Challenges and considerations in implementing clinical pharmacogenomics

Introduction

Pharmacogenomic (PGx) testing is relevant. A study performed in the Netherlands that tested 40 variants in eight genes for 200 patients found that 90% of the patients carried at least one actionable PGx result (1). In the United States (US), it was estimated that approximately 7% of Food and Drug Administration (FDA)-approved medication and about 18% of outpatient prescriptions are affected by actionable germline PGx (2).

The design and implementation of PGx testing, which involves clinical validation, data analysis, and reporting of results are labor-intensive and complicated. To add to these challenges, PGx testing is constantly changing due to ongoing research. This review article aims to give clinicians and laboratorians a brief reference for PGx testing implementation from the laboratory’s perspective. This article highlights recent milestones in clinical PGx and challenges that adult and pediatric PGx programs are facing and describes important laboratory aspects such as: available methodologies, basics of clinical validation and results reporting, and the complexity of data analysis.

Recent progress toward integration of PGx testing

In the past decade, clinical PGx met significant milestones. In the US, several academic medical centers have successfully implemented PGx programs (3,4). In 2020, the US FDA granted the private direct-to-consumer genetic testing company 23andMe clearance for a PGx report on two commonly prescribed medications, clopidogrel and citalopram (CYP2C19 genetic variants) (5). Perhaps the most important indicator for PGx progress in the US, however, is increased discussion surrounding qualified insurance reimbursement for testing.

Reimbursement has been a historic barrier to the mainstream adoption of PGx testing (6). Recently, one of the largest health insurance companies in the US, UnitedHealth Group, announced coverage for PGx testing based on specific criteria (7). UnitedHealth offers coverage options for patients with major depressive disorders or anxiety (7) for which a previous treatment has failed. UnitedHealth also offers an extended plan (8) providing coverage for patients with a clinically determined need for a medication with a known gene-drug interaction. The extended plan is provided under the condition that PGx testing would directly impact drug management and meets the standards of actionable evidence defined by the Clinical Pharmacogenetics Implementation Consortium (CPIC) or FDA labeling. With the recognition that PGx testing can provide more effective medication and ultimately save insurance companies thousands of dollars per participant, UnitedHealth Group has identified several current procedural terminology (CPT) codes that institutions can apply to testing services.

At nearly the same time, investigators at the University of Florida (UF) in Gainesville, one of the leading centers for PGx testing in the US, published a retrospective analysis of reimbursement claims. In their retrospective analysis of over 1,000 outpatient claims submitted for PGx testing, the reimbursement rate was 46%, with a significantly higher rate observed for PGx panels (72%) as compared to single-gene tests (43%). The study found that the amount reimbursed ranged from 36% to 48% among payers indicating that, currently, the patient may be responsible for over half the cost of PGx testing (9).

UF has played a major role in providing transparent resources for the funding, development, and implementation of clinical PGx testing in the US. For many years, publications from institutions like UF, which were developing PGx programs, focused primarily on clinical validation. This recent study focusing on reimbursement statistics marks a turning point for the future of clinical PGx testing in the US, where not having precedence for insurance reimbursement to off-set the cost of providing testing has been a major roadblock.

PGx progress in the US is marked by healthcare reimbursement. In some counties, which have nationalized healthcare systems, PGx progress is marked by initiatives to provide testing through government-run healthcare systems. Notably, the United Kingdom (UK) announced a pilot program for PGx testing of patients prior to initiation of any statin, antidepressant, or proton pump inhibitor (10). This move comes as the drug cost burden to the UK National Health Service (NHS) increases dramatically to support the aging population (11).

In Southeast Asia, some countries have established national initiatives for PGx testing have focused on preventing Steven-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), severe and often fatal reactions to taking incompatible drugs. The frequency of the genetic variant HLA-B*15:02, which is highly associated with carbamazepine-induced SJS/TEN, can be as high at 20% in some Southeast Asian countries (12). As a result, successful nationally funded PGx program have been implemented in both Thailand and Singapore (13,14). Hopefully, with more reports in both the mainstream media and clinical journals demonstrating improved patient care and a reduced national cost-burden associated with more effective drug treatments, more countries will continue to implement PGx testing.

Challenges for clinical implementation

The successful implementation of PGx testing into routine practice faces many of the same hurdles any new testing might, especially within the context of physician awareness and utilization. More recent medical school graduates may be aware of several drug-gene interactions (e.g., such as warfarin and VKORC1 gene variants, or azathioprine and TPMT mutations) (15). It is notable that successful implementations of PGx testing at US institutions have recommended dedicated PGx experts to champion available testing and translate complicated results into recommended actions (16).

Availability, accessibility, and standardization of PGx testing can also be considered a barrier to widespread implementation. Variability in test offerings across different clinics and laboratories can create confusion among patients and providers attempting to navigate through the offerings available to them. In an ideal world, testing for variants should be standardized across various manufacturers and incorporated into uniform clinical decision-making algorithms.

Even if PGx testing is available in the electronic health system (EHS), not all healthcare professionals who provide care to patients have access to the EHS where PGx results are reported, for example community pharmacists (17). There is still a technical barrier to retain and transfer records in a standardized format between hospitals.

PGx research studies continue to provide new evidence, and guidelines are constantly updated. As many healthcare institutions face even more limited resources, supporting testing which requires a dedicated team to keep on top of the latest evidence and adapt testing accordingly may not be feasible. Not to mention, the cost associated with performing PGx testing can also present as a significant hurdle in the implementation of in-house testing.

