Review

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Understanding the pharmacogenetics of selective serotonin reuptake inhibitors 1.

Introduction

2.

Areas covered

3.

Candidate gene studies

4.

GWAS and methods based on genome-wide data

5.

Conclusion

6.

Expert opinion

Chiara Fabbri, Alessandro Minarini, Tomihisa Niitsu & Alessandro Serretti† †

University of Bologna, Institute of Psychiatry, Department of Biomedical and NeuroMotor Sciences, Bologna, Italy

Introduction: The genetic background of antidepressant response represents a unique opportunity to identify biological markers of treatment outcome. Encouraging results alternating with inconsistent findings made antidepressant pharmacogenetics a stimulating but often discouraging field that requires careful discussion about cumulative evidence and methodological issues. Areas covered: The present review discusses both known and less replicated genes that have been implicated in selective serotonin reuptake inhibitors (SSRIs) efficacy and side effects. Candidate genes studies and genome-wide association studies (GWAS) were collected through MEDLINE database search (articles published till January 2014). Further, GWAS signals localized in promising genetic regions according to candidate gene studies are reported in order to assess the general comparability of results obtained through these two types of pharmacogenetic studies. Finally, a pathway enrichment approach is applied to the top genes (those harboring SNPs with p < 0.0001) outlined by previous GWAS in order to identify possible molecular mechanisms involved in SSRI effect. Expert opinion: In order to improve the understanding of SSRI pharmacogenetics, the present review discusses the proposal of moving from the analysis of individual polymorphisms to genes and molecular pathways, and from the separation across different methodological approaches to their combination. Efforts in this direction are justified by the recent evidence of a favorable cost-utility of gene-guided antidepressant treatment. Keywords: antidepressant, depression, gene, genome-wide, genome-wide association studies, pathway, pharmacogenetics, pharmacogenomics, polymorphism, serotonin reuptake inhibitor Expert Opin. Drug Metab. Toxicol. [Early Online]

1.

Introduction

Antidepressant drugs, especially selective serotonin (5-HT) reuptake inhibitors (SSRIs), are largely used to treat a range of psychiatric disorders, the most common of which are mood disorders (65.3%), followed by anxiety disorders (16.4%) [1]. In the US, the rate of antidepressant treatment increased from 5.84% in 1996 to 10.12% in 2005, and regarded almost all sociodemographic groups [2]. Despite this widespread use, symptom remission during antidepressant treatment is reached in only one-third of patients and poor tolerability is a common cause of early treatment discontinuation [3]. No reliable clinical, environmental, demographic or biological predictor(s) of SSRI response and side effects have been identified so far, thus treatment choice and dose are usually determined according to a trial and error principle. This results in high personal and socioeconomic burden, indeed depressive disorders are responsible for the most part of disability-adjusted life years caused by mental disorders (40.5%), followed by anxiety disorders (14.6%) [4]. The observation of clustering of antidepressant response in relatives of affected 10.1517/17425255.2014.928693 © 2014 Informa UK, Ltd. ISSN 1742-5255, e-ISSN 1744-7607 All rights reserved: reproduction in whole or in part not permitted

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C. Fabbri et al.

Article highlights. .

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Genetic polymorphisms represent the most promising and easily accessible biological marker of antidepressant response so far. The complexity of the genetic component of antidepressant response gives rise to the issue of methodological appropriateness of pharmacogenetic studies. Despite the limitations of candidate gene studies and genome-wide association studies (GWAS), several genetic regions provided encouraging results and overlapping between candidate studies and GWAS results exist. Technological and methodological innovations are expected to provide higher covering of genetic variants across the whole genome and particularly to increase the capability of capturing their interactions. Pharmacoeconomic studies investigating the cost-utility of genotype-guided antidepressant treatment are increasing in number and suggested favorable cost-utility profile of genotyping.

This box summarizes key points contained in the article.

individuals [3] gave rise to the hypothesis of a genetic contribution. Recent data confirmed the relevant contribution of common genetic variations to antidepressant efficacy, since they were estimated to explain 42% of individual differences [5] without including rare variants. Despite these encouraging results and several pharmacogenetic studies providing positive findings, no group of polymorphisms has been consistently demonstrated to explain a relevant proportion of the variance in antidepressant response [3], preventing the development and diffusion of genotype-based treatments. The complex polygenic modulation of antidepressant response set the problem of which is the most appropriate approach to disentangle it and put into question if methodological issues may be the main reason behind unsatisfactory outcomes of antidepressant pharmacogenetics. Anyway, genetic polymorphisms still remain the most promising and more easily applicable biological marker of antidepressant response in clinical practice. Further, the need of any reliable biological marker to guide antidepressant treatment results is of primary relevance in order to reduce the heavy burden of depressive and anxiety disorders. Thus, the present article will review pharmacogenetic knowledge focusing on the most commonly used antidepressant class, that is, SSRIs. Starting from the above-mentioned methodological issues, possible strategies to better integrate the available knowledge deriving from candidate gene studies and genome-wide association studies (GWAS) are suggested. 2.

Areas covered

Candidate gene studies and GWAS focused on SSRI pharmacogenetics were collected and discussed. MEDLINE database 2

was searched (articles published till January 2014) using any combination of the following terms: pharmacogenetics, gene, polymorphism, variation, genetic, candidate, genome-wide, GWAS, pathway, antidepressant, SSRI, efficacy, response, side effect, suicide, major depression, mood disorder, anxiety disorder and individual gene names according to the official nomenclature. GWAS signals localized in promising genetic regions derived from candidate gene studies were analyzed using Ricopili [6] - a tool for visualizing regions of interest in selected GWAS data sets available via the Broad Institute. Further, genes within a range of (0.1 cm + 300 kb) from single nucleotide polymorphisms (SNPs) with p < 0.0001 in a previous GWAS meta-analysis [7] (only results obtained in the subsample treated with SSRIs) were analyzed through the GeneMANIA plug-in [8] in Cytoscape [9] in order to identify potential interactions among them. 3.

Candidate gene studies

Candidate gene studies are focused on a limited number of genes that are selected on the base of evidence from molecular, cellular, animal or human studies that suggest a relevant biological function under the perspective of antidepressant action. Established candidates In this section, candidate genes confirmed by almost five studies are discussed, independently from the specific polymorphism(s) associated with phenotype. Indeed, the basic functional unities of genome can be identified in genes rather than individual polymorphisms. A summary of this group of candidate genes is provided in Table 1 for serotonergic genes and Table 2 for non-serotonergic genes. 3.1

5-HT transporter and 5-HT receptors The monoaminergic system represents a ‘historic’ piece of research in the field of SSRI pharmacogenetics. Indeed, SSRIs have been engineered starting from the hypothesis that a central deficit in monoamines and particularly 5-HT is pivotal in the pathogenesis of depression and anxiety disorders [3]. Clearly, this represents an oversimplification, but central 5-HT and norepinephrine (NE) are thought to have a key role in the regulation of anxiety, irritability, loneliness and guilt [10]. On the other hand, loss of pleasure, loss of interest, fatigue and loss of energy - each of which contributes to the loss of drive and motivation - are mainly associated with dysregulation of dopamine (DA) and NE neurotransmission [11]. The serotonin transporter (SERT) gene (SLC6A4) has been the most investigated in the field of SSRI pharmacogenetics, since it represents the main target of this class of antidepressants. A 44 bp insertion/deletion serotonin-transporter-linked polymorphic region (5-HTTLPR) and the rs25531 A/G have been particularly studied, since their functional effect on transcription. Briefly, the former variant can carry the 3.1.1

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

SSRI pharmacogenetics

Table 1. Summary of serotonin reuptake inhibitor pharmacogenetic findings for established serotonergic candidate genes. Established candidate genes are meant here as those described in paragraph 3.1. When several phenotypes/polymorphisms were investigated and results are positive only for some of them, only these results are reported and the others are meant to be negative. Gene

Polymorphism

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SLC6A4 5-HTTLPR

Diagnosis MDD+BP DSM IV MDD+BP DSM IV MDD+BP DSM IV MDD DSM IV MDD DSM IV MDD DSM IV MDD+BP DSM IV MDD DSM IV MDD DSM IV MDD DSM IV/ ICD10 MDE DSM IV

Drug(s)

Outcome w6 response

Paroxetine

w4 remission

[191]

Fluvoxamine ± pindolol

w6 remission

[192]

Paroxetine

w2 response

[193]

Citalopram

w4 response

[194]

Sertraline

[195]

Fluvoxamine or paroxetine ± lithium Paroxetine

w2, w4, w6, w8 response w6 remission w6 response

[40]

Paroxetine

w12 response

[197]

Escitalopram

w12 response

[39]

Paroxetine, fluoxetine, escitalopram, citalopram, sertraline Citalopram, fluoxetine, paroxetine Fluoxetine

w4 response

[17]

w3, w6 response and remission w6, w12, w18 response w4 response and remission w2, w4, w6 response

[198]

w12 response

[193]

Citalopram

w14 response and remission

[22]

Escitalopram

w12 remission

[23]

Citalopram

w8 response

[87]

Paroxetine

w12 response

Sertraline

w4, w12 response

Paroxetine

MDD MDD MDD MDD

Fluoxetine Escitalopram Fluvoxamine Sertraline

w1, w2, w3, w4, w6, w8 response and adverse events Adverse events Sexual dysfunction w1, w2, w4, w6 response w6 response

IV IV IV IV

L or L/L " outcome

Ref.

Fluvoxamine

MDD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV MDD in the elderly DSM IV MDD in the elderly DSM IV MDD and/or anxiety disorders in children/ adolescents PD DSM IV PTSD DSM IV MDD

DSM DSM DSM DSM

Result

Fluoxetine Paroxetine and fluvoxamine Paroxetine

[190]

[196]

[199] [200] [74]

L allele " outcome in females L/L " outcome

[18]

LL " outcome and # adverse events

[21]

S " adverse events Negative results S or S/S " outcome

[26] [29] [202] [203]

[201]

": Higher side effects; #: Lower side effects; 5-HTTLPR: Serotonin-transporter-linked polymorphic region; BP: Bipolar disorder; DSM: Diagnostic and Statistical Manual of Mental Disorders; GAD: Generalized anxiety disorder; MD: Major depression; MDD: Major depressive disorder; MDE: Major depressive episode; OCD: Obsessivecompulsive disorder; PD: Panic disorder; PTSD: Post-traumatic stress disorder; SNP: Single nucleotide polymorphism; SSRI: Serotonin reuptake inhibitor.

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

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Table 1. Summary of serotonin reuptake inhibitor pharmacogenetic findings for established serotonergic candidate genes. Established candidate genes are meant here as those described in paragraph 3.1. When several phenotypes/polymorphisms were investigated and results are positive only for some of them, only these results are reported and the others are meant to be negative (continued).

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Gene

Polymorphism

5-HTTLPR and STin2

5-HTTLPR, rs25531, STin2

5-HTTLPR and rs25531

STin2

Diagnosis

Drug(s)

Outcome

Result

Ref.

PD DSM IV MDD DSM IV MDD DSM IV OCD DSM IV MDD DSM IV PD DSM IV PD DSM IV MDD in the elderly DSM IV MDD DSM IV MDD DSM III MDD DSM IV MD ICD-10 MD DSM IV MDD DSM IV MD MDD DSM IV MDD ICD-10 MDD DSM IV/ICD-10 MDD DSM IV

Paroxetine Fluoxetine, sertraline

w2 response w2, w4, w6 response

[204] [14]

Escitalopram

w8 response

[205]

Fluvoxamine

w12 response and % improvement w8 response

MDD ICD-10 GAD in the elderly DSM IV MD MD

SSRIs

Escitalopram Paroxetine

S " reduction in compulsion scores Negative results

[206] [207]

Paroxetine

w2 response and side effects w10 response

Fluoxetine

w4 response

L/L " outcome

[41]

Fluoxetine, paroxetine, citalopram Fluoxetine, paroxetine

w6 response

L/L and 12/12 " outcome

[45]

Fluoxetine, sertraline Several serotonergic antidepressants SSRIs

w6 response and remission Adverse events

Sertraline Fluvoxamine

Response Drug-induced nausea

Paroxetine

Response, remission and adverse reactions w12 response and remission w12 remission

Escitalopram

Fluoxetine Paroxetine, fluvoxamine

MDD DSM IV MDD DSM IV

Citalopram

MDD DSM IV MDD

Fluoxetine, sertraline Fluvoxamine

Escitalopram

[44]

S and 12/12 " outcome S/S or S " adverse events

Adverse events w6 response

Citalopram

[209]

w6 response

Paroxetine

Escitalopram

[208]

w5 response and remission w12 response

w12 remission w2, w4, w6 response w12 response and remission, adverse effects w12 response and % improvement, side effects w6 response and remission w6 response

[43] [27] [28]

L/L and 9-10/9-10 " outcome Negative results

[46]

S/S " outcome

[50]

L " outcome

[39]

L/L and 9/12 " outcome; S-A-12 haplotype # outcome " LA outcome

[47]

[40] [49]

[35] [77]

" LA outcome to fluvoxamine S or LG " adverse effects S " adverse effects S and 9/12 " outcome Negative results

[36] [15] [24] [25]

[43] [42]

": Higher side effects; #: Lower side effects; 5-HTTLPR: Serotonin-transporter-linked polymorphic region; BP: Bipolar disorder; DSM: Diagnostic and Statistical Manual of Mental Disorders; GAD: Generalized anxiety disorder; MD: Major depression; MDD: Major depressive disorder; MDE: Major depressive episode; OCD: Obsessivecompulsive disorder; PD: Panic disorder; PTSD: Post-traumatic stress disorder; SNP: Single nucleotide polymorphism; SSRI: Serotonin reuptake inhibitor.

4

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

SSRI pharmacogenetics

Table 1. Summary of serotonin reuptake inhibitor pharmacogenetic findings for established serotonergic candidate genes. Established candidate genes are meant here as those described in paragraph 3.1. When several phenotypes/polymorphisms were investigated and results are positive only for some of them, only these results are reported and the others are meant to be negative (continued). Gene

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HTR1A

Polymorphism rs6295

10 SNPs among which rs6295, rs6294, rs1800044 rs1800042

HTR2A

rs1800042, rs6295 rs7997012

rs7997012, rs9316233, rs2224721 rs6313, rs6314

rs6311, rs6313, rs7997012, rs1928040 17 SNPs among which rs6314, rs1923882, rs3125 rs6311 rs6313, rs6306 rs6311 rs6311 rs6313 rs6313, rs6311, rs7997012, rs6313, rs6311

Diagnosis

Drug(s)

Outcome

Result

Ref.

