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Pharmacogenomics\nPharmacogenetics/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 development and on the market, 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 [7,15,16,17,127]. \nThe potential implications of pharmacogenomics in clinical trials and molecular therapeutics is that a particular disease could be treated according to genomic and biological markers, selecting medications and diseases which are optimized for individual patients or clusters of patients with a similar genomic profile. For many medications, interindividual differences are mainly due to SNPs 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). \nThe application of these procedures to CNS disorders is an extremely difficult task, since most neuropsychiatric diseases are complex disorders in which many different genes might be involved [6]. In addition, it is very unlikely that a single drug would 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. This is particularly important in the elderly population. In fact, the average number of drugs taken by patients with dementia ranges from 6 to over 10 per day depending upon their physical and mental conditions. Nursing home residents receive, on average, 7-8 medications each month, and over 30% of residents have monthly drug regimes of nine or more medications, including (in descending order) analgesics, antipyretics, gastrointestinal agents, electrolytic and caloric preparations, CNS agents, anti-infective agents, and cardiovascular agents [128]. In population-based studies, over 35% of patients older than 85 years are moderate or chronic antidepressant users [129]. Polypharmacy, drug-drug interactions, adverse reactions, and non-compliance are substantial therapeutic problems in the pharmacological management of elderly patients [130], adding further complications and costs to the patients and their caregivers. Over 25% of elderly individuals receive at least one of more than 30 potentially inappropriate medications in 10 health maintenance organizations (HMOs) of the USA [131]. Although drug effect is a complex phenotype which depends on many factors, it is estimated that genetics accounts for 20% to 95% of variability in drug disposition and pharmacodynamics [132]. Under these circumstances, therapeutics optimization is a major goal in neuropsychiatric disorders and in the elderly population, and novel pharmacogenetic and pharmacogenomic procedures may help in this endeavour [6,7,8,9,10,11,12,13,14,15,16,25,99,100,101,102,103,104,133].\nThe 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 genomewide sets of SNPs associated with drug response are now feasible and may offer the potential to personalize therapeutics [7].\nThe vast majority of drugs in current use, and many psychotropics, are metabolized by enzymes known to be genetically variable, including: (i) esterases: butyrylcholinesterase, paraoxonase/arylesterase; (ii) transferases: N-acetyltransferase, sulfotransferase, thiol methyltransferase, thiopurine methyltransferase, catechol-O-methyltransferase, glutathione-S-transferases, UDP-glucuronosyltransferases, glucosyltransferase, histamine methyltransferase; (iii) reductases: NADPH: quinine oxidoreductase, glucose-6-phosphate dehydrogenase; (iv) oxidases: alcohol dehydrogenase, aldehydehydrogenase, monoamine oxidase B, catalase, superoxide dismutase, trimethylamine N-oxidase, dihydropyrimidine dehydrogenase; and (v) cytochrome P450 enzymes, such as CYP1A1, CYP2A6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A5 and many others [6,7,16]. Polymorphic variants in the genes encoding these enzymes can induce alterations in drug metabolism modifying the efficacy and safety of the prescribed drugs.\nDrug metabolism includes phase I reactions (i.e., oxidation, reduction, hydrolysis) and phase II conjugation reactions (i.e., acetylation, glucuronidation, sulphation, methylation). The principal enzymes with polymorphic variants involved in phase I reactions are the following: CYP3A4/5/7, CYP2E1, CYP2D6, CYP2C19, CYP2C9, CYP2C8, CYP2B6, CYP2A6, CYP1B1, CYP1A1/2, epoxide hydrolase, esterases, NQO1 (NADPH-quinone oxidoreductase), DPD (dihydropyrimidine dehydrogenase), ADH (alcohol dehydrogenase), and ALDH (aldehyde dehydrogenase). Major enzymes involved in phase II reactions include the following: UGTs (uridine 5'-triphosphate glucuronosyl transferases), TPMT (thiopurine methyltransferase), COMT (catechol-O-methyltransferase), HMT (histamine methyl-transferase), STs (sulfotransferases), GST-A (glutathione S-transferase A), GST-P, GST-T, GST-M, NATS (N-acetyl transferases), and others [6,7,11,16].