There are many considerations to the cost of testing. First there is the initial cost of purchasing an analyzer, reagents, and array kits, dedicating appropriate laboratory space and trained staff to performing validations and then patient evaluations. These are substantial costs to laboratories that are already operating with significant challenges to securing staffing and generating enough revenue to support capital acquisitions.

At the level of patient testing, insurance plans may not cover PGx testing, creating a financial barrier for patients. Potential lack of reimbursement by insurance providers may be due to the relative paucity of cost-effective analyses for PGx testing. While several studies have had favorable results, including studies of HLA risk alleles for drug hypersensitivity reactions (18), other studies have shown a lack of cost-effectiveness. Notably, despite the fact that VKORC1 and CYP2C9 variants account for roughly 31–35% of phenotypic variability in warfarin dosing, studies on the cost-effectiveness of genotype-guided dosing have estimated a cost of greater than $170,000 per quality-adjusted life-year (QALY) (19); $50,000 per QALY is the most commonly used threshold for cost-effectiveness in the US, although recent surveys have shown the use of increased thresholds ranging from $100,000 to $150,000 (20). However, as the cost of PGx testing continues to decrease with advancements in technology, such analyses will likely become more favorable over time.

Both traditional reactive single-gene testing and preemptive testing face the abovementioned challenges. Preemptive testing usually uses multi-gene panels and aims to optimize medication use by having PGx information available at the point of prescribing and avoids delay of care (21-23). Some of the challenges are especially true for preemptive testing, such as cost, availability, and results reporting and data storage. Compared to reactive single-gene testing, which have been reported to cost from $100 to $500, preemptive testing can be more expensive, about double the cost (although lower cost per gene than single-gene testing) because more variants are covered (24,25). It may take hospitals more time to plan, design, and implement a PGx panel than a single-gene test. Furthermore, it could be months or years until a patient needs medication potentially covered by the PGx panel, and the patient might have relocated or may be seen at another healthcare facility, possibly lacking access to previous PGx results from which they may have benefited. In this way, PGx results are not immune from the same accessibility issues plaguing any other form of medical record. However, in the case of PGx testing, convincing an institution to invest in a currently less profitable in-house testing program given these quotidian challenges, can be an uphill battle.

An alternative for many providers may be to send specimens out for PGx testing to a reference laboratory. The testing may not be any less costly, especially to the patient, however the institution would not need to sustain the resources needed to support testing in-house.

Pediatric considerations

Compared to adults, PGx testing among children is still in its infancy. The implementation of PGx tests for pediatric patients faces similar challenges as those for adults, as well as some others. What makes PGx testing for children even more difficult to implement is that most PGx studies were performed in adults. Clinical studies involving children are often difficult to perform, due to enrollment challenges as well as getting Institutional Review Board (IRB)-approval for working with this vulnerable population. Moreover, even though some clinical trials were performed in children, the reproducibility for those studies is sometimes poor, partly because of the small and heterogeneous population (26). Children’s metabolic and hemostatic systems are still developing. They differ from adults not just with respect to body size and therefore cannot be seen as a small adult. Children and adults could show differences in the dynamic expression of some metabolic enzymes, drug transporter, and drug targets (27,28). The drug response between children, or even within a child, could show significant variation because of their continuous growth (29).

Two questions which have been widely discussed are “Is separate evidence always needed for pediatric patients?” and “Is it appropriate to extrapolate adult’s data to children for certain drugs?”. CPIC and Pharmacogenomics Knowledge Base (PharmGKB) are now providing pediatric recommendations for some of the drugs, when evidence is available (30,31). For each gene-drug pair, CPIC provides recommendations for whether sufficient data supporting the suitability to extrapolate adult data was found, and if the applicability of adult data to pediatric patient is unclear. Similarly, the PharmGKB created a pediatric column under the clinical annotations section for each drug. The pediatric box will be checked if sufficient evidence supports the use of the drug in pediatric patients (31).

Harmonization of pediatric recommendation across different PGx consortia is a key step and will facilitate the development of PGx in children. Because of the smaller population, continuous growth, and more inter- and intra-individual variation of children, more collaborative studies will be especially beneficial to the development of pediatric PGx. More knowledge about age-related changes in the expression of drug-metabolizing enzymes and transporters is essential for the establishment of guidelines and consensus (28,32).

Laboratory aspects: which variants should be included?

Clinicians and laboratories sometimes find there is a need to develop traditional reactive panels or preemptive custom-designed panels for a certain patient population or clinical studies. “Which variants to include?” is a question often encountered. Regardless of whether the panel will be broad-based (23,33,34), disease-specific (35,36), or for a specific gene (37-39), considerations during selection of variants should include clinical evidence and clinical actionability. Some helpful references include CPIC, PharmGKB, and the Dutch Pharmacogenetics Working Group (DPWG), which have standard systems for grading levels of evidence linking genotypes to phenotypes (30,31,40). The PGx Working Group of the Association for Molecular Pathology (AMP) provides recommendations defining a minimum set of variants that should be included in genotyping assays for specific genes (tier 1 variants), as well as an extended panel of tier 2 variants (41). Pharmacogene Variation Consortium (PharmVar) and the FDA drug labels can also be used as references for drug-gene phenotypes (42).

Labor time, methodology, and complexity of data analysis, affect the turnaround time, which is especially important for traditional reactive PGx testing. All these factors limit the size of the panel and can be used as a guidance to include or exclude variants with low-to-intermediate level of evidence.

The advantages and disadvantages of two common methodologies

There are various methodologies for PGx testing, including single variant (quantitative) polymerase chain reaction (PCR) assays and PCR-based arrays, next-generation sequencing (NGS), microarray (43,44), a method which combines competitive PCR with matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry (45), and high-resolution melt analysis (46). Here, the pros and cons of the two most popular methods for PGx testing—PCR-based array technology and NGS, will be discussed.

