Pharmacogenomics in neurology: Current state and future steps

NEUROLOGICAL PROGRESS

Pharmacogenomics in Neurology: CurrentState and Future Steps

Andrew Chan, MD,1 Munir Pirmohamed, MD, PhD, FRCP,2 and Manuel Comabella, MD3

In neurology, as in any other clinical specialty, there is a need to develop treatment strategies that allow stratificationof therapies to optimize efficacy and minimize toxicity. Pharmacogenomics is one such method for therapyoptimization: it aims to elucidate the relationship between human genome sequence variation and differential drugresponses. Approaches have focused on candidate approaches investigating absorption-, distribution-, metabolism,and elimination (ADME)-related genes (pharmacokinetic pathways), and potential drug targets (pharmacodynamicpathways). To date, however, only few genetic variants have been incorporated into clinical algorithms. Unfortunately,a large number of studies have thrown up contradictory results due to a number of deficiencies, including small samplesizes, inadequate phenotyping, and genotyping strategies. Thus, there still exists an urgent need to establishbiomarkers that could help to select for patients with an optimal benefit to risk relationship. Here we review recentadvances, and limitations, in pharmacogenomics for agents used in neuroimmunology, neurodegenerative diseases,ischemic stroke, epilepsy, and primary headaches. Further work is still required in all of these areas, which really needsto progress on several fronts, including better standardized phenotyping, appropriate sample sizes through multicentercollaborations and judicious use of new technological advances such as genome-wide approaches, next generationsequencing and systems biology. In time, this is likely to lead to improvements in the benefit-harm balance ofneurological therapies, cost efficiency, and identification of new drugs.

ANN NEUROL 2011;70:684–697

It is well known that drug response, be it efficacy or toxicity,

differs between individuals. This may be related to pharma-

cokinetic (absorption, distribution, metabolism, and excretion)

or pharmacodynamic (action of the drug on its targets) fac-

tors. Interindividual variability in drug response can be related

to both environmental and genetic factors. There is increasing

interest in the latter, an area known as pharmacogenomics,

which can be defined as the study of variation in DNA and

RNA, at the whole genome level, and its effects on drug

response. Pharmacogenomics is gradually superseding the term

pharmacogenetics, but these terms are often used interchange-

ably. The underlying basis of how variation in our DNA or

RNA affects drug response may be related to changes in the

expression, activity, and substrate specificity of the gene prod-

uct, but increasingly many markers are being identified using

whole genome technologies, in which the effect on gene func-

tion is either not known or the identified association repre-

sents a marker that is in linkage disequilibrium with a nearby

gene, which may or may not have been identified.

In general, the aim of pharmacogenomics is to

identify patients who are most likely to benefit from a

particular treatment or are at high risk for drug adverse

reactions, with the ultimate goal of facilitating the indi-

vidualization of therapy. Different fields in neurology,

particularly neuroimmunological diseases, have recently

experienced considerable therapeutic progress, with the

drawback of unanticipated, potentially severe adverse

effects and high costs. Therefore, there is an increasing

need to tailor therapeutic options to optimize the bene-

fit-risk relationship. This review focuses on current

knowledge of pharmacogenomics in neurology, recent

advances, and how it may evolve in the future.

Neuroimmunological Disorders

With neuroimmunological diseases, many patients benefit

from early immunotherapy, but there is still uncertainty

in identifying the patient groups that are most likely to

profit and defining optimal time points.1 At the other

View this article online at wileyonlinelibrary.com. DOI: 10.1002/ana.22502

Received Dec 14, 2010, and in revised form May 6, 2011. Accepted for publication May 16, 2011.

Address correspondence to Dr Comabella, Centre d’Esclerosi Multiple de Catalunya, CEM-Cat, Unitat de Neuroimmunologia Clınica, Hospital

Universitari Vall d’Hebron (HUVH), Passeig de la Vall d’Hebron, 119-129, E-08035 Barcelona, Spain. E-mail: [email protected] and Dr Chan,

Department of Neurology, St. Josef-Hospital, Ruhr-University, Gudrunstr. 56, D-44791 Bochum, Germany. E-mail: [email protected]

From the 1Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Germany; 2Wolfson Centre for Personalised Medicine, Department of

Molecular and Clinical Pharmacology, University of Liverpool, Liverpool, UK; 3Centre d’Esclerosi Multiple de Catalunya, CEM-Cat, Unitat de

Neuroimmunologia Clınica, Hospital Universitari Vall d’Hebron (HUVH), Barcelona, Spain.

684 VC 2011 American Neurological Association

end of the clinical spectrum, patients with highly active

disease undergoing escalation therapy with drugs of nar-

row therapeutic index are likely to face potentially severe

adverse-effects. With immunosuppressive agents in partic-

ular there is high interindividual and intraindividual vari-

ation, occasionally culminating in ‘‘idiosyncratic’’ reac-

tions, which makes therapeutic drug monitoring of

limited utility. There is therefore a need for biomarkers

that allow individual risk stratification.

Multiple Sclerosis

Interferon-betaInterferon-beta (IFNb) was the first disease-modifying

therapy approved by the U.S. Food and Drug Adminis-

tration (FDA) to treat multiple sclerosis (MS) patients

and has proven efficacy in reducing clinical and radiolog-

ical disease activity. However, it is estimated that about

20% to 55% of patients will experience a lack of

response to treatment. Unfortunately, response criteria to

IFNb are discernible only after 1 or 2 years of follow-up,

and hence many patients are treated without any benefit

and at high socioeconomic cost. Several candidate gene

studies have pursued the identification of genetic variants

associated with response to IFNb, mostly evaluating type-I

IFN pathway or IFNb responsive genes (Table 1).2–5

However, these studies have yielded inconclusive results.

