Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology.

Pharmacogenomics (PGx), the study of inherited genomic variation and drug response or safety, is a vital tool in precision medicine. In oncology, testing to identify PGx variants offers patients the opportunity for customized treatments that can minimize adverse effects and maximize the therapeutic benefits of drugs used for cancer treatment and supportive care. Because individuals of shared ancestry share specific genetic variants, PGx factors may contribute to outcome disparities across racial and ethnic categories when genetic ancestry is not taken into account or mischaracterized in PGx research, discovery, and application. Here, we examine how the current scientific understanding of the role of PGx in differential oncology safety and outcomes may be biased toward a greater understanding and more complete clinical implementation of PGx for individuals of European descent compared with other genetic ancestry groups. We discuss the implications of this bias for PGx discovery, access to care, drug labeling, and patient and provider understanding and use of PGx approaches. Testing for somatic genetic variants is now the standard of care in treatment of many solid tumors, but the integration of PGx into oncology care is still lacking despite demonstrated actionable findings from PGx testing, reduction in avoidable toxicity and death, and return on investment from testing. As the field of oncology is poised to expand and integrate germline genetic variant testing, it is vital that PGx discovery and application are equitable for all populations. Recommendations are introduced to address barriers to facilitate effective and equitable PGx application in cancer care.

Comparison of targeted vs. expanded pharmacogenomic testing: What are we missing?

BACKGROUND: Pharmacogenomics (PGx) is used as a medication management strategy by a small but growing number of institutions. PGx allows prescribers to individually treat patients concordant with their genes. Recent litigation for preventable PGx-mediated adverse events highlights the need to accelerate PGx implementation for patient safety. Genetic variations cause drug metabolism, transport, and target changes, affecting medication response and tolerability. PGx testing often consists of targeted testing aimed at specific gene-drug pairs or disease states. Conversely, expanded panel testing can evaluate all known actionable gene-drug interactions, enhancing proactive clarity regarding patient response. OBJECTIVES: Evaluate the divergence of targeted PGx testing with a single gene-drug pair test (cardiac), a two-gene panel, and a focused psychiatric panel compared to expanded PGx testing. METHODS: An expanded PGx panel (≥25 genes) was compared to a single gene-drug pair test of CYP2C19/clopidogrel, a dual gene test of CYP2C19/CYP2D6, a 7-gene psychiatric list, and a 14-gene psychiatric panel to inform specific depression and pain management drugs. The expanded panel provided a baseline to evaluate total PGx variations compared to those possibly missed by targeted testing. RESULTS: Targeted testing did not identify up to 95% of total PGx gene-drug interactions discovered. The expanded panel reported all gene-drug interactions for any medication with Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance or U.S. Food and Drug Administration (FDA) labeling for that gene. Single gene CYP2C19/clopidogrel testing missed or did not report on ∼95% of total interactions, CYP2C19/CYP2D6 testing missed or did not report ∼89%, and the 14-gene panel missed or did not report on ∼73%. The 7-gene list missed ∼20% of discovered potential PGx interactions but was not designed to identify gene-drug interactions. CONCLUSIONS: Targeted PGx testing for limited genes or by specialty may miss or not report significant portions of PGx gene-drug interactions. This can lead to potential patient harm from the missed interactions and subsequent failed therapies and/or adverse reactions.

Implementing comprehensive pharmacogenomics in a community hospital-associated primary care setting.

