Evaluation of the availability of clinical pharmacogenomics within the USA and utilization within an academic healthcare system

Introduction

Background

Pharmacogenomics aims to characterize how interindividual differences in genetics contribute to variability in drug responses. Within the USA, there are more than 20,000 drugs approved by the Food and Drug Administration (FDA); conversely, there is a relatively small number of genes and enzymes responsible for drug metabolism (1). Due to significant genetic heterogeneity, the population frequency of actionable genetic variants is high (2,3). One study conducted in Switzerland, which comprised predominantly of individuals of European descent, demonstrated that over 95% of participants had one clinically actionable variant that would influence care (4). Dozens of medications including anti-depressants, immunosuppressants, and chemotherapeutics, are impacted by variants in drug-metabolizing genes (1,5-9). Identification of clinically actionable gene-drug pairs has the potential to significantly advance medication safety, efficacy, and tolerability.

Genes that encode drug-metabolizing enzymes are highly polymorphic, representing some of the most diverse genes within the genome (10). Among these polymorphisms, a subset of variants impact enzyme function and subsequent medication metabolism (11). Decades of research encompassing molecular biology, pharmacology, and human genetics led to the identification of actionable gene-drug pairs (7,9,12-17). To support clinical translation, expert working groups have assembled variant-level data within centralized resources such as the ClinPGx (formerly the Pharmacogenomics Knowledge Database, PharmGKB, www.clinpgx.org). Organizations such as the Dutch Pharmacogenomics Working Group (DPWG) and the Clinical Pharmacogenomics Implementation Consortium (CPIC) have further advanced clinical adoption through the categorization of actionable variants and the development of genotype specific dosing recommendations. To date, CPIC has published 28 guidelines encompassing 32 genes and over 100 drugs (www.cpicpgx.org).

In addition to the diversity of genes and alleles evaluated, there is also heterogeneity in the analytical methods employed for pharmacogenomics, further complicating standardization. Single nucleotide polymorphisms occur with high frequency and should be assessed on each allele to accurately describe metabolic status (18-20). In addition, copy number variation (CNV), including full gene duplications and fusion events, is observed in genes such as CYP2D6 and CYP2B6 (21-24). Other genomic features that complicate sensitive, specific, and uniform variant detection include the presence of pseudogenes, guanine and cytosine (GC) rich regions, and short tandem repeats (20,25-29). While several commercial assays are available, they often include only a limited selection of genes and variants, necessitating the use of custom assays to detect clinically significant variants not covered with standard panels (30). Among the most commonly cited analytical methods for pharmacogenomic testing are polymerase chain reaction (PCR), next-generation sequencing (NGS), microarray, and mass spectrometry (31-33), with the choice of platform largely dependent on existing infrastructure, clinical need, institutional expertise, and anticipated order volume.

Rationale and knowledge gap

To identify pharmacogenes with the most significant impact on patient outcomes, clinically actionable gene-drug pairs were identified by review of ClinPGx and CPIC guidelines (34,35). Only pharmacogenes reported in CPIC and ClinPGx with a high level of evidence and clear clinical guidelines were included. Rather than presenting individual genes, many of which impact the metabolism of multiple drugs, pharmacogenes were grouped based on medication class to demonstrate the impact of altered metabolism within the context of patient care. Data from the FDA Table of Pharmacogenomic Associations (https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations) was used to directly connect the impact of pharmacogenomic variants to clinical risk and outcomes (Table 1).

Although several studies have demonstrated improved clinical outcomes with pharmacogenomic guided care (3,6,36-41), these findings do not necessarily reflect how and when testing is applied in routine clinical practice, outside of controlled trial settings. The lack of clinical context makes it challenging to identify at-risk and underserved populations. The scarcity of real-world utilization data also poses significant challenges for healthcare systems aiming to implement in-house testing or integrate referral testing into clinical workflows. Pharmacogenomic testing is often conducted at reference laboratories with limited clinical history, such that information regarding care setting, gene-drug pair of interest, and ordering indication is unavailable. Evidence based utilization management is essential to identify clinical populations at highest risk for adverse drug events and ensure that testing is accessible, appropriately applied, and inclusive of the relevant genes and variants that are most likely to influence therapeutic outcomes. Strategic implementation of pharmacogenomics has the potential to significantly advance patient safety by decreasing adverse drug reactions, reducing the risk of severe or life-threatening toxicity (3,4,12,40,42), and improving immunosuppressive management following solid organ transplant (7,8,36,43).