Methods such as PCR-based array may be more efficient for high throughput broad-based screening. There are numerous commercially available pre-designed platforms and these typically include variants with known genotype-phenotype linkages in the CPIC or DPWG guidelines (23,47). Some of these panels have been implemented in large-scale clinical studies (48,49). The advantage of using a broad-based panel with pre-selected variants is that it interrogates many variants, including those potentially or theoretically associated with drug response. However, it poses challenges to data processing and interpretation. Furthermore, the included variants may not be of interest in certain clinical settings. Some institutions have developed custom-designed array-based genotyping platforms for clinical trials (50-52). This approach allows reasonably short turnaround time and straightforward interpretation, with shortcomings. The array-based approach employs allelic discrimination platforms, which lack the ability to detect rare and structural variants. Another limitation is that for variants known to be triallelic, the genotyping requires a combination of two assays (22).

NGS is commonly categorized into three approaches. The first one is WGS, which sequences the entire genome. It is known for its power in variant discovery and commonly used in research (53,54). Because it is not quite feasible to routinely perform genome-wide sequencing for a large number of patients in most clinical settings, due to the turnaround time, cost, and labor, this is not a widely adopted approach for clinical PGx testing. The second one is WES, which only focus on sequencing the exons (i.e., the coding regions) of the genome, and therefore may miss functional variants in non-coding regions. For example, some variants in non-coding regions could be of clinical significance: UGT1A1*28, CYP2C19*17, and CYP3A5*3 (55). Targeted sequencing is the last approach, which only sequences selected genes of interest (56,57). It is particularly suitable for well-known variants with established functions in clinical PGx testing. For example, a multicenter study developed an NGS-based platform (PGRNseq) which targeted 84 genes associated with pharmacogenetic phenotypes (58). Panels targeting CYP2D6, CYP2C19, UGT1A1*28 have been developed (59-61).

Despite the strengths of NGS, the technology comes with limitations because of its short-read-based nature, which could cause inaccurate genotype calls (62,63). NGS could generate wrong genotyping calls for highly polymorphic and complexed genes, such as the CYP2D6 and the HLA genes (64,65). It has been shown that regions of high sequence homology can cause wrongful alignment of reads to the reference genome, and lead to false negative or false positive results (66). Regions of high sequence homology can be found in various locations, within a gene, on different genes, or in pseudogenes. CYP2D6 has two highly homologous neighboring pseudogenes: CYP2D7 and CYP2D8 (67). Copy number variations (CNVs) in the CYP2D6 gene are common, and hybrid alleles have been identified in this gene. Both affect the interpretation of CYP2D6 genotyping, sequencing, and phenotype prediction. Certain other drug metabolizing genes of the CYP subfamily share high-sequence similarity, such as CYP2C9 and CYP2C19 (68) [exhibit 92% amino acid sequence identity and 96% sequence similarity (69)], which may result in erroneous variant calls if NGS alone is used for genotyping. Note that orthogonal study of NGS platform does not always have a 100% accuracy, and there is potential inaccuracy in genotyping for CYP genes. Sanger sequencing can be used for confirmation when novel variants are detected.

Although NGS technology has been advancing quickly and it is anticipated that Sanger confirmation may not be needed in the future, laboratories should keep up to date about NGS standards, recommendations and guidelines. The current Molecular Pathology Checklist of the College of American Pathologists (CAP) requires CAP-accredited laboratories to perform confirmatory testing of NGS results as indicated in its policy and records correlation of the NGS confirmatory results, and notes that laboratories must determine during validation whether and when confirmatory testing is indicated (70). The American College of Medical Genetics and Genomics (ACMG) suggests that laboratories should establish and make available a confirmatory testing policy for each variant class. In case a validated approach is absent, laboratories should continue orthogonal confirmation (71).

Because of the abovementioned limitations, NGS platforms such as the PGRNseq did not include CYP2D6, CYP2A6, and HLA-B (58). A possible solution would be to complement the NGS PGx panel using target sequencing with WGS or a PCR-based assay (37,72,73), which would be specific for those complex genes. Multiplex ligation-dependent probe amplification (MLPA) is also a possible robust and cost-effective option for complex gene interrogation (74).

Data analysis

Various types of PGx platforms are being offered by different institutions across the world. Some platforms use NGS, while others use array-based or mass spectrometry-based methods (23,58,59,75). To date, PCR-based arrays are far more commonly used than the other technologies (56). Like many clinical tests in which each case requires manual interpretation, such as immunofixation or anatomic pathology specimens, data analysis and interpretation of PGx testing results could contribute a significant increase in turnaround time.

A custom-designed PGx panel, which uses the OpenArray technology, will be used here to demonstrate the level of complexity for data analysis. After a sample is processed using a real-time PCR system, the images of the OpenArray plates should be reviewed for quality assurance (76). The data file is then processed by a software provided by the manufacturer for SNP autocalling. Even though autocalling helps to save time, tracings and calls in each allele discrimination plot must be reviewed manually for all patient samples, quality controls, and standards (patient samples of known results). Note that for variants for which not all possible results were covered during the validation (e.g., the rare variants of RYR1-related disease), when the alternative allele is detected post-go-live, the call should be confirmed by another method, such as Sanger sequencing.

