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

Empowering personalized pharmacogenomics with generative AI solutions

OBJECTIVE

This study evaluates an AI assistant developed using OpenAI's GPT-4 for interpreting pharmacogenomic (PGx) testing results, aiming to improve decision-making and knowledge sharing in clinical genetics and to enhance patient care with equitable access.

MATERIALS AND METHODS

The AI assistant employs retrieval-augmented generation (RAG), which combines retrieval and generative techniques, by harnessing a knowledge base (KB) that comprises data from the Clinical Pharmacogenetics Implementation Consortium (CPIC). It uses context-aware GPT-4 to generate tailored responses to user queries from this KB, further refined through prompt engineering and guardrails.

RESULTS

Evaluated against a specialized PGx question catalog, the AI assistant showed high efficacy in addressing user queries. Compared with OpenAI's ChatGPT 3.5, it demonstrated better performance, especially in provider-specific queries requiring specialized data and citations. Key areas for improvement include enhancing accuracy, relevancy, and representative language in responses.

DISCUSSION

The integration of context-aware GPT-4 with RAG significantly enhanced the AI assistant's utility. RAG's ability to incorporate domain-specific CPIC data, including recent literature, proved beneficial. Challenges persist, such as the need for specialized genetic/PGx models to improve accuracy and relevancy and addressing ethical, regulatory, and safety concerns.

CONCLUSION

This study underscores generative AI's potential for transforming healthcare provider support and patient accessibility to complex pharmacogenomic information. While careful implementation of large language models like GPT-4 is necessary, it is clear that they can substantially improve understanding of pharmacogenomic data. With further development, these tools could augment healthcare expertise, provider productivity, and the delivery of equitable, patient-centered healthcare services.

Current Progress on the Influence Human Genetics Has on the Efficacy of Tyrosine Kinase Inhibitors Used to Treat Chronic Myeloid Leukemia

The use of tyrosine kinase inhibitors (TKIs) has become the mainstay of treatment in patients suffering from chronic myeloid leukemia (CML), an adult leukemia caused by a reciprocal translocation between chromosomes 9 and 22, which creates an oncogene resulting in a myeloproliferative neoplasm. These drugs function by inhibiting the ATP-binding site on the fusion oncoprotein and subsequently halting proliferative activity. The goal of this work is to investigate the current state of research into genetic factors that influence the efficacy of four FDA-approved TKIs used to treat CML. This overview attempts to identify genetic criteria that could be considered when choosing one drug over the others and to identify where more research is needed. Our results suggest that the usual liver enzymes impacting patient response may not be a major factor affecting the efficacy of imatinib, nilotinib, and bosutinib, and yet, that is where most of the past research has focused. More research is warranted on the impact that human polymorphisms of the CYP enzymes have on dasatinib. The impact of polymorphisms in UGT1A1 should be investigated thoroughly in other TKIs, not only nilotinib. The role of influx and efflux transporters has been inconsistent thus far, possibly due to failures to account for the multiple proteins that can transport TKIs and the impact that tumors have on transporter expression. Because physicians cannot currently use a patient's genetic profile to better target their treatment with TKIs, it is critical that more research be conducted on auxiliary pathways or off-target binding effects to generate new leads for further study. Hopefully, new avenues of research will help explain treatment failures and improve patient outcomes.

Should secondary pharmacogenomic variants be actively screened and reported when diagnostic genome-wide sequencing is performed in a child?

This white paper was prepared by the Global Alliance for Genomics and Health Regulatory and Ethics Work Stream's Pediatric Task Team to review and provide perspective with respect to ethical, legal, and social issues regarding the return of secondary pharmacogenomic variants in children who have a serious disease or developmental disorder and are undergoing exome or genome sequencing to identify a genetic cause of their condition. We discuss actively searching for and reporting pharmacogenetic/genomic variants in pediatric patients, different methods of returning secondary pharmacogenomic findings to the patient/parents and/or treating clinicians, maintaining these data in the patient's health record over time, decision supports to assist using pharmacogenetic results in future treatment decisions, and sharing information in public databases to improve the clinical interpretation of pharmacogenetic variants identified in other children. We conclude by presenting a series of points to consider for clinicians and policymakers regarding whether, and under what circumstances, routine screening and return of pharmacogenomic variants unrelated to the indications for testing is appropriate in children who are undergoing genome-wide sequencing to assist in the diagnosis of a suspected genetic disease.

CYP2D6-guided opioid therapy for adults with cancer pain: A randomized implementation clinical trial

INTRODUCTION

The CYP2D6 enzyme metabolizes opioids commonly prescribed for cancer-related pain, and CYP2D6 polymorphisms may contribute to variability in opioid response. We evaluated the feasibility of implementing CYP2D6-guided opioid prescribing for patients with cancer and reported pilot outcome data.