Objective

This study aims to estimate the availability of pharmacogenetic testing within the USA and examine the relative frequency of specific pharmacogene reporting. Accessibility of pharmacogenomics was estimated through review of data from a large USA-centric proficiency testing (PT) program. To understand ordering trends and identify areas of unmet need, 18 months of data were reviewed from an in-house pharmacogenomics service within our quaternary care healthcare system. A utilization review of pharmacogenomic testing was conducted to identify pharmacogenes that were of highest interest to providers, while also comparing their utilization rates to the clinical impact of corresponding gene-drug pairs.

Methods

Prioritization of actionable gene drug pairs

Clinically actionable gene-drug pairs were defined based on CPIC guidelines or ClinPGx databases with evidence levels of A or 1A, respectively. The clinical impact of an unknown variant was summarized based on the available literature, clinical practice guidelines published by CPIC, and the FDA table of pharmacogenomic associations (https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations). Medication label information for illustrative gene-drug pairs was obtained through the FDA online label repository (https://open.fda.gov/fdalabels/) and sources referenced above.

Analysis of College of American Pathology (CAP) PT reports

To estimate the number of laboratories performing pharmacogenomic testing, CAP PT summary reports released in 2023 and 2024 were reviewed. Two sets of CAP surveys are distributed each calendar year, designated surveys A and B. Results were reviewed from pharmacogenomic surveys representing the B survey of 2023 and both A and B surveys from 2024. Only genes that were included in all surveys were presented. The 2023-B survey results were used to gather overall response rates. For analyte specific reporting rates, the highest number of responders for any of the specimens available was selected. The average number of participants by method was calculated across all specimens and analytes on a gene specific basis.

In-house pharmacogenomics testing

Within Johns Hopkins Hospital (Baltimore, MD, USA), an 11-gene clinically actionable pharmacogenomics panel was validated and was inclusive of actionable alleles within the following pharmacogenes: ABCG2, CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A5, DPYD, NUDT15, SLCO1B1, TPMT, and VKORC1 (Table 2). The assay was validated using blood collected via venipuncture, and testing was performed using a TaqMan-based PCR assay on QuantStudio 12K Flex (Thermo Fisher Scientific, Waltham, MA, USA). All results were integrated into the electronic health records (EHR) using the Epic genomics module. Analytical details for this assay have been previously described (44). The 11-gene panel was initially made available at mid-Atlantic hospitals within the Johns Hopkins Medical Institutions, and during month 6, a standalone assay for NUDT15/TPMT was launched. At this time, both tests were also extended to Johns Hopkins All Children’s Hospital (St. Petersburg, FL, USA).

For pharmacogenomic tests ordered in the mid-Atlantic region, an e-consultation with an expert pharmacist is also available to facilitate result interpretation. The e-consult allows the ordering provider to submit clinical questions, medication lists, and primary concerns for review to identify the impact of the results on current and future care (44).

Utilization of pharmacogenomic testing

Over the course of the first 18 months in operation, pharmacogenomic orders were tracked and documented in a secure file by the laboratory, including information regarding the order type, location, and select patient information. Turnaround time (TAT) was defined as the number of days required to post a final result relative to receipt in the laboratory. A retrospective chart review was conducted to determine the gene(s) of interest and their impact on patient management. To perform chart review and collect relevant data, chart notes within 7 days of order collection were viewed to determine the care setting, medical service attending to the patient, and identify the clinical note describing the rationale for testing. If there was no clear rationale in the clinical note prior to testing, notes describing the results were reviewed, as well as consults to pharmacy, and active medication lists.

Ethical consideration

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Institutional Review Board of Johns Hopkins University (No. IRB00284790). All data were retrospectively collected and reviewed for continuous quality improvement in accordance with institutional policies as exempt, non-human subjects research; therefore, informed consent was not required.

