Pharmacogenomics of antipsychotics efficacy for schizophrenia

Authors


Masatoshi Takeda, MD, PhD, Department of Psychiatry, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan. Email: mtakeda@psy.med.osaka-u.ac.jp

Abstract

Central nervous system disorders are the third greatest health problem in developed countries, and schizophrenia represents some of the most disabling ailments in young individuals. There is an abuse and/or misuse of antipsychotics, and recent advances in pharmacogenomics pose new challenges for the clinical management of this complex disorder. Schizophrenia is a multi-factorial/polygenic complex disorder in which hundreds of different genes are potentially involved, leading to the phenotypic expression of the disease in conjunction with epigenetic and environmental phenomena. Consequently, structural and functional genomic changes induce proteomic and metabolomic defects associated with the disease phenotype.

Disease-related genomic profiles and genetic variants in genes involved in drug metabolism are responsible for drug efficacy and safety. About 20% of Caucasians are defective in CYP2D6 enzymes, which participate in the metabolism of 25–30% of central nervous system drugs. Approximately 40% of antipsychotics are substrates of CYP2D6 enzymes, 23% are substrates of CYP3A4, and 18% are substrates of CYP1A2. In order to achieve a mature discipline of pharmacogenomics of schizophrenia it would be effective to accelerate: (i) the education of physicians and the public in the use of genomic screening in daily clinical practice; (ii) the standardization of genetic testing for major categories of drugs; (iii) the validation of pharmacogenomic procedures according to drug category and pathology; (iv) the regulation of ethical, social, and economic issues; and (v) the incorporation of pharmacogenomic procedures of drugs in development and drugs on the market in order to optimize therapeutics.

PHARMACOGENOMICS RELATES TO the application of genomic technologies, such as genotyping, gene sequencing, gene expression, genetic epidemiology, transcriptomics, proteomics, metabolomics, and bioinformatics, to drugs in clinical use, applying the large-scale systematic approaches of genomics to speed up the discovery of drug response markers, whether they act at the level of drug target, drug metabolism, or disease pathways.1–4 Pharmacogenomic approach is totally different from that of pharmacogenetics, in which the former approach covers the whole range of genes throughout the genome possibly involved with the response to drug response while the latter and is mainly concerned with specific candidate genes.

In the early stage of pharmacotherapy of schizophrenia (SCZ), antipsychotic drugs were accompanied by severe adverse side-effects, such as extrapyramidal signs and tardive dyskinesia. Even though second-generation antipsychotics (SGA) have been introduced to reduce side-effects,5,6 there are still many cases with SGA-related adverse side-effects, with vulnerable subjects in particular.7–12 SGA are widely used in elderly patients with various diagnosis, such as delirium,13–15 delusion,16 hallucination,17 catatonia,18 and behavioral and psychological problems of many types of dementia.19–24 Due to adverse side-effects, SGA are no longer recommended to elderly subjects.25,26

Not all SCZ patients are responsive to antipsychotic treatment, including first-generation antipsychotics (FGA) and SGA. Clozapine is often used for treatment-resistant SCZ patients, but it has not yet given an entirely satisfactory response. Iloperidone (dopamine [D2A, D3], serotonin [5-HT1A, 5-HT6], and noradrenaline α2C receptor antagonist) and asenapine (dopamine D2 receptor antagonist, and an antagonist of a host of serotonin 5-HT receptor subtypes, except 5-HT1a and 5-HT1b) were approved by the Food and Drug Administration (FDA) in 2009, and new drugs for SCZ are under development, such as LY2140023 (acting on metabolic glutamate receptor) and others, targeting better efficacy in SCZ patients.

Antipsychotics, including FGA, SGA, and future drugs, do not always give the maximum efficacy to SCZ patients in clinical settings. This is because patients are treated in a trial-and-error manner without any scientific data or empirical evidence. There are few data available concerning which antipsychotic drug should be prescribed and/or what is the optimum dose for each SCZ patient. If we could determine the optimum dose and proper drug for the patient based on pharmacogenomic evidence, a much higher response rate would be achievable. Pharmacogenomic protocol would be able to implement a better treatment regime to SCZ patients with the maximum efficacy and the fewest adverse effects of the drug.

For a better pharmacological regime for SCZ patients, the pharmacogenomic approach is essential because there are hundreds of genes involved in drug kinetics and metabolism. Furthermore many genes are involved with the complex pathogenetic process of SCZ, which could be influenced by antipsychotics.

The potential implication of pharmacogenomics in the clinical setting is the treatment of SCZ patients according to genomic and biological markers, selecting optimal medications for individual patients or for clusters of patients with a similar genomic profile. For many medications, interindividual differences are mainly due to single-nucleotide polymorphism (SNP) in genes encoding drug-metabolizing enzymes, drug transporters, and/or drug targets (e.g. genome-related defective enzymes, receptors and proteins, which alter metabolic pathways leading to disease phenotype expression).

The application of these procedures to CNS disorders is an extremely difficult task because most neuropsychiatric diseases, including SCZ, are complex disorders in which many different genes might be involved.1 In addition, it is very unlikely that a single drug will be able to reverse the multifactorial mechanisms associated with neuronal dysfunction in most CNS processes with a complex phenotype affecting mood, personality, behavior, cognition, and functioning. This heterogeneous clinical picture usually requires the utilization of different drugs administered simultaneously.

