PARC report: a health-systems focus on reimbursement and patient access to pharmacogenomics testing

Does Preoperative Pharmacogenomic Testing of Patients Undergoing TKA Improve Postoperative Pain? A Randomized Trial

BACKGROUND

Pharmacogenomics is an emerging and affordable tool that may improve postoperative pain control. One challenge to successful pain control is the large interindividual variability among analgesics in their efficacy and adverse drug events. Whether preoperative pharmacogenomic testing is worthwhile for patients undergoing TKA is unclear.

QUESTIONS/PURPOSES

(1) Are the results of preoperative pharmacogenetic testing associated with lower postoperative pain scores as measured by the Overall Benefit of Analgesic Score (OBAS)? (2) Do the results of preoperative pharmacogenomic testing lead to less total opioids given? (3) Do the results of preoperative pharmacogenomic testing lead to changes in opioid prescribing patterns?

METHODS

Participants of this randomized trial were enrolled from September 2018 through December 2021 if they were aged 18 to 80 years and were undergoing primary TKA under general anesthesia. Patients were excluded if they had chronic kidney disease, a history of chronic pain or narcotic use before surgery, or if they were undergoing robotic surgery. Preoperatively, patients completed pharmacogenomic testing (RightMed, OneOME) and a questionnaire and were randomly assigned to the experimental group or control group. Of 99 patients screened, 23 were excluded, one before randomization; 11 allocated patients in each group did not receive their allocated interventions for reasons such as surgery canceled, patients ultimately undergoing spinal anesthesia, and change in surgery plan. Another four patients in each group were excluded from the analysis because they were missing an OBAS report. This left 30 patients for analysis in the control group and 38 patients in the experimental group. The control and experimental groups were similar in age, gender, and race. Pharmacogenomic test results for patients in the experimental group were reviewed before surgery by a pharmacist, who recommended perioperative medications to the clinical team. A pharmacist also assessed for clinically relevant drug-gene interactions and recommended drug and dose selection according to guidelines from the Clinical Pharmacogenomics Implementation Consortium for each patient enrolled in the study. Patients were unaware of their pharmacogenomic results. Pharmacogenomic test results for patients in the control group were not reviewed before surgery; instead, standard perioperative medications were administered in adherence to our institutional care pathways. The OBAS (maximum 28 points) was the primary outcome measure, recorded 24 hours postoperatively. A two-sample t-test was used to compare the mean OBAS between groups. Secondary measures were the mean 24-hour pain score, total morphine milligram equivalent, and frequency of opioid use. Postoperatively, patients were assessed for pain with a VAS (range 0 to 10). Opioid use was recorded preoperatively, intraoperatively, in the postanesthesia care unit, and 24 hours after discharge from the postanesthesia care unit. Changes in perioperative opioid use based on pharmacogenomic testing were recorded, as were changes in prescription patterns for postoperative pain control. Preoperative characteristics were also compared between patients with and without various phenotypes ascertained from pharmacogenomic test results.

RESULTS

The mean OBAS did not differ between groups (mean ± SD 4.7 ± 3.7 in the control group versus 4.2 ± 2.8 in the experimental group, mean difference 0.5 [95% CI -1.1 to 2.1]; p = 0.55). Total opioids given did not differ between groups or at any single perioperative timepoint (preoperative, intraoperative, or postoperative). We found no difference in opioid prescribing pattern. After adjusting for multiple comparisons, no difference was observed between the treatment and control groups in tramadol use (41% versus 71%, proportion difference 0.29 [95% CI 0.05 to 0.53]; nominal p = 0.02; adjusted p > 0.99).

CONCLUSION

Routine use of pharmacogenomic testing for patients undergoing TKA did not lead to better pain control or decreased opioid consumption. Future studies might focus on at-risk populations, such as patients with chronic pain or those undergoing complex, painful surgical procedures, to test whether pharmacogenomic results might be beneficial in certain circumstances.

LEVEL OF EVIDENCE

Level I, therapeutic study.

A model for the return and referral of all clinically significant secondary findings of genomic sequencing

Secondary findings (SFs) identified through genomic sequencing (GS) can offer a wide range of health benefits to patients. Resource and capacity constraints pose a challenge to their clinical management; therefore, clinical workflows are needed to optimise the health benefits of SFs. In this paper, we describe a model we created for the return and referral of all clinically significant SFs, beyond medically actionable results, from GS. As part of a randomised controlled trial evaluating the outcomes and costs of disclosing all clinically significant SFs from GS, we consulted genetics and primary care experts to determine a feasible workflow to manage SFs. Consensus was sought to determine appropriate clinical recommendations for each category of SF and which clinician specialist would provide follow-up care. We developed a communication and referral plan for each category of SFs. This involved referrals to specialised clinics, such as an Adult Genetics clinic, for highly penetrant medically actionable findings. Common and non-urgent SFs, such as pharmacogenomics and carrier status results for non-family planning participants, were directed back to the family physician (FP). SF results and recommendations were communicated directly to participants to respect autonomy and to their FPs to support follow-up of SFs. We describe a model for the return and referral of all clinically significant SFs to facilitate the utility of GS and promote the health benefits of SFs. This may serve as a model for others returning GS results transitioning participants from research to clinical settings.