PD DSM IV MD DSM IV MDD DSM IV MD DSM IV MDD DSM IV Depressive disorders MDD DSM IV MDD DSM IV

Sertraline, paroxetine Fluvoxamine

w6 response

C " outcome

[210]

w6 response

G # outcome

[52]

Citalopram

w12 response

[53]

Fluoxetine

w4 response

[54]

Fluoxetine

w4 response

G " outcome

[41]

SSRIs

w4 response

Negative results

[56]

SSRIs

w6 response and remission w12 response

Negative results

[57]

Depressive disorders DSM IV MD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV

Fluvoxamine

w12 response and remission

A " outcome

[59]

Fluoxetine

w4 response

[54]

Citalopram

w12 response and remission w12 response and remission w12 response and remission

rs6295C/C " outcome in females A " outcome

Fluoxetine

Citalopram Escitalopram

[55]

[62] [63]

rs9316233 G and rs2224721 A " outcome rs6314 C/T " outcome; rs6313 C/C " adverse events rs6311-rs6313rs1928040 G-C-T " outcome rs6314 A, rs1923882 T, rs3125 G " outcome

[64]

MDD ICD-10

Paroxetine

Response, remission and adverse reactions

MDD DSM IV

Fluvoxamine, paroxetine or sertraline Fluoxetine

w8 response and remission w12 response

Paroxetine

w12 response

G/G " outcome

[211]

Fluvoxamine, paroxetine Citalopram

w6 response

[68]

Fluvoxamine

w4 response and w12 remission w6 response

rs6306 TT trend of # outcome Negative findings Negative findings

[69]

Fluoxetine

w4 response

Negative findings

[41]

SSRIs

w6 response and remission Adverse events

Negative results

[57]

CC "side effects and " discontinuation

[71]

MDD DSM IV

OCD DSM IV MD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV MD DSM IV

Paroxetine

[50]

[65]

[55]

[70]

": Higher side effects; #: Lower side effects; 5-HTTLPR: Serotonin-transporter-linked polymorphic region; BP: Bipolar disorder; DSM: Diagnostic and Statistical Manual of Mental Disorders; GAD: Generalized anxiety disorder; MD: Major depression; MDD: Major depressive disorder; MDE: Major depressive episode; OCD: Obsessivecompulsive disorder; PD: Panic disorder; PTSD: Post-traumatic stress disorder; SNP: Single nucleotide polymorphism; SSRI: Serotonin reuptake inhibitor.

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

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Table 1. Summary of serotonin reuptake inhibitor pharmacogenetic findings for established serotonergic candidate genes. Established candidate genes are meant here as those described in paragraph 3.1. When several phenotypes/polymorphisms were investigated and results are positive only for some of them, only these results are reported and the others are meant to be negative (continued).

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Gene

Polymorphism

rs6313 rs7997012 TPH1

rs1800532

rs1800532

TPH2

19 SNPs among which rs1800532, -7180T/G, -5806T/G 14 SNPs among which rs1843809, rs1386492, rs1487276 rs10897346, rs1487278 rs2171363 rs4570625

Diagnosis

Drug(s)

Outcome

Result

Ref.

Sexual dysfunction

GG " sexual dysfunction [72]

Drug-induced nausea

Negative findings

[76]

Negative findings

[78]

AA " side effects

[34]

Fluvoxamine ± pindolol

Serotonin toxicity in overdose Response and adverse events w6 response

[80]

Paroxetine ± pindolol

w4 response

AA or A # outcome in subjects not taking pindolol

Citalopram

w8 remission

A # outcome

[81]

Citalopram

w4 response and w12 remission

[70]

MDD DSM IV MDD DSM IV MD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV

Fluvoxamine

w6 response

A # outcome in psychotic and melancholic MDD Negative results

Fluoxetine

w4 response

[41]

SSRIs

[83]

Fluvoxamine

w6 response and adverse effects w6 response and remission Drug-induced nausea

Fluoxetine

w12 response

-7180 C and -5806 T " outcome

MDD DSM IV

Fluoxetine

w12 response

[55] rs1843809 T, rs1386492 A and rs1487276 C " outcome

w3 response

rs10897346 C # outcome C/T " outcome G " outcome and interaction with 5-HTTLPR

Depressive SSRIs disorders Depressive Paroxetine or anxiety disorders DSM IV Mixed DSM IV Serotonergic diagnosis antidepressants MD SSRIs MD DSM IV MD DSM IV MDD DSM IV MDD DSM IV

SSRIs

MD SSRIs DSM IV MDD Fluoxetine, citalopram Depressive and Citalopram anxiety disorders

w8 response w8 response

[79]

[82]

[57] [49] [55]

[85] [86] [87]

": Higher side effects; #: Lower side effects; 5-HTTLPR: Serotonin-transporter-linked polymorphic region; BP: Bipolar disorder; DSM: Diagnostic and Statistical Manual of Mental Disorders; GAD: Generalized anxiety disorder; MD: Major depression; MDD: Major depressive disorder; MDE: Major depressive episode; OCD: Obsessivecompulsive disorder; PD: Panic disorder; PTSD: Post-traumatic stress disorder; SNP: Single nucleotide polymorphism; SSRI: Serotonin reuptake inhibitor.

16-repeat long (L) allele, which is associated with a twice basal SERT expression compared to the 14-repeat short (S) allele, while the rs25531 G variant in conjunction with the L allele (LG) may result in a reduced expression of SLC6A4, equivalent to that conferred by the S allele [3]. Cumulative evidence from reviews and meta-analyses mainly suggests that the L allele of 5-HTTLPR is a predictor of better SSRI response in Caucasian populations, while in Asian populations the same allele may be associated with poorer SSRI outcome [3,12]. Recently, this hypothesis that arose from pharmacogenetics has found 6

support in functional neuroimaging. Indeed, in Chinese Han subjects, in contrast to Caucasians, the L allele was demonstrated to confer vulnerability to anxiety and depression and to weak top-down emotional control between the prefrontal cortex (PFC) and amygdala [13], which may confer higher risk of poor SSRI response. A molecular explanation of the stratification effect deriving from ethnicity is provided by the observation of different functional effect of the S allele between Caucasians and Asians. In detail, Asian S/S subjects show higher SERT function expressed as rate of platelet 5-HT

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

SSRI pharmacogenetics

Table 2. Summary of serotonin reuptake inhibitor pharmacogenetic findings for established non-serotonergic candidate genes. Established candidate genes are meant here as those described in paragraph 3.1. When several phenotypes/polymorphisms were investigated and results are positive only for some of them, only these results are reported and the others are meant to be negative. Gene

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COMT

BDNF

Polymorphism rs4680 (Val108/158Met)

23 SNPs among which rs13306278 and rs9332381 rs6265 (Val66Met)

Several SNPs among which rs6265 Several SNPs, among which rs10835210 and rs6265 rs6265

ABCB1

18 SNPs among which rs2049046 and rs6265 rs6265, rs7124442, rs7103411 rs1045642, rs2032582, rs1128503 rs1045642 rs2032582 95 SNPs

15 SNPs 81 SNPs

Diagnosis MDD DSM IV MDD DSM IV MDD DSM IV MD MDD DSM IV MDD DSM IV MD DSM IV MDD DSM IV MDD DSM IV

Drug(s)

Outcome

Paroxetine

w4 response

Fluvoxamine

w6 response

Fluoxetine

w4, w8 response

Fluoxetine Citalopram

w12 remission w4 response, w12 remission w6 response

Paroxetine SSRIs Fluoxetine

Result A (Met) " outcome

Ref. [88] [89]

A (Met) " outcome in males A (Met) " outcome A/A # outcome

[90]

Negative results

[93]

w6 response and remission w4 response

[36] [92]

[94] [31]

Citalopram

w12 response and remission

MDD DSM IV MDD DSM IV MDD DSM IV

Fluoxetine

w4 response

Fluvoxamine

w2, w4, w6 response

Fluoxetine

w6 response and remission; side effects

MDD DSM IV MDD in the elderly DSM IV MDD in the elderly DSM IV MDD DSM IV

Citalopram Escitalopram

w8 response and remission w12 remission

Paroxetine

w8 response

Met # outcome

[106]

Escitalopram

w12 response and remission

rs10835210 associated with outcome

[64]

Citalopram

Negative results

[62]

SSRIs

w12 response and remission w5 response

rs2049046 T " outcome

[111]

SSRIs

w6 response

rs7124442 TT # outcome

[110]

Paroxetine

w6 response

[115]

Citalopram

w4 response

rs1045642 T and C-G-T haplotype # outcome rs2032582 G " outcome

P-gp substrates among which paroxetine and citalopram Paroxetine

w4, w5 and w6 response and remission

rs2032583 C and rs2235015 T " outcome

[117]

w8 response and remission w8 response and remission

rs2032583 C and rs2235040 A " outcome Haplotypic association with remission

[118]

MDD DSM IV MD DSM IV MD DSM IV MD DSM IV MDD DSM IV MD DSM IV

MD DSM IV MDD DSM IV

Fluoxetine

rs13306278 C and rs9332381 G " outcome in White non-Hispanic Val/Met trend of " outcome Val/Met " outcome

[95]

Val/Met " outcome; Met # insomnia and sexual dysfunction Met " outcome

[103]

[101] [102]

[104] [105]

[116]

[119]

": Higher side effects; #: Lower side effects; BDNF: Brain-derived neurotrophic factor; BP: Bipolar disorder; COMT: Catechol-O-methyl transferase; DSM: Diagnostic and Statistical Manual of Mental Disorders; GAD: Generalized anxiety disorder; MD: Major depression; MDD: Major depressive disorder; OCD: Obsessivecompulsive disorder; PD: Panic disorder; PTSD: Post-traumatic stress disorder; SNP: Single nucleotide polymorphism; SSRI: Serotonin reuptake inhibitor.

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

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Table 2. Summary of serotonin reuptake inhibitor pharmacogenetic findings for established non-serotonergic candidate genes. Established candidate genes are meant here as those described in paragraph 3.1. When several phenotypes/polymorphisms were investigated and results are positive only for some of them, only these results are reported and the others are meant to be negative (continued).

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Gene

Polymorphism

Diagnosis

rs6946119, rs28401781, rs4148739, rs3747802 20 SNPs

MDD DSM IV MDD DSM IV

rs61615398, rs2032582, rs1045642 rs1045642, rs2032582 rs1128503, rs2032582, rs1045642 rs2032582 and rs1128503 rs1128503, rs2032582, rs1045642, rs2032583, rs2235040, rs2235015 rs2032582 rs1045642, rs2032582, rs1128503

Drug(s)

Outcome

SSRIs

w6 response

Escitalopram

w8 response and remission

Depression ICD-10

Paroxetine

w2, w4 response

MD DSM IV MDD DSM IV MDD DSM IV MDD DSM IV

Paroxetine

w6 response

Citalopram Escitalopram

w12 response and remission; adverse events w8 remission

SSRIs

Adverse effects

Eating disorder

Paroxetine

Several diagnosis

SSRIs

Drug-induced sexual dysfunction Switching and discontinuation

Result rs28401781 A and rs4148739 G " outcome rs1882478-rs2235048rs2235047-rs1045642rs6949448 T-T-T-C-C # remission Negative results, but rs61615398 interacted with CYP2D6 genotypes Negative results

Ref. [120] [121]

[122]

[123] [124]

" Dose needed to remit for C rs1045642 rs2032583 C and rs2235040 A " adverse events rs2032582 A " sexual dysfunction T-T-T haplotype " risk of discontinuation

[125] [127]

[128] [129]

": Higher side effects; #: Lower side effects; BDNF: Brain-derived neurotrophic factor; BP: Bipolar disorder; COMT: Catechol-O-methyl transferase; DSM: Diagnostic and Statistical Manual of Mental Disorders; GAD: Generalized anxiety disorder; MD: Major depression; MDD: Major depressive disorder; OCD: Obsessivecompulsive disorder; PD: Panic disorder; PTSD: Post-traumatic stress disorder; SNP: Single nucleotide polymorphism; SSRI: Serotonin reuptake inhibitor.

uptake, while the contrary has been reported in Caucasians [14]. Therefore, ethnic background should be taken into account in gene-related studies. It should be nevertheless underlined that results regarding 5-HTTLPR and SSRI efficacy are not univocal, and ethnic stratification is only a possible explanation of the conflicting results. Given that contradictory results were found in the same ethnic group treated with the same class of antidepressants in some cases, another possible confounding factor is represented by peculiarity of the pharmacodynamic profile shown by different SSRIs. In detail, the level of selectivity and affinity of each SSRI molecule for the SERT may influence pharmacogenetic associations. This hypothesis is supported by previous studies that found a selective impact of 5-HTTLPR and rs25531 on paroxetine response compared to fluvoxamine response [15]. Other hypothesized stratification factors are gender and age. Indeed, the deleterious effect of the S allele may be particularly evident in females, as suggested in both major depressive disorder (MDD) [16,17] and panic disorder [18]. These findings may be explained by the demonstration that rates of 5-HT synthesis, as well as 5-hydroxy-indoleacetic acid levels in the cerebrospinal fluid, are sexually dimorphic in humans and tryptophan depletion may induce depressive symptomatology only in women [19]. The activity of the 5-HT system (as well as other neurotransmitter systems) is also influenced by aging [20], and age-related alterations of the 5-HT system occur at multiple levels, such as the density 8

of 5-HT neurons in the raphe nuclei, the metabolism and level of 5-HT in the central nervous stystem (CNS), the expression of the SERT and 5-HT receptors [20]. Pharmacogenetic studies performed in elderly populations supported higher risk of SSRI-induced adverse events [21] and worse SSRI response [22,23] in S carriers. Further, microstructural white matter abnormalities in frontolimbic networks were demonstrated in elderly depressed S carriers [23]. Thus, if the elderly and women are groups with some higher biological liability of the serotonergic system, the S allele may be more detrimental in these patients compared to young people and males. 5-HTTLPR S allele has been repeatedly associated to SSRI-induced side effects [21,24-28], with the exception of sexual dysfunction [29]. Similarly to the therapeutic effect, the association between the polymorphism and side effects was selective to Caucasians [3]. The S allele may be also a risk factor for antidepressant-induced mania according to a meta-analysis, which anyway underlined the high heterogeneity among studies and the confounding effect deriving from concomitant mood stabilizer treatment in some of them [30]. Studies focused on the tri-allelic 5-HTTLPR/rs25531 variant showed several negative findings [25,31-33], despite some of them confirmed that LA may predict better outcome [15,34-36]. Despite the quite common description of 5-HTTLPR as a tri-allelic locus (LG, LA, and S), the allelic variation in the SLC6A4 upstream regulatory region is more complex. Indeed,

