguous identification

of the variant when each one oligonucleotide has a distinct to the molecular mass. This

technology is being used to identify genes that involved in polygenic traits or complex

diseases and drug response for either genetic association or pharmacogenetics studies or

their application in personalized medicine and forensic toxicology.16

Sequenom

approaches genetic association and pharmacogenetics in two ways. Firstly, sequenom

is an automated mass spectrometry platform for the analysis of nucleic acids providing a

uniquely sensitive and quantitative measurement to detect both genetic risk markers and

disease onset and progression markers.17,18

Secondly, sequenom system is totally

flexible and allows many tests to be multiplexed and for a single sample.19

In genetic association and pharmacogenetic studies, hundreds of SNPs representing

many different candidate genes are being genotyped using Mass spectrometry based

195

assays. The studies are searching for disease associated genes, their corresponding

alleles and their link to treatment responsiveness.16,20

For example, various molecular

genetic studies have used mass spectrometry based assays to search for genes that are

associated with drug and nicotine addiction and treatment.16,20

the results of these

pharmacogenetics studies may be a useful tool not only when prescribing drugs to

patients, but also to elucidate the drug(s) involved in intoxication cases.16,21

An

increased knowledge of pharmacogenetics may contribute to safer use of drugs in the

future.22,23

In the forensic toxicology, the use of SNP genotyping remains uncommon,21

but has the

potential to be highly relevant.21,24,25

It is a challenge for the forensic toxicologist to

draw the right conclusion from the analytical results in post-mortem toxicology.26

An

individual’s response to drug and drug treatment varies due to genetic differences,

which can cause adverse drug reactions or even occasional death.21,24,25

Using high

throughput genotyping methods such as MALDI-TOF mass spectrometry-based

systems to obtain additional information about an individual’s metabolic capacity may

contribute to a better interpretation of forensic toxicological results. Various studies

have investigated SNPs involved in drug transporters and receptors such as dopamine

transporter 1 (SLC6A3) and dopamine receptor 4 (DRD4) as they can explain some of

the interindividual pharmacodynamics variability within individuals.22,23

Different

studies have also investigated the effect of polymorphisms in morphine related gene

sequences such as UDP-glucuronosyltransferase 2 B7 (UGT2B7) and mu opioid

receptor (OPRM1) genes.22,23

This may lead to an increased understanding of the

importance of polymorphisms in drug transporters, drug metabolizing enzymes and

receptors for investigations of adverse drug reactions and fatal intoxication.21,24,25

The objective of this study was to use the Sequenom MassARRAY® platform (iPLEX

GOLD) method for genotyping 68 SNPs within nine drug dependence candidate genes

(DRD1, DRD2, DRD3, DRD4, DRD5, OPRM1, SLC6A3, BDNF and COMT) and to

investigate the distribution of minor allele frequencies (MAFs) in drug and non-drug

dependent Jordanian Arab population.

196

MATERIAL AND METHODS

This research was approved by the Human Research Ethics Committee of The

University of Western Australia (Ref No. RA/4/1/4344).

Subjects

Altogether 460 subjects were analysed in this study. Of the samples tested, 220 were

from drug dependent male subjects of Arab descent. The control group were an

ethnically homogenous Jordanian Arab population. The two groups were matched on

the basis of age, sex (male) and ethnic (Arab) origin. The mean ages (±SD) of the

patients and controls were 32.70 (± 8.4) and 31.5 (± 5.6) years, respectively.

Blood Samples and DNA Extraction

Trained Jordanian health care professionals around Jordan have drawn the blood

samples into standard EDTA tubes. The tubes were posted to Princess Haya

Biotechnology Centre (PHBC), The King Abdullah Hospital University, Jordan

University of Science and Technology, Irbid, Jordan. After the blood samples were

received at the PHBC, genomic DNA was extracted at the DNA extraction unit of

PHBC using the Gentra Puregene® Blood Kit (Qiagen, Valencia, CA, USA) according

to the recommendations of the manufacturer. Briefly, 300μl of whole blood from each

sample was mixed with 200μl of lysis buffer (50mM Tris pH 8.0, 100mM EDTA,

100mM NaCl, 1% SDS) and 40μl of Proteinase K, followed by 100μl of isopropanol

and 500μl of inhibitor removal buffer (5M guanidine-HCl, 20mM Tris-HCl pH 6.6).

The DNA was washed with buffer (20mM NaCl; 2mM Tris- HCl; pH 7.5) centrifuged

twice at 2,000 rpm, washed again using cold 70% ethanol and centrifuged at 3,000 rpm.

The supernatant was discarded, leaving a pellet that contained purified genomic DNA.

The purified total DNA was diluted to 500 or 1000 μl with 1x TE buffer (Gentra system

Inc.). 10 μl of the total volume was used to measure the concentration of the sample.

DNA quantity (ng/µl) and purity (A260/280) were verified using a Nano-Drop ND-1000

UV-Vis Spectrophotometer (NanoDrop Technology, Wilmington, DE) and the

concentration of the sample was subsequently adjusted to approximately 100ng/µL.

Purified DNA was stored at -80°C until use.

DNA Dilutions

The DNA samples were transferred From the DNA extraction unit to the DNA dilution

unit. The concentration of the DNA was adjusted to 20ng/μl (50-500 μl) and this was

197

confirmed with the Nano-Drop ND-1000 UV-Vis Spectrophotometer (NanoDrop

Technology, Wilmington, DE). If the concentration did not match with the desired

concentration, it was adjusted by adding more of the stock DNA. A second dilution of

5ng/μl (50-500 μl) was prepared from the first dilution and the concentration. All

dilutions were made in 96-well plates using an automated robotic system. In order to

control for possible problems on the plates every 96-well dilution plate consisted of 2 to

4 plate specific blind duplicates and 2 water controls.

Candidate Gene and SNP Selections

In this study, 68 SNPs within nine genes (DRD1, DRD2, DRD3, DRD4, DRD5,

OPRM1, BDNF, SLC6A3 and COMT) were selected from public databases including:

the SNP database of the National Centre for Biotechnology Information (NCBI)

(http://www.ncbi.nlm.nih.gov/SNP/), the Applied Biosystems SNP database

(http://www.appliedbiosystems.com) and the International HapMap Project

(http://www.hapmap.org/). The list of genes, their SNPs, their reference numbers, and

chromosomal positions are shown in Table 1.

Assay Design

Assay design for the Sequenom MassARRAY™ system uses Assay DESIGN 2.0

software. A screen shot of the program is shown in Figure 1.

Figure 1. Assay DESIGN 2.0 software screen shot

http://www.ncbi.nlm.nih.gov/SNP/

http://www.appliedbiosystems.com/

http://www.hapmap.org/

198

The software uses the mass of each SNP product to investigate possible multiplex

combinations within the same termination mix group of ddNTPs. The termination point

and the number of incorporated nucleotides are sequence specific. This mass difference

makes it possible to identify allele specific products. The mass difference between all

possible products in an assay was set to 50 Daltons. The software designs forward and

reverse primers and extension primers for each SNP in the multiplex group (Sequenom

Inc., 2004). All the primer sequences proposed by the software were checked against

the human genome sequence using BLAST (Basic Local Alignment Search Tool) in

order to verify their uniqueness. The software was used to design two multiplexes from

the selected SNPs. The first multiplex set (A) comprised 36 of the SNPs and the second

multiplex set (B) comprised 32 of the SNPs. The primers (forward, reverse and

extension) are shown in Table 2.

DNA Genotyping

Samples that met the quantitative requirements for this study were sent to the Australian

Genome Research Facility (AGRF; Melbourne Node, Melbourne, Australia) to assess if

the DNA integrity also met the multiplexing requirements of SNPs for genotyping.

Genotyping using the Sequenom MassARRAY® system (iPLEX GOLD) (Sequenom,

San Diego, CA, USA) was performed at the AGRF according to the manufacturer’s

recommendations (Sequenom, San Diego, CA, USA). Briefly, PCR and single base

extension primers (SBE) were designed using MassARRAY assay design 3.1 software

(Sequenom MassARRAY system) that allows iPLEX reactions for SBE designs with

the modified masses associated with the termination mix. Manufacturer’s instructions

for the multiplex reaction were followed for the whole process, including PCR

amplification (Sequenom, San Diego, CA, USA), shrimp alkaline phosphatase (SAP)

enzyme (Sequenom, San Diego, CA, USA) treatment to dephosphorylate dNTPs

unincorporated in the PCR, SBE reactions using an iPLEX GOLD assay (Sequenom,

San Diego, CA, USA), and the clean-up with a resin kit (Sequenom, San Diego, CA,

USA) to desalt the iPLEX reaction products. PCR and all protocol conditions are

available upon request. Reaction products were dispensed onto a 384-element

SpectroCHIP bioarray (Sequenom) using a MassARRAY Nanodispenser and assayed

on the MassARRAY platform. Mass differences were detected with matrix-assisted

laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).

MassARRAY Workstation v.3.3 software was used to process and analyse the iPLEX

SpectroCHIP bioarray. Typer Analyzer v.4.0.2 software was used to analyse all

199

genotypes obtained from the assays. See figure 1 for a representative scatter plot. This

plot is of the rs4350392 SNP within the DRD2 gene coloured according to genotype

calls: AA (green), CA (yellow) and CC (blue) and no call (red) (Figure 1).

Quality Control (QC)

Several quality control measures were used. These included HWE, PCR products using

agarose gels, checks for skewed allele callings and manual checking of the alleles and.

The genotype results of each marker were accepted only if the success rate was at least

90%, all duplicates were identical, water controls were clean, the SNP was in HWE and

no Mendelian errors were observed.


The call rate and discrepancy rate were calculated using Microsoft Excel 2000

(Microsoft Corp., www.microsoft.com). The HWE provides a measure of whether an

evolutionary event has influenced an allele frequency. Theoretically calculated,

expected and seen genotype frequencies are compared to each other and a Pearson χ2

test is used to test whether they are significantly different. In this study, the estimated

genotype frequencies were calculated as follows: p2 + 2pq + q2 = 1,27

in which p

represents the frequency of one allele, and q represents the frequency of the other allele.

The software package PLINK28

was used to calculate the minor allele frequency (MAF)

and HWE p-values for distribution of genotypes at each locus for the cases and controls.

200

RESULTS

The Sequenom MassARRAY® system (iPLEX GOLD) (Sequenom, San Diego, CA,

USA) was used to genotype 220 drug dependent individuals and 240 healthy control

from an ethnically homogenous population.

Two multiplexes were run: one with 36 SNPs and the other with 32. In total, 460 DNA

samples were assayed for 68 SNPs (31, 280 genotypes) with a pass rate of higher than

96%. Only 3 SNPs in multiplex A (rs165599, rs3778156, rs2734838) and one SNP in

multiplex B (rs6347) failed to produce results. Of the 460 samples successful assayed,

40 samples had 3 failed SNPs/sample and the other 420 samples had a 100% pass rate.

Overall, 64 SNPs (94%) passed the quality control for throughput genotyping. The

average (±SD) rate of genotype discrepancy across the 64 SNPs was only 0.04%

(±0.00085%) for the whole cohort. An example of the Sequenom iPLEX Gold assay

results is shown in Figure 2. In this rs4350392 scatter plot, green denotes the AA

genotype, yellow denotes the CA genotype, blue denotes the CC genotype and red

denotes a no call.

All polymorphisms met the criteria for HWE in both case and control groups except for

two SNPs within the DRD2 gene (rs1800496, rs7125415) and three SNPs within the

BDNF1 gene (rs1401635, rs11030102, rs17309930). The minor alleles of the

successful genotyped SNPs and their frequencies for both cases and controls are shown

in Table 3.

201

Table 1. List of genes, their SNPs and positions, and genotyping data based on the whole cohort (460 subjects).

Gene Gene Location SNP _ID Positiona SNP SNP Location Discrepancy rate

b Call rate

c

DRD1 5q35.1 rs5326 174,802,802 G>A 5'-UTR 0.25% 99%

OPRM1 6q24-q25

rs1074287 154,390,502 A>G Upstream (5'-UTR) 0.00% 100%

rs6912029 154,402,201 G>T Upstream (5'-UTR) 0.00% 100%


rs1799971 154,402,490 A>G Exon 1 0.00% 100%

rs510769 154,403,712 A>G Intron 1 0.00% 100%

rs511435 154,410,240 A>G Intron 1 0.00% 100%

rs524731 154,416,785 C>A Intron 1 0.05% 99%

rs3823010 154,420,845 A>G Intron 1 0.00% 100%

rs1381376 154,434,951 A>G Intron 1 0.00% 100%

rs3778151 154,435,373 C>T Intron 1 0.00% 100%

rs3778156 154,446,006 A>G Exon 2 Fail 0%

rs563649 154,449,660 A>G Exon 2 0.00% 100%

rs2075572 154,453,697 C>G Intron 2 0.00% 100%

rs540825 154,456,139 T>A Intron 3 0.07% 99%

rs675026 154,456,256 G>A Intron 3 0.12% 99%

rs562859 154,456,266 A>G Intron 3 0.25% 99%

rs548646 154,459,840 A>C/G>T Intron 3 0.52% 97%

rs648007 154,464,304 C>T Intron 3 0.00% 100%

rs9322447 154,466,013 G>A Intron 3 0.00% 100%

rs609148 154,472,707 C>T Intron 3 0.07% 99%

rs606148 154,477,679 G>T Intron 3 0.00% 100%

rs632395 154,478,944 C>T Intron 3 0.00% 100%

rs648893 154,480,321 C>T Intron 3 0.07% 99%

rs671531 154,482,434 A>G Downstream (3'-UTR) 0.00% 100%

202

Table 1. (Continued)


b Call rate

c

DRD2 11q23

rs1800496 112,788,698 C>T Exon 7 0.00% 100%

rs6277 112,788,669 T>C 3'-UTR 0.15% 99%

rs2511521 112,790,509 T>C Intron 4 0.00% 100%

rs12574471 112,821,446 C>T Intron 1 0.00% 100%

rs2283265 112,790,746 G>T Intron 4 0.00% 100%

rs6279 112,786,283 C>G 3'-UTR 0.00% 100%

rs4581480 112,829,684 T>C 5'-UTR 0.00% 100%

rs4350392 112,840,927 C>A 5'-UTR 0.00% 100%

rs10891556 112,857,971 G>T 5'-UTR 0.00% 100%

rs7103679 112,808,884 C>T Intron 1 0.00% 100%

rs4938019 112,846,601 T>C Intron 1 0.00% 100%

rs1076560 112,788,898 G>T Intron 5 0.00% 100%

rs2075654 112,794,276 G>A Intron 2 0.00% 100%

rs7125415 112,815,891 C>T 5'-UTR 0.00% 100%

rs4648317 112,836,742 C>T Intron 1 0.00% 100%

rs1125394 112,802,395 A>G Intron 1 0.00% 100%

rs4648318 112,818,599 A>G Intron 1 0.00% 100%

rs12363125 112,791,126 A>G Intron 5 0.00% 100%

rs2734838 112,791,711 C>T Intron 5 Fail 0%

rs2734836 112,796,449 G>A Intron 2 0.05% 99%

rs12364283 112,852,165 T>C 5'-UTR 0.00% 100%

rs1799978 112,851,561 A>G 5'-UTR 0.00% 100%

rs6275 112,788,687 C>T Exon 7 0.15% 99%

rs1800497 112,776,038 C>T Exon 8 0.00% 100%

rs1079597 112,801,496 A>G Intron 1 0.00% 100%

203



b Call rate

c

DRD2 11q23 rs1799732 112,851,462 -C 5'-UTR 0.00% 100%

rs1800498 112,796,798 C>T Intron 2 0.00% 100%

DRD3 3q13.3 rs6280 115,373,505 C>T Exon 1 0.07% 99%

DRD4 11p15.5 rs3758653 626,399 C>T 5'-UTR 0.05% 99%

DRD5 4p16.1 rs10033951 938,867,8 C>T 5'-UTR 0.05% 99%

SLC6A3 5p15.3

rs2963238 149,742,7 A>C Intron 1 0.12% 99%

rs6347 141,141,2 A>G Exon 9 Fail 0%

rs6876225 145,903,6 C>A Intron 11 0.00% 100%

rs11564773 144,981,3 A>G Intron 14 0.00% 100%

rs1042098 144,781,5 T>C 3'-UTR 0.15% 99%

BDNF1 11p13

rs7103873 276,568,93 C>G Intron 1 0.07% 99%

rs1401635 276,505,67 C>G Intron 1 0.00% 100%

rs11030102 276,381,72 C>G Intron 1 0.00% 100%

rs17309930 277,050,69 A>C,G>T Intron 1 0.07% 99%

rs6265 276,364,92 G>A 3'-UTR 0.00% 100%

COMT 22q11.21

rs737865 183,101,09 T>C 5'-UTR 0.00% 100%

rs4680 183,312,71 A>G Exon 2 0.00% 100%

rs165599 183,367,81 A>G 3'-UTR Fail 0% a. Chromosome positions are based on NCBI Human Genome Assembly Build 36.3. b. Ratio of the number of discordant genotypes to the number of duplicates. c. Ratio of the number of valid genotypes to the number of subjects genotyped (N = 460) at each locus.

204

Table 2. Primer sequences and multiplex compositions after assay design.