BACKGROUND: Pharmacogenomics (PGx) is an emerging field. Many drug-gene interactions are known but not yet routinely addressed in clinical practice. Therefore, there is a significant gap in care, necessitating development of implementation strategies. OBJECTIVE: The objective of the study was to assess the impact of implementing a PGx practice model which incorporates comprehensive pharmacogenomic risk evaluation, testing and medication optimization administered by 7 PGx-certified ambulatory care pharmacists embedded across 30 primary care clinic sites. METHODS: Pharmacogenomic services were implemented in 30 primary care clinics within the Cincinnati, Ohio area. Patients are identified for pharmacogenomic testing using a clinical decision support tool (CDST) that is fully integrated in the electronic medical record (EMR) or by provider designation (e.g., psychotropic drug failure). Pharmacogenomic testing is performed via buccal swab using standardized clinic processes. Discrete data results are returned directly into the EMR/CDST for review by PGx-certified ambulatory care pharmacists. Recommendations and prescriptive changes are then discussed and implemented as a collaborative effort between pharmacist, primary care provider, specialists, and patient. RESULTS: A total of 422 unique interactions were assessed by the embedded ambulatory care PGx pharmacists (N = 7) during this interim analysis. About half (213) were pharmacogenomic interactions, and of these, 124 were actionable. When an intervention was actionable, 82% of the time a change in medication was recommended. The underlying reasons for recommending therapy alterations were most commonly ineffective therapy (43%), adverse drug reaction prevented (34%), or adverse drug reaction observed (13%). CONCLUSION: Variations in drug metabolism, response, and tolerability can negatively impact patient outcomes across many disease states and treatment specialties. Incorporation of pharmacogenomic testing with accessible clinical decision support into the team-based care model allows for a truly comprehensive review and optimization of medications. Our initial analysis suggests that comprehensive PGx testing should be considered to enhance medication safety and efficacy in at-risk patients.

Validation of Pharmacogenomic Interaction Probability (PIP) Scores in Predicting Drug-Gene, Drug-Drug-Gene, and Drug-Gene-Gene Interaction Risks in a Large Patient Population.

Utilizing pharmacogenomic (PGx) testing and integrating evidence-based guidance in drug therapy enables an improved treatment response and decreases the occurrence of adverse drug events. We conducted a retrospective analysis to validate the YouScript® PGx interaction probability (PIP) algorithm, which predicts patients for whom PGx testing would identify one or more evidence-based, actionable drug-gene, drug-drug-gene, or drug-gene-gene interactions (EADGIs). PIP scores generated for 36,511 patients were assessed according to the results of PGx multigene panel testing. PIP scores versus the proportion of patients in whom at least one EADGI was found were 22.4% vs. 22.4% (p = 1.000), 23.5% vs. 23.4% (p = 0.6895), 30.9% vs. 29.4% (p = 0.0667), and 27.3% vs. 26.4% (p = 0.3583) for patients tested with a minimum of 3-, 5-, 14-, and 25-gene panels, respectively. These data suggest a striking concordance between the PIP scores and the EAGDIs found by gene panel testing. The ability to identify patients most likely to benefit from PGx testing has the potential to reduce health care costs, enable patient access to personalized medicine, and ultimately improve drug efficacy and safety.

Assessment of a Manual Method versus an Automated, Probability-Based Algorithm to Identify Patients at High Risk for Pharmacogenomic Adverse Drug Outcomes in a University-Based Health Insurance Program.

We compared patient cohorts selected for pharmacogenomic testing using a manual method or automated algorithm in a university-based health insurance network. The medication list was compiled from claims data during 4th quarter 2018. The manual method selected patients by number of medications by the health system's list of medications for pharmacogenomic testing. The automated method used YouScript's pharmacogenetic interaction probability (PIP) algorithm to select patients based on the probability that testing would result in detection of one or more clinically significant pharmacogenetic interactions. A total of 6916 patients were included. Patient cohorts selected by each method differed substantially, including size (manual n = 218, automated n = 286) and overlap (n = 41). The automated method was over twice as likely to identify patients where testing may reveal a clinically significant pharmacogenetic interaction than the manual method (62% vs. 29%, p < 0.0001). The manual method captured more patients with significant drug-drug or multi-drug interactions (80.3% vs. 40.2%, respectively, p < 0.0001), higher average number of significant drug interactions per patient (3.3 vs. 1.1, p < 0.0001), and higher average number of unique medications per patient (9.8 vs. 7.4, p < 0.0001). It is possible to identify a cohort of patients who would likely benefit from pharmacogenomic testing using manual or automated methods.