METHODS

Adult patients from two cancer centers were prospectively enrolled into a hybrid implementation-effectiveness clinical trial and randomized to CYP2D6-genotype-guided opioid selection, with clinical recommendations, or usual care. Implementation metrics, including provider response, medication changes consistent with recommendations, and patient-reported pain and symptom scores at baseline and up to 8 weeks, were assessed.

RESULTS

Most (87/114, 76%) patients approached for the study agreed to participate. Of 85 patients randomized, 71% were prescribed oxycodone at baseline. The median (range) time to receive CYP2D6 test results was 10 (3-37) days; 24% of patients had physicians acknowledge genotype results in a clinic note. Among patients with CYP2D6-genotype-guided recommendations to change therapy (n = 11), 18% had a change congruent with recommendations. Among patients who completed baseline and follow-up questionnaires (n = 48), there was no difference in change in mean composite pain score (-1.01 ± 2.1 vs. -0.41 ± 2.5; p = 0.19) or symptom severity at last follow-up (3.96 ± 2.18 vs. 3.47 ± 1.78; p = 0.63) between the usual care arm (n = 26) and genotype-guided arm (n = 22), respectively.

CONCLUSION

Our study revealed high acceptance of pharmacogenetic testing as part of a clinical trial among patients with cancer pain. However, provider response to genotype-guided recommendations was low, impacting assessment of pain-related outcomes. Addressing barriers to utility of pharmacogenetics results and clinical recommendations will be critical for implementation success.

Clinician Response to Pharmacogenetic Clinical Decision Support Alerts

The objective of this study was to characterize clinician response following standardization of pharmacogenetic (PGx) clinical decision support alerts at University of Florida (UF) Health. A retrospective analysis of all PGx alerts that fired at a tertiary academic medical center from August 2020 through May 2022 was performed. Alert acceptance rate was calculated and compared across six gene-drug pairs, patient care setting, and clinician specialty. The disposition of the triggering medication was compared with the alert response and evaluated for congruence. There were a total of 818 alerts included for analysis of alert response, 557 alerts included in acceptance rate calculations, and 392 alerts with sufficient information to assess clinical response. The overall acceptance rate was 63%. The alert response and clinical response were congruent for 47% of alerts. Alert response was influenced by the triggering gene-drug pair, clinician specialty, patient care setting, and specialty of the provider who initially ordered the PGx test. Clinical response was mostly incongruent with alert response. Alert acceptance is influenced by the triggering gene-drug pair, clinician specialty, and care setting. Alert response is not a reliable surrogate marker for clinical action. Any future research into the impact of clinical decision support (CDS) alerts should focus on clinical response rather than alert response. Given the reliance on CDS alerts to enhance uptake of PGx, it is worthwhile to further investigate their impact on prescribing and patient outcomes.

Patient-Level Exposure to Actionable Pharmacogenomic Medications in a Nationally Representative Insurance Claims Database

BACKGROUND

The prevalence of exposure to pharmacogenomic medications is well established but little is known about how long patients are exposed to these medications.

AIM

Our objective was to describe the amount of exposure to actionable pharmacogenomic medications using patient-level measures among a large nationally representative population using an insurance claims database.

METHODS

Our retrospective cohort study included adults (18+ years) from the IQVIA PharMetrics® Plus for Academics claims database with incident fills of 72 Clinical Pharmacogenetics Implementation Consortium level A, A/B, or B medications from January 2012 through September 2018. Patient-level outcomes included the proportion of days covered (PDC), number of fills, and average days supplied per fill over a 12-month period.

RESULTS

Over 1 million fills of pharmacogenetic medications were identified for 605,355 unique patients. The mean PDC for all medications was 0.21 (SD 0.3), suggesting patients were exposed 21% (77 days) of the year. Medications with the highest PDC (0.55-0.89) included ivacaftor, tamoxifen, clopidogrel, HIV medications, transplant medications, and statins; with the exception of statins, these medications were initiated by fewer patients. Pharmacogenomic medications were filled an average of 2.8 times (SD 3.0, range 1-81) during the year following the medication's initiation, and the average days supplied for each fill was 22.3 days (SD 22.4, range 1-180 days).

CONCLUSION

Patient characteristics associated with more medication exposure were male sex, older age, and comorbid chronic conditions. Prescription fill data provide patient-level exposure metrics that can further our understanding of pharmacogenomic medication utilization and help inform opportunities for pharmacogenomic testing.