Part of the problem is the inconsistent definition of

response to IFNb in the different studies. Also, studies

investigating human leukocyte antigen (HLA) class I and

II genes and IFNb-treatment response were largely nega-

tive. However, the development of neutralizing antibodies

associated with IFNb-treatment appears to be HLA class

II–mediated6 (see Table 1). Two recent whole-genome asso-

ciation studies revealed associations of genes involved in

neurogenesis and neuroprotection such as GPC5 (glypican

5) and NPAS3 (neuronal PAS domain protein 3), and in

neurotransmission such as the AMPA type glutamate recep-

tor GRIA37,8 (see Table 1). This suggests that genetic deter-mination of IFNb-response is complex and polygenic in

nature. Of note, the association of GPC5 with IFNb-response was recently validated in an independent candidate

gene study.9 Despite the recent progress in the field of

IFNb pharmacogenetics, more effort is needed to define

clinical and paraclinical response criteria in order to validate

the top candidates in large and independent cohorts.

Glatiramer AcetateGlatiramer acetate (GA), the first noninterferon approved

for treatment of relapsing-remitting MS, shows similar

efficacy to IFNb and a similar proportion of nonres-

ponders to therapy. In contrast to IFNb, the fact that the

mechanism of action of GA does not involve binding and

activation of a specific receptor makes the a priori selec-

tion of genes that may be related with treatment response

even more difficult. Only 2 studies have evaluated the

influence of allelic variants in the response to GA. In the

first,10 HLA-DRB1*1501 was reported to be associated

with GA efficacy. More recently,11 a panel of candidate

genes was selected mostly based on their implication in

MS pathogenesis and the mechanism of action of GA.

Even though the association with the HLA-DRB1*1501

could not be confirmed, the authors proposed the T-cell

receptor b (TRBb; rs71878) and cathepsin S (CTSS;rs2275235) as candidates for GA response (see Table 1).

MitoxantroneTreatment with the immunosuppressant mitoxantrone

(MX) is restricted to escalation therapy where other

immune therapies have failed or as first line therapy for

malignant MS forms.12 This accounts for the risk poten-

tial, most prominently cardiotoxicity, gonadotoxicity, and

treatment-related acute leukemia. Recently, several single-

nucleotide gene polymorphisms (SNPs) in the adenosine

triphosphate (ATP)-binding cassette (ABC)-transporter

genes ABCB1 and ABCG2 were identified as potential

predictors for MX-clinical response (see Table 1), which

was corroborated by functional experimental data.13 In

addition, an unusual case of early cardiotoxicity was associ-

ated with a rare ABC-genotype14 (Table 2). ABC transport-

ers are expressed in the membranes of many tissues and

also in the central nervous system (CNS), protecting organs

from endogenous and exogenous toxins, and thus variation

in their activity is a biologically plausible mechanism for

the association seen with MX.15–17 Clearly there may be

additional (pharmacokinetic) pathways which influence the

response to MX: this is the subject of a prospective multi-

center German trial. If confirmed, this may aid in individu-

alized MX treatment regimen (dosage, therapy intervals,

and safety monitoring) because optimal therapy after the

maximal MX dosage has been reached is unclear.12

AzathioprineThe purine analogue azathioprine (AZA) is used in neu-

roimmunological diseases of neuromuscular transmission

(eg, myasthenia gravis, Lambert-Eaton myasthenic syn-

drome), muscles (eg, polymyositis, dermatomyositis),

peripheral nerves (eg, chronic inflammatory demyelinat-

ing polyneuropathy), and CNS (eg, sarcoid, vasculitis,

multiple sclerosis). Hematological toxicity that can be

fatal in about 0.3% of cases is the most severe adverse

effect.18 Methylation by thiopurine-methyl transferase

(TPMT) is the rate limiting step in the conversion of

thiopurines to inactive metabolites. Due to the strong

Chan et al: Pharmacogenomics in Neurology

November 2011 685

TABLE 1: Summary of Genes Associated with Treatment Efficacy in Neurological Disorders

Diseases Drug Reference Genes Comment

Multiplesclerosis

IFNb Cunningham andcolleagues (2005)2

CTSS (rs1136774);IFNAR1 (GTn repeat);MX1 (rs2071430/rs17000900); PSMB8(rs2071543)

Association withresponse

Wergeland andcolleagues (2005)5

IL10 (rs1800896/rs1800871/rs1800872)

Trend toward decreaseMRI activity

Martınez andcolleagues (2006)3

IFNG (intronicCAn repeat)


Hoffmann andcolleagues (2008)6

HLA-DRB1*0401and *0408

Association withNAB development

Byun andcolleagues (2008)7a

COL25A1 (rs794143);ERC2 (rs10510779);FAM19A1 (rs4855469);GPC5 (rs10492503,rs9301789); HAPLN1(rs4466137); LOC442331(rs6944054); NPAS3(rs4128599)


Comabella andcolleagues (2009)8a

ADAR (rs2229857);CIT (rs7308076);GRIA3 (rs12557782);IFNAR2 (rs2248202);STARD13 (rs9527281);ZFAT (rs733254);ZFHX4 (rs11787532)


Cenit andcolleagues (2009)9

GPC5 (rs10492503) Association withresponse

O’Doherty andcolleagues (2009)4b

GBP1 (rs12089335);IL10RB (rs2834167);JAK2 (rs1887429);PIAS1 (rs10162905)

Combination ofgenes associatedwith response

Multiplesclerosis

Glatirameracetate

Fusco andcolleagues (2001)10

HLA-DRB1*1501 Association withresponse

Grossman andcolleagues (2007)11

TRBb (rs71878);CTSS (rs2275235)


Mitoxantrone Cotte andcolleagues (2009)13

ABCB1 (rs1045642;rs2032582); ABCG2(rs2231137; rs2231142)


