Rapid evidence review of the comparative effectiveness, harms, and cost-effectiveness of pharmacogenomics-guided antidepressant treatment versus usual care for major depressive disorder

Efficacy and safety of pharmacogenomic-guided antidepressant prescribing in patients with depression: an umbrella review and updated meta-analysis

Aim

To determine the efficacy and safety of pharmacogenomics (PGx)-guided antidepressant prescribing in patients with depression through an umbrella review and updated meta-analysis.

Methods

A comprehensive systematic search was conducted on PsycINFO, PubMed, Embase and the Cochrane databases. The pooled effect sizes of randomized controlled trials (RCTs) were expressed as mean differences for continuous data and risk ratios for noncontinuous data.

Results

Patients who received PGx-guided medications were 41% to 78% more likely to achieve remission and 20% to 49% more likely to respond to antidepressants than patients receiving treatment-as-usual (TAU).

Conclusion

PGx-guided antidepressant prescribing improves the treatment of depression. However, the significance and magnitude of the benefit varies widely between studies and different PGx testing panels.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022321324.

Pharmacogenomics-Guided Pharmacotherapy in Patients with Major Depressive Disorder or Bipolar Disorder Affected by Treatment-Resistant Depressive Episodes: A Long-Term Follow-up Study

Treatment-resistant depression (TRD) reduces affected patients’ quality of life and leads to important social health care costs. Pharmacogenomics-guided treatment (PGT) may be effective in the cure of TRD. The main aim of this study was to evaluate the clinical changes after PGT in patients with TRD (two or more recent failed psychopharmacological trials) affected by bipolar disorder (BD) or major depressive disorder (MDD) compared to a control group with treatment as usual (TAU). We based the PGT on assessing different gene polymorphisms involved in the pharmacodynamics and pharmacokinetics of drugs. We analyzed, with a repeated-measure ANOVA, the changes between the baseline and a 6 month follow-up of the efficacy index assessed through the Clinical Global Impression (CGI) scale, and depressive symptoms through the Hamilton Depression Rating Scale (HDRS). The PGT sample included 53 patients (26 BD and 27 MDD), and the TAU group included 52 patients (31 BD and 21 MDD). We found a significant within-subject effect of treatment time on symptoms and efficacy index for the whole sample, with significant improvements in the efficacy index (F = 8.544; partial η² = 0.077, p < 0.004) and clinical global impression of severity of illness (F = 6.818; partial η² = 0.062, p < 0.01) in the PGT vs. the TAU group. We also found a significantly better follow-up response (χ² = 5.479; p = 0.019) and remission (χ² = 10.351; p = 0.001) rates in the PGT vs. the TAU group. PGT may be an important option for the long-term treatment of patients with TRD affected by mood disorders, providing information that can better define drug treatment strategies and increase therapeutic improvement.

Economic Impact of the Application of a Precision Medicine Model (5SPM) on Psychotic Patients

BACKGROUND

Schizophrenia is a severe mental disorder that often manifests within the first three decades of life. Its prognosis is uncertain and may result in a prolonged treatment that could extend throughout the entire lifespan of the patient. Antipsychotic drugs are characterized by a high interindividual variability when considering therapeutic effect and emergence of adverse effects. Such interindividual variability is thought to be associated primarily with pharmacokinetic matters.

OBJECTIVE

The objective of this study was to evaluate the economic impact of the application of the 5-Step Precision Medicine model (5SPM), an approach based on the pharmacogenetic analysis of the primary genes involved in the metabolism of the therapy for each patient, restructuring treatment as necessary.

PATIENTS AND METHODS

One hundred eighty-eight psychiatry patients were analysed for single nucleotide polymorphisms on genes CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A5 and ABCB1. Information on patients' diagnosis, pharmacotherapy, and hospitalizations was collected.

RESULTS

We achieved a cost-benefit ratio of 3.31-3.59 with a reduction of direct cost (hospitalizations plus pharmacotherapy) with a reduction of total cost in 67% of the patients who underwent the clinical intervention.