\n\n5.1. Pharmacogenetics of Psychotropic Drugs\nHistorically, the vast majority of pharmacogenetic studies of CNS disorders have been addressed to evaluate the impact of cytochrome P450 enzymes on drug metabolism. Conventional targets for psychotropic drugs were the neurotransmitters dopamine, serotonin, noradrenaline, GABA, ion channels, acetylcholine and their respective biosynthetic and catalyzing enzymes, receptors and transporters [134]; however, in the past few years many different genes have been associated with both pathogenesis and pharmacogenomics of neuropsychiatric disorders. Some of these genes and their products constitute potential targets for future treatments. New developments in genomics, including whole genome genotyping approaches and comprehensive information on genomic variation across populations, coupled with large-scale clinical trials in which DNA collection is routine, now provide the impetus for a next generation of pharmacogenetic studies and identification of novel candidate drugs [135,136,137].\nThe typical paradigm for the pharmacogenetics of phase I drug metabolism is represented by the cytochrome P-450 enzymes, a superfamily of microsomal drug-metabolizing enzymes. P450 enzymes comprise a superfamily of heme-thiolate proteins widely distributed in bacteria, fungi, plants and animals. 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 monooxigenase 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.\nThe 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 [138,139,140,141]. 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 (EMs), intermediate metabolizers (IMs), poor metabolizers (PMs) and ultrarapid metabolizers (UMs). \nMost 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 (CAR), and pregnane X receptor (PXR) [142,143]. 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 CYPs [143]. CYPs are also expressed in the CNS, and a complete characterization of constitutive and induced CYPs in the brain is essential for understanding the role of these enzymes in neurobiological functions and in age-related and xenobiotic-induced neurotoxicity [144]. 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 activities [145]. \nThere are substantial differences between individuals in the effects of psychotropic drugs in the treatment of neuropsychiatric disorders. Pharmacogenetic studies of psychotropic drug response have focused on determining the relationship between variation in specific candidate genes and the positive and adverse effects of drug treatment [134,146,147]. Approximately 18% of neuroleptics are major substrates of CYP1A2 enzymes, 40% of CYP2D6, and 23% of CYP3A4; 24% of antidepressants are major substrates of CYP1A2 enzymes, 5% of CYP2B6, 38% of CYP2C19, 85% of CYP2D6, and 38% of CYP3A4; 7% of benzodiazepines are major substrates of CYP2C19 enzymes, 20% of CYP2D6, and 95% of CYP3A4 [6,7]. About 80% of patients with resistant depression, 60% of patients non-responsive to neuroleptics, 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 [7].\nThe clinical impact of the cytochrome P450 (CYP) enzyme CYP2D6 poor metabolizer (PM) genotype in patients taking antipsychotic medication has been investigated in a retrospective study. 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 syndrome or tardive dyskinesia (EPS/TD) was 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 noncompliance 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 [148]. \nThe 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 schizophrenic patients. The patients investigated were CYP2D6 extensive metabolizers (EMs; CYP2D6*1/*1, *1/*10, and *10/*10) and CYP2D6 poor metabolizers (PMs; CYP2D6*1/*5 and *10/*5). For the CYP3A5 genotype, patients were CYP3A5*1 expressors (*1/*1 and *1/*3) and CYP3A5 nonexpressors (*3/*3). The plasma levels of RIS (2.03 ng/mL per milligram for EMs vs. 5.57 ng/mL per milligram for PMs) and 9-OH-RIS (5.06 ng/mL per milligram for EMs vs. 0.22 ng/mL per milligram for PMs) were significantly different among CYP2D6 genotype groups, but the CYP2D6 EMs (7.09 ng/mL per milligram) and PMs (5.79 ng/mL per milligram) did not show differences in the levels of the active moiety. CYP3A5 nonexpressors exhibited higher plasma concentrations of both RIS and 9-OH-RIS than its expressors. In the case of 9-OH-RIS, CYP3A5 nonexpressors exhibited significantly higher concentrations than CYP3A5 expressors. Concentrations of the active moiety were also significantly different between the CYP3A5 nonexpressors and expressors. According to these results reported by Kang et al. [149], 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 [149]. \nRisperidone 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 [150].