The pharmacogenomic outcome depends upon many different determinant factors including: (i) genomic profile; (ii) disease phenotype; (iii) concomitant pathology; (iv) genotype–phenotype correlations; (v) nutritional conditions; (vi) age and gender; (vii) pharmacological profile of the drugs; (viii) drug–drug interactions; (ix) gene expression profile; (x) transcriptomic cascade; (xi) proteomic profile; and (xii) metabolomic networking. The dissection and further integration of all these factors is of paramount importance for the assessment of the pharmacogenomic outcome in terms of safety and efficacy. Pharmacogenomic approaches based on genome-wide sets of SNP associated with drug response are now feasible and may offer the potential to personalize therapeutics.3

PHARMACOKINETICS OF ANTIPSYCHOTICS

The vast majority of drugs in current use and many psychotropics are metabolized by enzymes known to be genetically variable, including: (i) esterases: butyrylcholinesterase and paraoxonase/arylesterase; (ii) transferases: N-acetyltransferase, sulfotransferase, thiol methyltransferase, thiopurine methyltransferase, catechol-O-methyltransferase, glutathione-S-transferases, UDP-glucuronosyl-transferases, glucosyltransferase and histamine methyltransferase; (iii) reductases: nicotinamide adenine dinucleotide phosphate (NADPH; quinine oxidoreductase and glucose-6-phosphate dehydrogenase); (iv) oxidases: alcohol dehydrogenase, aldehyde dehydrogenase, monoamine oxidase B, catalase, superoxide dismutase, trimethylamine N-oxidase and dihydropyrimidine dehydrogenase; and (v) cytochrome P450 enzymes, such as CYP1A1, CYP2A6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A5 and many others.1–4 Polymorphic variants in these genes can induce alterations in drug metabolism, modifying the efficacy and safety of the prescribed drugs.

Drug metabolism includes phase I reactions (i.e. oxidation, reduction, hydrolysis) and phase II conjugation reactions (i.e. acetylation, glucuronidation, sulfation, methylation). The principal enzymes with polymorphic variants involved in phase I reactions are as follows: CYP3A4/5/7, CYP2E1, CYP2D6, CYP2C19/9/8, CYP2B6, CYP2A6, CYP1B1, CYP1A1/2, epoxide hydrolase, esterases, NADPH-quinone oxidoreductase (NQO1), dihydropyrimidine dehydrogenase (DPD), alcohol dehydrogenase (ADH), and aldehyde dehydrogenase (ALDH). Major enzymes involved in phase II reactions include the following: uridine 5′-triphosphate glucuronosyl transferases (UGT), thiopurine methyltransferase (TPMT), catechol-O-methyltransferase (COMT), histamine methyl-transferase (HMT), sulfotransferases (ST), glutathione S-transferase (GST)-A, GST-P, GST-T, GST-M, NAT2 (N-acetyl transferase), NAT1, and others.1,3

Historically, the vast majority of pharmacogenetic studies of SCZ have been addressed to evaluate the impact of cytochrome P450 enzymes on drug metabolism.27–30 The typical paradigm for the pharmacokinetics of phase I drug metabolism is represented by the cytochrome P450 enzymes, a superfamily of microsomal drug-metabolizing enzymes. The P450 enzymes are encoded in genes of the CYP superfamily and act as terminal oxidases in multicomponent electron transfer chains, which are called P450-containing monooxygenase systems. Some of the enzymatic products of the CYP gene superfamily can share substrates, inhibitors and inducers, whereas others are quite specific for their substrates and interacting drugs.

The microsomal, membrane-associated, P450 isoforms, CYP3A4, CYP2D6, CYP2C9, CYP2C19, CYP2E1, and CYP1A2, are responsible for the oxidative metabolism of over 90% of marketed drugs. About 60–80% of the psychotropic agents currently used for the treatment of neuropsychiatric disorders are metabolized via enzymes of the CYP family, especially CYP1A2, CYP2B6, CYP2C8/9, CYP2C19, CYP2D6 and CYP3A4. CYP3A4 metabolizes more drug molecules than all other isoforms together. Most of these polymorphisms exhibit geographic and ethnic differences.31–34 These differences influence drug metabolism in different ethnic groups in which drug dosage should be adjusted according to their enzymatic capacity, differentiating normal or extensive metabolizers (EM), intermediate metabolizers (IM), poor metabolizers (PM) and ultrarapid metabolizers (UM).

Most drugs act as substrates, inhibitors or inducers of CYP enzymes. Enzyme induction enables some xenobiotics to accelerate their own biotransformation (auto-induction) or the biotransformation and elimination of other drugs. A number of P450 enzymes in the human liver are inducible. Induction of the majority of P450 enzymes occurs by an increase in the rate of gene transcription and involves ligand-activated transcription factors, aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor.35,36 In general, binding of the appropriate ligand to the receptor initiates the induction process that cascades through a dimerization of the receptors, their translocation to the nucleus and binding to specific regions in the promoters of CYP.36 CYP are also expressed in the CNS, and a complete characterization of constitutive and induced CYP in the brain is essential for understanding the role of these enzymes in neurobiological functions and in age-related and xenobiotic-induced neurotoxicity.37 CYP2D6 mRNA expression is detected in all regions of the human brain where it may be involved in the metabolism of amines and steroids and in the regulation of diverse CNS activities38 (Table 1).

Table 1.  CYP450 enzyme and antipsychotic metabolism
 1A22D63A4
ChlorpromazineChlorpromazineChlorpromazine
P450 substrateFluphenazineFluphenazineHaloperidol
HaloperidolHaloperidolPerphenazine
PerphenazinePerphenazinePimozide
ClozapineThioridazineAripiprazole
OlanzapineAripiprazoleClozapine
ZiprasidoneQuetiapineRisperidone
 RisperidoneZiprasidone
P450 inhibitor ChlorpromazineHaloperidol
 FluphenazinePimozide
 Haloperidol 
 Perphenazine 
 Thioridazine 
 Aripiprazole 
 Clozapine 
 Risperidone 

Approximately 40% of antipsychotics are major substrates of CYP2D6 enzymes, 23% are major substrates of CYP3A4, and 18% are major substrates of CYP1A2. Eighty-five percent of antidepressants are major substrates of CYP2D6 enzymes, 38% are major substrates of CYP3A4, 38% are major substrates of CYP2C19, 24% are major substrates of CYP1A2, and 5% are major substrates of CYP2B6. Ninety-five percent of benzodiazepines are major substrates of CYP3A4 enzymes, 20% are major substrates of CYP2D6, and 7% are major substrates of CYP2C19.1,3 About 80% of patients with resistant depression, 60% of patients non-responsive to antipsychotics, and 50–70% of patients with paradoxical responses to benzodiazepines are carriers of mutant variants of the CYP2D6, CYP2C9 and CYP3A4 genes, falling within the categories of poor or ultra-rapid metabolizers.1,3,39,40