CONCLUSION

A rational Precision Medicine-based approach to psychiatric patients could result in a reduction on number of drugs required to control exacerbations, and the underlying pathologies, reducing the risk of adverse effects and improving adherence to treatment, leading to a potential decrease in direct costs. This methodology has been shown to be cost-dominant and, being based on a pharmacogenetic analysis, it has a lifelong nature, as the data obtained can be applied to other medical disciplines.

Economic evaluation in psychiatric pharmacogenomics: a systematic review

Nowadays, many relevant drug-gene associations have been discovered, but pharmacogenomics (PGx)-guided treatment needs to be cost-effective as well as clinically beneficial to be incorporated into standard health care. To address current challenges, this systematic review provides an update regarding previously published studies, which assessed the cost-effectiveness of PGx testing for the prescription of antidepressants and antipsychotics. From a total of 1159 studies initially identified by literature database querying, and after manual assessment and curation of all of them, a mere 18 studies met our inclusion criteria. Of the 18 studies evaluations, 16 studies (88.89%) drew conclusions in favor of PGx testing, of which 9 (50%) genome-guided interventions were cost-effective and 7 (38.9%) were less costly compared to standard treatment based on cost analysis. More precisely, supportive evidence exists for CYP2D6 and CYP2C19 drug-gene associations and for combinatorial PGx panels, but evidence is limited for many other drug-gene combinations. Amongst the limitations of the field are the unclear explanation of perspective and cost inputs, as well as the underreporting of study design elements, which can influence though the economic evaluation. Overall, the findings of this article demonstrate that although there is growing evidence on the cost-effectiveness of genome-guided interventions in psychiatric diseases, there is still a need for performing additional research on economic evaluations of PGx implementation with an emphasis on psychiatric disorders.

Predictive association of ABCB1 C3435T genetic polymorphism with the efficacy or safety of lopinavir and ritonavir in COVID-19 patients

Lopinavir and ritonavir are substrates of permeability glycoprotein encoded by ABCB1. The efficacy and safety of these drugs is unknown in COVID-19 patients affected by ABCB1 genetic variability. Patients carrying one or two copies of the ABCB1 C3435T were predictively considered as risk phenotypes. It was predicted that risk phenotypes due to carrying either one or two copies of ABCB1 C3435T were highly prevalent in Europe (76.8%; 95% CI: 75-78), followed by America (67%; 95% CI: 65-69), Asia (63.5%; 95% CI: 62-65) and Africa (41.4%; 95% CI: 37-46), respectively. It is hypothesized that a considerable proportion of COVID-19 patients treated with lopinavir/ritonavir inheriting ABCB1 C3435T genetic polymorphism may be predisposed to either therapeutic failure or toxicity.

Identification of populations likely to benefit from pharmacogenomic testing

OBJECTIVES

Pharmacogenomic testing (PGX) implementation is rapidly expanding, including pre-emptive testing funded by health systems. PGX continues to develop an evidence base that it saves money and improves clinical outcomes. Identifying the potential impact of pre-emptive testing in specific populations may aid in the development of a business case.

METHODS

We utilized a software tool that can evaluate patient drug lists and identified groups of patients most likely to benefit from implementation of a PGX testing program in a major medical system population.

RESULTS

Medication lists were obtained for sixteen patient groups with a total of 82 613 patients. The percent of patients in each group with testing 'Recommended', 'Strongly recommended', or 'Required' ranged from 12.7% in the outpatient pediatric psychiatry group to 75.7% in the any adult inpatient age >50 years group. Some of the highest yield drugs identified were citalopram, simvastatin, escitalopram, metoprolol, clopidogrel, tramadol, and ondansetron.

CONCLUSION

We demonstrate a significant number of patients in each group may have benefit, but targeting certain ones for pre-emptive testing may result in the initial highest yield for a health system.

Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression

Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

Pharmacogenetic Testing: A Tool for Personalized Drug Therapy Optimization

Pharmacogenomics is a study of how the genome background is associated with drug resistance and how therapy strategy can be modified for a certain person to achieve benefit. The pharmacogenomics (PGx) testing becomes of great opportunity for physicians to make the proper decision regarding each non-trivial patient that does not respond to therapy. Although pharmacogenomics has become of growing interest to the healthcare market during the past five to ten years the exact mechanisms linking the genetic polymorphisms and observable responses to drug therapy are not always clear. Therefore, the success of PGx testing depends on the physician's ability to understand the obtained results in a standardized way for each particular patient. The review aims to lead the reader through the general conception of PGx and related issues of PGx testing efficiency, personal data security, and health safety at a current clinical level.