NGS data analysis could be considerably more complex and labor-intensive than that of array-based methods. Sophisticated bioinformatics pipelines are needed to decipher the raw data output of NGS-based methods into clinically actionable findings. Such data pipelines begin with base calling and thereby culminate in the translation of raw signal data derived from a sequencing reaction into consecutive base sequences in the conventional fastq file format. If multiple samples are simultaneously analyzed, a de-multiplexing step is also needed to separate the respective reads belonging to each input sample. The determined base sequences are then aligned to reference sequences (or aligned de novo if no reference sequences are available) so that sequence or structural variants can be identified in a process called variant calling. Numerous alignment tools such as Burrows-Wheeler Aligner (BWA), Bowtie2, and Mapping and Assembly with Quality (MAQ) are available as commercial or open-source software for this key step (77). Aligned, or mapped, sequence reads are saved in the conventional sequence alignment and map (SAM) or binary alignment and map (BAM) file format (BAM files are compressed SAM files), which serves as the input of variant calling software. Popular variant calling algorithms or software include Genome Analysis Toolkit (GATK), UnifiedGenotyper, Samtools (mpileup and bcftools), and Freebayes (78-80). Annotation and interpretation of these variants thus provide clinical utility to the NGS dataset.

In recent years, there have been several examples of the use of NGS for PGx variant detection (81), ranging from targeted sequencing to WES to WGS approaches. Concurrently, there has been growing interest in the development of PGx-specific tools for variant calling and interpretations. The more widely available tools are Stargazer, Astrolabe, and Aldy. In a recent study, their performance was specifically evaluated based on their ability to call different CYP2D6 variants (81,82). Even so, a persisting challenge in the application of NGS to clinical PGx is how variants of uncertain significance (VUS)—defined as part of the five-tier terminology system jointly recommended by the ACMG and AMP for sequence variant classification (83) should be handled. Particularly with approaches based on WES or WGS, numerous VUS or incidental findings would likely be elucidated. In fact, one of the promises of utilizing NGS for PGx is its potential for large-scale profiling, which would inevitably lead to the identification of novel pharmacogenetic variants. This would not only lead to the arduous task of correlating pharmacogene variants belonging to the very complex drug metabolism gene network to poor, intermediate, extensive, and ultra-rapid metabolizer drug response phenotypes, but also necessitate interpretations and guidance for their actionability and clinical significance (84). Although clinical utility has been identified for pharmacogenetic incidental or secondary genetic findings (85), the reporting of these findings remains controversial (86). Institutions may set up their data analysis pipeline to only evaluate/report pre-determined, known variants, while research is being performed to better characterize the functionality of the other variants.

Analytical and clinical validation

Analytical validation is one of the key steps of clinical implementation of PGx. For pre-designed PGx testing platforms, validation requires accuracy and precision assessments to ensure the reliability of the assays, which is straightforward. For custom-designed testing platforms, one may want to design a more vigorous validation plan to challenge the assays’ accuracies and precisions. Analytical sensitivity and specificity studies may also be performed to determine minimum DNA concentrations and evaluate interferences, respectively.

Regardless of the type of platform, clinical validation requires a great deal of coordination between the clinical team identifying and recruiting study participants and collecting specimens, the laboratory operating the PGx testing platform and generating test results, and the pharmacy to ensure that reports and recommendations are accurate (87). Most validations begin with an IRB-approved clinical trial or study for which participants are identified by clinicians and recruited for PGx evaluation. Once the appropriate specimen is collected from the participant, the laboratory evaluates the analytical performance (e.g., accuracy, precision, and analytical sensitivity) of the PGx platform by comparing results obtained from the in-house platform to a reference method (e.g., Sanger sequencing) (22,87). In the US, the laboratory performing clinical PGx testing for use in patient management should be a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory, which requires appropriate accreditation through an approved accreditation agency (88).

While the laboratory performs the analytical validation, the laboratory, pharmacy, and clinical teams must work together to evaluate the clinical validity and utility. From the accuracy performance data and participant history (known adverse drug response, etc.), sensitivity, specificity, and positive and negative predictive values can be determined.

Results reporting

Another major component of evaluating clinical utility comes from the design and content included in the report of patient results. Ideally, these results must be made available to clinicians through the EHS or alternate secure web-based portal. Results and any recommendations for action must be clear. Reports that are too technical or require the clinical team to do additional research to decipher the actionable implications of the raw data will not be utilized appropriately.

Various institutions developed integration processes and clinical decision support tools which fit the requirements of their EHS (89-92). Some of these institutions alert providers who prescribe a medication with the patient’s relevant PGx results, while others opt to alert providers that relevant PGx tests exist. For both approaches, good practices in PGx data reporting include: (I) avoid using excessive text to display complex PGx results; (II) avoid alert fatigue; (III) the reporting system should be EHS-compatible but not EHS-specific, to have the system accessible within and outside of the institution’s EHS; and (IV) having the ability to integrate reactive and preemptive PGx data, which displays PGx results and interpretations relevant to both current medication as well as alternatives (89).

The design and content of the PGx result reports are typically developed and evaluated as part of the clinical validation. The Center for Personalized Therapeutics at The University of Chicago (CPTC) has published a series of investigations describing both the development of their PGx testing report design (89,93,94), and the clinical performance as a function of measurable clinician behavior (95) upon receipt of patient PGx results. Patient results generated by the CPTC are reported according to a traffic-light classification system indicating the likelihood of adverse effects (i.e., a red light indicating high likelihood) based on CPIC guidelines, and recommended action (i.e., prescription of alternative therapeutic). The clinician initially sees a colored traffic light associated with risk that is available to click on for access to the full PGx testing result. The subsequent evaluation of this report format revealed a high clinical utility, with PGx reports accompanied by a red traffic light symbol were viewed by clinical teams 100% of the time, of which 50% resulting in documented changes to therapeutics (95).