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10 additional sequence variants were identified, suggesting that the alleles reported as S and L are divided into four and six kinds of allelic variants, respectively, that show significant different distribution between Caucasian and Asian populations [37]. Rare variants for the L allele (16A, 16D and 16F) and two others for the S allele (14A and 14B) were examined in depressed patients to improve predictability [38]. Overall, the presence of these rare polymorphisms added 0.6% to the previous 7% with no difference in response rate by the presence of a different S allele. Subjects with the excess of 16F L allele (A of the A/G polymorphism together with a T instead of a C in the sixth nucleotide of the sixth repeat) may have more treatment resistant depression. Another functional variant of SLC6A4 has been particularly investigated given its functional effect, that is, a 17 bp VNTR within intron 2 (STin2). 9, 10 or 12 copies of a 16--17 bp repeat can be found and may influence gene transcription with a synergistic effect with 5-HTTLPR. Particularly, the 12 repeat variant was shown to cause higher gene expression in vitro and in vivo [3]. This polymorphism was repeatedly investigated, but results were mainly negative [16,39-42], while positive results suggested better response in L allele carriers in Asian samples [43-45], including elderly patients [43], and better response in S allele carriers in Caucasians [46,47]. No apparent stratification effect deriving from the antidepressant class used was retrieved. Meta-analytic results further suggested an opposite effect of the variant in Caucasians versus Asians, but results remained contradictory with evidence of high heterogeneity among studies [48]. Similarly, no association was found between STin2 and SSRI-induced side effects [27,28,49,50], as confirmed by a meta-analysis [48]. Given that additive effects of multiple genes are likely to contribute to pharmacogenomic associations, recent studies were interested in identifying molecules that interact with SERT and modulate its function, such as ITGB3 -- an integrin subunit that is required for SERT activity and modulates SERT [51] -- and microRNAs (miRNAs), which regulate SERT expression. Interestingly, the reduction of SERT expression obtained by miRNA regulates serotonergic neurotransmission more rapidly than pharmacological blockade of SERT [3], suggesting a strategy for developing fast-acting antidepressants. Among 5-HT receptors, the most established candidate genes for involvement in SSRI efficacy are HTR1A and HTR2A. HTR1A has been considered a good candidate since several antidepressants desensitize this inhibitory autoreceptor, consistently to the antidepressant effect of pindolol (an HTR1A blocker). rs6295 (1019C/G), in the upstream regulatory region of HTR1A, has received particular attention since the G allele causes an upregulation of the gene [3] and may contrast the therapeutic effect of antidepressants. Some pharmacogenetic studies confirmed the hypothesis reporting worse fluvoxamine [52], citalopram [53] and fluoxetine [54] response in G allele carriers, while one finding in the opposite direction [41] regarded an Asian sample treated with fluoxetine. Interestingly, the study by Yu et al. suggested that rs6295 CC

genotype was a female-specific factor for the prediction of a beneficial outcome [54]. On the other hand, several negative studies exist [55-57], as well as a recent meta-analysis that also performed subanalyses stratified by ethnic origin and treatment (SSRIs and non-SSRIs) [58]. Another variant that was hypothesized to modulate SSRI efficacy is HTR1A rs1800042, since it is in linkage disequilibrium (LD) with rs6295. Nevertheless, findings are scarce and contradictory [54,59]. Despite the contradictory and inconclusive findings reported for rs6295 and rs1800042, several studies demonstrated that HTR1A knockout mice and HTR1A receptor block by small interference RNA are characterized by increased 5-HT release in the PFC and robust antidepressant-like effects [60]. It is reasonable to hypothesize that in previous pharmacogenetic studies the focus on only one/two polymorphisms within HTR1A may have limited the understanding of the role of the gene. Recent findings suggested the involvement of other HTR1A SNPs in SSRI efficacy, as well as the interaction between HTR1A and HTR1B [53]. HTR1B is another subtype of 5-HT inhibitory autoreceptor. Insufficient evidence regarding the role of the gene in SSRI pharmacogenetics is available, but an interesting modulating effect of HTR1B rs130058 on the risk of suicidal ideation during treatment was suggested to depend from the antidepressant class (SSRIs vs non-SSRIs [61]). HTR2A codes for a G protein-coupled receptor that shows mainly excitatory activity and was demonstrated to be involved in depression and antidepressant mechanisms of action. Behavioral studies demonstrate that 5-HT2A receptor antagonists elicit antidepressant-like activities and potentiate the antidepressant-like effect of fluoxetine in rodents [3]. Several HTR2A variants have been studied in the field of SSRI pharmacogenetics, despite the possible functional impact of these variants is still unknown. rs7997012 was identified as a modulator of antidepressant efficacy that seems selective to SSRIs in a large sample [62,63]. In another study, other HTR2A SNPs (rs9316233 and rs2224721) but not rs7997012 were among the best predictors of escitalopram response [64]. Another positive finding regarded rs6314 and paroxetine treatment [50]. The poor replication of results may be linked to the complex (multilocus) nature of the genetic factors involved, as confirmed by several studies suggesting interactions among different HTR2A polymorphisms and other genes. Indeed, some HTR2A haplotypes may predict SSRI efficacy [55,65] and an interaction between HTR2A rs6313 (in LD with rs6311) and glutamate receptor, ionotropic, kainate 4 (GRIK4) (that codes for glutamate receptor, ionotropic, kainate 4) rs1954787 was reported to affect citalopram efficacy [66]. HTR2A x GRIK4 interaction does not seem surprising, since the serotonergic-glutamatergic molecular connections at multiple levels, thus the joined study of both systems has been suggested to provide a more informative perspective than the study of each individual system [67]. Under this perspective, the sequence of negative findings -- including a meta-analysis [58] -- provided by studies focused on only few HTR2A variants [41,57,68-70] may be at

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least partly explained. HTR2A may also modulate SSRIinduced side effects, particularly rs6311/rs6313 variants may affect the risk of paroxetine discontinuation [71], sexual dysfunction [72], fluvoxamine- and paroxetine-induced gastrointestinal side effects [73,74], and preliminary evidence suggested the involvement of rs7997012 in side effect risk [34]. On the other hand, some studies did not confirm the association between rs6311 and fluvoxamine- or paroxetine-induced nausea [75,76]. As HTR2A receptor plays a role in the modulation of cognition, rs6311 may also be associated with reductions in attention during SSRI treatment in elderly people [77]. Meta-analytic results confirmed that rs6311 GG and rs6313 CC genotypes may be risk factors for side effects development, particularly when considering only SSRItreated subjects [48]. The effect of rs6313 on adverse events seems to be limited to side effects and not to influence 5-HT toxicity after drug overdose [78]. Other subtypes of serotonergic receptors were less studied in the pharmacogenetic field, but interestingly some of them may be involved especially in the modulation of side effects risk. Particularly, HTR2C rs6644093 may be a selective predictor of serotonergic antidepressant-induced adverse reactions, while HTR3A and HTR3B were associated with paroxetine- and fluvoxamine-induced gastrointestinal side effects [3]. 5-HT and other monoamines metabolism The levels of monoamines in the CNS are largely dependent from the activity of the enzymes involved in their synthesis and breakdown. Tryptophan hydroxylase (TPH) catalyzes the rate-limiting step in 5-HT biosynthesis while catechol-Omethyl transferase (COMT) and monoamine oxidase (MAO) are involved in the degradation of monoamines. MAOA and MAOB genes are not discussed in this paragraph, since SSRI pharmacogenetic studies mainly provided negative findings, while evidence suggesting a possible association between MAOA/MAOB variants mainly regarded non-SSRI antidepressants [3]. Despite TPH2 is the TPH isoform predominantly expressed in the CNS, TPH1 and TPH2 are expressed at similar levels in some brain areas that are relevant to depressive and anxiety disorders pathogenesis (e.g., frontal cortex, thalamus, hippocampus and amygdala [3]). TPH1 rs1800532 (A218C) A allele is associated with decreased 5-HT synthesis, and according to the monoaminergic theory of depression it may determine predisposition to anxious/depressive symptoms and worse antidepressant efficacy [3]. Pharmacogenetic studies that show agreement with the molecular and imaging findings mainly used SSRI treatments [70,79-81], suggesting a selective effect on this class of antidepressants. Studies that did not report any association were mainly performed on Asian samples [41,82,83] but not only [55,57]. A possible selective effect of rs1800532 on response in major depression with psychotic and melancholic features was reported [70]. Despite clinical features may remain a confounding factor, a recent 3.1.2

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meta-analysis did not find any effect of the polymorphism on SSRI efficacy neither in Caucasians nor in Asians [58]. rs1800532 did not demonstrate any evidence of involvement in the modulation of SSRI-induced side effects [49,83], an effect (nonspecific for SSRIs) on the risk of body weight gain apart [84]. Other polymorphisms within TPH1 were only marginally investigated, but associations between -7180T/G, -5806T/G and fluoxetine response were reported [55], suggesting that a better covering of TPH1 variability may be useful to clarify the role of the gene. Results regarding TPH2 are more heterogeneous in terms of investigated variants. The most promising polymorphisms are rs1843809, rs1386492, rs1487276 [55], rs10897346, 1487278 [85] and rs2171363 [86]. In children and adolescents with depressive and anxiety disorders, response to citalopram was influenced by TPH2 G703T (rs4570625), and showed an interaction with SLC6A4. Indeed patients with the combination of TPH2 -703G and 5-HTTLPR L alleles were the most likely to respond [87]. The hypothesis of an additive effect on antidepressant response exerted by TPH2 and SLC6A4 is plausible since the key role of both genes in the modulation of 5-HT neurotransmission. COMT is a highly polymorphic gene, but the greatest part of studies were focused on the functional rs4680 (Val108/158Met). The Val/Val homozygote catabolizes DA at up four times the rate of Met/Met homozygote, with a possible impact on bioavailability of DA in the frontal cortex and clinical response to SSRIs [11]. Consistently with the monoaminergic theory, six pharmacogenetic studies reported the Met variant as the favorable allele for SSRI response [36,88-91], with an allele dose effect (better outcome in Met/Met carriers and intermediate outcome in Met/Val carriers). One of these studies [90] suggested a possible gender-dimorphic effect of rs4680, since Val/Val poorer antidepressant response was observed only in males. To the best of our knowledge, only one study demonstrated an opposite effect of the polymorphism on SSRI efficacy [92], while some others showed no evidence of association [31,93,94], as well as a meta-analysis [58]. All the cited studies were unfortunately performed on samples of relatively small size, while in a larger sample rs13306278 was the only COMT polymorphism that impacted on symptom remission; this variant was associated with altered ability to bind nuclear proteins [95]. Growth factors and cellular signaling The neurotrophic hypothesis of MDD was formulated after the observation that hippocampus atrophy following stress was reversed by antidepressants in parallel to an increase in the expression of neurotrophic factors, in particular brainderived neurotrophic factor (BDNF) [96]. The neurotrophic hypothesis is not independent from the monoaminergic one, since 5-HT depletion negatively modulates long-term potentiation (LTP), suggesting that 5-HT facilitates LTP induction [97]. 3.1.3

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The BDNF rs6265 (196G/A or Val66Met) polymorphism was particularly investigated, since it has been reported to affect intracellular trafficking and activity-dependent secretion of BDNF. The Met/Met mice show decreased basal levels of BDNF in the hippocampus and do not show any BDNF increase during fluoxetine treatment. Further, Met/Met mice have impaired survival of newly born cells in the dentate gyrus [98]. In humans, the Met allele was associated with poorer episodic memory and abnormal hippocampal activation [99]. On the other hand, animal models suggested that too high BDNF levels may have a detrimental effect on mood and especially anxiety [100]. The positive heterosis effect supported by this study found confirmation in Asian samples of depressed patients treated with SSRIs [101-103], including a recent meta-analysis [58]. Conversely, other studies reported a more favorable outcome in Met allele carriers treated with the most SSRIs (citalopram or escitalopram) [104,105], also in quite large Caucasian samples [62,64]. Regarding geriatric depression, two studies obtained opposite findings (better [105] and worse [106] response in Met carriers, treatments were escitalopram and paroxetine, respectively). Excluding studies performed in particular populations (e.g., geriatric depression), studies that reported a positive heterosis effect were performed in Asian samples treated with fluoxetine or fluvoxamine [101-103] while negative studies were performed in Caucasian samples treated with escitalopram or citalopram. The level of selectivity for the SERT may modulate the impact of the BDNF gene on the efficacy of each specific SSRI drug. This hypothesis is supported from the demonstration of higher increase in BDNF gene expression induced by citalopram combined with electroconvulsive therapy (ECT) than citalopram alone, even without any difference in treatment outcome [107]. ECT is hypothesized to exert its antidepressant activity through a modulation of 5-HT, NE and DA neurotransmission in different brain areas [108]. Thus, the antidepressant profile of modulation of different monoamines may modify the effect of BDNF variants on treatment outcome. Age is another modulating factor, as discussed for 5-HTTLPR, given that modifications in neurotrophic factor level normally occur with aging. Consistently, neural plasticity is severely affected in aging, as demonstrated by changes in hippocampal morphology and impairment in LTP [109]. As reported above, pharmacogenetic studies on geriatric depressive samples showed contradictory results that may be related to still poorly understood modifications of BDNF production/function in the elderly. Besides ethnicity, SSRI drug type and age, clinical type of depression was suggested as a possible modulating factor of BDNF effect on treatment response. Indeed, rs6265 Met/ Met (i.e., AA) and rs7103411 TT were found to be predictors of worse response selectively in melancholic depression, as well as rs7124442 TT predicted worse outcome particularly in anxious depression [110]. These results however were not specific for SSRI antidepressants. On the other hand, an association between BDNF rs2049046 and response was

reported to be selective to SSRIs compared to other antidepressant classes, without influence from mood stabilizer co-medication [111]. Second messengers are pivotal for the transmission and amplification of signals that originate through the binding of molecules such as growth factors, neurotransmitters and hormones with the respective receptors. Guanine nucleotide-binding protein (G protein), b polypeptide 3 (coded by the GNB3 gene), is involved in the generation of second messengers in response to a number of signals, with a possible modulating effect on a number of cellular responses. GNB3 rs5443 (C825T) T allele was associated with the occurrence of a splice variant that appears to have reduced biological activity [3] and was hypothesized to affect antidepressant efficacy. Despite the interesting functional impact of the SNP, rs5443 is not discussed in detail in this context because only one pharmacogenetic study found that the T allele predicted better SSRIs response, while several negative findings exist [3]. The study performed on the largest sample [112] found an association between rs5443 TT genotype and better response to nortriptyline but not escitalopram, suggesting an antidepressant class-specific effect. A recent meta-analysis confirmed that the effect of this SNP is probably not specific to SSRI antidepressants [58]. SSRI transport and metabolism P-glycoprotein (P-gp) (coded by the ABCB1 gene) and the CYP superfamily represent the most investigated among genes involved in antidepressant pharmacokinetics. P-gp is an ATP-dependent drug efflux pump for xenobiotic compounds that limit uptake and accumulation of some lipophilic drugs into key organs such as the brain. Among Pgp substrates, several SSRIs are included (citalopram, fluoxetine, fluvoxamine, paroxetine, sertraline [113]). Recently, also escitalopram was demonstrated to be a substrate of P-gp. In rats, the administration of a P-gp inhibitor enhanced the brain distribution of escitalopram by 70 -- 80%, increased 5-HT turnover inhibition in the PFC, and antidepressant-like effects of escitalopram [114], suggesting that P-gp inhibitors may be useful for antidepressant augmentation in treatment-resistant depression. Some ABCB1 SNPs, that is, rs2032582 and rs1045642, were demonstrated to alter P-gp expression and/or function [3], pointing out a possible modulation effect on antidepressant efficacy. This hypothesis was confirmed by pharmacogenetic studies on paroxetine [115] and citalopram [116]. Two further SNPs (rs2032583 and rs2235040) were associated with paroxetine efficacy by two independent studies [117,118], and one ABCB1 haplotype has been selectively associated with fluoxetine response [119]. A wider covering of ABCB1 genetic variability suggested that rs28401781 and rs4148739 may also modulate SSRI response [120], as well as intron 26-27 regions [121]. On the other hand, the association between rs2032582rs1045642 and paroxetine [122,123] or citalopram [124] response was not confirmed by other studies. Given that several different polymorphisms within the gene were related to SSRI efficacy 3.1.4

