Pharmacogenomics || Association Studies in Pharmacogenomics

Abstract

Single nucleotide polymorphisms (SNPs) are common genetic variations presentin DNA that are thought to account for most of the genetic variations that occurbetween individuals. The near completion of the sequencing of the human ge-nome and the development of high-throughput genotyping technologies, providethe means to assemble SNPs into maps that can be used for the dissection ofcomplex genetic traits. This approach, reflected in the branch of genetics knownas pharmacogenomics, can be used to identify gene variations that determine indi-vidual drug responses and has the ability to elucidate the gene networks that de-termine drug efficacy and associated toxicity in individuals. This chapter focuseson the methodologies that use SNP maps for the identification of genetic factorsmodulating drug responses. With this information, new drugs can be developedthat target individual genetic profiles to achieve maximal efficacy and minimalside effects.

4.1Introduction

In a large number of instances, the optimal dosage regimen of clinically usefuldrugs is limited by variability in the way individuals respond to these drugs, bothin terms of drug efficacy and side effects, i.e., adverse drug reactions (ADRs). Thelatter are a major cause of non-compliance and failure of treatment, particularlyfor chronic pathologies. For instance, the daily doses required to treat patientsvary by 20-fold for the antithrombotic drug warfarin, by 40-fold for the antihyper-tensive drug propranolol, and by 60-fold for l-dopa, the standard treatment forParkinson’s disease [1]. Furthermore, both the duration of treatment and the pro-gression of Parkinson’s disease require increasing amounts of l-dopa and as moredrug is given, side effects increase. Other drugs have clinical utility in a subset ofpatients with a given pathology, e. g., antipsychotics that are ineffective in 30% ofschizophrenics [2], suggesting the fact that such drugs are only effective in pa-tients with specific disease etiologies.

57

4

Association Studies in PharmacogenomicsLaurent Essioux, Benoit Destenaves, Philippe Jais and François Thomas

Pharmacogenomics: The Search for Individualized Therapies.Edited by J. Licinio and M.-L. Wong

Copyright © 2002 Wiley-VCH Verlag GmbH & Co. KGaAISBNs: 3-527-30380-4 (Paper); 3-527-60075-2 (Electronic)

Inter-individual variability in drug response and ADRs are major public healthproblems. In the United States in 1998, costs related to hospital admissions forunderdosing, overdosing and prescription of unnecessary drugs exceeded US$ 100billion a year [3]. Furthermore, this 1998 report indicated that ADRs are responsi-ble for approximately 7% of hospitalizations with fatalities occurring in 0.3% ofcases, making ADRs the fourth to sixth leading cause of death in the UnitedStates. As a whole, each year ADR-associated hospitalizations cost from US$30billion to US $ 150million [3]. Thus, a significant proportion of the drugs pre-scribed for disease treatment does not either result in the desired outcome or pro-duce side effects that limit drug utility. The ability to understand by pharmacoge-nomics the reasons for variations in drug effects at the individual level has the po-tential to improve responses, to prevent ADRs and make both the practice andcost of medicine less of a societal burden. In addition, there is considerable dis-cussion in the pharmaceutical industry regarding the use of pharmacogenomicsto reduce both costs and risk in the drug development process.

4.2Variability and ADR in Drug Response: Contribution of Genetic Factors

A number of non-genetic factors can cause drug response variability, including re-nal, pulmonary, cardiovascular or hepatic system status, age and gender, drug-drug interactions, environmental and nutritional factors, pathogenesis and sever-ity of the disease. In addition to the causal relationship of such factors in deter-mining drug response and ADR variability, a growing body of evidence also de-monstrates that multiple genetic factors contribute to this variability. An exampleof such multiple interaction is illustrated in patients undergoing warfarin therapy,for which a number of genetic and non-genetic factors have been shown to affectresponse (Tab. 4.1).

Early evidence that genetic factors are associated with drug response variabilityis based on the phenotypic differences of drug metabolizing enzymes in indivi-duals showing ADRs. The reduced activity of a phase II liver metabolism enzymeand the frequency of neurotoxicity of the antitubercular drug isoniazid having analmost identical geographical distribution has provided the first evidence that a ge-netic factor was involved in ADR [4]. More recently, the reduced activity of thephase II liver metabolism enzyme has been shown to be due to a homozygous in-activating polymorphism of the enzyme N-acetyl transferase 2 (NAT2]. Other ex-amples include: hemolysis due to the antimalarial drug chloroquine in patientswith glucose-6-phosphate dehydrogenase deficiency, prolonged muscle relaxationcaused by suxamethonium in patients with plasma cholinesterase deficiency, andmore recently, lupus-like syndrome induced by procainamide in patients with mu-tations in the liver enzyme CYP2D6. Familial studies have provided further evi-dence for the role of genetic factors in drug variability. For instance, a high con-cordance rate of chloramphenicol-induced aplastic anemia occurs in identicaltwins [5], as well as a high concordance rate of lithium response in bipolar first-

4 Association Studies in Pharmacogenomics58

degree relatives of bipolar probands (e.g., they often have a similar response pat-tern) [6].