Results

Estimating availability of pharmacogenomic testing

To estimate the number of laboratories performing pharmacogenomic testing, CAP PT summary reports from 2023 to 2024 were reviewed. Relative to other high volume laboratory testing with thousands of PT subscribers, the number of laboratories participating in pharmacogenomics was low. Among participating laboratories, there was significant variability in reporting rates across genes, ranging from 57 to 183 subscribers, depending on the gene (Figure 1A). CYP2C19 was the most highly represented gene, with 183 subscribers or 72.6% of all respondents reporting results (Figure 1A). Genes with high clinical impact, such as TPMT, DPYD, NUDT15, and UGT1A1 (Table 1), have some of the lowest response rates, with only 64–83 subscribers, representing 25.4–32.9% of laboratories subscribed to the CAP pharmacogenomic PT program (Figure 1A).

Figure 1 Estimating availability of pharmacogenomic testing utilizing CAP respondent data (2023-B). (A) The number of PGx reporters by gene. (B) Average reporting rates by method in 2023. CAP, College of American Pathology; NGS, next-generation sequencing; PCR, polymerase chain reaction; PGx, pharmacogenomics; PT, proficiency testing.

PT surveys also report the analytical methodologies employed on a per gene basis. In 2023, PCR was the most common method utilized for pharmacogenomics testing and was performed for 58.9% of testing across all genes (Figure 1B). NGS-based methods and mass spectrometry were also employed for a significant proportion of testing, representing 14.7% and 11.8% of responses, respectively. Less commonly employed methods included bead or microarrays, Sanger sequencing, pyrosequencing, and invader chemistry; cumulatively, these other methods were performed in approximately 15% of responses. The same data were reviewed from the 2024-B PT report, indicating very little change from year to year (Figure S1).

For a majority of genes, the analytical methodology largely reflected pooled trends in Figure 1B, with PCR representing the primary method, followed by a smaller proportion performing analysis by next generation sequencing or mass spectrometry (Figure S2). However, there was a subset of genes (TPMT, DPYD, NUDT15, and UGT1A1) where Sanger sequencing was more common, representing 10.3–18.8% of responses on a per gene basis (Figure S2J-S2M).

Laboratory metrics following implementation of in-house pharmacogenomics testing within the Johns Hopkins Health System

Within our own health system, an 11-gene pharmacogenomics panel (Table 2) was developed and deployed clinically in July 2023. Six months after the launch of the panel, a standalone assay for TPMT and NUDT15 was also made available. Over the course of months 7–18, standalone orders were placed with similar frequency as panel-based testing, averaging approximately 8 orders per month compared to 10 monthly panel orders towards the end of the observation period (Figure 2A). The median TAT for the pharmacogenomic panel was 4.5 days, ranging from 1 to 7 days, excluding the period impacted by reagent shortage (Figure S3). The standalone assay demonstrated faster TAT than the panel, with a median TAT of 3 days, typically released within 3–4 days of receipt in the laboratory (Figure 2B).

Figure 2 PGx volumes over the first 18 months of testing. (A) Monthly order volumes stratified by panel and standalone orders. (B) Average turnaround time for the panel (solid line) and the standalone NUDT15/TPMT assay (dashed line). *, Outliers removed from month 11 due to supply chain disruptions. Data with outliers included are shown in Figure S3. PGx, pharmacogenomics; TAT, turnaround time.

Health care system utilization of pharmacogenomics

Patients who were ≤18 years of age, ≥60 years of age, and self-reported their race as white were more likely to have pharmacogenomic testing performed than any other demographic group (Table 3). The percentage of 75.0 of orders was placed for inpatients, and 80.1% of orders were placed by providers at one of two urban hospitals located in Baltimore City (Table 3). The percentage of 60.3 of orders was for the actionable pharmacogenomics panel, constituting 94 orders over 18 months. The percentage of 31.1 of orders was placed by general internal medicine providers, a group that consists primarily of hospitalists and residents. The departments of oncology and psychiatry were the second most prevalent users, each placing 17.8% of orders for the full panel (Table 3).