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and negative studies were focused on only 2-3 SNPs, the hypothesis of an involvement of ABCB1 seems reasonable while the exact regions that are implicated were not definitively identified. Despite this degree of uncertainty, the association between rs2032582 and SSRI efficacy found encouraging results at meta-analytic level [58] and rs1045642 (in LD with rs2032582) showed an impact on escitalopram dose needed for remission in MDD [125]. Further, the clinical application of ABCB1 genotyping has been recently tested in patients treated with P-gp substrates (not only SSRIs), providing the evidence that in rs2032583 and rs2235015 major homozygotes an increase in dose was associated with a shorter duration of hospital stay, whereas other treatment strategies did not improve outcome [126]. This encouraging finding may also be related to ABCB1 impact on SSRI-induced side effects [127,128] and risk of treatment discontinuation, despite this association is still debated [129]. While P-gp is pivotal in regulating the access of lipophilic drugs into the brain, CYP superfamily has a major role in their oxidation and reduction, thus regulating drug plasma levels. The main isoforms involved in SSRI metabolism are CYP2D6 (fluoxetine, fluvoxanime, paroxetine), CYP2C19 (fluoxetine, fluvoxanime, escitalopram), CYP2C9 (fluoxetine, fluvoxanime), CYP3A4 (escitalopram), while sertraline is equally metabolized by CYP2D6, CYP2C19, CYP2C9 and CYP1A2 [130]. The genes coding for these isoforms are highly polymorphic, and alleles may show normal, partially or totally defective activity, defining some theoretical metabolizing groups. CYP genotypes and related metabolizing status have been demonstrated to affect antidepressant pharmacokinetics. In detail, the most confirmed evidence is the effect of CYP2D6 polymorphisms on fluoxetine and paroxetine pharmacokinetics, and CYP2C19 polymorphisms on citalopram and escitalopram pharmacokinetics [3]. Theoretical dose adjustments have been calculated on the base of these findings [131], despite no clear evidence of correlation between SSRI plasma levels and response has been demonstrated [130]. Pharmacogenetic studies mainly did not find any association between CYP polymorphisms and SSRI response [3]. Consequently, the genotyping of CYP2D6 and CYP2C19 (e.g., AmpliChipTM test) has not been recommended so far since the lack of validated prescription guidelines. Costeffectiveness trials that include pretreatment genotyping of CYP2D6, CYP2C19, CYP2C9 and CYP1A2 polymorphisms are providing first results. Preliminary findings suggested that genotyping can identify subjects at risk of high health-care utilization, greater number of medical absence days and greater disability claims [132]. Less replicated candidate genes In this section, candidate genes replicated by less than five studies but showing particular relevance under the perspective of SSRI mechanisms of action are discussed. The focus is pointed again on genes rather than individual polymorphisms, similarly to paragraph 3.1. 3.2

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Glutamatergic system Besides the monoaminergic theory has been central in the last decades, the contribution of dysfunction in the glutamatergic system has been recently underlined. Indeed, monoamine depletion paradigms failed to find a final common pathway for antidepressant efficacy [133]. The glutamatergic theory posits that the glutamate may shape the risk of depression, influencing the neuronal fate (neurotoxicity) or the unfolding of new neuronal nets (neuroplasticity) [3]. Compounds with glutamatergic activity have demonstrated antidepressant properties, for example molecules that downregulated the glycine B site and the NMDA receptor [133]. Further, also SSRI antidepressant action may be partially mediated through the modulation of the glutamatergic system. Indeed, repetitive administration of fluoxetine induced maturation of telencephalic dendritic spines that is associated with the presence of a higher proportion of GluA2- and GluN2A-containing glutamate receptors [134]. Despite the encouraging findings provided by preclinical studies, SSRI pharmacogenetics still provided few studies focused on glutamatergic genes. GRIK4 was identified as candidate gene as the reduced anxiety and antidepressant-like phenotype of the KO mice [135]. While a preliminary study on the first two-third of the STAR*D (Sequenced Treatment Alternatives for Depression) cohort provided negative findings for all glutamate NMDA and kainate receptor genes [62], a following study on the whole STAR*D sample identified GRIK4 rs1954787 G allele as a predictor of citalopram efficacy [66]. The result was independently replicated in a sample treated with SSRIs or SNRIs [136], and the possible functional effect of the SNP on gene expression makes the finding even more encouraging [137]. On the other hand, negative findings regarding GRIK4 gene were obtained in samples treated with antidepressants of mixed classes and in some cases combinations with mood stabilizers and/or antipsychotics [3]. Besides GRIK4, glutamate receptor, metabotropic 7 (GRM7) represents a promising candidate gene for involvement in early (second week) SSRI response [67], given the observation that glutamatergic-based antidepressants are faster in their action than their monoaminergic counterparts. Other glutamatergic genes were reported to be possible predictors of citalopraminduced sexual dysfunction (GRIK2, GRIA1, GRIA3 and GRIN3A) [138] and treatment-emergent suicidal ideation (GRIA3, GRIK2) [139], but these findings still remain without replication. 3.2.1

Neuropeptides In recent years, a prominent role of various neuropeptides in anxiety and mood disorders emerged. Given the typical neurovegetative symptoms of MDD (sleep-wake cycle disruption, gastrointestinal symptoms, arousal alterations), several peptides that are involved in the regulation of circadian rhythms have been recently investigated in depressed subjects. Disruptions in the diurnal rhythms of the release of 3.2.2

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melatonin, vasoactive intestinal polypeptide (VIP), adrenocorticotropic hormone (ACTH), IGF-1 and growth hormone were described in MDD, and they are combined with disruption in the expression of peptides involved in the regulation of the biological clock (PERIOD1, PERIOD2, CRY1, BMAL1, NPAS2 and GSK-3b). Several of these disruptions (i.e., PER1, CRY1, melatonin, VIP, ACTH and IGF-1) were found to persist after escitalopram treatment, suggesting that these neurobiological alterations may contribute to the risk of MDD recurrence or incomplete remission [140]. Besides peptides involved in circadian rhythms regulation, neuropeptide Y (NPY), leptin and galanin recently gained attention since their role in anxiety, mood, feeding and cognition [141,142]. These neuropeptides also exert modulating influence on the monoaminergic system and the hypothalamic-pituitary-adrenal (HPA) axis [143] and have antidepressant-like effects in animal models [144,145]. In depressed patients, a robust suppression of cerebrospinal fluid NPY levels has been found [146] and treatment with citalopram led to an increase in NPY cerebrospinal fluid concentrations [147], providing further support to the relevance of the gene in antidepressant mechanisms of action. In particular, rs16147 has been considered a good candidate SNP, since it is located in the promoter of NPY and has been shown to alter gene expression [148]. Preliminary evidence suggests that the less active rs16147 C allele conferred slow and worse response to antidepressant treatment, even if not only SSRI antidepressants were used in this study [149]. The effect of rs16147 on response was observed particularly in anxious depression and was combined with stronger bilateral amygdala activation in response to threatening faces in an allele-dose fashion. Further studies should focus on NPY gene to clarify its contribution to SSRI efficacy. Despite preclinical and first clinical data are also encouraging for galanin (e.g., galanin has been reported to have an antidepressant effect in patients with depression under standard antidepressant treatment [150]), few pharmacogenetic findings still exist. rs948854 in the preprogalanin gene (i.e., the gene coding for the proteic precursor of galanin) was demonstrated to play a role in anxiety and depressive vegetative symptoms, as well in antidepressant response, with a gender-specific effect that is related to the role of estrogens in the regulation of HPA-axis [151]. The SNP was associated with higher HPA-axis activity before treatment in a consistent way. Even in this case, the study was not focused on SSRIs but mixed antidepressant classes were used, thus further studies are needed to provide clearer statements. As reported above, leptin has biological functions that go beyond the modulation of appetite and energy balance. Modifications of leptin levels in the course of antidepressant treatment suggest a link between antidepressant action, resolution of psychopathology and regulation of leptin release in both rodents and humans [152,153]. In depressed patients, nominal associations of several polymorphisms in the upstream vicinity of rs10487506 were associated with antidepressant response across

different samples, among which the STAR*D. Further, unfavorable treatment outcome was accompanied with decreased leptin mRNA and leptin serum levels, but without selectivity of these last findings for SSRI antidepressants [142]. Endogenous opioid peptides have also been implicated in the modulation of anxiety [154] and depression [155]. Interestingly, women with MDD who did not respond to antidepressant treatment demonstrated attenuated µ-opioid receptor binding potential when compared with women with MDD who did respond to medication, as well as depressive state itself was associated with a defect in the endogenous opioid neurotransmission [155]. A pharmacogenetic study suggested that rs540825 in the OPRM1 gene (coding for the µ-opioid receptor) may be a modulator of citalopram response in MDD [156]. The polymorphism is a nonsynonymous SNP in the final exon of the OPRM1 gene that results in a histidine to glutamine change in the intracellular domain of the receptor. In the same study, the rs1799971 -- which results in an asparagine to aspartic acid amino acid substitution and produces a threefold increase in binding to b-endorphin and 10% reduction of protein level -- provided negative findings, whereas it was associated with non-SSRI antidepressant response [157]. Ion channels Mouse models have recently provided compelling evidence implicating some neuronal ion channels in antidepressant mechanisms of action. In detail, hyperpolarization-activated cyclic nucleotide-gated (HCN) 1 gene -- coding for a potassium/sodium channel -- and particularly KCNK2 -- a neuronal potassium channel -- have been proposed as promising candidates. TREK1 is inhibited by therapeutic doses of SSRIs, and mice lacking the KCNK2 gene are resistant to depression-like behavior, leading to speculation that KCNK2 inhibition may contribute to the therapeutic effects of SSRIs [158]. In humans, genetic variations in KCNK2 were associated with anhedonic symptoms of depression [159]. In depressed patients treated with citalopram, KCNK2 gene was associated with treatment response, as well as with response to different antidepressant therapies in treatment-resistant subjects [160]. Nevertheless, pharmacogenetic replication of these results is still lacking. HCN1 gene has not been studied yet by pharmacogenetic studies in humans, but animal models suggested it is involved in the antidepressant effect. HCN channels underlie the hyperpolarization-activated current, a functionally important current in the nervous system [161]. HCN localization in the CNS is most striking in dendrites of hippocampal and cortical pyramidal neurons -- both of which are relevant to depression -- and spatially enriched hyperpolarization-activated current controls local dendritic processes such as Ca2+ spikes and synaptic plasticity [162]. Mutant mice lacking HCN1 demonstrated antidepressant-like behaviors [163]; following experiments showed that the selective HCN1 knockdown in the dorsal hippocampal CA1 region was enough to produce antidepressant- and anxiolytic-like behaviors, in association with an upregulation of BDNF signaling pathways [164]. The reported 3.2.3

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findings highly suggest the usefulness of investigating this gene in clinical pharmacogenetic studies. Inflammation and immune system Inflammation is hypothesized to play a subtle role in the pathophysiology of MDD, as demonstrated by abnormal peripheral inflammatory biomarkers and HPA-axis functioning in these subjects [3]. In line with these findings, treatment outcome of MDD is influenced by the antidepressant-induced modulation of cytokines, which occurs through a direct or indirect action on intracellular cAMP, 5-HT metabolism and HPA-axis activity [165]. Corticotropin releasing hormone (CRH) receptors 1 and 2 (CRHR1 and CRHR2) hold a primary role within the HPA-axis since they are the mediators of the effects of glucocorticoids in the CNS. Interestingly, CRHR1 antagonists have demonstrated antidepressant-like effects in animals [166]. The CRHR1 rs242941 G/G genotype and one haplotype block including two SNPs in LD with rs242941 (rs1876828 and rs242939) were associated with SSRI response [167], particularly in anxious depression [168]. Nevertheless, the result was not confirmed by a further study [169], neither in a numerous sample. The latter study suggested the role of another CRHR1 SNP (i.e., rs12942300, that is 40 Kbp from rs242941). Studies focused on the CRHR2 gene suggested the involvement of rs2270007 [169] and rs2267716-rs255105 [170] in citalopram response. Besides CRH receptors, the glucocorticoid receptor (GR), (i.e., coded by the NR3C1 gene) represents the target of glucocorticoid hormones and is expressed in almost every cell in the body. GR regulates the transcription rate of genes controlling the development, metabolism and immune response through the binding with specific chromatin sequences. In the absence of hormone, GR resides in the cytosol complexed with a variety of proteins including FK506-binding protein 52 (FKBP5) that regulates GR sensitivity [3]. Pharmacogenetic studies suggested several NR3C1 polymorphisms as putative modulators of SSRI efficacy (rs852977, rs10482633 and rs10052957 [64]), but the effect does not appear specific for SSRI antidepressants, while negative results were provided for BcII [171]. Studies focused on FKBP5 firstly suggested that the gene does not play a major role in SSRI response [3], while recent studies on more numerous cohorts suggested that rs4713916 may modulate remission to citalopram [172] and rs352428 may affect SSRI response [173]. Unfortunately, among pharmacogenetic studies that investigated NR3C1 and FKBP5 only a minor part was focused on SSRI antidepressants, but they mainly investigated the efficacy of heterogeneous pharmacological treatments [3]. An effect of FKBP5 was anyway hypothesized on the risk of suicidal ideation during SSRI treatment [61,174] and may be related to the high HPA-axis dysfunctions associated with suicidal behavior. Interesting but preliminary results were reported for CRHbinding protein gene that encodes for a plasma protein involved in the inactivation of CRH. This gene is

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hypothesized to act as a selective modulator of citalopram efficacy in African-American and Hispanic populations, but not in Caucasians [170]. Given that the brain is not a sanctuary for the immune system as hypothesized in the past, inflammatory cytokines represent putative modulators of neurotransmitter metabolism, neuroendocrine function and neural plasticity [3]. The IL-1b gene is considered a good candidate for involvement in the modulation of SSRI response, since its multiple and reciprocal interactions with the monoaminergic, cholinergic and GABAergic systems in the CNS [11]. Further, pretreatment IL-1b expression is higher in antidepressant nonresponders and is significantly decreased after treatment [175]. Pharmacogenetic studies further support the role of IL-1b gene in the modulation of antidepressant efficacy. In detail, the promoter SNP rs16944 has been selectively associated with SSRI response by some studies [176,177].

GWAS and methods based on genome-wide data

4.

In the 2000s, GWAS were proposed as innovative approach to study human complex traits and rapidly gained approval and enthusiasm. Alongside a series of advantages - mainly the genotyping of hundreds of thousands of polymorphisms within the whole genome without any a priori hypothesis - some limitations of GWAS emerged. In detail, i) the available platforms are able to provide only a relative narrow genomic covering (e. g., less of 50% in the STAR*D); ii) current GWAS technology does not allow the reliable genotyping of rare variants (< 1% of the population); iii) the risk of the so-called flip-flop phenomenon [178], since the functional effect of the genotyped variants is largely unknown and polymorphisms apparently associated with outcomes may be only in LD with variants truly associated; iv) the difficulty in balancing the risk of false-positive and false-negative findings [179]. Other limitations that are specific of pharmacogenetics in psychiatry are represented by: i) difficulty in collecting a sufficiently powered sample size since the consistent administration of a drug for a period of several weeks is required; ii) the lack of objective measures of efficacy (e.g., laboratory tests) and tendency to high placebo effect that anyway remains undetermined in previous GWAS since they lacked a placebo arm. Two GWAS were performed on cohorts with a substantial part that was treated with SSRI monotherapy, that is, the STAR*D [180] and the Genomebased Therapeutic Drugs for Depression (GENDEP) [181]. Among the top STAR*D findings, the RAR-related orphan receptor A gene shows the highest biological rationale for association with antidepressant response, since it is involved in the regulation of the circadian rhythm [182]. In the GENDEP study instead, the best findings were localized within two intergenic regions, but copy number variants that may influence the expression of genes at longer distances through changes of the chromatin structure have been identified in both regions [181]. Results of both GWAS have been extensively