Multiplex Gene SNP_IDa 1st-PCR Primer 2nd-PCR Primer Extend Primer

Multiplex A

DRD2 rs1799732 ACGTTGGATGCAAAACAAGGGATGGCGGAA ACGTTGGATGAAAGGAGCTGTACCTCCTCG CCCTCCTACCCGTTC

DRD2 rs12574471 ACGTTGGATGACCCAGGTATCTGTGTGAAG ACGTTGGATGTGCATCCTTGCCTGCAGGTG GCCTTCCTGAGGCAT

COMT rs4680 ACGTTGGATGTTTTCCAGGTCTGACAACGG ACGTTGGATGACCCAGCGGATGGTGGATTT CACACCTTGTCCTTCA

DRD2 rs1800498 ACGTTGGATGGGGTGTGAAGAAAAGAGCCT ACGTTGGATGTGGTGATTTAGTAGCAGAGG AAAAGAGCCTTGGGTT

DRD2 rs1799978 ACGTTGGATGACTGCGCTCCCACCCACAC ACGTTGGATGAGAAGACTGGCGAGCAGAC ACCCACACCCAGAGTAA

COMT rs165599 ACGTTGGATGGGTCAGAAAGGTGTGAATGC ACGTTGGATGGGGCACCTGTTAGCCCCAT GGCTTCGTTTCCCAGGC

DRD2 rs1076560 ACGTTGGATGTGGTGTTTGCAGGAGTCTTC ACGTTGGATGACAAGTTCCCAGGCATCAGC GAGGAGTCTTCAGAGGG

OPRM rs1799971 ACGTTGGATGGGTGCGGTTCGGACCGCAT ACGTTGGATGGGTTCCTGGGTCAACTTGTC CTCATGGGTCGGACAGGT

DRD2 rs2511521 ACGTTGGATGTCCATGGAACTCAGACTGGG ACGTTGGATGTTCCCCCTTGCCCCAACTG TTAGGGCTCAGAGATCAG

DRD2 rs4648318 ACGTTGGATGCTGCTGATATTCCATGGGAC ACGTTGGATGAGTCTAAAGCAAATGGAACC AAGCAAACACAAATCTCTC

DRD2 rs1125394 ACGTTGGATGCTTCCTCTTTTACAGAATAG ACGTTGGATGAGTACCTGGAAGTCATGTGC ACTTATCAGCATTCCAAGG

BDNF rs17309930 ACGTTGGATGACCAATCCTCTTTACATGAC ACGTTGGATGCCATTTGGAATATATTGGGC CTCTCCCTGACTTTTCCATT

DRD2 rs4648317 ACGTTGGATGTGGCATTGGGCCTTCACTAC ACGTTGGATGGAAGACGAGATGAAAGCCAG GGACCAGGATGCTGGAGCCT

OPRM1 rs675026 ACGTTGGATGCCTTCGTGGAAATACTGCTC ACGTTGGATGCGGGCAACTGAAGAATAATC GGGTACAGCCCGTCTGGTGG

DRD2 rs2075654 ACGTTGGATGCTTATCTTCTCCCAGATAC ACGTTGGATGCTCCGTATAAGTTGGCTGTC CTTCCCAGATACATAAGACCA

OPRM1 rs3778156 ACGTTGGATGAGAGAACATGGCCGAATGTC ACGTTGGATGGTATCTCTCGTTCCATTTCC TGCCGAATGTCATTGCACTAC

DRD2 rs4350392 ACGTTGGATGAGTTTGCAGTTGTTCAGGAC ACGTTGGATGGTCTTTCAAGCCTTCCTACC GTGGAAGCCTGATCTAGGGAG

OPRM1 rs511435 ACGTTGGATGCCTCCTTAAGGGTTAGGAAC ACGTTGGATGGAATTTTGCCCACGCTCCAC AACGCATAACCTAAAAGCGTGA

DRD2 rs7125415 ACGTTGGATGGAGCCTATTGCTTTCAGAAC ACGTTGGATGAGAGCCCTGTAGGATAAAGC TAACCAGGATTGCAATTAAGGT

BDNF1 rs1401635 ACGTTGGATGAGATGTCTCTGGTCCTTGTG ACGTTGGATGTGAGTGAGTGAGTTGGTTCC CCCCGGTCCTTGTGAGTTCCTTT

OPRM1 rs3823010 ACGTTGGATGACCAAAGTCTTTTGTGGAA ACGTTGGATGAAAGGGCCACTATTCTGAAC AAGTCTTTTGTGGAAATGTGATG

DRD2 rs7103679 ACGTTGGATGAGGGCTTTGGGAATGAGGTC ACGTTGGATGTTTATGTTCTCTGGCCGAGG CCTTCGGTCTCCATTTCCTCTGCC

DRD2 rs2734838 ACGTTGGATGGCTCACAACCGTGTTTTTAA ACGTTGGATGATGTTTAAGGCTGCCTGGTC TGTTTTTAATACTCTGGTATCTGC

OPRM1 rs12205732 ACGTTGGATGAAGACTGTCATCCTGTAGGG ACGTTGGATGGCTTTGTAACTTTGTAGCTG CCCCCCTGTAGGGTAAAGTAACAT

DRD2 rs12364283 ACGTTGGATGCTTTCTTCTGCAGCAATTAG ACGTTGGATGCTGTGTGTGACTTCTGAATC CTCCCAACTGTCCTCAGTTTGCCAG

BDNF1 rs11030102 ACGTTGGATGGGCCATTGCATGTATGTGTC ACGTTGGATGCCAGCCCTGACCCTAATTTT ATTGCATGTATGTGTCTTTTTGTAA

COMT rs737865 ACGTTGGATGCAATAAAAAGCAACAGGAC ACGTTGGATGCCTGCTAACAGACCTGCTTT GGTGAAAAGCAACAGGACACAAAAA

OPRM1 rs563649 ACGTTGGATGGTCTTTAGATCATGCAGGTC ACGTTGGATGGAAGAAGTTTCTCCCCAAAAG CCTCGATCATGCAGGTCTATAACCAA

OPRM1 rs3778151 ACGTTGGATGCTTTCATTTTCAAGATAGC ACGTTGGATGCTATTGTGTTCTGGAGCTTG GAACGATAGCTAATTGAGAACAAGCA

OPRM1 rs671531 ACGTTGGATGTCTCACTGGTTTCTCCATAC ACGTTGGATGGGCATAAGGATTCTGAGCTG CCCACTTCTCCATACTGCAGGCTCCCC

205


Multiplex Gene SNP_ID 1st-PCR Primer 2nd-PCR Primer Extend Primer

Multiplex A

OPRM rs510769 ACGTTGGATGCAGATATATGGCATTTCAC ACGTTGGATGTTGTGTTGGTGTTGATGTG CTATCTGGCATTTCACATTCACATGTA

SLC6A3 rs11564773 ACGTTGGATGTCATGACATTCTCCTGGCTG ACGTTGGATGCGACAGGACCAGAGGGAAC AACAGTGCCCAGCCCTGTGTGGGCATC

DRD2 rs6275 ACGTTGGATGACCACCAGCTGACTCTCCC ACGTTGGATGATTCTTCTCTGGTTTGGCGG GGGTTACTCTCCCCGACCCGTCCCACCA

DRD2 rs4938019 ACGTTGGATGGCCCAAACTACACTAAGCTG ACGTTGGATGAGTAGACTCTTAGGAGGCCC GAGGCACTAAGCTGATACCTATATTCAA

OPRM rs9322447 ACGTTGGATGAGGAGTTGTGGACAAACTAC ACGTTGGATGTTCCAGGAGTCCTTCCATTC GGGCTGAAACACAAATCATAATCTCTGA

DRD2 rs6279 ACGTTGGATGTCAGGGCCCAGAGGCTGAG ACGTTGGATGGTGTGAACTGTCCATCTCTC GGGTAAGGGCCCAGAGGCTGAGTTTTCT

Multiplex B

DRD2 rs6277 ACGTTGGATGACCACCAGCTGACTCTCCC ACGTTGGATGATTCTTCTCTGGTTTGGCGG TCTCCACAGCACTCC

OPRM1 rs648893 ACGTTGGATGACTGTGTGTGTACTGCAGTC ACGTTGGATGCATAATAGTGCCAGTTCCCC AGTCTGGTCCCATCG

DRD4 rs3758653 ACGTTGGATGAAAGTGCTTGCAAAGCGCAG ACGTTGGATGGAAAATACCTCTCAGGTCAC AAAGCGCAGCAGAGA

SLC6A3 rs6347 ACGTTGGATGTCATCTACCCGGAAGCCATC ACGTTGGATGATACCCAGGGTGAGCAGCAT CGCTCCCTCTGTCCTC

DRD1 rs5326 ACGTTGGATGGTCAGTCAGATTTCCAGGAG ACGTTGGATGCTCTGCCAGGGAAGCAATCT GAGGAGTCCTCCCCAC

OPRM1 rs609148 ACGTTGGATGGAGATGTTATAAGGTGTCCC ACGTTGGATGTTTCTAAGCCAAAGTTCAG GCCTGAGCTCAGATGAA

DRD3 rs6280 ACGTTGGATGCTGGCACCTGTGGAGTTCT ACGTTGGATGTCTGGGCTATGGCATCTCTG CGTGTAGTTCAGGTGGC

BDNF1 rs6265 ACGTTGGATGTTTTCTTCATTGGGCCGAAC ACGTTGGATGCATCATTGGCTGACACTTTC CCAACAGCTCTTCTATCA

DRD2 rs4581480 ACGTTGGATGAAAATCATGTGACCCAGCCC ACGTTGGATGATGACGGAGATGCCGGCACA CCCCAGCCCTGTCTTTAG

BDNF1 rs7103873 ACGTTGGATGTCCCTAGAGGACCTTTTACC ACGTTGGATGAACCCCTTTTAAAGAATGG TTTTACCCCCAAATGTAGA

OPRM1 rs540825 ACGTTGGATGAGTGGTCCAGGGTACACAAC ACGTTGGATGAGTGCCTACCTATACCTTCC GGGACACAACCAAGCAGCC

OPRM1 rs6912029 ACGTTGGATGTACGCAGAGGAGAATGTCAG ACGTTGGATGAGAAACCAGTCCTGGCTGAG GGAGAATGTCAGATGCTCA

DRD2 rs1800496 ACGTTGGATGCCACCACCAGCTGACTCTC ACGTTGGATGATTCTTCTCTGGTTTGGCGG CACCCGCTGACTCTCCCCGAC

OPRM1 rs548646 ACGTTGGATGCTTCATCTCCAAAGTAGCAC ACGTTGGATGCATTCATGTCAATCAGCTTG TTTACTGTTTTGTCTAACCTG

DRD2 rs2283265 ACGTTGGATGTCAGATCCTGTCACTGACAC ACGTTGGATGATGAGGAAACAGGCTCATAG ATGCGTGACCTTAGGCAAGTT

DRD2 rs1800497 ACGTTGGATGTCAAGGGCAACACAGCCATC ACGTTGGATGTGTGCAGCTCACTCCATCCT CGCCCTCCTCAAAGTGCTGGTC

OPRM1 rs606148 ACGTTGGATGTAATTAGAATTCCAGAAGG ACGTTGGATGGTCCCATTGTATTTAAGTCC TTAGAATTCCAGAAGGAGAAAA

SLC6A3 rs2963238 ACGTTGGATGACTGAAACACGCTGCTGCTG ACGTTGGATGAGAGCAGGAGCGTCTACAC AACTCTGCTGCTGGATCCAAATG

OPRM1 rs2075572 ACGTTGGATGGCCTTAAGTTAGCTCTGGTC ACGTTGGATGCATAAGGTGTTAATACTGCC CAGATCTGGTCAAGGCTAAAAAT

OPRM1 rs1381376 ACGTTGGATGTCTTTGGCAATAGGAGAGGG ACGTTGGATGAAACCATACCTTCCCAGCTC AGAGGGATAAAAATGAGAATCAA

OPRM1 rs632395 ACGTTGGATGGATCTCCCGGCAAGAAAAGT ACGTTGGATGGAGCACGACCAGTGCATTTT GGCACGGCAAGAAAAGTAGTGGA

DRD2 rs10891556 ACGTTGGATGTTGATTCACTCACTGGGAGG ACGTTGGATGTGCTAACACCACATCCATCC TCCATCACTGGGAGGGGCAAGTAC

OPRM1 rs524731 ACGTTGGATGAATAATTGAGTCTCCTTCC ACGTTGGATGCATGTCTAAGCATATTGTGG AATCATTGAGTCTCCTTCCAATTAA

OPRM1 rs562859 ACGTTGGATGCGGGCAACTGAAGAATAATC ACGTTGGATGCCTTCGTGGAAATACTGCTC CTGCAGAATAATCATGCTTAACTCA

206


Multiplex Gene SNP_ID 1st-PCR Primer 2nd-PCR Primer Extend Primer

Multiplex B

DRD2 rs12363125 ACGTTGGATGGCAGGGTCCATGACACTAAA ACGTTGGATGAAGAGTTGCCGCCTTCAGTC GGGGCCATGACACTAAATAACAAGG

DRD2 rs1110976 ACGTTGGATGCATGTTGGTACAGCTACCAC ACGTTGGATGAGTTTGTGTACATAGAGCCC ACCGGGCAGAGGCTGGGGTCTGCTG

DRD2 rs2734836 ACGTTGGATGTGTGGGCATTGCACTTTATC ACGTTGGATGCACCTATGAGTGGGATAAGC TGGGAATTGCACTTTATCTCATGTAT

OPRM1 rs1074287 ACGTTGGATGAACCTTAGAACTCTATCTGG ACGTTGGATGGGAAGGACTGCATTTTGTGG CTGTTATTCTATTGTACTGTGGCTGA

SLC6A3 rs6876225 ACGTTGGATGTGGACAGCCCGACTCACCT ACGTTGGATGTCACAGTCCGTTGTGAGTCC GCATCTTCCCCTCCCAACACAGAGGCG

OPRM1 rs648007 ACGTTGGATGCTTCTGAAGCAATCAGAAAG ACGTTGGATGAATGCTTCAGCTAAGGCTTG AGCAATCAGAAAGAAATTCAGTTATTA

DRD5 rs10033951 ACGTTGGATGCTGAACATGAGACTGCTTGG ACGTTGGATGGGTTCAAACATTGCTAAGTGC AACTAACATGAGACTGCTTGGAGACTG

SLC6A3 rs1042098 ACGTTGGATGACTGGCTCAAGGTGTAGAGG ACGTTGGATGTGGTTTGTTCGTGTCTCTCC CCGATACGAAGACCCCAGGAAGTCATCC

SNPs in the bold failed to produce results.

207

Figure 2. Representative scatter plot from sequenom data. The scatter plot illustrates rs4350392 (C>A) SNP within DRD2 gene. The X and Y

axes denote the mass measurement for the two alleles (C, A, low mass allele versus high mass allele) at the rs4350392 SNP. Each point

represents the measurements for a single individual. The points in the scatter plot are colored according to the genotype calls.

208

Table 3. List of SNPs, their minor allele frequencies, and HWE p-values for genotypic distribution at each locus based on the cases (220) and

controls (240).

Gene SNP _ID

Cases (n =220) Controls (n = 240)

MAa MAF

b HWE

c p-value MA

a MAF

b HWE

c p-value

OPRM1

rs1074287 G 0.212 0.724 G 0.221 0.792

rs6912029 T 0.009 0.892 T 0.025 0.691

rs12205732 A 0.009 0.892 A 0.027 0.666

rs1799971 G 0.148 0.239 G 0.103 0.731

rs510769 A 0.191 0.657 A 0.196 0.622

rs511435 A 0.182 0.742 A 0.179 0.570

rs524731 A 0.179 0.992 A 0.179 0.856

rs3823010 A 0.105 0.062 A 0.113 0.981

rs1381376 A 0.110 0.101 A 0.115 0.923

rs3778151 C 0.290 0.001 C 0.127 0.513

rs563649 A 0.082 0.186 A 0.104 0.098

rs2075572 G 0.559 0.497 G 0.481 0.148

rs540825 T 0.249 0.571 T 0.272 0.388

rs675026 A 0.608 0.329 A 0.349 0.988

rs562859 G 0.341 0.404 G 0.359 0.936

rs548646 T 0.342 0.240 T 0.351 0.338

rs648007 T 0.344 0.337 T 0.350 0.461

rs9322447 A 0.489 0.899 A 0.452 0.989

rs609148 T 0.258 0.118 T 0.272 0.610

rs606148 T 0.076 0.467 T 0.067 0.268

rs632395 T 0.076 0.225 T 0.071 0.838

rs648893 C 0.252 0.139 C 0.270 0.701

rs671531 A 0.352 0.040 A 0.352 0.525

209


Gene SNP _ID


MAa MAF

b HWE

c p-value MA

a MAF

b HWE

c p-value

DRD1 rs5326 A 0.172 0.461 A 0.144 0.290

DRD2

rs1800496 T 0.027 <1.0 E-06 T 0.015 0.819

rs6277 T 0.427 0.928 T 0.460 0.255

rs2511521 C 0.370 0.241 C 0.383 0.174

rs12574471 T 0.195 0.005 T 0.161 0.565

rs2283265 T 0.140 0.055 T 0.088 0.498

rs6279 C 0.406 0.102 C 0.437 0.097

rs4581480 G 0.096 0.992 G 0.075 0.125

rs4350392 T 0.173 0.790 T 0.217 0.299

rs10891556 T 0.187 0.457 T 0.235 0.333

rs7103679 T 0.120 0.042 T 0.081 0.613

rs4938019 G 0.170 0.507 G 0.217 0.299

rs1076560 T 0.157 0.219 T 0.108 0.225

rs2075654 T 0.120 0.042 T 0.075 0.745

rs7125415 A 0.100 <1.0 E-06 A 0.117 0.867

rs4648317 A 0.170 0.858 A 0.217 0.299

rs1125394 G 0.147 0.142 G 0.092 0.431

rs4648318 G 0.362 0.060 G 0.362 0.061

rs12363125 T 0.486 0.830 T 0.473 0.102

rs2734836 A 0.146 0.046 A 0.091 0.454

rs12364283 C 0.098 0.938 C 0.129 0.004

rs1799978 C 0.142 0.045 C 0.110 0.480

rs6275 A 0.404 0.123 A 0.429 0.183

rs1800497 T 0.202 0.404 T 0.156 0.363

210


Gene SNP _ID


MAa MAF

b HWE

c p-value MA

a MAF

b HWE

c p-value

DRD2

rs1079597 T 0.222 0.405 T 0.108 0.225

rs1799732 -C 0.123 0.844 -C 0.048 0.001

rs1800498 C 0.509 0.586 C 0.467 0.195

DRD3 rs6280 C 0.356 0.669 C 0.366 0.017

DRD4 rs3758653 C 0.264 0.032 C 0.281 0.807

DRD5 rs10033951 T 0.306 0.073 T 0.325 0.691

SLC6A3

rs2963238 A 0.436 0.357 A 0.462 0.967

rs6876225 A 0.027 0.677 A 0.036 0.188

rs11564773 G 0.043 0.338 G 0.044 0.404

rs1042098 C 0.363 0.587 C 0.342 0.261

BDNF1

rs7103873 C 0.463 0.252 C 0.081 0.613

rs1401635 C 0.232 <1.0 E-06 C 0.213 0.654

rs11030102 G 0.164 <1.0 E-06 G 0.138 0.184

rs17309930 A 0.136 <1.0 E-06 A 0.127 0.486

rs6265 A 0.184 0.488 A 0.161 0.702

COMT rs737865 C 0.364 0.578 C 0.787 0.312

rs4680 A 0.475 0.662 A 0.498 0.366 a. MA: Minor allele. b. MAF: Minor allele frequency. c. HWE: Hardy-Weinberg equilibrium.

211

DISCUSSION

Several high throughput single nucleotide polymorphism (SNP) genotyping

technologies are currently available.16

Each offers a unique combination of scale,

accuracy, throughput and cost.16

However, there is no single technology or platform

that can satisfy all users and study designs.14,15

The Sequenom MassARRAY platform

has several advantages for users such as accuracy of SNP genotyping assay with modest

multiplexing and minimal assay setup costs due to the use of unmodified

oligonucleotide primers.15

It utilizes a homogeneous reaction format with a single

extension primer to generate allele-specific products with a high throughput of more

than 100,000 genotypes per day per system.14,15

In this study, a convenient and reliable method of Sequenom MassARRAY iPLEX Gold

platform was used to genotype 68 SNPs within nine drug dependence candidate genes

in two multiplex reactions. In this array we focused on genes of particular interest for

drug, alcohol and neuropsychiatric researchers because they were reported to be

involved in drug dependence and other neurological and psychiatric disorders.22,23

The

NCBI, dbSNP, HapMap databases were used to select SNPs within these genes, which

may be used for future genetic association studies. The chosen SNPs were selected

because in the previous studies they showed the greatest potential to distinguish

between substance dependent individuals and control subjects.22,23

Therefore, the

distribution of SNPs through the selected genes was optimal.

The Sequenom MassARRAY system can measure up to 36 SNPs per sample with up to

384 samples per assay.10,29

In this study, we were able to design one multiplex with 36

SNPs and another one with 32 SNPs. In total, 460 DNA samples were assayed for these

68 SNPs with a pass rate of higher than 96.6%. Three SNPs in multiplex A (rs165599,

rs3778156, rs2734838) and one SNP (rs6347) in Multiplex B failed to produce results

indicating some problems affecting the overall genotyping success rate. Other

genotyping errors may have also influenced the results, either by yielding false positive

or false negative results. This could be due to primer design or PCR condition problems

or this assay may not always meet the minimal criteria of mass spectral quality as

determined in real-time by the MassARRAY software.18

212

When assessing minor allele frequency (MAF), some of the genotyped SNPs had MAFs

lower than 5% in both drug dependent individuals and controls. These included

rs6912029 and rs12205732 in the OPRM1 gene, rs1800496 in the DRD2 gene and

rs6876225 and rs1156773 in the SLC6A4 gene. As the Arab population has been

historically quite isolated and is now considered to be ethnically genetically

homogenous population, the chance of detecting rare disease allele in this population

may be increased. Isolated populations such as the Arab study sample offer an

advantage for genetic studies,30

as the number of different variations in the genes behind

phenotypes is expected to be smaller than in more heterogeneous populations.30

Therefore even a small study sample from an isolated population, like in this study, can

give very realistic results. However, there is a risk that these rare variants are hard to

replicate in other populations.30

Overall, the results of this study indicated that a Sequenom MassARRAY based

targeted drug dependence candidate SNP panel covering majority of currently tested

SNPs can be implemented in a molecular diagnostic laboratory as a reliable and

efficient high throughput method for simultaneous detection of multiple SNPs.