These examples have opened a challenging area of pharmacology, called phar-macogenetics, which focuses on the discovery of the genetic factors responsiblefor the variability in drug responses. Polymorphisms in the genes coding forproteins involved in drug absorption, distribution, metabolism and excretion(ADME), as well as in drug response can significantly influence the in vivo re-sponse to drugs. More recently, technologies enabling high-throughput analysis ofgenetic polymorphisms and expression have led to a new era for pharmaco-

4.2 Variability and ADR in Drug Response: Contribution of Genetic Factors 59

Tab. 4.1 Known causes of individual variability in warfarin therapy in thromboembolism

Causes of variation Consequence on warfarin therapy

Extra-genetic factors

Association to miconazole, thyroid hormones,and others

Unknown

Association to allopurinol, amiodarone,fluoroquinolones, carbamazepine, pheno-barbital, rifampicin, and others

Inhibition of CYP activity by the drugs

Association to cholestyramine, and sucralfate Inhibition of digestive absorptionInteraction with non-steroidal anti-inflammatory drugs, androgens, fibrates,sulfamethoxazole, and others

Binding of drugs to albumin that increaseswarfarin free-circulating level

Food intake Increase of alimentary vitamin K1 intake,resulting in decreased anticoagulant effect

Alcohol intake Induction of liver-metabolism during chronicalcohol intake

Hepatic insufficiency Decrease of liver metabolism, leading toincreased efficacy of warfarin

Renal insufficiency Decrease of renal elimination, leading toincreased efficacy of warfarin

Genetic Factors

Albumin Reduced warfarin affinity of some albuminvariants (18)

�1-acid glycoprotein Variable warfarin affinity of some �1-acid gly-coprotein variants (19)

CYP2C9 Multiple allelic variants of CYP2C9 may ac-count for the occurrence of poor metabolizersand increased efficacy of warfarin (84)

CYP2C19 Effects of CYP2C19 polymorphisms on themetabolism of warfarin are not yet elucidated,although this enzyme is known to metabolizewarfarin (85)

genomics. Nowadays, the differences between pharmacogenetics and pharmaco-genomics is somewhat subtle and arbitrary, such that these terms are often usedinterchangeably [4].

4.3Multiple Inherited Genetic Factors Influence the Outcome of Drug Treatments

4.3.1Background

The in vivo response to drug treatment is a complex and highly dynamic processwhich involves multiple factors. As many as 50 proteins, e.g., carrier proteins,transporters, metabolizing enzymes, receptors and their transduction componentstake part in pharmacodynamic response to a drug. Many genes coding for suchproteins contain polymorphisms that alter the activity or the level of expression ofthe encoded proteins. Thus, the response of a given drug in a given individual re-flects the interaction of multiple variable genetic factors that cause important var-iations in drug metabolism, distribution and action on its target.

4.3.2Liver Metabolism Enzymes

Although hydrophilic drugs can be eliminated unchanged by passive filtration orexcretion, other drugs, due to their lipophilic nature, cannot. Thus liver metabo-lism, which involves the synthesis of more hydrophilic polar residues, is a majorpathway for the elimination of drugs in the bile. Such liver metabolism enzymes,mainly found in the endoplasmic reticulum of hepatocytes, can be classified intotwo groups: phase I or phase II. A number of these display wild type and otherphenotypes, the latter resulting from genetic polymorphisms. Poor metabolism re-sults in the accumulation of drug substrate and is typically a codominant autoso-mal trait due to deletions, null mutations or inactivating mutations in metaboliz-ing enzymes. Ultra-extensive metabolism resulting in increased drug metabolismis usually due to gene duplication and results in an autosomal dominant trait [7].However, there is no absolute correspondence between phenotype and genotype.This can be due to incorrect phenotype assignment by co-administration of drugsthat may change gene expression, confounding effects of disease, and allelic het-erogeneity due to the second allele product.

Phase I enzymes are responsible for modification of functional groups, throughhydrolysis, oxidation, reduction or hydroxylation. Phase I metabolism is mostlycarried out by the monooxygenase heme–thiolate protein superfamily, referred toas cytochromeP450 isoenzymes (CYP). Approximately 80 different forms of P450have been characterized in humans, each of which have a distinct catalytic speci-ficity and unique regulation [8]. Because of this diversity, the high frequency ofCYP genetic variations may be explained by their redundant or dispensable na-


ture. In humans, it appears that approximately half a dozen CYP enzymes are re-sponsible for metabolizing the vast majority of prescribed drugs. Among these,three (CYP2D6, CYP3A4, CYP2C19] are polymorphic and responsible for mostADRs demonstrated to date [8]:1) CYP2D6 is involved in the metabolism of more than 30 drugs. At least 16 dif-

ferent genetic variations including point mutations resulting in early stop co-dons and amino acid substitutions, microsatellite nucleotide repeats, and geneamplifications or deletions are the cause of differences in the enzymatic activ-ity of CYP2D6, which ranges from complete deficiency to ultrafast metabolism.The CYP2D6 poor-hydroxylator phenotype, the so-called sparteine-debrisoquinallele, is caused by homozygous inheritance of inactivating alleles. The fre-quency of this trait varies extensively among ethnic groups, ranging from 1%in Arabs to 30% in Chinese [9]. Consequently, clinical responses to standarddoses of drugs metabolized by CYP2D6, such as haloperidol, chlorpromazine,codeine, and antiarrhythmics, vary from increased risk of ADRs at recom-mended doses in poor metabolizers, to therapeutic failure in ultrafast metaboli-zers [9].

2) CYP3A4 is involved in the metabolism of the largest number of drugs and in-fluences their intestinal absorption. Although wide phenotypic variations havebeen found in human liver CYP3A4 activity [10], possibly caused by a commonpolymorphism in the promoter region [11], the resulting changes in drug re-sponses remain to be elucidated. CYP2C19 contains several inactivating muta-tions, whose frequencies vary among ethnic populations [9].