The clinical impact of the cytochrome P450 (CYP) enzyme CYP2D6 poor metabolizer (PM) genotype in patients taking antipsychotic medication was investigated in a retrospective study.41 The impaired metabolic capacity of the PM genotype results in higher steady-state plasma concentrations at a given dose, thus increasing the risk of toxic effects from medication. Extrapyramidal signs or tardive dyskinesia were significantly more frequent among PM patients than among the matched IM and EM control subjects. This finding was further supported by the significantly higher prevalence of non-compliance among the same PM patients. Genetically encoded differences in the rate of drug metabolism through CYP2D6 can predict antipsychotic side-effects and prompts the question of whether genotyping early in the course of illness to facilitate adjustment of pharmacotherapy will improve treatment outcomes and reduce side-effects.41

The effects of the CYP2D6 and CYP3A5 genotypes on the steady-state plasma levels of risperidone (RIS), 9-hydroxyrisperidone (9-OH-RIS), and the active moiety (RIS plus 9-OH-RIS) were studied in SCZ patients.42 The patients investigated were CYP2D6 extensive metabolizers (EM; CYP2D6*1/*1, *1/*10, and *10/*10) and CYP2D6 poor metabolizers (PM; CYP2D6*1/*5 and *10/*5). For the CYP3A5 genotype, patients were CYP3A5*1 expressors (*1/*1 and *1/*3) and CYP3A5 non-expressors (*3/*3). The plasma levels of RIS (2.03 ng/mL per milligram for EM vs 5.57 ng/mL per milligram for PM) and 9-OH-RIS (5.06 ng/mL per milligram for EM vs 0.22 ng/mL per milligram for PM) were significantly different among CYP2D6 genotype groups, but the CYP2D6 EM (7.09 ng/mL per milligram) and PM (5.79 ng/mL per milligram) did not show differences in the levels of the active moiety. CYP3A5 non-expressors exhibited higher plasma concentrations of both RIS and 9-OH-RIS than its expressors. In the case of 9-OH-RIS, CYP3A5 non-expressors exhibited significantly higher concentrations than CYP3A5 expressors. Concentrations of the active moiety were also significantly different between the CYP3A5 non-expressors and expressors. According to these results reported by Kang et al., both CYP2D6 and CYP3A5 genotypes affect plasma levels of RIS and 9-OH-RIS, whereas the active moiety levels are influenced only by the CYP3A5 genotype but not by the CYP2D6 genotype.42

Risperidone is converted to 9-hydroxyrisperidone by CYP2D6. The CYP2D6*10 polymorphism (which is a prevalent mutant allele among East Asians) and the presence of co-medication exert significant influences on the pharmacokinetics of risperidone.43

Some studies attempt to determine whether testing for cytochrome P450 (CYP) polymorphisms in adults entering antipsychotic treatment for SCZ leads to improvement in outcomes, is useful in medical, personal or public health decision-making, and is a cost-effective use of health-care resources.44

PHARMACODYNAMICS OF ANTIPSYCHOTICS

After chlorpromazine application to psychotic patients in 1952, many antipsychotics have been developed and are now widely used for pharmacological treatment of SCZ patients. The typical or first-generation antipsychotics (FGA) are compounds with dopamine receptor antagonizing activity. In the late 1990s the atypical or SGA were introduced, which include serotonin-dopamine antagonists (SDA), multi-acting receptor-targeted antipsychotics (MARTA), and dopamine partial agonists, which are now prescribed as the first choice antipsychotics for SCZ patients. Even though both FGA and SGA are widely used for SCZ patients, about 30% of SCZ patients are not responsive to these drugs and we urgently need to identify the responder to specific antipsychotic treatment using genetic information. Considering the changes in neurotransmitters in the brain of SCZ, especially in the dopamine and serotonin, there are many studies reporting the association of genetic polymorphism in receptor and transporter genes of dopamine and serotonin system. Albeit the huge number of research papers on genes regulating neurotransmitter function, we are still in a premature stage to reconcile all of the data into the integrated understanding.

The pathogenesis of SCZ is still to be elucidated even though some susceptible genes for SCZ have been identified. The polymorphism of those susceptible genes has been studied for the correlations with response to antipsychotic treatment. The data demonstrating the correlations between gene polymorphism and response to antipsychotic treatment will be briefly reviewed in this article, arbitrarily classified into five categories: (i) genes directly regulating neurotransmission; (ii) susceptible genes for SCZ; (iii) genes possibly related with pathophysiology of SCZ; (iv) mitochondrial genes; and (v) genes newly identified by genome-wide association (GWA) studies.

There are many papers exploring the association between gene polymorphisms and treatment response and/or adverse side-effects. Only the data reporting treatment response will be briefly reviewed in this article. Findings of the genetic polymorphisms related to adverse side-effects are not included due to space limitations. Some review papers are available that focus on genetic factors and adverse side-effects, such as weight gain and metabolic syndrome.45

(1) Genes directly regulating neurotransmission

Targets that show promise for pharmacological focus in SCZ include the dopamine receptors in the prefrontal cortex, and the serotonin receptors in the prefrontal cortex and the anterior cingulate cortex.46 Many antipsychotics have been developed based on the dopamine hyperactivity hypothesis of SCZ. Therefore there are many papers reporting the link between antipsychotic response and genetic variations in dopamine receptors (DRD1-5) and dopamine transporter.

Dopamine receptor D2

Dopamine receptor D2 (DRD2) blocking is the major mechanism of action of FGA and SGA. The DRD2 gene has a -141C Ins/Del polymorphism in the promoter region and the -141C Ins-allele is demonstrated to increase mRNA expression of the DRD2 gene. Ins/Ins showed better improvement to nemonapride and bromperidol treatment with Japanese acute SCZ patients (n = 49),47 and also to chlorpromazine treatment with Chinese patients (n = 135).48 However, the studies of clozapine response in Caucasians (n = 151),49 Chinese (n = 146)49 and African-Americans (n = 49)50 showed no positive association between -141C Ins/Del polymorphism and treatment response.

