Pharmacogenomic aspects of bipolar disorder: An update

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Pharmacogenomic aspects of bipolar disorder: An update M. Buddea,b, D. Degnerb, J. Brockmöllerc, T.G. Schulzea,b,n a

Institute of Psychiatric Phenomics and Genomics, Clinical Center of the University of Munich, Nussbaumstr. 7, 80336 Munich, Germany b University Medical Center Göttingen, Department of Psychiatry and Psychotherapy, Von-Siebold-Str. 5, 37075 Göttingen, Germany c University Medical Center Göttingen, Department of Clinical Pharmacology, Robert-Koch-Str. 40, 37075 Göttingen, Germany Received 17 July 2015; received in revised form 31 January 2017; accepted 9 February 2017

KEYWORDS

Abstract

Manic-depressive illness; Personalized medicine; Mood disorders; Mood stabilizers; Lithium; Genetics

The hopes for readily implementable precision medicine are high. For many complex disorders, such as bipolar disorder, these hopes critically hinge on tangible successes in pharmacogenetics of treatment response or susceptibility to adverse events. In this article, we review the current state of pharmacogenomics of bipolar disorder including latest results from candidate genes and genome-wide association studies. The majority of studies focus on response to lithium treatment. Although a host of genes has been studied, hardly any replicated findings have emerged so far. Very small samples sizes and heterogeneous phenotype definition may be considered the major impediments to success in this field. Drawing from current experiences and successes in studies on diagnostic psychiatric phenotypes, we suggest several approaches for our way forward. & 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction With a lifetime-prevalence of about 1%, bipolar disorder (BD) is a common life-long psychiatric illness often leading to a significant reduction in quality of life or even disability. Pharmacotherapy, supplemented by psychotherapy and n Corresponding author at: Institute of Psychiatric Phenomics and Genomics, Clinical Center of the University of Munich, Nussbaumstr. 7, 80336 Munich, Germany.

psychoeducation, constitutes the mainstay of treatment of BD, both in acute episodes and in maintenance therapy. As in other fields of medicine, the individual differences of patients in response to pharmacotherapy and also in their susceptibility to adverse events are important challenges for clinicians. For some drugs, like for example lithium, there is knowledge on clinical and demographic factors that may influence response (Gershon et al., 2009; Kleindienst et al., 2005). However, findings are ambiguous and usually these factors alone are not sufficient for response

http://dx.doi.org/10.1016/j.euroneuro.2017.02.001 0924-977X/& 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

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prediction on an individual level. During the last years, there have been many attempts to reduce these challenges by clarifying the genetic basis of pathways and mechanisms of drug action, aiming at the possibility to offer a more personalized treatment. This line of research has become known as pharmacogenetics or, in the genome-wide world of today, pharmacogenomics. The field of pharmacogenetics dates back to research in the 1950s demonstrating effects of genetics on drug response, but has been studied more extensively since promising progress in the Human Genome Project could be foreseen (Gardiner and Begg, 2006). The main goals of pharmacogenetics are to elucidate the effect of genetic variation on different responses to drugs as well as on the susceptibility to adverse events, and, by doing this, to offer a treatment more tailored to the particular needs and biological conditions of the individual patient than nowadays (“personalized/individualized medicine”). Furthermore, by gaining deeper insight into the molecular mechanism of drug actions, this knowledge can be used in new drug discovery. Looking closer at the pharmacogenetics of a specific medication, pharmacodynamic and pharmacokinetic aspects can be distinguished. Pharmacodynamics is the study of the biochemical and physiological effects of drugs on the body (e.g. mechanisms of drug action like receptor binding and interactions with proteins), while pharmacokinetics deals with the absorption, distribution, metabolism, and excretion of drugs in the body. Genetic variation can have a great effect on both aspects. During the last years, the body of literature dealing with pharmacogenetics of BD has been growing substantially. Here, we would like to give a comprehensive overview of the state of pharmacogenetic research in BD and appraise its potential clinical utility. Focusing on pharmacodynamic aspects and based on previous original and other review work (e.g. Alda 2015; Can et al., 2014; Geoffroy et al., 2014; McCarthy et al., 2010; Severino et al., 2013), we would like to give an easy-to-follow overview of where we Table 1

stand and, based on this, devise concepts for future research directions. The emphasis of this review lies on response as adverse events are not diagnosis-specific and a detailed presentation of their genetic underpinnings would be beyond the scope of this review.