Pharmacogenetics in psychiatric care, a call for uptake of available applications

In this narrative, we evaluate the role of pharmacogenetics in psychiatry from a pragmatic clinical perspective and address current barriers of clinical implementation of pharmacogenetics. Pharmacogenetics has been successfully implemented to improve drug therapy in several clinical areas, but not psychiatry. Yet, psychotropics account for more than one-third of the drugs for which pharmacogenetic guidelines are available and drug therapy in mental disorders is suboptimal with insufficient effectiveness and frequent adverse events. The limited application of pharmacogenetics in psychiatry is influenced by several factors; e.g. the complexity of psychotropic drug metabolism, possibly impeding the clinical understanding of the benefits of pharmacogenetics. Also, recommendations for most psychotropics classify pharmacogenetic testing only as (potentially) beneficial, not as essential, possibly because life-threatening adverse events are often not involved in these drug-gene interactions. Implementing pharmacogenetics in psychiatry could improve the current practice of time-consuming switching of therapies causing undue delays associated with worse outcomes. We expect pharmacogenetics in psychiatry to expedite with panel-based genotyping, including clinically relevant variants, which will address the complex enzymatic metabolism of psychotropic drugs. Until then, we stress that available pharmacogenetic testing should be seen as an integrated companion, not a competitor, in current clinical psychiatric care.

Pharmacogenetics in Psychiatry: An Update on Clinical Usability

Using pharmacogenetics in guiding drug therapy experiences a steady increase in uptake, although still leads to discussions as to its clinical use. Psychiatry constitutes a field where pharmacogenomic testing might help in guiding drug therapy. To address current challenges, this minireview provides an update regarding genotyping (SNP analysis/arrays/NGS), structural variant detection (star-alleles/CNVs/hybrid alleles), genotype-to-phenotype translations, cost-effectiveness, and actionability of results (FDA/CPIC/PharmGKB) regarding clinical importance of pre-emptive pharmacogenomic testing for prescription of antidepressants and antipsychotics.

Integrating Genomics into Psychiatric Practice: Ethical and Legal Challenges for Clinicians

Psychiatric genomics is a rapidly growing field that holds much promise for improving risk prediction, prevention, diagnosis, treatment selection, and understanding of the pathogenesis of patients' symptoms. The field of psychiatry (i.e., professional organizations, mental health clinicians, educational institutions), however, needs to address numerous challenges to promote the responsible translation of genomic technologies and knowledge into psychiatric practice. The goal of this article is to review how clinicians currently encounter and use genomics in the clinic, to summarize the existing literature on how clinicians feel about the use of genomics in psychiatry, and to analyze foreseeable ethical and legal challenges for the responsible integration of genomics into psychiatric care at the structural and clinic levels. Structural challenges are defined as aspects of the larger system of psychiatric practice that constitute potential barriers to the responsible integration of genomics for the purposes of psychiatric care and prevention. These structural challenges exist at a level where professional groups can intervene to set standards and regulate the practice of psychiatry and genomics. Clinic-level challenges are day-to-day issues clinicians face when managing genomic tests in the clinic. We discuss the need for action to mitigate these challenges and maximize the clinical and social utility of psychiatric genomics, including the following: expanding genomics training among mental health clinicians; establishing practice guidelines that consider potential clinical, psychological, and social implications of psychiatric genomics; promoting an integrated care model for managing genomics in psychiatry; emphasizing patient engagement and informed consent when managing genomic testing in psychiatric care.

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

Pharmacogenomic Testing in Psychiatry: Ready for Primetime?

