The Pharmacogenomics Global Research Network Implementation Working Group: global collaboration to advance pharmacogenetic implementation.

Pharmacogenetics promises to optimize treatment-related outcomes by informing optimal drug selection and dosing based on an individual's genotype in conjunction with other important clinical factors. Despite significant evidence of genetic associations with drug response, pharmacogenetic testing has not been widely implemented into clinical practice. Among the barriers to broad implementation are limited guidance for how to successfully integrate testing into clinical workflows and limited data on outcomes with pharmacogenetic implementation in clinical practice. The Pharmacogenomics Global Research Network Implementation Working Group seeks to engage institutions globally that have implemented pharmacogenetic testing into clinical practice or are in the process or planning stages of implementing testing to collectively disseminate data on implementation strategies, metrics, and health-related outcomes with the use of genotype-guided drug therapy to ultimately help advance pharmacogenetic implementation. This paper describes the goals, structure, and initial projects of the group in addition to implementation priorities across sites and future collaborative opportunities.

Evaluating the extent of reusability of CYP2C19 genotype data among patients genotyped for antiplatelet therapy selection.

PURPOSE: Genotype-guided antiplatelet therapy is increasingly being incorporated into clinical care. The purpose of this study is to determine the extent to which patients initially genotyped for CYP2C19 to guide antiplatelet therapy were prescribed additional medications affected by CYP2C19. METHODS: We assembled a cohort of patients from eight sites performingCYP2C19 genotyping to inform antiplatelet therapy. Medication orders were evaluated from time of genotyping through one year. The primary endpoint was the proportion of patients prescribed two or more CYP2C19 substrates. Secondary endpoints were the proportion of patients with a drug-genotype interaction and time to receiving a CYP2C19 substrate. RESULTS: Nine thousand one hundred ninety-one genotyped patients (17% nonwhite) with a mean age of 68 ± 3 years were evaluated; 4701 (51%) of patients received two or more CYP2C19 substrates and 3835 (42%) of patients had a drug-genotype interaction. The average time between genotyping and CYP2C19 substrate other than antiplatelet therapy was 25 ± 10 days. CONCLUSIONS: More than half of patients genotyped in the setting of CYP2C19-guided antiplatelet therapy received another medication impacted by CYP2C19 in the following year. Given that genotype is stable for a patient's lifetime, this finding has implications for cost effectiveness, patient care, and treatment outcomes beyond the indication for which it was originally performed.

Correction: Evaluating the extent of reusability of CYP2C19 genotype data among patients genotyped for antiplatelet therapy selection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Genome-wide association analysis of common genetic variants of resistant hypertension.

Resistant hypertension (RHTN), defined as uncontrolled blood pressure (BP) ≥ 140/90 using three or more drugs or controlled BP (<140/90) using four or more drugs, is associated with adverse outcomes, including decline in kidney function. We conducted a genome-wide association analysis in 1194 White and Hispanic participants with hypertension and coronary artery disease from the INternational VErapamil-SR Trandolapril STudy-GENEtic Substudy (INVEST-GENES). Top variants associated with RHTN at p < 10-4 were tested for replication in 585 White and Hispanic participants with hypertension and subcortical strokes from the Secondary Prevention of Subcortical Strokes GENEtic Substudy (SPS3-GENES). A genetic risk score for RHTN was created by summing the risk alleles of replicated RHTN signals. rs11749255 in MSX2 was associated with RHTN in INVEST (odds ratio (OR) (95% CI) = 1.50 (1.2-1.8), p = 7.3 × 10-5) and replicated in SPS3 (OR = 2.0 (1.4-2.8), p = 4.3 × 10-5), with genome-wide significance in meta-analysis (OR = 1.60 (1.3-1.9), p = 3.8 × 10-8). Other replicated signals were in IFLTD1 and PTPRD. IFLTD1 rs6487504 was associated with RHTN in INVEST (OR = 1.90 (1.4-2.5), p = 1.1 × 10-5) and SPS3 (OR = 1.70 (1.2-2.5), p = 4 × 10-3). PTPRD rs324498, a previously reported RHTN signal, was among the top signals in INVEST (OR = 1.60 (1.3-2.0), p = 3.4 × 10-5) and replicated in SPS3 (OR = 1.60 (1.1-2.4), one-sided p = 0.005). Participants with the highest number of risk alleles were at increased risk of RHTN compared to participants with a lower number (p-trend = 1.8 × 10-15). Overall, we identified and replicated associations with RHTN in the MSX2, IFLTD1, and PTPRD regions, and combined these associations to create a genetic risk score.

