Pharmacogenomic insights in psychiatric care: uncovering novel actionability, allele-specific CYP2D6 copy number variation, and phenoconversion in 15,000 patients.

Pharmacogenomic testing has emerged as an aid in clinical decision making for psychiatric providers, but more data is needed regarding its utility in clinical practice and potential impact on patient care. In this cross-sectional study, we determined the real-world prevalence of pharmacogenomic actionability in patients receiving psychiatric care. Potential actionability was based on the prevalence of CYP2C19 and CYP2D6 phenotypes, including CYP2D6 allele-specific copy number variations (CNVs). Combined actionability additionally incorporated CYP2D6 phenoconversion and the novel CYP2C-TG haplotype in patients with available medication data. Across 15,000 patients receiving clinical pharmacogenomic testing, 65% had potentially actionable CYP2D6 and CYP2C19 phenotypes, and phenotype assignment was impacted by CYP2D6 allele-specific CNVs in 2% of all patients. Of 4114 patients with medication data, 42% had CYP2D6 phenoconversion from drug interactions and 20% carried a novel CYP2C haplotype potentially altering actionability. A total of 87% had some form of potential actionability from genetic findings and/or phenoconversion. Genetic variation detected via next-generation sequencing led to phenotype reassignment in 22% of individuals overall (2% in CYP2D6 and 20% in CYP2C19). Ultimately, pharmacogenomic testing using next-generation sequencing identified potential actionability in most patients receiving psychiatric care. Early pharmacogenomic testing may provide actionable insights to aid clinicians in drug prescribing to optimize psychiatric care.

Characterization and prediction of individual functional outcome trajectories in schizophrenia spectrum disorders (PREDICTS study): Study protocol.

Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual's functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A Genotypes and Serotonin Reuptake Inhibitor Antidepressants.

Serotonin reuptake inhibitor antidepressants, including selective serotonin reuptake inhibitors (SSRIs; i.e., citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline), serotonin and norepinephrine reuptake inhibitors (i.e., desvenlafaxine, duloxetine, levomilnacipran, milnacipran, and venlafaxine), and serotonin modulators with SSRI-like properties (i.e., vilazodone and vortioxetine) are primary pharmacologic treatments for major depressive and anxiety disorders. Genetic variation in CYP2D6, CYP2C19, and CYP2B6 influences the metabolism of many of these antidepressants, which may potentially affect dosing, efficacy, and tolerability. In addition, the pharmacodynamic genes SLC6A4 (serotonin transporter) and HTR2A (serotonin-2A receptor) have been examined in relation to efficacy and side effect profiles of these drugs. This guideline updates and expands the 2015 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and CYP2C19 genotypes and SSRI dosing and summarizes the impact of CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A genotypes on antidepressant dosing, efficacy, and tolerability. We provide recommendations for using CYP2D6, CYP2C19, and CYP2B6 genotype results to help inform prescribing these antidepressants and describe the existing data for SLC6A4 and HTR2A, which do not support their clinical use in antidepressant prescribing.

Pharmacogenomics in psychiatry - the challenge of cytochrome P450 enzyme phenoconversion and solutions to assist precision dosing.

Pharmacogenomic (PGx) testing of cytochrome P450 (CYP) enzymes may improve the efficacy and/or safety of some medications. This is facilitated by increased availability and affordability of genotyping, the development of clinical practice PGx guidelines and regulatory support. However, the common occurrence of CYP phenoconversion, a mismatch between genotype-predicted CYP phenotype and the actual CYP phenotype, currently limits the application of PGx testing for precision dosing in psychiatry. This review proposes a stepwise approach to assist precision dosing in psychiatry via the introduction of PGx stewardship programs and innovative PGx education strategies. A future perspective on delivering precision dosing for psychiatrists is discussed that involves innovative clinical decision support systems powered by model-informed precision dosing.

Interactions between neuroticism and stressful life events predict response to pharmacotherapy for major depression: A CAN-BIND 1 report.

Exposure to stressful life events and individual differences in the personality trait neuroticism are important risk factors that interact to predict major depressive disorder (MDD). Less is known about their effect on treatment response in depression. Here, we examine whether stressful life events experienced prior to and during treatment interact with neuroticism to predict response to 16-week pharmacotherapy for MDD. Participants included 159 outpatients with MDD who were initially treated with 8 weeks of escitalopram. Those who responded to the initial treatment continued on escitalopram monotherapy, whereas non-responders received 8 weeks of adjunctive aripiprazole. Personality was assessed using the NEO-Five Factor Inventory, and stressful life events were assessed using the Life Events and Difficulties Schedule, a rigorous contextual interview that includes independent ratings of threatening life events. High baseline neuroticism was associated with a lower likelihood of response when patients experienced one or more negative life events before treatment. Secondary analyses indicated that this effect was specific to neuroticism, and not better accounted for by its self-criticism or negative affect facets. Our results suggest that assessing personality and stressful life events at baseline can help clinicians assess which patients will respond to antidepressant therapy and which may need treatment augmentation.