213

ACKNOWLEDGMENTS


Western Australia. Funding for this project was provided in part by Centre for Forensic

Science and Unit for Research and Education in Alcohol and Drugs of the School of

Psychiatry and Clinical Neurosciences, The University of Western Australia. The

supporting sources had no influence on the analysis, writing, or submission of the

manuscript. We gratefully thank and appreciate all the technical help and support from

the AGRF, in particular, Shane Herbert and David Hawkes, along with their staff from

the Perth and Melbourne Nodes, respectively. Laith Al-Eitan is a PhD Scholar at the

University of Western Australia supported by the Jordanian University of Science and

Technology, Jordan.

214

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217

CHAPTER 6

CUSTOM GENOTYPING FOR SUBSTANCE

ADDICTION SUSCEPTIBILITY GENES IN

JORDANIAN OF ARAB DESCENT

This chapter was published for publication in the BMC Research Notes Journal.

218

Chapter 6

Custom Genotyping for Substance Addiction

Susceptibility Genes in Jordanian of Arab Descent

The aim of the study was to identify gene(s) and mechanisms associated with substance

addiction in patients of Arab descent within the Jordanian population. Data on genetic

variations in the Middle Eastern population is limited and recent advances in DNA

technology have provided the opportunity to study this ethnic group. In present study

high throughput DNA arrays were used to study single nucleotide polymorphisms

(SNPs) and their influence on substance addiction among Arabs.

In the present study we examined eight candidate genes, the dopamine receptors DRD1,

DRD2, DRD2, DRD3 and DRD5, and solute carrier family 6, member 3 (SLC6A3) or

dopamine active transporter (DAT), brain-derived neurotrophic factor (BDNF) and

catechol-O-Methyltransferase (COMT) for their association with substance addiction.

We examined 220 Jordanian Arab individuals with substance addiction and 240

matched controls.

Six SNPs within the DRD2 gene were found to be associated with the substance

addiction in the Arab population. The highest statistical association SNPs were found

at rs1799732 in the C/−C promoter and rs1125394 in A/G intron 1 regions of the

DRD2 gene which codes for dopamine receptor D2. Allele frequency comparisons

between cases and controls also revealed a significant difference at two regions:

rs1799732 (C/-C, located in 5'-UTR) and rs2283265 (G/T, located intron 4). Previous

studies have shown an association with the DRD2 gene in different populations such as

Chinese and German (Xu et al., 2004). It has been suggested that DRD2, plays an

important role in dopamine secretion and the signal pathways of dopaminergic reward

and drug addiction mechanisms. This is the first study that proposes to investigate

genetic associations of SNPs within different genes with substance addiction. Fine

genetic association analysis in a larger cohort of Arab population will be essential to

validate the results obtained from this study.

219


listed. The DNA extraction, SNP selection; experimental design and statistical analysis

were conducted by AL-Eitan with assistance from the co-author listed. Patient sample

selection and DNA extraction were done through collaboration with Dr Jaradat at

Princess Haya Biotechnology Centre (PHBC). DNA samples were sent to the

Australian Genome Research Facility (AGRF; Melbourne Node, Melbourne, Australia)

for genotyping. Dr Hulse and Dr Tay assisted in all stages of the study. A part of the

work described in this chapter, a digital poster was prepared and presented by AL-

Eitan at the Human Genome Meeting (HGM) in Genetic and Genomics in Personalised

Medicine, Sydney, Australia.

220

Custom Genotyping for Substance Addiction Susceptibility Genes in Jordanian of

Arab Descent

Laith N. AL-Eitan , Saied A. Jaradat , Gary K. Hulse , Guan K. Tay


6009, Australia




Centre, The University of Western Australia, Crawley, WA 6009, Australia

Unit for Research and Education in Alcohol and Drugs, Queen Elizabeth II


Australia

Abbreviated title: Substance Addiction Susceptibility Genes in Arabs

Keywords: SNP, DRD2, Opiates, Cocaine, Association, Substance

Addiction, Jordan, Arab.


Western Australia


Laith N. AL-Eitan




Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285


[email protected]

1 2 4,3 1

1

2

3

4


221

ABSTRACT

Background: Both environmental and genetic factors contribute to individual

susceptibility to initiation of substance use and vulnerability to addiction. Determining

genetic risk factors can make an important contribution to understanding the processes

leading to addiction. In order to identify gene(s) and mechanisms associated with

substance addiction, a custom platform array search for a genetic association in a

case/control of homogenous Jordanian Arab population was undertaken. Patients

meeting the DSM-IV criteria for substance dependence (n = 220) and entering eight

week treatment program at two Jordanian Drug Rehabilitation Centres were genotyped.

In addition, 240 healthy controls were also genotyped. The sequenom MassARRAY

system (iPLEX GOLD) was used to genotype 49 single nucleotide polymorphisms

(SNPs) within 8 genes (DRD1, DRD2, DRD3, DRD4, DRD5, BDNF, SLC6A3 and

COMT).

Results: This study revealed six new associations involving SNPs within DRD2 gene

on chromosome 11. These six SNPs within the DRD2 were found to be most strongly

associated with substance addiction in the Jordanian Arabic sample. The strongest

statistical evidence for these new association signals were from rs1799732 in the C/−C

promoter and rs1125394 in A/G intron 1 regions of DRD2, with the overall estimate of

effects returning an odds ratio of 3.37 (χ2 (2, N = 460) = 21, p-value = 0.000026) and

1.78 (χ2 (2, N = 460) = 8, p-value = 0.001), respectively. It has been suggested that

DRD2, dopamine receptor D2, plays an important role in dopamine secretion and the

signal pathways of dopaminergic reward and drug addiction.

Conclusion: This study is the first to show a genetic link to substance addiction in a

Jordanian population of Arab descent. These findings may contribute to our

understanding of drug addiction mechanisms in Middle Eastern populations and how to

manage or dictate therapy for individuals. Comparative analysis with different ethnic

groups could assist further improving our understanding of these mechanisms.

222

BACKGROUND

Substance addiction and dependency has been influenced by both genetic and

environmental risk factors [1]. It has been estimated that genetic factors contribute to

40%–60% of the vulnerability to drug addiction, and environmental factors provide the

remainder [2-6]. However, there is also evidence for shared genetic vulnerability to two

or more drugs such as cannabis, sedatives, stimulants and opiates which may explain the

finding that addicted patients are often dependent on more than one category of drug [2-

9]. The presence of unique and shared genetic factors for substance addiction [5,7]

leads to the hypothesis that there is an association between specific genetic

polymorphisms and increased risk of substance addiction.

Genetic susceptibility to addiction is the result of the interaction of many genes related

to the central nervous system (CNS) [9-12]. In this system, dopamine is thought to be

the primary neurotransmitter involved in the mechanisms of reward and reinforcement

[13-16]. The function of dopamine is mediated by two classes of dopamine receptors

termed D1 like and D2 like families. The D1 like family (D1 and D5 dopamine receptors)

mediate a reduction in the drive to seek reinforcement effects, in contrast to the family

of D2-like receptors (including D2, D3, and D4 dopamine receptors) mediate both reward

and reinforcement effects [6,15-19]. The dopamine receptor gene family, which

comprises DRD1 (MIM *126449), DRD2 (MIM *126450), DRD3 (MIM *126451),

DRD4 (MIM * 126452) and DRD5 (MIM *126453) is a prime candidate gene family

for influencing substance abuse because this gene family is thought to play one of the

most important roles in the neurobehavioral signaling pathways implicated in substance

addiction [15,18].

Several studies have implicated a role for the products of dopamine receptor gene

variants in mediating the behavioral and neurochemical properties of opiates such as

heroin [8,16]. It has also been suggested that the endogenous dopamine system may

also contribute to the development of dependence on other drugs of abuse such as

alcohol, cannabis, cocaine and amphetamines [7,11,12]. Various studies have shown

that dopamine receptors are involved in reinforcement of drug use in addicted

individuals [15]. Other neurotransmitters are also thought to play a role in

reinforcement including the dopamine active transporter (DAT; gene symbol SLC6A3,

MIM *126455) [20], neurotrophines such as Brain-derived neurotrophic factor (gene

symbol BDNF, MIM *113505) [21-24] and enzymes systems such as catechol-O-

223

methyltransferase (gene symbol COMT, MIM *116790) [25]. All of these genes are

expressed within the meso-corticolimibic dopamine system or associated structures such

as the nucleus accumbens, ventral tegmental area, amygdala, prefrontal cortex,

hippocampus and cerebral cortex [5,11,12,21].

Human molecular genetic studies are also implicating the dopamine receptor gene

family in substance use disorders. The rs5326 SNP is located in the 5'-UTR of DRD1

gene and has been linked to heroin dependence in African Americans [26]. While there

are no similar confirmed associations between DRD2 gene and substance addiction [15,

27], some variants within DRD2 gene such as the rs1799732 SNP (C/-C, 5'-UTR)

warrant further investigation as these variants have a functional effect on gene

expression [28]. The DRD3 gene has been reported to be associated with substance

abuse [29] and cocaine [30] and heroin abuse [31] but others have not reported

association with abuse of either drug [32, 33]. The rs3758653 SNP located in the 5'-

UTR of the DRD4 gene has been reported to be associated with heroin dependence in

53 heroin Hungarian addicts [34]. The DRD5 gene has not been the subject of many

genetic studies.

The dopamine active transporter (DAT, SLC6A3) is widely distributed throughout the

brain in areas of dopaminergic activity [20]. The DA transporter DAT1 mediates the

active reuptake of DA from the synapse and is a principal regulator of dopaminergic

neurotransmission. Its addictive effects are thought to be principally mediated through

blockage of DAT, resulting in a substantial increase in the concentration of extracellular

DA and stimulation of neurons in brain regions involved in reward and reinforcement

behavior [35]. Family and twin studies suggest that DAT1 is a substantial genetic factor

in the vulnerability of individuals to cocaine dependence after exposure [36-38].

Therefore, polymorphic functional variants in the DAT gene may act to modify

susceptibility to substance abuse and dependence.

Brain-derived neurotrophic factor (BDNF) is a member of the nerve growth factor

family. This family is a group of structurally related secretory proteins widely

expressed in neurons and their target cells [39]. Induced by cortical neurons, BDNF is

required to support existing neurons in the brain and help in the growth and

differentiation of new neurons and synapses [40-42]. Studies in animals and humans

suggest that BDNF influences the dopaminergic and serotonergic functions that are

heavily linked to substance addiction [43-47]. In mice, BDNF administration or BDNF

224

genetic knockouts have shown that this factor can alter drug preference or drug-induced

behavior. In humans, Uhl et al. (2001) used 1494 SNPs to scan for vulnerability genes

for polysubstance abuse. Using 1004 European American and African American

samples; they found that positive association markers flank the BDNF gene and

Val66Met at rs6265 position was associated with drug addiction vulnerability [46].

Recently, various studies have shown that the Val66Met substitution in the prodomain

may affect intracellular trafficking and activity-dependent secretion of BDNF [47,48].

Overall these animal and human studies indicate that BDNF may be involved in the

mechanisms underlying substance addiction [49].

Catechol-O-methyltransferase (COMT) is one of several enzymes that metabolises

catecholamine such as dopamine, epinephrine and norepinephrine and play a role in the

reinforcement mechanism [5,7]. Nikoshkov et al. (2008) suggests that heroin addicts

with homozygous genotype at position rs4680 Met158/Met158 have a significant up-

regulation of COMT gene expression [50]. In contrast, heroin addicts with the

heterozygous genotype (Vall158/Met158) or homozygous genotype of Vall158 at this

position show a down-regulation of COMT gene expression. Goldman (2005) reported

that the Val158 variant catabolizes dopamine up to four times the rate of its methionine

counterpart, resulting in significant lower synaptic dopamine levels following

neurotransmitter release. This ultimately reduces dopaminergic stimulation of the post-

synaptic neuron [5]. Therefore, due to the role of COMT in prefrontal dopamine

degradation, the Val158Met polymorphism is thought to be associated with increased

risk of substance addiction by modulating dopamine signaling in the frontal lobes.

In the present study, we examined 49 SNPs within eight candidate genes, the dopamine

receptors DRD1, DRD2, DRD3, DRD4 and DRD5, the dopamine transporter (SLC6A3)

brain-derived neurotrophic factor (BDNF) and catechol-O-methyltransferase (COMT)

for genetic association analysis with substance addiction in Arab individuals. To the

best of our knowledge, this report is the first genetic association study for substance

addiction in a Middle Eastern population of Arab descent. These findings may prove

crucial to our understanding of substance addiction mechanisms in Arab populations.

At the individual level, this knowledge may improve patient management and treatment.

225


2.1.1. Subjects

All substance addiction subjects were recruited from the National Centre for

Rehabilitation of Addicts (NCRA) at Jordanian Ministry of Health and the Drug

Rehabilitation Centre at the Jordanian Public Security Directorate (DRC-PSD). They

were diagnosed as having substance addiction using DSM-IV criteria (American

Psychiatric Association, 1994) [51]. A semi-structured interview based on the

Addiction Severity Index (ASI) criteria [52] was used to collect clinical and

demographic data for each subject. The clinical and demographic data were collected

by an administering officer from each of the addiction treatment Centres. The clinical

data included current drug of abuse, age at first use of drug, onset and years of drug use,

substance and psychiatric treatment, drug overdose and history of substance abuse.

Demographic data collected included date of birth, gender, nationality was also

provided. All data was coded and no specific individual was identified. The mean age

(±SD) of these subjects was 32.7 (±8.4) years with an age range of 18 to 58 years.

In addition, 240 healthy males from an ethnically homogenous Jordanian Arab

population with no lifetime history of psychosis or mood disorders, or alcohol or heroin

dependence according to the DSM-IV, were used as controls. These controls were

recruited from the Blood Bank of the King Abdullah Hospital University, Jordan

University of Science and Technology. These controls were frequency matched by age,

sex and ethnicity to the case subjects. The mean age (±SD) of the controls was 31.5 (±

5.6) years with an age range of 18 years to 54 years.


Association Code of Ethics (2006) and the World Medical Association Declaration of



Australia (2007). Ethical approval to conduct this research was granted by the Human

Research Ethics Committee of The University of Western Australia (Ref No.


Jordanian Ministry of Health (Ref No. Development/Trainees/535) and by the

Institutional Review Board of the Jordan University of Science and Technology (Ref

226

No. RA/16/1/2010). Written informed consent was obtained from all subjects and

controls before participation in the study.

2.1.2. DNA Extraction

After blood was drawn into EDTA tubes, genomic DNA was extracted using the Gentra

Puregene® Blood Kit (Qiagen, Valencia, CA, USA) according to the recommendations

of the manufacturer. Briefly, 300μl of whole blood from each sample was mixed with

200μl of lysis buffer (50mM Tris pH 8.0, 100mM EDTA, 100mM NaCl, 1% SDS) and

40μl of Proteinase K. 100μl of isopropanol and 500μl of Inhibitor Removal Buffer (5M

guanidine-HCl, 20mM Tris-HCl pH 6.6) was then added. The DNA was washed with a

buffer (20mM NaCl; 2mM Tris- HCl; pH 7.5) and centrifuged twice at 2,000 rpm. The

DNA was washed using cold 70% ethanol, centrifuged at 3,000 rpm and the supernatant

was discarded, leaving a pellet that contained purified genomic DNA. The DNA pellet

was diluted in TE buffer (1mM EDTA; 10mM Tris-HCl, pH 7.5) to a concentration of

approximately 50ng.μl-1. DNA concentration (ng/µl) and purity (A260/280) were also

verified using the Nano-Drop ND-1000 UV-Vis Spectrophotometer (NanoDrop

Technology, Wilmington, DE) and subsequently adjusted to approximately 100ng/µL.

Purified DNA was stored at -80°C before use.

2.1.3. Genotyping

In this study, 49 single nucleotide polymorphisms (SNPs) within eight genes (DRD1,

DRD2, DRD3, DRD4, DRD5, BDNF, SLC6A3 and COMT) were selected from public

databases including the SNP database of the National Centre for Biotechnology

Information (NCBI; http://www.ncbi.nlm.nih.gov/SNP/), the Applied Biosystems SNP

database (http://www.appliedbiosystems.com) and the International HapMap Project

(http://www.hapmap.org/). The positions of the SNPs in these selected genes and the

relative distance to the translation initiation site are given in Table 2.

SNP genotyping using the sequenom MassARRAY® system (iPLEX GOLD)

(Sequenom, San Diego, CA, USA) was performed according to the manufacturer’s

recommendations (Sequenom, San Diego, CA, USA). Briefly, PCR and single base

extension primers (SBE) were designed using MassARRAY assay design 3.1 software

(Sequenom MassARRAY system) that allows iPLEX reactions for SBE designs with

the modified masses associated with the termination mix. Manufacturer’s instructions

for the multiplex reaction were followed in the whole process, including the PCR



227

amplification (Sequenom, San Diego, CA, USA), the shrimp alkaline phosphatase

(SAP) enzyme (Sequenom, San Diego, CA, USA) treatment to dephosphorylate dNTPs

unincorporated in the PCR, the SBE reactions using an iPLEX GOLD assay

(Sequenom, San Diego, CA, USA), and the clean-up with a resin kit (Sequenom, San

Diego, CA, USA) to desalt the iPLEX reaction products. PCR and SBE primers

sequences and all protocol conditions are available upon request. Reaction products

were dispensed onto a 384-element SpectroCHIP bioarray (Sequenom) using a

MassARRAY nanodispenser and assayed on the MassARRAY platform. Mass

differences were detected with matrix-assisted laser desorption/ionization time-of-flight

mass spectrometry (MALDI-TOF MS). MassARRAY Workstation v.3.3 software was

used to process and analyse the iPLEX SpectroCHIP bioarray. Typer Analyzer v.4.0.2

software was used to analyse all genotypes obtained from the assays. Scatter plots of

rs1125394 and rs1799732 SNPs within DRD2 gene were colored according to genotype

calls: AA (green), GA (yellow) GG (blue) and no call (red) (Figure 1).

2.1.3. Quality Control (QC)

Data cleaning was performed using the PLINK software developed by Purcell et al

(2007) [49]. Using this software, the genotype results of each marker are accepted only

if the success rate is at least 90%. SNPs were excluded from the analysis based on the

following criteria: (1) minor allele frequency (MAF) < 0.05, or (2) missingness per SNP

> 5%, or (3) significant deviation (p-value < 1.0 E-06) from the Hardy-Weinberg

equilibrium (HWE).

2.1.4. Statistical Methods

2.1.4.1. Hardy-Weinberg Equilibrium

The HWE provides a measure of wether an evolutionary event has influenced an allele

frequency. Theoretically calculated, expected and seen genotype frequencies are

compared to each other and a Pearson χ2 test is used to test whether they significantly

differ from each other. In this study, the estimated genotype frequencies were calculated

as follows: p2 + 2pq + q2 = 1, in which p represents the frequency of one allele, and q

represents the frequency of the other allele [53]. Significant deviations from HWE may

indicate genotyping errors.

228

2.1.4.2 Genetic Association Analysis

The software package PLINK [54] was used to test for association between genetic

variants and substance addiction.

2.1.4.3. Correction for Multiple Testing

In order to correct for the effect of multiple testing for a given phenotype, the effective

number of SNPs using the method of Li and Ji (2005) was estimated [55], which

employs a modification of an earlier approach by Nyholt (2004) [56]. After obtaining

the effective number of SNPs (Nem), a modified Bonferroni procedure was applied to

identify a target alpha level (0.05/ Nem) that would maintain an overall significance level

of 0.05 or less.

229

3. RESULTS

3.1. The Study Sample

Altogether 460 individuals were analysed in this study to identify potential candidate

genes related to substance addiction. The study sample consisted of 220 Jordanian Arab

individuals with substance addiction and 240 matched controls. The average age (±SD)

was 32.70 (± 8.4) and 31.5 (± 5.6) years, respectively. No drug dependent individual or

control had any psychiatric diseases according to the DSM-IV criteria assessment.

There were no significant differences found between individuals with drug dependence

and controls with regard to age and sex. Clinical and demographic data including

gender, age, current drug abuse, dependence variables, drug overdose or toxicity,

history of drug use and substance and psychiatric treatment is given in Table 1.

3.2. HWE Test

HWE tests were performed in case and control groups for the studied polymorphisms

respectively. All polymorphism were in HWE in both case and control groups except

for the three SNPs within DRD2 gene (rs1801028, rs2734838, and rs1110976) and two

SNPs within SLC6A3 gene (rs27048, rs6347). Two SNPs for COMT gene (rs1544325,

rs2239393) had p-values < 1.0 E-06 and were excluded from the study.