Integrating pharmacogenomic testing into paired germline and somatic genomic testing in patients with cancer

Precision medicine has revolutionized clinical care for patients with cancer through the development of targeted therapy, identification of inherited cancer predisposition syndromes and the use of pharmacogenetics to optimize pharmacotherapy for anticancer drugs and supportive care medications. While germline (patient) and somatic (tumor) genomic testing have evolved separately, recent interest in paired germline/somatic testing has led to an increase in integrated genomic testing workflows. However, paired germline/somatic testing has generally lacked the incorporation of germline pharmacogenomics. Integrating pharmacogenomics into paired germline/somatic genomic testing would be an efficient method for increasing access to pharmacogenomic testing. In this perspective, the authors argue for the benefits of implementing a comprehensive approach integrating somatic and germline testing that is inclusive of pharmacogenomics in clinical practice.

Cardiovascular precision medicine - A pharmacogenomic perspective

Precision medicine envisages the integration of an individual's clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success. As only up to half of patients respond to medication prescribed following the current one-size-fits-all treatment strategy, the need for a more personalised approach is evident. One of the routes to transforming healthcare through precision medicine is pharmacogenomics (PGx). Around 95% of the population is estimated to carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Whilst there are compelling examples of pharmacogenomic implementation in clinical practice, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases and look at the key enablers and barriers to PGx implementation in clinical practice.

Pharmacogenomics in practice: a review and implementation guide

Considerable efforts have been exerted to implement Pharmacogenomics (PGx), the study of interindividual variations in DNA sequence related to drug response, into routine clinical practice. In this article, we first briefly describe PGx and its role in improving treatment outcomes. We then propose an approach to initiate clinical PGx in the hospital setting. One should first evaluate the available PGx evidence, review the most relevant drugs, and narrow down to the most actionable drug-gene pairs and related variant alleles. This is done based on data curated and evaluated by experts such as the pharmacogenomics knowledge implementation (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC), as well as drug regulatory authorities such as the US Food and Drug Administration (FDA) and European Medicinal Agency (EMA). The next step is to differentiate reactive point of care from preemptive testing and decide on the genotyping strategy being a candidate or panel testing, each of which has its pros and cons, then work out the best way to interpret and report PGx test results with the option of integration into electronic health records and clinical decision support systems. After test authorization or testing requirements by the government or drug regulators, putting the plan into action involves several stakeholders, with the hospital leadership supporting the process and communicating with payers, the pharmacy and therapeutics committee leading the process in collaboration with the hospital laboratory and information technology department, and healthcare providers (HCPs) ordering the test, understanding the results, making the appropriate therapeutic decisions, and explaining them to the patient. We conclude by recommending some strategies to further advance the implementation of PGx in practice, such as the need to educate HCPs and patients, and to push for more tests' reimbursement. We also guide the reader to available PGx resources and examples of PGx implementation programs and initiatives.

Evaluation of Potential Racial Disparities in CYP2C19-Guided P2Y12 Inhibitor Prescribing After Percutaneous Coronary Intervention

Black patients suffer worse outcomes after percutaneous coronary intervention (PCI) than White patients. Inequities in antiplatelet prescribing may contribute to this health disparity. We compared P2Y12 inhibitor prescribing by race following CYP2C19 genotyping to guide antiplatelet therapy selection after PCI. Patients from 9 sites that performed clinical CYP2C19 genotyping after PCI were included. Alternative therapy (e.g., prasugrel or ticagrelor) was recommended for CYP2C19 no-function allele carriers, in whom clopidogrel is predicted to be less effective. The primary outcome was choice of P2Y12 inhibitor (clopidogrel vs. alternative therapy) based on genotype. Of 3,342 patients included, 2,448 (73%) were White, and 659 (20%) were Black. More Black than White patients had a no-function allele (34.3% vs. 29.7%, P = 0.024). At hospital discharge following PCI, 44.2% of Black and 44.0% of White no-function allele carriers were prescribed alternative therapy. At the time of the last follow-up within 12 months, numerically fewer Black (51.8%) than White (56.7%) no-function allele carriers were prescribed alternative therapy (P = 0.190). However, the difference was not significant after accounting for other factors associated with P2Y12 inhibitor selection (odds ratio 0.79, 95% confidence interval 0.58-1.08). Alternative therapy use did not differ between Black (14.3%) and White (16.7%) patients without a no-function allele (P = 0.232). Among real-world patients who received CYP2C19 testing after PCI, P2Y12 inhibitor prescribing rates did not differ between Black and White patients. Our data suggest an absence of racial disparity in genotype-guided antiplatelet prescribing among patients receiving CYP2C19 testing.