Psychiatric medications were the most common drug class prompting pharmacogenomic testing, representing 35.6% of all panels or 32 orders, closely followed by immunosuppressants (32.2% of panels or 29 orders). Nearly all testing for immunosuppressants was performed for gene-drug interactions between CYP3A5 and tacrolimus, representing the most common specific gene-drug pair assessed in our population. Chemotherapeutics represented 15.6% of all panels, constituting 14 orders. Pharmacogenomics was also employed to guide management of patients prescribed platelet inhibitors, beta-blockers, and anti-arrythmics (Table 4). The percentage of 43.6 of all testing, including 41 orders, was performed proactively; 65.9% of proactive orders were placed for immunosuppressants, followed by platelet inhibitors (12.1%). All of the proactive immunosuppressant orders were conducted for CYP3A5 and tacrolimus as a component of pre-operative evaluation for solid organ transplant. Of the panel-based orders, there were no instances of proactive orders prior to chemotherapy administration within this evaluation period.

Discussion

Key findings

The findings of this study underscore significant challenges within the field of pharmacogenomics, including access to testing, panel standardization, and barriers associated with evaluating effective utilization. Our analysis of CAP PT surveys reveals that the highest impact pharmacogenes are among those least commonly assessed in laboratories currently offering pharmacogenetics. Reduced representation of these pharmacogenes may restrict access to testing and disproportionately impact specific populations with the highest risk of severe adverse drug reactions, chiefly patients prescribed select chemotherapeutics and immunosuppressants.

Internally, our data reveal that pharmacogenomics is utilized effectively within specific contexts, such as immunosuppressant management for solid organ transplant patients, particularly when tacrolimus is part of the treatment regimen. Strong utilization was observed for the standalone TPMT/NUDT15 assay as well indicating positive utilization in oncology, particularly in pediatric practices. However, DPYD utilization remained low, particularly considering recent FDA recommendations (45,46), highlighting a clear opportunity to increase education to advance patient safety institutionally. To address this disparity, we have also developed and deployed a standalone assay for DPYD, which went live after the study period.

Strengths and limitations

One limitation to our analysis is that the availability of pharmacogenomics testing within USA laboratories may be underestimated, as our analysis was based on laboratory participation in CAP PT surveys. Although all laboratories performing clinical testing must perform PT within the USA, there is no requirement to subscribe to commercial programs. While commercial PT challenges are common, as they make it easier to maintain compliance with federal regulations, provide peer data, and include rare variants that are not routinely observed in general populations, they are not always utilized. Laboratories that perform alternative PT or utilize other commercial PT vendors are not represented in this data. Despite these limitations, the CAP surveys provide key data demonstrating the reporting of gene specific results and the analytical method, which are difficult to find in the literature.

In-house pharmacogenomics testing facilitated access to testing and results for patients and providers. The amount of detailed clinical and laboratory data represents a key strength of this study. Monitoring orders over a period of 18 months, we were able to gather specific data to trace ordering trends, identify the clinical rationale for testing, and examine gene-drug pairs of interest for our population. This analysis allowed us to recognize areas with strong utilization and identify populations with unmet needs. Although the pharmacogenomics program continues to grow, our sample size over 18 months remained relatively low and is representative of a single healthcare system, which limits the generalizability of results. Prior to development of the in-house testing, there was no readily accessible or EHR integrated mechanism to order pharmacogenomics, which may have contributed to slow uptake after testing became electronically orderable.

Comparison with similar research

Due to the analytical challenges associated with performing pharmacogenomic testing, there is a range of methods utilized, with several publications describing methods across analytical platforms. The analytical methods are largely dependent on the gene(s) assessed, the expertise of the performing lab, and the infrastructure within the lab (20). There are several published methods describing development of PCR-based pharmacogenomic assays (47-49) and pipelines for next generation sequencing (50,51). In addition to traditional sequencing methods, there are also methods for detection of variants via mass spectrometry (52-54), which led to the development of a commercially available platform, MassArray, from Agena Bioscience (55,56).