3) Polymorphisms of CYP2C19 cause differences in metabolism of omeprazole, aproton pump inhibitor used for treatment of gastroduodenal ulcers or refluxesophagitis. Such polymorphisms result in resistance to treatment at a stan-dard dose regimen in nearly 20% of European Caucasians, and in an evenhigher percentage of Asians [12].

Phase II conjugation of drugs can occur alone or after phase I metabolism. PhaseII enzymes link large endogenous polar moieties to drug molecules in order toenhance their excretion in urine or bile. Several of these proteins have an impor-tant role in interindividual variations of drug response, such as uridine diphos-phate glucuronosyltransferase 1A1 (UGT1A1], NAT2 and thiopurine S-methyltrans-ferase (TPMT). Firstly, UGT1A1 detoxifies various lipophilic chemicals and endog-enous substances including bilirubin. This gene contains a promoter polymorph-ism that alters the level of expression of the encoded enzyme, resulting in a widevariation of drug metabolism [13]. Secondly, the homozygous occurrence of NAT2inactivating mutations results in a slow-inactivator phenotype. This phenotype isresponsible for increased dose-dependent toxicity due to the accumulation of non-metabolized drugs such as hydralazine-induced lupus, isoniazid-induced neuropa-thies, and sulfonamide-induced hypersensitivity reactions in some ethnic groups[4]. Thirdly, some polymorphisms of TPMT reduce biotransformation of thiopur-ine drugs, such as azathioprine and 6-mercaptopurine, and can lead to potentiallyfatal hematopoietic toxicity in some patients [14].

4.3 Multiple Inherited Genetic Factors Influence the Outcome of Drug Treatments 61

4.3.3Transporters

Drug disposition does not only depend on liver metabolism and passive glomeru-lar filtration, but also on absorption and excretion across tissues that have a bar-rier function such as blood-brain endothelium, as well as biliary, intestinal and tu-bular renal epithelia [15]. The total number of transporters is still unknown de-spite important efforts dedicated into their identification. The best characterizedtransporter remains the human multidrug resistant protein (MDR1], a member ofthe P-glycoprotein family, that is known to be polymorphic [16]. This transporterpumps a large number of structurally diverse drugs out of the cell and back intothe intestinal lumen, e.g., chemotherapic agents, cyclosporine, HIV protease inhi-bitors and other CYP3A4 metabolites [16]. Although there are no clear structuralfeatures defining MD1R substrates, most of these molecules are lipophilic and/oramphipathic, and contain one or more aromatic rings. A polymorphism in exon26 of MDR1 correlates with reduced absorption of digoxin, and is, therefore, pro-posed as a possible cause of inter-individual response variability and ADRs [17].Other less characterized transporters include a second P-glycoprotein coded by thehuman MDR3 gene (also known as MDR2 in mouse), the canalicular multidrug-resistant protein (cMRP, also known as cMOAT or MRP2], and the organic aniontranporters, OATP and OAPT2. Despite the functional role of transporters in drugdisposition, their polymorphic nature and eventual involvement in drug responsevariability remains to be elucidated.

4.3.4Plasma Binding Proteins

Several physiological or pathological conditions including renal failure, cirrhosis,and inflammatory states, can modify the circulating level of plasma binding pro-teins, mainly serum albumin and �1-acid glycoprotein, resulting in drug responsevariability. While a number of polymorphisms have been identified in these plas-ma binding proteins, only a few studies have related these to variations in drugresponse. For instance, some albumin variants such as Canterbury (Lys313Asn)and Parklands (Asp365His), cause a decrease in high-affinity binding of warfarin,salicylate, and diazepam [18]. Genetic variants of �1-acid glycoprotein can affectbinding of imipramine, warfarin and mifepristrone [19] and, consequently, theirfree-concentration in the plasma.

4.3.5Drug Targets

Most drugs interact with specific target proteins in order to exert their pharmaco-logical effects. These include membrane receptors and enzymes, such as the �2-adrenoceptor, insulin receptor, various serotonergic receptors, angiotensin convert-ing enzyme (ACE), and HMG CoA reductase. More rarely, drugs link to signal


transduction molecules such as the peroxisome proliferator activated receptor �-2(PPAR-�-2]. Most of these drug targets exhibit polymorphisms that can alter the sen-sitivity to specific drugs [4]. Polymorphisms can play a major role in response vari-ation when minimal inter-individual variation in plasma levels of a drug and its me-tabolites are seen, but major pharmacodynamic differences occur. Additional com-plications exist when multiple targets participate in the overall drug response. Ar-ranz et al. [20] suggest that at least six receptors can influence the response to theatypical neuroleptic clozapine. While these findings have not been replicated to date[21], they represent a first approach to understand the genetics of multiple targetingof one of the most important psychiatric drugs available today.

Target genes involved in drug response can also be involved in disease suscepti-bility. For example, polymorphisms of the cholesteryl ester transfer protein (CETP)are associated with both coronary atherosclerosis and response to pravastatin [22].In addition, �2-adrenoceptor polymorphisms are associated with severity of asth-ma [23] and variability in antagonist responses used in treatment [24]. ACE poly-morphisms are associated with both susceptibility to essential hypertension [25]and response to therapeutically effective ACE inhibitors [25].

In most cases, ADRs result from the drug interaction with a target distinctfrom the therapeutic one. For example, polymorphisms in the 3’-untranslated re-gion of the prothrombin gene result in enhanced risk of cerebral vein thrombosisand embolism in women receiving oral contraceptives [26]. Furthermore, the ma-lignant hyperthermia induced by anesthetics such as halothane or succinylcho-line, results from multiple mutations in the ryanodine receptor whose conse-quences segregate in certain families as an autosomal dominant trait [27]. In con-trast, other ADRs can be caused by interaction of a drug with the target involvedin drug therapeutic response. For instance, a polymorphism of the dopamine D3-receptor is associated with an increased risk of tardive dyskinesia induced by typi-cal neuroleptics [28], this gene being also associated with response to this familyof drugs [29].

