Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics

ANKS1B encoded AIDA-1 regulates social behaviors by controlling oligodendrocyte function

Heterozygous deletions in the ANKS1B gene cause ANKS1B neurodevelopmental syndrome (ANDS), a rare genetic disease characterized by autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and speech and motor deficits. The ANKS1B gene encodes for AIDA-1, a protein that is enriched at neuronal synapses and regulates synaptic plasticity. Here we report an unexpected role for oligodendroglial deficits in ANDS pathophysiology. We show that Anks1b-deficient mouse models display deficits in oligodendrocyte maturation, myelination, and Rac1 function, and recapitulate white matter abnormalities observed in ANDS patients. Selective loss of Anks1b from the oligodendrocyte lineage, but not from neuronal populations, leads to deficits in social preference and sensory reactivity previously observed in a brain-wide Anks1b haploinsufficiency model. Furthermore, we find that clemastine, an antihistamine shown to increase oligodendrocyte precursor cell maturation and central nervous system myelination, rescues deficits in social preference in 7-month-old Anks1b-deficient mice. Our work shows that deficits in social behaviors present in ANDS may originate from abnormal Rac1 activity within oligodendrocytes.

Individual Prediction of Optimal Treatment Allocation Between Electroconvulsive Therapy or Ketamine using the Personalized Advantage Index

Introduction

Electroconvulsive therapy (ECT) and ketamine are two effective treatments for depression with similar efficacy; however, individual patient outcomes may be improved by models that predict optimal treatment assignment. Here, we adapt the Personalized Advantage Index (PAI) algorithm using machine learning to predict optimal treatment assignment between ECT and ketamine using medical record data from a large, naturalistic patient cohort. We hypothesized that patients who received a treatment predicted to be optimal would have significantly better outcomes following treatment compared to those who received a non-optimal treatment.

Methods

Data on 2526 ECT and 235 mixed IV ketamine and esketamine patients from McLean Hospital was aggregated. Depressive symptoms were measured using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Patients were matched between treatments on pretreatment QIDS, age, inpatient status, and psychotic symptoms using a 1:1 ratio yielding a sample of 470 patients (n=235 per treatment). Random forest models were trained and predicted differential patientwise minimum QIDS scores achieved during acute treatment (min-QIDS) scores for ECT and ketamine using pretreatment patient measures. Analysis of Shapley Additive exPlanations (SHAP) values identified predictors of differential outcomes between treatments.

Results

Twenty-seven percent of patients with the largest PAI scores who received a treatment predicted optimal had significantly lower min-QIDS scores compared to those who received a non-optimal treatment (mean difference=1.6, t=2.38, q<0.05, Cohen's D=0.36). Analysis of SHAP values identified prescriptive pretreatment measures.

Conclusions

Patients assigned to a treatment predicted to be optimal had significantly better treatment outcomes. Our model identified pretreatment patient factors captured in medical records that can provide interpretable and actionable guidelines treatment selection.

The pharmacogenetics of the new-generation antipsychotics - A scoping review focused on patients with severe psychiatric disorders

Exploring the possible correlations between gene variations and the clinical effects of the new-generation antipsychotics is considered essential in the framework of personalized medicine. It is expected that pharmacogenetic data will be useful for increasing the treatment efficacy, tolerability, therapeutic adherence, functional recovery, and quality of life in patients with severe psychiatric disorders (SPD). This scoping review investigated the available evidence about the pharmacokinetics, pharmacodynamics, and pharmacogenetics of five new-generation antipsychotics, i.e., cariprazine, brexpiprazole, aripiprazole, lumateperone, and pimavanserin. Based on the analysis of 25 primary and secondary sources and the review of these agents' summaries of product characteristics, aripiprazole benefits from the most relevant data about the impact of gene variability on its pharmacokinetics and pharmacodynamics, with significant consequences on this antipsychotic's efficacy and tolerability. The determination of the CYP2D6 metabolizer status is important when administering aripiprazole, either as monotherapy or associated with other pharmacological agents. Allelic variability in genes encoding dopamine D2, D3, and serotonin, 5HT2A, 5HT2C receptors, COMT, BDNF, and dopamine transporter DAT1 was also associated with different adverse events or variations in the clinical efficacy of aripiprazole. Brexpiprazole also benefits from specific recommendations regarding the CYP2D6 metabolizer status and the risks of associating this antipsychotic with strong/moderate CYP2D6 or CYP3A4 inhibitors. US Food and Drug Administration (FDA) and European Medicines Agency (EMA) recommendations about cariprazine refer to possible pharmacokinetic interactions with strong CYP3A4 inhibitors or inducers. Pharmacogenetic data about cariprazine is sparse, and relevant information regarding gene-drug interactions for lumateperone and pimavanserin is yet lacking. In conclusion, more studies are needed to detect the influence of gene variations on the pharmacokinetics and pharmacodynamics of new-generation antipsychotics. This type of research could increase the ability of clinicians to predict favorable responses to specific antipsychotics and to improve the tolerability of the treatment regimen in patients with SPD.

Peripheral biomarkers of treatment-resistant schizophrenia: Genetic, inflammation and stress perspectives

Treatment-resistant schizophrenia (TRS) often results in severe disability and functional impairment. Currently, the diagnosis of TRS is largely exclusionary and emphasizes the improvement of symptoms that may not be detected early and treated according to TRS guideline. As the gold standard, clozapine is the most prescribed selection for TRS. Therefore, how to predict TRS in advance is critical for forming subsequent treatment strategy especially clozapine is used during the early stage of TRS. Although mounting studies have identified certain clinical factors and neuroimaging characteristics associated with treatment response in schizophrenia, the predictors for TRS remain to be explored. Biomarkers, particularly for peripheral biomarkers, show great potential in predicting TRS in view of their predictive validity, noninvasiveness, ease of testing and low cost that would enable their widespread use. Recent evidence supports that the pathogenesis of TRS may be involved in abnormal neurotransmitter systems, inflammation and stress. Due to the heterogeneity of TRS and the lack of consensus in diagnostic criteria, it is difficult to compare extensive results among different studies. Based on the reported neurobiological mechanisms that may be associated with TRS, this paper narratively reviews the updates of peripheral biomarkers of TRS, from genetic and other related perspectives. Although current evidence regarding biomarkers in TRS remains fragmentary, when taken together, it can help to better understand the neurobiological interface of clinical phenotypes and psychiatric symptoms, which will enable individualized prediction and therapy for TRS in the long run.

Epigenetics for Drug Discovery: Dissecting the Effect of High Antipsychotic Dosage and D2 Blockage on Peripheral DNA Methylation

INTRODUCTION

The relationship between genetic polymorphisms of antipsychotic drug-metabolizing agents and drug receptors has been often investigated. DNA methylation is a form of epigenetic modification that regulates gene expression. Few studies have analyzed the relationship between genome-wide methylation patterns and antipsychotic dosage. The primary aim of this pilot study was to investigate the association between antipsychotic dosage and genome-wide DNA methylation in patients with schizophrenia (SCZ).

METHODS

Current dosage of antipsychotic medications was assessed in 136 patients with SCZ. Dosage was standardized using three different methods: chlorpromazine equivalent dose (CPZe), defined daily dose (DDD), and percentage of Lexicomp maximum dose (PM%). DNA methylation was measured in white blood cells. Antipsychotic dosage was the primary outcome variable in a model, including genome-wide methylation status as the main predictor.

RESULTS

This study did not show any association between DNA methylation and dosage variation for CPZe, PM%, and DDD. However, the probe cg271403389 was consistently associated with antipsychotic dosage across the three standardization methods. When looking at the genomic location of the most significant probes, we found that 15% were intergenic, 23% were in the distal promoter, 9% in the 3'untranslated region, 32% in the gene body, 3% in the 5' untranslated region, 15% in the proximal promoter, and 3% in the first exon.