Ischemicstroke

Clopidogrel Hulot andcolleagues (2010)31

CYP2C19 (*2 allelecarriers)


Epilepsy AED Siddiqui andcolleagues (2003)35

ABCB1 (rs1045642) Association withresponse

Parkinson’sdisease

Pyridoxinec Tan andcolleagues (2005)49

COMT (lowactivity allele)


Entacapone Corvol andcolleagues (2011)50

COMT (highactivity allele)


Pramipexole Liu andcolleagues (2009)51

DRD3 (rs6280) Association withresponse

ANNALS of Neurology

686 Volume 70, No. 5

genotype-phenotype correlation between TPMT-defi-

ciency and thiopurine toxicity, AZA therapy is an excel-

lent example of the potential impact of pharmacogenetics

in clinical practice with a recommendation for TPMT

genotyping and/or phenotyping by the FDA (http://

www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Phar-

macogenetics/ucm083378.htm; Table of Pharmacoge-

nomic Biomarkers in Drug Labels). AZA treatment is

contraindicated in patients with low/absent TPMT activ-

ity (�0.3%) due to potentially life threatening myelotox-

icity. In these ‘‘poor TPMT metabolizers’’ 2 inactive al-

leles lead to less than 5U/ml blood TPMT activity. More

common is partial TPMT deficiency (�10%) with inter-

mediate TPMT activity (5–10U/ml) reflecting only 1

active allele; here, slow dose escalation is recommended,

starting with 50% of the normal dosage. TPMT defi-

ciency can be detected by genotyping of the three com-

mon deficiency alleles with high sensitivity19 (see Table

TABLE 1 (Continued)

Diseases Drug Reference Genes Comment

Alzheimer’sdisease

Donepezil Greenberg andcolleagues (2000)69;Petersen andcolleagues (2005)70;Bizzarro andcolleagues (2005)71;Harold andcolleagues (2006)79;Choi andcolleagues (2008)72;Pilotto andcolleagues (2009)80

APOE (�4 carriers);CHAT (rs733722);CYP2D6 (rs1080985)


CDP-choline Alvarez andcolleagues (1999)75

APOE (�4noncarriers)

Decreased treatmentresponses

Rosiglitazone Risner andcolleagues (2006)76


Increased treatmentresponses


Bapineuzumab Salloway andcolleagues (2009)77


Increased treatmentresponses

Galantamine Harold andcolleagues (2006)79

CHAT (rs733722) Association withresponse

Rivastigmine Harold andcolleagues (2006)79;Scacchi andcolleagues (2009)78

ACHE (rs2571598);CHAT (rs733722)


Clusterheadache

Triptans Schurks andcolleagues (2007)88

GNB3 (825T allele) Association withresponse

aSNPs falling in intergenic regions are not included.bOnly the most significant combination is included.cIt refers to response to high dose of pyridoxine as adjunct therapy to levodopa.ABCB1 ¼ ATP-binding cassette, subfamily B (MDR/TAP), member 1; ABCG2 ¼ ATP-binding cassette, subfamily G (WHITE),member 2; ACHE ¼ acetylcholinesterase; ADAR ¼ adenosine deaminase, RNA-specific; AED ¼ antiepileptic drugs; APOE: apoli-poprotein E; CDP ¼ cytidine diphosphate; CHAT ¼ choline O-acetyltransferase; CIT ¼ citron (rho-interacting, serine/threoninekinase 21); COL25A1 ¼ collagen, type XXV, alpha 1; COMT ¼ catechol-O-methyltransferase; CTSS ¼ cathepsin S; CYP2C19 ¼cytochrome P450 ¼ family 2, subfamily C, polypeptide 19; CYP2D6 ¼ cytochrome P450, family 2, subfamily D, polypeptide 6;DRD3 ¼ dopamine receptor D3; ERC2 (CAST) ¼ ELKS/RAB6-interacting/CAST family member 2; FAM19A1 ¼ family withsequence similarity 19 (chemokine (C-C motif )-like), member A1; GBP1 ¼ guanylate binding protein 1, interferon-inducible,67kDa; GNB3 ¼ guanine nucleotide binding protein (G protein), beta polypeptide 3; GPC5 ¼ glypican 5; GRIA3 ¼ glutamatereceptor, ionotrophic, AMPA 3; HAPLN1 ¼ hyaluronan and proteoglycan link protein 1; IFNb ¼ interferon-beta; IFNAR1,IFNAR2 ¼ interferon receptors 1 and 2; IFNG ¼ interferon gamma; IL10 ¼ interleukin 10; IL10RB ¼ interleukin 10 receptor,beta; JAK2 ¼ Janus kinase 2; MRI ¼ magnetic resonance imaging; MX1 ¼ myxovirus (influenza virus) resistance 1, interferon-inducible protein p78; NAB ¼ neutralizing antibodies; NPAS3 ¼ neuronal PAS domain protein 3; PIAS1 ¼ protein inhibitor ofactivated STAT, 1; PSMB8 ¼ proteasome (prosome, macropain) subunit, beta type, 8; STARD13 ¼ StAR-related lipid transfer(START) domain containing 13; TRBb ¼ T-cell receptor b; ZFAT ¼ zinc finger and AT hook domain containing; ZFHX4 ¼ zincfinger homeobox 4.