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reviewed elsewhere [3], while suicidal ideation during antidepressant treatment deserves here some attention given the clinical relevance of this dramatic adverse event. In the GENDEP study, two suggestive signals were found within Kv channelinteracting protein 4 (KCNIP4) and near elongation protein 3 homolog (ELP3) in subjects treated with escitalopram [183], while the papilin, proteoglycan-like sulfated glycoprotein and IL28RA (IFN, lambda receptor 1) genes were predictors of the phenotype in the STAR*D [184]. Papilin is a component of the extracellular matrix whose relevance remains unknown in relation to suicide and antidepressant treatment, while KCNIP4 and ELP3 are involved in several neuronal functions but their possible role in suicide is still unclear. On the other hand, IL28RA calls the attention to the known inflammatory mechanisms that are associated with depression and suicide, as well as for the FKBP5 gene (see paragraph 3.2.4). Given the hypothesis that sample sizes of previous individual GWAS could be insufficient to provide adequate statistical power, recently GWAS meta- and mega-analysis have been performed. In particular, a meta-analysis was focused on STAR*D and GENDEP subsample treated with escitalopram [7], providing as top finding an intergenic region on chromosome 5 with no evidence of transcription so far. As reported above, regulatory regions may modulate gene expression also at longer distances, thus this finding should not be overlooked. Instead of further discussing findings of previous GWAS, the reporting of GWAS results in genetic regions identified by the candidate gene approach represents an interesting perspective to study the replication of findings across the two methodologies. Data set used corresponds to results reported by a previous GWAS meta-analysis [7]. Among genes considered as established candidates in this review (paragraph 3.1), SLC6A4 and HTR2A regions show interesting signals (Figure 1). Among less replicated candidate genes (paragraph 3.2), GWAS signal overlapping was more evident in CRHR2, GRIK4, GRM7, KCNK2, NPY and OPRM1 genetic regions (Supplementary Figure 1). Molecular pathways involved in SSRI efficacy Recently, pathway analysis has been proposed in order to balance the limitations of GWAS hypothesis free approach. The basic principle of the method is the analysis of variants within genes involved in the same biological pathway. Pathway analysis may yield more insights into disease biology because it overcomes the genetic heterogeneity bias (e.g., due to population stratification, differential rates of genotyping error between the groups under analysis), and setting the variants within the same pathway as the unit of analysis may increase power to detect associations and replicate findings [185]. As in the previous paragraph, a cumulative evidence on individual candidate genes derived from GWAS data was reported, in this paragraph molecular pathways involving genes within a range of (0.1 cm + 300 kb) from SNPs with p < 0.0001 in a previous GWAS meta-analysis [7] were reported (Table 3). The pathways involved were mainly related 4.1

to cell proliferation and apoptosis (including neuronal apoptosis) and immune system modulation. Both these processes have been extensively implicated in the mechanisms of antidepressant action accordingly to the inflammation [165] and neurotrophic [96] hypotheses of depression. Other identified pathways converged into corticosteroid and lipid metabolism, which are interestingly connected to HPA-axis dysfunction and neurosteroids role in stress and depression. Indeed, neurosteroids are potent and effective neuromodulators that are synthesized from cholesterol in the brain. Neurosteroids and their synthetic derivatives influence the function of multiple signaling pathways including GABA and glutamate receptors [186]. The other identified pathways are involved in protein metabolism or assembly, DNA transcription, and second messengers. These processes show clearly less specificity for antidepressant mechanisms of action. Anyway, a list of the genes included in each of the identified pathways was reported in Table 3 in order to provide some suggestions about putative candidate genes. 5.

Conclusion

Previous pharmacogenetic studies often provided findings that are difficult to summarize and interpret. This difficulty partly derived from heterogeneity in clinical features, type of treatment, trial design, genotyped polymorphisms and sample ethnicity, all of which reduce the comparability among studies. Taking into account these factors of stratification, the involvement of SLC6A4, HTR1A, HTR2A, BDNF and ABCB1 genes in SSRI response seems plausible (Tables 1 and 2). Among innovative candidates, CRHR2, GRIK4, GRM7, KCNK2, NPY and OPRM1 are promising genes taking into account both candidate studies and genome-wide findings. The rapid progress in technology and reduction in genotyping costs provide the opportunity to clarify the role of these genes and other genes potentially involved. The increasing amount of GWAS data paves the way to large mega-analysis (e.g., [187]), and sequencing technologies are spreading in genetic research. The possibility to extend both sample size and covering of genetic variability represents pivotal issues to improve the knowledge in the field, since they were among the main limitations of previous studies. Further, the need of shifting from the study of individual polymorphisms to genes and molecular pathways represents an equal importance issue. Indeed, polymorphisms are not independent from each other and the same can be stated for genes. The study of gene x environment interactions and epigenetic modulation of gene expression have encountered parallel development, together with genome-wide transcriptomics. Epigenetics (modulation of gene expression dependent from non-sequence modifications, e.g., DNA methylation) and transcriptomics (RNA sequencing) represent levels of regulation that are complementary to genetics and can fill the gaps in biological mechanisms of SSRI action that are left after genetic analysis. Progress in technology provides the

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A.

1

0.8

0.6

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Observed (–logP)

40 6

4 20 2

0

0

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25600

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25800

Chromosome 17 (kb) B.

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40

Observed (–logP)

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46100

46300

Recombination rate (cM/Mb)

8

46500

Chromosome 13 (kb)

Figure 1. Results reported by a previous genome-wide association studies meta-analysis [7] in SLC6A4 (A) and HTR2A (B) genetic regions are reported. Plots were obtained using Ricopili [6]. Colors in the legend indicate the level of linkage disequilibrium among the identified SNPs.

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Table 3. Molecular pathways involved in serotonin reuptake inhibitor efficacy (results obtained by including genes within a range of [0.1 cm + 300 kb] from SNPs with p < 0.0001 in a previous meta-analysis of genome-wide studies [7]). Biological process

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Cell proliferation, cell damage and apoptosis

Pathway p53 binding Induction of apoptosis by extracellular signals Cellular component disassembly Induction of apoptosis by intracellular signals Positive regulation of cysteine-type endopeptidase activity involved in apoptotic process Positive regulation of neuron apoptosis Positive regulation of cellular component organization Regulation of cellular response to stress

Immune system

Apoptotic signaling pathway Nuclear body Cellular response to radiation Cellular response to starvation Regulation of cellular component biogenesis Macromolecular complex disassembly Cellular response to biotic stimulus Cytokine-mediated signaling pathway

Regulation of defense response Regulation of innate immune response Regulation of type I IFN-mediated signaling pathway Leukocyte apoptosis Regulation of response to cytokine stimulus Immune response-activating signal transduction Positive regulation of immune response Protein assembly

Protein and peptide metabolism

Protein heterodimerization activity Regulation of protein complex assembly Peptidyl-lysine modification Positive regulation of peptidase activity Positive regulation of endopeptidase activity* Positive regulation of cysteine-type endopeptidase activity* Ubiquitin protein ligase binding* Activation of protein kinase activity Small conjugating protein ligase binding Unfolded protein binding Peptidyl-lysine acetylation Positive regulation of protein serine/threonine kinase activity Protein acetylation

Genes

q-value

USP7, TP53, SMARCA4, SIRT1, SETD7, EHMT2, DAXX VAV2, TNF, SSTR3, MAP3K5, DAXX, BAX, BAK1 SOD1, SMARCA4, RPS11, RPL7A, RPL7, RPL18, PMAIP1, BAX BAK1, BAX, MAP3K5, PMAIP1, SIRT1, TP53 TP53, TNF, SIRT1, PMAIP1, MAP3K5, BAX

6.91e-05

TP53, PCSK9, BAX TP53, TNF, SMAD2, PMAIP1, PCSK9, HCK, DNMT1, BAX, ARF6 SIRT1, PMAIP1, MAP3K7, MAP4K5, MAP3K5, DAXX, CEBPG TNF, PMAIP1, MCL1, BAX TP53, TFIP11, SIRT1, HSPA1A, GFI1, DAXX TP53, SIRT1, RUVBL2, BAK1 TP53, SIRT1, PMAIP1, PCSK9 TNF, RAF1, PMAIP1, HSPA1A, HCK, BAX, ARF6 TNF, SMARCA4, RPS11, RPL7A, RPL7, RPL18 TP53, TNF, HCK, GFI1 TYK2, TNF, PIN1, NUP210, IFNAR2, IFNAR1, HLA-DMB, HLA-DMA, HCK, GFI1, CEBPA, CDC37, ARIH1 TYK2, TNF, MEF2A, MAP3K7, IFNAR2, IFNAR1, HCK,GFI1, CHUK, CDC37 TYK2, MEF2A, MAP3K7, IFNAR2, IFNAR1, HCK, GFI1, CHUK, CDC37 TYK2, IFNAR2, IFNAR1, CDC37 TP53, SIRT1, CTSL1, BAX TYK2, IFNAR2, IFNAR1, GFI1, CDC37 MEF2A, MAP3K7, HLA-DMB, HLA-DMA, HCK, GFI1, CHUK SIRT1, MEF2A, MAP3K7, HLA-DMB, HLA-DMA, HCK, GFI1, CHUK TP53, MEF2A, MCL1, GTF2A2, GTF2A1, EXT2, CEBPG, BAX, BAK1 TNF, PMAIP1, HCK, BAX, ARF6 TAF10, SUPT7L, SIRT1, SETD7, RUVBL2, RUVBL1, MAP3K7, EHMT2 ADRM1, BAX, MAP3K5, PMAIP1, SIRT1, TNF, TP53 TP53, TNF, SIRT1, PMAIP1, MAP3K5, BAX, ADRM1 TP53, TNF, SIRT1, PMAIP1, MAP3K5, BAX USP7, TP53, SMAD2, HSPA1A, DAXX, ARIH1 DAXX, MAP3K5, MAP3K7, MAP4K5, PICK1, RAF1, SOD1, TNF USP7, TP53, SMAD2, HSPA1A, DAXX, ARIH1 RUVBL2, PFDN6, HSPA2, HSPA1A, CDC37 TAF10, SUPT7L, SIRT1, RUVBL2, RUVBL1, MAP3K7 TNF, SOD1, SIRT1, MAP4K5, MAP3K7, MAP3K5, DAXX

0.0053 0.0063 0.0074 0.0081

0.0178 0.0203 0.0275 0.0275 0.0275 0.0309 0.0323 0.0401 0.0428 0.0441 7.20e-05

0.0080 0.0018 0.0150 0.0176 0.0275 0.0408 0.0428 0.0018 0.0177 0.0018 0.0018 0.0018 0.0081 0.0082 0.0082 0.0082 0.0086 0.0125 0.0150 0.0178

*These molecular pathways are involved also in apoptosis.

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Table 3. Molecular pathways involved in serotonin reuptake inhibitor efficacy (results obtained by including genes within a range of [0.1 cm + 300 kb] from SNPs with p < 0.0001 in a previous meta-analysis of genome-wide studies [7]) (continued). Biological process

Pathway

Genes

Protein acylation

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DNA transcription

Histone modification Covalent chromatin modification Regulation of sequence-specific DNA binding transcription factor activity mRNA catabolic process Nuclear chromatin Nuclear-transcribed mRNA catabolic process

Histone acetyltransferase complex Positive regulation of sequence-specific DNA binding transcription factor activity Energetic metabolism Regulation of mitochondrial membrane potential Mitochondrial outer membrane Corticosteroid and Regulation of steroid biosynthetic process lipid metabolism Regulation of lipid biosynthetic process Regulation of steroid metabolic process Cholesterol homeostasis Sterol homeostasis Second messengers Activation of MAPK activity Positive regulation of JUN kinase activity

TAF10, SUPT7L, SIRT1, RUVBL2, RUVBL1, MAP3K7 TAF10, SUPT7L, SIRT1, RUVBL2, RUVBL1, MAP3K7 TP53, TAF10, SUPT7L, SIRT1, SETD7, RUVBL2, RUVBL1, MAP3K7, EHMT2, DNMT1 TP53, TAF10, SUPT7L, SIRT1, SETD7, RUVBL2, RUVBL1, MAP3K7, EHMT2, DNMT1 USP7, TNF, SMARCA4, SIRT1, MAP3K7, HCK, GTF2A2, GFI1, CHUK¸ CEBPG RPS11, RPL7A, RPL7, RPL18, LSM3, LSM2, HSPA1A, EXOSC5 TP53, SMARCA4, SIRT1, RUVBL2, RUVBL1, MEF2A RPS11, RPL7A, RPL7, RPL18, LSM3, LSM2, EXOSC5 TAF10, SUPT7L, RUVBL2, RUVBL1, MAP3K7 TNF, SMARCA4, MAP3K7, GTF2A2, CHUK, CEBPG SOD1, PMAIP1, BAX, BAK1 RAF1, PMAIP1, MCL1, BAX, BAK1 TNF, SOD1, SIRT1, GFI1 TNF, SOD1, SIRT1, LDLR, GFI1 TNF, SOD1, SIRT1, GFI1 SIRT1, PCSK9, LIPG, LDLR SIRT1, PCSK9, LIPG, LDLR TNF, SOD1¸ MAP4K5, MAP3K7, MAP3K5, DAXX MAP4K5, MAP3K7, MAP3K5, DAXX

q-value

0.0205 0.0018 0.0018 0.0058 0.0078 0.0143 0.0185 0.0203 0.0402 0.0018 0.0143 0.0084 0.0152 0.0224 0.0323 0.0323 0.0085 0.0239

*These molecular pathways are involved also in apoptosis.

opportunity to extend the study of biological predictors of SSRI response from genetics to genomics and more in general to ‘omics,’ giving a preferential way to enter into biological mechanisms that are still partly unknown. The identification of molecular targets for innovative antidepressant drugs would be a natural consequence, offering the opportunity of significant changes in the treatment and prognosis of depression. 6.

Expert opinion

Despite the efforts aimed to the identification of genes involved in SSRI efficacy, clinical applications of antidepressant pharmacogenetics are still in the first experimental phase. Given previous pharmacogenetic results concerning 5HTTLPR, models of cost-effectiveness of antidepressant treatment guided by 5-HTTLPR genotype have been investigated. Incorporating into the model the joint effect of 5-HTTLPR on antidepressant response and tolerability, cost-effectiveness acceptability showed a > 80% probability of being under the commonly accepted threshold (three times Gross Domestic Product [GDP] according to the WHO) [188]. The hypothesis of favorable cost-effectiveness ratio of treatment guided by 5-HTTLPR versus treatment as usual was confirmed by a 18

further simulation using data from 27 European states stratified by GDP. This study demonstrated that cost-effectiveness was favorable in > 90% in high-income countries (Euro A) [189]. Given these encouraging results, recent studies began to investigate the cost-effectiveness of genotyping in real-world clinical practice. A retrospective blinded study evaluated health-care utilization measures for patients with depressive or anxiety disorder in relation to an interpretive pharmacogenomic test that was based on DNA variations in CYP genes (CYP2D6, CYP2C19, CYP2C9 and CYP1A2), SLC6A4 and HTR2A. Subjects that were classified as ‘at risk’ according to the genebased interpretive report and drug regimen had 69% more total health-care visits, 67% more general medical visits, greater than threefold more medical absence days and greater than fourfold more disability than subjects categorized as having no risk or intermediate risk [132]. Present technology allows to provide a gene-based interpretive report in two working days, a time that is compatible with routine clinical practice. Nevertheless, a number of kits are currently commercially available, but sufficient stability across products is still lacking (i.e., different genetic markers are included in the available kits) and their prediction value is still not clinically overwhelming. Besides the high heterogeneity across previous pharmacogenetic studies,

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another reason of this lack of consistence is that previous evidence in literature largely regards individual polymorphisms, mRNA or proteins, without any integration of information derived by complementary levels of regulation. Indeed, protein level and functionality are influenced by genetic variants but also non-sequence variants (i.e., epigenetics) and RNA editing and degradation. Further, variability in protein transport and metabolism can affect drug pharmacodynamics and pharmacokinetics. Given the multiple levels of biological regulation, recent studies are moving from individual polymorphisms and individual genes to whole genotyping of multiple regions, epigenetics, RNA sequencing or candidate approach, study of the level of selected proteins in serum or lymphocytes. The extension from individual genes/molecules to molecular systems moved the focus on networks, which probably represents the most suitable strategy to identify interrelated groups of genes that are critically involved in antidepressant action. Bibliography Papers of special note have been highlighted as either of interest () or of considerable interest () to readers. 1.

Mark TL. For what diagnoses are psychotropic medications being prescribed? A nationally representative survey of physicians. CNS Drugs 2010;24:319-26

2.

