Genome-wide association studies of placebo and duloxetine response in major depressive disorder

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

Genetic risk for chronic pain is associated with lower antidepressant effectiveness: Converging evidence for a depression subtype

INTRODUCTION

Chronic pain and depression are highly comorbid and difficult-to-treat disorders. We previously showed this comorbidity is associated with higher depression severity, lower antidepressant treatment effectiveness and poorer prognosis in the Australian Genetics of Depression Study.

OBJECTIVE

The current study aimed to assess whether a genetic liability to chronic pain is associated with antidepressant effectiveness over and above the effect of genetic factors for depression in a sample of 12,863 Australian Genetics of Depression Study participants.

METHODS

Polygenic risk scores were calculated using summary statistics from genome-wide association studies of multisite chronic pain and major depression. Cumulative linked regressions were employed to assess the association between polygenic risk scores and antidepressant treatment effectiveness across 10 different medications.

RESULTS

Mixed-effects logistic regressions showed that individual genetic propensity for chronic pain, but not major depression, was significantly associated with patient-reported chronic pain (Pain^PRS OR = 1.17 [1.12, 1.22]; MD^PRS OR = 1.01 [0.98, 1.06]). Significant associations were also found between lower antidepressant effectiveness and genetic risk for chronic pain or for major depression. However, a fully adjusted model showed the effect of Pain^PRS (adjOR = 0.93 [0.90, 0.96]) was independent of MD^PRS (adjOR = 0.96 [0.93, 0.99]). Sensitivity analyses were performed to assess the robustness of these results. After adjusting for depression severity measures (i.e. age of onset; number of depressive episodes; interval between age at study participation and at depression onset), the associations between Pain^PRS and patient-reported chronic pain with lower antidepressant effectiveness remained significant (0.95 [0.92, 0.98] and 0.84 [0.78, 0.90], respectively).

CONCLUSION

These results suggest genetic risk for chronic pain accounted for poorer antidepressant effectiveness, independent of the genetic risk for major depression. Our results, along with independent converging evidence from other studies, point towards a difficult-to-treat depression subtype characterised by comorbid chronic pain. This finding warrants further investigation into the implications for biologically based nosology frameworks in pain medicine and psychiatry.

Identification of Endocannabinoid Predictors of Treatment Outcomes in Major Depressive Disorder: A Secondary Analysis of the First Canadian Biomarker Integration Network in Depression (CAN-BIND 1) Study

INTRODUCTION

An increasing number of studies are examining the link between the endocannabinoidome and major depressive disorder (MDD). We conducted an exploratory analysis of this system to identify potential markers of treatment outcomes.

METHODS

The dataset of the Canadian Biomarker Integration Network in Depression-1 study, consisting of 180 patients with MDD treated for eight weeks with escitalopram followed by eight weeks with escitalopram alone or augmented with aripiprazole was analyzed. Association between response Montgomery-Asberg Depression Rating Scale (MADRS; score reduction≥50%) or remission (MADRS score≤10) at weeks 8 and 16 and single nucleotide polymorphisms (SNPs), methylation, and mRNA levels of 33 endocannabinoid markers were examined. A standard genome-wide association studies protocol was used for identifying SNPs, and logistic regression was used to assess methylation and mRNA levels.

RESULTS

Lower methylation of CpG islands of the diacylglycerol lipase alpha gene (DAGLA) was associated with non-remission at week 16 (DAGLA; OR=0.337, p<0.003, q=0.050). Methylation of DAGLA was correlated with improvement in Clinical Global Impression (p=0.026), Quick Inventory of Depressive Symptomatology (p=0.010), and Snaith-Hamilton Pleasure scales (p=0.028). We did not find any association between SNPs or mRNA levels and treatment outcomes.

DISCUSSION

Methylation of DAGLA is a promising candidate as a marker of treatment outcomes for MDD and needs to be explored further.