2. Experimental procedures 2.1 Defining the phenotypes and drugs of interest When discussing pharmacogenomics of BD, one has first to delineate the target phenotypes and the drugs of interest for these phenotypes, as one can think of many clinical phenotypes necessitating specific pharmacological management within an overall treatment regime for BD (e.g. thyroid hormone replacement therapy in patients on chronic lithium treatment; concomitant use of benzodiazepines or simple sleeping pills in BD patients). Thus, we decided to focus our review on the core symptomatology of bipolar I (BD I) and bipolar II (BD II) disorder and the drugs typically used to treat it. As a guideline, we focused on the first line treatment recommendations published by the Canadian Network for Mood and Anxiety Treatments (CANMAT) and the International Society for Bipolar Disorders (ISBD) (Yatham et al., 2013) as summarized in Table 1. As to our knowledge, there is no literature on pharmacogenetics of the listed combination therapies, we focused on those drugs recommended for monotherapy: lithium, lamotrigine, valproate, olanzapine, risperidone, quetiapine, aripiprazole, ziprasidone, asenapine and paliperidone.

2.2 PubMed search The data base PubMed was searched for English-language articles published until May 2016 using the search terms “bipolar disorder” or “manic-depressive illness” cross-referenced with the abovenamed medications, “pharmacogenetics” or “gene” or “polymorphism”, and “response” or “adverse events”. In addition, the reference lists from the identified publications were reviewed manually.

First line pharmacological treatment for BD (Yatham et al., 2013).

Acute mania (BD I)

Monotherapy: lithium, divalproex, divalproex ER, olanzapine, risperidone, quetiapine, quetiapine XR, aripiprazole, ziprasidone, asenapine, paliperidone ER Adjunctive therapy with lithium or divalproex: risperidone, quetiapine, olanzapine, aripiprazole, asenapine

Acute depression (BD I)

Monotherapy: lithium, lamotrigine, quetiapine, quetiapine XR Combination therapy: lithium or divalproex + SSRIa, olanzapine + SSRIa, lithium + divalproex, lithium or divalproex + bupropion

Maintenance therapy (BD I) Monotherapy: lithium, lamotrigine (limited efficacy in preventing mania), divalproex, olanzapine, quetiapine, risperidone LAI, aripiprazole Adjunctive therapy with lithium or divalproex: quetiapine, risperidone LAI, aripiprazole, ziprasidone Monotherapy: quetiapine, quetiapine XR Acute depression (BD II) Maintenance therapy (BD II) Monotherapy: lithium, lamotrigine, quetiapine ER or XR = extended release; SSRI = selective serotonin reuptake inhibitor; LAI = long-acting injection. a Except paroxetine.

Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

Pharmacogenomic aspects of bipolar disorder: An update 2.3 Further selection criteria All identified publications were checked for the following criteria in order to increase the comparability of the study results: First of all, only studies applying a case-control design were selected, with the case-control-status being the drug response or the occurrence of adverse events. By applying this criterion, we excluded all animal and in-vivo studies as well as studies comparing healthy controls with responders to a drug (e.g. lithium). Secondly, as in some studies mixed diagnostic samples were studied, at least half of the patients of the analyzed sample had to suffer from BD. The by far biggest number of candidate gene association studies deals with lithium response (49 studies), while there were only two articles each dealing with response to lamotrigine and valproate, respectively. Three publications analyzed the pharmacogenetics of response to mood stabilizers without differentiating between the single drugs. Regarding all second generation antipsychotics, we could identify only one study on olanzapine response.