DISCUSSION

This study shows the importance of investigating the relationship between DNA methylation and optimal antipsychotic dosage to personalize treatment in SCZ. Future studies require larger prescription databases to build on the results of this analysis.

Pharmacogenetics of Antipsychotic Treatment in Schizophrenia

Antipsychotics are the mainstay treatment for schizophrenia. There is large variability between individuals in their response to antipsychotics, both in efficacy and adverse effects of treatment. While the source of interindividual variability in antipsychotic response is not completely understood, genetics is a major contributing factor. The identification of pharmacogenetic markers that predict antipsychotic efficacy and adverse reactions is a growing area of research and holds the potential to replace the current trial-and-error approach to treatment selection in schizophrenia with a personalized medicine approach.In this chapter, we provide an overview of the current state of pharmacogenetics in schizophrenia treatment. The most promising pharmacogenetic findings are presented for both antipsychotic response and commonly studied adverse reactions. The application of pharmacogenetics to schizophrenia treatment is discussed, with an emphasis on the clinical utility of pharmacogenetic testing and directions for future research.

Sex differences in social functioning of patients with schizophrenia depending on the age of onset and severity of the disease

AIM

Schizophrenia manifests differently in women and men. This disease starts at a young age, leads to disability at working age. The aim of our work was to study sex differences, association between social factors and different parameters of the clinical picture and the course of the disease.

METHODS

This study was performed using population of Russian patients (men: 345, women: 310). Patients were examined using DSM-V, Bush-Francis catatonia rating scale (BFCRS), Positive and Negative Syndrome Scale (PANSS), 4-Items Negative Symptoms Assessment (NSA-4) and Frontal Assessment Battery (FAB).

RESULTS

Sex differences were mainly shown through negative symptoms, which were more severe in male patients. Men were shown to experience a decrease in social functioning and earlier age of onset. A positive family history further influenced negative symptoms and age of onset. When comparing scores before and after inpatient treatment (4 weeks), sex differences were not so pronounced. Female patients and patients with high levels of education, no conflictual relationship with family and active labour activity showed a later age of onset of the prodromal events and manifestation age. The decrease in the number of social contacts correlated with lower age of disability. The association between social factors and the severity of psychotic symptoms was shown across DSM-V, PANSS, NSA-4 and FAB, but not for BFCRS. Social factors were associated with negative symptoms of schizophrenia, but not with positive.

CONCLUSION

For successful treatment of patients with schizophrenia, the discussed factors must be considered and schizophrenia treatment methods should be primarily aimed at improving social functioning.

Cooperative Studies Program (CSP) #572: A Study of Serious Mental Illness in Veterans as a Pathway to personalized medicine in Schizophrenia and Bipolar Illness

BACKGROUND

Personalization of psychiatric treatment includes treatment of symptoms, cognition and functional deficits, suicide, and medical co-morbidities. VA Collaborative Study 572 examined a large sample of male and female veterans with schizophrenia (n=3,942) and with bipolar disorder (n=5,414) with phenotyping and genomic analyses. We present the results to date and future directions.

METHODS

All veterans received a structured diagnostic interview and assessments of suicidal ideation and behavior, PTSD, and health. Veterans with schizophrenia were assessed for negative symptoms and lifetime depression. All were assessed with a cognitive and functional capacity assessment. Data for genome wide association studies were collected. Controls came from the VA Million Veteran Program.

RESULTS

Suicidal ideation or behavior was present in 66%. Cognitive and functional deficits were consistent with previous studies. 40% of the veterans with schizophrenia had a lifetime major depressive episode and PTSD was present in over 30%. Polygenic risk score (PRS) analyses indicated that cognitive and functional deficits overlapped with PRS for cognition, education, and intelligence in the general population and PRS for suicidal ideation and behavior correlated with previous PRS for depression and suicidal ideation and behavior, as did the PRS for PTSD.

DISCUSSION

Results to date provide directions for personalization of treatment in SMI, veterans with SMI, and veterans in general. The results of the genomic analyses suggest that cognitive deficits in SMI may be associated with general population features. Upcoming genomic analyses will reexamine the issues above, as well as genomic factors associated with smoking, substance abuse, negative symptoms, and treatment response.

Protocol for a pharmacogenomic study on individualised antipsychotic drug treatment for patients with schizophrenia

BACKGROUND

Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.

AIMS

We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.

METHOD

This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.

RESULTS

This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.

CONCLUSION

This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.

Protein-protein interactions at the NMDA receptor complex: From synaptic retention to synaptonuclear protein messengers

N-methyl-d-aspartate receptors (NMDARs) are glutamate-gated ion channels that support essential functions throughout the brain. NMDARs are tetramers composed of the GluN1 subunit in complex with GluN2- and GluN3-type regulatory subunits, resulting in the formation of various receptor subtypes throughout the central nervous system (CNS), characterised by different kinetics, biophysical and pharmacological properties, and the abilities to interact with specific partners at dendritic spines. NMDARs are expressed at high levels, are widely distributed throughout the brain, and are involved in several physiological and pathological conditions. Here, we will focus on the GluN2A- and GluN2B-containing NMDARs found at excitatory synapses and their interactions with plasticity-relevant proteins, such as the postsynaptic density family of membrane-associated guanylate kinases (PSD-MAGUKs), Ca²⁺/calmodulin-dependent kinase II (CaMKII) and synaptonuclear protein messengers. The dynamic interactions between NMDAR subunits and various proteins regulating synaptic receptor retention and synaptonuclear signalling mediated by protein messengers suggest that the NMDAR serves as a key molecular player that coordinates synaptic activity and cell-wide events that require gene transcription. Importantly, protein-protein interactions at the NMDAR complex can also contribute to synaptic dysfunction in several brain disorders. Therefore, the modulation of the molecular composition of the NMDAR complex might represent a novel pharmacological approach for the treatment of certain disease states.

Identification of novel risk loci with shared effects on alcoholism, heroin, and methamphetamine dependence

Different substance dependences have common effects on reward pathway and molecular adaptations, however little is known regarding their shared genetic factors. We aimed to identify the risk genetic variants that are shared for substance dependence (SD). First, promising genome-wide significant loci were identified from 3296 patients (521 alcoholic/1026 heroin/1749 methamphetamine) vs 2859 healthy controls and independently replicated using 1954 patients vs 1904 controls. Second, the functional effects of promising variants on gene expression, addiction characteristics, brain structure (gray and white matter), and addiction behaviors in addiction animal models (chronic administration and self-administration) were assessed. In addition, we assessed the genetic correlation among the three SDs using LD score regression. We identified and replicated three novel loci that were associated with the common risk of heroin, methamphetamine addiction, and alcoholism: ANKS1B rs2133896 (P_meta = 3.60 × 10^-9), AGBL4 rs147247472 (P_meta = 3.40 × 10^-12), and CTNNA2 rs10196867 (P_meta = 4.73 × 10^-9). Rs2133896 in ANKS1B was associated with ANKS1B gene expression and had effects on gray matter of the left calcarine and white matter of the right superior longitudinal fasciculus in heroin dependence. Overexpression of anks1b gene in the ventral tegmental area decreased addiction vulnerability for heroin and methamphetamine in self-administration rat models. Our findings could shed light on the root cause for substance dependence and will be helpful for the development of cost-effective prevention strategies for general addiction disorders.