Pharmacogenomic testing in clinical psychiatry has grown at an accelerated pace in the last few years and is poised to grow even further. Despite robust evidence lacking regarding efficacy in clinical use, there continues to be growing interest to use it to make treatment decisions. We intend this article to be a primer for a clinician wishing to understand the biological bases, evidence for benefits, and pitfalls in clinical decision-making. Using clinical vignettes, we elucidate these headings in addition to providing a perspective on current relevance, what can be communicated to patients, and future research directions. Overall, the evidence for pharmacogenomic testing in psychiatry demonstrates strong analytical validity, modest clinical validity, and virtually no evidence to support clinical use. There is definitely a need for more double-blinded randomized controlled trials to assess the use of pharmacogenomic testing in clinical decision-making and care, and until this is done, they could perhaps have an adjunct role in clinical decision-making but minimal use in leading the initial treatment plan.

Research Lifecycle to Increase the Substantial Real-world Impact of Research: Accelerating Innovations to Application

BACKGROUND

US health care systems face a growing demand to incorporate innovations that improve patient outcomes at a lower cost. Funding agencies increasingly must demonstrate the impact of research investments on public health. The Learning Health System promotes continuous institutional innovation, yet specific processes to develop innovations for further research and implementation into real-world health care settings to maximize health impacts have not been specified.

OBJECTIVE

We describe the Research Lifecycle and how it leverages institutional priorities to support the translation of research discoveries to clinical application, serving as a broader operational approach to enhance the Learning Health System.

METHODS

Developed by the US Department of Veterans Affairs Office of Research and Development Research-to-Real-World Workgroup, the Research Lifecycle incorporates frameworks from product development, translational science, and implementation science methods. The Lifecycle is based on Workgroup recommendations to overcome barriers to more direct translation of innovations to clinical application and support practice implementation and sustainability.

RESULTS

The Research Lifecycle posits 5 phases which support a seamless pathway from discovery to implementation: prioritization (leadership priority alignment), discovery (innovation development), validation (clinical, operational feasibility), scale-up and spread (implementation strategies, performance monitoring), and sustainability (business case, workforce training). An example of how the Research Lifecycle has been applied within a health system is provided.

CONCLUSIONS

The Research Lifecycle aligns research and health system investments to maximize real-world practice impact via a feasible pathway, where priority-driven innovations are adapted for effective clinical use and supported through implementation strategies, leading to continuous improvement in real-world health care.

Moving pharmacoepigenetics tools for depression toward clinical use

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide, and over half of patients do not achieve symptom remission following an initial antidepressant course. Despite evidence implicating a strong genetic basis for the pathophysiology of MDD, there are no adequately validated biomarkers of treatment response routinely used in clinical practice. Pharmacoepigenetics is an emerging field that has the potential to combine both genetic and environmental information into treatment selection and further the goal of precision psychiatry. However, this field is in its infancy compared to the more established pharmacogenetics approaches.

METHODS

We prepared a narrative review using literature searches of studies in English pertaining to pharmacoepigenetics and treatment of depressive disorders conducted in PubMed, Google Scholar, PsychINFO, and Ovid Medicine from inception through January 2019. We reviewed studies of DNA methylation and histone modifications in both humans and animal models of depression.

RESULTS

Emerging evidence from human and animal work suggests a key role for epigenetic marks, including DNA methylation and histone modifications, in the prediction of antidepressant response. The challenges of heterogeneity of patient characteristics and loci studied as well as lack of replication that have impacted the field of pharmacogenetics also pose challenges to the development of pharmacoepigenetic tools. Additionally, given the tissue specific nature of epigenetic marks as well as their susceptibility to change in response to environmental factors and aging, pharmacoepigenetic tools face additional challenges to their development.

LIMITATIONS

This is a narrative and not systematic review of the literature on the pharmacoepigenetics of antidepressant response. We highlight key studies pertaining to pharmacoepigenetics and treatment of depressive disorders in humans and depressive-like behaviors in animal models, regardless of sample size or methodology. While we discuss DNA methylation and histone modifications, we do not cover microRNAs, which have been reviewed elsewhere recently.

CONCLUSIONS

Utilization of genome-wide approaches and reproducible epigenetic assays, careful selection of the tissue assessed, and integration of genetic and clinical information into pharmacoepigenetic tools will improve the likelihood of developing clinically useful tests.