3. Results 3.1 Response 3.1.1 Lithium – candidate gene association studies Although the use of lithium as the mainstay of maintenance therapy in BD has been heavily challenged for more than a decade now by the increasing use of antipsychotic and antiepileptic drugs, this is not mirrored by the available pharmacogenetic research. Most pharmacogenetic studies in BD focus on lithium response. Table 2 provides an overview of the history of candidate gene association studies from 1998 until today, showing the diverse panel of more than 50 candidate genes that has been studied so far as well as the developments in sample sizes. Further details about the studies, which are listed in Table 2, are shown in Supplementary Table 2. For most of the genes it is well known which neurotransmitter systems and pathways they are involved in. They play important roles in the inositol signaling pathway, the circadian signaling system, various transmitter systems (adrenergic, noradrenergic, dopaminergic, GABAergic, glutamatergic and serotonergic) as well as the BDNF/TrkB signaling pathway (Geoffroy et al., 2014). But there are also genes whose functionality is not yet completely understood. This seems to be due to the different strategies to choose ones candidate genes: The most common ones are to either look for genes, which are known to play a role in a specific transmitter system or pathway, which is again known to be involved in the drug's mechanism of action, or to choose genes that have been shown to be associated with the illness. Another approach has been used by Perlis et al. (2009), who first conducted a GWAS in 359 BD patients from the STEP-BD cohort receiving lithium and then carried out a replication analysis in another sample for those SNPs that had demonstrated the greatest evidence of association with lithium response (GRIA2, TENM4, SDC2 and SV2B). As can be seen from Table 2, replications of findings are quite rare. For eight genes (INPP1,GSK3B, NR1D1, SLC6A4, COMT, DRD1, BDNF and NTRK2), positive findings could be replicated in at least a second study. However, for all of

3 these genes there are also articles that could not report corresponding associations. 3.1.2 Lithium – genomewide association studies (GWAS) Subsequent to the aforementioned first GWAS by Perlis et al. (2009), three other GWAS on lithium response have been published to date. Like Perlis, Squassina et al. (2011a, 2011b) did not report any genomewide significant findings. However, it is noteworthy that their sample of 52 BD patients lacks the power to detect small effects. In another GWAS conducted in a sample of 294 Han Chinese BD patients from Taiwan, a SNP of the GADL1 gene reached genomewide significance (Chen et al., 2014). However, this finding could not be replicated in independent samples from Taiwan and Japan so far (Consortium in Lithium Genetics, 2014; Ikeda et al., 2014). Furthermore, the top hit (rs17026688) is absent in non-Asian populations and can thus not be vetted in available samples of Caucasian ethnicity. The largest GWAS to date on lithium response (Hou et al., 2016), totaling 2563 uniformly phenotyped BD patients from more than 20 sites across four continents has been published very recently by the Consortium on Lithium Genetics (ConLiGen; Schulze et al. 2010; Manchia and Alda, 2013). Four linked SNPs of a single locus located on chr21q21.1 reached genome-wide significance. The associated chromosomal region contains no known protein-coding genes, but two genes for long, non-coding RNAs, AL157359.3 and AL157359.4. Long, non-coding RNAs seem to play an important role in gene regulation, especially in the CNS (Hou et al., 2016). If replicated, this locus could be a possible target for functional studies.Continuously being extended, the ConLiGen sample will furthermore serve as a valuable resource for future genotype-phenotype dissection analyses in lithium response and related traits. 3.1.3. Other medications For the other first-line treatment options for BD, there are significantly less case-control-association studies so far. Nevertheless, there are some preliminary pharmacogenetic findings on the response to lamotrigine, valproate, and olanzapine/fluoxetine combination treatment. Perlis et al. (2010) found significant associations of seven genes (ANKK1, DRD2, DRD4, DBH, HRH1, MC2R, NR3C1) and lamotrigine response in a sample of 85 patients. The same study revealed a relationship between polymorphisms in the genes DRD3, HRH1 and MC2R and response to an olanzapine/ fluoxetine combination treatment in 88 patients. Kim et al. (2009) found an association between a polymorphism in XBP1 and the response to prophylactic treatment with valproate in 51 bipolar patients. Shortly after that, the same research group reported that, in a sample of 144 manic BD patients., carriers of the Val allele in the COMT gene showed better therapeutic response to mood stabilizers (lithium, valproate and carbamazepine) than those with the Met/Met allele (Lee and Kim, 2010). The approach to analyze more than one mood stabilizer at once has also been used in two studies on Han Chinese BD patients (Wang et al., 2012; Wang et al., 2013), where the researchers found an association of the BDNF and NTRK2 genes and the response to lithium and valproate. However, these results need further investigation, because various mood stabilizers

Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

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Table. 2

Candidate genes analyzed regarding lithium response.

Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

Pharmacogenomic aspects of bipolar disorder: An update

Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

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The numbers in the cells indicate, how many studies have been published on the association between a candidate gene and lithium response in each year. The colours code the sample sizes: red: N≤100; yellow: N = 101-200; blue: N ≥200. * = significant associations with lithium found; number without * or n.s. = non-significant a robust lithium response prediction when GSK3B and NR1D1 genotypes considered together (McCarthy et al., 2011) b association of GSK3B haplotypes and lithium response (Iwahashi et al., 2014) c significant interaction between BDNF and SLC6A4 polymorphism and lithium response (Rybakowski et al., 2007) d In one study, patients were treated with lithium and valproate. The total sample included 342 patients (Wang et al., 2012) e Patients in this sample were treated with lithium, valproate and lamotrigine. The total sample included 144 patients (Lee & Kim 2010). f Patients in this sample were treated with lithium and valproate. The total sample included 284 patients (Wang et al., 2013). Benedetti et al., 2005; Bonaccorso et al., 2015; Bremer et al., 2007; Campos-de-Sousa et al., 2010; Consortium on Lithium Genetics et al., 2014; Dimitrova et al., 2005; Dmitrzak-Weglarz et al., 2005; Dmitrzak-Weglarz et al., 2008; Drago et al., 2010; Iwahashi et al., 2014; Kakiuchi and Kato, 2005; Lin et al., 2013; Løvlie et al., 2001; Mamdani et al., 2008; Mamdani et al., 2007; Manchia et al., 2009a; Manchia et al., 2009b; Masui et al., 2006a; Masui et al., 2006b; McCarthy et al., 2011; Michelon et al., 2006; Mitjans et al., 2015; Pisanu et al., 2013; Rybakowski et al., 2005a; Rybakowski et al., 2005b; Rybakowski et al., 2007; Rybakowski et al., 2009; Rybakowski et al., 2011; Rybakowski et al., 2012; Rybakowski et al., 2013; Rybakowski et al., 2014; Serretti et al., 1998; Serretti et al., 1999a; Serretti et al., 1999b; Serretti et al., 2000; Serretti et al., 2001; Serretti et al., 2002; Serretti et al., 2004; Squassina et al., 2008; Squassina et al., 2009; Szczepankiewicz et al., 2006; Szczepankiewicz et al., 2009a; Szczepankiewicz et al., 2009b; Szczepankiewicz et al., 2011; Sjøholt et al., 2004; Steen et al., 1998; Tharoor et al., 2013

Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

Pharmacogenomic aspects of bipolar disorder: An update were used in un-blinded conditions in the study. More homogeneous treatment conditions would be desirable.