Gene expression and response prediction to amisulpride in the OPTiMiSE first episode psychoses

A fundamental shortcoming in the current treatment of schizophrenia is the lack of valid criteria to predict who will respond to antipsychotic treatment. The identification of blood-based biological markers of the therapeutic response would enable clinicians to identify the subgroup of patients in whom conventional antipsychotic treatment is ineffective and offer alternative treatments. As part of the Optimisation of Treatment and Management of Schizophrenia in Europe (OPTiMiSE) programme, we conducted an RNA-Seq analysis on 188 subjects with first episode psychosis, all of whom were subsequently treated with amisulpride for 4 weeks. We compared gene expression on total RNA from patients' blood before and after treatment and identified 32 genes for which the expression changed after treatment in good responders only. These findings were replicated in an independent sample of 24 patients with first episode psychosis. Six genes showed a significant difference in expression level between good and poor responders before starting treatment, allowing to predict treatment outcome with a predictive value of 93.8% when combined with clinical features. Collectively, these findings identified new mechanisms to explain symptom improvement after amisulpride medication and highlight the potential of combining gene expression profiling with clinical data to predict treatment response in first episode psychoses.

Identifying key transcription factors for pharmacogenetic studies of antipsychotics induced extrapyramidal symptoms

INTRODUCTION

We explore the transcription factors involved in the molecular mechanism of antipsychotic (AP)-induced acute extrapyramidalsymptoms (EPS) in order to identify new candidate genes for pharmacogenetic studies.

METHODS

Protein-protein interaction (PPI) networks previously created from three pharmacogenomic models (in vitro, animal, and peripheral blood inhumans) were used to, by means of several bioinformatic tools; identify key transcription factors (TFs) that regulate each network. Once the TFs wereidentified, SNPs disrupting the binding sites (TFBS) of these TFs in the genes of each network were selected for genotyping. Finally, SNP-basedassociations with EPS were analyzed in a sample of 356 psychiatric patients receiving AP.

RESULTS

Our analysis identified 33 TFs expressed in the striatum, and 125 SNPs disrupting TFBS in 50 genes of our initial networks. Two SNPs (rs938112,rs2987902) in two genes (LSMAP and ABL1) were significantly associated with AP induced EPS (p < 0.001). These SNPs disrupt TFBS regulated byPOU2F1.

CONCLUSION

Our results highlight the possible role of the disruption of TFBS by SNPs in the pharmacological response to AP.

Smoking Cessation Pharmacotherapy Based on Genetically-Informed Biomarkers: What is the Evidence?

INTRODUCTION

Pharmacogenomic studies have used genetic variants to identify smokers likely to respond to pharmacological treatments for smoking cessation.

METHODS

We performed a systematic review and meta-analysis of primary and secondary analyses of trials of smoking cessation pharmacotherapies. Eligible were trials with data on a priori selected single nucleotide polymorphisms, replicated non-single nucleotide polymorphisms, and/or the nicotine metabolite ratio. We estimated the genotype × treatment interaction as the ratio of risk ratios (RRR) for treatment effects across genotype groups.

RESULTS

We identified 18 trials (N = 9017 participants), including 40 active (bupropion, nicotine replacement therapy [NRT], varenicline, or combination therapies) versus placebo comparisons and 16 active versus active comparisons. There was statistical evidence of heterogeneity across rs16969968 genotypes in CHRNA5 with regard to both 6-month abstinence and end-of-treatment abstinence in non-Hispanic black smokers and end-of-treatment abstinence in non-Hispanic white smokers. There was also heterogeneity across rs1051730 genotypes in CHRNA3 with regard to end-of-treatment abstinence in non-Hispanic white smokers. There was no clear statistical evidence for other genotype-by-treatment combinations. Compared with placebo, NRT was more effective among non-Hispanic black smokers with rs16969968-GG with regard to both 6-month abstinence (RRR for GG vs. GA or AA, 3.51; 95% confidence interval [CI] = 1.19 to 10.30) and end-of-treatment abstinence (RRR for GG vs. GA or AA, 5.84; 95% CI = 1.89 to 18.10). Among non-Hispanic white smokers, NRT effectiveness relative to placebo was comparable across rs1051730 and rs169969960 genotypes.

CONCLUSIONS

We did not identify widespread differential effects of smoking cessation pharmacotherapies based on genotype. The quality of the evidence is generally moderate.

IMPLICATIONS

Although we identified some evidence of genotype × treatment interactions, the vast majority of analyses did not provide evidence of differential treatment response by genotype. Where we find some evidence, these results should be considered preliminary and interpreted with caution because of the small number of contributing trials per genotype comparison, the wide confidence intervals, and the moderate quality of evidence. Prospective trials and individual-patient data meta-analyses accounting for heterogeneity of treatment effects through modeling are needed to assess the clinical utility of genetically informed biomarkers to guide pharmacotherapy choice for smoking cessation.

Pharmacogenetics of Antipsychotic Drug Treatment: Update and Clinical Implications

Numerous genetic variants have been shown to be associated with antipsychotic response and adverse effects of schizophrenia treatment. However, the clinical application of these findings is limited. The aim of this narrative review is to summarize the most recent publications and recommendations related to the genetics of antipsychotic treatment and shed light on the clinical utility of pharmacogenetics/pharmacogenomics (PGx). We reviewed the literature on PGx studies with antipsychotic drugs (i.e., antipsychotic response and adverse effects) and commonly used commercial PGx tools for clinical practice. Publications and reviews were included with emphasis on articles published between January 2015 and April 2018. We found 44 studies focusing on antipsychotic response and 45 studies on adverse effects (e.g., antipsychotic-induced weight gain, movement disorders, hormonal abnormality, and clozapine-induced agranulocytosis/granulocytopenia), albeit with mixed results. Overall, several gene variants related to antipsychotic response and adverse effects in the treatment of patients with schizophrenia have been reported, and several commercial pharmacogenomic tests have become available. However, further well-designed investigations and replication studies in large and well-characterized samples are needed to facilitate the application of PGx findings to clinical practice.

A joint study of whole exome sequencing and structural MRI analysis in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide and influenced by both environmental and genetic factors. Genetic studies of MDD have focused on common variants and have been constrained by the heterogeneity of clinical symptoms.

METHODS

We sequenced the exome of 77 cases and 245 controls of Han Chinese ancestry and scanned their brain. Burden tests of rare variants were performed first to explore the association between genes/pathways and MDD. Secondly, parallel Independent Component Analysis was conducted to investigate genetic underpinnings of gray matter volume (GMV) changes of MDD.

RESULTS

Two genes (CSMD1, p = 5.32×10-6; CNTNAP5, p = 1.32×10-6) and one pathway (Neuroactive Ligand Receptor Interactive, p = 1.29×10-5) achieved significance in burden test. In addition, we identified one pair of imaging-genetic components of significant correlation (r = 0.38, p = 9.92×10-6). The imaging component reflected decreased GMV in cases and correlated with intelligence quotient (IQ). IQ mediated the effects of GMV on MDD. The genetic component enriched in two gene sets, namely Singling by G-protein coupled receptors [false discovery rate (FDR) q = 3.23×10-4) and Alzheimer Disease Up (FDR q = 6.12×10-4).

CONCLUSIONS

Both rare variants analysis and imaging-genetic analysis found evidence corresponding with the neuroinflammation and synaptic plasticity hypotheses of MDD. The mediation of IQ indicates that genetic component may act on MDD through GMV alteration and cognitive impairment.

Delineating significant genome-wide associations of variants with antipsychotic and antidepressant treatment response: implications for clinical pharmacogenomics

Background

Genome-wide association studies (GWAS) have significantly contributed to the association of many clinical conditions and phenotypic characteristics with genomic variants. The majority of these genomic findings have been deposited to the GWAS catalog. So far, findings uncovering associations of single nucleotide polymorphisms (SNPs) with treatment efficacy in mood disorders are encouraging, but not adequate.

