Pharmacogenetics and Imaging-Pharmacogenetics of Antidepressant Response: Towards Translational Strategies

The association of FKBP5 gene polymorphism with genetic susceptibility to depression and response to antidepressant treatment- a systematic review

BACKGROUND

Given the inconsistencies in current studies regarding the impact of FKBP5 gene polymorphisms on depression, arising from variations in study methods, subjects, and treatment strategies, this paper provides a comprehensive review of the relationship between FKBP5 gene polymorphisms and genetic susceptibility to depression, as well as their influence on response to antidepressant treatment.

METHODS

Electronic databases were searched up to April 11, 2023, for all literature in English and Chinese on depression, FKBP5 gene polymorphisms, and antidepressant treatment. Data extraction and quality assessment were performed for key study characteristics. Qualitative methods were used to synthesize the study results.

RESULTS

A total of 21 studies were included, with the majority exhibiting average to moderate quality. Six SNPs (rs3800373, rs1360780, rs9470080, rs4713916, rs9296158, rs9394309) were broadly implicated in susceptibility to depression, while rs1360780 and rs3800373 were linked to antidepressant treatment sensitivity. Additionally, rs1360780 was associated with adverse reactions to antidepressant drug treatment. However, these associations were largely unconfirmed in replication studies.

CONCLUSIONS

Depression is recognized as a polygenic genetic disorder, with multiple genes contributing, each exerting relatively small effects. Future studies should explore not only multiple gene interactions but also epigenetic changes. Presently, research on FKBP5 in affective disorders remains notably limited, highlighting the necessity for further investigations in this domain.

'Omics' of suicidal behaviour: A path to personalised psychiatry

Psychiatric disorders, including suicide, are complex disorders that are affected by many different risk factors. It has been estimated that genetic factors contribute up to 50% to suicide risk. As the candidate gene approach has not identified a gene or set of genes that can be defined as biomarkers for suicidal behaviour, much is expected from cutting edge technological approaches that can interrogate several hundred, or even millions, of biomarkers at a time. These include the '-omic' approaches, such as genomics, transcriptomics, epigenomics, proteomics and metabolomics. Indeed, these have revealed new candidate biomarkers associated with suicidal behaviour. The most interesting of these have been implicated in inflammation and immune responses, which have been revealed through different study approaches, from genome-wide single nucleotide studies and the micro-RNA transcriptome, to the proteome and metabolome. However, the massive amounts of data that are generated by the '-omic' technologies demand the use of powerful computational analysis, and also specifically trained personnel. In this regard, machine learning approaches are beginning to pave the way towards personalized psychiatry.

How Can Drug Metabolism and Transporter Genetics Inform Psychotropic Prescribing?

Many genetic variants in drug metabolizing enzymes and transporters have been shown to be relevant for treating psychiatric disorders. Associations are strong enough to feature on drug labels and for prescribing guidelines based on such data. A range of commercial tests are available; however, there is variability in included genetic variants, methodology, and interpretation. We herein provide relevant background for understanding clinical associations with specific variants, other factors that are relevant to consider when interpreting such data (such as age, gender, drug-drug interactions), and summarize the data relevant to clinical utility of pharmacogenetic testing in psychiatry and the available prescribing guidelines. We also highlight areas for future research focus in this field.

Four Actionable Bottlenecks and Potential Solutions to Translating Psychiatric Genetics Research: An Expert Review

BACKGROUND

Psychiatric genetics has had limited success in translational efforts. A thorough understanding of the present state of translation in this field will be useful in the facilitation and assessment of future translational progress.

PURPOSE

A narrative literature review was conducted. Combinations of 3 groups of terms were searched in EBSCOhost, Google Scholar, and PubMed. The review occurred in multiple steps, including abstract collection, inclusion/exclusion criteria review, coding, and analysis of included papers.

RESULTS

One hundred and fourteen articles were analyzed for the narrative review. Across those, 4 bottlenecks were noted that, if addressed, may provide insights and help improve and increase translation in the field of psychiatric genetics. These 4 bottlenecks are emphasizing linear translational frameworks, relying on molecular genomic findings, prioritizing certain psychiatric disorders, and publishing more reviews than experiments.