November 2011 687

TABLE2:GenesAssociatedwithAdverseDrugReactionsin

NeurologicalDisord

ers

Diseases

Drug

Genes

AdverseReactions

Valid

Genomic

Biomarker?a

References

Neuroim

munological

disorders

Azathioprine

TPMT(alleles*2,*3A,*3C)

Myelotoxicity

Yes

Wusk

andcolleagues

(2004)

19

Multiplesclerosis

Mitoxantron

eABCB1(rs1045642;rs2032582)/

ABCG2(rs2231142)

Cardiotoxicity

No

Dorrandcolleagues

(2009)

14

Ischem

icstroke

Warfarin

CYP2C

9(alleles*2

and*3)/

VKORC1(�

1639G)

Increasedbleedingrisk

Yes

Wadeliusandcolleagues

(2007)

22

Epilepsy

Carbamazepine

HLA-B*1502/HLA-B*3101

Hypersensitivity

reaction

s-SJS/TEN

Yes

Pirmoham

ed(2006)

39;McC

ormack

andcolleagues

(2011)

43;Ozeki

and

colleagues

(2011)

44

Parkinson’sdisease

Levodopaand

dopam

ineagon

ists

APOE/DRD2/DRD3/CKK

Hallucinations

No

Fuente-Fernandez

andcolleagues

(1999)

52;Makoffandcolleagues

(2000)

53;Goetz

andcolleagues

(2001)

54;

Wangandcolleagues

(2003)

55

APOE/DAT/ACE

Psychosis

No

Feldman

andcolleagues

(2006)

56;Kaiser

andcolleagues

(2003)

57;Lin

and

colleagues

(2007)

58

DRD2/DRD4/HCRT/COMT

Sleepdisturbancesb

No

Pausandcolleagues

(2004)

59;Rissling

andcolleagues

(2004)

60;Risslingand

colleagues

(2005)

61;Frauscher

and

colleagues

(2004)

62

DRD2/OPRM1

Motor

complication

scNo

Strongandcolleagues

(2006)

63;Wang

andcolleagues

(2001)

64


Tacrine

ABCB4/GST

M1/GST

T1

Liver

toxicity

No

Alfirevicandcolleagues

(2007)

82;

Simon

andcolleagues

(2000)

83

Peripheralneuropathy

Oxaliplatin

GST

P1(A313G

)dNeuropathy

No

Ruzzoandcolleagues

(2007)

95;McLeod

andcolleagues

(2010)

97;Lecom

teand

colleagues

(2006)

98;Goekkurtand

colleagues

(2009)

99

Vincristine

CYP3A

5(allele*3)

No

Egbelakin

andcolleagues

(2011)

101

Paclitaxel

ABCB1

No

Sissungandcolleagues

(2006)

102

a Itrefersto

validgenom

icbiom

arkersin

thecontext

ofFDA-approveddruglabels.

bSleepdisturbancesincludeexcessivedaytimesleepinessandsleepattacks.

c Motor

complicationsincludedyskinesiasandwearing-offandon

-offphenom

ena.

dItrefersto

aSN

Psubstitution

inexon

5that

givesrise

toIle105Valam

inoacid

substitution

.ABCB1,

ABCB4¼

ATP-bindingcassette,subfam

ilyB(M

DR/TAP),mem

bers1and4respectively;ABCG2¼

ATP-bindingcassette,subfam

ilyG

(WHITE),mem

ber2;

ACE¼

angiotensinIconverting

enzyme(peptidyl-dipeptidaseA)1;

APOE¼

apolipoprotein

E;ATP¼

adenosinetriphosphate;COMT¼

catechol-O

-methyltransferase;CYP2C

9¼

cytochromeP450,

family2,

subfam

ilyC,polypeptide

9;CYP3A

5¼

cytochromeP450,

family3,

subfam

ilyA,polypeptide5;

DAT¼

dopam

inetransporter(officialsymbolandnam

e¼

SLC6A

3-solute

carrierfamily6(neurotransm

ittertransporter,dopam

ine),

mem

ber3;

DRD2,

DRD3,

DRD4¼

dopam

inereceptorsD2,

D3,

andD4;

FDA¼

U.S.FoodandDrugAdministration;GST

M1,

GST

P1,

GST

T1¼

glutathioneS-transferasemu,pi,andtheta1variants,

respectively;HCRT¼

hypocretin(orexin)neuropeptideprecursor;OPRM1¼

opioid

receptor,mu1;

SNP¼

single-nucleotidepolym

orphism;SJS/TEN

¼Stevens-John

sonsyndrome/toxicepidermalnecrol-

ysis;TPMT¼

thiopurine-methyl

transferase;VKORC1¼

vitamin

Kepoxidereductasecomplex,

subu

nit1.

ANNALS of Neurology


2). Phenotyping of TPMT enzyme activity can overcome

the difficulties caused by rare TPMT variants, but can

yield false-negative results in individuals who have

received blood transfusions during the past 3 months.

Whereas TPMT genotyping is considered cost effective

for several non-neurological indications, the average cost

per identified TPMT-deficient individual of approxi-

mately £5.000–8.000 (TPMT phenotyping) in Europe is

still considerable.20 Additional arguments against routine

TPMT testing include the relatively low prevalence of

complete TPMT deficiency, wide variation in allele fre-

quencies between different ethnic groups, and incomplete

sensitivity of non-TPMT-associated adverse effects that

still necessitate laboratory monitoring. Allelic variants of

other enzymes implicated in thiopurine metabolism (eg,

glutathione S-transferase [GST-M1],21 inosine triphos-

phate pyrophosphatase [ITPA]) could in the future be

used in combination with TPMT testing if their utility is

confirmed in larger independent studies.

Neurovascular Diseases: Ischemic Stroke

Antithrombotic agents and anticoagulants are commonly

used in secondary prevention of stroke. Anticoagulation

with warfarin in patients with atrial fibrillation is over

twice as effective compared with aspirin. Despite guide-

lines recommending the use of warfarin in the elderly

population, the high prevalence of comorbid conditions

coupled with the difficulties in maintaining a therapeutic

dosage and the potentially severe hemorrhagic adverse

effects have been major drawbacks.