\nSome studies attempt to determine whether testing for cytochrome P450 (CYP) polymorphisms in adults entering antipsychotic treatment for schizophrenia (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 [151].\nTargets that show promise for pharmacologic focus in SCZ and psychosis include the dopamine receptors (especially D1) in the prefrontal cortex, the serotonin receptors in the prefrontal cortex and anterior cingulate cortex, the glutamatergic excitatory synapse, the acetylcholine nicotinic receptors in the hippocampus, the acetylcholine muscarinic receptors, and the brain gamma-aminobutyric acid (GABA) system [152,153]. In addition to cytochrome P450 enzymes, many other gene products influence both efficacy and safety of psychotropic drugs [6,7,134,137,154,155,156]. \nTo detect potential predictor gene variants for risperidone response in schizophrenic subjects, Ikeda et al. [157] performed a convergent analysis based on (i) a genomewide (100K SNP) SNP pharmacogenetic study of risperidone response and (ii) a global transcriptome study of genes with mRNA levels influenced by risperidone exposure in mouse prefrontal cortex. Fourteen genes were highlighted as of potential relevance to risperidone activity in both studies: ATP2B2, HS3ST2, UNC5C, BAG3, PDE7B, PAICS, PTGFRN, NR3C2, ZBTB20, ST6GAL2, PIP5K1B, EPHA6, KCNH5, and AJAP1. \nAnalysis of polymorphic variants in 5-HT2A receptors (5-HT2AR-A-1438G) revealed that schizophrenic carriers of the G/G genotype receiving olanzapine showed a significant tendency toward improvement in the PANSS positive syndrome score in comparison with patients who did not have a G gene (AA and AG) [158]. \nHuman 5-HT7 receptor may be involved in the pharmacodynamics of risperidone and may influence clinical response of the drug. A pharmocogenetics study of this receptor may therefore 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 [159]. \nAripiprazole acts as a partial agonist at dopamine D2 (DRD2) and D3 (DRD3) and serotonin 1A (HTR1A) receptors and as an antagonist at serotonin 2A receptors (HTR2A) [160]. 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 (Positive and Negative Syndrome Scale) performance after aripiprazole treatment [161].\nChen et al. [160] investigated whether the efficacy of aripiprazole can be predicted by a functional DRD3 gene polymorphism Ser9Gly (rs6280) as modified by clinical factors 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 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 [160].\nThe 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 aripirazole effectively increased the levels of GSK-3beta phosphorylation and Bcl-2 at doses of five 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 [162].\nThe prototypical atypical antipsychotic agent, clozapine, is more efficacious for refractory SCZ than the ‘typical’ antipsychotics. Since 2002, at least 22 association studies have shown that 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. [163] might indicate that DTNBP1 gene modulates the effects of both the atypical antipsychotic clozapine and the typical antipsychotic haloperidol and that SCZ patients with different DTNBP1 diplotypes, haplotypes, genotypes, or alleles might have different responses to these antipsychotics [163]. \nDisruption of the Reelin and GABAergic signaling systems have been observed in psychiatric disorders including autism, SCZ, bipolar disorders (BD), and major depression. Chronic administration of psychotropic medications (clozapine, fluoxetine, haloperidol, lithium, olanzapine, and valproic acid) used in the treatment of psychiatric disorders alters levels of Reelin, its receptor Vldlr, downstream molecules Gsk3beta, Dab-1, and Gad65/67 in rat prefrontal cortex as measured by qRT-PCR and SDS-PAGE and western blotting. qRT-PCR revealed that mRNAs for Reelin, Vldlr, Dab-1, Gsk3beta, and Gad65 were each significantly altered by at least one of the drugs tested, and in the case of Reelin, Dab-1, and Gsk3beta, by multiple drugs, suggest that the Reelin signaling and GABAergic systems are affected by commonly used psychotropic medications [164]. 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 [165].