Olfson M, Marcus SC. National patterns in antidepressant medication treatment. Arch Gen Psychiatry 2009;66:848-56

3.

Fabbri C, Di Girolamo G, Serretti A. Pharmacogenetics of antidepressant drugs: an update after almost 20 years of research. Am J Med Genet B Neuropsychiatr Genet 2013;162B:487-520

4.

5.

..

6.

Whiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013;382:1575-86

7.

..

In conclusion, the lacking of any genotyping test approved by guidelines for use in routine clinical practice may become the past. As a matter of fact, resources to overcome the limits of previous pharmacogenetic studies are currently more and more available at both technological and theoretical level.

Declaration of interest The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No funding or any financial interest is related to the present paper. Financial disclosure is referred to general (past or current) assignments.

GENDEP; Mars; Star D. I. Common genetic variation and antidepressant efficacy in major depressive disorder: a meta-analysis of three genome-wide pharmacogenetic studies. Am J Psychiatry 2013;170:207-17 Meta-analysis of the main genomewide studies investigating antidepressant response.

8.

Montojo J, Zuberi K, Rodriguez H, et al. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop. Bioinformatics 2010;26:2927-8

9.

Demchak B, Ono K, Hull T, et al. Cytoscape. 2014. Available from: www.cytoscape.org/ [Last accessed 12 May 2014]

10.

Nutt D, Demyttenaere K, Janka Z, et al. The other face of depression, reduced positive affect: the role of catecholamines in causation and cure. J Psychopharmacol 2007;21:461-71

11.

Tansey KE, Guipponi M, Hu X, et al. Contribution of common genetic variants to antidepressant response. Biol Psychiatry 2013;73:679-82 First study estimating the overall contribution of genome-wide association studies (GWAS) SNPs to the variance in serotonin reuptake inhibitor (SSRI) response.

12.

Broad., Institute. Ricopili. 2014. Available from: www. broadinstitute.org/mpg/ricopili [Last accessed 12 May 2014]

.

Porcelli S, Drago A, Fabbri C, Serretti A. Mechanisms of antidepressant action: an integrated dopaminergic perspective. Prog Neuropsychopharmacol Biol Psychiatry 2011;35:1532-43 Porcelli S, Fabbri C, Serretti A. Meta-analysis of serotonin transporter gene promoter polymorphism (5HTTLPR) association with antidepressant efficacy. Eur Neuropsychopharmacol 2012;22:239-58 Most recent meta-analysis investigating SLC6A4 5-HTTLPR in antidepressant response.

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

13.

Long H, Liu B, Hou B, et al. The long rather than the short allele of 5HTTLPR predisposes Han Chinese to anxiety and reduced connectivity between prefrontal cortex and amygdala. Neurosci Bull 2013;29:4-15

14.

Myung W, Lim SW, Kim S, et al. Serotonin transporter genotype and function in relation to antidepressant response in Koreans. Psychopharmacology (Berl) 2013;225:283-90

15.

Kato M, Nonen S, Serretti A, et al. 5-HTTLPR rs25531A > G differentially influence paroxetine and fluvoxamine antidepressant efficacy: a randomized, controlled trial. J Clin Psychopharmacol 2013;33:131-2

16.

Smits KM, Smits LJ, Peeters FP, et al. The influence of 5-HTTLPR and STin2 polymorphisms in the serotonin transporter gene on treatment effect of selective serotonin reuptake inhibitors in depressive patients. Psychiatr Genet 2008;18:184-90

17.

Gressier F, Bouaziz E, Verstuyft C, et al. 5-HTTLPR modulates antidepressant efficacy in depressed women. Psychiatr Genet 2009;19:195-200

18.

Perna G, Favaron E, Di Bella D, et al. Antipanic efficacy of paroxetine and polymorphism within the promoter of the serotonin transporter gene. Neuropsychopharmacology 2005;30:2230-5

19

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

C. Fabbri et al.

19.

Pitychoutis PM, Zisaki A, Dalla C, Papadopoulou-Daifoti Z. Pharmacogenetic insights into depression and antidepressant response: does sex matter? Curr Pharm Des 2010;16:2214-23

20.

Rodriguez JJ, Noristani HN, Verkhratsky A. The serotonergic system in ageing and Alzheimer’s disease. Prog Neurobiol 2012;99:15-41

21.

Murphy GM Jr, Hollander SB, Rodrigues HE, et al. Effects of the serotonin transporter gene promoter polymorphism on mirtazapine and paroxetine efficacy and adverse events in geriatric major depression. Arch Gen Psychiatry 2004;61:1163-9

22.

23.

24.

25.

Nakamura M, Ueno S, Sano A, Tanabe H. The human serotonin transporter gene linked polymorphism (5-HTTLPR) shows ten novel allelic variants. Mol Psychiatry 2000;5:32-8

29.

Strohmaier J, Wust S, Uher R, et al. Sexual dysfunction during treatment with serotonergic and noradrenergic antidepressants: clinical description and the role of the 5-HTTLPR. World J Biol Psychiatry 2011;12:528-38

38.

Smeraldi E, Serretti A, Artioli P, et al. Serotonin transporter gene-linked polymorphic region: possible pharmacogenetic implications of rare variants. Psychiatr Genet 2006;16:153-8

39.

30.

Biernacka JM, McElroy SL, Crow S, et al. Pharmacogenomics of antidepressant induced mania: a review and meta-analysis of the serotonin transporter gene (5HTTLPR) association. J Affect Disord 2012;136:e21-9

Huezo-Diaz P, Uher R, Smith R, et al. Moderation of antidepressant response by the serotonin transporter gene. Br J Psychiatry 2009;195:30-8

40.

Dogan O, Yuksel N, Ergun MA, et al. Serotonin transporter gene polymorphisms and sertraline response in major depression patients. Genet Test 2008;12:225-31

41.

Hong CJ, Chen TJ, Yu YW, Tsai SJ. Response to fluoxetine and serotonin 1A receptor (C-1019G) polymorphism in Taiwan Chinese major depressive disorder. Pharmacogenomics J 2006;6:27-33

42.

Ito K, Yoshida K, Sato K, et al. A variable number of tandem repeats in the serotonin transporter gene does not affect the antidepressant response to fluvoxamine. Psychiatry Res 2002;111:235-9

43.

Kim H, Lim SW, Kim S, et al. Monoamine transporter gene polymorphisms and antidepressant response in koreans with late-life depression. JAMA 2006;296:1609-18

44.

Kim DK, Lim SW, Lee S, et al. Serotonin transporter gene polymorphism and antidepressant response. Neuroreport 2000;11:215-19

45.

Min W, Li T, Ma X, et al. Monoamine transporter gene polymorphisms affect susceptibility to depression and predict antidepressant response. Psychopharmacology (Berl) 2009;205:409-17

46.

Bozina N, Peles AM, Sagud M, et al. Association study of paroxetine therapeutic response with SERT gene polymorphisms in patients with major depressive disorder. World J Biol Psychiatry 2008;9:190-7

47.

Mrazek DA, Rush AJ, Biernacka JM, et al. SLC6A4 variation and citalopram response. Am J Med Genet B Neuropsychiatr Genet 2009;150B:341-51

31.

Alexopoulos GS, Murphy CF, Gunning-Dixon FM, et al. Serotonin transporter polymorphisms, microstructural white matter abnormalities and remission of geriatric depression. J Affect Disord 2009;119:132-41

32.

Kraft JB, Slager SL, McGrath PJ, Hamilton SP. Sequence analysis of the serotonin transporter and associations with antidepressant response. Biol Psychiatry 2005;58:374-81

33.

Ruhe HG, Ooteman W, Booij J, et al. Serotonin transporter gene promoter polymorphisms modify the association between paroxetine serotonin transporter occupancy and clinical response in major depressive disorder. Pharmacogenet Genomics 2009;19:67-76

Hu XZ, Rush AJ, Charney D, et al. Association between a functional serotonin transporter promoter polymorphism and citalopram treatment in adult outpatients with major depression. Arch Gen Psychiatry 2007;64:783-92 Maron E, Tammiste A, Kallassalu K, et al. Serotonin transporter promoter region polymorphisms do not influence treatment response to escitalopram in patients with major depression. Eur Neuropsychopharmacol 2009;19:451-6 Perlis RH, Mischoulon D, Smoller JW, et al. Serotonin transporter polymorphisms and adverse effects with fluoxetine treatment. Biol Psychiatry 2003;54:879-83

27.

Popp J, Leucht S, Heres S, Steimer W. Serotonin transporter polymorphisms and side effects in antidepressant therapy -- a pilot study. Pharmacogenomics 2006;7:159-66

20

37.

Shiroma PR, Drews MS, Geske JR, Mrazek DA. SLC6A4 polymorphisms and age of onset in late-life depression on treatment outcomes with citalopram: a Sequenced Treatment Alternatives to Relieve Depression (STAR*D) report. Am J Geriatr Psychiatry 2013. [Epub ahead of print]

26.

28.

the occurrence of adverse events during treatment with selective serotonin reuptake inhibitors. Int Clin Psychopharmacol 2007;22:137-43

Smits K, Smits L, Peeters F, et al. Serotonin transporter polymorphisms and

34.

35.

36.

Gudayol-Ferre E, Herrera-Guzman I, Camarena B, et al. The role of clinical variables, neuropsychological performance and SLC6A4 and COMT gene polymorphisms on the prediction of early response to fluoxetine in major depressive disorder. J Affect Disord 2010;127:343-51

Staeker J, Leucht S, Laika B, Steimer W. Polymorphisms in serotonergic pathways influence the outcome of antidepressant therapy in psychiatric inpatients. Genet Test Mol Biomarkers 2014;18:20-31 Dreimuller N, Tadic A, Dragicevic A, et al. The serotonin transporter promoter polymorphism (5-HTTLPR) affects the relation between antidepressant serum concentrations and effectiveness in major depression. Pharmacopsychiatry 2012;45(3):108-13 Gudayol-Ferre E, Herrera-Guzman I, Camarena B, et al. Prediction of remission of depression with clinical variables, neuropsychological performance, and serotonergic/ dopaminergic gene polymorphisms. Hum Psychopharmacol 2012;27:577-86

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

SSRI pharmacogenetics

48.

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

49.

50.

51.

52.

Kato M, Serretti A. Review and metaanalysis of antidepressant pharmacogenetic findings in major depressive disorder. Mol Psychiatry 2010;15:473-500 Takahashi H, Yoshida K, Ito K, et al. No association between the serotonergic polymorphisms and incidence of nausea induced by fluvoxamine treatment. Eur Neuropsychopharmacol 2002;12:477-81 Wilkie MJ, Smith G, Day RK, et al. Polymorphisms in the SLC6A4 and HTR2A genes influence treatment outcome following antidepressant therapy. Pharmacogenomics J 2009;9:61-70 Oved K, Morag A, Pasmanik-Chor M, et al. Genome-wide expression profiling of human lymphoblastoid cell lines implicates integrin beta-3 in the mode of action of antidepressants. Transl Psychiatry 2013;3:e313 Serretti A, Artioli P, Lorenzi C, et al. The C(-1019)G polymorphism of the 5HT1A gene promoter and antidepressant response in mood disorders: preliminary findings. Int J Neuropsychopharmacol 2004;7:453-60

a comprehensive meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2013;45:183-94 Most recent meta-analysis of the main genes involved in antidepressant and SSRI response.

.

59.

60.

61.

62.

Suzuki Y, Sawamura K, Someya T. The effects of a 5-hydroxytryptamine 1A receptor gene polymorphism on the clinical response to fluvoxamine in depressed patients. Pharmacoeconomics J 2004;4:283-6 Ferres-Coy A, Santana N, Castane A, et al. Acute 5-HT(1)A autoreceptor knockdown increases antidepressant responses and serotonin release in stressful conditions. Psychopharmacology (Berl) 2013;225:61-74 Perroud N, Bondolfi G, Uher R, et al. Clinical and genetic correlates of suicidal ideation during antidepressant treatment in a depressed outpatient sample. Pharmacogenomics 2011;12:365-77 McMahon FJ, Buervenich S, Charney D, et al. Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet 2006;78:804-14

53.

Villafuerte SM, Vallabhaneni K, Sliwerska E, et al. SSRI response in depression may be influenced by SNPs in HTR1B and HTR1A. Psychiatr Genet 2009;19:281-91

63.

Peters EJ, Slager SL, Jenkins GD, et al. Resequencing of serotonin-related genes and association of tagging SNPs to citalopram response. Pharmacogenet Genomics 2009;19:1-10

54.

Yu YW, Tsai SJ, Liou YJ, et al. Association study of two serotonin 1A receptor gene polymorphisms and fluoxetine treatment response in Chinese major depressive disorders. Eur Neuropsychopharmacol 2006;16:498-503

64.

Uher R, Huezo-Diaz P, Perroud N, et al. Genetic predictors of response to antidepressants in the GENDEP project. Pharmacogenomics J 2009;9:225-33

55.

56.

57.

58.

Peters EJ, Slager SL, McGrath PJ, et al. Investigation of serotonin-related genes in antidepressant response. Mol Psychiatry 2004;9:879-89 Levin GM, Bowles TM, Ehret MJ, et al. Assessment of human serotonin 1A receptor polymorphisms and SSRI responsiveness. Mol Diagn Ther 2007;11:155-60 Illi A, Setala-Soikkeli E, Viikki M, et al. 5-HTR1A, 5-HTR2A, 5-HTR6, TPH1 and TPH2 polymorphisms and major depression. Neuroreport 2009;20:1125-8 Niitsu T, Fabbri C, Bentini F, Serretti A. Pharmacogenetics in major depression:

65.

Kishi T, Yoshimura R, Kitajima T, et al. HTR2A is associated with SSRI response in major depressive disorder in a Japanese cohort. Neuromolecular Med 2010;12(3):237-42

66.

Paddock S, Laje G, Charney D, et al. Association of GRIK4 with outcome of antidepressant treatment in the STAR*D cohort. Am J Psychiatry 2007;164:1181-8

67.

Fabbri C, Drago A, Serretti A. Early antidepressant efficacy modulation by glutamatergic gene variants in the STAR ()D. Eur Neuropsychopharmacol 2013;23(7):612-21

68.

Cusin C, Serretti A, Zanardi R, et al. Influence of monoamine oxydase A and serotonin receptor 2A polymorphisms in SSRIs antidepressant activity.

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

Int J Neuropsychopharmacol 2002;5:27-35 69.

Sato K, Yoshida K, Takahashi H, et al. Association between -1438G/A promoter polymorphism in the 5-HT(2A) receptor gene and fluvoxamine response in Japanese patients with major depressive disorder. Neuropsychobiology 2002;46:136-40

70.

Arias B, Fabbri C, Gressier F, et al. TPH1, MAOA, serotonin receptor 2A and 2C genes in citalopram response: possible effect in melancholic and psychotic depression. Neuropsychobiology 2013;67:41-7

71.

Murphy GM Jr, Kremer C, Rodrigues HE, Schatzberg AF. Pharmacogenetics of antidepressant medication intolerance. Am J Psychiatry 2003;160:1830-5

72.

Bishop JR, Moline J, Ellingrod VL, et al. Serotonin 2A -1438 G/A and G-protein Beta3 subunit C825T polymorphisms in patients with depression and SSRIassociated sexual side-effects. Neuropsychopharmacology 2006;31:2281-8

73.

Suzuki Y, Sawamura K, Someya T. Polymorphisms in the 5hydroxytryptamine 2A receptor and CytochromeP4502D6 genes synergistically predict fluvoxamineinduced side effects in japanese depressed patients. Neuropsychopharmacology 2006;31:825-31

74.