Pharmacogenetic tests and depressive symptom remission: a meta-analysis of randomized controlled trials

Aim: To conducted a systematic review and meta-analysis of prospective, randomized controlled trials (RCTs) that examined pharmacogenetic-guided decision support tools (DSTs) relevant to depressive symptom remission in major depressive disorder (MDD). Patients & methods: Random-effects meta-analysis was performed on RCTs that examined the effect of DSTs on remission rates in MDD. RCT quality was assessed using the Cochrane Collaboration Criteria. Results & conclusion: A total of 1737 eligible subjects from five RCTs were examined. Individuals receiving pharmacogenetic-guided DST therapy (n = 887) were 1.71 (95% CI: 1.17–2.48; p = 0.005) times more likely to achieve symptom remission relative to individuals who received treatment as usual (n = 850). Pharmacogenetic-guided DSTs might improve symptom remission among those with MDD.

Transcutaneous auricular vagus nerve stimulation in treating major depressive disorder: A systematic review and meta-analysis

BACKGROUND

Transcutaneous auricular vagus nerve stimulation (taVNS), as a noninvasive intervention, has beneficial effects on major depressive disorder based on clinical observations. However, the potential benefits and clinical role of taVNS in the treatment of major depressive disorder are still uncertain and have not been systematically evaluated. Therefore, we performed a systematic review and meta-analysis to evaluate the effectiveness and safety of taVNS in treating major depressive disorder.

METHODS

Four electronic databases, namely, Embase, MEDLINE, the Cochrane Library and PsycINFO, were searched for all related trials published through May 1, 2018. We extracted the basic information and data of the included studies and evaluated the methodological quality with the Cochrane risk of bias tool and the nonrandomized studies-of interventions (ROBINS-I) tool. A meta-analysis of the comparative effects was conducted using the Review Manager 5.3 software.

RESULTS

A total of 423 citations from the databases were searched, and 4 studies with 222 individuals were included in the meta-analysis. The taVNS technique could decrease 24-item HAMD scores more than the sham intervention (MD: -4.23, 95% CI: -7.15, -1.31; P = .005) and was also more effective in decreasing Self-Rating Depression Scale scores ((MD: -10.34, 95% CI: -13.48, -7.20; P < .00001), Beck Depression Inventory scores (MD: -10.3, 95% CI: -18.1, -2.5; P = .01) and Self-Rating Anxiety Scale scores (MD: -6.57, 95% CI: -9.30, -3.84; P < .00001). However, there was no significant difference in the Hamilton Anxiety Rating Scale scores between the taVNS and sham taVNS groups (MD: -1.12, 95% CI: -2.56, 0.32; P = .13). No obvious adverse effects of taVNS treatment were reported in the included studies.

CONCLUSION

The results of the analysis preliminarily demonstrated that taVNS therapy can effectively ameliorate the symptoms of major depressive disorder, providing an alternative technique for addressing depression. However, more well-designed RCTs with larger sample sizes and follow-ups are needed in future studies to confirm our findings.

Pharmacogenetic profile and major depressive and/or bipolar disorder treatment: a retrospective, cross-sectional study

Aim: To compare pharmacogenetic test predictions with self-reported treatment experience and side effect tolerability among patients with depression taking psychotherapeutic medications. Methods: Subjects completed a survey recalling medication effectiveness and side effects and then underwent pharmacogenetic testing. Results: Our 15 gene pharmacogenetic panel predicted efficacy (p < 0.001) but did not predict side effect tolerability (p = 0.70) in a group of 352 patients. The pharmacogenetic panel and reported efficacy corresponded 60% of the time and medication tolerability agreed 71% of the time. Conclusion: Pharmacogenetic testing may be a useful adjunct to predict efficacy of medications used to treat depression.

Polygenic risk scores for major depressive disorder and neuroticism as predictors of antidepressant response: Meta-analysis of three treatment cohorts

There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10-5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

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.

Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing

The accrual and analysis of genomic sequencing data have identified specific genetic variants that are associated with major depressive disorder. Moreover, substantial investigations have been devoted to identifying gene-drug interactions that affect the response to antidepressant medications by modulating their pharmacokinetic or pharmacodynamic properties. Despite these advances, individual responses to antidepressants, as well as the unpredictability of adverse side effects, leave clinicians with an imprecise prescribing strategy that often relies on trial and error. These limitations have spawned several combinatorial pharmacogenetic testing products that are marketed to physicians. Typically, combinatorial pharmacogenetic decision support tools use algorithms to integrate multiple genetic variants and assemble the results into an easily interpretable report to guide prescribing of antidepressants and other psychotropic medications. The authors review the evidence base for several combinatorial pharmacogenetic decision support tools whose potential utility has been evaluated in clinical settings. They find that, at present, there are insufficient data to support the widespread use of combinatorial pharmacogenetic testing in clinical practice, although there are clinical situations in which the technology may be informative, particularly in predicting side effects.

Pharmacogenetic tests to guide drug treatment in depression: Comparison of the available testing kits and clinical trials

The empirical approach to drug choice and dosing in depression often results into inadequate response and side effects. Pharmacogenetic (PGx) testing appears a promising way to implement personalized treatments. A systematic review was performed to identify available PGx tests, compare the genes they include with clinical guidelines and drug labels, and assess the quality of published clinical studies. ~40 commercial PGx tests are available and potential benefits were estimated for nine of them by clinical studies. The most part of studies are observational (9/21) or non-randomized case-control trials that compared standard care with PGx-guided treatment (6/21), six randomized controlled trials (RCTs) are available. The only genes included in all the available PGx tests and with recommendations in current clinical guidelines and drug labels are CYP2D6 and CYP2C19. There is heterogeneity among outcome measures across studies (response, remission, improvement, health care utilization, medication tolerability), as well as in trial design. Relatively weak clinical benefits were reported by RCTs and higher clinical benefits by non-RCTs, but the last group showed greater risk of bias. Lack of patient and rater's blindness, retrospective design and possible confounders (concomitant medications and medical diseases, lack of wash out prior to inclusion, no assessment of compliance etc.) were the main issues. Estimations of cost savings provided heterogeneous findings. Variants in CYP2D6 and CYP2C19 have already adequate support for clinical application. The development of future PGx tests should include best practices for clinical evidence development and for health economic assessment.

The functional variant rs334558 of GSK3B is associated with remission in patients with depressive disorders

Purpose

GSK3B and AKT1 genes have been implicated in the pathogenesis of a number of psychiatric and neurological disorders. Furthermore, their genetic variants are associated with response to antidepressant pharmacotherapy. As the evidence is still incomplete and inconsistent, continuing efforts to investigate the role of these two genes in the pathogenesis and treatment of brain disorders is necessary. The aim of our study was thus to evaluate the association of variants of these two genes with depressive disorders and drug treatment response.

Patients and methods

In the present study, 222 patients with a depressive disorder who underwent pharmacological antidepressant treatment were divided into remitters and non-remitters following a 28-day course of pharmacotherapy. The association of a depressive disorder and remission rates with polymorphisms rs334558 in the GSK3B gene and rs1130214 and rs3730358 in the AKT1 gene was evaluated with a chi-square test.

Results

Neither of the studied genetic variants was associated with a depressive disorder. Furthermore, frequencies of alleles and genotypes for rs1130214 and rs3730358 were not different in the groups of remitters and non-remitters. However, the activating allele T of the functional polymorphism rs334558 was significantly associated with remission, when all types of antidepressant drugs were included. This association continued as a trend when only patients taking selective serotonin reuptake inhibitors were considered.

Conclusion

The present study provides support that the functional polymorphism rs334558 of GSK3B may play a role as a useful genetic and pharmacogenetic biomarker in the framework of personalized medicine approach.

The revolution of personalized psychiatry: will technology make it happen sooner?

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

Pharmacogenetic Decision Support Tools: A New Paradigm for Late-Life Depression?

Clinicians still employ a "trial-and-error" approach to optimizing treatment regimens for late-life depression (LLD). With LLD affecting a significant and growing segment of the population, and with only about half of older adults responsive to antidepressant therapy, there is an urgent need for a better treatment paradigm. Pharmacogenetic decision support tools (DSTs), which are emerging technologies that aim to provide clinically actionable information based on a patient's genetic profile, offer a promising solution. Dozens of DSTs have entered the market in the past 15 years, but with varying level of empirical evidence to support their value. In this clinical review, we provide a critical analysis of the peer-reviewed literature on DSTs for major depression management. We then discuss clinical considerations for the use of these tools in treating LLD, including issues related to test interpretation, timing, and patient perspectives. In adult populations, newer generation DSTs show promise for the treatment of major depression. However, there are no primary clinical trials in LLD cohorts. Independent and comparative clinical trials are needed.