Taq 1(Allele 1, Allele 2) polymorphism of DRD2 has also been studied extensively for its correlations with treatment response. The A1 allele is associated with better improvement to nemonapride treatment with Japanese acute SCZ (n = 25), and A1/A2 showed greater improvement with haloperidol treatment with European acute SCZ (n = 57), but there are papers reporting no association with clozapine,50 risperidone,51 or chlorpromazine treatment.48

Dopamine receptor D4

The dopamine receptor D4 (DRD4) is polymorphic with the number of 48 base pair repeats in the third exon (VNTR). DRD4 with different repeat numbers (2-, 4-, or 7- repeat) shows different affinity to spiperone and clozapine. This VNTR of DRD4 shows no link to clozapine treatment response in many reports,52–56 however, there are at least three reports that support the hypothesis that the 4-repeat allele might be associated with better response.57–59

Dopamine receptor D3

The Ser9Gly polymorphism of dopamine receptor D3 (DRD3) could disturb the membrane insertion of this receptor molecule with biological consequence. Studies with FGA treatment response showed the Gly allele is more frequent among poor responders, however, recent studies with SGA showed a positive link of the Gly allele with a better response.60,61

Chen et al.62 investigated whether the efficacy of aripiprazole can be predicted by a functional Ser9Gly (rs6280) polymorphism of DRD3 gene in Han Chinese hospitalized patients with acutely exacerbated SCZ. Although the Ser carriers have numerically larger score reductions when compared with non-carriers in almost all positive and negative syndrome scale (PANSS) dimensions, the difference of their effects is statistically not significant; however, the clinical factors, including dosage of aripiprazole, age, duration of illness, and diagnostic subtype could influence PANSS performance after aripiprazole treatment, suggesting that DRD3 Ser9Gly polymorphism may not contribute significantly to inter-individual differences in therapeutic efficacy of aripiprazole, but some clinical factors may predict treatment efficacy.62

Dopamine transporter

VNTR of the dopamine transporter gene showed no link with response to clozapine, or SGA (quetiapine, risperidone or olanzapine) treatment in at least three papers in the literature.60,63,64

Serotonin receptor 2A

The serotonin receptor 2A (5HT2A) receptor has been most thoroughly studied among 5HT receptors. Three polymorphisms, T102C, -1438-G/A, and His452Tyr, have been studied for correlations with response to clozapine treatment.

The T102C polymorphism showed a weak link with clozapine treatment in six papers and the meta-analysis of these data showed C/C carriers showed significantly worse response to clozapine.65 The C/C genotype with Chinese SCZ patients, however, was associated with better response to risperidone treatment,66 showing different response patterns with different ethnic backgrounds or with different drugs.

Analysis of A-1438G polymorphic variants also revealed different association results by different cohorts or by different antipsychotics. For example, SCZ carriers of the G/G genotype receiving olanzapine showed a significant tendency toward improvement in the PANSS positive syndrome score in comparison with AA or AG American patients (n = 41),67 but the A/A genoptype was found to give better response to risperidone treatment with Turkish patients (n = 63).68

Aripiprazole acts as a partial agonist at DRD2, DRD3 and serotonin 1A (HTR1A) receptors.69 Since aripiprazole acts as an antagonist at HTR2A, genetic variants of HTR2A may be important in explaining variability in response to aripiprazole. The GG/CC genotype group of HTR2A A-1438G/T102C polymorphisms predicts poor aripiprazole response specifically for negative symptoms. In addition, the clinical factors, including dosage of aripiprazole, age, duration of illness, and diagnostic subtype, were found to influence PANSS performance after aripiprazole treatment.69

As for His452Tyr polymorphism, Tyr452 is shown to be associated with poor response to clozapine.65

Serotonin receptor 1A

The serotonin receptor 1A (5HT1A) receptor may modulate some of the negative, cognitive, and affective symptoms of SCZ and a functional polymorphism in the promoter region of the 5-HT(1A) receptor gene is reportedly associated with depression and suicidal behavior. The -1019C/G polymorphism of the 5HT1A promoter region was studied with SCZ patients and this polymorphism is shown to be associated with changes in negative and depressive symptoms but not positive symptoms.70

Serotonin receptor 2C

The serotonin receptor 2C (5HT2C) receptor is believed to be involved in negative symptoms and cognitive impairment of SCZ. The gene coding for 5HT2C, located on X chromosome, has a polymorphism in position 68 (G68C) resulting in amino acid substitution (Cys23Ser). There are papers reporting that Ser23 is associated with good response to clozapine,71,72 but there are at least four papers reporting no link with clozapine response.

Serotonin receptor 6

A polymorphism 267-T/C of serotonin receptor 6 (5HT6) genes showed positive association with T/T showing better response to clozapine and risperidone.73,74

Serotonin receptor 7

Gene variation of serotonin receptor 7 (5HT7) is expected to be useful in developing individualized therapy; however, no significant correlation of HTR7 with antipsychotic efficacy was detected in either genotype or haplotype analysis in the Chinese population.75

Serotonin transporter

The serotonin transporter (5HTT) gene has two polymorphisms with functional consequences: 5HTTLPR L/S and VNTR12. 5HTTRLP is the insertion/deletion type of polymorphism with a 44-base pair repeat in the promoter region of the 5HTT gene. The long form (L allele) shows higher promoter activity than the short form (S allele). VNTR12 is also a regulating transcription level of the gene, but there is disagreement on the functional consequence. The association studies of 5HTTRLP with antipsychotic response have given different results. There are reports showing the association of S allele with poorer response to clozapine with Caucasian patients (n = 200), association of L allele with better improvement to risperidone with Chinese patients (n = 129) and Slovenian patients (n = 65), and no link to clozapine treatment with Chinese patients (n = 90). No link has been reported with HTTRLP and VNTR12 polymorphisms in response to SGA and clozapine with Caucasian patients.76,77

(2) Susceptible genes of schizophrenia

Genetic research has identified several candidate susceptible genes for SCZ, including DTNBP1 (6p22), NRG1 (8p12-21), DISC1 (1q42), COMT (22q1.1), G72 (13q32-34), RGS4 (1q21-22), Akt1 (14q22-32), PPP3C (8p21), GRM3 (7q21-22) and DAAO (12q24),78 some of which were studied for the correlations between their polymorphisms and treatment response.

