Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.

Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. Participants (n = 714) from the Genome-Based Therapeutic Drugs for Depression study received escitalopram or nortriptyline over 12 weeks. Remission was defined as scoring ≤ 7 on the Hamilton Rating Scale. Predictors included demographic, clinical, and genetic variables (at 0 weeks) and measures of symptom severity (at 0, 2, 4, and 6 weeks). Longitudinal descriptors extracted with growth curves and topological data analysis were used to inform prediction of remission. Repeated assessments produced gradual and drug-specific improvements in predictive performance. By Week 4, models' discrimination in all samples reached levels that might usefully inform treatment decisions (area under the receiver operating curve (AUC) = 0.777 for nortriptyline; AUC = 0.807 for escitalopram; AUC = 0.794 for combined sample). Decisions around switching or modifying treatments for depression can be informed by repeated symptom assessments collected during treatment, but not until 4 weeks after the start of treatment.

Haplotype phasing of CYP2D6: an allelic ratio method using Agena MassARRAY data.

Pharmacogenomics aims to use the genetic information of an individual to personalize drug prescribing. There is evidence that pharmacogenomic testing before prescription may prevent adverse drug reactions, increase efficacy, and reduce cost of treatment. CYP2D6 is a key pharmacogene of relevance to multiple therapeutic areas. Indeed, there are prescribing guidelines available for medications based on CYP2D6 enzyme activity as deduced from CYP2D6 genetic data. The Agena MassARRAY system is a cost-effective method of detecting genetic variation that has been clinically applied to other genes. However, its clinical application to CYP2D6 has to date been limited by weaknesses such as the inability to determine which haplotype was present in more than one copy for individuals with more than two copies of the CYP2D6 gene. We report application of a new protocol for CYP2D6 haplotype phasing of data generated from the Agena MassARRAY system. For samples with more than two copies of the CYP2D6 gene for which the prior consensus data specified which one was present in more than one copy, our protocol was able to conduct CYP2D6 haplotype phasing resulting in 100% concordance with the prior data. In addition, for three reference samples known to have more than two copies of CYP2D6 but for which the exact number of CYP2D6 genes was unknown, our protocol was able to resolve the number for two out of the three of these, and estimate the likely number for the third. Finally, we demonstrate that our method is applicable to CYP2D6 hybrid tandem configurations.

Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.

BACKGROUND: Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment. AIMS: To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants. METHOD: Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses. RESULTS: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, ßpooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I2 = 3.61%); this was also found for an IMD index combining these features (n = 372, ßpooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I2= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (ßpooled = 0.16) and the IMD index (ßpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission. CONCLUSIONS: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Dimensions of temperament and character as predictors of antidepressant discontinuation, response and adverse reactions during treatment with nortriptyline and escitalopram.

BACKGROUND: Personality traits may predict antidepressant discontinuation and response. However, previous studies were rather small, only explored a few personality traits and did not include adverse drug effects nor the interdependency between antidepressant discontinuation patterns and response. METHODS: GENDEP included 589 patients with unipolar moderate-severe depression treated with escitalopram or nortriptyline for 12 weeks. Seven personality dimensions were measured using the self-reported 240-item Temperament and Character Inventory-Revised (TCI-R). We applied Cox proportional models to study discontinuation patterns, logistic and linear regression to investigate response and remission after 8 and 12 weeks, and mixed-effects linear models regarding time-varying treatment response and adverse drug reactions. RESULTS: Low harm avoidance, low cooperativeness, high self-transcendence and high novelty seeking were associated with higher risks for antidepressant discontinuation, independent of depressed mood, adverse drug reactions, drug, sex and age. Regression analyses showed that higher novelty seeking and cooperativeness scores were associated with a greater likelihood of response and remission after 8 and 12 weeks, respectively, but we found no correlations with response in the mixed-effects models. Only high harm avoidance was associated with more self-reported adverse effects. CONCLUSIONS: This study, representing the largest investigation between several personality traits and response to two different antidepressants, suggests that correlations between personality traits and antidepressant treatment response may be confounded by differential rates of discontinuation. Future trials on personality in the treatment of depression need to consider this interdependency and study whether interventions aiming at improving compliance for some personality types may improve response to antidepressants.

Logistic Regression With Machine Learning Sheds Light on the Problematic Sexual Behavior Phenotype.