Methods

Statistical, genomic, and literature information was retrieved from EBI’s GWAS catalog, while we also searched for potential clinical information/clinical guidelines in well-established pharmacogenomics databases regarding the assessed drug-SNP correlations of the present study.

Results

Here, we provide an overview of significant genome-wide associations of SNPs with the response to commonly prescribed antipsychotics and antidepressants. Up to date, this is the first study providing novel insight in previously reported pharmacogenomics associations for antipsychotic/antidepressant treatment. We also show that although there are published CPIC guidelines for antidepressant agents, as well as the FDA labels include genome-based drug prescription information for both antipsychotic and antidepressant treatments, there are no specific clinical guidelines for the assessed drug-SNP correlations of this study.

Conclusions

Our present findings suggest that more effort should be implemented towards identifying GWA-significant antipsychotic and antidepressant pharmacogenomics correlations. Moreover, additional functional studies are required in order to characterise the potential role of the assessed SNPs as biomarkers for the response of patients to antipsychotic/antidepressant treatment.

A functional HTR1A polymorphism, rs6295, predicts short-term response to lurasidone: confirmation with meta-analysis of other antipsychotic drugs

Stimulation of the serotonin (5-HT)1A receptor (HTR1A) has been shown to contribute to the mechanism of action of some atypical antipsychotic drugs (APDs), including clozapine and lurasidone. A meta-analysis of rs6295, a functional polymorphism located at the promoter region of HTR1A, showed association with clinical response in schizophrenic patients treated with atypical APD. We have now tested whether other SNPs related to rs6295 predict response to lurasidone. We first evaluated whether rs358532 and rs6449693, tag SNPs for rs6295, predicted response to lurasidone, using data from two clinical trials of acutely psychotic schizophrenia patients with European (EUR, n = 171) or African (AFR, n = 131) ancestry; we then determined if those findings could be replicated in a third trial of lurasidone of similar design. Weekly changes (up to 6 weeks) in the Positive and Negative Syndrome Scale (PANSS) Total score and its five subscales were used to assess response. In EUR, a significant association, or trends for association, were observed for PANSS Total (p = 0.035), positive (p = 0.039), negative (p = 0.004), and disorganization (p = 0.0087) subscales, at week 1-6. There was a trend for replication with PANNS Total (p = 0.036) in the third trial. No significant association was observed in AFR or the placebo group. Meta-analysis of five studies, including the three with lurasidone, showed that rs6295 was associated with improvement in positive (p = 0.023) and negative (p ≤ 0.0001) symptoms in EUR patients with schizophrenia. This is the first study to show a significant association between functional HTR1A polymorphisms and treatment response to lurasidone. The meta-analysis provides additional evidence that rs6295 could be a race-dependent biomarker for predicting treatment response to APDs in schizophrenic patients with European Ancestry.

Haploinsufficiency in the ANKS1B gene encoding AIDA-1 leads to a neurodevelopmental syndrome

Neurodevelopmental disorders, including autism spectrum disorder, have complex polygenic etiologies. Single-gene mutations in patients can help define genetic factors and molecular mechanisms underlying neurodevelopmental disorders. Here we describe individuals with monogenic heterozygous microdeletions in ANKS1B, a predicted risk gene for autism and neuropsychiatric diseases. Affected individuals present with a spectrum of neurodevelopmental phenotypes, including autism, attention-deficit hyperactivity disorder, and speech and motor deficits. Neurons generated from patient-derived induced pluripotent stem cells demonstrate loss of the ANKS1B-encoded protein AIDA-1, a brain-specific protein highly enriched at neuronal synapses. A transgenic mouse model of Anks1b haploinsufficiency recapitulates a range of patient phenotypes, including social deficits, hyperactivity, and sensorimotor dysfunction. Identification of the AIDA-1 interactome using quantitative proteomics reveals protein networks involved in synaptic function and the etiology of neurodevelopmental disorders. Our findings formalize a link between the synaptic protein AIDA-1 and a rare, previously undefined genetic disease we term ANKS1B haploinsufficiency syndrome.

Synapse-to-Nucleus Signaling in Neurodegenerative and Neuropsychiatric Disorders

Synapse-to-nucleus signaling is critical for converting signals received at synapses into transcriptional programs essential for cognition, memory, and emotion. This neuronal mechanism usually involves activity-dependent translocation of synaptonuclear factors from synapses to the nucleus resulting in regulation of transcriptional programs underlying synaptic plasticity. Acting as synapse-to-nucleus messengers, amyloid precursor protein intracellular domain associated-1 protein, cAMP response element binding protein (CREB)-regulated transcription coactivator-1, Jacob, nuclear factor kappa-light-chain-enhancer of activated B cells, RING finger protein 10, and SH3 and multiple ankyrin repeat domains 3 play essential roles in synapse remodeling and plasticity, which are considered the cellular basis of memory. Other synaptic proteins, such as extracellular signal-regulated kinase, calcium/calmodulin-dependent protein kinase II gamma, and CREB2, translocate from dendrites or cytosol to the nucleus upon synaptic activity, suggesting that they could contribute to synapse-to-nucleus signaling. Notably, some synaptonuclear factors converge on the transcription factor CREB, indicating that CREB signaling is a key hub mediating integration of synaptic signals into transcriptional programs required for neuronal function and plasticity. Although major efforts have been focused on identification and regulatory mechanisms of synaptonuclear factors, the relevance of synapse-to-nucleus communication in brain physiology and pathology is still unclear. Recent evidence, however, indicates that synaptonuclear factors are implicated in neuropsychiatric, neurodevelopmental, and neurodegenerative disorders, suggesting that uncoupling synaptic activity from nuclear signaling may prompt synapse pathology, contributing to a broad spectrum of brain disorders. This review summarizes current knowledge of synapse-to-nucleus signaling in neuron survival, synaptic function and plasticity, and memory. Finally, we discuss how altered synapse-to-nucleus signaling may lead to memory and emotional disturbances, which is relevant for clinical and therapeutic strategies in neurodegenerative and neuropsychiatric diseases.

Prediction of Response to Drug Therapy in Psychiatric Disorders

Reprinted with permission from Open Biol. 8: 180031. The Royal Society.

The ANKS1B gene and its associated phenotypes: focus on CNS drug response

The ANKS1B gene was a top finding in genome-wide association studies (GWAS) of antipsychotic drug response. Subsequent GWAS findings for ANKS1B include cognitive ability, educational attainment, body mass index, response to corticosteroids and drug dependence. We review current human association evidence for ANKS1B, in addition to functional studies that include two published mouse knockouts. The several GWAS findings in humans indicate that phenotypically relevant variation is segregating at the ANKS1B locus. ANKS1B shows strong plausibility for involvement in CNS drug response because it encodes a postsynaptic effector protein that mediates long-term changes to neuronal biology. Forthcoming data from large biobanks should further delineate the role of ANKS1B in CNS drug response.