Ultimately, the goal of PGx testing is to minimize adverse drug events and improve the quality of patient care. This requires multidisciplinary teamwork to evaluate that every part of the testing process, particularly that the post-analytical analysis and result reporting, is providing information that will be viewed, understood, and utilized by the clinical teams.

Conclusions

Despite the challenges, clinical PGx has become more widely used in the past two decades. Major advancements in the clinical integration of PGx testing in the US come from the FDA-regulated inclusion of drug-gene interactions on drug labels. Worldwide, some nations have begun laying the groundwork for including PGx testing programs in government-funded national healthcare systems. Although the outlook for the incorporation of PGx testing into mainstream healthcare looks promising, these programs require a substantial investment of resources. This review discusses key considerations for PGx test implementation and aims to serve as a reference for pharmacists, clinicians, and laboratorians looking to provide PGx testing within their institutions. Implementing PGx testing not only requires selecting the appropriate variants and testing methodologies to serve the clinical needs, but an understanding of all the supporting infrastructure and education needed for effective test utilization to improve the treatment and management of patients.

Acknowledgments

Funding: None.

Footnote

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-24-36/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-24-36/coif). X.M.R.v.W. serves as an unpaid editorial board member of Journal of Laboratory and Precision Medicine from September 2023 to August 2025. X.M.R.v.W. received travel support for attending Association for Diagnostics & Laboratory Medicine (ADLM), Rocky Mountain ADLM Local Section, and American Society for Clinical Pathology meetings; received consulting honoraria from Dialectica, has patents planned, issued, or pending with Beckman Coulter; has leadership or fiduciary roles as owner of van Wijk Consulting LLC, per-diem laboratory director at Fred Hutchinson Cancer Center, chair of ADLM Industry Division, board member of Rocky Mountain ADLM Local section, editorial board member for Journal of Laboratory and Precision Medicine, member of ADLM Science & Practice Core Committee, member of ADLM Policy & External Affairs Core Committee, member of Academy of Diagnostics & Laboratory Medicine’s Awards Committee, member of American Society for Clinical Laboratory Science Political Action Committee, and advisor for CLSI expert panel on Clinical Chemistry and Toxicology; has stock options with Danaher and is a former employee of Beckman Coulter (part of Danaher). C.W.C. is recording secretary/board member for ADLM Chicago Section. 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.

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/.