OBJECTIVES: There has been a longstanding debate about whether the mechanisms involved in problematic sexual behavior (PSB) are similar to those observed in addictive disorders, or related to impulse control or to compulsivity. The aim of this report was to contribute to this debate by investigating the association between PSB, addictive disorders (internet addiction, compulsive buying), measures associated with the construct known as reward deficiency (RDS), and obsessive-compulsive disorder (OCD). METHODS: A Canadian university Office of the Registrar invited 68,846 eligible students and postdoctoral fellows. Of 4710 expressing interest in participating, 3359 completed online questionnaires, and 1801 completed the Mini-International Neuropsychiatric Interview. PSB was measured by combining those screening positive (score at least 6) on the Sexual Addiction Screening Test-Revised Core with those self-reporting PSB. Current mental health condition(s) and childhood trauma were measured by self-report. OCD was assessed by a combination of self-report and Mini-International Neuropsychiatric Interview data. RESULTS: Of 3341 participants, 407 (12.18%) screened positive on the Sexual Addiction Screening Test-Revised Core. On logistic regression, OCD, attention deficit, internet addiction, a family history of PSB, childhood trauma, compulsive buying, and male gender were associated with PSB. On multiple correspondence analysis, OCD appeared to cluster separately from the other measures, and the pattern of data differed by gender. CONCLUSIONS: In our sample, factors that have previously been associated with RDS and OCD are both associated with increased odds of PSB. The factors associated with RDS appear to contribute to a separate data cluster from OCD and to lie closer to PSB.

Corrigendum: Validation of single nucleotide variant assays for human leukocyte antigen haplotypes HLA-B*15:02 and HLA-A*31:01 across diverse ancestral backgrounds.

[This corrects the article DOI: 10.3389/fphar.2021.713178.].

Identifying the Common Genetic Basis of Antidepressant Response.

Background: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction. Methods: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA. Results: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response. Conclusions: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

The feasibility and acceptability of mobile application-based assessment of suicidality using self-report components of a novel tool, the Suicide Ideation and Behavior Assessment Tool (SIBAT).

The aim of this study was to assess the validity of a mobile application-based self-report questionnaire in the assessment of suicidality. We developed a program for the administration of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT). We invited university students and trainees enrolled in a study of addictions to complete this component of the SIBAT using the program on their mobile devices or personal computer. 196 participants completed all required modules of the SIBAT, with 97 using their mobile device and 99 using their personal computer. Rates of completed questionnaires between the two groups were compared, as were the responses to the items and the total scores. There was a significant difference between proportions of scale completion in both groups, with a greater number of participants who used a personal computer to complete the scale not responding to all questions compared to participants who used a mobile device to complete the scale. Data collected via mobile device showed good concurrent validity with data collected via personal computer. A trend toward greater disclosure of suicidality was observed in the mobile device group however, replication of these findings using larger sample sizes is needed.

Decreased Medial Prefrontal Cortex Glutamate Levels in Perimenopausal Women.

Objective: There is an increased risk of experiencing depression during perimenopause (PM), a period of rapidly changing female hormone concentrations. Women at particular risk of developing major depression (MD) during PM are those with history of mood sensitivity to female hormone fluctuations i.e., women with a history of premenstrual dysphoric disorder (PMDD) and/or post-partum depression (PPD). Depressive symptomology has been associated with fluctuations of glutamate (Glu) levels in the medial prefrontal cortex (MPFC) in MD patients as well as PMDD and PPD patients. The objective of the study was to compare MPFC Glu levels in healthy perimenopausal and reproductive-aged (RD) women. Methods: Medial prefrontal cortex Glu levels in healthy perimenopausal (n = 15) and healthy RD women (n = 16) were compared via Magnetic Resonance Spectroscopy (MRS) scan using a 3 Tesla (T) magnet. Absence of depressive symptomology and psychiatric comorbidity was confirmed via semi-structured interview. Participants were scanned during the early follicular phase (FP) of the menstrual cycle (MC). Results: Mean MPFC Glu concentrations were decreased in the PM group compared to RD group (PM mean = 0.57 ± 0.03, RD mean = 0.63 ± 0.06, t = -3.84, df = 23.97, p = 0.001). Conclusion: Perimenopause is associated with decreases in MPFC Glu levels. This decrease may be contributing to the increased risk of experiencing depression during PM. Further research should assess MPFC Glu levels in perimenopausal women suffering from MD.

Methodology for clinical genotyping of CYP2D6 and CYP2C19.