Prediction of response to drug therapy in psychiatric disorders

Personalized medicine has become increasingly relevant to many medical fields, promising more efficient drug therapies and earlier intervention. The development of personalized medicine is coupled with the identification of biomarkers and classification algorithms that help predict the responses of different patients to different drugs. In the last 10 years, the Food and Drug Administration (FDA) has approved several genetically pre-screened drugs labelled as pharmacogenomics in the fields of oncology, pulmonary medicine, gastroenterology, haematology, neurology, rheumatology and even psychiatry. Clinicians have long cautioned that what may appear to be similar patient-reported symptoms may actually arise from different biological causes. With growing populations being diagnosed with different psychiatric conditions, it is critical for scientists and clinicians to develop precision medication tailored to individual conditions. Genome-wide association studies have highlighted the complicated nature of psychiatric disorders such as schizophrenia, bipolar disorder, major depression and autism spectrum disorder. Following these studies, association studies are needed to look for genomic markers of responsiveness to available drugs of individual patients within the population of a specific disorder. In addition to GWAS, the advent of new technologies such as brain imaging, cell reprogramming, sequencing and gene editing has given us the opportunity to look for more biomarkers that characterize a therapeutic response to a drug and to use all these biomarkers for determining treatment options. In this review, we discuss studies that were performed to find biomarkers of responsiveness to different available drugs for four brain disorders: bipolar disorder, schizophrenia, major depression and autism spectrum disorder. We provide recommendations for using an integrated method that will use available techniques for a better prediction of the most suitable drug.

A systematic review of genome-wide association studies of antipsychotic response

Clinical symptom response to antipsychotic medications is highly variable. Genome-wide association studies (GWAS) provide a 'hypothesis-free' method of interrogating the genome for biomarkers of antipsychotic response. We performed a systematic review of GWAS findings for antipsychotic efficacy or effectiveness. 14 studies met our inclusion criteria, ten of which examined antipsychotic response using quantitative rating scales to measure symptom improvement. 15 genome-wide significant loci were identified, seven of which were replicated in other antipsychotic GWAS publications: CNTNAP5, GRID2, GRM7, 8q24 (KCNK9), PCDH7, SLC1A1 and TNIK. Notably, four replicated loci are involved in glutamatergic pathways. Additional validation and evaluation of the biological significance of these markers is warranted. These markers should also be evaluated for clinical utility, especially in the context of other validated pharmacogenomic variants (e.g., CYP450 genes). These findings may generate new avenues for development of novel antipsychotic treatments.

Association Study of KCNH7 Polymorphisms and Individual Responses to Risperidone Treatment in Schizophrenia

Risperidone has been used to treat the symptoms of schizophrenia and to reduce its relapse. However, the responses to treatment show great variability among patients. The potassium channel has been reported as an effective target for antipsychotics. KCNH7, a member of the voltage-gated K⁺ channel Kv11 family, is primarily expressed in the brain. Here, we assessed the genetic association of KCNH7 with risperidone responses in 393 schizophrenia patients. The patients were treated with risperidone for 6 weeks. The reduction rates of Positive and Negative Syndrome Scale (PANSS) scores were determined to quantify drug response. We also examined the associations between six single-nucleotide polymorphisms (SNPs) of KCNH7 and the risperidone responses for a total of 6 weeks. The SNP rs77699177 (C > T) in the KCNH7 gene intron was significantly associated with the treatment response reflected by the PANSS reduction rate (CC, 55.8 ± 23.0; TC, 70.9 ± 20.3, P = 0.000110), indicating that patients with the TC genotype have better efficacy for antipsychotic therapy. The rs2241240 SNP also showed a significant association with treatment responses after 6 weeks of treatment (P = 0.00256). The findings indicate that the voltage-gated K⁺ channel KCNH7 is a potential functional marker for the identification of the response to risperidone treatment in schizophrenia patients. Note: The study was registered under clinical trial number ChiCTR-RNC-09000522 (http://www.chictr.org/).

Effect of Damaging Rare Mutations in Synapse-Related Gene Sets on Response to Short-term Antipsychotic Medication in Chinese Patients With Schizophrenia: A Randomized Clinical Trial

IMPORTANCE

The underlying mechanism for individual differences in patient response to antipsychotic medication remains unknown.

OBJECTIVE

To discover genes and gene sets harboring rare variants associated with short-term antipsychotic medication efficacy.

DESIGN, SETTING, AND PARTICIPANTS

In this multicenter, open-label, randomized clinical trial conducted between July 6, 2010, and December 31, 2011, 3023 patients recruited in China of Chinese Han descent with schizophrenia with total Positive and Negative Syndrome Scale (PANSS) score ≥ 60 received a 6-week treatment of antipsychotic medications randomly chosen from 5 atypical and 2 typical antipsychotic medications. Whole-exome sequencing (WES) was performed in 316 participants (grouped into those with the best response [n=156] and those who had no response [n=160] to the antipsychotic medication prescribed), according to the total PANSS score reduction rate after 6 weeks of treatment. Validation was performed using targeted sequencing in an independent sample of 1920 patients. Data analyses was performed between March 15, 2016, and March 1, 2017.

MAIN OUTCOMES AND MEASURES

Drug efficacy at week 6 was assessed according to the change in PANSS scores from baseline. Extremely good and extremely poor responders were selected for an initial WES association study, from which a subset of genes showing putative association was selected for independent replication with a targeted sequencing approach.

RESULTS

Of the 3023 patients (1549 [51.24%] female and 1474 [48.8%] male; mean [SD] age, 31.2 [7.9] years), 2336 (77.3%) were eligible for genetic analysis. After quality-control exclusions, 316 patients (10.5%) were included for WES and 1920 (63.5%) were included for replication. In the WES discovery stage, 2 gene sets (reduced NMDA [N-methyl-D-aspartate]-mediated synaptic currents and reduced AMPA [α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid]-mediated synaptic currents) were found to be enriched with rare damaging variants in the nonresponder group, suggesting the involvement of these gene sets in antipsychotic medication efficacy. Reduced NMDA-mediated synaptic currents gene set was further replicated in an independent sample using targeting sequencing. No statistically significant differences in antipsychotic drug response were found among the patients who received different antipsychotic drugs.

CONCLUSIONS AND RELEVANCE

Genetic variation in glutamatergic or NMDA neurotransmission is implicated in short-term antipsychotic medication efficacy; WES may have utility in the study of rare genetic variation in pharmacogenetics.

TRIAL REGISTRATION

Chinese Clinical Trials Registry Identifier: ChiCTR-TRC-10000934.

Clozapine Pharmacogenetic Studies in Schizophrenia: Efficacy and Agranulocytosis

Clozapine is an efficacious atypical antipsychotic for treatment-refractory schizophrenia. Clinical response and appearance of adverse events vary among individual patients receiving clozapine, with genetic and non-genetic factors potentially contributing to individual variabilities. Pharmacogenetic studies investigate associations between genetic variants and drug efficacy and toxicity. To date, most pharmacogenetic studies of clozapine have been conducted through candidate gene approaches. A recent advance in technology made it possible to perform comprehensive genetic mapping underlying clinical phenotypes and outcomes, which allow novel findings beyond biological hypotheses based on current knowledge. In this paper, we will summarize the studies on clozapine pharmacogenetics that have extensively examined clinical response and agranulocytosis. While there is still limited evidence on clozapine efficacy, recent genome-wide studies provide further evidence of the involvement of the human leukocyte antigen (HLA) region in clozapine-induced agranulocytosis.