3.2 Adverse events Despite the fact that pharmacogenetic research is often dominated by the research on adverse events, there are hardly any findings dealing with this topic in BD. A very common adverse event of lithium treatment is a decrease in urinary concentrating ability, which can – in worst case scenarios – lead to chronic interstitial nephropathy. Rybakowski et al. (2013) recently reported an association between a glycogen synthase kinase-3 beta (GSK3B) gene polymorphism and kidney function in 78 long-term lithium-treated bipolar patients. Both mood stabilizers and second generation antipsychotics often lead to weight gain, obesity, and other metabolic conditions. For second generation antipsychotics, there is already a substantial amount of literature on pharmacogenetics (for a review see e.g. Brennan, 2014). For example, a very recent study by Bonaccorso and colleagues (2015) reports an association of the BDNF Met66 allele with increased weight gain due to treatment with risperidone or olanzapine in patients diagnosed with BD, schizophrenia and schizoaffective disorder. However, for mood stabilizers there is a lack of research, with the work of a group from Taiwan being an exception. Chang et al. (2010) present preliminary evidence that a polymorphism of the GNB3 gene is associated with valproate-related metabolic abnormalities. They replicated these initial findings in a longitudinal study with BD II patients (Chen et al., 2016). Recently, 16 candidate genes were analyzed in the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) sample regarding their effects on weight gain caused by mood stabilizers and second generation antipsychotics (Creta et al., 2015). In a sample of nearly 486 BD patients, weight gain was associated with FTO, TBC1D1, MTHFR and HRH1. Besides weight gain, other very serious adverse events are antiepileptic drug-induced hypersensitivity reactions such as the Stevens-Johnson syndrome. A recent metaanalysis (Bloch et al., 2014) concluded that carriers of the HLA-B*15:02 allele in Asian populations are at increased risk of developing hypersensitivity to several antiepileptic drugs including carbamazepine, lamotrigine and phenytoin. A couple of studies have been published on genetic risk factors of antidepressant induced mania in BD. Although a meta-analysis by Daray et al. (2010) reports an association between the serotonin transporter gene promoter polymorphism (SLC6A4) and antidepressant induced mania, all in all the results remain ambiguous (Frye et al., 2015). Another very serious adverse event that may occur during treatment with antidepressants – especially serotonin reuptake inhibitors (SSRIs) – is the so called treatmentemergent suicidal ideation (TESI). A few articles have addressed the question of a genetic background for TESI (for a review, see Brent et al., 2010), among which there are two studies with sample sizes of significantly more than 1000 patients from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (Laje et al., 2007;

7 Perlis et al., 2007). Two GWAS of TESI have been published (Laje et al., 2009; Menke et al., 2012) that unfortunately were underpowered for detecting genome-wide significant findings. Associations of TESI and candidate genes involved in various systems and pathways have been reported. However, Brent et al. (2010) conclude that the only replicated ones are for associations with glutamatergic genes, namely GRIK2 and GRIA3.

4. Discussion In summary, our review shows that pharmacogenetic and pharmacogenomic studies of BD have been around for about two decades now. In other words, this line of research has been on the scientific agenda virtually as long as genetic studies into diagnostic categories such as schizophrenia, BD, or major depressive disorder. However, while the latter line of research has witnessed major efforts and achievements, in particular in the last five years due to large-scale multinational collaborations, this cannot be said for psychiatric pharmacogenetics, and even less so for pharmacogenetics of BD. First and foremost, sample sizes have remained incredibly small, in particular in light of the large samples we have become accustomed with for studies on diagnostic phenotypes. Secondly, pharmacogenetic studies of BD are still mostly confined to candidate gene association studies. While this is most readily explained by the small sample sizes, the overwhelming majority of these studies employ samples that are even well below those typical for studies on diagnostic phenotypes right before the advent of the GWAS era. Thirdly, the majority of these (mostly underpowered) studies focus on lithium response. This may be due to the fact that the other first-line treatment options for BD are used more often for other indications like schizophrenia (atypical antipsychotics like olanzapine, risperidone, quetiapine, aripiprazole and ziprasidone) or epilepsy (anticonvulsants like lamotrigine and valproate). A closer look at the studies involving response to lithium reveals that a host of candidate genes representing various pathways and mechanisms have been investigated, but only few replicated associations have been found. And even those replicated associations should be interpreted with caution, because these replications are also accompanied by non-replications and they have been – in part – published by the same workgroup, with the extent of overlap between the samples not being fully clear.