Although some work has been published in this space (57-59), the majority of large-scale utilization data comes from clinical trials, where specific criteria are used to recommend or perform testing. The lack of standardization of gene and variant specific testing further confounds result interpretation and increases the visibility of the most common tests, which may not have the highest impact clinically. One research group attempted to estimate broad utilization of pharmacogenomics through the examination of medical and pharmacy claims from the USA from 2013 to 2017. CYP2C19 was identified as the most commonly assessed gene from USA insurance claims (60). It should be noted that this work examined a smaller subset of pharmacogenes, including CYP2C19, CYP2D6, CYP2C9, VKORC1, UGT1A1, and HLA class 1 typing. However, clinical laboratory data obtained from PT surveys in 2024 and 2025 demonstrate that CYP2C19 testing is likely more accessible than other pharmacogenes (Figure 1A). This finding is supported by the availability of CYP2C19 testing at the point of care (61-63), primarily developed for the management of antiplatelet medications, including clopidogrel (15,64,65). In 2024, the American Heart Association released a scientific statement summarizing the evidence base and recommending CYP2C19 testing prior to administration of select P2Y12 inhibitors to reduce the risk of ischemic events for patients with acute coronary syndrome and percutaneous coronary intervention (42).

More recently, work has been published describing a framework for how healthcare systems and larger communities can build infrastructure to support integration of precision medicine. An interdisciplinary group, largely from the University of Minnesota and Mayo Clinic, has described their efforts in providing pharmacogenomics education to students in pharmacy, MS, PhD, and residency programs (66). This investment has led to advances in pharmacogenomic research and implementation in the state and broader region (67-71).

Implications and actions needed

Pharmacogenomic testing necessitates expertise and is often conducted primarily in subspecialty laboratories, a factor that complicates the timely access to critical information for both healthcare providers and patients. The underreporting of high impact genes represents a significant barrier to effective patient care, particularly for populations at elevated risk for severe adverse drug reactions. To address these disparities, healthcare systems must devise strategies to enhance access to pharmacogenomic testing. This may include developing in-house testing capabilities or solidifying partnerships with referral laboratories to ensure that pharmacogenomic data is both readily available and actionable.

In our specific patient population, utilization of pharmacogenomics for immunosuppressants and NUDT15 and TPMT was appropriate. Although ordering rates overall were low, a gradual increase in order volumes over the course of 18 months was observed. Prior to the establishment of an in-house test, access to pharmacogenomic testing was poor without clear pathways for ordering, which likely contributed to a lack of physician awareness. Although in-house testing increased accessibility of testing, there remains a need to better integrate pharmacogenomics into the standard of care through the creation of order sets, best practice alerts, and engagement of interdisciplinary teams. Additional barriers to testing include requirements for pre-authorization, concern for lack of coverage, and heterogeneity in payor outcomes.

In-house utilization was particularly low for DPYD testing, which represents a significant care gap. After the first 18 months in operation, a standalone DPYD assay was validated, composed of all variants recommended in the joint consensus guidelines from CPIC, DPWG, American College of Medical Genetics and Genomics (ACMG), and Association for Molecular Pathology (AMP) (72). The new test announcement was accompanied by education on DPYD testing with the use of fluoropyrimidines to ensure widespread awareness and understanding of the new testing capabilities. Looking ahead, we are considering additional methods to facilitate clinical uptake of pharmacogenomic testing, including the incorporation of standalone assays into order sets to ensure equitable access to pharmacogenomics across the healthcare system.

Conclusions

Pharmacogenomics plays a crucial role in precision medicine and serves to decrease the likelihood of adverse drug reactions and support effective treatment regimens while tailoring medication delivery to the patient. An analysis of CAP PT surveys reveals that pharmacogenes with the most significant clinical impact may be among the least accessible. Across the Johns Hopkins Health System, the integration of pharmacogenomics has proceeded gradually, with notable success in pre-transplant assessments, particularly regarding CYP3A5 for immunosuppressant management. To further advance the field, there is an urgent need to enhance education and awareness around pharmacogenomics, particularly for patient populations at high risk for severe and life-threatening adverse events.

Acknowledgments

All work and data herein are original. Portions of this manuscript were presented as unpublished data at the PLUGS (Patient Centered Utilization Guidance Services) Summit in Seattle, Washington in April 2025.

Footnote

Data Sharing Statement: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-25/dss

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-25/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-25-25/coif). M.A.M. reports receiving payments from National Institutes of Health (NIH) and ViiV; travel support for presenting at annual meeting from ADLM; and royalties or licenses in Elsevier. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Institutional Review Board of Johns Hopkins University (No. IRB00284790). All data were retrospectively collected and reviewed for continuous quality improvement in accordance with institutional policies as exempt, non-human subjects research; therefore; informed consent was not required.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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