Nihal El Rouby

Pharmacogenomics (PGx), utilizing a patient's DNA to individualize medication prescribing, minimizes adverse events and improves patient outcomes. However, the integration of PGx into primary care remains sparse. PGx testing encompasses multiple components and can be considered a complex health intervention. We conducted an in‐person workshop with early adopters from the Pharmacogenomics Global Research Network Implementation Working Group, followed by qualitative interviews with key opinion leaders, to identify the core functions and forms necessary for implementing PGx testing in primary care. Workshop participants agreed on six core functions necessary to implement PGx‐guided medication prescribing in primary care settings: patient identification, test ordering, laboratory testing, returning results, providing medication recommendations, and modifying pharmacotherapy. An additional function, leadership engagement, was proposed by key opinion leaders. There was considerable variation in the forms, depending on local context and available resources. In this position paper, we discuss why it is critical to separately define the core functions required to implement PGx testing in primary care settings and the customizable forms used for local execution. Defining core functions provides an actionable first step to guide scalable and context‐sensitive integration of PGx testing into routine primary care practice, balancing the need for both fidelity and adaptation in real‐world implementation.

BackgroundSecond-generation antipsychotics (SGAs) are commonly prescribed in pediatric populations and are associated with adverse metabolic effects. However, real-world data characterizing screening of metabolic syndrome (MetS) among youth remain limited.MethodsWe conducted a retrospective longitudinal study using electronic health records (EHR) data from Cincinnati Children’s Hospital Medical Center. We included 23,886 patients aged 5–19 years, prescribed at least one SGA. Patients were considered assessable for MetS if at least three of the five MetS criteria were recorded ≥4 weeks after an SGA. MetS cases had at least three abnormal criteria, including: body mass index (BMI) percentile ≥95th percentile, systolic or diastolic blood pressure (BP) ≥90th percentile for age, sex, and height, high-density lipoprotein (HDL) cholesterol ≤40 mg/dL, triglycerides ≥110 mg/dL, and glucose ≥100 mg/dL or hemoglobin A1c ≥5.6%. Controls did not meet the MetS definition and had at least three normal criteria.FindingsOf 23,886 eligible patients, 5,573 (23.3%) had sufficient data for MetS ascertainment; 1,423 (25.5%) met the MetS definition. Cases had higher baseline BMI z-score than controls (1.69 vs 0.32; p<0.001). Higher baseline BMI z-score (adjusted odds ratio (aOR) 3.86, 95% CI 3.51–4.25) and higher BMI z-score change closest to 52 weeks (aOR 3.07, 95% CI 2.69–3.51) were independently associated with MetS.InterpretationMetS was common among pediatric patients prescribed SGAs who had sufficient data for phenotype ascertainment. Sparse laboratory-based measurements limited MetS ascertainment in routine clinical practice.

Dual antiplatelet therapy (DAPT) with aspirin and clopidogrel is commonly used for secondary prevention following acute ischemic stroke (AIS) and neurointerventional procedures. However, the efficacy of clopidogrel is reduced in carriers of CYP2C19 no‐function alleles. While CYP2C19 ‐guided DAPT is well‐established for some cardiovascular indications, its use in neurovascular settings has remained limited until recently. This study characterized current implementation strategies for CYP2C19 ‐guided DAPT for neurovascular indications across institutions in the Pharmacogenomics Global Research Network Implementation Working Group. A REDCap survey was distributed to 54 institutions targeting those currently utilizing or planning to implement CYP2C19 testing to guide clopidogrel use for neurovascular indications. The survey collected data on implementation status, workflows, testing logistics, clinical decision support (CDS), and guideline concordance. Fifteen institutions met inclusion criteria; 80% were in the United States and 87% were academic centers. CYP2C19 testing was used for neurovascular indications at 12 sites, most commonly for AIS, intracranial aneurysm repair, and intracranial stenting. Implementation was often built on infrastructure previously established for cardiovascular indications. Most sites used multigene panels, offered testing in both inpatient and outpatient settings, and returned results within 7 days. Eighty percent of sites provided CDS to support application of results. All sites recommended an alternative P2Y12 inhibitor (e.g., ticagrelor) for poor metabolizers, and most did for intermediate metabolizers. CYP2C19 ‐guided antiplatelet therapy for neurovascular indications is expanding, with many implementations leveraging preexisting infrastructure from cardiovascular settings. Broader adoption and outcome‐focused research are needed to optimize the clinical utility of genotype‐guided DAPT in neurovascular care.

Fluoropyrimidines are a vital component of chemotherapy regimens. Deleterious DPYD variants reduce activity of dihydropyrimidine dehydrogenase, the rate‐limiting enzyme of fluoropyrimidine catabolism, resulting in reduced fluoropyrimidine clearance and elevated risk of life‐threatening toxicities. DPYD genotype‐guided fluoropyrimidine therapy can mitigate the risk of severe life‐threatening toxicities, but adoption of testing globally has been limited. We developed a 91‐item survey investigating global DPYD implementation strategies to gain insight into common practices and successful strategies. The survey was disseminated to Pharmacogenomics Global Research Network Implementation Working Group members consisting of 54 health care sites across 15 countries. Survey responses were received from 28 sites (52%) across 9 countries. Over 80% of sites implemented, or planned to implement, a preemptive testing strategy (i.e., before a fluoropyrimidine is administered) leveraging the electronic health record (EHR) to disseminate DPYD results to providers. All sites created infrastructure to support DPYD testing (e.g., order sets, EHR decision support), but 70% of sites indicated reliance on clinicians to remember test ordering. Only 2 sites reported high DPYD testing rates (> 75%) among patients planned to receive a fluoropyrimidine. Most sites (57%) used in‐house clinical laboratories that tested for the majority of DPYD Tier 1 variants. Among sites that had implemented DPYD testing, the median turnaround time was 10 days. Few sites indicated that a high percentage (> 75%) of DPYD results were returned before fluoropyrimidine administration. Our results suggest that additional implementation strategies are needed, addressing barriers and facilitators of DPYD testing.