Unmanaged Pharmacogenomic and Drug Interaction Risk Associations with Hospital Length of Stay among Medicare Advantage Members with COVID-19: A Retrospective Cohort Study.

Unmanaged pharmacogenomic and drug interaction risk can lengthen hospitalization and may have influenced the severe health outcomes seen in some COVID-19 patients. To determine if unmanaged pharmacogenomic and drug interaction risks were associated with longer lengths of stay (LOS) among patients hospitalized with COVID-19, we retrospectively reviewed medical and pharmacy claims from 6025 Medicare Advantage members hospitalized with COVID-19. Patients with a moderate or high pharmacogenetic interaction probability (PIP), which indicates the likelihood that testing would identify one or more clinically actionable gene-drug or gene-drug-drug interactions, were hospitalized for 9% (CI: 4-15%; p < 0.001) and 16% longer (CI: 8-24%; p < 0.001), respectively, compared to those with low PIP. Risk adjustment factor (RAF) score, a commonly used measure of disease burden, was not associated with LOS. High PIP was significantly associated with 12-22% longer LOS compared to low PIP in patients with hypertension, hyperlipidemia, diabetes, or chronic obstructive pulmonary disease (COPD). A greater drug-drug interaction risk was associated with 10% longer LOS among patients with two or three chronic conditions. Thus, unmanaged pharmacogenomic risk was associated with longer LOS in these patients and managing this risk has the potential to reduce LOS in severely ill patients, especially those with chronic conditions.

Democratizing genomics: Leveraging software to make genetics an integral part of routine care.

Genetic testing can provide definitive molecular diagnoses and guide clinical management decisions from preconception through adulthood. Innovative solutions for scaling clinical genomics services are necessary if they are to transition from a niche specialty to a routine part of patient care. The expertise of specialists, like genetic counselors and medical geneticists, has traditionally been relied upon to facilitate testing and follow-up, and while ideal, this approach is limited in its ability to integrate genetics into primary care. As individuals, payors, and providers increasingly realize the value of genetics in mainstream medicine, several implementation challenges need to be overcome. These include electronic health record integration, patient and provider education, tools to stay abreast of guidelines, and simplification of the test ordering process. Currently, no single platform offers a holistic view of genetic testing that streamlines the entire process across specialties that begins with identifying at-risk patients in mainstream care settings, providing pretest education, facilitating consent and test ordering, and following up as a "genetic companion" for ongoing management. We describe our vision for using software that includes clinical-grade chatbots and decision support tools, with direct access to genetic counselors and pharmacists within a modular, integrated, end-to-end testing journey.

Clinical impact of pharmacogenetic profiling with a clinical decision support tool in polypharmacy home health patients: A prospective pilot randomized controlled trial.