5. Future demands Pharmacogenetic research in BD is still somehow at a state of infancy. With the objective of producing more comparable results, there is a need for large cohorts of patients, well characterized for treatment regimes, and response as well as adverse events. While it is often very hard to realize in clinical settings, treatment conditions should ideally be as homogeneous as possible. Another source of heterogeneity between studies is the inconsistent definition of response or adverse events. Therefore, the development of consensus phenotype definitions that allow for assessments in naturalistic settings and adherence to them are crucial. The four GWAS nicely show

Please cite this article as: Budde, M., et al., Pharmacogenomic aspects of bipolar disorder: An update. European Neuropsychopharmacology (2017), http://dx.doi.org/10.1016/j.euroneuro.2017.02.001

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two very different approaches to measure lithium response. Perlis et al. (2009) used prospective survival analysis, i.e. time to recurrence of mood episodes, for measuring lithium response. In the other three GWAS (Squassina et al., 2011a, 2011b; Chen et al., 2014; Hou et al., 2016) response was defined retrospectively by applying the “Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder" scale (Grof et al., 2002; Manchia and Alda, 2013). The scale measures the degree of clinical improvement (change in frequency and severity of mood symptoms) on lithium treatment compared to periods off lithium (A score), but additionally takes into account clinical factors considered relevant in determining whether the observed clinical change is in fact due to the lithium treatment rather than spontaneous improvement or an effect of additional medication (B score). The total score is then derived by subtracting the total B score from the A score. This scale can be used to define a dichotomous (good vs. poor response) as well as a continuous phenotype. As mentioned above, a considerably big amount of pharmacogenetic research in BD deals with response to drugs and only a small proportion with adverse events. A way to potentially facilitate research on adverse events could be a more systematic assessment of adverse events in the clinical work and linking medical records to research protocols whenever possible and in line with regulations set forth by IRBs and ethics committees. Another way to go would be to encourage collaborative projects with pharmaceutical and other biomedical companies. Furthermore an establishment of pharmacogenetic task forces within large consortia, currently operating at the forefront of psychiatric genetics, would be desirable.

6. Conclusion Psychopharmacogenetic research, in particular in BD, is lagging behind overall psychiatric genetic research. This is to some degree understandable because the logistics and efforts needed to establish sufficiently large sample sizes are much greater than for studies on diagnostic phenotypes. GWAS have unequivocally shown that the genetic liability to psychiatric disorders is highly polygenic and thus research into etiopathological factors may most likely not deliver clues that can be readily implemented into clinical use. Thus, one may consider revisiting current efforts to go for larger and larger, and thus less meticulously phenoytped samples of diagnostic categories. Instead, some of this energy and financial resources could be diverted into projects that tackle what is closer to patients’ and clinicians’ everyday measure of success: response to treatment and adverse events.

Role of the funding source The funding bodies had no further role in study design; in the collection, analysis and interpretation of data; in the writing report; or in the decision to submit the paper for publication.

Author's contributions MB, TGS designed the overall design of this review. MB wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.

Conflict of interest None.

Acknowledgements MB and TGS were supported by the Deutsche Forschungsgemeinschaft (Clinical Research Group 241; Grant number SCHU 1603/4-1 & SCHU 1603/5-1; PsyCourse; Grant number SCHU 1603/7-1). JB was supported by the Deutsche Forschungsgemeinschaft (Clinical Research Group 241; Grant number BR 2471/1-1). TGS was supported by the Dr. Lisa Oehler Foundation (Kassel, Germany).

Appendix A.

Supporting information

Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/ j.euroneuro.2017.02.001.

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