Many antidepressants, atomoxetine, and several antipsychotics are metabolized by the cytochrome P450 enzymes CYP2D6 and CYP2C19, and guidelines for prescribers based on genetic variants exist. Although some laboratories offer such testing, there is no consensus regarding validated methodology for clinical genotyping of CYP2D6 and CYP2C19. The aim of this paper was to cross-validate multiple technologies for genotyping CYP2D6 and CYP2C19 against each other, and to contribute to feasibility for clinical implementation by providing an enhanced range of assay options, customizable automated translation of data into haplotypes, and a workflow algorithm. AmpliChip CYP450 and some TaqMan single nucleotide variant (SNV) and copy number variant (CNV) data in the Genome-based therapeutic drugs for depression (GENDEP) study were used to select 95 samples (out of 853) to represent as broad a range of CYP2D6 and CYP2C19 genotypes as possible. These 95 included a larger range of CYP2D6 hybrid configurations than have previously been reported using inter-technology data. Genotyping techniques employed were: further TaqMan CNV and SNV assays, xTAGv3 Luminex CYP2D6 and CYP2C19, PharmacoScan, the Ion AmpliSeq Pharmacogenomics Panel, and, for samples with CYP2D6 hybrid configurations, long-range polymerase chain reactions (L-PCRs) with Sanger sequencing and Luminex. Agena MassARRAY was also used for CYP2C19. This study has led to the development of a broader range of TaqMan SNV assays, haplotype phasing methodology with TaqMan adaptable for other technologies, a multiplex genotyping method for efficient identification of some hybrid haplotypes, a customizable automated translation of SNV and CNV data into haplotypes, and a clinical workflow algorithm.

Internal consistency and concurrent validity of self-report components of a new instrument for the assessment of suicidality, the Suicide Ideation and Behavior Assessment Tool (SIBAT).

This study aimed to assess the internal consistency of self-report components of the Suicide Ideation and Behavior Assessment Tool (SIBAT) and validate it with relevant elements of the Mini International Neuropsychiatric Interview (MINI). The SIBAT is a newly developed instrument for the evaluation of suicidality. In this study, we invited university students and trainees participating in a study of addictions to complete the self-report component of the SIBAT as an add-on study. We evaluated the internal consistency of the self-report component of the SIBAT and validated it against the suicidality component of the MINI. Data were analysed using both complete case analysis and multiple imputation. SIBAT data were collected for 394 participants, 314 of whom had also completed the MINI. The internal consistency of modules 2, 3, and 5 of the SIBAT was high. Each item from module 5 had a statistically significant association with the corresponding item from the MINI. The sum of scores from modules 2 and 3 had a moderate correlation with the assessment of suicide risk determined by the MINI, and a strong correlation with the total score of SIBAT module 5. The completion median time of modules 2, 3 and 5 was 14.3 min.

Validation of Single Nucleotide Variant Assays for Human Leukocyte Antigen Haplotypes HLA-B*15:02 and HLA-A*31:01 Across Diverse Ancestral Backgrounds.

The human leukocyte antigen haplotypes HLA-B*15:02 and HLA-A*31:01 have been linked to life-threatening adverse drug reactions to the anticonvulsants carbamazepine and oxcarbazepine. Identification of these haplotypes via pharmacogenetic techniques facilitates implementation of precision medicine to prevent such reactions. Using reference samples from diverse ancestral origins, we investigated the test analytical validity (i.e., ability to detect whether or not the haplotypes were present or absent) of TaqMan assays for single nucleotide variants previously identified as potentially being able to "tag" these haplotypes. A TaqMan custom assay for rs10484555 and an inventoried assay for rs17179220 and were able to identify with 100% sensitivity and 100% specificity HLA-B*15:02 and HLA-A*31:01 respectively. A custom assay for rs144012689 that takes into account a neighboring single nucleotide variant with manual calling was also able to identify HLA-B*15:02 with 100% sensitivity and 100% specificity. A custom assay for rs106235 identified HLA-A*31:01 with 100% sensitivity and 95% specificity. The slight reduction in specificity for the latter was owing to another haplotype (HLA-A*33:03) also being detected. While any positive call using the rs106235 assay could therefore be further investigated, as the presence of the HLA-A*31:01 haplotype confers adverse drug reaction risk, the absence of false negatives (indexed by sensitivity) is more important than false positives. In summary, we present validated TaqMan assay methodology for efficient detection of HLA haplotypes HLA-B*15:02 and HLA-A*31:01. Our data are relevant for other genotyping technologies that identify, or have the potential to identify, these haplotypes using single nucleotide variants.