4.4. Quality Control (QC)

All the genotyped SNPs were checked for HWE and Mendelian errors. All duplicates

were identical, water controls were clean, markers were in HWE and no Mendelian

errors were observed. Genotypes determined by sequenom MassARRAY® system

(iPLEX GOLD) for all 49 SNPs were highly accurate with an average success rate of

100%. The genotype discrepancy average (±SD) rate across the 49 loci was only 0.02%

(±0.06%) in the whole cohort (460 subjects).

4.5. The Candidate Genes and the SNPs

The NCBI, dbSNP and HapMap databases were used for the SNP selection. The goal

was to select SNPs that had significant functional relevance, covered the genes of

interest as widely as possible, and had been previously genotyped. Using these criteria,

a total of 49 SNPs were selected (Table 1). Of these, 41 (82%) passed quality control

and were used in the association analysis.

230

4.6. Association of SNPs Candidate Genes with Substance Addiction

Association analysis of eight genes (DRD1, DRD2, DRD3, DRD4, DRD5, SLC6A3,

BDNF and COMT) with substance addiction was performed using PLINK Software

[54]. The association p- values from the PLINK genetic association analysis are shown

in Table 3.

4.6.1. Dopamine Receptor Genes

The top scoring SNPs for association with substance addiction were from the DRD2

gene (Figure 1). The significant p-values for genotypic frequency ranged from 0.03 to

0.000026 for six SNPs within DRD2 gene on chromosome 6 (Table 3). The strongest

statistical evidence for these new association signals were from rs1799732 in the C/−C

promoter and rs1125394 in A/G intron 1 regions of DRD2, with the overall estimate of

effects returning an odds ratio of 3.37 (χ2 (2, N = 460) = 21, p-value = 0.000026) and

1.78 (χ2 (2, N = 460) = 8, p-value = 0.001), respectively. The p-values for allelic

frequency ranged from 0.01 to 0.0001 for five SNPs (rs2283265 (G/T, intron 4),

rs1125394 (A/G, rs2075654, intron 1), rs2734836 (G/A, intron 2), and rs1799732 (C/-C,

5'-UTR) (data not shown) within DRD2 gene. The strongest statistical evidence of

allelic frequency for these new association signals were from rs1799732 (χ2 (1, N =

460) = 15, p-value = 0.0001) and rs2283265 (χ2 (1, N = 460) = 8, p-value = 0.005).

4.6.2. Solute Carrier Family 6, Member 3(SLC6A3), Brain-Derived Neurotrophic

Factor (BDNF) and Catechol-O-Methyltransferase (COMT) Genes

There were no significant difference of genotype (Table 3) or allele frequencies (data

not shown) of the studied SNPs in the SLC6A3, BDNF and COMT genes between

subjects with substance addiction and normal controls.

231

Table 1. Characteristics of 220 substance abuse patients of Arab origin in this study

Category Subcategory Value (n) Percentage (%)/Mean ± SD

Demographic data

Gender Male 220 100.0%

Female 0 0.0%

Age (years)

18-20 12 5.5%

21-39 165 75.0%

+40 43 19.5%

Drug/alcohol problem

Current drug abuse

Nicotine 203 92.0%

Opiates 185 84.0%

Cannabis 128 58.0%

Alcohol 117 53.0%

Amphetamine 31 14.0%

Cocaine 7 3.0%

Dependence

Age first drug use (years) 220 18.7 ± 10.1

Age of onset (years) 220 20.3 ± 10.9

Duration (years) 220 7.6 ± 6.6

Frequency (days/week) 220 3 ± 1.5

Drug overdose 100 45.5%

History of drug use 53 24.0%

Previous treatment

Substance treatment Alcohol 117 53.0%

Drugs 185 84.0%

Psychiatric treatment Inpatient 15 7.0%

Outpatient 20 9.0%

Mean (M) data are provided with ± Standard Deviation (SD).

232

Table 2. List of Genes, their SNPs and Positions, and Genotyping Data based on the Whole Cohort (460 subjects).


b Call rate

c

DRD1 5q35.1 rs5326 174802802 G>A 5'-UTR 0.25% 99%

DRD2 11q23

rs1800496 112788698 C>T Exon 7 0.00% 100%

rs6277 112788669 T>C 3'-UTR 0.15% 99%

rs2511521 112790509 T>C Intron 4 0.00% 100%

rs12574471 112821446 C>T Intron 1 0.00% 100%

rs2283265 112790746 G>T Intron 4 0.00% 100%

rs6279 112786283 C>G 3'-UTR 0.00% 100%

rs4581480 112829684 T>C 5'-UTR 0.00% 100%

rs4350392 112840927 C>A 5'-UTR 0.00% 100%

rs10891556 112857971 G>T 5'-UTR 0.00% 100%

rs7103679 112808884 C>T Intron 1 0.00% 100%

rs4938019 112846601 T>C Intron 1 0.00% 100%

rs1076560 112788898 G>T Intron 5 0.00% 100%

rs2075654 112794276 G>A Intron 2 0.00% 100%

rs7125415 112815891 C>T 5'-UTR 0.00% 100%

rs4648317 112836742 C>T Intron 1 0.00% 100%

rs1125394 112802395 A>G Intron 1 0.00% 100%

rs4648318 112818599 A>G Intron 1 0.00% 100%

rs12363125 112791126 A>G Intron 5 0.00% 100%

rs2734836 112796449 G>A Intron 2 0.05% 99%

rs12364283 112852165 T>C 5'-UTR 0.00% 100%

rs1799978 112851561 A>G 5'-UTR 0.00% 100%

rs6275 112788687 C>T Exon 7 0.15% 99%

rs1800497 112776038 C>T Exon 8 0.00% 100%

rs1079597 112801496 A>G Intron 1 0.00% 100%

233



b Call rate

c

DRD2 11q23 rs1799732 112851462 -C 5'-UTR 0.00% 100%

rs1800498 112796798 C>T Intron 2 0.00% 100%

DRD3 3q13.3 rs6280 115373505 C>T Exon 1 0.07% 99%

DRD4 11p15.5 rs3758653 626399 C>T 5'-UTR 0.05% 99%

DRD5 4p16.1 rs10033951 9388678 C>T 5'-UTR 0.05% 99%

SLC6A3 5p15.3

rs2963238 1497427 A>C Intron 1 0.12% 99%

rs6876225 1459036 C>A Intron 11 0.00% 100%

rs11564773 1449813 A>G Intron 14 0.00% 100%

rs1042098 1447815 T>C 3'-UTR 0.15% 99%

BDNF1 11p13

rs7103873 27656893 C>G Intron 1 0.07% 99%

rs1401635 27650567 C>G Intron 1 0.00% 100%

rs11030102 27638172 C>G Intron 1 0.00% 100%

rs17309930 27705069 A>C,G>T Intron 1 0.07% 99%

rs6265 27636492 G>A 3'-UTR 0.00% 100%

COMT 22q11.21 rs737866 18310109 T>C 5'-UTR 0.00% 100%

rs4680 18331271 A>G Exon 2 0.00% 100% d. Chromosome positions are based on NCBI Human Genome Assembly Build 36.3. e. Ratio of the number of discordant genotypes to the number of duplicates. f. Ratio of the number of valid genotypes to the number of subjects genotyped (N = 460) at each locus.

234

Table 3. Association of genes SNPs with opiate drug dependence

Gene SNP _ID SNP M_Aa F_A

b F_U

c Pearson Chi-Square p-value

d OR CMH

e

DRD1 rs5326 G>A A 0.172 0.144 1.32 0.256 0.256

DRD2

rs1800496 C>T T 0.027 0.015 1.86 0.173 1.903

rs6277 T>C T 0.427 0.460 1.05 0.307 0.873

rs2511521 T>C G 0.370 0.383 0.16 0.685 0.946

rs12574471 C>T T 0.196 0.161 1.85 0.173 1.265

rs2283265 G>T T 0.146 0.087 8.70 0.001 2.785

rs6279 C>G C 0.406 0.435 0.79 0.374 0.888

rs4581480 T>C C 0.095 0.075 1.29 0.256 1.308

rs4350392 C>A A 0.173 0.217 2.82 0.093 0.755

rs10891556 G>T T 0.187 0.235 3.18 0.074 0.748

rs7103679 C>T T 0.121 0.083 3.48 0.062 1.506

rs4938019 T>C C 0.170 0.216 3.13 0.076 0.743

rs1076560 G>T T 0.157 0.108 4.73 0.031 1.531

rs2075654 G>A A 0.120 0.075 5.43 0.021 1.689

rs7125415 C>T T 0.100 0.118 0.78 0.376 0.829

rs4648317 C>T T 0.170 0.216 3.12 0.076 0.743

rs1125394 A>G G 0.152 0.091 8.00 0.001 1.780

rs4648318 A>G G 0.379 0.361 0.29 0.593 1.080

rs12363125 A>G C 0.513 0.472 1.52 0.217 1.170

rs2734836 G>A A 0.146 0.090 7.00 0.001 1.720

rs12364283 T>C G 0.097 0.129 2.24 0.134 0.730

rs1799978 A>G G 0.141 0.110 2.03 0.154 1.330

rs6275 C>T T 0.404 0.429 0.60 0.440 0.900

rs1800497 C>T T 0.195 0.156 2.37 0.123 1.310

235


Gene SNP _ID SNP M_Aa F_A

b F_U

c Pearson Chi-Square p-value

d OR CMH

e

DRD2 rs1799732 -C -C 0.146 0.067 21.00 0.260E-4 3.370

rs1800498 C>T C 0.509 0.466 1.66 0.190 1.180

DRD3 rs6280 C>T C 0.355 0.370 0.21 0.645 0.938

DRD4 rs3758653 C>T C 0.263 0.287 0.66 0.410 0.886

DRD5 rs10033951 C>T T 0.306 0.325 0.36 0.550 0.917

SLC6A3

rs2963238 A>C A 0.436 0.462 0.63 0.427 0.899

rs6876225 C>A A 0.027 0.035 0.49 0.480 0.764

rs11564773 A>G G 0.043 0.437 0.00 0.978 0.991

rs1042098 T>C C 0.363 0.342 0.42 0.515 1.094

BDNF1

rs7103873 C>G C 0.463 0.510 2.02 0.154 0.827

rs1401635 C>G C 0.231 0.212 0.50 0.481 1.118

rs11030102 C>G G 0.163 0.138 1.17 0.279 1.221

rs17309930 A>C,G>T A 0.136 0.127 0.17 0.679 1.084

rs6265 G>A A 0.182 0.161 0.748 0.387 1.164

COMT rs737866 T>C C 0.363 0.312 2.69 0.100 1.259

rs4680 A>G A 0.475 0.502 0.67 0.411 0.897 a. M_A: minor allele for whole cohort sample b. F_A: minor allele frequency in affected individuals (substance addiction cases). c. F_U: minor allele frequency in unaffected individuals (healthy controls) d. p-value: two tailed p-value from the 2x2 allele count chi-squared test; p <0.05 (Bonferroni-adjusted). e. OR CMH: allelic odds ratio from the 2x2xK Cochran-Mantel-Haenszel’s test.

236

Figure 1. Representative Scatter plot from sequenom data. The left panel (a) and right panel (b) illustrate the scatter plot of rs1799732 and

rs1125394 SNPs within DRD2 gene, respectively. These two SNPs showed the strongest statistical evidence for association with substance

addiction in Arab population. The X and Y axes on both plots denote the mass height measurement for the two alleles (C, C.DEL, low

mass allele versus high mass allele) at the rs1799732 SNP (panel a) and for the two alleles (G, A, low mass allele versus high mass allele)

at the rs1125394 SNP (panel b). Each point represents the measurements for a single individual. The points in the both panels are colored

according to the genotype calls. For example in the left panel (a), green color denotes –C genotype; yellow color denotes C/-C genotype

and blue color denotes CC genotype and red color denotes no call. Genotypes determined by sequenom MassARRAY® system (iPLEX

GOLD) for all 49 SNPs were highly accurate with average success rate 100%. Genotype discrepancy average (±SD) rate across the 49 loci

were only 0.02% (±0.06%) in the whole cohort (460 subjects).

237

DISCUSSION

Although epidemiologic studies have shown that substance addiction is strongly

influenced by genetic factors, the number and identity of vulnerability genes remain

unknown [1,3-8]. This is the first study to examine eight candidate genes for

association with substance addiction in individuals of Arab descent. These eight genes

were the Dopamine receptors (DRD1, DRD2, DRD2, DRD3 and DRD5), Solute Carrier

Family 6, Member 3 (SLC6A3), Brain-Derived Neurotrophic Factor (BDNF) and

Catechol-O-Methyltransferase (COMT). Altogether 460 individuals were genotyped

using 49 SNPs from these eight genes. Of the samples tested, 220 were from substance

addicted male subjects of Arab descent. The control group were an ethnically

homogenous Jordanian Arab population with no lifetime history of psychosis, mood

disorders or substance dependence.

Both dopamine and non-dopamine neurochemical pathways through neurotransmitters

(SLC6A3), neurotrophic factors (BDNF) and enzymes (COMT) are influenced by drugs

and their psychoactive and addictive effects [8,10,12]. Dopamine is one of the main

neurotransmitters involved in the stimulation of reward pathways, which is the

important feature of substance addiction [13,15,57]. It has been suggested that

dopamine receptor genes play a role in the genetics of substance addiction [58-60].

Previous studies have emphasized the importance of dopamine gene family specifically

DRD2 gene as a general risk factor for substance dependence rather than a marker of

risk for a particular drug [13,15,18]. However, various genetic association studies

reported that there are inconsistencies in the frequency of alleles within DRD2 gene in

different populations. For example, Barr and Kidd reported that the A1 allele frequency

differs dramatically among the population studied from as low as 0.09 to as high as

0.075 [61].

As many studies indicated that multiple substances influence dopaminergic system

activity, the investigation of substance addiction may result in a complete examination

of gene risk [7,11,12]. In this study, none of the polymorphisms within the eight genes

differed significantly for allele or genotype frequencies, with exception of six

polymorphisms (rs2283265, rs10765560, rs2075654, rs1125394, rs2734836 and

rs1799732) within the DRD2 gene. The strongest statistical evidence for these

association signals was found within the DRD2 gene at two sites: rs1799732 (C/-C, 5'-

238

UTR) and rs1125394 (A/G, intron 1). The strongest evidence of allelic frequency for

these association signals were from rs1799732.

The rs1799732 (C/-C, 5'-UTR) is of particular interest because there is evidence that

this allele has a functional effect on DRD2 gene expression [27]. The dopaminergic

system is involved in reward and reinforcing mechanisms in the brain [13,57]

specifically the positive reinforcing effects of substance addiction [59]. Animal and

human studies of addiction indicate that DRD2 plays a critical role in the mechanism of

reward and reinforcement behavior [60-63]. Various animal studies reported that opiate

rewarding effects were absent in mice lacking D2 receptors, while DRD2

overexpression in transgenic mice led to reduced self-administration of alcohol [60,62].

A positron emission tomography study of human brain showed that D2 receptor density

in the brain decreased significantly in alcoholic compared with control subjects [63,64].

These findings suggest that genetically determined variation in DRD2 expression and

function can alter reward responses to a variety of substances and may contribute to

vulnerability to heroin dependence in humans. For example, DRD2 gene was

previously studied by Xu et al (2004) to examine the susceptibility of this gene with

heroin dependence in Chinese and German population [65]. This study found that

genetic polymorphisms, specifically rs1799732 (C/-C), within DRD2 gene play a role as

a susceptibility gene with heroin dependence in Chinese but not in German population

[65].

Association with substance addiction was not seen in the studied SNPs within SLC6A3,

BDNF and COMT genes. Conflicting results have been published in various studies on

the influence of these genes on the increased risk of substance addiction [35-37, 42-46].

Candidate gene analysis is problematic because the prior probability of seeing true

association is exceptionally low [66], unless a very strong case of specific phenotype for

involvement of a particular gene can be made. This is not applied to substance

addiction because compelling biological evidence implicating particular

neurotransmitter receptors in addiction is absent, with the possible exception of the

opioid receptor gene family, and prior probability is impossible to determine [58]. Thus

p-values of 0.05 are more likely to be chance occurrences, especially when using cases

and controls where hidden population stratification as confounding factor is an inherent

danger. However, a risk of population stratification as a confounding factor was not

found in this study because the Jordanian Arab population are considered to be

239

genetically homogenous population. This offers an advantage for genetic studies. For

example, the numbers of different variations in the genes behind phenotypes are

expected to be smaller than in more heterogeneous populations. This increases the

probability to find genetic associations [67]. Therefore, even a small study sample from

a genetically homogenous population, like the sample of subjects used in this study, can

give accurate results.

In this study, genotyping was carried out by sequenom MassARRAY® system for 49

SNPs. The NCBI, dbSNP, HapMap databases and previous published data were used to

select the studied SNPs, yielding reliable candidate SNPs database for genetic

association analysis. In this array we focused on genes of particular interest for drug,

alcohol and neuropsychiatric researchers because they were reported to be involved in

drug dependence and other neurological and psychiatric disorders [4-12]. The chosen

SNPs were also selected because they showed the greatest potential to distinguish

between substance addicts’ individuals and control subjects in previous studies [4-12].

Therefore, the distribution of SNPs through the selected genes was optimal.

Various studies showed a risk of false positive results due to population stratification.

However, a risk of false positive results was not found in this study because genotypic

frequencies of chosen SNPs in the patients and controls met HWE expectations. In

addition, it is likely that there were genotyping errors. However, genotyping errors

were minimized by genotyped each patient twice in order to avoid technical errors as

evidenced by the low average rate of genotype discrepancy. Genotyping was conducted

for patients under the same conditions and during the same period. Genotypes were

also evaluated by investigators who were blind to the status of the subject and any

discrepancies were resolved by test replication.

A confounding factor which could have contributed to the observed variations in the

between this study and previous studies is the heterogeneity of population based on

gender [66, 67]. However in our study, only male individuals with substance addiction

were genotyped. Therefore, the generalisation of the results to all substance addicts’

individuals is limited. Another confounding factor is differences in phenotype in

addiction such as polysubstance use, severity of addiction and the use of unstructured

clinical interviews to obtain phenotypic data could affect the genetic association

analysis. However, these confounding factors are not found in our study as a specific

240

clinical structural interview was designed based on the DSM-IV criteria and the

Addiction Severity Index (ASI) for collecting clinical and phenotypic data [52]. The

careful and extensive interview based phenotypic data collection has been performed by

highly trained psychiatrist consultants, yielding exceptionally reliable phenotype data.

In addition, the study sample is strongly enriched with regular substance addicts’

individuals giving more statistical power.

CONCLUSION

Overall our results indicate that the DRD1, DRD3, DRD4, DRD5, SLC6A3, BDNF and

COMT genes are not likely to be a major genetic risk factor for substance addiction in

the Arab population, with the exception of strong association between substance

addiction and DRD2 gene. However, it has been proposed that defects in various

combinations of these genes for these neurotransmitters results in the Reward

Deficiency Syndrome (RSD) and that indivuals at risk for abuse of the unnatural

rewards [68]. Because of its importance, DRD2 gene was a major candidate gene [68-

70]. Several studies in the past decade have shown that in various subject groups the

DRD2 gene is associated with alcoholism, drug abuse, smoking, obesity, compulsive

gambling, and several personality traits [69,70]. A range of other dopamine, opioid,

cannabinoid, norepinephrine, and related genes have since been considered to be

candidate genes. Like other behavioral disorders, these genes are polygenically

inherited and each gene accounts for only a small per cent of the variance [68,69].

Techniques such as the Multivariate Analysis of Associations, which simultaneously

examine the contribution of multiple genes is required for understanding the genetic

makeup of polygenic disorders. In the future research could be also directed towards

using a genome-wide association analysis and including more specific case-control

study with a wider set of phenotypes.

241

COMPETING INTEREST

The authors declare that they have not competing interest.

AUTHORS’ CONTRIBUTIONS

This manuscript was prepared by AL-EITAN with support from the co-author listed.