References

1. Bank PCD, Swen JJ, Schaap RD, et al. A pilot study of the implementation of pharmacogenomic pharmacist initiated pre-emptive testing in primary care. Eur J Hum Genet 2019;27:1532-41. [Crossref] [PubMed]
2. Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature 2015;526:343-50. [Crossref] [PubMed]
3. Volpi S, Bult CJ, Chisholm RL, et al. Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects. Clin Pharmacol Ther 2018;103:778-86. [Crossref] [PubMed]
4. Van Heukelom J, Morgan J, Massmann A, et al. Evolution of pharmacogenomic services and implementation of a multi-state pharmacogenomics clinic across a large rural healthcare system. Front Pharmacol 2023;14:1274165. [Crossref] [PubMed]
5. US Food and Drug Administration. FDA authorizes first direct-to-consumer test for detecting genetic variants that may be associated with medication metabolism. 2018. [Cited 2024 Mar 18]. Available online: https://www.fda.gov/news-events/press-announcements/fda-authorizes-first-direct-consumer-test-detecting-genetic-variants-may-be-associated-medication
6. Virelli CR, Mohiuddin AG, Kennedy JL. Barriers to clinical adoption of pharmacogenomic testing in psychiatry: a critical analysis. Transl Psychiatry 2021;11:509. [Crossref] [PubMed]
7. UnitedHealthcare. Pharmacogenetic Panel Testing. 2022 [Cited 2024 Mar 18]. Available online: https://www.uhcprovider.com/content/dam/provider/docs/public/policies/comm-medical-drug/pharmacogenetic-testing.pdf
8. UnitedHealthcare^® Medicare Advantage Medical Policy (Pharmacogenomics Testing). UnitedHealthcare Medicare Advantage. 2024. Available online: https://www.uhcprovider.com/content/dam/provider/docs/public/policies/medadv-mp/pharmacogenomics-testing.pdf
9. Lemke LK, Alam B, Williams R, et al. Reimbursement of pharmacogenetic tests at a tertiary academic medical center in the United States. Front Pharmacol 2023;14:1179364. [Crossref] [PubMed]
10. Connelly D. Pharmacogenomic testing pilot to start in general practice from June 2023. The Pharmaceutical Journal 2023. Available online: https://pharmaceutical-journal.com/article/news/pharmacogenomic-testing-pilot-to-start-in-general-practice-from-june-2023
11. Robinson J. NHS drug costs in England see biggest rise for five years to almost £18bn. The Pharmaceutical Journal 2022. Available online: https://pharmaceutical-journal.com/article/news/nhs-drug-costs-in-england-see-biggest-rise-for-five-years-to-almost-18bn
12. Nguyen AH, Sukasem C, Nguyen QN, et al. The pharmacogenomics of carbamazepine-induced cutaneous adverse drug reaction in the South of Vietnam. Front Pharmacol 2023;14:1217516. [Crossref] [PubMed]
13. Sukasem C, Biswas M, Lungchukiet P, et al. Clinical pharmacogenomics implementation in Thailand: a dream come true. Pharmacogenomics 2023;24:297-301. [Crossref] [PubMed]
14. Chumnumwat S, Lu ZH, Sukasem C, et al. Southeast Asian Pharmacogenomics Research Network (SEAPharm): Current Status and Perspectives. Public Health Genomics 2019;22:132-9. [Crossref] [PubMed]
15. Basyouni D, Shatnawi A. Pharmacogenomics Instruction Depth, Extent, and Perception in US Medical Curricula. J Med Educ Curric Dev 2020;7:2382120520930772. [Crossref] [PubMed]
16. Cicali EJ, Weitzel KW, Elsey AR, et al. Challenges and lessons learned from clinical pharmacogenetic implementation of multiple gene-drug pairs across ambulatory care settings. Genet Med 2019;21:2264-74. [Crossref] [PubMed]
17. Haga SB, Allen LaPointe NM, Moaddeb J. Challenges to integrating pharmacogenetic testing into medication therapy management. J Manag Care Spec Pharm 2015;21:346-52. [Crossref] [PubMed]
18. Zhou Y, Krebs K, Milani L, et al. Global Frequencies of Clinically Important HLA Alleles and Their Implications For the Cost-Effectiveness of Preemptive Pharmacogenetic Testing. Clin Pharmacol Ther 2021;109:160-74. [Crossref] [PubMed]
19. Eckman MH, Rosand J, Greenberg SM, et al. Cost-effectiveness of using pharmacogenetic information in warfarin dosing for patients with nonvalvular atrial fibrillation. Ann Intern Med 2009;150:73-83. [Crossref] [PubMed]
20. Neumann PJ, Kim DD. Cost-effectiveness Thresholds Used by Study Authors, 1990-2021. JAMA 2023;329:1312-4. [Crossref] [PubMed]
21. Fernandez CA, Smith C, Yang W, et al. Concordance of DMET plus genotyping results with those of orthogonal genotyping methods. Clin Pharmacol Ther 2012;92:360-5. [Crossref] [PubMed]
22. Tang NY, Pei X, George D, et al. Validation of a Large Custom-Designed Pharmacogenomics Panel on an Array Genotyping Platform. J Appl Lab Med 2021;6:1505-16. [Crossref] [PubMed]
23. Brown AM, Renaud Y, Ross C, et al. Development of a broad-based ADME panel for use in pharmacogenomic studies. Pharmacogenomics 2014;15:1185-95. [Crossref] [PubMed]
24. Anderson HD, Crooks KR, Kao DP, et al. The landscape of pharmacogenetic testing in a US managed care population. Genet Med 2020;22:1247-53. [Crossref] [PubMed]
25. Haidar CE, Crews KR, Hoffman JM, et al. Advancing Pharmacogenomics from Single-Gene to Preemptive Testing. Annu Rev Genomics Hum Genet 2022;23:449-73. [Crossref] [PubMed]
26. Maagdenberg H, Vijverberg SJ, Bierings MB, et al. Pharmacogenomics in Pediatric Patients: Towards Personalized Medicine. Paediatr Drugs 2016;18:251-60. [Crossref] [PubMed]
27. Mooij MG, Nies AT, Knibbe CA, et al. Development of Human Membrane Transporters: Drug Disposition and Pharmacogenetics. Clin Pharmacokinet 2016;55:507-24. [Crossref] [PubMed]
28. Kearns GL, Abdel-Rahman SM, Alander SW, et al. Developmental pharmacology--drug disposition, action, and therapy in infants and children. N Engl J Med 2003;349:1157-67. [Crossref] [PubMed]