Point-of-care companion diagnostic tests for personalizing psychiatric medications: fulfilling an unmet clinical need

Over the last decade stable isotope-labeled substrates have been used as probes for rapid, point-of-care, non-invasive and user-friendly phenotype breath tests to evaluate activity of drug metabolizing enzymes. These diagnostic breath tests can potentially be used as companion diagnostics by physicians to personalize medications, especially psychiatric drugs with narrow therapeutic windows, to monitor the progress of disease severity, medication efficacy and to study in vivo the pharmacokinetics of xenobiotics. Several genotype tests have been approved by the FDA over the last 15 years for both cytochrome P450 2D6 and 2C19 enzymes, however they have not been cleared for use in personalizing medications since they fall woefully short in identifying all non-responders to drugs, especially for the CYP450 enzymes. CYP2D6 and CYP2C19 are among the most extensively studied drug metabolizing enzymes, involved in the metabolism of approximately 30% of FDA-approved drugs in clinical use, associated with large individual differences in medication efficacy or tolerability essentially due to phenoconversion. The development and commercialization via FDA approval of the non-invasive, rapid (<60 min), in vivo, phenotype diagnostic breath tests to evaluate polymorphic CYP2D6 and CYP2C19 enzyme activity by measuring exhaled ¹³CO₂ as a biomarker in breath will effectively resolve the currently unmet clinical need for individualized psychiatric drug therapy. Clinicians could personalize treatment options for patients based on the CYP2D6 and CYP2C19 phenotype by selecting the optimal medication at the right initial and subsequent maintenance dose for the desired clinical outcome (i.e. greatest efficacy and minimal side effects).

Economic Evaluation of Implementing a Novel Pharmacogenomic Test (IDgenetix®) to Guide Treatment of Patients with Depression and/or Anxiety

BACKGROUND

The response to therapeutics varies widely in patients with depression and anxiety, making selection of an optimal treatment choice challenging. IDgenetix^®, a novel pharmacogenomic test, has been shown to improve outcomes by predicting the likelihood of response to different psychotherapeutic medications.

OBJECTIVE

The objective of this study was to estimate the cost effectiveness of implementing a novel pharmacogenomic test (IDgenetix^®) to guide treatment choices in patients with depression and/or anxiety compared with treatment as usual from the US societal perspective.

METHODS

We developed a discrete event simulation to compare clinical events, quality-adjusted life-years, and costs of the two treatment strategies. Target patients had a Hamilton Rating Scale for Depression Score ≥ 20 and/or a Hamilton Rating Scale for Anxiety score ≥ 18 at baseline. Remission, response, and no response were simulated based on the observed rates in the IDgenetix^® randomized controlled trial. Quality-adjusted life-years and direct and indirect costs attributable to depression and anxiety were estimated and compared over a 3-year time horizon. We conducted extensive deterministic and probabilistic sensitivity analyses to assess the robustness of the results.

RESULTS

The model predicted cumulative remission rates of 78 and 66% in IDgenetix^® and treatment as usual groups, respectively. Estimated discounted quality-adjusted life-years were 2.09 and 1.94 per patient for IDgenetix^® and treatment as usual, respectively, which resulted in 0.15 incremental quality-adjusted life-years (95% credible interval 0.04-0.28). The total costs after accounting for a US$2000 test cost were US$14,124 for IDgenetix^® compared with US$14,659 for treatment as usual, suggesting a US$535 (95% credible interval - 2902 to 1692) cost saving per patient in the IDgenetix^® group. Incremental quality-adjusted life-year gain (0.49) and cost savings (US$6800) were substantially larger in patients with severe depression (Hamilton Rating Scale for Depression score ≥ 25).

CONCLUSION

Using the IDgenetix^® test to guide the treatment of patients with depression and anxiety may be a dominant strategy, as it improves quality-adjusted life-years and decreases overall costs over a 3-year time horizon.