DNA extraction, SNPs selection; experimental design and statistical analysis were

conducted by AL-EITAN with assistance from the co-author listed. Patient samples

selection and DNA extraction were done through a collaboration link with JARADAT

at Princess Haya Biotechnology Centre (PHBC). JARADAT, HULSE and TAY were

assisted in designing the study to proof reading the manuscripts. All authors read and

approved the final manuscript.

ACKNOWLEDGMENTS


Western Australia. We gratefully acknowledge the contribution of participating patients

whose cooperation made this study possible. We also would like to thank the Genomics

Research Group at Princess Haya Biotechnology Center (PHBC) for their technical

support. We gratefully thank and appreciate all the technical help and support from the

AGRF, in particular, Shane Herbert and David Hawkes, along with their staff from the

Perth and Melbourne Nodes, respectively. Funding for this project was provided in part

by Centre for Forensic Science and Unit for Research and Education in Alcohol and

Drugs of the School of Psychiatry and Clinical Neurosciences, The University of

Western Australia.

242

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Nogami H, Briggs AH, Cohn JB: Allelic association of human dopamine D2

receptor gene in alcoholism. JAMA 1990; 263(15):2055-60.

http://www.ncbi.nlm.nih.gov/pubmed?term=Blum%20K%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Noble%20EP%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Sheridan%20PJ%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Montgomery%20A%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Ritchie%20T%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Jagadeeswaran%20P%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Nogami%20H%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Briggs%20AH%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed?term=Cohn%20JB%5BAuthor%5D&cauthor=true&cauthor_uid=1969501

http://www.ncbi.nlm.nih.gov/pubmed/1969501

248

CHAPTER 7

POLYMORPHISMS IN THE µ-OPIOID RECEPTOR

GENE IN JORDANIAN ARABS WITH OPIATE

DRUG DEPENDENCE

This chapter was published in the ScienceMED an International Journal of Medical

Sciences.

249

CHAPTER 7

Polymorphisms in the µ-Opioid Receptor Gene in

Jordanian Arabs with Opiate Drug Dependence

Chapter 7 was published in the ScienceMED an International Journal of Medical

Sciences. The aim of the study presented in this article was to detect and characterize

the genetic variations within the µ-Opioid receptor (OPRM1) gene that possibly

associated with susceptibility to opiate drug dependence in drug and non-drug

Jordanian Arab population.

Mu-opioid receptor gene (OPRM; MIM# 600018; Gene Bank NM_000914) is an

important component of the self-rewarding system; it also mediates the effects of

several important opioid analgesic agents and drugs such as heroin, methadone,

fentanyl (Paraternak, 1993), and especially heroin (Kreek, 1996). Rapid activation of

the mu opioid receptor by heroin and its analogs results in an euphoric effect that may

have conferred reinforcing or rewarding effects of the drug, and thus contributed to the

development of drug dependence (Koob, 1992, Wise, 1996)

Polymorphisms of the OPRM1 gene and their relationship to psychiatric phenotypes

have been reported. More than 300 Single nucleotide polymorphisms (SNPs) in the

human OPRM1 gene have been identified, some of which have been tested for

associations with a range of substance dependence (http://www.ncbi.nlm.nih.gov/SNP/).

In this chapter; we specifically investigated the genetic associations of a series of

markers spanning the coding sequence of OPRM1 gene with drug dependence. SNPs at

the OPRM1 locus in 220 drug dependent patients and 240 controls subjects were

genotyped using the sequenom MassARRAY system (iPLEX GOLD).

This chapter shows that genotype frequencies at three sites differed significantly

between cases and controls. The significant sites were rs6912029 (G-172T),

rs12205732 (G-1510A) and rs563649 (G-983A). Allele frequency comparisons between

cases and control also revealed a significant difference at two sites: rs6912029 (G-

172T), rs12205732 (G-1510A). This study was the first to investigate a genetic

association of OPRM1 variations with drug dependence in males of Arab descent.


250

These findings may provide crucial knowledge to understand the drug dependence

mechanisms in Middle Eastern population of Arab descent.

This manuscript was prepared by Laith AL-Eitan with support from the co-author listed

SNPs selection; experimental design, all laboratory work and statistical analysis were

conducted by AL-Eitan with assistance from the co-author listed. A part of the work

described in this chapter was presented by AL-Eitan as a poster at the Up Close and

Personalized, International Congress on Personalized Medicine, 2 to 5 February, 2012,

Florence, Italy. All Authors proof read the manuscript.

251

Polymorphisms in the µ-Opioid Receptor Gene in Jordanian Arabs with Opiate

Drug Dependence

Laith N. AL-Eitan , Saied A. Jaradat , Ian R. Dadour , Guan K. Tay , Gary K. Hulse


6009, Australia




Centre, The University of Western Australia, Crawley, WA 6009, Australia

Unit for Research and Education in Alcohol and Drugs, Queen Elizabeth II


Australia

Abbreviated title: Polymorphisms in the µ-Opioid Receptor Gene in Arabs

Keywords: OPRM1, SNP Genotyping, Association, Opiates, Drug

Dependence, Jordan, Arab.


Western Australia


Laith N. AL-Eitan




Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285


[email protected]

1 2 1 1 4,3

1

2

3

4


252

ABSTRACT

This study investigated the relationship between the gene coding of the µ-opioid

receptor (OPRM1) and the susceptibility to opiate drug dependence in Arab

populations. Single nucleotide polymorphisms (SNPs) at the OPRM1 locus in 460

subjects were genotyped using the sequenom MassARRAY system (iPLEX GOLD).

Of these subjects, 220 were male patients at a Jordanian Drug Rehabilitation Centre

who met the DSM- IV criteria for opiate drug dependence and 240 were healthy male

controls from an ethnically homogenous Jordanian Arab population. Genotype

distributions for all 22 SNPs were in Hardy-Weinberg equilibrium (HWE). The SNPs

associated with drug dependence were rs6912029 (G-172T), rs12205732 (G-1510A)

and rs563649 (G-983A) (p value < 0.05). It has been suggested that OPRM1 is the

main target of opiates and other drugs and plays an important role in opioid tolerance

and dependence. This study is the first to show a genetic association between OPRM1

and drug dependence in an Arab population. However, further studies using larger

samples and different populations are needed to confirm these findings and identify

individuals with increased risk for dependence.

253

INTRODUCTION

While environmental factors are likely to contribute to drug dependence, susceptibility

is also strongly influenced by genetic factors. Opiate dependent individuals have one of

the highest levels of genetic variance compared to other illicit drug users (Tsuang et al.,

1998). There are several gene families implicated in drug addiction. One such family is

the opioid receptor gene members (e.g. OPRM1). The endogenous opioid system is

considered one of the most important neurobehavioral signalling pathways implicated in

drug use (Koob and Simon, 2009). This opioid system consists of widely scattered

neurons that produce three opioids: beta-endorphin, the met- and leu-enkephalins, and

the dynorphins. These opioids act as neurotransmitters and neuromodulators at three

major classes of opioid (μ, δ, κ) receptors and produce analgesia. Several studies have

implicated the role of the products of opioid receptor gene variants in mediating the

behavioral and neurochemical properties of opiates such as heroin (Gelernter, et al.,

2007, Kim, et al., 2009). The endogenous opioid system may contribute not only to the

development of heroin dependence, but also other drugs of abuse such as alcohol,

cannabis, cocaine and amphetamines (Goldman, et al, 2006; Haile, et al., 2008, Koob

and Simon, 2009). Some drugs of abuse can cause a euphoric effect through rapidly

activating the μ-opioid receptors (MOR). The euphoric effects confer reinforcing or

rewarding effects of the drug, which constitute a key psychomotor mechanism for the

development of addiction (Bond et al., 1998).

Research studies into the genetics of drug dependence have focussed on OPRM1, which

is the primary target for opiates. Figure 1 shows the opiate drug pathway mechanisms

where specific targets are highlighted. Specifically, heroin is an opioid synthesised

from morphine, which in turn is a derivative of the opium poppy. Heroin is converted

into morphine invivo and activates the three opioid receptors (primarily MOR), which

modulates several physiological systems including the pain/reward systems. The

OPRM1 gene (6q24-q25; Gene ID: 4988) encodes MOR which is widely distributed in

the brain (Benyhe et al., 1985, Chen et al., 1993). The OPRM1 receptor is a membrane

of the G protein-coupled receptor family (Beyer et al., 2004). Over 300 OPRM1

sequence variants have been identified to date (Hoehe et al., 2000).

Most abundant among the missense variants in exon 1 of OPRM1 is the A118G single

nucleotide polymorphism (SNP) (Bergen et al., 1997). This polymorphism encodes an

254

Asn40Asp amino acid substitution that appears to cause a changing function. Bond et

al. (1998) reported that β-endorphin has a higher binding affinity (3 fold) at the Asp40

mutated receptor than for the receptor encoded by Asn40 (Beyer et al., 2004). In

addition, β-endorphin has more potency at the Asp40 variant in activating G protein-

coupled inwardly-rectifying potassium channels (GIRKs) compared to the receptor

encoded by the Asn40 allele (Haile et al., 2008). However, other studies reported that

the binding affinity or potency of β-endorphin for the variant receptor was not found to

be different from that for the normal receptor (Beyer et al., 2004). A recent meta-

analysis reported that the OPRM1 Asn40Asp does not appear to affect risk for drug

dependence (Arias et al., 2006), but other studies reported that they may influence

response to opioid antagonist treatment (Naltrexone) of drug dependence (Oroszi et al.,

2009). The Asp40 allele frequency varies considerably between different ethnic

backgrounds (Haile, 2008). For example, Asp40 was reported in less than 5% of

African-Americans (Gelernter et al., 1999) and 16% in European Americans (Zhang et

al., 2006), but is found in 58% of Asians (Kim et al., 2004).

In the present study, 22 SNPs spanning the coding sequence of the OPRM1 locus were

examined. To our knowledge, no previous study has evaluated a series of SNPs that

map the full region of the locus from upstream (5'-UTR) to downstream (3'-UTR).

These SNPs were chosen because they showed the greatest potential to distinguish

between drug dependent patients and control subjects in previous studies (Hoehe et al.,

2000). The relationship between the alleles and genotypes of these 22 SNPs and

susceptibility to drug dependence were investigated.

255


Subject

The study group consisted of 220 unrelated Jordanian Arab males meeting the

Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria (American

Psychiatric Association, 1994) for drug dependence (84% opioid, 14% amphetamine

and 4% Alcohol). The majority of these patients (92%) also had nicotine co-

dependence. Cannabis abuse was common (58%) and 53% were alcoholics. The

patients were undergoing a voluntary 8 week treatment program at two Jordanian Drug

Rehabilitation Centers. In addition, 240 controls from an ethnically homogenous

Jordanian Arab population with no lifetime history of psychosis or mood disorders,

alcohol or heroin dependence according to the DSM-IV were recruited from the Blood

Bank of the King Abdullah Hospital University, Jordan University of Science and

Technology. These controls were frequency matched by age, sex and ethnicity to the

case subjects. The mean ages (±SD) of the patients and controls were 32.7 ± (8.4) and

31.5 ± (5.6) years, respectively. Approval to study these patients was granted by

Human Research Ethics Committee of the University of Western Australia (UWA) (Ref

No. RA/4/1/4344). Each participant gave signed informed consent.

DNA Extraction and Genotyping

Genomic DNA was extracted from whole blood using a Gentra Puregene Kit (Qiagen,

Valencia, CA, USA). All individuals were genotyped for 22 SNPs within the OPRM1

gene (Figure 2) using the sequenom MassARRAY® system (iPLEX GOLD)

(Sequenom, San Diego, CA, USA). A scatter plot of rs6912029 SNP colored according

to genotype calls: AA (green), GA (yellow) and GG (blue) and no call (red) is shown in

Figure 3.


Hardy-Weinberg Equilibrium (HWE) was tested to determine if the population was

fulfilling the HWE at each variant locus. A Pearson’s Chi squared test was used for

genetic association between cases and controls. A p value < 0.05 was considered to be

statistically significant. All statistical analyses were performed using SPSS statistical

package 19.0 (SPSS Corp., Chicago, Illinois, USA). Bonferroni corrections were

applied for multiple comparisons.

256

RESULTS

The present results were based on 220 opiate drug dependent patients in treatment, all

who were males of Arab descent. The median age of the patients was 32 (range: 18 to

58) and 203 patients were smokers (92%). In addition, a control group of 240 subjects

(median age: 35, range: 18 to 62) of an ethnically homogenous Jordanian Arab

population.

Genotyping of the 22 OPRM1 SNP genotypes was highly accurate with average success

rate of 99.6%. Genotype discrepancy average rates across the 22 loci were only 0.05%

(±0.12%) in the Jordanian Arab samples. No significant deviation from Hardy-

Weinberg equilibrium was observed in Arab controls and cases.

Cases and controls were compared for genotype and allele frequencies across the 22

OPRM1 markers (Table 1). In the Arab population, genotype frequencies at three sites

differed significantly between cases and controls. The significant sites were rs6912029

(G-172T; p value = 0.04), rs12205732 (G-1510A, p value = 0.04) and rs563649 (G-

983A, p value = 0.049). Allele frequency comparisons between cases and controls were

significant at two sites: rs6912029 (G-172T, p value = 0.032, data not shown),

rs12205732 (G-1510A, p value = 0.032, data not shown).

CONCLUSION

In conclusion, conflicting results have been published on the influence of OPRM1

A118G on the increased risk of drug dependence (Lötsch et al., 2002, Kim et al., 2004)

but other functional SNPs have not been examined in detail. This is the first report of

an association between the OPRM1 G-172T and G-1510A polymorphisms and drug

dependence in an Arab population. These results suggest that the OPRM1 G-172T and

G-1510A may play role in drug dependence mechanisms in Middle Eastern populations.

Currently, these samples are being tested for a possible association of OPRM1

polymorphisms with treatment outcomes. This may lead to more accurate matching of

individuals to different treatment options and early identification of individuals of high

risk of relapse. However, our findings require replication in both Arab and other ethnic

groups to confirm these results and identify individuals at increased risk of dependence.

257

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KREEK M.J., YU L: Single nucleotide polymorphism in the human mu opioid

receptor gene alters beta-endorphin binding and activity: possible implications for

opiate addiction. Proc Natl Acad Sci USA, 95, 9608–9613. 1998.

7. BENYHE, S., TÓTH, G., KEVEI, J., SZUCS, M., BORSODI, A., DI GLÉRIA,

K., SZÉCSI, J., SULI-VARGHA, H., MEDZIHRADSZKY, K. Characterization

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A118G polymorphism on binding affinity, potency and agonist-mediated

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K.K., BERRETTINI W.H., CHURCH G.M. Sequence variability and candidate

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variation with substance dependence. Hum Mol Gene, 9, 2895-2908. 2000.

11. BERGEN A.W., KOSKOZKA J., PETERSON R., LONG J.C., VIRKKUUNEN

M., LINNOILA M., GOLDMAN D. Mu opioid receptor gene variants: lack of

association with alcohol dependence. Mol Psychiatry, 2, 490-497. 1997.

12. OROSZI G., ANTON R.F., O’MALLEY S., SWIFT R., PETTINATI H.,

COUPER D., YUAN Q., GOLDMAN D. OPRM1 Asn40Asp predicts response to

naltrexone treatment: A haplotype based approach. Exp Clin Psychopharmacol,

33, 383-393. 2009.

13. GELERNTER J., KRANZLER H., and CUBELLS J. Genetics of two mu opioid

receptor gene (OPRM1) exon I polymorphisms: population studies, and allele

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KRUPITSKY E, ZVARTAU E., GELERNTER J. Association between two mu-

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J.M., KIM M.J., OSLIN D.W. Association of functional opioid receptor

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16. LÖTSCH J., ZIMMERMANN M., DARIMONT J., MARX C., DUDZIAK R.,

SKARKE C., GEISSLINGER G. Does the A118G polymorphism at the mu-

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259

Figure 5. Opiate drug pathway mechanisms.

260

Figure 2. Schematic structure of the human μ-opioid receptor gene (OPRM1) based on the MOR-1 transcript variant (PubMed:

NM_000914.2). The positions of the 22 SNPs genotyped and their dbSNP IDs are also shown.

261

Figure 3. Representative Scatter plot from sequenom data.

262

Table 1. Association of OPRM1 SNPs with opiate drug dependence

SNP ID Chr Position SNP SNP Gene Location Pearson Chi-Square p-value

rs6912029 154402201 G>T Upstream (5'-UTR) 4.20 0.040

rs12205732 154400626 A>G Upstream (5'-UTR) 4.20 0.040

rs1799971 154402490 A>G Exon 1 0.03 0.980

rs510769 154403712 A>G Intron 1 0.03 0.980

rs511435 154410240 A>G Intron 1 0.03 0.980

rs524731 154416785 C>A Intron 1 0.02 0.990

rs3823010 154420845 A>G Intron 1 1.81 0.410

rs1381376 154434951 A>G Intron 1 1.73 0.420

rs3778151 154435373 C>T Intron 1 0.33 0.854

rs563649 154449660 A>G Exon 2 4.66 0.049

rs2075572 154453697 C>G Intron 3 3.76 0.150

rs540825 154456139 T>A Intron 3 1.76 0.410

rs675026 154456256 G>A Intron 3 0.53 0.770

rs562859 154456266 A>G Intron 3 0.63 0.737

rs548646 154459840 A>C/G>T Intron 3 0.11 0.940

rs648007 154464304 C>T Intron 3 0.03 0.990

rs9322447 154466013 G>A Intron 3 3.20 0.192

rs609148 154472707 C>T Intron 3 2.36 0.300

rs606148 154477679 G>T Intron 3 2.21 0.331

rs632395 154478944 C>T Intron 3 0.74 0.690

rs648893 154480321 C>T Intron 3 0.03 0.990

rs671531 154482434 A>G Downstream (3'-UTR) 1.07 0.585

263

CHAPTER 8

ΜU-OPIOID RECEPTOR (OPRM1) AS A PREDICTOR

OF TREATMENT OUTCOME IN OPIATE

DEPENDENT INDIVIDUALS OF ARAB DESCENT

This chapter was published in the Journal of Pharmacogenomics and Personalized

Medicine.

264

Chapter 8

Μu-Opioid Receptor (OPRM1) As a Predictor of

Treatment Outcome in Opiate Dependent Individuals of

Arab Descent

In the previous Chapter, we identified genetic variations within Opioid receptor

(OPRM1) gene that are associated with opiate drug dependence. The major aim in this

chapter was to investigate whether the genetic polymorphisms within the OPRM1 gene

in opiate dependent patients of Arab descent are associated with opiate consumption, a

range of dependence and clinical variables, psychiatric symptoms and treatment

outcomes. This chapter was published in the Journal of Pharmacogenomics and

Personalized Medicine.

The μ-opioid receptors (MOR) are a class of opioid receptors with high affinity for

enkephalins and beta-endorphin but low affinity for dynorphins (Gianoulakis and

Barcomb, 1987). They are also referred to as μ opioid peptide (MOP) receptors. The μ

receptor agonist is the opium alkaloid morphine and μ refers to morphine (Waldhoer,

Bartlett, Whistler, 2004). For example, heroin is an opioid synthesised from morphine,

which in turn is a derivative of the opium poppy. Heroin is converted into morphine in

vivo and activates the μ, δ and κ opioid receptors (primarily μ), which modulate several

physiological systems including the pain/reward systems (Pasternak, 1993, Uhl et al.,

1999, Sora et al., 2001, Kieffer and Gaveriaux-Ruff, 2002, Kreek et al., 2005, Johnson

et al., 2011). The OPRM1 gene has been the focus of several pharmacogenetic

investigations into treatment responses. In particular, the coding region polymorphism

A118G (Asn40Asp) has been the main focus of substance abuse research. The Asp40

genetic variant has recently been associated with favourable outcomes in nicotine

(Lerman et al., 2004), alcohol (Anton et al., 2008, Kim et al., 2009) and opiate (Kreek

et al., 2005; Mayer et al., 2007) treatment.