Dystrobrevin-binding protein 1

The prototypical SGA agent, clozapine, is more efficacious for refractory SCZ than the FGA. Since 2002, at least 22 association studies have shown that dystrobrevin-binding protein 1 (DTNBP1) can be associated with the risk for SCZ, and it has also been hypothesized that DTNBP1 might influence the response to antipsychotic treatments. Patients with diplotype ACCCTC/GTTGCC, genotypes T/T + T/C, or allele T of marker rs742105 (P1333) have better response to clozapine, and patients with diplotype ACCCTC/GCCGCC, genotype A/G, or allele A of marker rs909706 (P1583) have better response to haloperidol in European-Americans, African-Americans, and/or the combined sample. European-American patients with diplotype ACCCTC/GCCGCC have worse response to clozapine on positive symptoms. These results obtained by Zuo et al. might indicate that the DTNBP1 gene modulates the effects of both clozapine and haloperidol, and that SCZ patients with different DTNBP1 diplotypes, haplotypes, genotypes or alleles might have different responses to these antipsychotics.79

Neuregulin 1

Neuregulin 1 (NRG1) is a trophic factor containing an epidermal growth factor (EGF)-like domain through which it stimulates ErbB receptor tyrosine kinase. It belongs to a family of growth factors that are encoded by four individual genes (NRG14), of which NRG1 is most intensively studied. NRG1–ErbB signaling is involved in various processes of brain development. Loss of function of NRG1/ErbB4 or perturbation of NRG1 signaling can cause deficits in migration of pyramidal and GABAergic neurons, neurite outgrowth and axon projection, myelination of axons and synapse formation. The resulting anatomical abnormalities could underlie the altered neurotransmission and cortical function that leads to psychotic symptoms and cognitive impairments of SCZ. NRG1 has been demonstrated to be a susceptible gene for SCZ by multiple association studies and some NRG1 isoforms are abnormally expressed in SCZ patients. Polymorphism of NRG1 (SNP8NRG221533) was studied for response to SGA treatment with Finnish patients (n = 94), showing the TT genotype was overrepresented in the non-responders group compared with the responders (P = 0.013).80

Disrupted in Schizophrenia 1

The Disrupted in Schizophrenia 1 (DISC1) gene is linked to SCZ and other mental illnesses in multiple pedigrees. The neurobiology of DISC1 in the normal developing and adult brain and its roles in SCZ are not well understood. The biological role of DISC1 is, however, speculated from the following findings: (i) downregulation of DISC1 accelerates morphological development of adult-born neurons resulting in soma hypertrophy and enhancement of dendritic outgrowth; (ii) inhibition of DISC1 leads to mispositioning of new neurons in the molecular layer from overextended migration, suggesting that DISC1 serves as an interpreter that relays positional signals to the intracellular migratory machinery rather than a direct mediator of neuronal migration; (iii) new neurons with DISC1 knockdown exhibit more mature neuronal firing patterns; and (iv) downregulation of DISC1 accelerates synapse formation of newborn neurons. Taken together, these results indicate that DISC1 controls the tempo of the entire process of neuronal integration in the adult brain as a master regulator.

Three common missense variants of the DISC1 gene, rs3738401 (Q264R), rs6675281 (L607F) and rs821616 (S704C), have been variably associated with the risk of schizophrenia. These gene variants were shown to have a significantly positive association with clozapine response in French Caucasian patients. The association with Q264R (rs3738401) variant was most significantly associated with non-response to clozapine treatment.81

Catechol-O-methyl transferase

Catechol-O-methyl transferase (COMT) is the rate limiting monoamine metabolizing enzyme and the significance of functional val108Met polymorphism has been extensively studied for the susceptibility of SCZ, neurocognitive function and response to antipsychotic treatment. Met allele was shown to be associated with poor response to FGA,82 but no link with response to SGA treatment.83 Single-locus as well as detailed haplotype-based association analysis of the COMT gene with SCZ and antipsychotic treatment response was carried out using seven COMT polymorphisms in SCZ patients from a homogeneous south Indian population. Haplotype analysis showed a highly significant association of seven COMT marker haplotypes with SCZ, and allelic associations of two SNP (rs4633, rs4680) with drug response were also found. A significant association of markers located between intron 1 and intron 2 (rs737865, rs6269), and in exon 4 (rs4818, rs4680) with drug response was also detected, indicating that the interacting effects within the COMT gene polymorphisms may influence the disease status and response to risperidone.84

Regulator of G-protein signaling 4

Regulator of G-protein signaling 4 (RGS4) has been identified and confirmed by meta-analysis as a risk gene of SCZ.85 The expression level of the transcript and the protein86 of RGS4 is reportedly decreased in the frontal cortex of SCZ patients. RGS4 regulates G-protein-coupled receptor (GPCR) activity, including dopamine, acetylcholine and serotonin receptors, and RSG4 is part of a large family of regulators of G-protein signaling coded by genes lying near each other on chromosome 1, all of which act by shortening the duration of neurotransmitter signaling through GPCR. An exploratory study of RGS4 variants to antipsychotic treatment response was performed with SCZ patients recruited into the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Subjects with RGS4 rs951439 genotype CC responded better to perphenazine compared with ziprasidone.87 Similarly subjects with RGS4 rs2842030 genotype TT responded better to perphenazine treatment than to quetiapine, risperidone, or ziprasidone. An independent study with Chinese SCZ subjects showed that RGS4 SNP rs2661319, which is located in between rs951439 and rs2842030, predicted differential response to risperidone treatment.88

(3) Genes possibly related with pathophysiology of schizophrenia

Brain-derived neurotrophic factor

The val66met(G196A) polymorphism was investigated in two studies and no link with the response to SGA treatment was found.89,90 The SCZ patients with longer allele of the VNTR polymorphism of 172-176bp in the brain-derived neurotrophic factor (BDNF) gene were shown to respond better to SGA.91