Potential therapeutic benefits of cannabinoid products in adult psychiatric disorders: A systematic review and meta-analysis of randomised controlled trials.

The utility of cannabinoids and cannabinoid-based products (CBPs) as a pharmacological aid to treat psychiatric disorders in adulthood is still poorly understood despite a number of comprehensive general reviews discussing the topic. With a focus on randomized controlled trial (RCT) data, this review and meta-analysis aimed to aggregate and evaluate all current high-quality (Level-1) research that specifically assessed the effectiveness of a CBP on a diagnosed adult psychiatric disorder. The following databases, from their inception to September 2020, were included in the search: Academic Search Premier, PubMed, Ovid MEDLINE®, Web of Science™, PsycARTICLES, PsycINFO, CINAHL (Nursing and Allied Health), and Scopus. Risk of bias for each study was individually assessed using the revised Cochrane tool. Of the 2397 papers identified, thirty-one RCTs met criteria for inclusion: ten trials focused on treating cannabis use disorder, six on schizophrenia, five on opioid/tobacco use disorder, three on anxiety disorders, two on Tourette's disorder, two on anorexia nervosa, and one trial each for attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and obsessive compulsive disorder. This review finds limited evidence for the effectiveness of CBPs to acutely treat a narrow range of psychiatric symptoms. We report no evidence supporting the mid- to long-range effectiveness of any currently available CBP. In general, quality of the evidence was assessed as low- to moderate. Importantly, none of the studies discussed in this review presently endorse the use of cannabis flower as a method of treatment for any recognized psychiatric disorder. Larger, hypothesis driven RCTs are required prior to making further therapeutic recommendations.

Sequence2Script: A Web-Based Tool for Translation of Pharmacogenetic Data Into Evidence-Based Prescribing Recommendations.

Pharmacogenomic (PGx) testing has emerged as an effective strategy for informing drug selection and dosing. This has led to an increase in the use of PGx testing in the clinic and has catalyzed the emergence of a burgeoning commercial PGx testing industry. However, not all PGx tests are equivalent in their approach to translating testing results into prescribing recommendations, due to an absence of regulatory standards. As such, those generating and using PGx data require tools for ensuring the prescribing recommendations they are provided align with current peer-reviewed PGx-based prescribing guidelines developed by expert groups or approved product labels. Herein, we present Sequence2Script (sequence2script.com), a simple, free, and transparent web-based tool to assist in the efficient translation of PGx testing results into evidence-based prescribing recommendations. The tool was designed with a wide-range of user groups (e.g., healthcare providers, laboratory staff, researchers) in mind. The tool supports 97 gene-drug pairs with evidence-based prescribing guidelines, allows users to adjust recommendations for concomitant inhibitors and inducers, and generates a clinical report summarizing the patient's genotype, inferred phenotype, phenoconverted phenotype (if applicable), and corresponding prescribing recommendations. In this paper, we describe each of the tool's features, provide use case examples, and discuss limitations of and future development plans for the tool. Although we recognize that Sequecnce2Script may not meet the needs of every user, the hope is that this novel tool will facilitate more standardized use of PGx testing results and reduce barriers to implementing these results into practice.

Mobile App-Based Self-Report Questionnaires for the Assessment and Monitoring of Bipolar Disorder: Systematic Review.

BACKGROUND: Bipolar disorder is a chronic, progressive illness characterized by recurrent episodes of mania and depression. Self-report scales have historically played a significant role in the monitoring of bipolar symptoms. However, these tools rely on episodic memory, which can be unreliable and do not allow the clinician to monitor brief episodic symptoms or the course of symptoms over shorter periods of time. Mobile app-based questionnaires have been suggested as a tool to improve monitoring of patients with bipolar disorder. OBJECTIVE: This paper aims to determine the feasibility and validity of mobile app-based self-report questionnaires. METHODS: We performed a systematic review of the literature according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The PubMed, PsycInfo, Web of Science, Ovid MEDLINE, and EMBASE databases were searched for papers published in English that assessed adherence to and the validity of mobile app-based self-report questionnaires. Relevant studies published from database creation to May 22, 2020, were identified, and results examining the validity of and rates of adherence to app-based self-report questionnaires are reported. RESULTS: A total of 13 records were identified for inclusion in this review. Of these studies, 4 assessed the concurrent validity of mobile app-based self-report tools, with the majority of findings indicating significant associations between data collected using these tools and the Young Mania Rating Scale, Hamilton Depression Rating Scale-17, or Montgomery-Åsberg Depression Rating Scale (P<.001 to P=.24). Three studies comparing the variability or range of symptoms between patients with bipolar disorder and healthy controls suggested that these data are capable of differentiating between known groups. Two studies demonstrated statistically significant associations between data collected via mobile app-based self-report tools and instruments assessing other clinically important factors. Adherence rates varied across the studies examined. However, good adherence rates (>70%) were observed in all but 1 study using a once-daily assessment. There was a wide range of adherence rates observed in studies using twice-daily assessments (42%-95%). CONCLUSIONS: These findings suggest that mobile app-based self-report tools are valid in the assessment of symptoms of mania and depression in euthymic patients with bipolar disorder. Data collected using these tools appear to differ between patients with bipolar disorder and healthy controls and are significantly associated with other clinically important measures. It is unclear at this time whether these tools can be used to detect acute episodes of mania or depression in patients with bipolar disorder. Adherence data indicate that patients with bipolar disorder show good adherence to self-report assessments administered daily for the duration of the study periods evaluated.