CONCLUSIONS

These entwined bottlenecks are examined with one another. Awareness of these bottlenecks can inform stakeholders who work to translate and/or utilize psychiatric genetic information. Potential solutions include utilizing nonlinear translational frameworks as well as a wider array of psychiatric genetic information (e.g., family history and gene-environment interplay) in this area of research, expanding which psychiatric disorders are considered for translation, and when possible, conducting original research. Researchers are urged to consider how their research is translational in the context of the frameworks, genetic information, and psychiatric disorders discussed in this review. At a broader level, these efforts should be supported with translational efforts in funding and policy shifts.

No association between CYP2C19 genetic polymorphism with treatment remission to antidepressant venlafaxine in Han Chinese population

OBJECTIVE

Major depressive disorder (MDD) is a global mental health problem. As a serotonin-noradrenaline reuptake inhibitor (SNRI), the antidepressant venlafaxine is used to alleviate MDD clinically. Recent research has shown that Cytochrome P450 (CYP) enzymes affect venlafaxine efficacy by mediating its metabolism. The present study investigates genetic polymorphisms of cytochrome P450 family 2 subfamily C member 19 (CYP2C19) are associated with remission after venlafaxine treatment for MDD.

METHODS

A total of 175 Han Chinese patients with depression were recruited to accept a 6-week treatment with venlafaxine. Three single-nucleotide polymorphisms of CYP2C19 were selected from dbSNP and previous literature to compare the allele and genotype frequencies between patients in remission and nonremission. Seventeen items Hamilton Depression Scale (17-HAMD) was used to access the outcomes of patients' depressive symptoms through the study. Our results denied the role of CYP2C19 polymorphisms for remission after venlafaxine treatment in MDD patients.

RESULT & CONCLUSION

CYP2C19 genetic polymorphism may not have association with SNRI venlafaxine treatment remission in the Han Chinese population.

Pharmacogenetics of Sertraline Tolerability and Response in Pediatric Anxiety and Depressive Disorders

Objective: To determine whether genetic variants in a pharmacokinetic gene (the number of CYP2C19 reduced function alleles [RFAs]), and in pharmacodynamic genes (HTR2A, SLC6A4, and GRIK4) influence sertraline tolerability and response in a cohort of pediatric patients with anxiety and depressive disorders. Methods: A retrospective analysis was performed using the electronic medical record data of 352 patients <19 years of age being treated for anxiety and/or depressive disorders with sertraline and who underwent routine clinical CYP2C19 genotyping. Additional genotyping and analysis of variants in HTR2A, SLC6A4, and GRIK4 were conducted for 249 patients. Multivariate regression models testing for associations with CYP2C19 were adjusted for concomitant use of interacting medications. Combinatorial classification and regression tree (CART) analyses containing all pharmacokinetic and pharmacodynamic genes and clinical factors were performed. Results: The maximum sertraline dose during the initial titration period of sertraline was inversely associated with the number of CYP2C19 RFAs and sertraline dose at 60 (p = 0.025) and 90 days (p = 0.025). HTR2A rs6313 was associated with sertraline dose (p = 0.011) and time to the average maximum sertraline dose (p = 0.039). Regarding efficacy, the number of CYP2C19 RFAs was not associated with the sertraline dose at the time of response (p = 0.22), whereas for the pharmacodynamic genes, only HTR2A rs6313 was associated with response dose (p = 0.022). An association was observed between predicted expression levels of SLC6A4 and the duration on sertraline (p = 0.025). Combinatorial CART and multivariate regression analyses implicated that pharmacodynamic genes and clinical factors influence the maximum sertraline dose and response dose. The total number of side effects was not associated with any of the variants tested. Conclusion: Both pharmacokinetic and pharmacodynamic factors, in addition to clinical and demographic components, influence sertraline dose, response, and tolerability, thereby necessitating further research to assess for the validity of these pharmacogenetic associations in children and adolescents.