In this chapter; we investigate the genetic associations of a series of markers spanning

the coding sequence of OPRM1 gene with the responsiveness to the biopsychosocial

265

treatment in opiate dependent Arab population. Opiate dependent patients (N = 183)

were genotyped using the Sequenom MassARRAY® system (iPLEX GOLD).

Analysis of the relationship between treatment response and the OPRM1 SNPs showed

there was a significant difference in the genotyping distribution between the three

inclusion groups (good, moderate and poor responders) in response to the

biopsychosocial treatment at two sites (rs6912029 [G-172T] and rs12205732 [G-

1510A]) of the OPRM1 gene. In contrast, Previous studies have reported an

association of the OPRM1 A118G (Asn40Asp) polymorphism at position rs1799971

with different opioids (Klepatad et al., 2004; Lotsch et al., 2002, Oertel et al., 2006;

Romberg et al., 2005) and opiate antagonist treatment with naltrexone (Oroszi et al.,

2009, Gelernter et al., 2007). However in this study, we found no influence of this SNP

on responsiveness to treatment, as the allele and genotypes frequencies were similar in

the opiate dependent patients.

This study is the first to investigate a genetic association of a series of OPRM1 SNPs

with treatment outcome in opiate dependent Jordanian Arab population. This may lead

to more accurate matching of individuals to different treatment options including

pharmacotherapy and identify at an early stage persons at high risk for relapse and

therefore requiring more intensive intervention.

This manuscript was prepared by Laith Al-Eitan with support from the co-author listed.

DNA extraction, SNPs selection; experimental design and statistical analysis were

conducted by AL-Eitan with assistance from the co-author listed. Patient samples

selection and DNA extraction were done through a collaboration link with Dr Jaradat

at Princess Haya Biotechnology Centre (PHBC). DNA samples were sent to the

Australian Genome Research Facility (AGRF; Melbourne Node, Melbourne, Australia)

for genotyping. Dr Steve Su, a biostatistician at University of Western Australia’s

School of Mathematics and Statistics who provided support and advice regarding the

statistical methods and analysis. A part of the work described in this chapter was

presented by AL-Eitan as an oral presentation at The Australian Society for Medical

Research (ASMR) Symposium, 7 June, 2012, Western Australia, Australia. Dr Hulse

and Dr Tay were assisted in designing the study to proof reading the manuscripts. All

Authors proof read the manuscript.

266

Μu-Opioid Receptor (OPRM1) As a Predictor of Treatment Outcome in Opiate

Dependent Individuals of Arab Descent

Laith N. AL-Eitan1, Saied A. Jaradat 2 , Steve Y.S. Su 3 , Guan K. Tay 1

, Gary K. Hulse 5,4


6009, Australia 2 Princess Haya Biotechnology Center, Jordan University of Science and

Technology, Irbid 22110, Jordan 3 School of Mathematics and Statistics, The University of Western Australia,

Crawley, WA 6009, Australia 4 School of Psychiatry and Clinical Neurosciences, Queen Elizabeth II Medical



Australia

Abbreviated title: Μu-Opioid Receptor (OPRM1) and Treatment Response

Keywords: OPRM1, Association, Opiates, Dependence, Treatment response,

Arab.


Western Australia


Laith N. AL-Eitan




Phone: + 61 8 6488 7286

Fax: + 61 8 6488 7285


[email protected]


267

SUMMARY

Background: A number of research studies on genetics of opiate dependence have

focussed on the µ-opioid receptor (OPRM1), which is a primary target for opiates. This

study aims to identify genetic polymorphisms within the OPRM1 gene involved in

response to the biopsychosocial treatment in opiate dependent individuals of Arab

descent.

Methods: Unrelated Jordanian Nationals of Arab descent (N = 183) with opiate

dependence were selected for this study. These individuals, all males, met the DSM-IV

criteria for opiate dependence and were undergoing a voluntary 8 week treatment

program at a Jordanian Drug Rehabilitation Centre. All individuals were genotyped for

22 single nucleotide polymorphisms (SNPs) within the OPRM1 gene using the

Sequenom MassARRAY® system (iPLEX GOLD). Statistical analyses were carried

out using R package.

Results: Patients receiving the biopsychosocial treatment showed that there was a

significant difference in their OPRM1 SNPs genotyping distribution between good,

moderate and poor responders to the treatment at two sites (rs6912029 [G-172T], and

rs12205732 [G-1510A], p < 0.05, Fisher’s exact test).

Conclusion: This study is the first report of an association between the OPRM1 G-

172T and G-1510A polymorphisms and treatment response for opiate dependence.

Specifically, this study demonstrated that OPRM1 GG-172 and GG-1510 genotypes

being more frequent among patients who were non-responders to the biopsychosocial

treatment. However, further pharmacogenetic studies in a larger cohort of opiate

dependent patients of Arab descent are needed to confirm these findings and identify

individuals with increased chance of relapse.

268

INTRODUCTION

Opiates are considered one of the most addictive illicit drugs.1 Anthony et al. (1994)

reported that 23% of individuals who use opiates at least once in their lifetime, become

dependent, compared to17% for cocaine and 13% for other illicit drugs.2 Opiate

dependence has the highest propensity for causing physical harm to the user, and

societal harm through damage to family and social circles. The economic costs of

opiate dependence are also high and include the costs of health care, social care and

crime.3-6

Currently, the three major maintenance pharmacotherapies for the treatment of opioid

dependence are methadone, buprenorphine and naltrexone.7 When combined with

psychosocial services, these maintenance treatments are effective in reducing opiate use,

dangerous behavior and criminal activity, whilst improving the mental health of

patients.6,7

However, the majority of opiate-dependent individuals remain out of

treatment, and those who remain in treatment exhibit only 60–70% success rates.6

Therefore, it remains an essential goal to further the understanding of the factors

underlying poor treatment outcomes and assist in the development of more

individualized, optimized and ultimately more effective treatments for opiate drug

dependence.

Pharmacogenetic studies have shown that both methadone and buprenorphine displays a

large interindividual variability in its pharmacokinetics and contributes to high

interindividual variability in response to opiate dependence treatment.7-9

Meta-analyses

of many studies of the naltrexone hydrochloride medication have also suggested that the

effect size for response over placebo is in the small to moderate range.10-14

Several

genetic studies have suggested that naltrexone does not have therapeutic effects in some

alcohol-dependent individuals.14-17

Human laboratory studies have also reported that

alcohol increases endogenous opioid more in patients with a family history of alcohol

dependence.18

Apart from alcohol dependence, little attention devoted to possible

genetic factors affecting the treatment response to naltrexone in opiate drug dependence.

However, demonstrations of the role of the brain opioidergic system in mediating drug

tolerance and dependence have identified it as a potential source of interindividual

variability in the pharmacodynamics response to opioids (e.g. heroin, buprenorphine,

and methadone) and opiate antagonist such as naltrexone.14-17

269

Research into the genetics of opiate dependence has focused on the opioidergic system,

which is the primary target for opiates in particular, heroin.7,19

Heroin is converted into

morphine in vivo and activates the opioid receptors (μ, δ, κ).7,20,21

This modulates

several physiological process such as pain, reward, nociception, immune and gastric

function, and stress and treatment responses.7,20,21

The opioid receptor µ 1 (OPRM1) is

thought to account for most of the opioidergic effects.20-22

OPRM1 is also the primary

site of action of endogenous ligands such as β-endorphin and enkephalin23

and µ-opioid

receptor antagonists such as naltrexone,24

agonists such as methadone25

or partial

antagonists such as buprenorphine.20

Haile et al. (2008) reported that opiate’s

physiological and subjective effects are associated with enhanced release of β-

endorphin.19

The OPRM1 gene (6q24-q25; Gene ID: 4988) encodes the µ opioid receptor which is

widely distributed in the brain.26-28

The OPRM1 receptor is a membrane of the G

protein-coupled receptor family23,29

and over 300 OPRM1 sequence variants have been

identified to date.30,31

The A118G single nucleotide polymorphism (SNP) in exon 1 of

OPRM1 gene is the most frequently SNP found in human population.32

This

polymorphism encodes an Asn40Asp amino acid substitution that appears to be

associated with changes in function. Bond et al (1998) reported that β-endorphin has a

higher binding affinity (3 fold) at the Asp40 mutated receptor than at the receptor

encoded by Asn40.33,34

In addition, β-endorphin activates G protein-coupled inwardly-

rectifying potassium channels (GIRKs) more in the presence of the Asp40 allele than

the Asn40 allele.33,34

However, other studies reported that binding affinity or potency of

β-endorphin for the variant receptor is not different from the normal receptor.23,33-35

A

recent meta-analysis reported that the OPRM1 Asn40Asp does not appear to affect risk

for drug dependence,36

but other studies report it may influence response to opioid

antagonist treatment for alcohol dependence using naltrexone.37

Various studies have provided varied and conflicting results for an association between

opioid receptor gene polymorphism and treatment response.35-41

For example, Oslin et

al. (2003) found that individuals with the Asp40 allele had significantly lower rates of

relapse and took longer to resume heavy drinking than Asn40/Asn40 homozygous in a

sample of 130 European American alcoholics receiving naltrexone as treatment.42

A

similar result has been found in Korean alcoholics with higher therapeutic effect of

270

naltrexone in individuals who had the Asp40 variant genotype compared to the Asn40

genotype.39

As very few pharmacogenetics studies have been conducted on the treatment of opiate

dependence, such studies are required to better understand how opiate addicts respond

to specific drug treatments. This study aimed to identify genetic variations within the

OPRM1 gene involved in responsiveness to the biopsychosocial treatment in Jordanian

opiate dependent patients of Arab descent.

271

MARTIALS AND METHODS

Study Patients

Patients for this study were recruited from inpatient and outpatient programs at the

National Centre for Rehabilitation of Addicts (NCRA) at Jordanian Ministry of Health

and the Drug Rehabilitation Centre at the Jordanian Public Security Directorate (DRC-

PSD). Inclusion criteria were having diagnosis of opiate dependence and being

unrelated Jordanian Arab males and between 18 and 58 years of age. The Diagnostic

and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American

Psychiatric Association, 1994) was used to assess medical and psychiatric status of

patients.43

Psychiatric diagnosis was established using a structured baseline interview

which was based on the Addiction Severity Index (ASI) criteria.44

The clinical

diagnoses and structured clinical interviews were conducted by independent psychiatric

consultants. Patients were excluded if any of the following applied: axis-I comorbidity

such as diagnosis of schizophrenia, major depression, bipolar I and II disorder,

schizoaffective disorder, a serious medical illness or those receiving psychotropic

medications. Those patients with serious medical conditions such as neuroendocrine,

metabolic or cardiovascular diseases, neurodegenerative disorders or epilepsy were also

excluded. If patients used more than one substance, they were included only if their

major drug dependence was opiate. Initially, 500 patients were screened (see Figure 1).

Based on the inclusion and exclusion criteria mentioned above, 350 patients were

approached to participate in this study. Of these patients, 130 patients could not finish

the treatment program for clinical reasons. Of the reaming patients, 220 agreed to be

part of this study. A further 37 (16%) patients were then excluded from the final

analysis due to the type of drug dependence and the samples were only limited to opiate

dependence. In total, complete data was obtained from 183 patients with opiate

dependence.


Association Code of Ethics (2006) and the World Medical Association Declaration of



Australia (2007). Ethical approval to conduct this research was granted by the Human

Research Ethics Committee of The University of Western Australia (Ref No.


272

Jordanian Ministry of Health (Ref No. Development/Trainees/535) and by the

Institutional Review Board of the Jordan University of Science and Technology (Ref

No. RA/16/1/2010). Written informed consent was obtained from all patients before

participation in the study.

Study Design

At the initial screening, several procedures were undertaken to collect demographic and

clinical data. These included capturing demographic data such as date of birth, gender,

nationality, marital status, children and occupation. A medical history including drug

overdose and suicide attempts was also taken from their medical records. Information

on substance abuse was also collected from their medical records including type of

substance, cause of addiction, initial date of addiction, starting and last taken amount of

opiate, route of administration, withdrawal periods, and hospitalisation due to opiate

abuse. Data on smoking status and blood relatives with history of drug abuse was also

collected from their medical records. All data was coded and no specific individual was

identified.

Treatment Approach

All subjects who met the DSM-IV criteria for opiate dependence received

biopsychosocial treatment in the NCRA and DRC-PSD programs (see Figure 1).

Treatment consisted of seven day inpatient detoxification using a regime of diazepam

medication and seven week oral naltrexone as maintenance treatment. This is often

accompanied by withdrawal symptoms and occasionally fatal side effects.7 Four other

oral medications (Lofexidine, Loperamide, Metoclopramide and Ibuprofen) were used

to ameliorate other withdrawal symptoms such as stomach cramps; diarrhoea and

nutritional supplementation (vitamin B and thiamine) were provided for 4 weeks. The

NCRA and DRC-PSD treatment program also provide patients with psychosocial

support. Inpatients groups were offered counselling sessions 3 times a week for a total

of 8 weeks. Each session lasts for 2 hours. In addition, patients participate in 1 hour

individual counselling sessions per week over the 8 week treatment program. These

participants were described as patients completing treatment and continuing with

aftercare programs.

273

In the NCRA and DRC-PSD program, chlordiazepoxide is prescribed for outpatients as

it has lower abuse potential. Valium (diazepam) is used for inpatients as it has a faster

action and a higher dose effect. All patients are given four doses of diazepam every day

at early morning, mid-day, early evening and at bedtime and reviewed daily to assess

withdrawal symptoms. On admission, they are given four separate doses of 30mg. The

next day’s planned dosages are based on ongoing assessment of the patients’ symptoms

rather than the length of the course or the prescribed starting dose. The patients

recruited for the study were divided into three groups according to the severity of their

withdrawal symptoms. The first group of patients (lower-dose responders) had the

lowest severity of withdrawal symptoms and was medicated with 10mg diazepam four

times a day. The second group of patients (high-dose responders) had moderately

severe withdrawal symptoms and was medicated with equal to or greater than 20mg

diazepam four times a day. The third group of patients (high-dose non-responders) was

non-responders to treatment with severe withdrawal symptoms and medicated with

equal to or greater than 30mg diazepam four times a day. Four other oral medications

were used to ameliorate other withdrawal symptoms: Lofexidine, Loperamide,

Metoclopramide and Ibuprofen. Lofexidine, an alpha-adrenergic agonist was used to

reduce the other withdrawal symptoms such as chills, sweating, stomach cramps,

diarrhoea, muscle pain, runny nose and watering eyes. For these patients, 10 day course

of lofexidine treatment starting at 0.2mg two to four times a day and increasing 0.4mg

until a maximum of 2.4mg. Loperamide was used for treatment of diarrhea. All

patients were given an initial dose of 4mg after each loose stool up to a total of 16mg

per day. Metoclopramide was used for nausea and vomiting treatment. Patients were

prescribed 10mg/day up to a maximum of 30mg/day. Ibuprofen was used to reduce

fever and headaches and treat muscle pains. Patients were given an initial dose of

0.4mg Ibuprofen every 4 to 6 hours, increased to a maximum daily dose of 1.6mg

according to their response and tolerance to the drug. Supplementary vitamins (e.g.

vitamin B and thiamine) were also provided to patients who were malnourished.

For the patients who had successfully completed detoxification and withdrawal period

and had been opiate free for more than 7 days, the opiate antagonist, naltrexone, was

used as an aid to prevent relapse and promote abstinence. The drug was given at a

starting dose of 25mg/day, increased to 50mg/days. Sometimes, patients received an

average of 50mg naltrexone/day either delivered as 50mg/day or 100mg on Monday,

100mg on Wednesday and 150mg on Friday to aid compliance.

274

Patients completing the intensive program are offered an aftercare program which

includes one individual counselling session per week for up to 6 months. The treatment

approach is problem oriented and focuses on achieving well defined goals such as the

availability, accessibility, affordability and efficiency to all substance abuse patients in

need of treatment. These objectives are outlined in a treatment plan that is prepared at

admission and is updated for each new treatment program. Medical, behavioral,

supportive and relapse prevention strategies are drawn from different treatment models.7

In this study, the treatment team was kept blind to the genetic status of the patients.

Outcomes Measures

A semi-structured baseline interview was developed from the ASI criteria. These

interviews were used to collect demographic and clinical data of the 183 patients. The

family history of substance abuse was also obtained. This allowed the subset of drug

dependent patients whose addiction may be influenced by genetic factor to be

identified.45

Berrettini and Persico (1996) suggest that the likelihood of detecting

susceptibility genes is higher in these individuals.46

In treatment, end-of-treatment and

follow-up assessments were also conducted to provide valid estimates of opiate

abstinence. Retention and attrition from treatment were also recorded (see Figure 1).

The measures to assess outcome were number of negative urines. Urine drug screening

(UDS) was performed for all patients at the NCRA and DRC-PSD on the day of

admission to the treatment program. Every week during the treatment, after treatment

completion and each follow up, the patients were randomly screened for drugs using

The Multi Drug Test 10 Panel (Jant Pharmacal Corporation, CA, USA). This test is a

one-step immunoassay for the detection of cannabis, cocaine, phencyclidine, opiates,

methamphetamine, methadone, amphetamine, barbiturates, benzodiazepines and

oxycodeine. The number of negative UDS for opiate was adopted as a measure of

opiate abuse during treatment and after treatment. Missed urines samples were not

taken into account for data analysis.

The number of drug treatments and detoxification and rehabilitation treatments were

recorded as this offered another estimate of treatment retention. Attendance was

determined as duration from the date of admission to date of the last visit. The number

of treatment sessions attended by the patients including the total number of counselling

and self-help sessions was calculated. This measure was used to reflect participation in

275

the treatment process. The number of patients who dropped out treatment was also

considered. Attrition was defined as patients who stopped attending the treatment

program within 7 days of admission. Attendance at aftercare treatment was also

recorded. The opiate dependent patients (N = 183) were divided into good (n = 66),

moderate (n = 32) and poor (n = 85) responders to the biopsychosocial treatment based

on the biological, clinical and psychological outcome measures mentioned above.

SNP Selection and Genotyping

To date, 10 human OPRM1 splice variants have been identified. They contain the same

exons 1, 2 and 3 of normal human OPRM1 (Gene ID: 4988, Gene bank Accession #:

NM_000914.2, Gene Alias: KIAA0403, MOR, MOR1, OPRM), which has four exons.

However, they differ in their splicing downstream of exon 3. All splice variants result

in amino acid sequence changes to the C-terminus of the μ-opioid receptor and may

affect the activity of the receptor.47-49

Of these, the normal human OPRM1 gene has

been most extensively investigated (Table 1). In this study, 22 OPRM1 SNPs were

selected from public databases including: the SNP database of the National Centre for

Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/SNP/), the Applied

Biosystems SNP database (http://www.appliedbiosystems.com) and the International

HapMap Project (http://www.hapmap.org/). The positions of the SNPs in OPRM1 and

the relative distance to the translation initiation site are given in Table 1.

Genomic DNA was extracted from whole blood using a Gentra Puregene Kit (QIAGEN

Inc., Valencia, Calif.). All individuals were genotyped for the chosen 22 SNPs within

the OPRM1 gene using the sequenom MassARRAY® system (iPLEX GOLD)

(Sequenom, San Diego, CA, USA). Briefly, PCR and single base extension primers

(SBE) were designed using MassARRAY assay design 3.1 software (Sequenom

MassARRAY system) that allows iPLEX reactions for SBE designs with the modified

masses associated with the termination mix. Manufacturer’s instructions for the

multiplex reaction were followed in the whole process, including the PCR amplification

(Sequenom, San Diego, CA, USA), the shrimp alkaline phosphatase (SAP) enzyme

(Sequenom, San Diego, CA, USA) treatment to dephosphorylate dNTPs unincorporated

in the PCR, the SBE reactions using an iPLEX GOLD assay (Sequenom, San Diego,

CA, USA), and the clean-up with a resin kit (Sequenom, San Diego, CA, USA) to desalt

the iPLEX reaction products. PCR and SBE primers sequences and all protocol

conditions are available upon request. Reaction products were dispensed onto a 384-




276

element SpectroCHIP bioarray (Sequenom) using a MassARRAY Nanodispenser and

assayed on the MassARRAY platform. Mass differences were detected with matrix-

assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF

MS). MassARRAY Workstation v.3.3 software was used to process and analyse the

iPLEX SpectroCHIP bioarray. Typer Analyzer v.4.0.2 software was used to analyse all

genotypes obtained from the assays.