Genetic variation of pathogenic agents in microorganism-caused disorders canbe an additional source of individual drug sensibility. Since this article only fo-cuses on human polymorphisms, we will illustrate this concept with one example.The severity of chronic hepatitis C and its complications is influenced by varia-tions in host genes e. g., TAP2 polymorphism (transporter associated with antigenprocessing) [30], TNF-� promoter variants [31], TGF-�1 polymorphisms [32], andHLA classII and III alleles [33]. Moreover, drug response to interferon-� therapyin chronic hepatitis C patients also depends on variations in host genes includingpolymorphisms of IL-10 [34], interferon (IFN)-inducible MxA protein [35], andmannose-binding lectin [36] genes. Furthermore, polymorphisms in the sequenceof the hepatitis C virus, e.g., the “interferon sensitivity determining region” (ISDR)of HCV-1b genotype [37] and the complexity of the hypervariable region1 (HVR1][38], can also modulate the efficacy of interferon-�-2 therapy. This situation iseven further complicated by taking into account the evolution of the hypervariableregion of hepatitis C virus quasispecies in response to interferon-� therapy, thatlikely causes profound changes in virus-host interactions [39].

4.3 Multiple Inherited Genetic Factors Influence the Outcome of Drug Treatments 63

4.4Association Studies in Pharmacogenomics

4.4.1The Principles of Association Studies

Association studies are used to identify common alleles more frequently asso-ciated with phenotypic traits such as drug response or ADRs. These associated al-leles are called “causal polymorphisms” or “functional polymorphisms” and arerisk-conferring factors of the trait at the population level, while, at the individuallevel, they impact either on the cellular level of the gene product or its function.

All polymorphisms that can be identified by such an approach are common.This population-based approach thus postulates that common factors explain asubstantial genetic component of the trait. Numerous reports show that such poly-morphisms do exist in pharmacogenomics (Tab.4.2) and that this is a valid model[40]. If a more complex allelic structure is assumed where the effect of a gene ona trait is mediated by a multitude of rare functional polymorphisms (a modelsuch as BRCA1 for breast cancer), population-based approaches have no power toidentify causative genes. Whether this model is more frequent is currently un-known due to a paucity of knowledge and understanding of human genetic diver-sity [41].

If the polymorphism tested in an association study is a disease risk factor, itsfrequency is expected to increase with the studied trait. If the polymorphismtested is silent, but is correlated to a nearby causal polymorphism, an increase offrequency is still expected, although the effect will be milder, reflecting an indirectassociation of the tested polymorphism to the trait. The correlation between poly-morphisms, known as linkage disequilibrium (LD), is a cornerstone of indirect-based association studies. The stronger the LD between the polymorphisms andthe risk-conferring mutation, the stronger the effect found by an association studywould be. This population-based parameter quantifies the concordance of two al-leles of physically linked markers from an individual. Its intensity depends on bio-logical processes, such as crossing-over and recurrent mutations, which tend tolower it. Linkage disequilibrium is also influenced by population factors includinggeographic migration, population size, evolution, and biological selection. Thus,LD is specific for a given population. Due to the multiplicity of factors listedabove, all of which are critical to quantify, LD is extremely difficult to model andpredict.

4.4.2Study Design

The main potential drawback of genetic association studies is the bias resulting ofthe lack of ethnic matching between the groups under study, which can lead tospurious results [42, 43]. This bias is the best known and acute problem of thisdesign and affects the interpretation of the results. When comparing two groups


4.4 Association Studies in Pharmacogenomics 65

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(e. g., responders/non-responders), the two collected samples originating from dif-ferent source populations with different ethnicities, the result of the associationstudy might simply reflect this inherent difference. Most of these issues couldthen be overcome by the analysis of individuals recruited for pharmaceuticaltrials, as such controlled studies take into account major confounding factors. Aless obvious case is encountered when the study is designed in a mixed popula-tion, as different proportions of ethnic groups may be present in the analyzedpopulation [44].

During recruitment of patients, the ethnic background can be taken into consid-eration, but this process may be inadequate for association studies. In recentyears, methodological developments have focused on the assessment of the genet-ic homogeneity of the collected samples [44, 45] using sets of random markers(markers that a priori are not associated with the trait under study) to quantify thestratification effect between the groups under study. To some extent, this homoge-neity study guarantees that the association found using this type of sample couldnot be ascribed to a major difference in ethnic background. These approaches areeasily applied in genetic case control studies due to the availability of marker andgenotyping capabilities, illustrating how genomic information can improve thequality of genetic study design. Other approaches, such as detection of geneticoutliers [46] or the study of the genetic diversity of samples [45], can also provideimprovements to the assessment of genetic homogeneity in the near future [47].