CYP2D6 and Antipsychotic Treatment Outcomes in Children and Youth: A Systematic Review.

Objective: To systematically review the impact of CYP2D6 genetic variation on antipsychotic pharmacokinetics, efficacy, and adverse drug reactions among children and youth. Method: The published literature was systematically searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations and critically evaluated using standardized tools and consensus criteria. Results: A total of 20 eligible studies comprising 1078 children and youth were evaluated. The included studies were of fair to moderate quality and included mostly males, individuals of European ancestry, and those treated with risperidone. CYP2D6 poor metabolizers (PMs) were consistently shown to have increased concentrations of risperidone relative to normal metabolizers (NMs). PMs were also consistently shown to have a greater propensity to experience antipsychotic (primarily risperidone) associated adverse drug reactions relative to NMs. However, robust evidence for an association between CYP2D6 and efficacy was less apparent. Conclusion and Clinical Significance: The current knowledge base suggests that CYP2D6 genetic variation has an appreciable impact on antipsychotic pharmacokinetics and the propensity for adverse drug reactions, particularly among children receiving risperidone treatment. However, several limitations with the current literature (e.g., sample sizes, study design, sample heterogeneity) should be addressed in future studies. Assuming that future studies support the link between CYP2D6 genetic variation and antipsychotic outcomes, we would anticipate an increase in the implementation of CYP2D6-guided antipsychotic drug selection and dose optimization in child and adolescent psychiatric services.

Identification of high-impact gene-drug pairs for pharmacogenetic testing in Alberta, Canada.

OBJECTIVES: To facilitate decision-making and priority-setting related to Alberta's Pharmacogenomics (PGx) testing implementation strategy by identifying gene-drug pairs with the highest potential impact on prescribing practices in Alberta. PATIENTS AND METHODS: Annual drug dispensing data for Alberta from 2012 to 2016 for 57 medications with PGx-based prescribing guidelines were obtained, along with population estimates and demographics (age and ethnicity). Frequencies of actionable PGx genotypes by ethnicity were obtained from the Pharmacogenomics Knowledgebase (PharmGKB). Annual dispensing activity for each of the 57 medications was calculated for the full population (all ages) and children/youth (0-19 years). Alberta ethnicity data were cross-referenced with genetic frequency data for each of the main ethnic groups from PharmGKB to estimate the proportion of individuals with actionable genotypes. Actionable genotype proportions and drug dispensing frequencies were collectively used to identify high impact gene-drug pairs. RESULTS: We found (a) half of the drugs with PGx-based prescribing guidelines, namely, analgesics, proton pump inhibitors, psychotropics, and cardiovascular drugs, were dispensed at high frequencies (>1% of the entire population), (b) the dispensing rate for about one-third of these drugs increased over the 5-year study period, (c) between 1.1 and 45% of recipients of these drugs carried actionable genotypes, and (d) the gene-drug pairs with greatest impact in Alberta predominatly included CYP2C19 or CYP2D6. CONCLUSIONS: We uncovered specific patterns in drug dispensing and identified important gene-drug pairs that will inform the planning and development of an evidenced-based PGx testing service in Alberta, Canada. Adaptation of our approach may facilitate the process of evidence-based PGx testing implementation in other jurisdictions.

Are There Therapeutic Benefits of Cannabinoid Products in Adult Mental Illness?

A position statement developed by the Canadian Psychiatric Association's (CPA) Research Committee and approved by the CPA's Board of Directors on May 13, 2020.