Concise Review: Induced Pluripotent Stem Cell Research in the Era of Precision Medicine.

Recent advances in DNA sequencing technologies are revealing how human genetic variations associate with differential health risks, disease susceptibilities, and drug responses. Such information is now expected to help evaluate individual health risks, design personalized health plans and treat patients with precision. It is still challenging, however, to understand how such genetic variations cause the phenotypic alterations in pathobiologies and treatment response. Human induced pluripotent stem cell (iPSC) technologies are emerging as a promising strategy to fill the knowledge gaps between genetic association studies and underlying molecular mechanisms. Breakthroughs in genome editing technologies and continuous improvement in iPSC differentiation techniques are particularly making this research direction more realistic and practical. Pioneering studies have shown that iPSCs derived from a variety of monogenic diseases can faithfully recapitulate disease phenotypes in vitro when differentiated into disease-relevant cell types. It has been shown possible to partially recapitulate disease phenotypes, even with late onset and polygenic diseases. More recently, iPSCs have been shown to validate effects of disease and treatment-related single nucleotide polymorphisms identified through genome wide association analysis. In this review, we will discuss how iPSC research will further contribute to human health in the coming era of precision medicine. Stem Cells 2017;35:545-550.

Genetics of resistant hypertension: a novel pharmacogenomics phenotype.

Resistant hypertension (RHTN), defined as an uncontrolled blood pressure despite the use of multiple antihypertensive medications, is an increasing clinical problem associated with increased cardiovascular (CV) risk, including stroke and target organ damage. Genetic variability in blood pressure (BP)-regulating genes and pathways may, in part, account for the variability in BP response to antihypertensive agents, when taken alone or in combination, and may contribute to the RHTN phenotype. Pharmacogenomics focuses on the identification of genetic factors responsible for inter-individual variability in drug response. Expanding pharmacogenomics research to include patients with RHTN taking multiple BP-lowering medications may identify genetic markers associated with RHTN. To date, the available evidence surrounding pharmacogenomics in RHTN is limited and primarily focused on candidate genes. In this review, we summarize the most current data in RHTN pharmacogenomics and offer some recommendations on how to advance the field.

The influence of the CYP2C19*10 allele on clopidogrel activation and CYP2C19*2 genotyping.

BACKGROUND/OBJECTIVES: The polymorphic hepatic enzyme CYP2C19 catalyzes the metabolism of clinically important drugs such as clopidogrel, proton-pump inhibitors, and others and clinical pharmacogenetic testing for clopidogrel is increasingly common. The CYP2C19*10 single-nucleotide polymorphism (SNP) is located 1 bp upstream the CYP2C19*2 SNP. Despite the low frequency of the CYP2C19*10 allele, its impact on metabolism of CYP2C19 substrates and CYP2C19*2 genotyping makes it an important SNP to consider for pharmacogenetic testing of CYP2C19. However, the effect of the CYP2C19*10 allele on clopidogrel metabolism has not been explored to date. METHODS: We measured the enzymatic activity of the CYP2C19.10 protein against clopidogrel. DNA samples from two clinical studies were genotyped for CYP2C19*2 and *10 by pyrosequencing genotyping method. RESULTS: The catalytic activity of CYP2C19.10 in the biotransformation of clopidogrel and 2-oxo-clopidogrel was significantly decreased relative to the wild-type CYP2C19.1B. We also reported that the CYP2C19*10 SNP interferes with the CYP2C19*2 TaqMan genotyping assay, resulting in miscalling of CYP2C19*10/*2 as CYP2C19*2/*2. CONCLUSIONS: Our data provide evidence that CYP2C19.10 variant partially metabolizes clopidogrel and 2-oxo-clopidogrel, and the presence of CYP2C19*10 allele affects the CY2C19*2 TaqMan genotyping assay and results in misclassification of CYP2C19*10/*2 as CYP2C19*2/*2.