4.4.3Direct Approach: A Hypothesis-Driven Strategy

Historically, association studies in pharmacogenetics have used one or a few poly-morphisms per gene due to the lack of documentation regarding gene polymor-phisms and access to genotyping facilities. Such studies focused primarily onnon-conservative exonic markers or markers previously suspected to have a func-tional effect. In doing this, if an association exists, it is more likely to be directlymediated by the studied polymorphism, which explains why this has been called“direct strategy” or “hypothesis-driven strategy”. Several compelling examples ofthis approach have been obtained and are detailed in Tab.4.2. One critical point isthe choice of the marker to studying and compelling the data that supports itsfunctional role. For example, Kuivehoven et al. [22] have studied the effect of anintronic polymorphism of the CETP gene on atherosclerosis and cholesterol-lower-ing therapy. Although this polymorphism is intronic, it was chosen because pre-vious studies showed it to be associated with lipid transfer activity and CETP plas-ma concentration, making it a plausible functional polymorphism. After reportingan allele effect of the polymorphism in patients undergoing cholesterol-loweringtherapy, Kuivehoven et al. concluded that they could not ascribe this effect to thestudied polymorphism or to another non-genotyped polymorphism in LD with thestudied marker. The same concerns can be applied to all direct association stud-ies, even those involving non-conservative polymorphisms.


4.4.4Indirect Approach: A Hypothesis-Generating Strategy

With the availability of the sequence of the human genome and increased geno-typing throughput, new approaches can be used based on the study of severalpolymorphisms across one gene that are no longer selected on their putative func-tionality. The indirect approach is based on the assumption that alleles of theseunselected markers are in LD with the causative polymorphism(s) and may thusact as surrogate markers of the unknown causative polymorphisms and indirectlyreflect the association. The feasibility of this approach has been illustrated, e. g.,by the identification of a polymorphism in the 5’ promoter region of the 5-lipo-oxygenase gene (ALOX5], which influences the response of asthmatics to the 5-lipo-oxygenase inhibitor, ABT-761 [48].

By not pre-defining a model of association, the indirect approach is less restric-tive than a direct approach and can better be viewed as a hypothesis-generating,rather than a hypothesis-testing approach linking a gene to an observed trait andnot merely identifying risk-conferring alleles. This strategy completely alters theway in which pharmacogenomic association studies are designed. After a positiveassociation is revealed, other studies can be undertaken in order to refine the as-sociations and to replicate the previous results in other populations.

Compared to the direct approach, the indirect approach requires detailed docu-mentation of polymorphisms and their relation to the genes studied. In the lasttwo years, numerous studies have been published on this topic. As pointed out byCambien et al. [40], common polymorphisms are frequently in nearly completeLD in Caucasian populations. This has a major impact on haplotype diversity inthe gene [49, 50] and thus on the study design. In such genes, all common poly-morphisms do not have to be typed, as a subset is sufficient to provide an accu-rate image of the common diversity of the variation of the gene. The feasibility ofthis approach has been successfully validated for the ATM gene (ataxia-telangiecta-sia mutated) [51]. Assembling SNPs into haplotypes has demonstrated that haplo-type-based association studies for candidate genes have significant potential forthe detection of genetic background that contribute to disease. Nevertheless, theextreme variability of LD has to be taken into account when designing indirectassociation studies. Consequently, the pattern of haplotype diversities or LD mayfirst have to be described before choosing the proper set of markers to use. In ad-dition, these patterns are highly population-dependent as illustrated by Petersonet al. [50] in a study which provided a good description of the worldwide geneticsubdivision for the ALDH2 gene (aldehyde dehydrogenase 2) and demonstratedthat a common polymorphism is only present in individuals of Asian origin.

Many statistical methods have been developed for association studies [52, 53]that mostly consist of testing each polymorphism separately with the disease. Ithas been shown that the power of such methods decreases rapidly when the mar-kers are in low or even moderate LD with the risk-conferring polymorphism [54].One way to overcome this defect is to use haplotype-based tests [47, 55, 56] thatcombine different alleles of different markers. Haplotypes are likely to capture

4.4 Association Studies in Pharmacogenomics 67

more adequately the overall diversity of one chromosomal segment thus increas-ing the power to detect variations between groups. This was illustrated by an asso-ciation study of ApoE (apolipoprotein E) with Alzheimer’s disease where it wasfound that a set of markers, none of which are significantly associated in univari-ate analysis, are associated when using an haplotype-based test [57].

In addition, haplotype-based methods can increase the power of associationstudies since the allelic architecture of the risk factor is unknown. Recent exam-ples of association studies suggest that haplotypes can be the responsible factors[58]. The Apo E4 “allele”, which is associated with Alzheimer’s disease, is a possi-ble example since it results from the substitution of two of non-conservative poly-morphisms encoding for residues 112 and 158 [59].

4.5SNP Assembly into Maps

Association studies are based on organized maps of non-repetitive genomic se-quence variations, among which single nucleotide polymorphisms (SNPs) are byfar the most common in the human genome. SNPs, the simplest genomic varia-tions, are considered as the major source of genetic inter-individual variation.Other less frequent sequence variations due to insertion/deletion of one or morebases are not formally considered as SNPs, but can be detected by most of themethods used for SNP analysis [60]. An additional interesting source of poly-morphisms are VNTRs (variable number of tandem repeats), but their relativelyrare occurrence in the human genome (only several thousand are supposed to oc-cur throughout the entire genome) does not allow the development of an associa-tion strategy. It is estimated that SNPs occur on average every 1,330 bases whentwo human chromosomes are compared [60]. Therefore, a few million bases outof the 3 billion nucleotide pairs making up the human genome are thought to bepolymorphic, and are thus present at a sufficient density for association studies[61, 69]. In addition, SNPs are less prone to mutations than microsatellites, astheir rate of mutation is thought to be about 10–8 changes per nucleotide per gen-eration. This relative stability of SNPs is well-suited for indirect association stud-ies permitting a better maintenance of haplotypes across generations [61].