Identifying the genetic risk factors for treatment response to lurasidone by genome-wide association study: A meta-analysis of samples from three independent clinical trials

A genome-wide association study (GWAS) of response of schizophrenia patients to the atypical antipsychotic drug, lurasidone, based on two double-blind registration trials, identified SNPs from four classes of genes as predictors of efficacy, but none were genome wide significant (GWS). After inclusion of data from a third lurasidone trial, meta-analysis identified a GWS marker and other findings consistent with our first study. The primary end-point was change in Total Positive and Negative Syndrome Scale (PANSS) between baseline and last observation carried forward. rs4736253, a genetic locus near KCNK9, encoding the K_2P9.1 potassium channel, with a role in cognition and neurodevelopment, was the top marker in patients of European ancestry (EUR) (n = 264), reaching GWS (p = 4.78 × 10^-8). rs10180106 (p = 4.92 × 10^-7), located at an intron region of CTNNA2, a SCZ risk gene important for dendritic spine stabilization, was one of other best response markers for EUR patients. SNPs at STXBP5L (rs511841, p = 2.63 × 10^-7) were the top markers for patients of African ancestry (n = 158). The association between PTPRD, NRG1, and MAGI1 previously reported to be related to response to lurasidone in the first two trials, showed a trend of significant association in the third trial. None of these genetic loci showed significant associations with clinical response in the corresponding placebo groups (n = 107 for EUR; n = 58 for AFR). This meta-analysis yielded the first GWAS-based GWS biomarker for lurasidone response and additional support for the conclusion that genes related to synaptic biology and/or risk for SCZ are the strongest predictors of response to lurasidone in schizophrenia patients.

Biological Predictors of Clozapine Response: A Systematic Review

Background: Clozapine is the recommended antipsychotic for treatment-resistant schizophrenia (TRS) but there is significant variability between patients in the degree to which clozapine will improve symptoms. The biological basis of this variability is unknown. Although clozapine has efficacy in TRS, it can elicit adverse effects and initiation is often delayed. Identification of predictive biomarkers of clozapine response may aid initiation of clozapine treatment, as well as understanding of its mechanism of action. In this article we systematically review prospective or genetic studies of biological predictors of response to clozapine. Methods: We searched the PubMed database until 20th January 2018 for studies investigating "clozapine" AND ("response" OR "outcome") AND "schizophrenia." Inclusion required that studies examined a biological variable in relation to symptomatic response to clozapine. For all studies except genetic-studies, inclusion required that biological variables were measured before clozapine initiation. Results: Ninety-eight studies met the eligibility criteria and were included in the review, including neuroimaging, blood-based, cerebrospinal fluid (CSF)-based, and genetic predictors. The majority (70) are genetic studies, collectively investigating 379 different gene variants, however only three genetic variants (DRD3 Ser9Gly, HTR2A His452Tyr, and C825T GNB3) have independently replicated significant findings. Of the non-genetic variables, the most consistent predictors of a good response to clozapine are higher prefrontal cortical structural integrity and activity, and a lower ratio of the dopamine and serotonin metabolites, homovanillic acid (HVA): 5-hydroxyindoleacetic acid (5-HIAA) in CSF. Conclusions: Recommendations include that future studies should ensure adequate clozapine trial length and clozapine plasma concentrations, and may include multivariate models to increase predictive accuracy.

Five novel loci associated with antipsychotic treatment response in patients with schizophrenia: a genome-wide association study

BACKGROUND

Antipsychotic drugs improve schizophrenia symptoms and reduce the frequency of relapse, but treatment response is highly variable. Little is known about the genetic factors associated with treatment response. We did a genome-wide association study of antipsychotic treatment response in patients with schizophrenia.

METHODS

The discovery cohort comprised patients with schizophrenia from 32 psychiatric hospitals in China that are part of the Chinese Antipsychotics Pharmacogenomics Consortium. Patients who met inclusion criteria were randomly assigned (1:1:1:1:1:1) to six groups (olanzapine, risperidone, quetiapine, aripiprazole, ziprasidone, and haloperidol or perphenazine; those assigned to haloperidol or perphenazine were subsequently assigned [1:1] to one or the other) for 6 weeks. Antipsychotic response was quantified with percentage change on the Positive and Negative Syndrome Scale. Single-nucleotide polymorphisms (SNPs) were tested for their association with treatment response. Linkage-disequilibrium-independent SNPs that exhibited potential associations (ie, p<1 × 10^-5) were tested in a validation cohort comprising patients from the Chinese Antipsychotics Pharmacogenetics Consortium from five collaborative hospitals, who were treated with olanzapine, risperidone, or aripiprazole for 8 weeks.

FINDINGS

The discovery cohort contained 2413 patients and the validation cohort 1379 patients. In the discovery cohort, we identified three novel SNPs (rs72790443 in MEGF10 [p=1·37 × 10^-8], rs1471786 in SLC1A1 [p=1·77 × 10^-8], and rs9291547 in PCDH7 [p=4·48 × 10^-8]) that were associated with antipsychotic treatment response at a genome-wide significance level. These associations were confirmed in the validation cohort (p<0·05). In the combined sample of the discovery and validation cohorts, we identified five novel loci showing genome-wide significant associations with general antipsychotic treatment response (rs72790443 in MEGF10 [p=1·40 × 10^-9], rs1471786 in SLC1A1 [p=2·33 × 10^-9], rs9291547 in PCDH7 [p=3·24 × 10^-9], rs12711680 in CNTNAP5 [p=2·12 × 10^-8], and rs6444970 in TNIK [p=4·85 × 10^-8]). In antipsychotic-specific groups, after the combination of results from both samples, the rs2239063 SNP in CACNA1C was associated with treatment response to olanzapine (p=1·10 × 10^-8), rs16921385 in SLC1A1 was associated with treatment response to risperidone (p=4·40 × 10^-8), and rs17022006 in CNTN4 was associated with treatment response to aripiprazole (p=2·58 × 10^-8).

INTERPRETATION

We have identified genes related to synaptic function, neurotransmitter receptors, and schizophrenia risk that are associated with response to antipsychotics. These findings improve understanding of the mechanisms underlying treatment responses, and the identified biomarkers could eventually guide choice of antipsychotic in patients with schizophrenia.

FUNDING

National Key Technology R&D Program of China, National Natural Science Foundation of China.

DNA methylation and antipsychotic treatment mechanisms in schizophrenia: Progress and future directions

Antipsychotic response in schizophrenia is a complex, multifactorial trait influenced by pharmacogenetic factors. With genetic studies thus far providing little biological insight or clinical utility, the field of pharmacoepigenomics has emerged to tackle the so-called "missing heritability" of drug response in disease. Research on psychiatric disorders has only recently started to assess the link between epigenetic alterations and treatment outcomes. DNA methylation, the best characterised epigenetic mechanism to date, is discussed here in the context of schizophrenia and antipsychotic treatment outcomes. The majority of published studies have assessed the influence of antipsychotics on methylation levels in specific neurotransmitter-associated candidate genes or at the genome-wide level. While these studies illustrate the epigenetic modifications associated with antipsychotics, very few have assessed clinical outcomes and the potential of differential DNA methylation profiles as predictors of antipsychotic response. Results from other psychiatric disorder studies, such as depression and bipolar disorder, provide insight into what may be achieved by schizophrenia pharmacoepigenomics. Other aspects that should be addressed in future research include methodological challenges, such as tissue specificity, and the influence of genetic variation on differential methylation patterns.

Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes

Biomarkers which predict response to atypical antipsychotic drugs (AAPDs) increases their benefit/risk ratio. We sought to identify common variants in genes which predict response to lurasidone, an AAPD, by associating genome-wide association study (GWAS) data and changes (Δ) in Positive And Negative Syndrome Scale (PANSS) scores from two 6-week randomized, placebo-controlled trials of lurasidone in schizophrenia (SCZ) patients. We also included SCZ risk SNPs identified by the Psychiatric Genomics Consortium using a polygenic risk analysis. The top genomic loci, with uncorrected p<10^-4, include: 1) synaptic adhesion (PTPRD, LRRC4C, NRXN1, ILIRAPL1, SLITRK1) and scaffolding (MAGI1, MAGI2, NBEA) genes, both essential for synaptic function; 2) other synaptic plasticity-related genes (NRG1/3 and KALRN); 3) the neuron-specific RNA splicing regulator, RBFOX1; and 4) ion channel genes, e.g. KCNA10, KCNAB1, KCNK9 and CACNA2D3). Some genes predicted response for patients with both European and African Ancestries. We replicated some SNPs reported to predict response to other atypical APDs in other GWAS. Although none of the biomarkers reached genome-wide significance, many of the genes and associated pathways have previously been linked to SCZ. Two polygenic modeling approaches, GCTA-GREML and PLINK-Polygenic Risk Score, demonstrated that some risk genes related to neurodevelopment, synaptic biology, immune response, and histones, also contributed to prediction of response. The top hits predicting response to lurasidone did not predict improvement with placebo. This is the first evidence from clinical trials that SCZ risk SNPs are related to clinical response to an AAPD. These results need to be replicated in an independent sample.