Pharmacogenomics polygenic risk score: Ready or not for prime time?

Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.

Genomewide association analysis of warfarin dose requirements in Middle Eastern and North African populations.

To date, there has been no genomewide association study (GWAS) from the Middle East and North African (MENA) region to identify genetic variants associated with warfarin dose variability using this approach. In this study, we aimed to conduct the first GWAS of warfarin dose requirements in patients from the MENA region. A total of 132 Qatari (discovery) and 50 Egyptians (replication) were genotyped using Illumina Multi-Ethnic Global BeadChip Array. A GWAS was performed on log-transformed weekly warfarin dose in the studied population, adjusting for clinical characteristics and ancestry. The genomewide signals from the discovery cohort were tested in the Egyptian cohort. A GWAS meta-analysis, including the Qatari and Egyptian cohorts, was also performed and the output from this analysis was used in a gene-based analysis. The discovery analysis in Qatari identified five genomewide single-nucleotide polymorphisms (SNPs) in chromosome 16. These signals were replicated in the Egyptian cohort. Combining the two data through a GWAS meta-analysis strengthened the association in chromosome 16 with VKORC1 rs9934438 being the lead genomewide signal (ß = -0.17, 6 × 10-15 ). Other SNPs were identified in chromosome 10 at a p value less than 1 × 10-5 . The genetic variants within VKORC1 rs9934438 and CYP2C9 rs4086116 explained 39% and 27% of the variability in the weekly warfarin dose requirement in the Qatari and Egyptians, respectively. This is the first GWAS of warfarin dose variability in the MENA region. It confirms the importance of VKORC1 and CYP2C9 variants in warfarin dose variability among patients from the MENA region.

A randomized, cross-over trial of metoprolol succinate formulations to evaluate PK and PD end points for therapeutic equivalence.

There are limited comparison data throughout the dosing interval for generic versus brand metoprolol extended-release (ER) tablets. We compared the pharmacokinetics (PKs) and pharmacodynamics of brand name versus two generic formulations (drugs 1 and 2) of metoprolol ER tablets with different time to maximum concentration (Tmax ) in adults with hypertension. Participants were randomized to equal drug doses (50-150 mg/day) administered in one of two sequences (brand-drug1-brand-drug2 or brand-drug2-brand-drug1) and completed 24-h PK, digital heart rate (HR), ambulatory blood pressure (BP), and HR studies after taking each formulation for greater than or equal to 7 days. Metoprolol concentrations were determined by liquid chromatography tandem mass spectrometry, with noncompartmental analysis performed to obtain PK parameters in Phoenix WinNonlin. Heart rate variability (HRV) low-to-high frequency ratio was determined per quartile over the 24-h period. Thirty-six participants completed studies with the brand name and at least one generic product. Among 30 participants on the 50 mg dose, the primary PK end points of area under the concentration-time curve and Cmax were similar between products; Tmax was 6.1 ± 3.6 for the brand versus 3.5 ± 4.9 for drug 1 (p = 0.019) and 9.6 ± 3.2 for drug 2 (p < 0.001). Among all 36 participants, 24-h BPs and HRs were similar between products. Mean 24-h HRV low-to-high ratio was also similar for drug 1 (2.04 ± 1.35), drug 2 (1.86 ± 1.35), and brand (2.04 ± 1.77), but was more sustained over time for the brand versus drug 1 (drug × quartile interaction p = 0.017). Differences in Tmax between metoprolol ER products following repeated doses may have implications for drug effects on autonomic balance over the dosing interval.