Pharmacogenetics of P2Y12 receptor inhibitors

Oral P2Y12 inhibitors are commonly prescribed for cardiovascular disease and include clopidogrel, prasugrel, and ticagrelor. Each of these drugs has its strengths and weaknesses. Prasugrel and ticagrelor are more potent inhibitors of platelet aggregation and were shown to be superior to clopidogrel in preventing major adverse cardiovascular events after an acute coronary syndrome and percutaneous coronary intervention (PCI) in the absence of genotyping. However, both are associated with an increased risk for non-coronary artery bypass-related bleeding. Clopidogrel is a prodrug requiring bioactivation, primarily via the CYP2C19 enzyme. Approximately 30% of individuals have a CYP2C19 no function allele and decreased or no CYP2C19 enzyme activity. Clopidogrel-treated carriers of a CYP2C19 no function allele have decreased exposure to the clopidogrel active metabolite and lesser inhibition of platelet aggregation, which likely contributed to reduced clopidogrel efficacy in clinical trials. The pharmacogenetic data for clopidogrel are most robust in the setting of PCI, but evidence is accumulating for other indications. Guidance is available from expert consensus groups and regulatory agencies to assist with integrating genetic information into P2Y12 inhibitor prescribing decisions, and CYP2C19 genotype-guided antiplatelet therapy after PCI is one of the most common examples of clinical pharmacogenetic implementation. Herein, we review the evidence for pharmacogenetic associations with clopidogrel response and outcomes with genotype-guided P2Y12 inhibitor selection and describe guidance to assist with pharmacogenetic implementation. We also describe processes for applying genotype data for P2Y12 inhibitor therapy selection and remaining gaps in the field. Ultimately, consideration of both clinical and genetic factors may guide selection of P2Y12 inhibitor therapy that optimally balances the atherothrombotic and bleeding risks.

Identifying the prevalence of clinically actionable drug-gene interactions in a health system biorepository to guide pharmacogenetics implementation services

Understanding patterns of drug-gene interactions (DGIs) is important for advancing the clinical implementation of pharmacogenetics (PGx) into routine practice. Prior studies have estimated the prevalence of DGIs, but few have confirmed DGIs in patients with known genotypes and prescriptions, nor have they evaluated clinician characteristics associated with DGI-prescribing. This retrospective chart review assessed prevalence of DGI, defined as a medication prescription in a patient with a PGx phenotype that has a clinical practice guideline recommendation to adjust therapy or monitor drug response, for patients enrolled in a research genetic biorepository linked to electronic health records (EHRs). The prevalence of prescriptions for medications with pharmacogenetic (PGx) guidelines, proportion of prescriptions with DGI, location of DGI prescription, and clinical service of the prescriber were evaluated descriptively. Seventy-five percent (57,058/75,337) of patients had a prescription for a medication with a PGx guideline. Up to 60% (n = 26,067/43,647) of patients had at least one DGI when considering recommendations to adjust or monitor therapy based on genotype. The majority (61%) of DGIs occurred in outpatient prescriptions. Proton pump inhibitors were the most common DGI medication for 11 of 12 clinical services. Almost 25% of patients (n = 10,706/43,647) had more than one unique DGI, and, among this group of patients, 61% had a DGI with more than one gene. These findings can inform future clinical implementation by identifying key stakeholders for initial DGI prescriptions, helping to inform workflows. The high prevalence of multigene interactions identified also support the use of panel PGx testing as an implementation strategy.

Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population

BACKGROUND

Pharmacogenomics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population.

METHODS

A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines ('CPIC level A or B') in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles.

RESULTS

We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68).

CONCLUSIONS

Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

Pediatric CYP2D6 metabolizer status and post-tonsillectomy nausea and vomiting after ondansetron

The goal of this study was to determine whether CYP2D6 metabolizer status within the ondansetron-treated pediatric tonsillectomy population is associated with risk of postoperative nausea and vomiting (PONV) in the post-anesthesia care unit. We conducted a retrospective cohort study of pediatric patients (<18 years) who underwent tonsillectomy and received ondansetron on the day of the procedure. Data were obtained from BioVU, an institutional biobank that links DNA to de-identified electronic health record data. Subjects were tested for 10 CYP2D6 allelic variants and copy number variation, and genotype data translated into CYP2D6 metabolizer status. The cohort included 652 individuals, 105 (16.1%) of whom had PONV. Rates of PONV were similar across groups: ultrarapid metabolizers (UMs), 1 of 9 (11.1%); normal metabolizers (NMs), 64 of 354 (18.1%); intermediate metabolizers (IMs), 33 of 234 (14.1%); poor metabolizers (PMs), 6 of 39 (15.4%); and ambiguous phenotypes, 1 of 16 (6.3%). In multivariable analysis adjusted for age, sex, and time under anesthesia, CYP2D6 metabolizer status was not associated with PONV, with an odds ratio of 1.37 (95% confidence interval 0.9, 2.1) when comparing PM/IM versus NM/UM. In this large pediatric population, no significant differences were detected for PONV based on CYP2D6 metabolizer status. Further investigation is needed to determine mechanisms for ondansetron inefficacy in children.