Kato M, Fukuda T, Wakeno M, et al. Effects of the serotonin type 2A, 3A and 3B receptor and the serotonin transporter genes on paroxetine and fluvoxamine efficacy and adverse drug reactions in depressed Japanese patients. Neuropsychobiology 2006;53:186-95

75.

Yoshida K, Naito S, Takahashi H, et al. Monoamine oxidase A gene polymorphism, 5-HT 2A receptor gene polymorphism and incidence of nausea induced by fluvoxamine. Neuropsychobiology 2003;48:10-13

76.

Tanaka M, Kobayashi D, Murakami Y, et al. Genetic polymorphisms in the 5hydroxytryptamine type 3B receptor gene and paroxetine-induced nausea. Int J Neuropsychopharmacol 2008;11:261-7

77.

Lenze EJ, Dixon D, Nowotny P, et al. Escitalopram reduces attentional performance in anxious older adults with high-expression genetic variants at

21

C. Fabbri et al.

serotonin 2A and 1B receptors. Int J Neuropsychopharmacol 2013;16:279-88 78.

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

79.

80.

81.

82.

83.

84.

85.

86.

22

87.

Cooper JM, Newby DA, Whyte IM, et al. Serotonin toxicity from antidepressant overdose and its association with the T102C polymorphism of the 5-HT receptor. Pharmacogenomics J 2014. [Epub ahead of print] Serretti A, Zanardi R, Cusin C, et al. Tryptophan hydroxylase gene associated with paroxetine antidepressant activity. Eur Neuropsychopharmacol 2001;11:375-80 Serretti A, Zanardi R, Rossini D, et al. Influence of tryptophan hydroxylase and serotonin transporter genes on fluvoxamine antidepressant activity. Mol Psychiatry 2001;6:586-92 Ham BJ, Lee BC, Paik JW, et al. Association between the tryptophan hydroxylase-1 gene A218C polymorphism and citalopram antidepressant response in a Korean population. Prog Neuropsychopharmacol Biol Psychiatry 2007;31:104-7 Yoshida K, Naito S, Takahashi H, et al. Monoamine oxidase: a gene polymorphism, tryptophan hydroxylase gene polymorphism and antidepressant response to fluvoxamine in Japanese patients with major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2002;26:1279-83 Kato M, Wakeno M, Okugawa G, et al. No association of TPH1 218A/C polymorphism with treatment response and intolerance to SSRIs in Japanese patients with major depression. Neuropsychobiology 2007;56:167-71 Secher A, Bukh J, Bock C, et al. Antidepressive-drug-induced bodyweight gain is associated with polymorphisms in genes coding for COMT and TPH1. Int Clin Psychopharmacol 2009;24:199-203 Tzvetkov MV, Brockmoller J, Roots I, Kirchheiner J. Common genetic variations in human brain-specific tryptophan hydroxylase-2 and response to antidepressant treatment. Pharmacogenet Genomics 2008;18:495-506 Tsai SJ, Hong CJ, Liou YJ, et al. Tryptophan hydroxylase 2 gene is associated with major depression and antidepressant treatment response.

pharmacogenomics and selective serotonin reuptake inhibitor response. Pharmacogenomics J 2012;12:78-85

Prog Neuropsychopharmacol Biol Psychiatry 2009;33:637-41

88.

89.

90.

Rotberg B, Kronenberg S, Carmel M, et al. Additive effects of 5-HTTLPR (serotonin transporter) and tryptophan hydroxylase 2 G-703T gene polymorphisms on the clinical response to citalopram among children and adolescents with depression and anxiety disorders. J Child Adolesc Psychopharmacol 2013;23:117-22 Benedetti F, Colombo C, Pirovano A, et al. The catechol-O-methyltransferase Val(108/158)Met polymorphism affects antidepressant response to paroxetine in a naturalistic setting. Psychopharmacology (Berl) 2009;203:155-60 Benedetti F, Dallaspezia S, Colombo C, et al. Effect of catechol-O-methyltransferase Val(108/ 158)Met polymorphism on antidepressant efficacy of fluvoxamine. Eur Psychiatry 2010;25:476-8 Tsai SJ, Gau YT, Hong CJ, et al. Sexually dimorphic effect of catechol-O-methyltransferase val158met polymorphism on clinical response to fluoxetine in major depressive patients. J Affect Disord 2009;113:183-7

91.

Gudayol-Ferre E, Guardia-Olmos J, Pero-Cebollero M, et al. Prediction of the time-course pattern of remission in depression by using clinical, neuropsychological, and genetic variables. J Affect Disord 2013;150:1082-90

92.

Arias B, Serretti A, Lorenzi C, et al. Analysis of COMT gene (Val 158 Met polymorphism) in the clinical response to SSRIs in depressive patients of European origin. J Affect Disord 2006;90:251-6

93.

94.

95.

Szegedi A, Rujescu D, Tadic A, et al. The catechol-O-methyltransferase Val108/158Met polymorphism affects short-term treatment response to mirtazapine, but not to paroxetine in major depression. Pharmacogenomics J 2005;5:49-53 Illi A, Setala-Soikkeli E, Kampman O, et al. Catechol-O-methyltransferase val108/158met genotype, major depressive disorder and response to selective serotonin reuptake inhibitors in major depressive disorder. Psychiatry Res 2010;176:85-7 Ji Y, Biernacka J, Snyder K, et al. Catechol O-methyltransferase

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

96.

Stein DJ, Daniels WM, Savitz J, Harvey BH. Brain-derived neurotrophic factor: the neurotrophin hypothesis of psychopathology. CNS Spectr 2008;13:945-9

97.

Araragi N, Lesch KP. Serotonin (5-HT) in the regulation of depression-related emotionality: insight from 5-HT transporter and tryptophan hydroxylase-2 knockout mouse models. Curr Drug Targets 2013;14:549-70

98.

Bath KG, Jing DQ, Dincheva I, et al. BDNF Val66Met impairs fluoxetineinduced enhancement of adult hippocampus plasticity. Neuropsychopharmacology 2012;37:1297-304

99.

Egan MF, Kojima M, Callicott JH, et al. The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 2003;112:257-69

100. Govindarajan A, Rao BS, Nair D, et al. Transgenic brain-derived neurotrophic factor expression causes both anxiogenic and antidepressant effects. Proc Natl Acad Sci U S A 2006;103:13208-13 101. Tsai SJ, Cheng CY, Yu YW, et al. Association study of a brain-derived neurotrophic-factor genetic polymorphism and major depressive disorders, symptomatology, and antidepressant response. Am J Med Genet B Neuropsychiatr Genet 2003;123B:19-22 102. Yoshida K, Higuchi H, Kamata M, et al. The G196A polymorphism of the brainderived neurotrophic factor gene and the antidepressant effect of milnacipran and fluvoxamine. J Psychopharmacol 2007;21:650-6 103. Zou YF, Wang Y, Liu P, et al. Association of brain-derived neurotrophic factor genetic Val66Met polymorphism with severity of depression, efficacy of fluoxetine and its side effects in Chinese major depressive patients. Neuropsychobiology 2010;61:71-8 104. Choi MJ, Kang RH, Lim SW, et al. Brain-derived neurotrophic factor gene polymorphism (Val66Met) and citalopram response in major depressive disorder. Brain Res 2006;1118:176-82

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

SSRI pharmacogenetics

105.

Alexopoulos GS, Glatt CE, Hoptman MJ, et al. BDNF Val66met polymorphism, white matter abnormalities and remission of geriatric depression. J Affect Disord 2010;125(1-3):262-8

106.

Murphy GM Jr, Sarginson JE, Ryan HS, et al. BDNF and CREB1 genetic variants interact to affect antidepressant treatment outcomes in geriatric depression. Pharmacogenet Genomics 2013;23:301-13

107.

108.

Haghighi M, Salehi I, Erfani P, et al. Additional ECT increases BDNF-levels in patients suffering from major depressive disorders compared to patients treated with citalopram only. J Psychiatr Res 2013;47:908-15 Tsen P, El Mansari M, Blier P. Effects of repeated electroconvulsive shocks on catecholamine systems: electrophysiological studies in the rat brain. Synapse 2013;67:716-27

109.

Dwivedi Y. Involvement of brain-derived neurotrophic factor in late-life depression. Am J Geriatr Psychiatry 2013;21:433-49

110.

Domschke K, Lawford B, Laje G, et al. Brain-derived neurotrophic factor (BDNF) gene: no major impact on antidepressant treatment response. Int J Neuropsychopharmacol 2010;13:93-101

111.

112.

113.

114.

115.

Hennings JM, Kohli MA, Czamara D, et al. Possible associations of NTRK2 polymorphisms with antidepressant treatment outcome: findings from an extended tag SNP approach. PLoS One 2013;8:e64947 Keers R, Bonvicini C, Scassellati C, et al. Variation in GNB3 predicts response and adverse reactions to antidepressants. J Psychopharmacol 2011;25:867-74 O’Brien FE, Dinan TG, Griffin BT, Cryan JF. Interactions between antidepressants and P-glycoprotein at the blood-brain barrier: clinical significance of in vitro and in vivo findings. Br J Pharmacol 2012;165:289-312 O’Brien FE, O’Connor RM, Clarke G, et al. The P-glycoprotein inhibitor cyclosporin A differentially influences behavioural and neurochemical responses to the antidepressant escitalopram. Behav Brain Res 2014;261:17-25 Kato M, Fukuda T, Serretti A, et al. ABCB1 (MDR1) gene polymorphisms

are associated with the clinical response to paroxetine in patients with major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2008;32:398-404

125. Singh AB, Bousman CA, Ng CH, et al. ABCB1 polymorphism predicts escitalopram dose needed for remission in major depression. Transl Psychiatry 2012;2:e198

116.

Nikisch G, Eap CB, Baumann P. Citalopram enantiomers in plasma and cerebrospinal fluid of ABCB1 genotyped depressive patients and clinical response: a pilot study. Pharmacol Res 2008;58:344-7

126. Breitenstein B, Scheuer S, Pfister H, et al. The clinical application of ABCB1 genotyping in antidepressant treatment: a pilot study. CNS Spectr 2014;19(2):165-75

117.

Uhr M, Tontsch A, Namendorf C, et al. Polymorphisms in the drug transporter gene ABCB1 predict antidepressant treatment response in depression. Neuron 2008;57:203-9

118.

Sarginson JE, Lazzeroni LC, Ryan HS, et al. ABCB1 (MDR1) polymorphisms and antidepressant response in geriatric depression. Pharmacogenet Genomics 2010;20:467-75

119.

Dong C, Wong ML, Licinio J. Sequence variations of ABCB1, SLC6A2, SLC6A3, SLC6A4, CREB1, CRHR1 and NTRK2: association with major depression and antidepressant response in Mexican-Americans. Mol Psychiatry 2009;14:1105-18

120.

Huang X, Yu T, Li X, et al. ABCB6, ABCB1 and ABCG1 genetic polymorphisms and antidepressant response of SSRIs in Chinese depressive patients. Pharmacogenomics 2013;14:1723-30

121.

Lin KM, Chiu YF, Tsai IJ, et al. ABCB1 gene polymorphisms are associated with the severity of major depressive disorder and its response to escitalopram treatment. Pharmacogenet Genomics 2011;21:163-70

122.

Gex-Fabry M, Eap CB, Oneda B, et al. CYP2D6 and ABCB1 genetic variability: influence on paroxetine plasma level and therapeutic response. Ther Drug Monit 2008;30:474-82

123.

Mihaljevic Peles A, Bozina N, Sagud M, et al. MDR1 gene polymorphism: therapeutic response to paroxetine among patients with major depression. Prog Neuropsychopharmacol Biol Psychiatry 2008;32:1439-44

124.

Peters EJ, Slager SL, Kraft JB, et al. Pharmacokinetic genes do not influence response or tolerance to citalopram in the STAR*D sample. PLoS One 2008;3:e1872

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

127. de Klerk OL, Nolte IM, Bet PM, et al. ABCB1 gene variants influence tolerance to selective serotonin reuptake inhibitors in a large sample of Dutch cases with major depressive disorder. Pharmacogenomics J 2013;13(4):349-53 128. Zourkova A, Slanar O, Jarkovsky J, et al. MDR1 in paroxetine-induced sexual dysfunction. J Sex Marital Ther 2013;39:71-8 129. Noordam R, Aarts N, Hofman A, et al. Association between genetic variation in the ABCB1 gene and switching, discontinuation, and dosage of antidepressant therapy: results from the Rotterdam Study. J Clin Psychopharmacol 2013;33:546-50 130. Porcelli S, Fabbri C, Spina E, et al. Genetic polymorphisms of cytochrome P450 enzymes and antidepressant metabolism. Expert Opin Drug Metab Toxicol 2011;7:1101-15 131. Kirchheiner J, Brosen K, Dahl ML, et al. CYP2D6 and CYP2C19 genotype-based dose recommendations for antidepressants: a first step towards subpopulation-specific dosages. Acta Psychiatr Scand 2001;104:173-92 132. Winner J, Allen JD, Altar CA, Spahic-Mihajlovic A. Psychiatric pharmacogenomics predicts health resource utilization of outpatients with anxiety and depression. Transl Psychiatry 2013;3:e242 .. One of the first studies estimating the impact of pharmcogenetics on health resource utilization. 133. Krystal JH, Sanacora G, Blumberg H, et al. Glutamate and GABA systems as targets for novel antidepressant and mood-stabilizing treatments. Mol Psychiatry 2002;7(Suppl 1):S71-80 134. Ampuero E, Stehberg J, Gonzalez D, et al. Repetitive fluoxetine treatment affects long-term memories but not learning. Behav Brain Res 2013;247:92-100

23

C. Fabbri et al.

135. Catches JS, Xu J, Contractor A. Genetic ablation of the GluK4 kainate receptor subunit causes anxiolytic and antidepressant-like behavior in mice. Behav Brain Res 2012;228:406-14

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

136. Pu M, Zhang Z, Xu Z, et al. Influence of genetic polymorphisms in the glutamatergic and GABAergic systems and their interactions with environmental stressors on antidepressant response. Pharmacogenomics 2013;14:277-88 137. Pickard BS, Malloy MP, Christoforou A, et al. Cytogenetic and genetic evidence supports a role for the kainate-type glutamate receptor gene, GRIK4, in schizophrenia and bipolar disorder. Mol Psychiatry 2006;11:847-57 138. Perlis RH, Laje G, Smoller JW, et al. Genetic and clinical predictors of sexual dysfunction in citalopram-treated depressed patients. Neuropsychopharmacology 2009;34:1819-28 139. Laje G, Paddock S, Manji H, et al. Genetic markers of suicidal ideation emerging during citalopram treatment of major depression. Am J Psychiatry 2007;164:1530-8 140. Li SX, Liu LJ, Xu LZ, et al. Diurnal alterations in circadian genes and peptides in major depressive disorder before and after escitalopram treatment. Psychoneuroendocrinology 2013;38:2789-99 141. Rotzinger S, Lovejoy DA, Tan LA. Behavioral effects of neuropeptides in rodent models of depression and anxiety. Peptides 2010;31:736-56 142. Kloiber S, Ripke S, Kohli MA, et al. Resistance to antidepressant treatment is associated with polymorphisms in the leptin gene, decreased leptin mRNA expression, and decreased leptin serum levels. Eur Neuropsychopharmacol 2013;23:653-62 143. Picciotto MR, Brabant C, Einstein EB, et al. Effects of galanin on monoaminergic systems and HPA axis: potential mechanisms underlying the effects of galanin on addiction- and stress-related behaviors. Brain Res 2010;1314:206-18 144. Kuteeva E, Wardi T, Lundstrom L, et al. Differential role of galanin receptors in the regulation of depression-like behavior and monoamine/stress-related genes at the cell body level.