Oral AnticoagulantsWarfarin is the most widely used oral anticoagulant in

the world. The main issue with warfarin is identifying a

dose that maintains the international normalized ratio

(INR) within a range of 2–3. There is a 40-fold interin-

dividual variability in dose requirements with underdos-

ing and overdosing predisposing to thrombotic and hem-

orrhagic events, respectively.22 This is evidenced by the

fact that warfarin is often in the top 3 of drugs causing

adverse drug reaction (ADR)-related hospital admis-

sions.23 Several pharmacogenomics studies have shown

that variation in VKORC1 (the target of action for warfa-

rin) and CYP2C9 (the enzyme responsible for S-warfarin

metabolism) together with age and body mass index (or

weight) account for over 50% of the variance in the pre-

diction of the daily dose requirement22 (see Table 2).

This has led to a change in the warfarin label by the

FDA, the development of dosing algorithms by numer-

ous researchers, including the International Warfarin

Pharmacogenetics Consortium,24 and recently, a natural-

istic evaluation in the United States which showed that

warfarin genotyping reduced the risk of hospitalization.25

Similar data are also available for the other vitamin K

antagonists such as acenocoumarol and phenprocoumon.

However, many clinicians, including those involved in

the development of guidelines,26 feel that the evidence is

not adequate (ie, it is not randomized data) to justify

preprescription genotyping for patients being initiated on

oral anticoagulants.27 For this reason, there are at least 5

randomized trials currently being conducted globally to

determine if genotype-guided dosing for warfarin is supe-

rior to the current standards of clinical care. Whether

such stratified approaches or whether oral thrombin-

inhibitors and factor Xa-inhibitors will in the future take

over from warfarin will require further study.28

Antiplatelet AgentsAspirin and clopidogrel are mainly used in patients

deemed unsuitable for warfarin; eg, stroke of noncar-

dioembolic etiology.29 Over 30% of patients may show

clinical treatment failure with aspirin, but whether this is

true resistance or related to noncompliance or comorbid-

ities, is not clear. To date, no convincing pharmacoge-

nomic predictors of aspirin resistance have been identified.

Clopidogrel is metabolized by various P450 enzymes,

in particular CYP2C19.30 A number of studies have shown

that poor metabolizers for CYP2C19 are unable to form

the active metabolite of clopidogrel and therefore are at

higher risk of resistance to clopidogrel. This has now been

shown in a number of studies, and the association seems to

be strongest with stent thrombosis in patients with coro-

nary artery disease.31 However, there are other studies that

have shown no association with a composite end-point

which includes cerebrovascular events.32 Although routine

genotyping for CYP2C19 when using clopidogrel for pre-

venting cardiovascular and cerebrovascular events is not

routinely recommended, some centers (Scripps Institute,

Nashville, TN) are routinely testing their patients.

Epilepsy

Resistance to pharmacotherapy is a common and difficult

problem in epileptology. At the other end of the clinical

spectrum, there are rare, but potentially severe adverse

effects associated with different antiepileptic drugs (AEDs).

PharmacoresistanceResistance to treatment with AEDs is seen in 30% of

patients. Given that resistance is observed with multiple

drugs, it has been postulated that generic pharmacody-

namic or pharmacokinetic mechanisms may operate. The

latter area has focused particularly on the role of drug

transporters expressed in the blood brain barrier,


November 2011 689

specifically P-glycoprotein (Pgp).15 Pgp is overexpressed

in brain tissue obtained from patients undergoing surgery

for epilepsy, and is also upregulated following seizures.33

However, there is continuing controversy on whether any

of the AEDs are substrates for Pgp.34 The gene encoding

Pgp, ABCB1, is polymorphically expressed. The first

study in this area suggested an association between the

C3435T polymorphism in ABCB135 (see Table 1). How-

ever, while many subsequent studies have shown the

same association, at least an equal number have demon-

strated lack of an association with SNPs in ABCB1.36

Indeed, the latest meta-analysis has concluded that there

is no association with ABCB1 SNPs and pharmacoresist-

ance.37 There is also no association between seizure con-

trol and ABCB1 SNPs in newly diagnosed epilepsy

patients.38 Whether other transporters are important, or

indeed the role Pgp overexpression plays in the pharma-

coresistant phenotype, will require further study.

Severe Adverse Reactions to AEDsAromatic anticonvulsants can cause severe hypersensitiv-

ity reactions in a minority of patients, the most severe

form, toxic epidermal necrolysis (TEN), having a mortal-

ity rate of 30%39 (see Table 2). A study in Taiwanese

patients showed a strong association between carbamaze-

pine-induced Stevens-Johnson syndrome (SJS) and HLA-

B*1502.40 This led to changes in the carbamazepine drug

label in Taiwan, the United States, and the European

Union. The association with HLA-B*1502 and the blister-

ing skin reactions has been confirmed in Thai and Indian

patients, but is not seen in Japanese and Caucasian

patients.41 Interestingly, the association with HLA-B*1502

also seems to be phenotype-specific; ie, it is seen in SJS/

TEN but not in the commoner manifestation hypersensi-

tivity syndrome (in patients of any ethnicity). HLA-

B*1502 may also predispose patients to phenytoin-induced

blistering reactions, but this is not invariable.41 A recent

prospective study from Taiwan has also shown that avoid-

ing the use of carbamazepine in individuals who are posi-

tive for HLA-B*1502 can prevent SJS/TEN.42

Interestingly, recent genome-wide association stud-

ies in both Caucasian43 and Japanese44 patients have

shown that carbamazepine hypersensitivity, which

includes maculopapular exanthema, hypersensitivity syn-

drome, and SJS/TEN, is associated with an alternative al-

lele HLA-A*3101 (see Table 2). Although the association

was not as strong as that seen between HLA-B*1502 and

carbamazepine-induced SJS/TEN40 in Chinese patients,

by virtue of the fact that HLA-A*3101 predicts the

occurrence of mild as well severe reactions, the number

needed to test to prevent 1 reaction with HLA-A*3101 is

superior to that seen with HLA-B*1502.