The effects of aripiprazole and haloperidol have been studied in SH-SY5Y human neuroblastoma cells via BDNF-mediated signaling, glycogen synthase kinase-3beta (GSK-3beta), and B cell lymphoma protein-2 (Bcl-2). The effects of both drugs on BDNF gene promoter activity were studied in SH-SY5Y cells transfected with a rat BDNF promoter fragment (−108 to +340) linked to the luciferase reporter gene. Haloperidol was not associated with a significant difference in BDNF promoter activity. In contrast, aripiprazole was associated with increased BDNF promoter activity only with a dose of 10 µM (93%). Treatment with aripiprazole at 10 µM increased the levels of BDNF by 85% compared with control levels, whereas haloperidol had no effect. Cells treated with aripiprazole effectively increased the levels of GSK-3beta phosphorylation and Bcl-2 at doses of 5 and 10 µM (30% and 58% and 31% and 80%, respectively); however, haloperidol had no effects on p-GSK-3beta and Bcl-2 expression. This study seems to indicate that aripiprazole, but not haloperidol, may exert neuroprotective effects on human neuronal cells. The actions of signaling systems associated with BDNF may represent key targets for both aripiprazole and haloperidol, but the differential effects of both drugs suggest that the haloperidol-mediated responses may depend on different pharmacogenomic pathways.92

Reelin signaling

Abnormality in reelin and GABAergic signaling system is observed with psychiatric disorders, including autism, SCZ, bipolar disorder, and major depression. Chronic administration of psychotropic medications (clozapine, fluoxetine, haloperidol, lithium, olanzapine, and valproic acid) is shown to alter levels of reelin, its receptor (VLDL receptor), downstream molecules GSK3 beta, Dab-1, and GAD65/67 in rat prefrontal cortex as measured by quantitative real-time PCR (qRT-PCR) and Western blotting. mRNA for reelin, VLDL receptor, Dab-1, GSK3 beta, and GAD65 were each significantly altered by at least one of the drugs tested, and in the case of reelin, Dab-1, and GSK3 beta, by multiple drugs, suggesting that the reelin signaling and GABAergic systems are affected by commonly used psychotropic medications.93 Valproic acid facilitates chromatin remodeling when it is associated with clozapine or sulpiride but not with haloperidol or olanzapine. This remodeling might contribute to reelin- and GAD(67)-promoter demethylation and might reverse the GABAergic-gene-expression downregulation associated with SCZ morbidity.94

Synaptosomal associated protein-25

There is a hypothesis that antipsychotic medication may function to restore synaptic connectivity that is reduced in the brain of SCZ patients. It is interesting to know whether antipsychotics affect the marker proteins of the synaptic connectivity, such as synaptosomal-associated protein receptor (SNARE) protein, and vesicle-associated membrane protein-1 (VAMP-1). NRG-1 and SNARE protein transcripts in human brain aggregates were examined after 3-week exposure to plasma levels of clozapine or haloperidol. At the end of this exposure period, the mRNA levels of NRG-1, VAMP-1 and SNAP-25 were investigated. Clozapine, but not haloperidol, has the ability to upregulate NRG-1 (+3.58 fold change) and VAMP-1 (+1.92 fold change) while synaptosomal-associated protein-25 (SNAP-25) remains unchanged.95

The protein expression of SNAP-25, directly involved in the release of neurotransmitters, is shown to be linked to the polymorphism of the gene. The effect of Mnl1 and Tai1 polymorphisms was examined for the response to antipsychotics (clozapine, haloperidol, olanzapine, and risperidone) and Mnl1 (P = 0.01) and Tai1 (P = 0.03) polymorphisms are shown to be associated with changes in PANSS scores with Caucasian and Afro-American SCZ patients (n = 59).96

(4) Mitochondrial genes

An increasing number of experiments have found abnormalities in mitochondria in the brains of psychotics, which suggests that mitochondrial dysfunction or abnormal cerebral energy metabolism might play an important role in the pathophysiology of SCZ. Differential mitochondrial protein expressions were assessed for three groups with chlorpromazine (CPZ), clozapine (CLZ), or quetiapine (QTP) treatment and a control group. A total of 14 proteins, of which six belong to the respiratory electron transport chain (ETC) of oxidative phosphorylation (OXPHOS), showed significant changes in quantity including NADH dehydrogenase (ubiquinone) 1 alpha subcomplex 10 (Ndufa10), NADH dehydrogenase (ubiquinone) flavoprotein 2 (Ndufv2), NADH dehydrogenase (ubiquinone) Fe-S protein 3 (Ndufs3), F1-ATPase beta subunit (Atp5b), ATPase, H+ transporting, lysosomal, beta 56/58 kDa, isoform 2 (Atp6v1b2) and ATPase, H+ transporting, V1 subunit A, and isoform 1 (Atp6v1a1). These data show proteomic changes are induced by antipsychotics in rodents.97

(5) GWA study for efficacy of antipsychotics

Genome-wide expression profiling using microarrays, performed to study effects of FGA and SGA in the postmortem liver of SCZ patients, revealed that FGA affected genes associated with nuclear protein, stress responses and phosphorylation, whereas SGA affected genes associated with Golgi/endoplasmic reticulum and cytoplasm transport. Comparison between FGA and SGA further identified genes associated with lipid metabolism and mitochondrial function. Analyses on individual antipsychotics identified a set of genes (151 transcripts) that are differentially regulated by four antipsychotics, particularly by phenothiazines, in the liver of SCZ patients.98

Ma et al.99 used the label-free liquid chromatography tandem mass spectrometry (LC-MSE) to identify differentially expressed proteins in rat frontal cortex following subchronic treatment with haloperidol or olanzapine. LC-MSE profiling identified 531 and 741 annotated proteins in fractions I (cytoplasmic-) and II (membrane enriched-) in the two drug treatments. Fifty-nine of these proteins were altered significantly by haloperidol treatment, 74 by olanzapine and 21 were common to both treatments. Pathway analysis revealed that both drugs altered similar classes of proteins associated with cellular assembly/organization, nervous system development/function (particularly presynaptic function) and neurological disorders, which indicate a common mechanism of action. The top affected canonical signaling pathways differed between the two treatments. The haloperidol dataset showed a stronger association with Huntington's disease signaling, while olanzapine treatment showed stronger effects on glycolysis/gluconeogenesis.99

Recently, GWA studies with antipsychotic treatment have been performed. However, most of the GWA studies with antipsychotic treatment were focused on adverse side-effects, such as movement-related adverse effects,100 Parkinsonism,101 QT prolongation,102 and obesity.103 There are a few GWA studies focused on the efficacy and response of antipsychotics.