[Pharmacogenetics in psychiatry: state of the art]

In this article, the current literature on pharmacogenetics of antidepressants, antipsychotics and lithium are summarized by the section of Neurobiology and Genetics of the German Society of Psychiatry, Psychotherapy and Neurology (DGPPN). The publications of international expert groups and regulatory authorities are reviewed and discussed. In Germany, a statement on pharmacogenetics was also made by the gene diagnostics committee of the Ministry of Health. The DGPPN supports two recommendations: 1) to perform CYP2D6 genetic testing prior to prescription of tricyclic antidepressants and 2) to determine the HLA-B*1502 genotype in patients of Asian origin before using carbamazepine. The main obstacle for a broad application of pharmacogenetic tests in psychiatry remains the lack of large prospective studies, for both single gene-drug pair and cobinatorial pharmacogenetic tests, to evaluate the benefits of genetic testing. Psychiatrists, geneticists and funding agencies are encouraged to increase their efforts for the future benefit of psychiatric patients.

The Serotonin Syndrome: From Molecular Mechanisms to Clinical Practice

The serotonin syndrome is a medication-induced condition resulting from serotonergic hyperactivity, usually involving antidepressant medications. As the number of patients experiencing medically-treated major depressive disorder increases, so does the population at risk for experiencing serotonin syndrome. Excessive synaptic stimulation of 5-HT_2A receptors results in autonomic and neuromuscular aberrations with potentially life-threatening consequences. In this review, we will outline the molecular basis of the disease and describe how pharmacologic agents that are in common clinical use can interfere with normal serotonergic pathways to result in a potentially fatal outcome. Given that serotonin syndrome can imitate other clinical conditions, an understanding of the molecular context of this condition is essential for its detection and in order to prevent rapid clinical deterioration.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

Exploratory genome-wide association analysis of response to ketamine and a polygenic analysis of response to scopolamine in depression

Growing evidence suggests that the glutamatergic modulator ketamine has rapid antidepressant effects in treatment-resistant depressed subjects. The anticholinergic agent scopolamine has also shown promise as a rapid-acting antidepressant. This study applied genome-wide markers to investigate the role of genetic variants in predicting acute antidepressant response to both agents. The ketamine-treated sample included 157 unrelated European subjects with major depressive disorder (MDD) or bipolar disorder (BD). The scopolamine-treated sample comprised 37 unrelated European subjects diagnosed with either MDD or BD who had a current Major Depressive Episode (MDE), and had failed at least two adequate treatment trials for depression. Change in Montgomery-Asberg Depression Rating Scale (MADRS) or the 17-item Hamilton Depression Rating Scale (HAM-D) scale scores at day 1 (24 h post-treatment) was considered the primary outcome. Here, we conduct pilot genome-wide association study (GWAS) analyses to identify potential markers of ketamine response and dissociative side effects. Polygenic risk score analysis of SNPs ranked by the strength of their association with ketamine response was then calculated in order to assess whether common genetic markers from the ketamine study could predict response to scopolamine. Findings require replication in larger samples in light of low power of analyses of these small samples. Neverthless, these data provide a promising illustration of our future potential to identify genetic variants underlying rapid treatment response in mood disorders and may ultimately guide individual patient treatment selection in the future.

Genetic variants in major depressive disorder: From pathophysiology to therapy

In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.

Editorial: Functional Dyspepsia Treatment: Trials and Tribulations of Targeted Strategies

While functional dyspepsia (FD) is an exceedingly common disorder, the number of treatment options remains limited, and strategies for the individualized implementation of these therapies largely are lacking. In the current issue of American Journal of Gastroenterology, Saito and colleagues report on a secondary analysis of data from the Functional Dyspepsia Treatment Trial, specifically examining the role of two candidate genetic markers in predicting FD response to antidepressant treatments. Though the current study yielded a negative result, it nevertheless emphasizes the importance of our continued pursuit of therapeutic biomarkers in order to move beyond "one-size-fits-all" approaches to the treatment of FD and related disorders.