Construction of genome-wide SNP maps is an important step in characterizingand correlating genes with complex traits such as drug responses or ADRs. TheSNP Consortium, a joint effort of 10 leading pharmaceutical companies under theaegis of the Wellcome Trust as well as the Human Genome Sequence program,have facilitated the construction of such SNP maps. The current release of theSNP Consortium coupled with the analysis of clone overlaps by the InternationalHuman Genome Sequencing Consortium has anchored by “in silico” mapping of1.42 million SNPs to the human genome working draft, providing an average den-sity of one SNP every 1.91 kb with only 4% of the genome having gaps betweenSNPs greater than 80 kb [60]. Furthermore, the intensive integration of public datato privately owned genomic information has allowed Celera Genomics to develop


a high-density SNP map containing more than 2.1 million SNPs [62]. Overall, nu-cleotide frequency appears to be roughly homogenous across chromosomes, ex-cept for sex chromosomes in which diversity is much lower – but is much morevariable at a finer scale – suggesting that these polymorphisms do not occur byrandom and independent mutation [60, 62]. However, 93% of gene loci are pre-dicted to contain at least one SNP and 85% of the exons are within 5kb of thenearest SNP [60], making it possible to analyze in detail the coding regions of thegenome by indirect association studies. Interestingly, it is thought that only a verysmall proportion of SNPs (< 1%) are able to impact on protein function, and thatonly one or two thousand genetic variations contribute to the diversity of humanprotein structure [62].

Although the simple structure of SNPs is well suited for automated high-throughput genotyping, their scoring remains a bottleneck for association studies.Most current methods are based on PCR technology, analyzing either directly thevariable nucleotide polymorphism (single nucleotide primer extension methods,PCR-RFLP, allele-specific oligonucleotide hybridization, and pyrosequencing), orPCR products as a whole (dHPLC, SSCP and DGGE). High-density oligonucleo-tide microarrays and MALDI-TOF-MS (matrix-assisted laser desorption ionizationtime-of-flight mass spectrometry) have, in the past two years, been considered foruse in high-throughput genotyping. All of these technologies have their advan-tages and disadvantages, as detailed in Tab.4.3. Although most of these technolo-gies are robust, their price per genotype (US $ 0.50–$ 1.5], however, limits theirextensive use.

Other promising SNP scoring methods have been developed to fill the deficien-cies of the currently used technologies. For instance, some methods have been de-veloped for direct determination of haplotypes, in contrast to technologies thatonly estimate haplotype frequency. These direct haplotype scoring methods, suchas single molecule [63] or allele-specific amplification [64] and carbon nanotubeprobe technologies [65], should achieve maximum power in haplotype-based indi-rect association studies. Furthermore, pooling methods have been proposed as analternative to reduce the cost of genotyping. By mixing DNA from different indivi-duals, this method allows the simultaneous study of a large number of samplesfor a given polymorphism. Despite significant economic advantages and the ro-bustness of the method, the use of DNA pooling is hindered by the fact that itprohibits haplotype and subgroup analysis [66].

4.6Strategies for Pharmacogenomic Association Studies

4.6.1Candidate Genes

The candidate gene approach directly tests the association of selected genes withdrug response or ADR. The major sources of candidates which code for receptor,enzyme or ADME targets, are obtained through literature surveys. Most pharma-

4.6 Strategies for Pharmacogenomic Association Studies 69


Tab.

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cogenomic variations validated so far have been identified by this approach [4].Data mining of genomic and cDNA sequence databases has increased the num-ber of candidates by identifying genes that possess similar sequences to estab-lished candidates [67]. Another important class of candidates are those genes asso-ciated with disease susceptibility, as some of them may also participate in drug re-sponse (Tab.4.2). For instance, polymorphisms of the �-2-adrenoreceptor are asso-ciated with asthma susceptibility, as well as with variations in response to �-2-adrenergic agonists [24]. However, in some cases, no pathophysiology link hasbeen established between the disease susceptibility gene and the mechanism ofdrug action. This is the case for the association between response to the cholines-terase inhibitor tacrine and variants of the ApoE gene in Alzheimer’s disease [68].Another source of candidates involves construction of protein-protein interactionmaps for drug receptors and effectors. As a whole, choosing candidate genes isstrongly limited by our present knowledge of disease pathophysiology, mode of ac-tion, metabolism and distribution of the drug.

Gene expression profiling appears to be an extremely promising technology forthe identification of novel candidate genes by identifying genes that are over-/un-der-expressed in response to drugs studied in various cellular or animal models.Although limited by their inability to detect novel genes for which EST (expressedsequence tag) information is not available, microarrays are efficient for high-throughput gene expression profiling [69]. Several other technologies offer the op-portunity to discover novel genes that are differentially expressed, including differ-ential display, RDA (representational differential analysis), SSH (suppression sub-tractive hybridization) and GeneCalling. The use of such technologies in pharma-cotoxicology models has led to the identification of differentially expressed genesthat can be tested as putative targets for both drug response and ADRs by associa-tion studies [70, 71].

4.6.2Genome-Wide Scan

Genome-wide scans provide the means to identify many trait-related genetic fac-tors and, therefore, appear to be useful tools for genetic studies [67]. A critical fac-tor in such a strategy is the profile of LD throughout the genome, which influ-ences the number and choice of markers to be genotyped. Although population-based studies and large simulation studies have been carried out to evaluate thegenome-wide scan strategy [72], the estimation of the suitable number of markersstill remains at the level of “best guesses” or is driven by economic constraints(e.g., the genotyping cost). Taking a mean density of one marker per 30 kb, thenumber of markers to be genotyped should be roughly around 100000. Conse-quently, with an average cost of US$ 1 per genotype (without pooling the indivi-duals), the cost of this approach would be around US$ 100 million for a samplesize of 1 000 individuals, which is currently prohibitive in cost [73].