\nFlavin-containing monooxygenase 3 (FMO3) genotype data for European-, Latin-, African- and Asian-American SCZ patients administered olanzapine were compared to age-, gender-, and race/ethnicity-matched controls. SNPs and haplotypes associated with case-control status was undertaken to determine the potential role of FMO3 in olanzapine therapeutic response. For European Americans, significant differences in individual cases vs. controls were observed between FMO3 158 and 257 alleles and genotype frequencies and SCZ delusions, hallucinations, and weight gain/increased appetite, but this was not observed in a replicated population. For Latin Americans, a significant difference in individual cases vs. controls was observed for FMO3 158 and 257 for SCZ delusions as well as hallucinations and delusions. Sleepiness and weight gain were associated with allele 308. In African-Americans, a comparison of allele frequency and diagnosis showed a significant dependence on allele 158 in individual cases vs. controls. FMO3 genotype and allele frequency was not significantly associated with auditory hallucinations or delusions. For Asian-Americans, no significant difference in allele or genotype frequency and auditory hallucination and delusions was observed in individual cases vs. controls. In female Asian-Americans, allele frequency for FMO3 257 was significantly associated with diagnosis and in males, genotype frequency for FMO3 257 and diagnosis was significantly associated [166]. \nSingle-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 schizophrenic patients from a homogeneous south Indian population. Haplotype analysis showed highly significant association of seven COMT marker haplotypes with SCZ, and allelic associations of two SNPs (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 in SCZ patients [167]. \nOlanzapine is a second-generation antipychotic that may cause weight gain and metabolic syndrome in some cases. The peroxisome proliferator-activated receptor (PPARG) is an important gene in the progress of type II diabetes and metabolic syndrome. Significant differences were found between pre-treatment and post-treatment body mass index and weight change in Pro12Ala polymorphism of PPARG2 in Turkish patients [168]. \nTwenty-one loci of diverse candidate genes encoding dopamine, serotonin (5-HT), histamine, and adrenergic receptors, tumor necrosis factor-alpha, ghrelin, adiponectin, and peroxisome proliferator-activated receptor gamma-2, were analyzed as candidate genes for olanzapine-related body weight gain. Olanzapine-induced weight gain correlated negatively with baseline BMI and positively with clinical global improvement and the length of olanzapine treatment, but it did not correlate with the daily dose of olanzapine, concomitant antipsychotics, sex, age, or smoking. Four genetic variants, the 102T allele of HTR2A, the 825T allele of GNB3, the 23Cys allele of HTR2C, and the 64Arg/Arg genotype of ADRB3, were significantly associated with olanzapine-induced weight gain. Stepwise regression analysis revealed that the baseline BMI predicted 12.5% of the weight gain, and the 2 latter genetic factors added 6.8%. The patients with double and triple genetic risk factors showed 5.1% and 8.8% BMI increases, respectively, during olanzapine treatment, whereas the patients with a single or no risk factor showed approximately a 1% BMI increase [169]. \nGenetic predisposition to clozapine-induced weight gain has also been suggested. 10 genetic polymorphisms across 9 candidate genes, including the serotonin 2C, 2A, and 1A receptor genes (HTR2C/2A/1A); the histamine H1 and H2 receptor genes (H1R/H2R); the cytochrome P450 1A2 gene (CYPIA2); the β3 and α-adrenergic receptor genes (ADRB3/ADRAIA); and tumor necrosis factor alpha (TNFA) have been studied. Trends were observed for ADRB3, ADRA1A, TNFA, and HTR2C [170].\nSerotonin 2C and 2A receptor (5-HT2C and 5-HT2A) antagonisms are hypothesized to play a role in the metabolic adverse effects induced by olanzapine and clozapine. Associations have been reported between polymorphisms in 5-HT2C and 5-HT2A receptor coding genes, HTR2C and HTR2A, with antipsychotic-induced weight gain. Olanzapine-treated patients with HTR2C haplotype C (-759C, -697C, and 23Ser) had higher BMI and C peptide levels compared with patients with haplotype B (-759T, -697C, and 23Cys). The frequency of patients homozygous for the HTR2C haplotype A (-759C, -697G, and 23Cys) was significantly higher among clozapine-treated patients with obesity (BMI \u003e 30 kg/m2) compared with nonobese patients. Patients carrying the HTR2A haplotype 2 (-1438A, 102T, and 452His) had significantly higher C peptide levels compared with haplotype 3 (-1438A, 102T, and 452Tyr) carriers in the olanzapine group and in the overall study population. None of the haplotypes were associated with serum levels of insulin, triglycerides, and cholesterol or with homeostasis model assessment index for insulin resistance. Both HTR2C and HTR2A gene polymorphisms seem to be associated with the occurrence of metabolic abnormalities in patients treated with olanzapine or clozapine [171]. \nThe LEPR Q223R polymorphism (rs1137101) and the LEP promoter 2548G/A polymorphism (rs7799039) were postulated as candidate genes to be associated with obesity in patients using atypical antipsychotic drugs. In females, the LEPR 223QR and LEPR 223RR genotypes