Pharmacogenomic (PGx) testing using multi‐gene panels (mgPGx) is documented to improve clinical outcomes; however, real‐world data on its economic impact remain limited. This study aimed to evaluate the utility and economic value of mgPGx testing among Medicare patients within a community‐based health system. We identified Medicare Advantage patients within the primary care setting of a community‐based health system hospital who were taking ≥ 1 PGx‐guided medication using a stratification algorithm. In total, 1042 patients participated in mgPGx testing. We evaluated the prevalence of PGx medications, polypharmacy involving PGx medications, and actionable results (i.e., a phenotype with PGx guidance and a relevant PGx medication). A Total Cost of Care (TCOC) analysis was performed for a subset of patients ( n = 548) who underwent PGx testing and were matched to a control group that did not undergo PGx testing using propensity score matching. Total medical expenses over 12 months, both before and after testing, were compared. Forty‐four percent ( n = 454/1042) of patients were ≥ 3 PGx‐guided medications. Over one‐third of patients who were on ≥ 3 PGx medications had ≥ 2 actionable results (35.5%, n = 161/454). The TCOC analysis demonstrated a trend toward a net cost savings of $1827 per member per year (PMPY), with $1582 in medical savings and $245 in pharmacy savings. Polypharmacy with PGx medications is prevalent, and mgPGx led to cost savings. Further research with a larger sample size is needed to replicate the results and assess the long‐term impact on healthcare utilization and costs.

Objectives: Second‐generation antipsychotic (SGA) medications are frequently prescribed for mental health conditions; however, they are associated with an increased risk of metabolic syndrome (MetS). We aimed to identify genetic associations of SGA‐associated MetS (SGA–MetS) using genome‐wide approaches within the UK Biobank. We also set out to evaluate if genetically predicted obesity is associated with an increased risk of SGA–MetS. Methods: We defined SGA–MetS based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria using cross‐sectional data from 1318 UK Biobank participants who reported being on an SGA medication. An SGA–MetS case was defined as meeting three or more of the five NCEP‐ATP III criteria. We performed a genome‐wide association study (GWAS) and gene‐based analysis to identify significant variants and gene associations. We computed the polygenic risk score (PGS) for body mass index (BMI) using 2,100,302 variants validated for obesity and metabolic traits from imputed single‐nucleotide polymorphism (SNP) data. We tested the association of PGS–BMI with SGA–MetS using logistic regression. Results: GWAS identified suggestive associations ( p < 1 × 10 ⁻⁵ ) on chromosome 15. The variant rs12914956 in CHD2 was associated with increased risk of SGA (odds ratio (OR) = 1.73, 95% confidence interval (CI) = 1.4–2.4, p = 3.6 × 10 ⁻⁷ ). The gene‐based analysis identified significant gene associations with RBFOX1 ( p = 4.85 × 10 ⁻⁷ ), PTPRD ( p = 7.6 × 10 ⁻⁷ ), CSMD1 ( p = 2.2 × 10 ⁻⁶ ), and CHD2 ( p = 1.3 × 10 ⁻⁶ ). The PGS–BMI ( β = 0.23, p = 6.8 × 10 ⁻⁵ ), was associated with increased MetS in a model adjusted for age, sex, physical activity, alcohol consumption, antidepressant medications, schizophrenia diagnosis, and principal components of ancestry. Conclusion: Using a gene‐based analysis, we identified significant gene associations with SGA–MetS that have been previously associated with obesity and metabolic traits. The PGS–BMI was associated with MetS, suggesting that a genetic predisposition to a higher BMI may increase the risk of SGA–MetS. Future research should replicate the findings in a larger dataset with more diverse populations.

Second‐generation antipsychotics (SGA) are widely used for treating psychiatric disorders; however, their use is associated with an increased risk of metabolic syndrome (MetS). To identify common genetic associations of SGA‐induced metabolic syndrome (SGA‐MetS), we conducted a genome‐wide association study (GWAS) in a diverse patient population within the BioVU and BioMe electronic health records (EHRs)‐linked biobanks. Additionally, we performed Mendelian Randomization (MR) analysis to investigate the association between the individual metabolic parameters comprising MetS (body mass index [BMI], fasting glucose, blood pressure, HDL, and triglycerides) and SGA‐MetS. The meta‐analysis of European ancestry GWAS from BioVU and BioMe (N = 9248) identified a genome‐wide signal (rs61900075, β = −0.27, SE = 0.05, p = 1.6 × 10⁻⁸) on chromosome 11. Multiple associated variants met the suggestive level of association (p ≤ 10⁻⁵) in the PELO‐ITGA1 locus on chromosome 5 and were associated among the Hispanic Ancestry within BioMe. The meta‐analysis of the African Ancestry patients of BioVU and BioMe (N = 2018) identified multiple genome‐wide signals that were functionally mapped to NPPC‐DIS3L2 in chromosome 2. Finally, the inverse‐variance weighted average MR (BMI: OR = 1.2, 95% CI: 1.1–1.4, p = 0.002) showed that genetically predicted, higher BMI was associated with an increased risk of SGA‐MetS. Similar results were seen in the sensitivity analyses using the weighted median and Egger MR. This study identified novel variants for SGA‐MetS and suggested a role of BMI in increasing the risk of SGA‐MetS. The findings highlight the value of EHR biobanks for identifying the genetics underlying SGA‐MetS. The associations in chromosome 2 and 5 will need further replication.

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.