McClay et al.104 used a genome-wide approach to detect genetic variation underlying individual differences in response to treatment with the antipsychotics olanzapine, quetiapine, risperidone, ziprasidone and perphenazine. The sample consisted of 738 subjects with DSM-IV SCZ who took part in the CATIE study. The top statistical result that reached significance at the pre-specified threshold involved an SNP in an intergenic region on chromosome 4p15. In addition, SNP in Ankyrin Repeat and Sterile Alpha Motif Domain-Containing Protein 1B (ANKS1B) and in the contactin-associated protein-like 5 gene (CNTNAP5), which mediated the effects of olanzapine and risperidone on negative symptoms, were very close to the threshold for declaring significance. This study demonstrated the potential of GWA studies to discover novel genes that mediate the effects of antipsychotics, which could eventually help to tailor drug treatment to schizophrenic patients.104

A GWA study was performed in a phase 3 clinical trial of iloperidone.105 Genotypes of 407 patients were analyzed for 334 563 SNP. SNP associated with iloperidone efficacy were identified within the neuronal PAS domain protein 3 gene (NPAS3), close to a translocation breakpoint site previously observed in a family with SCZ. Five other loci were identified that include the XK, Kell blood group complex subunit-related family, member 4 gene (XKR4), the tenascin-R gene (TNR), the glutamate receptor, inotropic, AMPA 4 gene (GRIA4), the glial-cell-line-derived neurotrophic factor receptor-alpha2 gene (GFRA2), and the NUDT9P1 pseudogene located in the chromosomal region of the serotonin receptor 7 gene (HTR7).105

To detect potential predictor gene variants for risperidone response in SCZ subjects, Ikeda et al.106 performed a convergent analysis based on: (i) a genome-wide (100 K SNP) SNP study of risperidone response; and (ii) a global transcriptome study of genes with mRNA levels influenced by risperidone exposure in the mouse prefrontal cortex. Fourteen genes were highlighted as having potential relevance to risperidone activity in both studies: ATP2B2, HS3ST2, UNC5C, BAG3, PDE7B, PAICS, PTGFRN, NR3C2, ZBTB20, ST6GAL2, PIP5K1B, EPHA6, KCNH5, and AJAP1.106

PRACTICAL CONSIDERATIONS

The great variability in the therapeutic response of SCZ patients to conventional treatments (<20% effective responders), the heterogeneity of the disease and its complex pathogenesis, as well as the occurrence of neuropsychiatric disorders associated with cognitive deterioration, seem to suggest that: (i) it is very unlikely that a single drug may be able to halt disease progression after the onset of the disease; (ii) multifactorial interventions (as in other complex disorders, such as cardiovascular disease, cancer, AIDS etc.) might be an alternative strategy; (iii) the co-administration of many different drugs in patients with concomitant pathologies may represent an obstacle for an effective pharmacological management of SCZ because some drugs effective for a peripheral medical condition can exert a deleterious effect on brain function and brain perfusion with severe effects on cognition, behavior and psychomotor function; (iv) the co-administration of antipsychotic drugs should be carried out with extreme care as most antipsychotics deteriorate cognitive function, psychomotor activity, and cerebrovascular function; (v) the conventional procedures currently used in drug development (i.e. trial-and-error) and serendipity are not cost-effective nowadays; (vi) the reluctant attitude of the medical community to incorporate genomic procedures as diagnostic aids and disease biomarkers is not contributing to accelerating our understanding of SCZ and its biological diversity; and (vii) the underdeveloped field of pharmacogenomics is delaying the possibility of optimizing our limited therapeutic resources for the treatment of SCZ.1,3

A good example of application of pharmacogenomic protocol was shown with iloperidone, which was finally approved by the FDA in May 2009. A study using six genetic markers for iloperidone response as measured by changes in the PANSS score demonstrated that the six-marker genotype combinations defined four groups of patients with distinct probabilities of response.107 Over 75% of iloperidone-treated patients in the group with the optimal genotype combinations showed a 20% or greater improvement, compared with 37% for patients with other genotypes. These patients had a significant response by the first week of treatment, which was earlier than for patients with other genotype combinations. The odds of responding to iloperidone treatment with at least 20% improvement ranged from 2.4 to 3.6 for patients with one of the six favorable single-marker genotypes. The odds increased to 9.5 or greater for patients with the most favorable six-marker combinations. These results illustrate the combined use of genetic markers to predict enhanced response to iloperidone and support the application of pharmacogenomics to differentiate medication options and improve individualized treatments for SCZ.107 Several polymorphisms associated with the efficacy of iloperidone could be used together to predict clinical response to other antipsychotics and provide practical information for individualized treatment.

Pharmacogenomic approach will be even more important in the case of combination therapy of antipsychotics with other drugs, such as antidepressants. Selective serotonin reuptake inhibitor (SSRI) is widely used together with antipsychotics to treat both psychotic depression and depressive symptoms in SCZ. It has been suggested that co-administration of SSRI and antipsychotics may result in molecular changes different from their individual effects. Studies have been carried out on the acute effects of two SSRI, citalopram and escitalopram, alone or in combination with haloperidol, on the expression of Homer1a together with its splice variant ania-3, and p11, two genes linked, respectively, to dopaminergic and serotonergic neurotransmission and involved in synaptic plasticity. Homer1a and ania-3 were induced in the striatum by haloperidol, alone and in combination with SSRI, but not by SSRI alone. Haloperidol + citalopram co-administration induced a stronger Homer1a expression than haloperidol alone in the ventrolateral caudate-putamen. Homer1a was significantly downregulated in the parietal cortex by all treatments. These results show that haloperidol + citalopram combination exerts synergistic effects on Homer expression, suggesting that citalopram may influence the impact of haloperidol on dopaminergic neurotransmission. Homer1a and ania-3 are strongly induced in striatum by haloperidol, while they are not influenced by citalopram or escitalopram in this region. In the cortex the two transcripts are modulated by both haloperidol and SSRI, suggesting a possible role of both dopamine and serotonin in their cortical regulation.108