Multi-site Investigation of Genetic Determinants of Warfarin Dose Variability in Latinos.

We conducted a multi-site investigation of genetic determinants of warfarin dose variability in Latinos from the U.S. and Brazil. Patients from four institutions in the United States (n = 411) and Brazil (n = 663) were genotyped for VKORC1 c.-1639G> A, common CYP2C9 variants, CYP4F2*3, and NQO1*2. Multiple regression analysis was used in the U.S. cohort to test the association between warfarin dose and genotype, adjusting for clinical factors, with further testing in an independent cohort of Brazilians. In the U.S. cohort, VKORC1 and CYP2C9 variants were associated with lower warfarin dose (ß = -0.29, P < 2.0 × 10-16 ; ß = -0.21, P = 4.7 × 10-7 , respectively) whereas CYP4F2 and NQO1 variants were associated with higher dose (ß = 0.10, P = 2 × 10-4 ; ß = 0.10, P = 0.01, respectively). Associations with VKORC1 (ß = -0.14, P = 2.0 × 10-16 ), CYP2C9 (ß = -0.07, P = 5.6 × 10-10 ), and CYP4F2 (ß = 0.03, P = 3 × 10-3 ), but not NQO1*2 (ß = 0.01, P = 0.30), were replicated in the Brazilians, explaining 43-46% of warfarin dose variability among the cohorts from the U.S. and Brazil, respectively. We identified genetic associations with warfarin dose requirements in the largest cohort of ancestrally diverse, warfarin-treated Latinos from the United States and Brazil to date. We confirmed the association of variants in VKORC1, CYP2C9, and CYP4F2 with warfarin dose in Latinos from the United States and Brazil.

Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans.

Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model's ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10-15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2C19 and Proton Pump Inhibitor Dosing.

Proton pump inhibitors (PPIs) are widely used for acid suppression in the treatment and prevention of many conditions, including gastroesophageal reflux disease, gastric and duodenal ulcers, erosive esophagitis, Helicobacter pylori infection, and pathological hypersecretory conditions. Most PPIs are metabolized primarily by cytochrome P450 2C19 (CYP2C19) into inactive metabolites, and CYP2C19 genotype has been linked to PPI exposure, efficacy, and adverse effects. We summarize the evidence from the literature and provide therapeutic recommendations for PPI prescribing based on CYP2C19 genotype (updates at www.cpicpgx.org). The potential benefits of using CYP2C19 genotype data to guide PPI therapy include (i) identifying patients with genotypes predictive of lower plasma exposure and prescribing them a higher dose that will increase the likelihood of efficacy, and (ii) identifying patients on chronic therapy with genotypes predictive of higher plasma exposure and prescribing them a decreased dose to minimize the risk of toxicity that is associated with long-term PPI use, particularly at higher plasma concentrations.

Adverse Cardiovascular Outcomes and Antihypertensive Treatment: A Genome-Wide Interaction Meta-Analysis in the International Consortium for Antihypertensive Pharmacogenomics Studies.

