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Pharmacogenomic testing can be used to guide medication selection in patients with major depressive disorder (MDD). Currently, there is no consensus on which gene or genes to consider in medication management. Here, we assessed the clinical validity of the combinatorial pharmacogenomic algorithm to predict sertraline blood levels in a subset of patients enrolled in the Genomics Used to Improve DEpression Decisions (GUIDED) trial. Patients who reported taking sertraline within ≤2 weeks of the screening blood draw were included. All patients received combinatorial pharmacogenomic testing, which included a weighted assessment of individual phenotypes for multiple pharmacokinetic genes relevant for sertraline (CYP2C19, CYP2B6, and CYP3A4). Sertraline blood levels were compared between phenotypes based on: 1) the pharmacokinetic portion of the combinatorial pharmacogenomic algorithm, and 2) individual genes. When evaluated separately, individual genes (for CYP2C19 and CYP2B6) and the combinatorial algorithm were significant predictors of sertraline blood levels. However, in multivariate analyses that included individual genes and the combinatorial pharmacogenomic algorithm, only the combinatorial pharmacogenomic algorithm remained a significant predictor of sertraline blood levels. These findings support the clinical validity of the combinatorial pharmacogenomic algorithm, in that it is a superior predictor of sertraline blood levels compared to individual genes.

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OBJECTIVE: To evaluate the real-world impact of using a commercially available combinatorial pharmacogenomic (CPGx) test on medication management and clinical outcomes in children and adolescents treated at a tertiary care psychiatry practice. METHODS: A retrospective cohort study using our prospectively maintained database of patients undergoing CPGx testing was performed. Only patients with clinical data at the time of ordering CPGx test (pre-baseline), potential medication change visit (baseline) and 8-weeks follow-up (post-baseline) visit were included. Clinical Global Impression (CGI) scores for each visit were calculated. Appropriate statistical analysis, including one-sample t-test, paired t-test and Chi-square test was performed. RESULTS: Based on the inclusion criteria, 281 (75.9%) of the 370 patients with CPGx testing were included. Their mean age was 15.8 ± 4.5 years (111 females; 39.5%). The average number of medications significantly increased to 2.4 ± 1.2 on the post-baseline visit [t(280) = 8.34, p < 0.001). Medications were added in 123 (43.7%), replaced in 92 (32.7%) patients and remained unchanged in rest. There was no significant association between medication-related adverse effects and psychotropic medication change group (p = 0.27). The study population showed a significant improvement (p < 0.001) in the CGI severity, efficacy, and global improvement indices. CONCLUSION: In our experience of using CPGx test in a large cohort of children and adolescents during routine clinical practice, three-quarter of them underwent medication change. Additionally, we noted an improvement in clinical outcomes without impacting adverse effects. While the role of clinical judgement in medication changes in our cohort is likely, CPGx may supplement clinical decision making. However, the best use and benefit of CPGx in routine clinical practice needs further investigation.

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We evaluated the clinical validity of a combinatorial pharmacogenomic test and single-gene Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines against patient outcomes and medication blood levels to assess their ability to inform prescribing in major depressive disorder (MDD). This is a secondary analysis of the Genomics Used to Improve DEpression Decisions (GUIDED) randomized-controlled trial, which included patients with a diagnosis of MDD, and ≥1 prior medication failure. The ability to predict increased/decreased medication metabolism was validated against blood levels at screening (adjusted for age, sex, smoking status). The ability of predicted gene-drug interactions (pharmacogenomic test) or therapeutic recommendations (single-gene guidelines) to predict patient outcomes was validated against week 8 outcomes (17-item Hamilton Depression Rating Scale; symptom improvement, response, remission). Analyses were performed for patients taking any eligible medication (outcomes N=1,022, blood levels N=1,034) and the subset taking medications with single-gene guidelines (outcomes N=584, blood levels N=372). The combinatorial pharmacogenomic test was the only significant predictor of patient outcomes. Both the combinatorial pharmacogenomic test and single-gene guidelines were significant predictors of blood levels for all medications when evaluated separately; however, only the combinatorial pharmacogenomic test remained significant when both were included in the multivariate model. There were no substantial differences when all medications were evaluated or for the subset with single-gene guidelines. Overall, this evaluation of clinical validity demonstrates that the combinatorial pharmacogenomic test was a superior predictor of patient outcomes and medication blood levels when compared with guidelines based on individual genes.

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Pharmacogenomic tests used to guide clinical treatment for major depressive disorder (MDD) must be thoroughly validated. One important assessment of validity is the ability to predict medication blood levels, which reflect altered metabolism. Historically, the metabolic impact of individual genes has been evaluated; however, we now know that multiple genes are often involved in medication metabolism. Here, we evaluated the ability of individual pharmacokinetic genes (CYP2C19, CYP2D6, CYP3A4) and a combinatorial pharmacogenomic test (GeneSight Psychotropic®; weighted assessment of all three genes) to predict citalopram/escitalopram blood levels in patients with MDD. Patients from the Genomics Used to Improve DEpression Decisions (GUIDED) trial who were taking citalopram/escitalopram at screening and had available blood level data were included (N=191). In multivariate analysis of the individual genes and combinatorial pharmacogenomic test separately (adjusted for age, smoking status), the F statistic for the combinatorial pharmacogenomic test was 1.7 to 2.9-times higher than the individual genes, showing that it explained more variance in citalopram/escitalopram blood levels. In multivariate analysis of the individual genes and combinatorial pharmacogenomic test together, only the combinatorial pharmacogenomic test remained significant. Overall, this demonstrates that the combinatorial pharmacogenomic test was a superior predictor of citalopram/escitalopram blood levels compared to individual genes.