How to Implement a Pharmacogenetics Service at your Institution

The vast majority of patients possess one or more pharmacogenetic variants that can influence optimal medication use. When pharmacogenetic data are used to guide drug choice and dosing, evidence points to improved disease outcomes, fewer adverse effects, and lower healthcare spending. Although its science is well established, clinical use of pharmacogenetic data to guide drug therapy is still in its infancy. Pharmacogenetics essentially involves the intersection of an individual's genetic data with their medications, which makes pharmacists uniquely qualified to provide clinical support and education in this field. In fact, most pharmacogenetics implementations, to date, have been led by pharmacists as leaders or members of a multidisciplinary team or as individual practitioners. A successful large-scale pharmacogenetics implementation requires coordination and synergy among administrators, clinicians, informatics teams, laboratories, and patients. Because clinical implementation of pharmacogenetics is in its early stages, there is an urgent need for guidance and dissemination of shared experiences to provide a framework for clinicians. Many early adopters of pharmacogenetics have explored various strategies among diverse practice settings. This article relies on the experiences of early adopters to provide guidance for critical steps along the pathway to implementation, including strategies to engage stakeholders; evaluate pharmacogenetic evidence; coordinate laboratory testing, results interpretation and their integration into the electronic health record; identify reimbursement avenues; educate providers and patients; and maintain a successful program. Learning from early adopters' published experiences and strategies can allow clinicians leading a new pharmacogenetics implementation to avoid pitfalls and adapt and apply lessons learned by others to their own practice.

A clinician’s guide for counseling patients on results of a multigene pharmacogenomic panel

Disclaimer

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Purpose

This article explores approaches to pharmacogenomic counseling for patients who have undergone multigene panel testing by describing the collective experience of 5 institutions.

Summary

Multigene panel pharmacogenomic testing has the potential to unlock a myriad of information about a patient’s past, present, and future drug response. The multifaceted nature of drug response coupled with the complexity of genetic results necessitates some form of patient education through pharmacogenomic counseling. Published literature regarding disclosure of pharmacogenomic test results is limited. This article compares the counseling practices of pharmacists from 5 different institutions with pharmacogenomics clinics whose experience represents perspectives ranging from academia to community clinical environments. Overarching counseling themes discussed during result disclosure center around (1) pharmacogenomic results, (2) gene-drug interactions, (3) gene-drug-drug interactions, (4) drug changes (5) future, familial, or disease-risk implications, (6) updates in the interpretation and application of pharmacogenomic results, (7) gauging patient comprehension, and (8) sharing results and supplemental information.

Conclusion

Dedicating time to counseling patients on the results of a multigene pharmacogenomic panel is important given the lifelong applications of a test that is generally performed only once. The content and methods of disclosing test results shared by the experiences of pharmacists at 5 different institutions serve as guide to be further refined as research addresses effective communication strategies that enhance patient comprehension of pharmacogenomic results.

Opportunities for personalizing colorectal cancer care: an analysis of SEER-medicare data

United States clinical practice guidelines for metastatic colorectal cancer recommend use of medications impacted by genetic variants but do not recommend testing. We analyzed real-world treatment using a cancer registry and claims dataset to explore pharmacogenomic (PGx) medication treatment patterns and characterize exposure. In a cohort of 6957 patients, most (86.9%) were exposed to at least one chemotherapy medication with PGx guidelines. In a cohort of 2223 patients with retail pharmacy claims available, most (79.2%) were treated with at least one non-chemotherapy (79.2%) medication with PGx guidelines. PGx-associated chemotherapy exposure was associated with age, race/ethnicity, educational attainment, and rurality. PGx-associated non-chemotherapy exposure was associated with medication use and comorbidities. The potential impact of PGx testing is large and policies aimed at increasing PGx testing at diagnosis may impact treatment decisions for patients with metastatic colorectal cancer as most patients are exposed to medications with pharmacogenomics implications during treatment.