In a genome-wide scan study, a large proportion of the genotyped markers fallinto intergenic regions, and are likely to be not relevant for association studies.

4.6 Strategies for Pharmacogenomic Association Studies 71

This limits the value of high-density SNP maps in genome scans. Gene-widescans can be proposed as an alternative to genome-wide scans. This strategy isbased on analysis of marker sets lying in functionally interesting regions, i.e.genomic regions surrounding predicted or annotated gene sequences. To date, be-tween 32 000–40000 genes are estimated to lie in the human genome [62, 74].However, alternative approaches based on assemblies of ESTs estimate the totalnumber of genes to range between 84 000 and 120000 [75, 76]. Furthermore,among the already fully identified genes, only 60% of them belong to a predictedfamily for which a function might be anticipated by their sequence [62, 74]. Thus,a large number of genes remains to be discovered or annotated [77], and strate-gies based on current predictions may miss some genes that are still unknown.Ultimately, developing a SNP map organized around genes should be consideredfor association studies. This approach will document the extent of LD for eachgene in a given population, which is a valuable information for further studies.

4.7Expected Benefits of Pharmacogenomics in Drug R & D

4.7.1Background

Pharmaceutical drug discovery and development of small molecules is a risky, ex-pensive (around US$ 800million per marketed drug) and time-consuming processof about 10–12 years [78, 79], as summarized in Figure 4.1. While the time fromdiscovery to market is relatively stable, the number of new drugs that receive NewDrug Application (NDA) approval is decreasing [78, 79]. Thus the pharmaceuticalindustry is searching for ways to reverse this productivity trend (e.g., decreasedtime of development and/or decreased cost of development and/or increasednumber of drugs that obtain marketing approval). This is why new technologiessuch as high-throughput screening and combinatorial chemistry have been widelyadopted. The integration of pharmacogenomics in the drug development processhas the potential to reduce the amount of time for drug discovery and develop-ment, as well as to increase the odds of compounds that will ultimately obtainmarketing approval.

In order to understand how pharmacogenomics can be used in the drug re-search and development process it is necessary to understand what the aims ofeach phase of this process are, and are what the questions each phase is trying toanswer.

4.7.2Identification of New Targets

Pharmaceutical drug development of small molecules is initially based on the se-lection of candidate targets – mainly enzymes, receptors or circulating proteinsthat are currently targeted by 45, 28 and 11% of marketed compounds, respec-


4.7 Expected Benefits of Pharmacogenomics in Drug R & D 73

Fig.

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tively [80]. As a whole, current drug therapy is based on the targeting of approxi-mately 500 proteins [80], a number that should increase as there are at least30 000–40 000 protein-coding genes in the human genome [62, 74].

Pharmacogenomics offers interesting opportunities to discover novel targets re-sponsible for drug response. Such an approach is particularly useful for drugswith unclear mechanisms of action, such as drugs having multiple binding sites.In theory, such information can be further used to design novel compounds thatare specific for a targeted gene product. However, the validity of such anapproach, which has never been used on a large scale, remains to be determined.

4.7.3Pre-Clinical Development Phase

Optimization of lead compounds is a main step in the development of new drugcompounds. This involves an extensive study of the pharmacology (especiallypharmacokinetics) and toxicology of the compounds. These determine if the com-pound should enter clinical development. Here, pharmacogenomics offers thepossibility of eliminating candidates that interact with frequently polymorphic pro-teins, such as drug – metabolizing enzymes responsible for variable drug re-sponse and toxicity. Another application is predictive toxicology based on gene ex-pression profiling. The objective of this line of research is to correlate specific pro-files with toxicity in animals and to develop predictive models and databases fromthis information.

4.7.4Pre-Marketing Clinical Trials

Phase I: The preliminary studies in humans are designed to evaluate the pharma-cokinetics and the acute toxicity/tolerability of the potential drug. Usually, phase Istudies are carried out in a very small population [15–40] of healthy male volun-teers. Thus, the nature of the population tested and the small number of indivi-duals involved limits the scope of pharmacogenomics to the study of genetic varia-bility in drug metabolism.

Phase II: The first part of phase II clinical trials (phase IIa) typically involves theinitial assessment of compound efficacy in a limited number of patients [2–3 con-centrations with 50 or more patients per group or arm of the study) in compari-son with controls, and is used to determine the mean effective dose (MED) of thecompound in the disease, the frequency of administration and the therapeutic ef-ficacy and index (dose-producing efficacy versus dose-eliciting side effects) of a po-tential new drug. Genotyping of individuals from this portion of the clinical trialprocess for key proteins having an influence on the pharmacokinetics of candi-date drugs that may be altered by the disease, can be very helpful in the designand the selection of patients for subsequent stages of the trial. In many instances,patients – in contrast to healthy volunteers – have better tolerance to new medica-tions. The second part of phase II (phase IIb) is a confirmatory efficacy trial, usual-


ly double blind, placebo-controlled, which, depending on the power (number ofpatients and controls included in the study), can be used as a pivotal trial for re-gistration purposes. Frequently, the effects of a new compound are compared withthose of a reference (if available) already on the market to assess the efficacy ofthe new compound on a dose/dose comparative basis.