White matter microstructure in youth at risk for serious mental illness: A comparative analysis.

Identifying biomarkers of serious mental illness, such as altered white matter microstructure, can aid in early diagnosis and treatment. White matter microstructure was assessed using constrained spherical deconvolution of diffusion imaging data in a sample of 219 youth (age 12-25 years, 64.84% female) across 8 sites. Participants were classified as healthy controls (HC; n = 47), familial risk for serious mental illness (n = 31), mild-symptoms (n = 37), attenuated syndromes (n = 66), or discrete disorder (n = 38) based on clinical assessments. Fractional anisotropy (FA) and mean diffusivity (MD) values were derived for the whole brain white matter, forceps minor, anterior cingulate, anterior thalamic radiations (ATR), inferior fronto-occipital fasciculus, superior longitudinal fasciculus (SLF), and uncinate fasciculus (UF). Linear mixed effects models showed a significant effect of age on MD of the left ATR, left SLF, and left UF, and a significant effect of group on FA for all tracts examined. For most tracts, the discrete disorder group had significantly lower FA than other groups, and the attenuated syndromes group had higher FA compared to HC, with few differences between the remaining groups. White matter differences in MDD are most evident in individuals following illness onset, as few significant differences were observed in the risk phase.

Associations between plasma clozapine/N-desmethylclozapine ratio, insulin resistance and cognitive performance in patients with co-morbid obesity and ultra-treatment resistant schizophrenia.

Clozapine (CLZ), the sole antipsychotic with superior efficacy for ultra-treatment resistant schizophrenia (TRS), is limited by adverse effects, including metabolic dysregulation. Clozapine's main metabolite, N-desmethylclozapine (NDMC), has potent 5-HT2C antagonist properties which may explain this metabolic dysfunction, thus the CLZ:NDMC ratio is of particular interest. High insulin resistance states could be associated with CYP1A2 induction and lower CLZ:NDMC ratios. Additionally, lower CLZ:NDMC ratios have been associated with better cognitive, but worse metabolic functioning. This study investigated associations between metabolic and cognitive parameters with the CLZ/NDMC ratio. Primary outcomes included relationships between the CLZ:NDMC ratio to the homeostatic model assessment for insulin resistance (HOMA-IR) and Brief Assessment of Cognition in Schizophrenia (BACS) composite z-scores. Secondary outcomes assessed relationships between CLZ:NDMC ratios to fasting insulin, BMI, weight, fasting glucose, and BACS digit sequencing z-scores. 38 patients who were overweight or obese with schizophrenia or schizoaffective disorder completed fasting bloodwork, anthropometric, psychopathological, and cognitive assessments. Multivariate regressions found a statistically significant inverse association between the CLZ/NDMC ratio and HOMA-IR (B = - 1.028, SE B = .473, ß = - 0.348 p = 0.037), which may have been driven by fasting insulin levels (B = - 27.124, SE B = 12.081, ß = - 0.351 p = 0.031). The CLZ/NDMC ratio may predict insulin resistance/metabolic comorbidity among patients with TRS receiving clozapine.

Reverse translation of major depressive disorder symptoms: A framework for the behavioural phenotyping of putative biomarkers.

BACKGROUND: Reverse translating putative biomarkers of depression from patients to animals is complex because Major Depressive Disorder (MDD) is a highly heterogenous condition. This review proposes an approach to reverse translation based on relating relevant bio-behavioural functions in laboratory rodents to MDD symptoms. METHODS: This systematic review outlines symptom clusters assessed by psychometric tests of MDD and antidepressant treatment response including the Montgomery-Åsberg Depression Rating Scale, the Hamilton Depression Rating Scale, and the Beck Depression Inventory. Symptoms were related to relevant behavioural assays in laboratory rodents. RESULTS: The resulting battery of tests includes passive coping, anxiety-like behaviours, sleep, caloric intake, cognition, psychomotor functions, hedonic reactivity and aversive learning. These assays are discussed alongside relevant clinical symptoms of MDD, providing a framework through which reverse translation of a biomarker can be interpreted. LIMITATIONS: Certain aspects of MDD may not be quantified by tests in laboratory rodents, and their biological significance may not always be of clinical relevance. CONCLUSIONS: Using this reverse translation approach, it is possible to clarify the functional significance of a putative biomarker in rodents and hence translate its contribution to specific clinical symptoms, or clusters of symptoms.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression.

Importance: Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression. Objective: To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. Design, Setting, and Participants: This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. Interventions: All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. Main Outcomes and Measures: The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. Results: Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). Conclusions and Relevance: These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Integration of complementary biomarkers in patients with first episode psychosis: research protocol of a prospective follow up study.