29. O'Hara K. Pharmacokinetic changes with growth and development between birth and adulthood. J Pharm Pract Res 2017;47:313-8. [Crossref]
30. Relling MV, Klein TE. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin Pharmacol Ther 2011;89:464-7. [Crossref] [PubMed]
31. Whirl-Carrillo M, Huddart R, Gong L, et al. An Evidence-Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine. Clin Pharmacol Ther 2021;110:563-72. [Crossref] [PubMed]
32. Neville KA, Becker ML, Goldman JL, et al. Developmental pharmacogenomics. Paediatr Anaesth 2011;21:255-65. [Crossref] [PubMed]
33. Welch RA, Lazaruk K, Haque KA, et al. Validation of the performance of a comprehensive genotyping assay panel of single nucleotide polymorphisms in drug metabolism enzyme genes. Hum Mutat 2008;29:750-6. [Crossref] [PubMed]
34. Swen JJ, van der Wouden CH, Manson LE, et al. A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. Lancet 2023;401:347-56. [Crossref] [PubMed]
35. Garcia EP, Minkovsky A, Jia Y, et al. Validation of OncoPanel: A Targeted Next-Generation Sequencing Assay for the Detection of Somatic Variants in Cancer. Arch Pathol Lab Med 2017;141:751-8. [Crossref] [PubMed]
36. Collins KS, Pratt VM, Stansberry WM, et al. Analytical validity of a genotyping assay for use with personalized antihypertensive and chronic kidney disease therapy. Pharmacogenet Genomics 2019;29:18-22. [Crossref] [PubMed]
37. Leung EKY, Agolini E, Pei X, et al. Validation of an Extensive CYP2D6 Assay Panel Based on Invader and TaqMan Copy Number Assays. J Appl Lab Med 2017;1:471-82. [Crossref] [PubMed]
38. Twist GP, Gaedigk R, Leeder JS, et al. High-resolution melt analysis to detect sequence variations in highly homologous gene regions: application to CYP2B6. Pharmacogenomics 2013;14:913-22. [Crossref] [PubMed]
39. Chen C, Li S, Lu X, et al. High resolution melting method to detect single nucleotide polymorphism of VKORC1 and CYP2C9. Int J Clin Exp Pathol 2014;7:2558-64. [PubMed]
40. PharmGKB. DPWG: Dutch Pharmacogenetics Working Group. [Cited 2023 Jan 26]. Available online: https://www.pharmgkb.org/page/dpwg
41. PharmGKB. AMP's Minimum Sets of Alleles for PGx Testing. [Cited 2024 Mar 27]. Available online: https://www.pharmgkb.org/ampAllelesToTest
42. PharmVar. [Cited 2023 Jan 26]. Available online: https://www.pharmvar.org/
43. Chicurel ME, Dalma-Weiszhausz DD. Microarrays in pharmacogenomics--advances and future promise. Pharmacogenomics 2002;3:589-601. [Crossref] [PubMed]
44. Gan P, Hajis MIB, Yumna M, et al. Development and validation of a pharmacogenomics reporting workflow based on the illumina global screening array chip. Front Pharmacol 2024;15:1349203. [Crossref] [PubMed]
45. Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr Protoc Hum Genet 2009;Chapter 2:Unit 2.12.
46. Vossen RH, Aten E, Roos A, et al. High-resolution melting analysis (HRMA): more than just sequence variant screening. Hum Mutat 2009;30:860-6. [Crossref] [PubMed]
47. Agena Bioscience. VeriDose^® Core Panel for Pharmacogenetic Testing. [Cited 2024 Mar 26]. Available online: https://www.agenabio.com/products/panel/veridose-core-panel/
48. Pulley JM, Denny JC, Peterson JF, et al. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther 2012;92:87-95. [Crossref] [PubMed]
49. Hoffman JM, Haidar CE, Wilkinson MR, et al. PG4KDS: a model for the clinical implementation of pre-emptive pharmacogenetics. Am J Med Genet C Semin Med Genet 2014;166C:45-55. [Crossref] [PubMed]
50. Eadon MT, Desta Z, Levy KD, et al. Implementation of a pharmacogenomics consult service to support the INGENIOUS trial. Clin Pharmacol Ther 2016;100:63-6. [Crossref] [PubMed]
51. Truong TM, Apfelbaum J, Shahul S, et al. The ImPreSS Trial: Implementation of Point-of-Care Pharmacogenomic Decision Support in Perioperative Care. Clin Pharmacol Ther 2019;106:1179-83. [Crossref] [PubMed]
52. Reizine N, Vokes EE, Liu P, et al. Implementation of pharmacogenomic testing in oncology care (PhOCus): study protocol of a pragmatic, randomized clinical trial. Ther Adv Med Oncol 2020;12:1758835920974118. [Crossref] [PubMed]
53. Mizzi C, Peters B, Mitropoulou C, et al. Personalized pharmacogenomics profiling using whole-genome sequencing. Pharmacogenomics 2014;15:1223-34. [Crossref] [PubMed]
54. Sivadas A, Salleh MZ, Teh LK, et al. Genetic epidemiology of pharmacogenetic variants in South East Asian Malays using whole-genome sequences. Pharmacogenomics J 2017;17:461-70. [Crossref] [PubMed]
55. Caspar SM, Schneider T, Stoll P, et al. Potential of whole-genome sequencing-based pharmacogenetic profiling. Pharmacogenomics 2021;22:177-90. [Crossref] [PubMed]
56. van der Lee M, Kriek M, Guchelaar HJ, et al. Technologies for Pharmacogenomics: A Review. Genes (Basel) 2020;11:1456. [Crossref] [PubMed]
57. Schwarz UI, Gulilat M, Kim RB. The Role of Next-Generation Sequencing in Pharmacogenetics and Pharmacogenomics. Cold Spring Harb Perspect Med 2019;9:a033027. [Crossref] [PubMed]
58. Gordon AS, Fulton RS, Qin X, et al. PGRNseq: a targeted capture sequencing panel for pharmacogenetic research and implementation. Pharmacogenet Genomics 2016;26:161-8. [Crossref] [PubMed]
59. Klein K, Tremmel R, Winter S, et al. A New Panel-Based Next-Generation Sequencing Method for ADME Genes Reveals Novel Associations of Common and Rare Variants With Expression in a Human Liver Cohort. Front Genet 2019;10:7. [Crossref] [PubMed]
60. Han SM, Park J, Lee JH, et al. Targeted Next-Generation Sequencing for Comprehensive Genetic Profiling of Pharmacogenes. Clin Pharmacol Ther 2017;101:396-405. [Crossref] [PubMed]
61. Gulilat M, Lamb T, Teft WA, et al. Targeted next generation sequencing as a tool for precision medicine. BMC Med Genomics 2019;12:81. [Crossref] [PubMed]