were associated with a lower risk of obesity. In males, this association was not found. In females, the average body weight was 13.6 kg more in the LEPR 223QQ group compared with the LEPR 223RR group. No significant association was found between the LEP promoter 2548G/A polymorphism and obesity. LEPR Q223R polymorphism may be associated with obesity in women with a psychotic disorder treated with atypical antipsychotic drugs [172]. \nAn association has been reported between 5-hydroxytryptamine receptor 2C (HTR2C) polymorphisms and the occurrence of the metabolic syndrome in patients using antipsychotics. Primary determinants were polymorphisms in the promoter region of the HTR2C gene [HTR2C:c.1-142948(GT)n, rs3813929 (-759 C/T), and rs518147 (-697 G/C)] and an intragenic polymorphism (rs1414334:C \u003e G). The variants of HTR2C:c.1-142948(GT)n and rs1414334 were not significantly associated with the metabolic syndrome in the replication sample but did show significance in the pooled analysis. The variant rs1414334 C allele was specifically associated with the metabolic syndrome in patients using clozapine or risperidone. The increased risk for the metabolic syndrome is particularly strong in carriers of the rs1414334 C allele using clozapine or risperidone [173]. \nSequence variations in the glutamate transporter gene SLC1A1 (A/C/G haplotype at rs2228622-rs3780413-rs3780412) have been associated with susceptibility to atypical antipsychotic-induced obsessive-compulsive symptoms [174]. \nSeveral polymorphisms previously associated with the efficacy of the novel antipsychotic iloperidone could be used together to predict clinical response and provide practical information for individualized treatment. A recent study using 6 genetic markers of iloperidone response as measured by change in the Positive and Negative Syndrome Scale-Total (PANSS-T) score demonstrated that the 6-marker genotype combinations defined 4 groups of patients with distinct probabilities of response. 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. These results illustrate the combined use of genetic markers to predict enhanced response to iloperidone and support the application of pharmacogenetics to differentiate medication options and improve individualized treatments for SCZ [175]. \nGenome-wide expression profiling to study effects of typical antipsychotics and atypical antipsychotics in the post-mortem liver of SCZ patients using microarrays revealed that typical antipsychotics affected genes associated with nuclear protein, stress responses and phosphorylation, whereas atypical antipsychotics affected genes associated with Golgi/endoplasmic reticulum and cytoplasm transport. Comparison between typical antipsychotics and atypical antipsychotics 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 [176].\nGrowing genetic evidence has implicated a role for neuregulin-1 (NRG-1) in SCZ pathogenesis as well as alterations in SNAP receptor (SNARE) proteins at both gene and protein levels in post-mortem investigations. Clozapine has been shown to increase both NRG-1 levels and synaptic markers in rodents. Clozapine has the ability to upregulate NRG-1 (+3.58 fold change) and VAMP-1 (+1.92) while SNAP-25 remaines unchanged [177]. \nAn increasing number of experiments have found anomalies 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 using two-dimensional (2D) gel electrophoresis for three groups with chlorpromazine (CPZ), clozapine (CLZ), quetiapine (QTP) and a control group. A total of 14 proteins, of which 6 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, isoform 1 (Atp6v1a1). These data show proteomic changes induced by neuroleptics in rodents [178]. Ma et al. [179] used 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 data set showed a stronger association with Huntington’s disease signaling, while olanzapine treatment showed stronger effects on glycolysis/gluconeogenesis [179].\nSelective serotonin reuptake inhibitor (SSRI) and antipsychotic co-administration is a widely used strategy to treat both psychotic depression and depressive symptoms in SCZ. It has been suggested that co-administration of SSRIs and antipsychotics may result in molecular changes different from their individual effects. Studies have been carried out on the acute effects of two SSRIs, 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 SSRIs, but not by SSRIs alone. Haloperidol + citalopram co-administration induced a stronger Homer1a expression than haloperidol alone in the ventrolateral caudate-putamen. Homer1a was significantly down-regulated 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 SSRIs, suggesting a possible role of both dopamine and serotonin in their cortical regulation [180].\n"}