Consistency with Hardy-Weinberg equilibrium (HWE) was tested using an exact test

implemented in PowerMarker software; version 3.25.50

Continuous variables were

compared using the ANOVA F-test, Kruskal-Wallis test, Wilcoxon Rank Sum test,

Welch test and Student’s t –test as appropriate. Categorical variables were analysed

using Pearson’s χ2 tests, and Fisher exact test. A significance level of α=0.05 was

applied and a p < 0.05 was considered to be statistically significant. All statistical

analyses were carried out using R software (http://www.r-project.org/).

277

Figure 1. Flow chart of subjects, treatment approach and outcome measures.

Abbreviations: ASI, The Addiction Severity Index; DSM-IV, The

Diagnostic Manual of Mental Disorders, Fourth Edition.

278

RESULTS

Sample Characteristic

The study group comprised of 183 unrelated Jordanian Arab males meeting the DSM-

IV criteria (American Psychiatric Association, 1994) for opiate dependence. The

majority of these patients (92%) also had nicotine co-dependence. Cannabis abuse was

common (58%) and 53% were alcoholics. The mean age (±SD) of these patients was 33

± (8.6) years with a range of 18 to 58 years. The median age of the patients was 32

(range: 18 to 58). Both genotype and phenotype data were available for these patients.

All patients received biopsychosocial treatment at the NCRA and the DRC-PSD for 8

weeks. There were 66 (36%) good responders, 32 (18%) moderate responders and 85

(46%) poor responders. The description of the three groups, including demographic,

nicotine status, opiate consumption (beginning and last taken amount (grams per day)),

drug use, dependence variables, substance screening, drug toxicity, psychiatric status

and hospitalisation treatment is given in Table 2.

Association of SNP OPRM1 Genotypes with Opiate Dependence Variables and

Treatment Response

Genotypes determined by sequenom MassARRAY® system for the 22 OPRM1 SNPs

were highly accurate with an average success rate of 99.6%. The average genotype

discrepancy rate across the 22 loci was only 0.05% (±0.12%) in the samples.

For the 183 opiate dependent patients, no deviations from HWE was observed for the

allele and genotype frequencies for the 22 SNPs (p >0.5). When comparing the three

inclusion groups (good, moderate and poor responders), significant differences in

proportions among genotypes were observed at two sites of OPRM1 gene with response

to the biopsychosocial treatment (rs6912029 [G-172T], p = 0.0329 and rs12205732 [G-

1510A], p = 0.0333) (Table 4). Specifically, the GG-172 and GG-1510 carriers were

more frequent in non-responders to treatment. For example, The GG-172 carriers were

48% in the non-responders group (n = 88) whereases they were 18% in the moderate

responders group (n= 35) and they were 34% in the responders group (n = 70).

There were also significant differences in proportion of opiate use at treatment

admission at six sites of OPRM1 gene (rs2075572 [C644-83G], p = 0.011, and rs648893

279

[C1165-1189T], p = 0.014, rs609148 [G1165-8803T], p = 0.028, rs9322447

[G1164+11714A], p = 0.038, rs671531 [A1164+28135A G], p = 0.032, rs540825

[T1164+1839A], p = 0.045). However, there were no significant differences for the 22

SNPs for the remaining opiate dependence variables (age at first use, age at regular use,

year’s regular use and frequency of use) excluding the six sites mentioned above

concerning the frequency of opiate use (Table 3).

The duration since last positive urine screening for opiate was not significantly different

according to the different genotypes (p > 0.3, data not shown). Opiate drug

consumption and the number of drug treatments, detoxification and rehabilitation were

also not significantly different among opiate dependent patients (p > 0.2; data not

shown). Exploratory comparisons between SNPs OPRM1 and psychiatric status (e.g.

impulsive, depression, anxiety, craving euphoria and diminution of attention) at

admission and past history as obtained from their medical records (Family with history

of drug use, overdose toxicity and suicide attempts) found no significant group

differences in opiate dependent patients (p > 0.1, data not shown).

280

Table 1. List of SNPs within OPRM1 gene, their positions and genotyping data based on the whole cohort (N = 183)

SNP_ID Chr positiona SNP SNP gene location Discrepancy rate

b Call rate

c

rs6912029 154,402,201 G>T Upstream (5'-UTR) 0.00% 100%


rs1799971 154,402,490 A>G Exon 1 0.00% 100%

rs510769 154,403,712 A>G Intron 1 0.00% 100%

rs511435 154,410,240 A>G Intron 1 0.00% 100%

rs524731 154,416,785 C>A Intron 1 0.05% 99%

rs3823010 154,420,845 A>G Intron 1 0.00% 100%

rs1381376 154,434,951 A>G Intron 1 0.00% 100%

rs3778151 154,435,373 C>T Intron 1 0.00% 100%

rs563649 154,449,660 A>G Exon 2 0.00% 100%

rs2075572 154,453,697 C>G Intron 2 0.00% 100%

rs540825 154,456,139 T>A Intron 3 0.07% 99%

rs675026 154,456,256 G>A Intron 3 0.12% 99%

rs562859 154,456,266 A>G Intron 3 0.25% 99%

rs548646 154,459,840 A>C/G>T Intron 3 0.52% 97%

rs648007 154,464,304 C>T Intron 3 0.00% 100%

rs9322447 154,466,013 G>A Intron 3 0.00% 100%

rs609148 154,472,707 C>T Intron 3 0.07% 99%

rs606148 154,477,679 G>T Intron 3 0.00% 100%

rs632395 154,478,944 C>T Intron 3 0.00% 100%

rs648893 154,480,321 C>T Intron 3 0.07% 99%

rs671531 154,482,434 A>G Downstream (3'-UTR) 0.00% 100% g. Chromosome positions are based on NCBI Human Genome Assembly Build 36.3. h. Ratio of the number of discordant genotypes to the number of duplicates. i. Ratio of the number of valid genotypes to the number of subjects genotyped (N = 183) at each locus.

281

Table 2. Detailes of the 183 opiate dependent patients classified into three groups

Category Subcategory Good responders Moderate responders Poor responders p valuef

Demographics

Patients (n, %) (66/183) 36% (32/183) 18% (85/183) 46%

Agea [years] 31.0 [7.64] 33.1 [9.64] 34.5 [8.68] 0.0469

g

Marital (single, married, divorced) (%) (47%, 53%, 0%) (47%, 50%, 3%) (24%, 72%, 5%) 0.0055h

Education (%) 26% 16% 20% 0.4770i

Employment (%) 33% 31% 44% 0.3120i

Children (%) 41% 44% 75% <0.0001i

Family with history of drug use (%) 21% 31% 20% 0.4110i

Nicotine status

Nicotine (%) 80% 94% 82% 0.1830i

Agea when start smoking [years] 13.1 [7.33] 14.6 [5.00] 12.9 [6.99] 0.4880

g

Nicotine cigarette per daya (#) 23.9 [18.30] 34.8 [16.70] 29.6 [21.80] 0.0304

g

Opiate consumption

Beginning taken amounta (grams per

day)

0.495 [0.27] 0.362 [0.215] 0.864 [2.67] 0.3030g

Last taken amounta (grams per day) 0.996 [0.67] 0.86 [0.52] 1.43 [0.81] <0.0001

g

Other Drug useb

Alcohol (%) 61% 53% 67% 0.3590i

Cocaine (%) 3% 3% 2% 1.0000h

Benzodiazepines (%) 55% 31% 68% 0.0014i

Amphetamine (%) 14% 28% 8% 0.0203i

Cannabis (%) 56% 31% 60% 0.0184i

Opiate Dependence

Agea first opiate use (years) 22.1 [6.51] 22.8 [7.08] 22.3 [6.08] 0.8850

g

Agea of onset (years) 24.6 [6.68] 24.7 [7.64] 23.8 [6.76] 0.7150

g

Duration of opiate usea (years) 6.02 [3.48] 7.62 [4.96] 11.9 [6.9] <0.0001

j

Drug screening Positive admit UDSc (%) 0% 0% 98% <0.0001

i

282

Table 2 (continued)

Category Subcategory Good responders Moderate responders Poor responders p valuef

Medical history

d

Overdose (%) 34% 44% 53% 0.0657

i

Hepatitis (C virus infectious) 5% 6% 2% 0.5750

i

Psychiatric statUDS

Impulsive (%) 91% 84% 98% 0.0330i

Suicide attempts (%) 33% 41% 54% 0.0349i

Depression (%) 3% 38% 16% <0.0001i

Anxiety (%) 98% 100% 100% 0.5360h

Craving (%) 35% 62% 71% <0.0001i

Euphoria (%) 50% 38% 78% <0.0001i

Hospitalisationg

Numbera of drug treatments 2.6 [1.87] 2.3 [1.42] 2.5 [1.78] 0.6630

g

Numbera of detoxification treatments 2.6 [1.89] 2.3 [1.44] 2.5 [1.78] 0.6660

g

Numbera of rehabilitation treatments 2.9 [2.02] 2.5 [1.46] 2.8 [1.97] 0.6250

g

Numbera of counselling sessions 14.3 [11.00] 12.0 [11.10] 13.5 [10.90] 0.6290

g

Numbera of self-help groups 14.3 [11.00] 11.4 [11.00] 13.4 [11.10] 0.4810

g

f. Standard deviation in square brackets. g. Regardless wether prescribed or not as determined by substance screening tests at treatment entry. h. UDS: Urine Drug Screens at follow up treatment i. As obtained from their medical records at baseline. j. Accumulative months of hospitalisation treatment for psychiatric morbidity. k. Statistically significant if p < 0.05; the bold faced p-values are ones that are significant. l. ANOVA f-test. m. Fisher exact test. n. Pearson’s Chi squared test. o. Kruskal-Wallis test.

283

Table 3. Differences in genotyping distribution of OPRM1 SNPs with age at opiate initiation and transition to opiate dependence

Variable SNP_ID P value Variable SNP_ID P value

Age of first use

(mean: 22.3 [6.4])

rs6912029 0.381a

Age of regular use

(mean: 24.2[6.9])

rs6912029 0.237a

rs12205732 0.374a rs12205732 0.232

a

rs1799971 0.618b rs1799971 0.551

b

rs510769 0.649b rs510769 0.664

b

rs511435 0.619b rs511435 0.485

b

rs524731 0.652b rs524731 0.557

b

rs3823010 0.352b rs3823010 0.706

b

rs1381376 0.327b rs1381376 0.513

b

rs3778151 0.438b rs3778151 0.617

b

rs563649 0.442c rs563649 0.606

c

rs2075572 0.279d rs2075572 0.398

d

rs540825 0.306b rs540825 0.243

b

rs675026 0.572b rs675026 0.804

b

rs562859 0.556b rs562859 0.591

b

rs548646 0.476b rs548646 0.661

b

rs648007 0.462b rs648007 0.696

b

rs9322447 0.559d rs9322447 0.885

d

rs609148 0.228b rs609148 0.172

b

rs606148 0.173b rs606148 0.349

b

rs632395 0.184b rs632395 0.346

b

rs648893 0.831b rs648893 0.737

b

rs671531 0.448b rs671531 0.884

b

a. Wilcoxon rank sum test,

b. Kruskal Wallis test,

c. t-test,

d. f-test. Standard deviation in square brackets.

Abbreviation: SNP, single nucleotide polymorphism.

284

Table 4. Differences in genotyping distribution of OPRM1 SNPs with years of regular opiate use and frequency of opiate use

Variable SNP_ID P value Variable SNP_ID P value

Years of regular use

(mean: 9.0[6.2])

rs6912029 0.199a

Frequency of use

(mean: 3.6 [0.8])

rs6912029 1.000e

rs12205732 0.203a rs12205732 1.000

e

rs1799971 0.255b rs1799971 0.827

e

rs510769 0.919b rs510769 0.338

e

rs511435 0.957b rs511435 0.422

e

rs524731 0.970b rs524731 0.327

e

rs3823010 0.548b rs3823010 0.303

e

rs1381376 0.495b rs1381376 0.391

e

rs3778151 0.383b rs3778151 0.292

e

rs563649 0.062c rs563649 0.165

e

rs2075572 0.922d rs2075572 0.011

e

rs540825 0.876b rs540825 0.045

e

rs675026 0.838b rs675026 0.129

e

rs562859 0.725b rs562859 0.090

e

rs548646 0.785b rs548646 0.067

e

rs648007 0.796b rs648007 0.063

e

rs9322447 0.892d rs9322447 0.038

e

rs609148 0.898b rs609148 0.028

e

rs606148 0.373b rs606148 0.263

e

rs632395 0.245b rs632395 0.201

e

rs648893 0.896b rs648893 0.014

e

rs671531 0.660b rs671531 0.032

e

a. Wilcoxon rank sum test,

b. Kruskal Wallis test,

c. t-Test,

d. f-Test.

e. Fisher’s Exact Test. Standard deviation in square brackets.

Abbreviation: SNP, single nucleotide polymorphism.

285

Table 5. Association of OPRM1 SNPs with Treatment Outcome

SNP_ID /Genotype Good responders Moderate responders Poor responders p value

rs6912029

GG 34% 18% 48%

0.0329a GT 100% 0% 0%

TT 0% 0% 0%

rs12205732

GG 35% 18% 47%

0.0333a AG 100% 0% 0%

AA 0% 0% 0%

rs1799971

GG 33% 0% 67%

0.5440b AG 43% 18% 39%

AA 34% 18% 48%

rs510769

GG 36% 15% 48%

0.6040b AG 37% 22% 41%

AA 17% 17% 67%

rs511435

GG 38% 15% 47%

0.4740b AG 33% 24% 43%

AA 17% 17% 67%

rs524731

AA 17% 17% 67%

0.4400b AC 32% 25% 43%

CC 38% 15% 47%

rs3823010

GG 37% 16% 47%

0.8000a AG 33% 23% 43%

AA 25% 25% 50%

rs1381376

GG 36% 16% 48%

0.7600a AG 34% 24% 41%

AA 25% 25% 50%

286

Table 5. (continued)

SNP_ID /Genotype Good responders Moderate responders Poor responders p-value

rs3778151

CC 25% 25% 50%

0.8980a CT 36% 21% 42%

TT 36% 16% 47%

rs563649

GG 36% 17% 47%

0.9550b AG 35% 19% 45%

AA 0% 0% 0%

rs2075572

GG 42% 8% 50%

0.5080b GC 32% 20% 48%

CC 38% 19% 43%

rs540825

TT 38% 8% 54%

0.2440b TA 39% 11% 50%

AA 33% 23% 44%

rs675026

CC 35% 21% 44%

0.6940b CT 38% 16% 46%

TT 33% 10% 57%

rs562859

GG 35% 9% 57%

0.6690b AG 37% 16% 47%

AA 35% 21% 44%

rs548646

CC 33% 22% 46%

0.6360b CT 38% 17% 45%

TT 35% 9% 57%

rs648007

CC 33% 22% 46%

0.6310b CT 38% 16% 46%

TT 35% 9% 57%

287

Table 5. (continued)

SNP_ID/Genotype Good responders Moderate responders Poor responders p-value

rs9322447

GG 41% 17% 41%

0.2300b AG 29% 22% 49%

AA 44% 9% 47%

rs609148

CC 32% 23% 45%

0.2220b CT 41% 11% 48%

TT 40% 7% 53%

rs606148

GG 36% 16% 48%

0.4040a GT 38% 25% 38%

TT 0% 50% 50%

rs632395

CC 36% 16% 47%

0.6620a CT 36% 21% 43%

TT 0% 50% 50%

rs648893

CC 47% 7% 47%

0.2510b CT 39% 11% 50%

TT 33% 23% 44%

rs671531

GG 34% 21% 45%

0.5770b AG 36% 18% 46%

AA 39% 7% 54% a. Fisher’s exact test. b. Pearson’s Chi squared test.

Abbreviation: SNP, single nucleotide polymorphism

288

DISCUSSION

Opiate dependence is a significant public health issue with approximately 10 million

people abuse illicit opioids worldwide.51

Hulse et al. (1999) suggested that opiate

dependence is not only associated with high mortality rates and poor health among

dependent individuals, but also imposes excessively large economic and social costs

upon the community in general.3 Considerable medical, legal, and interpersonal harm,

including mortality, is associated with opiate use.3 The major causes of premature death

amongst Jordanian opiate users are accidental overdose, and infectious diseases.52

Moreover, a high prevalence of criminal activity and psychosocial difficulties are also

found among Jordanian heroin users.52

The extent of this serious problem has stimulated considerable interest in the

physiological and neurochemical processes involved in opioid dependence. In this

respect, there are growing evidence that opiate influenced by genetic factor.53-55

Several

studies have been undertaken to estimate the role that genetic factors play in the

vulnerability to opiate dependence.19,56-59

These studies have shown that the

endogenous opioid system in particular is considered to be one of the most important

signalling pathways involved in opiate dependence.60

This system includes the μ opioid

receptor which is the primary site of action for the most commonly used opioids

including morphine, heroin, fentanyl and µ-opioid receptor antagonists such as

naltrexone,24

agonists such as methadone25

or partial antagonists such as

buprenorphine.20

This system may also play a crucial role in mediating the

reinforcement effects of non-opioid drugs such as alcohol, cannabinoids, nicotine and

cocaine.60-62

In the current study, our aim was to identify genetic factors associated with

responsiveness to the biopsychosocial treatment offered for opiate drug dependent

patients at the NCRA and the DRC-PSD. Drug dependent patients of Arab descent

were genotyped for variance in OPRM1 gene. The genotyping was carried out by

sequenom MassARRAY® system for 22 OPRM1 SNPs. All OPRM1 polymorphism,

which were genotyped in this study, are reported in NCBI database

(http://www.ncbi.nlm.nih.gov/SNP/). This is, to our knowledge, the first attempt to

evaluate a series of SNPs spanning the coding sequence of the OPRM1 gene in opiate

Jordanian Arab population in relation to the response to the biopsychosocial treatment.


289

Various alcohol or drug dependence related association studies have expanded their

investigation to include up to 20 SNPs spanning the coding sequence of the OPRM1

gene; all include the A118G (Asn40Asp) variants. For instance, Japanese subjects

meeting ICD-10 criteria for methamphetamine (MAP) dependence and controls were

genotyped for 20 SNPs including 10 SNPs in the 3'UTR region.63

The study reported

that A118G and other SNPs were not associated with MAP dependence or psychosis,

and the rs2075572 G-allele was only significantly associated with increased risk for a

diagnosis of MAP dependence or psychosis (p = 0.011). Ten SNPs selected throughout

OPRM1 were examined within a Chinese population to investigate the relationship

between the SNPs and heroin-induced subjective responses.64

the study reported that

three SNPs in intron 1 were associated with an increased risk of positive responses on

first use of heroin and were likely to contribute to further heroin consumption; A118G

and rs2075572 were not associated with any differences in heroin-induced subjective

responses. Another association study of eight SNPs within OPRM1 in alcohol-l,

cocaine-, opioid- and polysubstance-dependent European Americans (EA) and African

Americans (AA) were genotyped.65

The EA and AA study reported that C-2044A

polymorphism was associated with the combination of alcohol and opioid dependence

in EA subjects, but not AA subjects. Again, A118G was not associated with any of the

substance-dependent phenotypes. A genetic association study on the role of OPRM1

genetic variations in a large case-control sample of alcohol- and drug- (cocaine and

opioid) dependent European Americans was conducted by Zhang et al. (2006).38

They

typed thirteen SNPs representing the major haplotypes observed in HapMap, all of

which are included in the present study. They found that seven SNPs (but not rs1799971

[A188G]) were associated with alcohol, opioid and cocaine dependency. Zhang et al.

(2006) found that the frequency of the rs524731 A and rs648893 T alleles was

significantly higher among EA than AA subjects. Finally, a case-control study of opiate

and non-opiate dependent Jordanian Arabs was recently conducted by Al-Eitan et al.