Cost-effectiveness of pharmacogenomic application

Cost-effectiveness analysis has been the most commonly applied framework for evaluating pharmacogenomics. Pharmacogenomic testing, which incurs high costs, is potentially relevant to large populations. For instance, the most common drugs metabolized by CYP2D6 account for 189 million prescriptions and $US12.8 bn annually in expenditures in the USA, which represent 5–10% of total utilization and expenditures for outpatient prescription drugs.109 Pharmacogenomics offer great potential to improve patients' health in a cost-effective manner; however, pharmacogenomics will not be applied to all drugs available in the market, and careful evaluations should be made prior to investing resources in research and development of pharmacogenomic-based therapeutics and making reimbursement decisions.110

Despite the effort of the pharmaceutical industry to demonstrate the benefits and cost-effectiveness of available drugs, the general impression in the medical community and in some governments is that some psychotropics and most anti-dementia drugs present in the market are not cost-effective.4 Conventional drugs for neuropsychiatric disorders are relatively simple compounds with unreasonable prices. Some new products, with higher prices, are not superior in their efficacy to conventional antipsychotics, antidepressants, and anxiolytics. There is an urgent need to assess the costs of new trials with pharmacogenomic strategies, and to implement pharmacogenomic procedures for the prediction of drug-related adverse events. Pharmacogenomics can help to reduce costs in drug development as well as the number of patients in clinical trials with high risk of toxicity. It has been suggested that the two critical strategies for pipeline genetics must make use of fewer patients: (i) the early identification of efficacy signals so that they can be applied early in development for targeted therapies; and (ii) identification of safety signals that can subsequently be validated prospectively during development using the least number of patients with adverse responses.111

CONCLUSIONS

The drug treatment of SCZ has made remarkable strides with the introduction of many new drugs. Improvement in terms of clinical outcome, however, has fallen short of expectations, with up to one-third of the patients continuing to experience clinical relapse or unacceptable medication-related side-effects in spite of efforts to identify optimal treatment regimes with one or more drugs. Potential reasons to explain this setback might be that: (i) the molecular pathology of SCZ is still poorly understood; (ii) drug targets are inappropriate, not fitting into the real etiology of the disease; (iii) most treatments are symptomatic, but not anti-pathogenic; (iv) the genetic component of SCZ is poorly defined; and (v) the understanding of genome-drug interactions is very limited.

Assuming that the human genome contains about 20 000–30 000 genes, at the present time only 0.31% of commercial drugs have been assigned to corresponding genes whose gene products might be involved in pharmacokinetic and pharmacodynamic activities of a given drug; and only 4% of the human genes have been assigned to a particular drug metabolic pathway. Supposing a theoretical number of 100 000 chemicals in current use worldwide, and assuming that practically all human genes can interact with drugs taken by human beings, each gene in the human genome should be involved in the metabolism and/or biopharmacological effect of 30–40 drugs; however, assuming that most xenobiotic substances in contact with our organism can influence genomic function, it might be possible that for 1 000 000 xenobiotics in daily contact with humans, an average of 350–500 xenobiotics have to be assigned to each one of the genes potentially involved in drug metabolism and/or the processing of xenobiotics. To fulfill this task, a single gene has to possess the capacity of metabolizing many different xenobiotic substances and at the same time many different genes have to cooperate in orchestrated networks in order to metabolize a particular drug or xenobiotic under sequential biotransformation steps. This is the reason why a pharmacogenomic approach is required for better drug use, in particular with antipsychotics. Considering the possible involvement of genes in antipsychotic response, pharmacogenomic approach, rather than pharmacogenetic protocol, which is focused on each gene, is essential in better pharmacotherapy of SCZ.

Numerous chemicals increase the metabolic capability of organisms by their ability to activate genes encoding various xenochemical-metabolizing enzymes, such as CYP, transferases and transporters. Many natural and artificial substances induce the hepatic CYP subfamilies in humans, and these inductions might lead to clinically important drug–drug interactions. Some of the key cellular receptors, which mediate such inductions, have been identified, including nuclear receptors, such as the constitutive androstane receptor (CAR, NR1I3), the retinoid X receptor (RXR, NR2B1), the pregnane X receptor (PXR, NR1I3), and the vitamin D receptor (VDR, NR1I1) and steroid receptors, such as the glucocorticoid receptor (GR, NR3C1).112 There is a wide promiscuity of these receptors in the induction of CYP in response to xenobiotics. Indeed, this adaptive system acts as an effective network where receptors share partners, ligands, DNA response elements and target genes, influencing their mutual relative expression.112,113

The optimization of CNS therapeutics requires the establishment of new postulates regarding: (i) the costs of medicines; (ii) the assessment of protocols for multifactorial treatment in chronic disorders; (iii) the implementation of novel therapeutics addressing causative factors; and (iv) the setting-up of pharmacogenomic strategies for drug development.

In performing pharmacogenomic studies in SCZ, it is necessary to rethink the therapeutic expectations of novel drugs, redesign the protocols for drug clinical trials, and incorporate biological markers as assessable parameters of efficacy and prevention. In addition to the characterization of genomic profiles, phenotypic profiling of responders and non-responders to conventional drugs is also important (and currently neglected).

To achieve a mature discipline of pharmacogenomics in SCZ, it would be convenient to accelerate the following processes: (i) to educate physicians and the public on the use of genetic/genomic screening in daily clinical practice; (ii) to standardize genetic testing for major categories of drugs; (iii) to validate pharmacogenomic procedures according to drug category and pathology; (iv) to regulate ethical, social, and economic issues; and (v) to incorporate pharmacogenomic procedures to drugs in development and drugs on the market in order to optimize therapeutics.

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