62. Tucker T, Marra M, Friedman JM. Massively parallel sequencing: the next big thing in genetic medicine. Am J Hum Genet 2009;85:142-54. [Crossref] [PubMed]
63. Kohane IS. (Mis)treating the pharmacogenetic incidentalome. Nat Rev Drug Discov 2012;11:89-90. [Crossref] [PubMed]
64. Gaedigk A. Complexities of CYP2D6 gene analysis and interpretation. Int Rev Psychiatry 2013;25:534-53. [Crossref] [PubMed]
65. Williams TM. Human leukocyte antigen gene polymorphism and the histocompatibility laboratory. J Mol Diagn 2001;3:98-104. [Crossref] [PubMed]
66. Mandelker D, Schmidt RJ, Ankala A, et al. Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing. Genet Med 2016;18:1282-9. [Crossref] [PubMed]
67. Yang Y, Botton MR, Scott ER, et al. Sequencing the CYP2D6 gene: from variant allele discovery to clinical pharmacogenetic testing. Pharmacogenomics 2017;18:673-85. [Crossref] [PubMed]
68. BoyleGESitkoKGallowayJGDeep mutational scanning of CYP2C19 reveals a substrate specificity-abundance tradeoff.bioRxiv 2023.10.06.561250. 2023. Available online: https://www.biorxiv.org/content/10.1101/2023.10.06.561250.abstract
69. Yao Y, Han WW, Zhou YH, et al. The metabolism of CYP2C9 and CYP2C19 for gliclazide by homology modeling and docking study. Eur J Med Chem 2009;44:854-61. [Crossref] [PubMed]
70. College of American Pathologists. Molecular Pathology Checklist. 2023. Available online: https://www.cap.org/laboratory-improvement/accreditation/accreditation-checklists
71. Rehder C, Bean LJH, Bick D, et al. Next-generation sequencing for constitutional variants in the clinical laboratory, 2021 revision: a technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2021;23:1399-415. [Crossref] [PubMed]
72. Twist GP, Gaedigk A, Miller NA, et al. Constellation: a tool for rapid, automated phenotype assignment of a highly polymorphic pharmacogene, CYP2D6, from whole-genome sequences. NPJ Genom Med 2016;1:15007. [Crossref] [PubMed]
73. Chen X, Shen F, Gonzaludo N, et al. Cyrius: accurate CYP2D6 genotyping using whole-genome sequencing data. Pharmacogenomics J 2021;21:251-61. [Crossref] [PubMed]
74. Schouten JP, McElgunn CJ, Waaijer R, et al. Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Res 2002;30:e57. [Crossref] [PubMed]
75. Ellis JA, Ong B. The MassARRAY(®) System for Targeted SNP Genotyping. Methods Mol Biol 2017;1492:77-94. [Crossref] [PubMed]
76. Hartshorne T, Le F, Lang J, et al. A high-throughput real-time PCR approach to pharmacogenomics studies. J Pharmacogenomics Pharmacoproteomics 2014;5:1000133.
77. Baker QB, Hammad M, Al-Rashdan W, et al. Comprehensive comparison of cloud-based NGS data analysis and alignment tools. Inform Med Unlocked 2020;18:100296. [Crossref]
78. DePristo MA, Banks E, Poplin R, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011;43:491-8. [Crossref] [PubMed]
79. Li H, Handsaker B, Wysoker A, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-9. [Crossref] [PubMed]
80. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv:1207.3907. 2012. Available online: https://arxiv.org/abs/1207.3907
81. Tafazoli A, Guchelaar HJ, Miltyk W, et al. Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice. Front Pharmacol 2021;12:693453. [Crossref] [PubMed]
82. Twesigomwe D, Wright GEB, Drögemöller BI, et al. A systematic comparison of pharmacogene star allele calling bioinformatics algorithms: a focus on CYP2D6 genotyping. NPJ Genom Med 2020;5:30. [Crossref] [PubMed]
83. Richards S, Aziz N, Bale S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015;17:405-24. [Crossref] [PubMed]
84. Zhou Y, Lauschke VM. Next-generation sequencing in pharmacogenomics - fit for clinical decision support? Expert Rev Clin Pharmacol 2024;17:213-23. [Crossref] [PubMed]
85. Lee EM, Xu K, Mosbrook E, et al. Pharmacogenomic incidental findings in 308 families: The NIH Undiagnosed Diseases Program experience. Genet Med 2016;18:1303-7. [Crossref] [PubMed]
86. Haga SB. Revisiting Secondary Information Related to Pharmacogenetic Testing. Front Genet 2021;12:741395. [Crossref] [PubMed]
87. Mroz P, Michel S, Allen JD, et al. Development and Implementation of In-House Pharmacogenomic Testing Program at a Major Academic Health System. Front Genet 2021;12:712602. [Crossref] [PubMed]
88. Kanegusuku ALG, Chan CW, O'Donnell PH, et al. Implementation of pharmacogenomics testing for precision medicine. Crit Rev Clin Lab Sci 2024;61:89-106. [Crossref] [PubMed]
89. Danahey K, Borden BA, Furner B, et al. Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system. J Biomed Inform 2017;75:110-21. [Crossref] [PubMed]
90. Caraballo PJ, Hodge LS, Bielinski SJ, et al. Multidisciplinary model to implement pharmacogenomics at the point of care. Genet Med 2017;19:421-9. [Crossref] [PubMed]
91. Bell GC, Crews KR, Wilkinson MR, et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc 2014;21:e93-9. [Crossref] [PubMed]
92. Rasmussen-Torvik LJ, Stallings SC, Gordon AS, et al. Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems. Clin Pharmacol Ther 2014;96:482-9. [Crossref] [PubMed]
93. O'Donnell PH, Bush A, Spitz J, et al. The 1200 patients project: creating a new medical model system for clinical implementation of pharmacogenomics. Clin Pharmacol Ther 2012;92:446-9. [Crossref] [PubMed]
94. O'Donnell PH, Danahey K, Jacobs M, et al. Adoption of a clinical pharmacogenomics implementation program during outpatient care--initial results of the University of Chicago "1,200 Patients Project". Am J Med Genet C Semin Med Genet 2014;166C:68-75. [Crossref] [PubMed]
95. O'Donnell PH, Wadhwa N, Danahey K, et al. Pharmacogenomics-Based Point-of-Care Clinical Decision Support Significantly Alters Drug Prescribing. Clin Pharmacol Ther 2017;102:859-69. [Crossref] [PubMed]