The mechanisms of these associations are unknown

but it has been postulated that the antigen derived from

carbamazepine is presented via the HLA alleles; however,

definitive proof that either HLA-B*1502 or HLA-A*3101

are the causal alleles are lacking. Further work is ongoing

in this area, for example as part of the International Serious

Adverse Event Consortium (http://www.saeconsortium.org),

which is likely to identify new associations.

Neurodegenerative Disorders

Parkinson’s DiseaseMarked interindividual variability in therapeutic drug

response and the occurrence of adverse events (especially

motor and psychotropic) with dopaminergic antiparkin-

sonian medications has prompted the search for genetic

determinants. With respect to drug efficacy, several stud-

ies have failed to find significant associations between

polymorphisms in dopamine transporters (DAT) or mon-

oamine degradation enzymes (COMT and MAOB) and

response to levodopa,45–47 or the COMT inhibitor tolca-

pone.48 However, Tan and colleagues,49 reported associa-

tion between low-activity COMT homozygotes and

response to pyridoxine as adjunct therapy to levodopa

(Table 1). In a more recent study by Corvol and col-

leagues,50 responses to the COMT inhibitor entacapone

were higher in levodopa-treated patients homozygotes for

the high-activity COMTH allele (COMTHH) compared to

low-activity homozygotes (COMTLL). In another study,

Liu and colleagues51 investigated the DRD2 and DRD3dopamine receptor polymorphisms and response to the

non-ergot dopamine receptor agonist pramipexole, and

observed higher response rates in Ser/Ser homozygotes

for the DRD3 Ser9Gly polymorphism (see Table 1).

Studies investigating potential associations between

allelic variants and occurrence of adverse drugs reactions

such as hallucinations, psychosis, sleep disturbances and

motor complications related with levodopa and dopa-

mine agonists treatment have yielded conflicting

results52–64 (see Table 2). Overall, although much effort

has been expended to investigate the role of pharmacoge-

netics in the interindividual variability observed with

antiparkinson drugs, the majority of the reported associa-

tions have not been replicated, most likely reflecting the

small sample size of the studies, inadequate gene cover-

age, and phenotype heterogeneity, suggesting the need

for better studies in the future.

Alzheimer’s Disease

Consistent with the ‘‘cholinergic hypothesis’’ of Alzhei-

mer’s disease, cholinesterase inhibitors are the mainstay

of therapy for patients with mild-to-moderate disease,

ANNALS of Neurology


but with marked variability in treatment response. The

APOE �4 allele is associated with an increased risk of

Alzheimer’s disease with 1 or 2 copies of this allele being

present in about 40% to 50% of patients. Early work

with tacrine suggested that drug efficacy may be reduced

in APOE �4 carriers,65,66 although later studies observed

either no differences or even higher responses in APOE

�4 carriers.67,68 Discrepant data also exist for donepezil

and differential treatment responses in APOE �4 car-

riers69–73 (see Table 1), while responses to galantamine

and rivastigmine appear to be similar between APOE �4

carriers and noncarriers.68,73,74

The influence of the APOE �4 status has also been

evaluated in other noncholinergic strategies (see Table 1):

APOE �4-negative patients were shown to have decreased

treatment responses to cytidine diphosphate (CDP)-chol-

ine75 but increased responses to the peroxisome prolifera-

tor-activated receptor c rosiglitazone.76 Interestingly, in a

recent phase 2 clinical trial with the monoclonal anti-

amyloid antibody bapineuzumab in Alzheimer’s disease,

post hoc analyses revealed positive effects only in APOE

�4 noncarriers.77

As for other candidate genes (see Table 1), positive

associations have been reported between polymorphisms

in (1) acetylcholinesterase and response to rivastigmine;78

(2) choline acetyltransferase and response to donepezil,

galantamine and rivastigmine;79 and (3) CYP2D6 and

response to donepezil;80 but (4) not between the low ac-

tivity butyrylcholinesterase K-variant and response to

rivastigmine or donezepil.78,81 Pharmacogenetic studies

investigating adverse drug reactions associated with ace-

tylcholinesterase inhibitors are limited to tacrine-induced

liver damage. A study by Alfirevic and colleagues82 sug-

gested that genetic variants in the ATP-binding cassette

transporter ABCB4 may influence tacrine-induced eleva-

tion of liver transaminases. Studies on glutathione-S-

transferase mu and theta null variants (GSTM1, GSTT1)in tacrine-induced liver toxicity have resulted in conflict-

ing results83–85 (see Table 2).

Despite the inconsistent results observed for most

therapies, APOE genotyping is routinely incorporated into

new clinical trials for Alzheimer’s disease to evaluate drug

efficacy in carriers and noncarriers of the APOE �4 allele.

Primary Headaches

Triptans are agonists of the 5-hydroxytryptamine (5-

HT)1B and 5-HT1D receptors and have proven efficacy

in the first-line treatment of severe acute migraine

attacks. However, some patients do not respond to treat-

ment or experience a recurrence of the headache after ini-

tial relief. Attempts to identify allelic variants in the 5-

HT1B gene associated with clinical response to triptans

have resulted in negative results, though only small num-

bers of patients were studied.86,87 A polymorphism

located in the G-protein b polypeptide 3 (GNB3;C825T) was interrogated for its potential association

with response to triptans in patients with cluster head-

ache88 (see Table 1). TC heterozygotes were characterized

by a nearly 3-fold increased probability of responding to

triptans compared with CC homozygotes. The GNB3encoded protein is located downstream of the 5-HT1B/

D receptor signaling cascade and the T allele has been

associated with enhanced signal transduction via G pro-

tein-coupled receptors.89 This polymorphism has not

been investigated in migraine and its association with the

response to triptans in patients with cluster headache

remains to be validated. In another study,90 a polymor-

phism in the hypocretin receptor 2 (HCRTR2; G1246A),a gene found to be associated with increased genetic

risk91 for cluster headache, failed to show association

with treatment response. In summary, pharmacogenetic

studies in primary headaches are sparse. Further work is

needed to identify the predictors of variability in

response to anti-migraine drugs, with carefully designed

studies that take the placebo response into account.