Comparison of FDA Table of Pharmacogenetic Associations and Clinical Pharmacogenetics Implementation Consortium guidelines

Purpose

Healthcare professionals need a clear understanding of information about gene-drug interactions in order to make optimal use of pharmacogenetic (PGx) testing. In this report, we compare PGx information in the US Food and Drug Administration (FDA) Table of Pharmacogenetic Associations with information presented in Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines.

Summary

Information from CPIC guidelines and the FDA Table of Pharmacogenetic Associations do not have a high level of concordance. Many drugs mentioned in CPIC guidelines are not listed in the FDA table and vice versa, and the same gene-drug association and dosing recommendation was reported for only 5 of the 126 drugs included in either source. Furthermore, classification of drugs in specific sections of the FDA table does not correlate well with CPIC-assigned or provisionally assigned clinical actionability levels. The Pharmacogenomics Knowledge Base (PharmGKB) clinical annotation levels are generally high for drugs mentioned in CPIC guidelines. PharmGKB clinical annotation levels are often unassigned or are lower level for drugs listed on the FDA table but not in CPIC guidelines. These differences may be due in part to FDA having access to PGx information that is unavailable in published literature and/or because PGx classifications are based on criteria other than clinical actionability.

Conclusion

There are important differences between the PGx information presented in the FDA Table of Pharmacogenetic Associations and in CPIC guidelines. FDA and CPIC have different perspectives when evaluating PGx associations and use different approaches and information resources when considering clinical validity related to specific medicines. Understanding how information sources developed by each group differ and can be used together to form a holistic view of PGx may be helpful in increasing adoption of these information sources in practice.

Clinical Opportunities for Germline Pharmacogenetics and Management of Drug-Drug Interactions in Patients With Advanced Solid Cancers

PURPOSE

Precision medicine approaches, including germline pharmacogenetics (PGx) and management of drug-drug interactions (DDIs), are likely to benefit patients with advanced cancer who are frequently prescribed multiple concomitant medications to treat cancer and associated conditions. Our objective was to assess the potential opportunities for PGx and DDI management within a cohort of adults with advanced cancer.

METHODS

Medication data were collected from the electronic health records for 481 subjects since their first cancer diagnosis. All subjects were genotyped for variants with clinically actionable recommendations in Clinical Pharmacogenetics Implementation Consortium guidelines for 13 pharmacogenes. DDIs were defined as concomitant prescription of strong inhibitors or inducers with sensitive substrates of the same drug-metabolizing enzyme and were assessed for six major cytochrome P450 (CYP) enzymes.

RESULTS

Approximately 60% of subjects were prescribed at least one medication with Clinical Pharmacogenetics Implementation Consortium recommendations, and approximately 14% of subjects had an instance for actionable PGx, defined as a prescription for a drug in a subject with an actionable genotype. The overall subject-level prevalence of DDIs and serious DDIs were 50.3% and 34.8%, respectively. Serious DDIs were most common for CYP3A, CYP2D6, and CYP2C19, occurring in 24.9%, 16.8%, and 11.7% of subjects, respectively. When assessing PGx and DDIs together, approximately 40% of subjects had at least one opportunity for a precision medicine-based intervention and approximately 98% of subjects had an actionable phenotype for at least one CYP enzyme.

CONCLUSION

Our findings demonstrate numerous clinical opportunities for germline PGx and DDI management in adults with advanced cancer.

Pharmacogenomic prescribing opportunities in percutaneous coronary intervention and bone marrow transplant patients

Aim: To evaluate the potential impact of preemptive multigene pharmacogenomic (PGx) testing on medication prescribing in real-world clinical settings. Patients & methods: Prescription frequencies for 65 medications with actionable PGx recommendations were collected in 215 percutaneous coronary intervention (PCI) and 131 allogeneic hematopoietic cell transplant (allo-HCT) patients. A simulation projected the number of PGx-guided prescribing opportunities. Results: In PCI and allo-HCT patients, respectively, 66.5 and 90.1% were prescribed at least one medication with actionable PGx prescribing recommendations. Simulations projected 26.5 and 41.2 total PGx-guided prescribing opportunities per 100 PCI and allo-HCT patients, respectively, if multigene PGx results were available. Conclusion: A multigene PGx testing strategy offers potential to optimize medication prescribing beyond clopidogrel and tacrolimus in PCI and allo-HCT patients.

Prescription Patterns and Relationship to Pharmacogenomics Testing in the Military Health System

INTRODUCTION

Clinical utilization of pharmacogenomics (PGx) testing is highly institutionally dependent, and little information is known about provider practices of PGx testing in the Military Health System (MHS). In this study, we aimed to characterize Clinical Pharmacogenetics Implementation Consortium (CPIC) actionable prescription (Rx) patterns and their temporal relationship with PGx testing in the MHS.