24

Neuropsychopharmacology 2008;33:2573-85 145. Lu XY, Kim CS, Frazer A, Zhang W. Leptin: a potential novel antidepressant. Proc Natl Acad Sci U S A 2006;103:1593-8 146. Heilig M. The NPY system in stress, anxiety and depression. Neuropeptides 2004;38:213-24 147. Nikisch G, Agren H, Eap CB, et al. Neuropeptide Y and corticotropinreleasing hormone in CSF mark response to antidepressive treatment with citalopram. Int J Neuropsychopharmacol 2005;8:403-10 148. Zhou Z, Zhu G, Hariri AR, et al. Genetic variation in human NPY expression affects stress response and emotion. Nature 2008;452:997-1001 149. Domschke K, Dannlowski U, Hohoff C, et al. Neuropeptide Y (NPY) gene: impact on emotional processing and treatment response in anxious depression. Eur Neuropsychopharmacol 2010;20:301-9 150. Murck H, Held K, Ziegenbein M, et al. Intravenous administration of the neuropeptide galanin has fast antidepressant efficacy and affects the sleep EEG. Psychoneuroendocrinology 2004;29:1205-11 151. Unschuld PG, Ising M, Roeske D, et al. Gender-specific association of galanin polymorphisms with HPA-axis dysregulation, symptom severity, and antidepressant treatment response. Neuropsychopharmacology 2010;35:1583-92 152. Dryden S, Brown M, King P, Williams G. Decreased plasma leptin levels in lean and obese Zucker rats after treatment with the serotonin reuptake inhibitor fluoxetine. Horm Metab Res 1999;31:363-6 153. Kraus T, Haack M, Schuld A, et al. Body weight, the tumor necrosis factor system, and leptin production during treatment with mirtazapine or venlafaxine. Pharmacopsychiatry 2002;35:220-5 154. Fatseas M, Denis C, Lavie E, Auriacombe M. Relationship between anxiety disorders and opiate dependence -- a systematic review of the literature: implications for diagnosis and treatment. J Subst Abuse Treat 2010;38:220-30

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

155. Kennedy SE, Koeppe RA, Young EA, Zubieta JK. Dysregulation of endogenous opioid emotion regulation circuitry in major depression in women. Arch Gen Psychiatry 2006;63:1199-208 156. Garriock HA, Tanowitz M, Kraft JB, et al. Association of mu-opioid receptor variants and response to citalopram treatment in major depressive disorder. Am J Psychiatry 2010;167:565-73 157. Cooper AJ, Rickels K, Lohoff FW. Association analysis between the A118G polymorphism in the OPRM1 gene and treatment response to venlafaxine XR in generalized anxiety disorder. Hum Psychopharmacol 2013;28:258-62 158. Heurteaux C, Lucas G, Guy N, et al. Deletion of the background potassium channel TREK-1 results in a depressionresistant phenotype. Nat Neurosci 2006;9:1134-41 159. Dillon DG, Bogdan R, Fagerness J, et al. Variation in TREK1 gene linked to depression-resistant phenotype is associated with potentiated neural responses to rewards in humans. Hum Brain Mapp 2010;31:210-21 160. Perlis RH, Moorjani P, Fagerness J, et al. Pharmacogenetic analysis of genes implicated in rodent models of antidepressant response: association of TREK1 and treatment resistance in the STAR(*)D study. Neuropsychopharmacology 2008;33:2810-19 161. Biel M, Wahl-Schott C, Michalakis S, Zong X. Hyperpolarization-activated cation channels: from genes to function. Physiol Rev 2009;89:847-85 162. Nolan MF, Malleret G, Dudman JT, et al. A behavioral role for dendritic integration: HCN1 channels constrain spatial memory and plasticity at inputs to distal dendrites of CA1 pyramidal neurons. Cell 2004;119:719-32 163. Lewis AS, Vaidya SP, Blaiss CA, et al. Deletion of the hyperpolarizationactivated cyclic nucleotide-gated channel auxiliary subunit TRIP8b impairs hippocampal Ih localization and function and promotes antidepressant behavior in mice. J Neurosci 2011;31:7424-40 164. Kim CS, Chang PY, Johnston D. Enhancement of dorsal hippocampal activity by knockdown of HCN1 channels leads to anxiolytic- and antidepressant-like behaviors. Neuron 2012;75:503-16

SSRI pharmacogenetics

165.

166.

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

167.

168.

169.

170.

171.

172.

173.

174.

Janssen DG, Caniato RN, Verster JC, Baune BT. A psychoneuroimmunological review on cytokines involved in antidepressant treatment response. Hum Psychopharmacol 2010;25:201-15

(TORDIA) study. Am J Psychiatry 2010;167:190-7 175.

Kehne JH. The CRF1 receptor, a novel target for the treatment of depression, anxiety, and stress-related disorders. CNS Neurol Disord Drug Targets 2007;6:163-82 Liu Z, Zhu F, Wang G, et al. Association study of corticotropinreleasing hormone receptor1 gene polymorphisms and antidepressant response in major depressive disorders. Neurosci Lett 2007;414:155-8 Licinio J, O’Kirwan F, Irizarry K, et al. Association of a corticotropin-releasing hormone receptor 1 haplotype and antidepressant treatment response in Mexican-Americans. Mol Psychiatry 2004;9:1075-82 Papiol S, Arias B, Gasto C, et al. Genetic variability at HPA axis in major depression and clinical response to antidepressant treatment. J Affect Disord 2007;104:83-90 Binder EB, Owens MJ, Liu W, et al. Association of polymorphisms in genes regulating the corticotropin-releasing factor system with antidepressant treatment response. Arch Gen Psychiatry 2010;67:369-79 Brouwer JP, Appelhof BC, van Rossum EF, et al. Prediction of treatment response by HPA-axis and glucocorticoid receptor polymorphisms in major depression. Psychoneuroendocrinology 2006;31:1154-63 Lekman M, Laje G, Charney D, et al. The FKBP5-gene in depression and treatment response–an association study in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Cohort. Biol Psychiatry 2008;63:1103-10 Ellsworth KA, Moon I, Eckloff BW, et al. FKBP5 genetic variation: association with selective serotonin reuptake inhibitor treatment outcomes in major depressive disorder. Pharmacogenet Genomics 2013;23:156-66 Brent D, Melhem N, Ferrell R, et al. Association of FKBP5 polymorphisms with suicidal events in the Treatment of Resistant Depression in Adolescents

176.

Cattaneo A, Gennarelli M, Uher R, et al. Candidate genes expression profile associated with antidepressants response in the GENDEP study: differentiating between baseline ’predictors’ and longitudinal ’targets’. Neuropsychopharmacology 2013;38:377-85 Yu YW, Chen TJ, Hong CJ, et al. Association Study of the Interleukin1beta (C-511T) Genetic Polymorphism with Major Depressive Disorder, Associated Symptomatology, and Antidepressant Response. Neuropsychopharmacology 2003;28:1182-5

177.

Tadic A, Rujescu D, Muller MJ, et al. Association analysis between variants of the interleukin-1beta and the interleukin1 receptor antagonist gene and antidepressant treatment response in major depression. Neuropsychiatr Dis Treat 2008;4:269-76

178.

Lin PI, Vance JM, Pericak-Vance MA, Martin ER. No gene is an island: the flip-flop phenomenon. Am J Hum Genet 2007;80:531-8

179.

.

180.

.

181.

.

Shen X, Carlborg O. Beware of risk for increased false positive rates in genomewide association studies for phenotypic variability. Front Genet 2013;4:93 Study that provides an interesting discussion about one of the major limitations of GWAS. Garriock HA, Kraft JB, Shyn SI, et al. A genomewide association study of citalopram response in major depressive disorder. Biol Psychiatry 2010;67:133-8 The largest GWAS investigating the pharmacogenetics of SSRIs. Uher R, Perroud N, Ng MY, et al. Genome-wide pharmacogenetics of antidepressant response in the GENDEP project. Am J Psychiatry 2010;167:555-64 One of the main GWAS investigating antidepressant response.

182.

Crumbley C, Wang Y, Kojetin DJ, Burris TP. Characterization of the core mammalian clock component, NPAS2, as a REV-ERBalpha/RORalpha target gene. J Biol Chem 2010;285:35386-92

183.

Perroud N, Uher R, Ng MY, et al. Genome-wide association study of increasing suicidal ideation during antidepressant treatment in the

Expert Opin. Drug Metab. Toxicol. (2014) 10(8)

GENDEP project. Pharmacogenomics J 2012;12:68-77 184. Laje G, Allen AS, Akula N, et al. Genome-wide association study of suicidal ideation emerging during citalopram treatment of depressed outpatients. Pharmacogenet Genomics 2009;19:666-74 185. Holmans P. Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits. Adv Genet 2010;72:141-79 186. Zorumski CF, Paul SM, Izumi Y, et al. Neurosteroids, stress and depression: potential therapeutic opportunities. Neurosci Biobehav Rev 2013;37:109-22 187. Tansey KE, Guipponi M, Perroud N, et al. Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. PLoS Med 2012;9:e1001326 .. One of the main genome-wide association study and meta-analysis of antidepressant response. 188. Serretti A, Olgiati P, Bajo E, et al. A model to incorporate genetic testing (5-HTTLPR) in pharmacological treatment of major depressive disorders. World J Biol Psychiatry 2011;12:501-15 189. Olgiati P, Bajo E, Bigelli M, et al. Should pharmacogenetics be incorporated in major depression treatment? Economic evaluation in high- and middle-income European countries. Prog Neuropsychopharmacol Biol Psychiatry 2012;36:147-54 . Pharmacoeconomic study estimating the cost-effectiveness ratio of SLC6A4 5-HTTLPR-guided antidepressant treatment. 190. Smeraldi E, Zanardi R, Benedetti F, et al. Polymorphism within the promoter of the serotonin transporter gene and antidepressant efficacy of fluvoxamine. Mol Psychiatry 1998;3:508-11 191. Zanardi R, Benedetti F, DiBella D, et al. Efficacy of paroxetine in depression is influenced by a functional polymorphism within the promoter of serotonin transporter gene. J Clin Psychopharmacol 2000;20:105-7 192. Zanardi R, Serretti A, Rossini D, et al. Factors affecting fluvoxamine antidepressant activity: influence of pindolol and 5-HTTLPR in delusional

25

C. Fabbri et al.

and nondelusional depression. Biol Psychiatry 2001;50:323-30

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Maastricht on 07/05/14 For personal use only.

193. Pollock BG, Ferrell RE, Mulsant BH, et al. Allelic Variation in the Serotonin Transporter Promoter Affects Onset of Paroxetine Treatment Response in LateLife Depression. Neuropsychopharmacology 2000;23:587-90 194. Arias B, Catalan R, Gasto C, et al. 5-HTTLPR polymorphism of the serotonin transporter gene predicts nonremission in major depression patients treated with citalopram in a 12-weeks follow up study. J Clin Psychopharmacol 2003;23:563-7 195. Durham LK, Webb SM, Milos PM, et al. The serotonin transporter polymorphism, 5HTTLPR, is associated with a faster response time to sertraline in an elderly population with major depressive disorder. Psychopharmacology (Berl) 2004;174:525-9 196. Serretti A, Cusin C, Rossini D, et al. Further evidence of a combined effect of SERTPR and TPH on SSRIs response in mood disorders. Am J Med Genet B Neuropsychiatr Genet 2004;129:36-40 197. Lotrich FE, Pollock BG, Kirshner M, et al. Serotonin transporter genotype interacts with paroxetine plasma levels to influence depression treatment response in geriatric patients. J Psychiatry Neurosci 2008;33:123-30 198. Illi A, Poutanen O, Setala-Soikkeli E, et al. Is 5-HTTLPR linked to the response of selective serotonin reuptake inhibitors in MDD? Eur Arch Psychiatry Clin Neurosci 2011;261:95-102 199. Rausch JL, Johnson ME, Fei Y, et al. Initial conditions of serotonin transporter kinetics and genotype: influence on ssri treatment trial outcome. Biol Psychiatry 2002;51:723-32

200. Yu YW, Tsai SJ, Chen TJ, et al. Association study of the serotonin transporter promoter polymorphism and symptomatology and antidepressant response in major depressive disorders. Mol Psychiatry 2002;7:1115-19 201. Mushtaq D, Ali A, Margoob MA, et al. Association between serotonin transporter gene promoter-region polymorphism and 4- and 12-week treatment response to sertraline in posttraumatic stress disorder. J Affect Disord 2012;136:955-62 202. Yoshida K, Ito K, Sato K, et al. Influence of the serotonin transporter gene-linked polymorphic region on the antidepressant response to fluvoxamine in Japanese depressed patients. Prog Neuropsychopharmacol Biol Psychiatry 2002;26:383-6 203. Umene-Nakano W, Yoshimura R, Ueda N, et al. Predictive factors for responding to sertraline treatment: views from plasma catecholamine metabolites and serotonin transporter polymorphism. J Psychopharmacol 2010;24(12):1764-71 204. Saeki Y, Watanabe T, Ueda M, et al. Genetic and pharmacokinetic factors affecting the initial pharmacotherapeutic effect of paroxetine in Japanese patients with panic disorder. Eur J Clin Pharmacol 2009;65:685-91 205. Won ES, Chang HS, Lee HY, et al. Association between serotonin transporter-linked polymorphic region and escitalopram antidepressant treatment response in Korean patients with major depressive disorder. Neuropsychobiology 2012;66:221-9 206. Di Bella D, Erzegovesi S, Cavallini MC, Bellodi L. Obsessive-compulsive disorder, 5-HTTLPR polymorphism and treatment response. Pharmacogenomics J 2002;2:176-81 207. Ng C, Sarris J, Singh A, et al. Pharmacogenetic polymorphisms and response to escitalopram and venlafaxine

Supplementary materials available online Figure 1.

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over 8 weeks in major depression. Hum Psychopharmacol 2013;28:516-22 208. Ishiguro S, Watanabe T, Ueda M, et al. Determinants of pharmacodynamic trajectory of the therapeutic response to paroxetine in Japanese patients with panic disorder. Eur J Clin Pharmacol 2011;67:1213-21 209. Kim W, Choi YH, Yoon KS, et al. Tryptophan hydroxylase and serotonin transporter gene polymorphism does not affect the diagnosis, clinical features and treatment outcome of panic disorder in the Korean population. Prog Neuropsychopharmacol Biol Psychiatry 2006;30:1413-18 210. Yevtushenko OO, Oros MM, Reynolds GP. Early response to selective serotonin reuptake inhibitors in panic disorder is associated with a functional 5HT1A receptor gene polymorphism. J Affect Disord 2010;123:308-11 211. Denys D, Van Nieuwerburgh F, Deforce D, Westenberg HG. Prediction of response to paroxetine and venlafaxine by serotonin-related genes in obsessivecompulsive disorder in a randomized, double-blind trial. J Clin Psychiatry 2007;68:747-53

Affiliation Chiara Fabbri1, Alessandro Minarini1, Tomihisa Niitsu1,2 & Alessandro Serretti†1 MD PhD † Author for correspondence 1 University of Bologna, Institute of Psychiatry, Department of Biomedical and NeuroMotor Sciences, Viale Carlo Pepoli 5, 40123 Bologna, Italy Tel: +39 051 6584233; Fax: +39 051 521030; E-mail: [email protected] 2 Chiba University Graduate School of Medicine, Research Center for Child Mental Development, Chiba, Japan

Understanding the pharmacogenetics of selective serotonin reuptake inhibitors.

The genetic background of antidepressant response represents a unique opportunity to identify biological markers of treatment outcome. Encouraging res...
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