Chemotherapy Neuropathy

Platinum-based chemotherapy, in particular with cis-

platin, remains a mainstay in the treatment of solid

tumors.92 Painful peripheral sensory neuropathy is the

most common dose-limiting adverse effect and can lead

to discontinuation of therapy. Neurotoxicity is more

common with cisplatin or oxaliplatin (15–70%) than

with carboplatin (4–6%).92,93 The mechanism is unclear,

but platinum-induced DNA adducts are thought to be

important with genetic alterations in DNA repair path-

ways in mice modulating adduct levels in dorsal root

ganglia and correlating with the degree of sensory

impairment.94 Several pharmacogenomic investigations

have focused on DNA repair mechanisms such as exci-

sion repair cross-complementation group 1 (ERCC1), but

have not clearly demonstrated a genotype-neurotoxicity

relationship.95–97 By contrast, several studies, among

them pharmacogenomic substudies from controlled clini-

cal trials, have demonstrated an association between poly-

morphisms in the glutathione S-transferases (GSTs) genes

and the risk of neurotoxicity95,97–99 (see Table 2). This

may be explained through the role of GSTs as detoxify-

ing enzymes, but other mechanisms involving regulation

of JNK-signaling may also be important.98 Genetic varia-

tion in the glyoxylate aminotransferase gene, which is

involved in oxalate metabolism, may be predictive of


November 2011 691

oxaliplatin-induced neuropathy, the mechanism based on

the fact that the oxaliplatin metabolite oxalate is directly

neurotoxic.100 Variation in genes responsible for drug

disposition has been implicated in the peripheral neurop-

athy associated with vincristine (CYP3A5)101 and pacli-

taxel (ABCB1)102 (see Table 2), but the findings have

uncertain clinical utility. In summary, further work is

required in this area with studies having enough power

to take into account potential confounders such as the

different underlying tumors, and multiple combination

chemotherapy. Additionally, the studies should determine

whether the antitumor response is correlated with the

potential for neurotoxicity. Given the multitude of associ-

ated gene loci, genetic screening for inherited neuropa-

thies prior to chemotherapy does not appear to be feasi-

ble. In clinically overt phenotypes (eg, Charcot-Marie-

Tooth disease) molecular testing can be performed103;

however, the clinical situation usually does not allow the

postponement of effective antitumor treatment.

Clinical Uptake of Genetic Tests

The translation of genetic tests (or other biomarkers)

into clinical practice is a long and complicated pro-

cess.104 Various obstacles including the lack of consistent

data on validation, inadequate evidence of clinical utility,

poor understanding of pathways of implementation, and

lack of evidence of operational effectiveness and impact

on public health, have to be overcome—in view of lack

of space, various issues that hamper clinical translation

are listed in Table 3.104 It is fair to state that most of the

associations in neurology, as highlighted in this article,

are stuck in the first translational gap with inadequate

evidence of clinical validity. In order to move from the

first to the second translational gap, the study designs

required to show clinical utility will depend to some

extent on the phenotype being investigated. For instance,

with warfarin, only data from a randomized controlled

trial may be acceptable to clinicians to change clinical

practice. However, this is of course not possible with rare

adverse events such as carbamazepine-induced SJS, where

observational data has led to some change in clinical

practice.

Apart from the phenotype being investigated, the

complexity of the genetic tests needed; whether alterna-

tive clinical approaches and drugs are available; and

whether the predictive criteria are accepted by regulators,

clinical guideline developers, and health technology asses-

sors are some other factors that are crucial in translating

a genetic test into clinical practice. Even when there is

good evidence of clinical utility of a test, uptake may be

hampered by lack of a testing infrastructure or by the

inability of clinicians to interpret test results. As we

move forward in this century, and new genetic sequenc-

ing technologies become more affordable, it is likely that

most drug response phenotypes will be pinned down to

multiple biomarkers. In such a situation, interpretation

will become even more difficult for clinicians—there is

TABLE 3: Translational Gaps in the Uptake of Pharmacogenetic Tests into Clinical Practice

Translational Gap Issues that Hamper Uptake

T1: Clinical validity Lack of replicated evidence of association (poor phenotyping, small sample sizes,population stratification, inadequate genotyping strategies)

Lack of functional correlates to the association

T2: Clinical utility Lack of data on effectiveness

Lack of data on cost effectiveness

Predictive test accuracy data

T3: Implementation Lack of acceptance in clinical guidelines

Lack of change in drug label by regulators

Lack of ability to undertake testing

Lack of ability to interpret test results or lack of decision support

Lack of reimbursement

Lack of education

Lack of patient and clinician acceptance

T4: Public health impact Lack of data on public health impact of genetic testing

Adapted from Pirmohamed104 and Khoury and colleagues.105

ANNALS of Neurology


therefore a necessity to ensure intelligent decision support

tools are developed alongside tests that allow for rapid

and accurate interpretation of test results, and thereby

appropriate drug prescription.