A computational algorithm for personalized medicine in schizophrenia

Despite advances in sequencing candidate genes and whole genomes, no method has accurately predicted who will or will not benefit from a specific antipsychotic medication among patients with schizophrenia. We propose a computational algorithm that utilizes a person-centered approach that directly identifies individual patients who will respond to a specific antipsychotic medication. The algorithm was applied to the data obtained from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. The predictors were either (1) 13 single-nucleotide polymorphisms (SNPs) and 53 baseline variables or (2) 25 SNPs and the same 53 baseline variables, depending on the existing findings and data availability. The outcome variables were either (1) improvement in the Positive and Negative Syndrome Scale (PANSS) (Yes/No) or (2) completion of phase 1/1A (Yes/No). Each of those four predictor-outcome combinations was tried for each of the five antipsychotic medications (Perphenazine, Olanzapine, Quetiapine, Risperidone, and Ziprasidone), leading to 20 prediction experiments. For 18 out of 20 experiments, all three performance measures were greater than 0.50 (sensitivity 0.51-0.79, specificity 0.52-0.79, accuracy 0.52-0.74). Notably, the model provided a promising prediction for Ziprasidone for the case involving completion of phase 1/1A (Yes/No) predicted by 13 SNPs and 53 baseline variables (sensitivity 0.75, specificity 0.74, accuracy 0.74). The proposed algorithm simultaneously used both genetic information and clinical profiles to predict individual patients' response to antipsychotic medications. As the method is not disease-specific but a general algorithm, it can be easily adopted in many other clinical practices for personalized medicine.

Synapse-to-nucleus communication: from developmental disorders to Alzheimer's disease

In the last decade several synaptonuclear protein messengers including Jacob, CRTC1, AIDA-1, ProSaP2/Shank3 and RNF10 have been identified and characterized as key players for modulation of synaptic transmission and synaptic plasticity. Activation of excitatory glutamatergic synapses leads to their shuttling from the synapse to the nucleus, mostly importin-mediated, and subsequent regulation of gene transcription needed for long lasting modifications of synaptic function. Accordingly, increasing evidences show that alterations of the activity of synaptonuclear messengers are correlated to synaptic failure as observed in different synaptopathies. Specifically, recent studies demonstrate that the modulation of the activity of synaptonuclear messengers could represent a novel molecular target in the pathogenesis of both neurodevelopmental disorders and neurodegenerative diseases such as Alzheimer's disease.

Developing tomorrow's antipsychotics: the need for a more personalised approach

There has been little pharmacological advance in the treatment of schizophrenia since the introduction of chlorpromazine in the 1950s. This may be set to change as recent advances in molecular biology offer the prospect of a better understanding of the pathophysiology of the disorder and allow investigation of the complex interplay of genetic and environmental risk factors. In this review I discuss future approaches to antipsychotic drug development, highlighting the need to better define symptom areas and develop drugs based on an understanding of neurobiological mechanisms. The development of biomarkers has the potential in future to improve differential diagnosis and help predict response to treatment. These developments herald the possibility of a more integrated drug discovery approach and the subsequent provision of more stratified healthcare, and hopefully significant improvements in patient care and improved long-term outcomes.

Antipsychotic Drug Responsiveness and Dopamine Receptor Signaling; Old Players and New Prospects

Antipsychotic drugs targeting dopamine neurotransmission are still the principal mean of therapeutic intervention for schizophrenia. However, about one third of people do not respond to dopaminergic antipsychotics. Genome wide association studies (GWAS), have shown that multiple genetic factors play a role in schizophrenia pathophysiology. Most of these schizophrenia risk variants are not related to dopamine or antipsychotic drugs mechanism of action. Genetic factors have also been implicated in defining response to antipsychotic medication. In contrast to disease risk, variation of genes coding for molecular targets of antipsychotics have been associated with treatment response. Among genes implicated, those involved in dopamine signaling mediated by D2-class dopamine receptor, including DRD2 itself and its molecular effectors, have been implicated as key genetic predictors of response to treatments. Studies have also reported that genetic variation in genes coding for proteins that cross-talk with DRD2 at the molecular level, such as AKT1, GSK3B, Beta-catenin, and PPP2R2B are associated with response to antipsychotics. In this review we discuss the relative contribution to antipsychotic drug responsiveness of candidate genes and GWAS identified genes encoding proteins involved in dopamine responses. We also suggest that in addition of these older players, a deeper investigation of new GWAS identified schizophrenia risk genes such as FXR1 can provide new prospects that are not clearly engaged in dopamine function while being targeted by dopamine-associated signaling molecules. Overall, further examination of genes proximally or distally related to signaling mechanisms engaged by medications and associated with disease risk and/or treatment responsiveness may uncover an interface between genes involved in disease causation with those affecting disease remediation. Such a nexus would provide realistic targets for therapy and further the development of genetically personalized approaches for schizophrenia.

Genome-wide association study of paliperidone efficacy

Supplemental Digital Content is available in the text.

Objective

Clinical response to the atypical antipsychotic paliperidone is known to vary among schizophrenic patients. We carried out a genome-wide association study to identify common genetic variants predictive of paliperidone efficacy.

Methods

We leveraged a collection of 1390 samples from individuals of European ancestry enrolled in 12 clinical studies investigating the efficacy of the extended-release tablet paliperidone ER (n₁=490) and the once-monthly injection paliperidone palmitate (n₂=550 and n₃=350). We carried out a genome-wide association study using a general linear model (GLM) analysis on three separate cohorts, followed by meta-analysis and using a mixed linear model analysis on all samples. The variations in response explained by each single nucleotide polymorphism (h²_SNP) were estimated.

Results

No SNP passed genome-wide significance in the GLM-based analyses with suggestive signals from rs56240334 [P=7.97×10⁻⁸ for change in the Clinical Global Impression Scale-Severity (CGI-S); P=8.72×10⁻⁷ for change in the total Positive and Negative Syndrome Scale (PANSS)] in the intron of ADCK1. The mixed linear model-based association P-values for rs56240334 were consistent with the results from GLM-based analyses and the association with change in CGI-S (P=4.26×10⁻⁸) reached genome-wide significance (i.e. P<5×10⁻⁸). We also found suggestive evidence for a polygenic contribution toward paliperidone treatment response with estimates of heritability, h²_SNP, ranging from 0.31 to 0.43 for change in the total PANSS score, the PANSS positive Marder factor score, and CGI-S.

Conclusion

Genetic variations in the ADCK1 gene may differentially predict paliperidone efficacy in schizophrenic patients. However, this finding should be replicated in additional samples.

Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers

BACKGROUND

Smoking cessation therapies are not effective for all smokers, and researchers are interested in identifying those subgroups of individuals (e.g. based on genotype) who respond best to specific treatments.

OBJECTIVES

To assess whether quit rates vary by genetically informed biomarkers within pharmacotherapy treatment arms and as compared with placebo. To assess the effects of pharmacotherapies for smoking cessation in subgroups of smokers defined by genotype for identified genome-wide significant polymorphisms.