Therapy response prediction in major depressive disorder: current and novel genomic markers influencing pharmacokinetics and pharmacodynamics

Major depressive disorder is connected with high rates of functional disability and mortality. About a third of the patients are at risk of therapy failure. Several pharmacogenetic markers especially located in CYP450 genes such as CYP2D6 or CYP2C19 are of relevance for therapy outcome prediction in major depressive disorder but a further optimization of predictive tools is warranted. The article summarizes the current knowledge on pharmacogenetic variants, therapy effects and side effects of important antidepressive therapeutics, and sheds light on new methodological approaches for therapy response estimation based on genetic markers with relevance for pharmacokinetics, pharmacodynamics and disease pathology identified in genome-wide association study analyses, highlighting polygenic risk score analysis as a tool for further optimization of individualized therapy outcome prediction.

Comorbid Chronic Pain and Depression: Shared Risk Factors and Differential Antidepressant Effectiveness

The bidirectional relationship between depression and chronic pain is well-recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study (N = 13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for 10 different antidepressants. Chronic pain was associated with an increased risk of depression (OR = 1.86 [1.37-2.54]), recent suicide attempt (OR = 1.88 [1.14-3.09]), higher use of tobacco (OR = 1.05 [1.02-1.09]) and misuse of painkillers (e.g., opioids; OR = 1.31 [1.06-1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR = 0.75 [0.68-0.83]), escitalopram (OR = 0.75 [0.67-0.85]) and venlafaxine (OR = 0.78 [0.68-0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR = 0.45 [0.30-0.67]), escitalopram (OR = 0.45 [0.27-0.74]) and citalopram (OR = 0.32 [0.15-0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.

Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine

Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.

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.

Analysis of the Deleterious Single-Nucleotide Polymorphisms Associated With Antidepressant Efficacy in Major Depressive Disorder

Major depressive disorder (MDD) is a serious mental disease with negative effects on both mental and physical health of the patient. Currently, antidepressants are among the major ways to ease or treat MDD. However, the existing antidepressants have limited efficacy in treating MDD, with a large fraction of patients either responding inadequately or differently to antidepressants during the treatment. Pharmacogenetics studies have found that the genetic features of some genes are associated with the antidepressant efficacy. In order to obtain a better understanding on the relationship between the genetic factors and antidepressant treatment response, we compiled a list of 233 single-nucleotide polymorphisms (SNPs) significantly associated with the antidepressant efficacy in treating MDD. Of the 13 non-synonymous SNPs in the list, three (rs1065852, rs3810651, and rs117986340) may influence the structures and function of the corresponding proteins. Besides, the influence of rs1065852 on the structure of CYP2D6 was further investigated via molecular dynamics simulations. Our results showed that compared to the native CYP2D6 the flexibility of the F-G loop was reduced in the mutant. As a portion of the substrate access channel, the lower flexibility of F-G loop may reduce the ability of the substrates to enter the channel, which may be the reason for the lower enzyme activity of mutant. This study may help us to understand the impact of genetic variation on antidepressant efficacy and provide clues for developing new antidepressants.

Drug-Placebo Additivity in Randomized Clinical Trials

In randomized clinical trials (RCTs), it is assumed that nonspecific effects beyond action of pharmacological agents are roughly equivalent in drug and placebo treatment groups. Hence, since the inception of RCTs, drug efficacy is determined by comparing outcomes in active to those in placebo control arms. However, quantitation of efficacy is based on an unproven assumption, that drug and placebo responses are always additive. Response to treatment in RCTs can be differentially influenced by the perturbing effects of patient expectations, side effects, and pharmacogenomic interactions in both drug and placebo arms. Ability to control for these effects requires understanding of when and where they arise, how to mitigate, analyze, and even leverage their impact. Here, we examine three factors that influence additivity: expectation, side effects, and pharmacogenomics. Furthermore, to provide novel insights into nonadditivity and solutions for managing it, we introduce systems pharmacogenomics, a network approach to integrating and analyzing the effects of the numerous interacting perturbations to which a patient is exposed in RCTs.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

Low lncRNA ZNF385D‑AS2 expression and its prognostic significance in liver cancer