We sought to identify genome-wide variants influencing antihypertensive drug response and adverse cardiovascular outcomes, utilizing data from four randomized controlled trials in the International Consortium for Antihypertensive Pharmacogenomics Studies (ICAPS). Genome-wide antihypertensive drug-single nucleotide polymorphism (SNP) interaction tests for four drug classes (ß-blockers, n = 9,195; calcium channel blockers (CCBs), n = 10,511; thiazide/thiazide-like diuretics, n = 3,516; ACE-inhibitors/ARBs, n = 2,559) and cardiovascular outcomes (incident myocardial infarction, stroke, or death) were analyzed among patients with hypertension of European ancestry. Top SNPs from the meta-analyses were tested for replication of cardiovascular outcomes in an independent Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) study (n = 21,267), blood pressure (BP) response in independent ICAPS studies (n = 1,552), and ethnic validation in African Americans from the Genetics of Hypertension Associated Treatment study (GenHAT; n = 5,115). One signal reached genome-wide significance in the ß-blocker-SNP interaction analysis (rs139945292, Interaction P = 1.56 × 10-8 ). rs139945292 was validated through BP response to ß-blockers, with the T-allele associated with less BP reduction (systolic BP response P = 6 × 10-4 , Beta = 3.09, diastolic BP response P = 5 × 10-3 , Beta = 1.53). The T-allele was also associated with increased adverse cardiovascular risk within the ß-blocker treated patients' subgroup (P = 2.35 × 10-4 , odds ratio = 1.57, 95% confidence interval = 1.23-1.99). The locus showed nominal replication in CHARGE, and consistent directional trends in ß-blocker treated African Americans. rs139945292 is an expression quantitative trait locus for the 50 kb upstream gene NTM (neurotrimin). No SNPs attained genome-wide significance for any other drugs classes. Top SNPs were located near CALB1 (CCB), FLJ367777 (ACE-inhibitor), and CES5AP1 (thiazide). The NTM region is associated with increased risk for adverse cardiovascular outcomes and less BP reduction in ß-blocker treated patients. Further investigation into this region is warranted.

Opportunity for Genotype-Guided Prescribing Among Adult Patients in 11 US Health Systems.

The value of utilizing a multigene pharmacogenetic panel to tailor pharmacotherapy is contingent on the prevalence of prescribed medications with an actionable pharmacogenetic association. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has categorized over 35 gene-drug pairs as "level A," for which there is sufficiently strong evidence to recommend that genetic information be used to guide drug prescribing. The opportunity to use genetic information to tailor pharmacotherapy among adult patients was determined by elucidating the exposure to CPIC level A drugs among 11 Implementing Genomics In Practice Network (IGNITE)-affiliated health systems across the US. Inpatient and/or outpatient electronic-prescribing data were collected between January 1, 2011 and December 31, 2016 for patients ≥ 18 years of age who had at least one medical encounter that was eligible for drug prescribing in a calendar year. A median of ~ 7.2 million adult patients was available for assessment of drug prescribing per year. From 2011 to 2016, the annual estimated prevalence of exposure to at least one CPIC level A drug prescribed to unique patients ranged between 15,719 (95% confidence interval (CI): 15,658-15,781) in 2011 to 17,335 (CI: 17,283-17,386) in 2016 per 100,000 patients. The estimated annual exposure to at least 2 drugs was above 7,200 per 100,000 patients in most years of the study, reaching an apex of 7,660 (CI: 7,632-7,687) per 100,000 patients in 2014. An estimated 4,748 per 100,000 prescribing events were potentially eligible for a genotype-guided intervention. Results from this study show that a significant portion of adults treated at medical institutions across the United States is exposed to medications for which genetic information, if available, should be used to guide prescribing.

Clinical Utility of Pharmacogene Panel-Based Testing in Patients Undergoing Percutaneous Coronary Intervention.

We aimed to estimate the utility of panel-based pharmacogenetic testing of patients undergoing percutaneous coronary intervention (PCI). Utilization of Clinical Pharmacogenetic Implementation Consortium (CPIC) level A/B drugs after PCI was estimated in a national sample of IBM MarketScan beneficiaries. Genotype data from University of Florida (UF) patients (n = 211) who underwent PCI were used to project genotype-guided opportunities among MarketScan beneficiaries with at least one (N = 105,547) and five (N = 12,462) years of follow-up data. The actual incidence of genotype-guided prescribing opportunities was determined among UF patients. In MarketScan, 50.0% (52,799/105,547) over 1 year and 68.0% (8,473/12,462) over 5 years had ≥ 1 CPIC A/B drug besides antiplatelet therapy prescribed, with a projected incidence of genotype-guided prescribing opportunities of 39% at 1 year and 52% at 5 years. Genotype-guided prescribing opportunities occurred in 32% of UF patients. Projected and actual incidence of genotype-guided opportunities among two cohorts supports the utility of panel-based testing among patients who underwent PCI.

