Review and Meta-analysis on the Impact of the ADRA2A Variant rs1800544 on Methylphenidate Outcomes in Attention-Deficit/Hyperactivity Disorder.

Background: Methylphenidate is among the most prescribed medications for treating attention-deficit/hyperactivity disorder (ADHD). However, nearly half of pediatric patients with ADHD do not respond to methylphenidate treatment. Pharmacogenetic testing can aid in identifying patients for whom methylphenidate is unlikely to be safe or effective, leading to improved methylphenidate outcomes and increased use of alternative treatment options for ADHD. This article aimed to summarize findings from studies of the ADRA2A gene variant, rs1800544, and its association with methylphenidate outcomes in ADHD. Methods: We systematically reviewed and meta-analyzed available literature on the impact of rs1800544 on methylphenidate outcomes in ADHD. Results: Fourteen studies met inclusion criteria for review, 9 of which were eligible for meta-analysis. The included studies compared methylphenidate outcomes in patients with ADHD categorized by rs1800544 genotype. G-allele carriers experienced significantly greater improvements in ADHD symptom scores (Swanson, Nolan, and Pelham Version-IV Scale or ADHD Rating Scale-IV) relative to noncarriers (odds ratio 3.08, 95% confidence interval 1.71-5.56, p = .0002) and greater response rates as measured by a ≥50% improvement in symptom scores (odds ratio 2.68, 95% confidence interval 1.23-5.82, p = .01); no significant difference in response rate as measured by Clinical Global Impressions score ≤2 was found. Stouffer's z-score method showed significant improvement across all methylphenidate outcomes in G-allele carriers relative to noncarriers (z = 3.03, p = .002). Conclusions: These findings suggest that carriers of rs1800544 may have improved ADHD outcomes following methylphenidate treatment. However, the extent to which these improvements are clinically impactful remain unclear. Additional studies are required to determine if rs1800544 carrier status should influence clinical recommendations for treatment of ADHD symptoms.

Combinatorial pharmacogenomic algorithm is predictive of sertraline metabolism in patients with major depressive disorder.

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.

Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder.

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.