Phase III: The entry of a new compound into phase III clinical trials is a keystep in the drug development process since such trials involve cohorts of a thou-sand patients or more in each arm and thus are extremely expensive. This phaseis used to further demonstrate the efficacy and safety of the new compoundthrough double-blind randomized studies. Phase II trials also involve assessmentof potential drug–drug interactions, use in the elderly population and continuousexposure of a cohort of at least 1000 patients to the compound for a year. Pharma-cogenomics may be key in the selection of individuals included in a phase IIItrial. Patients may be chosen not only according to their clinical phenotype, butalso according to their genotype, for instance by genotyping the genes coding forthe proteins with which the drug interacts. Another approach is to take into ac-count the contributing genetic factors, as co-variables, in the analysis of the clini-cal trials. Pharmacogenomics can also be used during the trial to identify keygenes involved in the efficacy and/or ADR profile of the drug, thus making it pos-sible to enhance the safety and efficacy of the drug. However, information regard-ing individual prescriptions will have to be validated prospectively.

4.7.5Post-Marketing Phase IV

Taking place after marketing authorization and continuing as long as the productis marketed, this phase places considerable emphasis on the cost-to-benefit ratio(pharmacoepidemiology) of new drugs so that they can be included in state andfederal formularies. The use of pharmacogenomics in this stage of drug assess-ment will provide a better understanding of the exact relationship between drugactivity/toxicity and polymorphisms. Drug interactions and the effect of the drugon pathologies other than those tested in the phase III trial are also studied. Herepharmacogenomics can also be used to increase the safety and/or efficacy of thetreatment by helping to identify of important genetic variations responsible forthe ADRs. However, the problem with rare or long-term ADRs involves the re-cruitment of a sufficient number of patients possessing the trait to design an as-sociation study. Phase IV studies also involve the assessment of alternative uses ofa drug that may only become apparent when the drug is used in patients. For in-stance, valproate, originally used as an anticonvulsant, was found post approval tobe effective in the treatment of bipolar affective disorder and in the prophylaxis ofmigraine.

4.7 Expected Benefits of Pharmacogenomics in Drug R & D 75

4.7.6Targeting Drugs to the Individual

In addition to facilitating the selection of individuals for clinical trails to reduceside effect risk, pharmacogenomics is anticipated to be useful in helping to selectdrugs for the individuals that will have the most benefit and least side effects [81].Drugs likely to be tested are those that are metabolized by specific enzymaticpathways and/or that have a narrow therapeutic window and/or are used to treatdisorders for which several other lines of treatment are available. The concept ofusing pharmacogenomics to tailor drug treatment to the individual has becomean ethic issue for public health [82] and raises a profit maker question for thepharmaceutical industry. If one drug is replaced by 2, 3 or 10 new ones that aretailored to individual patient subsets, unless the time and cost of development arereduced, the cost of drug development will increase by the same factors of 2, 3 or10 placing additional cost burdens on the health care system.

In some rare instances, pharmacogenomics has clearly been useful in medicalpractice. For example, testing for mutations in the dihydropyrimidine dehydrogen-ase gene, which codes for a rate-limiting enzyme in 5-FU catabolism, is now re-commended before initiation of patient treatment in order to prevent severe toxici-ty in individuals having a mutation of this gene [14]. Furthermore, inactivatingmutations of TPMT are frequently screened for before initiation of treatment withazathioprine, an immunosuppressant drug used in organ transplantation, to avoidfatal hematologic toxicity [83]. However, in most cases, multiple variable factorsare thought to participate to a phenotype. Thus, an individual genetic factor re-vealed by pharmacogenomics will only have a limited influence on the overallvariability, resulting in the fact that pharmacogenomics would be probabilistic. Forinstance, Drazen et al. have shown that a 5-lipooxygenase (ALOX5] promoter gen-otype is associated with a diminished clinical response to treatment with the nov-el antiasthmatic compound, ABT-761 (an analog of Zileuton) [48]. The genotypehas a 100% positive predictive value for non-response to the drug. However, be-cause the susceptibility genotype is uncommon, less than 10% of non-responderscan be attributed to this genotype, while other variable factors are thought to de-termine non-response to the drug.

4.8Conclusion

Pharmacogenomics has opened a new area in the field of pharmacology by pin-pointing genetic factors involved in drug response. Association studies appear tobe well suited for the study of such complex traits. In the past years, some signifi-cant results have been obtained either by direct association studies or more rarelyby indirect study design. However, the wide use of such studies remained re-stricted by the unavailability of relevant genetic information. The near completionof the human genome sequencing and the availability of high-density SNP maps


now provide the required tools. We can, therefore, anticipate that within the nexttwo years association studies will be widely used in order to elucidate drug re-sponse factors. Whether genome-wide analysis or candidate gene association stud-ies will be most successful remains a subject of debate. However, it is clear thathigh-throughput genotyping methods, massive calculation capacity, and high-qual-ity patient collections are and will be key factors for the success of these studies.

The place of pharmacogenomics in the drug development process remains a pi-votal question. We anticipate that pharmacogenomics will have a major impact byreducing both the resources and the time required for drug development. Firstly,pharmacogenomics appears to be a valuable tool in the identification of new ther-apeutic targets. Secondly, it should improve the design of clinical trials throughpatient stratification and re-definition of disease state. Thirdly, understanding thegenetic basis for drug ADME, therapeutic response and ADRs should lead to bet-ter medicine prescription and therapeutic strategies. Although pharmacogenomicshas not had the success initially planned, it has now the potential to improvehealth care delivery in the near future.

AcknowledgementsThe authors would like to thank Michael Williams, Said Meguenni and SandrineMacé for discussions and input.

4.9 References 77

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