Effect of Genetic and Nongenetic Factors on the Clinical Response to Mineralocorticoid Receptor Antagonist Therapy in Egyptians with Heart Failure.

This prospective cohort study evaluated the association between the renin angiotensin aldosterone system genotypes and response to spironolactone in 155 Egyptian patients with heart failure with reduced ejection fraction (HFrEF). Genotype frequencies for AGT rs699 were: CC = 16%, CT = 48%, and TT = 36%. Frequencies for CYP11B2 rs1799998 were: TT = 33%, TC = 50%, and CC = 17%. After 6 months of spironolactone treatment, change in the left ventricular ejection fraction (LVEF) differed by AGT rs699 (CC, 14.6%; TC, 7.9%; TT, 2.7%; P = 2.1E-26), and CYP11B2 rs1799998 (TT, 9.1%; TC, 8.7%; CC, 1.4%; P = 0.0006) genotypes. Multivariate linear regression showed that the AGT rs699 and CYP11B2 rs1799998 polymorphisms plus baseline serum potassium explained 71% of variability in LVEF improvement (P = 0.001), 63% of variability in serum potassium increase (P = 2.25E-08), and 39% of the variability in improvement in quality of life (P = 2.3E-04) with spironolactone therapy. These data suggest that AGT and CYP11B2 genotypes as well as baseline serum K are predictors of spironolactone response in HFrEF.

Examination of Metoprolol Pharmacokinetics and Pharmacodynamics Across CYP2D6 Genotype-Derived Activity Scores.

Recent CYP2D6 phenotype standardization efforts by CYP2D6 activity score (AS) are based on limited pharmacokinetic (PK) and pharmacodynamic (PD) data. Using data from two independent clinical trials of metoprolol, we compared metoprolol PK and PD across CYP2D6 AS with the goal of determining whether the PK and PD data support the new phenotype classification. S-metoprolol apparent oral clearance (CLo), adjusted for clinical factors, was correlated with CYP2D6 AS (P < 0.001). The natural log of CLo was lower with an AS of 1 (7.6 ± 0.4 mL/minute) vs. 2-2.25 (8.3 ± 0.6 mL/minute; P = 0.012), similar between an AS of 1 and 1.25-1.5 (7.8 ± 0.5 mL/minute; P = 0.702), and lower with an AS of 1.25-1.5 vs. 2-2.25 (P = 0.03). There was also a greater reduction in heart rate with metoprolol among study participants with AS of 1 (-10.8 ± 5.5) vs. 2-2.25 (-7.1 ± 5.6; P < 0.001) and no significant difference between those with an AS of 1 and 1.25-1.5 (-9.2 ± 4.7; P = 0.095). These data highlight linear trends among CYP2D6 AS and metoprolol PK and PD, but inconsistencies with the phenotypes assigned by AS based on the current standards. Overall, this case study with metoprolol suggests that utilizing CYP2D6 AS, instead of collapsing AS into phenotype categories, may be the most precise approach for utilizing CYP2D6 pharmacogenomics in clinical practice.

Prescribing Prevalence of Medications With Potential Genotype-Guided Dosing in Pediatric Patients.