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Aim: To perform a meta-analysis of prospective, two-arm studies examining the clinical utility of using the combinatorial pharmacogenomic test, GeneSight Psychotropic, to inform treatment decisions for patients with major depressive disorder (MDD). Patients & methods: The pooled mean effect of symptom improvement and pooled relative risk ratio (RR) of response and remission were calculated using a random effect model. Results: Overall, 1556 patients were included from four studies, with outcomes evaluated at week 8 or week 10. Patient outcomes were significantly improved for patients with MDD whose care was guided by the combinatorial pharmacogenomic test results compared with unguided care (symptom improvement Δ = 10.08%, 95% CI: 1.67-18.50; p = 0.019; response RR = 1.40, 95% CI: 1.17-1.67; p < 0.001; remission RR = 1.49, 95% CI: 1.17-1.89; p = 0.001). Conclusion: GeneSight Psychotropic guided care improves outcomes among patients with MDD.

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OBJECTIVE: We compared economic outcomes when elderly patients with neuropsychiatric disorders received psychotropic medications guided by a combinatorial pharmacogenomic (PGx) test. METHODS: This is a subanalysis of a 1-year prospective assessment of medication cost for patients with neuropsychiatric disorders receiving combinatorial PGx testing. Pharmacy claims were used to compare per member per year (PMPY) medication cost for patients ≥65 and <65 years old when medications were congruent or incongruent with the PGx test. Polypharmacy was also assessed. RESULTS: Congruent prescribing was associated with savings of US$3497 PMPY (P < .001) for patients ≥65 years and US$2467 PMPY (P < .001) for patients <65, compared to incongruent prescribing. Congruent prescribing in patients ≥65 treated by primary care providers was associated with US$4113 PMPY (P = .026) in savings, while congruent prescribing by psychiatrists was associated with US$120 PMPY (P = .719). Congruent prescribing was also associated with one fewer neuropsychiatric medication for patients ≥65 (P = .070). CONCLUSION: Congruence with PGx testing was associated with medication cost savings in elderly patients.

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OBJECTIVE: To estimate Canadian pharmacy cost savings associated with psychiatric medication prescribing that is guided by combinatorial pharmacogenomic testing in patients switching or augmenting their psychiatric medication. METHODS: Pharmacy claims data from a United States (US) pharmacy benefit manager were analyzed for 1662 patients who recently augmented or switched to a different antidepressant or antipsychotic medication and underwent combinatorial pharmacogenomic testing. Costs of prescription medications were translated to the Canadian healthcare system by matching drug names and doses using the Ontario Drug Benefit Formulary. One-year costs (2017 CAD) were compared between patients whose clinician prescribed antidepressants or antipsychotics that were consistent (congruent) or inconsistent (incongruent) with the combinatorial pharmacogenomic test recommendations. RESULTS: Patients whose psychiatric medication treatment was congruent with the combinatorial pharmacogenomic test report saved $1061 CAD per member per year (PMPY) on prescription medication costs relative to patients whose medications were incongruent with their test report (p<0.0001). For patients ages <65 and ≥65, prescription medication costs were $979 and $1178 CAD PMPY lower, respectively, for patients who followed the report recommendations (p=0.0004 and p=0.13). Prescription drug fills from the US pharmacy claims were concordant with the Canadian Formulary; 62% of fills matched at both the drug name and dose strength, 81% matched at drug name, and >99% matched at the therapeutic chapter. CONCLUSIONS: Antidepressant and antipsychotic prescribing that was congruent with combinatorial pharmacogenomic test guidance was associated with significant cost savings on Canadian prescription medications according to the Ontario Drug Benefit Formulary.

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BACKGROUND: Pharmacogenomic testing has recently become scalable and available to guide the treatment of major depressive disorder (MDD). The objective of the current meta-analysis was to determine if guidance from pharmacogenomic testing results in relatively higher rates of remission and response compared to treatment as usual (i.e., 'unguided' trial-and-error method) in adults with MDD. METHODS: Article databases were systematically searched from inception to December 2, 2017 for human studies assessing the clinical utility of pharmacogenomics in the acute treatment of MDD. Treatment outcomes in MDD may be defined continuously or categorically (i.e., response/remission). Herein, we delimit our focus on categorical outcomes. Using a random-effects model, data was pooled to determine the risk ratio (RR) of response and remission, respectively, in the pharmacogenomic-guided treatment group compared to the unguided group. RESULTS: Four randomized controlled trials (RCTs) and two open-label, controlled cohort studies were included. The pooled RR for treatment response comparing guided versus unguided treatment was 1.36 (95% confidence interval [CI] = 1.14 to 1.62; p = 0.0006; n = 799) in favour of guided treatment. The pooled RR for remission was 1.74 (95%CI = 1.09 to 2.77; p = 0.02, n = 735) also in favour of guided treatment. Heterogeneity in study results suggest that different genetic tests may variably impact response and remission rates. LIMITATIONS: The available evidence is limited, with significant methodological deficiencies. CONCLUSION: The current analysis provides preliminary support for improved response and remission rates in MDD when treatment is guided by pharmacogenomics.

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