Hepatocellular carcinoma (HCC) is a common digestive system disease with no curative treatment. Zinc finger protein 385D antisense RNA 2 (ZNF385D-AS2) is a long non-coding RNA (lncRNA) that has been predicted to function in human diseases, including several types of cancer. Yet, it has not been investigated in relation to liver cancer. Thus, the present study was designed with an aim to elucidate the prognostic significance of lncRNA ZNF385D-AS2 in HCC. The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) collection of data was utilized to analyze the expression of lncRNA ZNF385D-AS2 in liver cancer. Then Chi-square tests were used to evaluate the correlation between clinical characteristics and lncRNA ZNF385D-AS2 expression. The significance of lncRNA ZNF385D-AS2 in patient prognosis was evaluated using Kaplan-Meier curves and Cox analysis. Concomitantly, Gene Set Enrichment Analysis (GSEA) was performed to analyze the most closely related cytological behavior. Finally, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) and KOBAS software and data from the Gene Expression Omnibus (GEO) database to analyze the possible competing endogenous RNA (ceRNA) network pattern as well as the co-expression network in liver cancer. Based on the results, analysis of RNA-Seq gene expression data for 303 patients with primary tumors revealed low expression of ZNF385D-AS2 in liver cancer. Low expression of ZNF385D-AS2 was found to be significantly associated with sex (P=0.050), T stage (P=0.049), M stage (P=0.040), N stage (P<0.001) and clinical stage (P=0.037). Patients with ZNF385D-AS2 low-expression liver cancers had a shorter median overall survival compared with the patients with ZNF385D-AS2 high-expression liver cancers (P=0.0079). Cox analysis identified ZNF385D-AS2 low-expression as an independent prognostic variable (AUC=0.594) for overall survival in liver cancer patients. Co-expression and ceRNA predictive analysis data suggested that there may be a regulatory signaling axis between ZNF385D-AS2 and miR-96 and miR-182. In conclusion, our results suggests that low expression of ZNF385D-AS2 is predictive of a poor prognosis of liver cancer patients.

Systems pharmacogenomics - gene, disease, drug and placebo interactions: a case study in COMT

Disease, drugs and the placebos used as comparators are inextricably linked in the methodology of the double-blind, randomized controlled trial. Nonetheless, pharmacogenomics, the study of how individuals respond to drugs based on genetic substrate, focuses primarily on the link between genes and drugs, while the link between genes and disease is often overlooked and the link between genes and placebos is largely ignored. Herein, we use the example of the enzyme catechol-O-methyltransferase to examine the hypothesis that genes can function as pharmacogenomic hubs across system-wide regulatory processes that, if perturbed in andomized controlled trials, can have primary and combinatorial effects on drug and placebo responses.

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 comprehensive analysis of mitochondrial genes variants and their association with antipsychotic-induced weight gain

Antipsychotic Induced Weight Gain (AIWG) is a common and severe side effect of many antipsychotic medications. Mitochondria play a vital role for whole-body energy homeostasis and there is increasing evidence that antipsychotics modulate mitochondrial function. This study aimed to examine the role of variants in nuclear-encoded mitochondrial genes and the mitochondrial DNA (mtDNA) in conferring risk for AIWG. We selected 168 European-Caucasian individuals from the CATIE sample based upon meeting criteria of multiple weight measures while taking selected antipsychotics (risperidone, quetiapine or olanzapine). We tested the association of 670 nuclear-encoded mitochondrial genes with weight change (%) using MAGMA software. Thirty of these genes showed nominally significant P-values (<0.05). We were able to replicate the association of three genes, CLPB, PARL, and ACAD10, with weight change (%) in an independent prospectively assessed AIWG sample. We analyzed mtDNA variants in a subset of 74 of these individuals using next-generation sequencing. No common or rare mtDNA variants were found to be significantly associated with weight change (%) in our sample. Additionally, analysis of mitochondrial haplogroups showed no association with weight change (%). In conclusion, our findings suggest nuclear-encoded mitochondrial genes play a role in AIWG. Replication in larger sample is required to validate our initial report of mtDNA variants in AIWG.