In this project, we recruited a sample of 150 patients with first episode of psychosis with schizophrenia features (FEP) and 100 healthy controls. We assessed the differences between these two groups, as well as the changes between the acute phase of illness and subsequent remission among patients over 18-month longitudinal follow-up. The assessments were divided into four work packages (WP): WP1- psychopathological status, neurocognitive functioning and emotional recognition; WP2- stress response measured by saliva cortisol during a stress paradigm; cerebral blood perfusion in the resting state (with single photon emission computed tomography (SPECT) and during activation paradigm (with Transcranial Ultrasonography Doppler (TCD); WP3-post mortem analysis in histologically prepared human cortical tissue of post mortem samples of subjects with schizophrenia in the region that synaptic alteration was suggested by WP1 and WP2; WP4- pharmacogenetic analysis (single gene polymorphisms and genome wide association study (GWAS). We expect that the analysis of these data will identify a set of markers that differentiate healthy controls from patients with FEP, and serve as an additional diagnostic tool in the first episode of psychosis, and prediction tool which can be then used to help tailoring individualized treatment options. In this paper, we describe the project protocol including aims and methods and provide a brief description of planned post mortem studies and pharmacogenetic analysis.

Clinical Pharmacogenetics Implementation Consortium Guideline for Cytochrome P450 (CYP)2D6 Genotype and Atomoxetine Therapy.

Atomoxetine is a nonstimulant medication used to treat attention-deficit/hyperactivity disorder (ADHD). Cytochrome P450 (CYP)2D6 polymorphisms influence the metabolism of atomoxetine thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for atomoxetine based on CYP2D6 genotype (updates at www.cpicpgx.org).

Antipsychotics, Metabolic Adverse Effects, and Cognitive Function in Schizophrenia.

Cognitive impairment is a core symptom domain of schizophrenia. The effect of antipsychotics, the cornerstone of treatment in schizophrenia, on this domain is not fully clear. There is some evidence suggesting that antipsychotics may partially improve cognitive function, and that this improvement may vary depending on the specific cognitive domain. However, this research is confounded by various factors, such as age, duration/stage of illness, medication adherence, and extrapyramidal side effects that complicate the relationship between antipsychotics and cognitive improvement. Furthermore, antipsychotics-particularly the second generation, or "atypical" antipsychotics-can induce serious metabolic side effects, such as obesity, dyslipidemia and type 2 diabetes, illnesses which themselves have been linked to impairments in cognition. Thus, the inter-relationships between cognition and metabolic side effects are complex, and this review aims to examine them in the context of schizophrenia and antipsychotic treatment. The review also speculates on potential mechanisms underlying cognitive functioning and metabolic risk in schizophrenia. We conclude that the available literature examining the inter-section of antipsychotics, cognition, and metabolic effects in schizophrenia is sparse, but suggests a relationship between metabolic comorbidity and worse cognitive function in patients with schizophrenia. Further research is required to determine if there is a causal connection between the well-recognized metabolic adverse effects of antipsychotics and cognitive deficits over the course of the illness of schizophrenia, as well as, to determine underlying mechanisms. In addition, findings from this review highlight the importance of monitoring metabolic disturbances in parallel with cognition, as well as, the importance of interventions to minimize metabolic abnormalities for both physical and cognitive health.

Association study of the gamma-aminobutyric acid type a receptor gamma2 subunit gene with schizophrenia.

Schizophrenia (SCZ) is a severe neuropsychiatric disorder with a strong genetic basis. We analyzed eight GABRG2 and one DRD5 tag single-nucleotide polymorphisms for association with SCZ in 109 small nuclear families and 229 independent SCZ case-control pairs. The marker rs183294 in the 5' region of GABRG2 was found to be associated with SCZ in both samples with the C allele over-represented in SCZ cases and over-transmitted in SCZ families (combined z=9.18; p<1 x 10(-3)). Taken together, the results of the present study suggest that GABRG2 may be involved in SCZ susceptibility, but further studies are required.

Association of the HTR2C gene and antipsychotic induced weight gain: a meta-analysis.

The 5-HT2C receptor has been hypothesized to represent an important modulator in feeding behaviour. Evidence was based on the observation that knock-out mice for the 5-HT2C receptor gene (HTR2C) develop obesity and that many atypical antipsychotics with potent 5-HT2C antagonism may induce weight gain in susceptible individuals. Pharmacogenetic studies focusing mainly on the -759C/T promoter polymorphism (rs3813929) of the X-linked HTR2C gene revealed controversial results. We investigated the association of the HTR2C gene and weight gain using meta-analytical techniques, combining all published data while restricting our analysis to studies investigating the 759C/T. We also investigated whether ancestry (Caucasian vs. Asian) and clinical factors moderated any association. We found evidence for a slight association of -759C/T with weight gain and significance between studies for heterogeneity. Our meta-analysis provides support for the association of HTR2C in weight gain but indicates that firmly establishing the role of pharmacogenetics in clinical psychiatry requires much larger sample sizes that have been hitherto reported.