BACKGROUND: In polypharmacy patients under home health management, pharmacogenetic testing coupled with guidance from a clinical decision support tool (CDST) on reducing drug, gene, and cumulative interaction risk may provide valuable insights in prescription drug treatment, reducing re-hospitalization and emergency department (ED) visits. We assessed the clinical impact of pharmacogenetic profiling integrating binary and cumulative drug and gene interaction warnings on home health polypharmacy patients. METHODS AND FINDINGS: This prospective, open-label, randomized controlled trial was conducted at one hospital-based home health agency between February 2015 and February 2016. Recruitment came from patient referrals to home health at hospital discharge. Eligible patients were aged 50 years and older and taking or initiating treatment with medications with potential or significant drug-gene-based interactions. Subjects (n = 110) were randomized to pharmacogenetic profiling (n = 57). The study pharmacist reviewed drug-drug, drug-gene, and cumulative drug and/or gene interactions using the YouScript® CDST to provide drug therapy recommendations to clinicians. The control group (n = 53) received treatment as usual including pharmacist guided medication management using a standard drug information resource. The primary outcome measure was the number of re-hospitalizations and ED visits at 30 and 60 days after discharge from the hospital. The mean number of re-hospitalizations per patient in the tested vs. untested group was 0.25 vs. 0.38 at 30 days (relative risk (RR), 0.65; 95% confidence interval (CI), 0.32-1.28; P = 0.21) and 0.33 vs. 0.70 at 60 days following enrollment (RR, 0.48; 95% CI, 0.27-0.82; P = 0.007). The mean number of ED visits per patient in the tested vs. untested group was 0.25 vs. 0.40 at 30 days (RR, 0.62; 95% CI, 0.31-1.21; P = 0.16) and 0.39 vs. 0.66 at 60 days (RR, 0.58; 95% CI, 0.34-0.99; P = 0.045). Differences in composite outcomes at 60 days (exploratory endpoints) were also found. Of the total 124 drug therapy recommendations passed on to clinicians, 96 (77%) were followed. These findings should be verified with additional prospective confirmatory studies involving real-world applications in larger populations to broaden acceptance in routine clinical practice. CONCLUSIONS: Pharmacogenetic testing of polypharmacy patients aged 50 and older, supported by an appropriate CDST, considerably reduced re-hospitalizations and ED visits at 60 days following enrollment resulting in potential health resource utilization savings and improved healthcare. TRIAL REGISTRATION: ClinicalTrials.gov NCT02378220.

Cytochrome P-450 gene and drug interaction analysis in patients referred for pharmacogenetic testing.

PURPOSE: The results of a study of variant cytochrome P-450 (CYP) alleles and associated risks of drug-drug interactions (DDIs) and altered drug metabolism are reported. METHODS: The records of a pharmacogenetic testing laboratory were retrospectively analyzed to identify patients tested for polymorphisms of genes coding for five CYP isozymes important in drug metabolism (CYP2D6, CYP2C9, CYP2C19, CYP3A4, and CYP3A5) over a 16-month period. Based on the results of phenotyping, the patients were categorized by expected CYP isozyme activity (e.g., normal or poor metabolizer, expresser or nonexpresser). Using proprietary Web-based software, researchers analyzed phenotyping data and medication lists submitted by patients to determine the potential for DDIs, drug-gene interactions (DGIs), and drug-drug-gene interactions (DDGIs). RESULTS: In the mixed-race study population of more than 22,000 male and female patients (age range, 1-108 years; mean, 60 years), phenotypes associated with alterations of CYP metabolic pathways were common. Among patients in whom phenotypes for all five isozymes of interest were determined (n = 14,578), about 93% were not categorized as normal metabolizers of all five proteins. In many cases, potential interaction threats were rated by clinicians as severe enough to warrant implementation or consideration of a medication regimen change or dose adjustment. Analysis of patient-provided medication lists indicated frequent use of medications posing DDI, DGI, or DDGI risks. CONCLUSION: In a mixed-race population of over 20,000 U.S. patients, CYP gene polymorphisms associated with DDIs and other interaction threats were prevalent, and most individuals were not categorized as normal metabolizers of all five CYP isozymes of interest.

Genelex Corporation.

Comprehensive, personalized medication management and pharmacogenetic testing are important existing opportunities to reduce adverse medication events and improve overall healthcare outcomes. A primary barrier to the adoption of personalized pharmacology is the inadequacy of existing patient records, drug interaction tools and the 'interpretation gap'--the lack of physician decision support tools needed to interpret DNA test reports. GeneMedRx, an algorithm-driven, gene-drug interaction software, closes this gap. It helps physicians optimize medication regimens by correlating the genetic makeup of the patient with all the medicines they are taking. Portable personal health records created by GeneMedRx are the core product required for a much-needed comprehensive program of personalized medication management.