Future Directions in Pharmacogenomics

Although recent progress of pharmacogenomics in differ-

ent fields of clinical neurology is encouraging, unequivo-

cal proof of genetic associations with treatment response

is lacking in most cases, and the current situation is still

far from the ideal scenario in which individualized ther-

apy can be offered to patients with neurological disorders

(Figure). It does seem from the data currently available

that pharmacogenetic predictors of toxicity, eg, HLA-

B*1502 and carbamazepine-induced SJS, that can have a

clinical impact are more likely to be identified than

markers of efficacy. However, this may merely represent

‘‘low hanging fruit,’’ and the methodological difficulties

highlighted in the preceding sections, such as variable

definitions of treatment response, small sample sizes in

largely retrospective studies, and heterogeneity of pheno-

types, need to be addressed in future studies in order to

identify robust and clinically useful markers of drug

response, be it efficacy or toxicity.

As with any other field of genetics, associations

need to be accompanied or followed by functional stud-

ies, which would facilitate our interpretation of the

potential impact of respective genetic alterations, and an

understanding of the mechanisms. Functional validation

FIGURE 1: Moving toward individualized therapy through pharmacogenomics. In the process of treatment-related biomarker dis-covery, patients receiving a particular treatment (Tx. A) are followed up and classified into responders (R) and nonresponders (NR)based on clinical and/or paraclinical response criteria. Biomarkers associated with the response phenotype can be identified byapplying ‘‘-omics’’ technologies such as genomics, transcriptomics, or proteomics to biological samples derived from respondersand nonresponders. A crucial point in this process that may lead to discrepant results between studies is the use of uniformresponse criteria to each particular treatment. Additionally, the use of several ‘‘omics’’ (‘‘omics integration’’) may result in the dis-covery of more specific treatment-related biomarkers. In the validation process, candidate biomarkers will be determined in inde-pendent and prospective cohorts of patients, ideally before receiving treatment or in the first months of treatment, in order tomake predictions of the response of each particular patient to the treatment. Subsequently, patients will be followed up and clas-sified into responders and nonresponders by applying the same response criteria. Finally, the predictive accuracies of tested bio-markers will be calculated by relating the initial predictions made before treatment with the real response phenotypes observedafter follow-up. The ultimate goal of pharmacogenomic studies is individualized medicine, in which treatment will be administeredto the patient who is going to respond to it. In clinical neurology, most pharmacogenomic studies are in the discovery phase andhave led to the identification of a large number of treatment-related biomarkers. Unfortunately, only a few of the proposed bio-markers are being validated in prospective cohorts of patients. NR5 nonresponders; R5 responders; Tx A 5 treatment A; Tx B5treatment B. [Color figure can be viewed in the online issue, which is available at www.annalsofneurology.org.]


November 2011 693

of a genetic variant can be done in many different ways,

including relating it to changes in gene or protein expres-

sion levels by using in vitro or ex vivo techniques such as

real-time quantitative PCR for messenger RNA (mRNA),

or western blot, flow cytometry, enzyme-linked immuno-

sorbent assay (ELISA), and immunohistochemistry for

protein. On occasions, it may be necessary to express the

genetic variant in a cell line to determine whether it has

an effect on mRNA, protein or activity of the gene com-

pared with the wild-type. It may also be possible to deter-

mine the functional effect of a variant, particularly if it is

affecting the pharmacokinetics of a drug, by undertaking

pharmacokinetic-pharmacodynamic modeling. However,

we should also state that a pharmacogenetic test which has

good predictive accuracy, but for which there is no data of

a functional effect in vitro or in vivo, can still be used as a

clinical test, as it may be acting as a proxy for a functional

variant that is in linkage disequilibrium.

Technical aspects such as inadequate gene coverage

may add to difficulties in the interpretation of data. The

advent of genome-wide association studies (GWAS) has

allowed unbiased interpretation of associations at the

genome-wide level. This will undoubtedly be further

enhanced with the rapid advances in technologies such as

next generation sequencing, which will allow us to identify

both common and rare variants exerting strong or modest

effects. Therefore, the proactive definition of larger and

better phenotyped cohorts with standard outcome meas-

ures is an essential prerequisite to unequivocally identify

genetic predictors of response, if any. This will undoubt-

edly require multicenter, international collaborations and

broad consensus on phenotype definitions.

Acknowledgments

This research was supported by the German Bundesmi-

nisterium fur Bildung und Forschung (BMBF), German

competence Network Multiple Sclerosis (KKNMS)

(01GI0914 to A.C.); UK Department of Health (NHS

Chair of Pharmacogenetics), MRC, Wellcome Trust,

NIHR, and EU-FP7 (to M.P.).

Potential Conflict of Interest

A.C. has received grant(s) from the Bundesministerium fur

Bildung und Forschung (BMBF) (German Competence

Network Multiple Sclerosis, No. 01GI0914) and Merck

Serono; has been a member of the advisory boards of Bayer

Vital, Biogen Idec, Merck Serono, Novartis, and Sanofi-

Aventis; has given expert testimony for Sanofi Aventis; has

grants/grants pending from Bayer Vital, Biogen Idec,

Merck Serono, Novartis, and Teva; has received payment

for lectures including service on speakers bureaus from

BiogenIdec, Bayer Vital, Merck Serono, Novartis, Sanofi-

Aventis, and Teva; and has received payment for develop-

ment of educational presentations for course ‘‘Master

online in Neuroimmunology.’’ M.C. has consulted for

Bayer Schering Pharma, Biogen Idec, Merck Serono, and

Teva Pharmaceuticals; and has received payment for

lectures including service on speakers bureaus for Bayer

Schering Pharma, Merck Serono, Teva Pharmaceuticals,

and Novartis. M.P. is an NIHR Senior Investigator; and

has received grants from the MRC, UK Dept of Health,

Wellcome Trust, and NIHR.

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Pharmacogenomics in neurology: Current state and future steps

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