Importance: Genotype-guided prescribing in pediatrics could prevent adverse drug reactions and improve therapeutic response. Clinical pharmacogenetic implementation guidelines are available for many medications commonly prescribed to children. Frequencies of medication prescription and actionable genotypes (genotypes where a prescribing change may be indicated) inform the potential value of pharmacogenetic implementation. Objective: To assess potential opportunities for genotype-guided prescribing in pediatric populations among multiple health systems by examining the prevalence of prescriptions for each drug with the highest level of evidence (Clinical Pharmacogenetics Implementation Consortium level A) and estimating the prevalence of potential actionable prescribing decisions. Design, Setting, and Participants: This serial cross-sectional study of prescribing prevalences in 16 health systems included electronic health records data from pediatric inpatient and outpatient encounters from January 1, 2011, to December 31, 2017. The health systems included academic medical centers with free-standing children's hospitals and community hospitals that were part of an adult health care system. Participants included approximately 2.9 million patients younger than 21 years observed per year. Data were analyzed from June 5, 2018, to April 14, 2020. Exposures: Prescription of 38 level A medications based on electronic health records. Main Outcomes and Measures: Annual prevalence of level A medication prescribing and estimated actionable exposures, calculated by combining estimated site-year prevalences across sites with each site weighted equally. Results: Data from approximately 2.9 million pediatric patients (median age, 8 [interquartile range, 2-16] years; 50.7% female, 62.3% White) were analyzed for a typical calendar year. The annual prescribing prevalence of at least 1 level A drug ranged from 7987 to 10 629 per 100 000 patients with increasing trends from 2011 to 2014. The most prescribed level A drug was the antiemetic ondansetron (annual prevalence of exposure, 8107 [95% CI, 8077-8137] per 100 000 children). Among commonly prescribed opioids, annual prevalence per 100 000 patients was 295 (95% CI, 273-317) for tramadol, 571 (95% CI, 557-586) for codeine, and 2116 (95% CI, 2097-2135) for oxycodone. The antidepressants citalopram, escitalopram, and amitriptyline were also commonly prescribed (annual prevalence, approximately 250 per 100 000 patients for each). Estimated prevalences of actionable exposures were highest for oxycodone and ondansetron (>300 per 100 000 patients annually). CYP2D6 and CYP2C19 substrates were more frequently prescribed than medications influenced by other genes. Conclusions and Relevance: These findings suggest that opportunities for pharmacogenetic implementation among pediatric patients in the US are abundant. As expected, the greatest opportunity exists with implementing CYP2D6 and CYP2C19 pharmacogenetic guidance for commonly prescribed antiemetics, analgesics, and antidepressants.

Development and Cross-Validation of High-Resolution Melting Analysis-Based Cardiovascular Pharmacogenetics Genotyping Panel.

AIMS: This study was designed to develop a high-resolution melting (HRM) analysis-based cardiovascular (CV) pharmacogenetics (PGx) genotyping panel for the Canon DNA Genetic Analyzer multiplex genotyping platform and cross-validate its performance with the TaqMan®-based OpenArray® method. METHODS: The CV PGx genotyping panel containing 17 single nucleotide polymorphisms (SNPs) selected from 5 genes (CYP2C9, CYP2C19, CYP4F2, SLCO1B1, and VKORC1) and the CYP2C cluster was used to compare genotyping results between analysis methods. Genomic DNA from 223 clinical samples was used to genotype the 17 SNPs on the Canon DNA Genetic Analyzer and TaqMan OpenArray Quant Studio Real-Time PCR (polymerase chain reaction) System. RESULTS: The concordance between the Canon DNA analyzer and TaqMan-based OpenArray genotyping results for the 17 SNPs ranged from 99.10% to 100% where SNPs (rs4244285, rs12248560, rs4986893, rs72552267, rs28399504, rs4149056, rs28371686, rs9332131, rs72558189, rs9923231, rs12777823), (rs41291556, rs1799853, rs7900194, rs28371685, rs2108622), and (rs1057910) showed 100%, 99.60%, and 99.10% concordance, respectively. CONCLUSION: These results show that the HRM analysis-based CV PGx genotyping panel performed well when compared with TaqMan-based OpenArray. The multiple genetic variant testing capability, efficient turnaround time and reproducibility of both assays formats suggest that the PGx panel with the DNA analyzer or other real-time PCR instruments with HRM assay analysis capability can be used for PGx testing in both research and clinical practice settings.