The role of depression pharmacogenetic decision support tools in shared decision making

Patients discontinue antidepressant medications due to lack of knowledge, unrealistic expectations, and/or unacceptable side effects. Shared decision making (SDM) invites patients to play an active role in their treatment and may indirectly improve outcomes through enhanced engagement in care, adherence to treatment, and positive expectancy of medication outcomes. We believe decisional aids, such as pharmacogenetic decision support tools (PDSTs), facilitate SDM in the clinical setting. PDSTs may likewise predict drug tolerance and efficacy, and therefore adherence and effectiveness on an individual-patient level. There are several important ethical considerations to be navigated when integrating PDSTs into clinical practice. The field requires greater empirical research to demonstrate clinical utility, and the mechanisms thereof, as well as exploration of the ethical use of these technologies.

Pharmacoeconomics of genotyping-based treatment decisions in patients with chronic pain

INTRODUCTION

Genotyping-based treatment decisions may optimize treatment response and minimize adverse drug events (ADEs) in patients with chronic pain.

OBJECTIVES

To estimate the financial impact of genotyping-based treatment decisions in patients with moderate to severe chronic pain in a managed care setting.

METHODS

A budget impact model was built with a 1-year time horizon to estimate costs of genotyping-based treatment decisions in a 1000-patient cohort. The model includes drug costs, type and cost of ADEs, distribution of treatments used, and genotyping costs. Event rates and health care costs were derived from primary literature. Three patient cohorts were assessed with and without genotyping-based treatment decisions: no genetic testing; 50% genetic testing; and 100% genetic testing. Sensitivity analysis was performed varying costs, adherence, and the percentage of patients treated according to genotyping results.

RESULTS

Medical and ADE costs varied by patient severity and genotyping rates. Without genotyping, drug and ADE costs ranged from $1,544,377 to $24,313,844. With genotyping-based treatment, total costs ranged from $1,780,922 to $18,868,032. Sensitivity analysis, varying costs, adherence, and genotyping rates suggested genotyping improves outcomes and is cost saving in patients with chronic pain.

CONCLUSION

Genotyping-based treatment costs are offset by reduced medication utilization and adverse event costs. Genotyping should be considered for patients with chronic pain in clinical practice and within clinical trials.

Remote Ischemic Conditioning: A Novel Non-Invasive Approach to Prevent Post-Stroke Depression

Post-stroke depression (PSD) is a common neuropsychiatric complication of stroke. However, due to the high expense and side effects of pharmacotherapy and the difficult-to-achieve of psychotherapy, the prevention and treatment of PSD are still far from satisfaction. Inflammation hypothesis is now playing an essential role in the pathophysiological mechanism of PSD, and it may be a new preventive and therapeutic target. Remote ischemic conditioning (RIC) is a non-invasive and easy-to-use physical strategy, which has been used to protect brain (including ischemic and hemorrhagic stroke), heart and many other organs in clinical trials. The underlying mechanisms of RIC include anti-inflammation, anti-oxidative stress, immune system regulation and other potential pathways. Our hypothesis is that RIC is a novel approach to prevent PSD. The important implications of this hypothesis are that: (1) RIC could be widely used in clinical practice to prevent PSD if our hypothesis were verified; and (2) RIC would be thoroughly explored to test its effects on other neurobehavioral disorders (e.g., cognitive impairment).

Antidepressant prescribing in the precision medicine era: a prescriber's primer on pharmacogenetic tools

About half of people who take antidepressants do not respond and many experience adverse effects. These detrimental outcomes are in part a result of the impact of an individual's genetic profile on pharmacokinetics and pharmcodynamics. If known and made available to clinicians, this could improve decision-making and antidepressant therapy outcomes. This has spurred the development of numerous pharmacogenetic-based decision support tools. In this article, we provide an overview of pharmacogenetic decision support tools, with particular focus on tools relevant to antidepressants. We briefly describe the evolution and current state of antidepressant pharmacogenetic decision support tools in clinical practice, followed by the evidence-base for their use. Finally, we present a series of considerations for clinicians contemplating use of these tools and discuss the future of antidepressant pharmacogenetic decision support tools.