METHODS

Using data from the Military Health System Management Analysis and Reporting Tool (M2) database, this retrospective cohort study included all patients receiving at least one PGx test and at least one CPIC actionable Rx from January 2015 to August 2020 (845 patients, 1,471 PGx, 7,725 index CPIC actionable Rxs). Rx patterns and temporal relationships with PGx testing were characterized via descriptive statistics. Binomial regression was used to determine which patient and provider characteristics were associated with a patient receiving a PGx test within 30 days of an index Rx.

RESULTS

Patients had a median of 9 index CPIC actionable Rx's (range 1-26). Pain medications were most commonly prescribed (N = 794, 94% patients with at least 1 Rx). However, pain medication had the lowest Rx-PGx match rate (40%) compared to an average of 62% Rx-PGx match rate for all CPIC drugs. Antidepressants were also commonly prescribed (N = 668, 79.1% patients with at least 1 Rx), and antidepressants had the highest Rx-PGx match rate of 86.7%. A minority of providers (20%, N = 249) ordered the majority of PGx tests (86.1%, N = 1,266) and only 8.3% of PGx tests (N = 398) matched to a CPIC actionable drug within 30 days of the test (defined by Rxs ordered within 30 days before or after the PGx test). However, approximately 39.8% of patients (N = 317) had at least one drug match to a PGx test within 30 days. The largest predictor of whether a patient received a PGx test within 30 days of any index Rx was whether or not a specific psychiatry provider ordered the PGx test (odds ratio; OR 3.7, 95% CI 2.13-6.54, P < 0.001). Neither the CPIC level of evidence nor FDA PGx actionable or informative labels had a significant effect on PGx test timing.

CONCLUSIONS

PGx testing was generally limited to high Rx-drug users and was found to be an under-utilized resource. PGx testing did not typically follow CPIC guidelines. Implementing PGx testing protocols, simplifying PGx test-ordering by incorporating at minimum CYP2D6, CYP2C19, and CYP2C9 into PGx-testing panels, and unifying providers' PGx knowledgebase in the MHS are feasible and would improve the clinical utilization of PGx tests in the MHS.

Life-time Actionable Pharmacogenetic Drug Use: A Population-based Cohort Study in 86 040 Young People With and Without Mental Disorders in Denmark

Objective To describe life-time use of current actionable pharmacogenetic (PGx) somatic and psychotropic drugs according to international PGx consortia in people with and without hospital-diagnosed mental disorders in the Danish population.

Methods Population- and register-based observational drug utilization study in 56 065 individuals with mental disorders, i. e. attention-deficit/hyperactivity disorder, autism, bipolar disorder, depression and schizophrenia, and a random, representative sample of 29 975 individuals of the Danish population, born between 1981 and 2005. Individuals were followed from 1995 or birth until 2016 (for a maximum of 22 years). We report prevalence and incidence rates of PGx drug use by age, sex and mental disorders based on redeemed prescriptions between 1995 and 2016.

Results Of the 69 PGx drugs, prescriptions of 39 drugs had been redeemed by the study population by 35 years of age. The use of at least 1 PGx drug varied between 23.1% in males without mental disorders and 97.2% in females with schizophrenia. Males with ADHD or autism were the youngest first-time PGx drug users at a mean of 11.6 years. The mean number of different PGx drugs used was 1.2 in males without mental disorders and 5.6 in individuals with schizophrenia. The prevalence of different PGx drugs linked to more than one gene was 25.3% in males without mental disorders to 94.1% in females with schizophrenia.

Conclusion PGx drugs are commonly used by younger people, more often by individuals with mental disorders and by females. Panel-based PGx testing could contribute to treatment decisions at a very young age.

Revisiting Secondary Information Related to Pharmacogenetic Testing

Incidental or secondary findings have been a major part of the discussion of genomic medicine research and clinical applications. For pharmacogenetic (PGx) testing, secondary findings arise due to the pleiotropic effects of pharmacogenes, often related to their endogenous functions. Unlike the guidelines that have been developed for whole exome or genome sequencing applications for management of secondary findings (though slightly different from PGx testing in that these refer to detection of variants in multiple genes, some with clinical significance and actionability), no corresponding guidelines have been developed for PGx clinical laboratories. Nonetheless, patient and provider education will remain key components of any PGx testing program to minimize adverse responses related to secondary findings.