(2012) to investigate the genetic association of twenty two SNPs spanning the coding

sequence of the OPRM1 locus with opiate dependence.66

The study reported that three

SNPs (rs6912029 [G-172T], rs12205732 [G-1510A] and rs563649 [G-983A]) were

associated with opiate dependence.66

In the present study, these 22 SNPs were also

tested for a possible association of OPRM1 polymorphisms with response to the

biopsychosocial treatment.

290

Analysis of the relationship between treatment response and the OPRM1 SNPs

genotypes showed there was a significant difference in the genotyping distribution

between the three inclusion groups at two sites (rs6912029 (G-172T), and rs12205732

(G-1510A)) of the OPRM1 gene located in the upstream (5'-UTR) region. UTRs are

known to play crucial roles in the post-transcriptional regulation of gene expression,

including modulation of the transport of mRNAs out of the nucleus and of translation

efficiency67

, subcellular localization68

and stability69

. The importance of UTRs in

regulating gene expression is underlined by the finding that mutations that alter the

UTR can lead to serious pathology.

Previous studies have reported an association of the OPRM1 118A>G with different

opioids 22,70-73

and opiate antagonist “naltrexone” treatment.37,72

However, we found no

influence of this SNP on responsiveness to treatment, as the allele and genotypes

frequencies were similar in the opiate dependent patients with a p > 0.05. A recent

study demonstrated that individuals with the Asp40 variants of the OPRM1 gene

showed favourably higher relapse prevention rates when receiving naltrexone

treatment.42

A recent clinical trial also showed that the variants of the OPRM1 gene did

not have any preferential effect on naltrexone treatment in alcoholics.70

The functional

importance of treatment on any of the variants of the OPRM1 gene is still being

elucidated. Although, earlier studies in transfected cells reported that the OPRM1-

Asp40 (118G) variant had a threefold higher affinity for beta-endorphin than OPRM1-

Asn40 (G118) which would suggest enhanced function,

23 this was not reported by

others.33,34

In addition, recent in vitro transfection studies have suggested that the

Asp40 allele might be associated with lower OPRM1 protein expression than the Asp40

allele.35

As a result, higher frequency of this allele would have been more frequent in

the individuals with poor treatment response.

Various genetic studies have reported that the frequency of Asp40 allele can vary

significantly between populations with drug dependence from as low as 5% in African

Americans15

to 16% European Americans38

to 26% in this study of Arab decent66

and as

high as 58% among those of Asian descent.39

Although the complexity of opiate

dependence and the differences in ethnicity have influences on the OPRM1 results, all

the previous studies have confidence in the hypothesis that the Asp40 allele might be

associated with lower OPRM1 protein expression than the Asn40 allele.35

As a result,

higher frequency of the Asp40 allele would have been expected in the poor responder

291

group as compared to the responder group. Similarly, this study found that opiate

dependent patients with poor treatment response had higher frequency of Asp40 allele

and this was in agreement with proposed theory.

In a search for the relationship between the dependence variables of opiate dependence

and the OPRM1 SNPs genotypes, none of the analysed SNPs in this study were

associated with the age of opiate initiation, transition to opiate dependence and regular

use and opiate consumption (beginning and last taken amount (grams per day)).

However, the data indicated that there were significant differences in opiate frequency

use at only six sites (rs2075572 [C644-83G], rs648893 [C1165-1189T], rs609148

[G1165-8803T], rs671531 [A1164+28135A G], rs9322447 [G1164+11714A], rs540825

[T1164+1839A]) of the OPRM1 gene with p < 0.05. Previous studies have indicated

that OPRM1 polymorphism may be associated with clinical variables such as treatment

duration, hospitalization for drug treatments, detoxification, rehabilitation, counselling

and self-help for substance abuse, psychiatric symptoms, history of drug use, overdose

toxicity and follow up measures (urine screening test).15,39, 45,46,62,71

Unfortunately, the p

values concerning the clinical variables in this study were not significant.

This discrepancy between the study results could be related to the past history, the

psychiatric status and the subgroups of drug dependent individuals with violent

behaviour. This might be due to a bias in classifying the drug dependent individuals.

There are some possible confounding factors that should be taken into consideration

when assessing the patients with drug dependence such as the methods used for defining

violent, antisocial behaviour and the early onset of dependence. Several studies have

based patients’ assessment on collecting the phenotypic data by only a self-report,

interviewing the patients by health professional workers or obtaining the data from their

medical records, including this study.

Previous studies have shown that some OPRM1 markers deviate from the Hardy –

Weinberg Equilibrium (HWE) in different populations.38,74

There are many possible

confounding factors for this deviation including population stratification, genotyping

errors and true association with phenotypes.74

In this study the genotypic frequencies of

the OPRM1 markers met HWE expectations. Other studies have also reported sample

bias or genotyping errors. Our patients were from one geographic origin and were

100% native Jordanians of Arab ancestry, which is a population known to be genetically

292

homogenous. Genotyping errors were minimized by genotyped each patients twice in

order to avoid technical errors as evidenced by the low average rate of genotype

discrepancy. SNPs Genotyping was conducted for patients under the same conditions

and during the same period. Genotypes were also evaluated by investigators who were

blind to the status of the subject and any discrepancies were resolved by test replication.

Finally, only male individuals with opiate dependence were studied. Therefore, the

generalisation of the results to all drug dependent patients is limited.

In conclusion, this is the first report of an association between the OPRM1 G-172T and

G-1510A polymorphisms and response to the biopsychosocial treatment. Specifically,

the GG-172 and GG-1510 carriers were more frequent in non-responders to treatment.

This may lead to more accurate matching of individuals to different treatment options

and early identification of persons of high risk of relapse and therefore requiring more

intensive intervention. However, further pharmacogenetic studies are needed to confirm

the present findings on the influence of the OPRM1 G-172T and G-1510A on the

response to treatment in opiate dependent patients of Arab descent.

293

ACKNOWLEDGMENTS


Western Australia. We gratefully acknowledge the contribution of participating patients

whose cooperation made this study possible. Funding for this project was provided in

part by Centre for Forensic Science and Unit for Research and Education in Alcohol and

Drugs of the School of Psychiatry and Clinical Neurosciences, The University of

Western Australia.

DISCLOSURE

The authors report no conflicts of interest in this work.

294

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CHAPTER 9

CONCLUSION AND FINAL REMARKS

302

The overarching goal of this project was to describe the pattern and severity of

substance abuse in a group of patients presenting for treatment at two Jordanian drug

rehabilitation treatment centres (Chapter 2) and to identify genetic markers within

selected candidate genes associated with increased risk of substance dependence and/or

responsiveness to treatment (Chapter 3 to 8). The clinical, epidemiological and

pharmacogenetic characteristics of substance dependent patients of Arab descent have

not been previously described in the scientific literature. Therefore, there is an urgent

need for epidemiological and genetic studies of the Jordanian population and population

from other Arab nations (see review in Chapter 1). The study described in Chapter 2

used epidemiological data from a structured baseline survey based on the 5th

edition of

the Addiction Severity Index (ASI) to describe a substance abuse population undergoing

treatment in Jordan. The study described in Chapter 5 used a Sequenom

MassARRAY® iPLEX Platform method to genotype single nucleotide polymorphisms

(SNPs) within genes of interest. Further studies described in Chapter 3, Chapter 6 and

Chapter 7 used genetic markers to analyse associations with substance dependence

and/or clinical and biological outcomes following a pharmacological and behavioral

treatment program (Chapter 4, Chapter 8). Several complementary genetic

approaches were used (outlined in Figure 1 of Chapter 1).

In Chapter 1, the major issues associated with substance dependence were discussed.

Currently, According to the World Health Organization (WHO), 76.3 million people

worldwide currently abuse or are dependent on alcohol and at least 15.3 million people

have drug use disorders (WHO 2012). These disorders are affected by environmental,

genetic and behavioral factors [1]. As genetic factors contributed 40 to 60% of the

etiology of substance dependence [1], it is important to map disease genes by

comparison disease and control as well as by preforming comparative analysis across

different ethnic groups.

In Jordan, substance abuse and dependence is a major concern. A survey conducted by

the World Health Organisation (WHO) in 2005 in Jordan reported that of 2,471 students

surveyed, aged between 18 to 25 years, 3% reported using drugs [2]. Furthermore, a

recent study of 5,000 Jordanian students, also aged from 18 to 25 years, showed that

tobacco, sedatives, alcohol and stimulants are commonly used by students [3]. These

observations emphasise the necessity of considering prevention for substance abuse in

Jordan.

303

The objectives of this project were established to use epidemiological data to describe a

substance abuse in this population undergoing treatment in Jordan and to identify

genetic variations and genes influencing susceptibility to substance dependence and/or

responsiveness to treatment.

Chapter 1 explains why drugs continue to be used and abused, why drug abuse

represents a significant social burden, and why effective treatment for drug dependence

is required. For each substance, the origin of substance, the pharmacology of the

substance, the mechanisms of substance dependence and the toxicology and

pharmacotherapy of the substance are described. Chapter 1 also touches on the

implications of genetic research, with specific emphasis on the molecular genetics and

pharmacogenetics of substance dependence, focusing primarily on genes that have been

associated with, or have evidence for linkage to, drug dependence.

Chapter 2 assesses the pattern and severity of substance abuse as voluntarily reported

by patients undergoing biopsychosocial treatment. This chapter also investigates the

nature and extent of transitions in route of heroin administration. A sample of substance

dependent patients (N = 220) of Jordanian Arab population were characterised.

Epidemiological and clinical data were collected through the collaboration of four

institutions [4]: The National Centre for Rehabilitation of Addicts (NCRA) at The

Jordanian Ministry of Health, the Drug Rehabilitation Centre at The Jordanian Public

Security Directorate, Jordan University of Science and Technology and The University

of Western Australia. An assessment tool based on the Addiction Severity Index (ASI)

[5] was developed and used to assess the nature and severity of these problems. This

study showed that opiates, specifically heroin, were the most common substance used

by the patients and the main route of administration was chasing. Heroin abusers were

found to be at ongoing risk of switching from chasing to injecting, which suggests route

of administration should be taken into consideration in treatment programs. This study

will allow researchers to assess clinical and epidemiological features that could

contribute to the development of the substance abuse treatment regimes for population

of Arab countries. This work could also lead to future studies of clinical,

epidemiological, genetic association and pharmacogenetic/omics studies to control

substance dependence.

304

Chapter 3 describes the distribution of allele, haplotype and genotype frequencies of

serotonin transporter gene SLC6A4 polymorphisms (5-HTTLPR and rs25531) in drug

and non-drug dependent individuals from the Jordanian Arab population. This chapter

also examines the genetic association of these variants in a drug dependent population

from the same area. The results of this study show that the “LL” genotype of 5-

HTTLPR gene has a higher serotonin transporter function than other genotypes,

resulting in increased serotonin uptake and a reduced level of intra-synaptic serotonin

[6]. Jordanian male addicts of Arab descent (n = 192) meeting the DSM-IVcriteria [7]

for drug dependence and 230 healthy male controls from an ethnically homogenous

Jordanian Arab population were examined. Toward this, both bi-allelic and tri-allelic

approaches for genotyping the 5-HTTLPR and rs25531 markers based on Wendland et

al’s suggestion were used. This study showed that the frequency of 5-HTTLPR

(LL/LS/SS) genotypes were significant different between drug dependent individuals

and controls. Specifically, that drug dependent subjects had a higher frequency of “L”

allele. The identification of polymorphisms that are associated with diseases in

different ethnic backgrounds may enable researchers in the future to assess if the same

gene variations are associated with the aetiology of drug dependence and further assess

the strength of the association. Moreover, comparative analysis with different ethnic

groups could assist in understanding the mechanisms that causes drug dependence. The

comparative analysis revealed the genotype frequencies of 5-HTTLPR (LL/LS/SS) in

the Arab population to be approximately similar to that previously reported for Italian

[8] and Israeli [9] populations.

Chapter 4 examined the influences of SLC6A4 gene polymorphisms (5-HTTLPR and

rs25531) on clinical and biological measures of outcome in a sample of Arab drug

dependent patients (N = 192) undergoing pharmacological and behavioural treatment.

These patients were undergoing an 8-week pharmacological and behavioural inpatient

treatment program. Patients were stratified according to receptor polymorphism using a

biallelic (Group A: LL versus Group B: SS and LS genotype) and a triallelic approach

(Group A′: LA/LA versus Group B′: non-LA/LA genotype). Recognition of the role of

serotonergic systems in drug dependence as well as their interaction with the clinical

outcomes of treatment has led to increased interest in the modulating role of

polymorphisms within 5-HT relevant genes [10]. A number of studies have been

directed towards assessing the relationship between 5-HTTLPR and rs25531

polymorphisms and the expression of 5-HTT in drug addiction [10,11]. However, most

305

of these studies recruited patients from European backgrounds with a primary diagnosis

of alcohol dependence [11]. The influence of 5-HTTLPR status on various clinical and

biological drug dependence outcomes have not been studied in patients of Arab descent.

This study reported that the biallelic 5-HTTLPR genotype was associated with

responsiveness to treatment within drug dependent patient undergoing combined

pharmacological and behavioral treatment, although responsiveness to treatment was

not affected by the triallelic polymorphism. However, the study did show that the

biallelic LL or the triallelic LA/LA genotype of 5-HTTLPR gene may be a genetic risk

factor for drug dependant patients. Patients with this genotype may be at increased risk

of relapse; first use drugs at an early age and develop dependence early, and have a

higher frequency of drug use. This study provides additional clinical knowledge that

may be used to establish a new pharmacogenetic approach to reduce the severity of drug

consumption and alcohol intake and improve drug abstinence based on variations in the

serotonin gene.

Chapter 5 describes the use of a Sequenom MassARRAY® iPLEX Platform method to

genotype 68 single nucleotide polymorphisms (SNPs) within nine drug dependence

candidate genes (DRD1, DRD2, DRD3, DRD4, DRD5, OPRM1, SLC6A3, BDNF and

COMT) and to investigate the distribution of minor allele frequencies (MAFs) in drug

and non-drug dependent Jordanian Arab populations. These genes are of particular

interest to drug and alcohol researchers as they could be involved in the mechanism of

drug dependence [12,13]. They are also of interest to the general neuropsychiatric

community as they could be involved in other neurological and psychiatric disorders

[12,13] (Kreek et al., 2005, Haile, Kosten and Kosten, 2008). In this study, two

multiplex genotyping assays for 460 subjects of Arab descent (220 drug dependent

individuals and 240 controls without a history of drug abuse) were evaluated. The

multiplex assays included 68 SNPs (multiplex A comprised 36 SNPs and multiplex B

comprised 32 SNPs). The results of this study indicated that a Sequenom MassARRAY

based targeted drug dependence candidate SNP panel covering majority of currently

tested SNPs can be implemented in a molecular diagnostic laboratory as a reliable and

efficient high throughput method for simultaneous detection of multiple SNPs.

306

Chapter 6 and 7 examined the genes that may influence susceptibility to opiate and

substance dependence in Jordanian individuals of Arab descent. The Sequenom the

MassARRAY® iPLEX Platform was used to study 68 SNPs throughout the nine genes

described in Chapter 5. Genetic association studies for substance dependence

conducted for a number of populations (see Table 2 and 3 for review in Chapter 1)

have shown that substance dependence susceptibility genetic variations reside in a

number of different genes. To the best of our knowledge, this study is the first custom

genotyping screen in focusing on identification of the genetic variations involved in the

development of substance dependence among the Jordanian population. Custom

genotyping for substance addiction susceptibility genes in this Arab population was first

used to explore susceptibility variants and variations at DRD2 (Dopamine receptor D2)

on 11q23 were shown to be associated with substance addiction in Jordanian individuals

of Arab descent. It has been suggested that DRD2, dopamine receptor D2, plays an

important role in dopamine secretion and the signal pathways of dopaminergic reward

and drug addiction [14,15].

Chapter 7, the genotype frequencies at three sites differed significantly between cases

and controls in the Jordanian population. The significant sites were rs6912029 (G-

172T), rs12205732 (G-1510A) and rs563649 (G-983A). Allele frequency comparisons

between cases and control also revealed a significant difference at two sites: rs6912029

(G-172T), rs12205732 (G-1510A). It has been suggested that OPRM1 is the main

target of opiates and other drugs and plays an important role in opioid tolerance and

dependence [12,13,16]. This study is the first to investigate a genetic association of

OPRM1 variations with drug dependence in males of Arab descent. These findings may

prove crucial to understanding the drug dependence mechanisms in Middle Eastern

populations of Arab descent.

Chapter 8 investigated whether the genetic polymorphisms within the OPRM1 gene in

opiate dependent patients of Arab descent are associated with opiate consumption, a

range of dependence and clinical variables, psychiatric symptoms and treatment

outcomes. In order to uncover novel SNPs or potentially functional, a series of SNPs

spanning the coding sequence of the OPRM1 gene in opiate Jordanian Arab population

undergoing biopsychosocial treatment were evaluated. Toward this, opiate dependent

patients (N = 183) were undergoing a voluntary 8 week treatment program at the

Jordanian Drug Rehabilitation Centre genotyped using the Sequenom MassARRAY®

307

system (iPLEX GOLD). Within the opiate dependent patients, two SNPs (rs6912029

(G-172T), and rs12205732 (G-1510A)) located in the upstream (5'-UTR) region in

OPRM1 gene were associated (p value < 0.05) with responsiveness to biopsychosocial

treatment. Specifically, the GG-172 and GG-1510 carriers were more frequent in non-

responders to treatment. UTRs are known to play crucial roles in the post-

transcriptional regulation of gene expression, including modulation of the transport of

mRNAs out of the nucleus and of translation efficiency [17], subcellular localization

[18] and stability [19]. The importance of UTRs in regulating gene expression is

underlined by the finding that mutations that alter the UTR can lead to serious

pathology. This study is the first to investigate a genetic association of a series of

OPRM1 SNPs with treatment outcome in opiate dependent Jordanian Arab population.

This may lead to more accurate matching of individuals to different treatment options

including pharmacotherapy and identify at an early stage persons at high risk for relapse

and therefore requiring more intensive intervention.

In conclusion, the field of human genetics examining complex phenotypic traits is

evolving at a fast pace since sequencing technologies have advanced. Much of the

evolution of the field can be traced to rapidly advancing genotyping technologies that

increase genotype throughput and reduce genotyping costs. These technologies allow

researchers to better address complex phenotypes utilizing both common variants and

rare variants simultaneously (SNPs, CNVs, microsatellite, for review see Chapter 1,

Section VI) and better understand the role of genetics on complex phenotypes. These

technologies also enable the identification of genes involved in polygenic traits,

complex diseases and drug responses. Knowledge of these genes can then be used in

genetic association or pharmacogenetics studies and applied to personalized medicine

and forensic toxicology.

Overall, this project has examined a number of genes that appear to be involved in the

vulnerability to and the treatment of substance dependence in a Middle Eastern

population of Arab descent. The results of this study provided additional clinical,

epidemiological and genetic knowledge, that may be useful in the context of further

genetic and pharmacogenetics analyses to reduce the severity of drug consumption and

alcohol intake and improve drug abstinence. The evidence for the role of these studied

genes in this ethnic background remains tenuous. To further elucidate the genetic

variability that may contribute to the vulnerability and treatment of substance

308

dependence, further studies are required to identify both new alleles as well as to

confirm the role of previously identified alleles. However, genetics is only one factor

that contributes to the development of substance dependence. Environmental factors are

also of importance. Evaluation of the interaction of genes with environmental factors is

required to further our knowledge into the physiology of substance dependence.

Investigations are also required to know how specific genes and their specific alleles

interact with each other and with the environment. Therefore, to develop novel

pharmacotherapies as well as behavioral therapies and to create new prevention and

treatment programs, the roles of genes, their variants, and the environment in which

they are expressed need to be further elucidated. Future research could be directed

towards using a genome-wide association analysis and including more specific case-

control study with a wider set of phenotypes. In the next decade, I anticipate there will

be many examples of the clinical and forensic utilities of genetic and pharmacogenetics

analyses in either personalised medicine or forensic molecular toxicology.

309

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Pharmacogenetic Characteristics of Substance Abuse ... · population of Arab descent and phenotypic and genotypic data were systematically compiled in a database for study purposes