SEARCH METHODS

We searched the Cochrane Tobacco Addiction Group specialised register, clinical trial registries, and genetics databases for trials of pharmacotherapies for smoking cessation from inception until 16 August 2016.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) that recruited adult smokers and reported pharmacogenomic analyses from trials of smoking cessation pharmacotherapies versus controls. Eligible trials included those with data on a priori genome-wide significant (P < 5 × 10-8) single-nucleotide polymorphisms (SNPs), replicated non-SNPs, and/or the nicotine metabolite ratio (NMR), hereafter collectively described as biomarkers.

DATA COLLECTION AND ANALYSIS

We used standard methodological procedures expected by Cochrane. The primary outcome was smoking abstinence at six months after treatment. The secondary outcome was abstinence at end of treatment (EOT). We conducted two types of meta-analyses- one in which we assessed smoking cessation of active treatment versus placebo within genotype groups, and another in which we compared smoking cessation across genotype groups within treatment arms. We carried out analyses separately in non-Hispanic whites (NHWs) and non-Hispanic blacks (NHBs). We assessed heterogeneity between genotype groups using T², I², and Cochrane Q statistics.

MAIN RESULTS

Analyses included 18 trials including 9017 participants, of whom 6924 were NHW and 2093 NHB participants. Data were available for the following biomarkers: nine SNPs (rs1051730 (CHRNA3); rs16969968, rs588765, and rs2036527 (CHRNA5); rs3733829 and rs7937 (in EGLN2, near CYP2A6); rs1329650 and rs1028936 (LOC100188947); and rs215605 (PDE1C)), two variable number tandem repeats (VNTRs; DRD4 and SLC6A4), and the NMR. Included data produced a total of 40 active versus placebo comparisons, 16 active versus active comparisons, and 64 between-genotype comparisons within treatment arms.For those meta-analyses showing statistically significant heterogeneity between genotype groups, we found the quality of evidence (GRADE) to be generally moderate. We downgraded quality most often because of imprecision or risk of bias due to potential selection bias in genotyping trial participants. Comparisons of relative treatment effects by genotypeFor six-month abstinence, we found statistically significant heterogeneity between genotypes (rs16969968) for nicotine replacement therapy (NRT) versus placebo at six months for NHB participants (P = 0.03; n = 2 trials), but not for other biomarkers or treatment comparisons. Six-month abstinence was increased in the active NRT group as compared to placebo among participants with a GG genotype (risk ratio (RR) 1.47, 95% confidence interval (CI) 1.07 to 2.03), but not in the combined group of participants with a GA or AA genotype (RR 0.43, 95% CI 0.15 to 1.26; ratio of risk ratios (RRR) GG vs GA or AA of 3.51, 95% CI 1.19 to 10.3). Comparisons of treatment effects between genotype groups within pharmacotherapy randomisation armsFor those receiving active NRT, treatment was more effective in achieving six-month abstinence among individuals with a slow NMR than among those with a normal NMR among NHW and NHB combined participants (normal NMR vs slow NMR: RR 0.54, 95% CI 0.37 to 0.78; n = 2 trials). We found no such differences in treatment effects between genotypes at six months for any of the other biomarkers among individuals who received pharmacotherapy or placebo.

AUTHORS' CONCLUSIONS

We did not identify widespread differential treatment effects of pharmacotherapy based on genotype. Some genotype groups within certain ethnic groups may benefit more from NRT or may benefit less from the combination of bupropion with NRT. The reader should interpret these results with caution because none of the statistically significant meta-analyses included more than two trials per genotype comparison, many confidence intervals were wide, and the quality of this evidence (GRADE) was generally moderate. Although we found evidence of superior NRT efficacy for NMR slow versus normal metabolisers, because of the lack of heterogeneity between NMR groups, we cannot conclude that NRT is more effective for slow metabolisers. Access to additional data from multiple trials is needed, particularly for comparisons of different pharmacotherapies.

Progress in genome-wide association studies of schizophrenia in Han Chinese populations

Since 2006, genome-wide association studies of schizophrenia have led to the identification of numerous novel risk loci for this disease. However, there remains a geographical imbalance in genome-wide association studies, which to date have primarily focused on Western populations. During the last 6 years, genome-wide association studies in Han Chinese populations have identified both the sharing of susceptible loci across ethnicities and genes unique to Han Chinese populations. Here, we review recent progress in genome-wide association studies of schizophrenia in Han Chinese populations. Researchers have identified and replicated the sharing of susceptible genes, such as within the major histocompatibility complex, microRNA 137 (MIR137), zinc finger protein 804A (ZNF804A), vaccinia related kinase 2 (VRK2), and arsenite methyltransferase (AS3MT), across both European and East Asian populations. Several copy number variations identified in European populations have also been validated in the Han Chinese, including duplications at 16p11.2, 15q11.2-13.1, 7q11.23, and VIPR2 and deletions at 22q11.2, 1q21.1-q21.2, and NRXN1. However, these studies have identified some potential confounding factors, such as genetic heterogeneity and the effects of natural selection on tetraspanin 18 (TSPAN18) or zinc finger protein 323 (ZNF323), which may explain the population differences in genome-wide association studies. In the future, genome-wide association studies in Han Chinese populations should include meta-analyzes or mega-analyses with enlarged sample sizes across populations, deep sequencing, precision medicine treatment, and functional exploration of the risk genes for schizophrenia.

QKI6B mRNA levels are upregulated in schizophrenia and predict GFAP expression

Schizophrenia is a highly heritable disorder with a heterogeneous symptomatology. Research increasingly indicates the importance of the crucial and often overlooked glial perturbations within schizophrenia. Within this study, we examined an isoform of quaking (a gene encoding an RNA-binding protein that is exclusively expressed in glial cells), known as QKI6B, and a prototypical astrocyte marker, glial fibrillary acidic protein (GFAP), postulated to be under the regulation of QKI. The expression levels of these genes were quantified across post-mortem brain samples from 55 schizophrenic individuals, and 55 healthy controls, using real-time PCR. We report, through an analysis of covariance (ANCOVA) model, an upregulation of both QKI6B, and GFAP in the prefrontal cortex of brain samples of schizophrenic individuals, as compared to control samples. Previous research has suggested that the QKI protein directly regulates the expression of several genes through interaction with a motif in the target's sequence, termed the Quaking Response Element (QRE). We therefore examined if QKI6B expression can predict the outcome of GFAP, and several oligodendrocyte-related genes, using a multiple linear regression approach. We found that QKI6B significantly predicts the expression of GFAP, but does not predict oligodendrocyte-related gene outcome, as previously seen with other QKI isoforms.

GWAS analysis of treatment resistant schizophrenia: interaction effect of childhood trauma

Aims: In the current study, we aimed to compare the prevalence of adverse lifetime events in treatment resistant and non-treatment resistant schizophrenia in a genome-wide association study. Materials & methods: Our sample consisted of 84 Caucasian participants with schizophrenia spectrum disorders, assessed cross-sectionally to collect information regarding drug effectiveness and childhood trauma. Using a genome-wide association analysis, we tested single-nucleotide polymorphisms for their association with resistance to antipsychotics defined according to American Psychiatric Association criteria. Two models were tested: a main model and an interaction model with the childhood trauma. Results: Our analysis failed to demonstrate a significant relationship among 1,178,234 single-nucleotide polymorphisms and treatment-resistance in both the main model and in the childhood trauma interaction model. Conclusion: Even though we could not find any significant association, treatment resistance has clinical relevance and it may be determined by the interaction between biological and non biological factors.