Translational Pharmacogenomics: Discovery, Evidence Synthesis and Delivery of Race-Conscious Medicine

Response to medications, the principal treatment modality for acute and chronic diseases, is highly variable, with 40-70% of patients exhibiting lack of efficacy or adverse drug reactions. With ~ 15-30% of this variability explained by genetic variants, pharmacogenomics has become a valuable tool in our armamentarium for optimizing treatments and is poised to play an increasing role in clinical care. This review presents the progress made toward elucidating genetic underpinnings of drug response including discovery of race/ancestry-specific pharmacogenetic variants and discusses the current evidence and evidence framework for actionability. The review is framed in the context of changing demographics and evolving views related to race and ancestry. Finally, it highlights the vital role played by cohort studies in elucidating genetic differences in drug response across race and ancestry and the informal collaborations that have enabled the field to bridge the "bench to bedside" translational gap.

Evaluation of population-level pharmacogenetic actionability in Alabama

The evolution of evidence and availability of Clinical Pharmacogenetic Implementation Consortium (CPIC) guidelines have enabled assessment of pharmacogenetic (PGx) actionability and clinical implementation. However, population‐level actionability is not well‐characterized. We leveraged the Alabama Genomic Health Initiative (AGHI) to evaluate population‐level PGx actionability. Participants (>18 years), representing all 67 Alabama counties, were genotyped using the Illumina Global Screening array. Using CPIC guidelines, actionability was evaluated using (1) genotype data and genetic ancestry, (2) prescribing data, and (3) combined genotype and medication data. Of 6,331 participants, 4230 had genotype data and 3386 had genotype and prescription data (76% women; 76% White/18% Black [self‐reported]). Genetic ancestry was concordant with self‐reported race. For CPIC level A genes, 98.6% had an actionable genotype (99.4% Blacks/African; 98.5% White/European). With the exception of DPYD and CYP2C19, the prevalence of actionable genotypes by gene differed significantly by race. Based on prescribing, actionability was highest for CYP2D6 (70.9%), G6PD (54.1%), CYP2C19 (53.5%), and CYP2C9 (47.5%). Among participants prescribed atenolol, carvedilol, or metoprolol, ~ 50% had an actionable ADRB1 genotype, associated with decreased therapeutic response, with higher actionability among Blacks compared to Whites (62.5% vs. 47.4%; p < 0.0001). Based on both genotype and prescribing frequencies, no significant differences in actionability were observed between men and women. This statewide effort highlights PGx population‐level impact to help optimize pharmacotherapy. Almost all Alabamians harbor an actionable genotype, and a significant proportion are prescribed affected medications. Statewide efforts, such as AGHI, lay the foundation for translational research and evaluate “real‐world” outcomes of PGx.

UGT1A1 Guided Cancer Therapy: Review of the Evidence and Considerations for Clinical Implementation

Simple Summary

The use of multi-gene testing platforms to individualize treatment is rapidly expanding into routine oncology practice. UGT1A1, which encodes for the uridine diphosphate glucuronosyltransferase (UGT) 1A1 enzyme, is commonly included on multi-gene molecular testing assays. UGT1A1 polymorphisms may influence drug-induced toxicities of numerous medications used in oncology. However, guidance for incorporating UGT1A1 results into therapeutic decision-making is sparse and can differ depending on the referenced resource. We summarize the literature describing associations between UGT1A1 polymorphisms and toxicity risk with irinotecan, belinostat, pazopanib, and nilotinib. Resources that provide recommendations for UGT1A1-guided drug prescribing are reviewed, and considerations for implementation into patient care are provided.

Abstract

Multi-gene assays often include UGT1A1 and, in certain instances, may report associated toxicity risks for irinotecan, belinostat, pazopanib, and nilotinib. However, guidance for incorporating UGT1A1 results into therapeutic decision-making is mostly lacking for these anticancer drugs. We summarized meta-analyses, genome-wide association studies, clinical trials, drug labels, and guidelines relating to the impact of UGT1A1 polymorphisms on irinotecan, belinostat, pazopanib, or nilotinib toxicities. For irinotecan, UGT1A1*28 was significantly associated with neutropenia and diarrhea, particularly with doses ≥ 180 mg/m², supporting the use of UGT1A1 to guide irinotecan prescribing. The drug label for belinostat recommends a reduced starting dose of 750 mg/m² for UGT1A1*28 homozygotes, though published studies supporting this recommendation are sparse. There was a correlation between UGT1A1 polymorphisms and pazopanib-induced hepatotoxicity, though further studies are needed to elucidate the role of UGT1A1-guided pazopanib dose adjustments. Limited studies have investigated the association between UGT1A1 polymorphisms and nilotinib-induced hepatotoxicity, with data currently insufficient for UGT1A1-guided nilotinib dose adjustments.