Implementation of CYP2D6-guided opioid therapy at Cincinnati Children's Hospital Medical Center

PURPOSE

We describe the implementation of CYP2D6-focused pharmacogenetic testing to guide opioid prescribing in a quaternary care, nonprofit pediatric academic medical center.

SUMMARY

Children are often prescribed oral opioids after surgeries, for cancer pain, and occasionally for chronic pain. In 2004, Cincinnati Children's Hospital Medical Center implemented pharmacogenetic testing for CYP2D6 metabolism phenotype to inform codeine prescribing. The test and reports were updated to align with changes over time in the testing platform, the interpretation of genotype to phenotype, the electronic health record, and Food and Drug Administration (FDA) guidance. The use of the test increased when a research project required testing and decreased as prescribing of oxycodone increased due to FDA warnings about codeine. Education about the opioid-focused pharmacogenetic test was provided to prescribers (eg, the pain and sickle cell teams) as well as patients and families. Education and electronic health record capability increased provider compliance with genotype-guided postsurgical prescribing of oxycodone, although there was a perceived lack of utility for oxycodone prescribing.

CONCLUSION

The implementation of pharmacogenetic testing to inform opioid prescribing for children has evolved with accumulating evidence and guidelines, requiring changes in reporting of results and recommendations.

Potential Impact of Pharmacogenomic Single Nucleotide Variants in a Rural Caucasian Population

BACKGROUND

In the US adverse drug reactions (ADRs) are estimated to cause 100 000 fatalities and cost over $136 billion annually. A patient's genes play a significant role in their response to a drug. Pharmacogenomics aims to optimize drug choice and dose for individual patients by characterizing patients' pharmacologically relevant genes to identify variants of known impact.

METHODS

DNA was extracted from randomly selected remnant whole blood samples from Caucasian patients with previously performed complete blood counts. Samples were genotyped by mass spectrometry using a customized pharmacogenomics panel. A third-party result interpretation service used genotypic results to predict likely individual responses to frequently prescribed drugs.

RESULTS

Complete genotypic and phenotypic calls for all tested Cytochrome P450 isoenzymes and other genes were obtained from 152 DNA samples. Of these 152 unique genomic DNA samples, 140 had genetic variants suggesting dose adjustment for at least one drug. Cardiovascular and psychiatry drugs had the highest number of recommendations, which included United States Food and Drug Administration warnings for highly prescribed drugs metabolized by CYP2C19, CYP2C9, CYP2D6, HLA-A, and VKORC1.

CONCLUSIONS

Risk for each drug:gene pairing primarily depends upon the degree of predicted enzyme impairment or activation, width of the therapeutic window, and whether parent compound or metabolite is pharmacologically active. The resulting metabolic variations range from risk of toxicity to therapeutic failure. Pharmacogenomic profiling likely reduces ADR potential by allowing up front drug/dose selection to fit a patient's unique drug-response profile.

Pharmacogenomic and Statistical Analysis

Genetic variants can alter response to drugs and other therapeutic interventions. The study of this phenomenon, called pharmacogenomics, is similar in many ways to other types of genetic studies but has distinct methodological and statistical considerations. Genetic variants involved in the processing of exogenous compounds exhibit great diversity and complexity, and the phenotypes studied in pharmacogenomics are also more complex than typical genetic studies. In this chapter, we review basic concepts in pharmacogenomic study designs, data generation techniques, statistical analysis approaches, and commonly used methods and briefly discuss the ultimate translation of findings to clinical care.

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.

Financing and Reimbursement Models for Personalised Medicine: A Systematic Review to Identify Current Models and Future Options

BACKGROUND

The number of healthcare interventions described as 'personalised medicine' (PM) is increasing rapidly. As healthcare systems struggle to decide whether to fund PM innovations, it is unclear what models for financing and reimbursement are appropriate to apply in this context.

OBJECTIVE

To review financing and reimbursement models for PM, summarise their key characteristics, and describe whether they can influence the development and uptake of PM.

METHODS

A literature review was conducted in Medline, Embase, Web of Science, and Econlit to identify studies published in English between 2009 and 2021, and reviews published before 2009. Grey literature was identified through Google Scholar, Google and subject-specific webpages. Articles that described financing and reimbursement of PM, and financing of non-PM were included. Data were extracted and synthesised narratively to report on the models, as well as facilitators, incentives, barriers and disincentives that could influence PM development and uptake.

RESULTS

One hundred and fifty-three papers were included. Research and development of PM was financed through both public and private sources and reimbursed largely through traditional models such as single fees, Diagnosis-Related Groups, and bundled payments. Financial-based reimbursement, including rebates and price-volume agreements, was mainly applied to targeted therapies. Performance-based reimbursement was identified mainly for gene and targeted therapies, and some companion diagnostics. Gene therapy manufacturers offered outcome-based rebates for treatment failure for interventions including Luxturna®, Kymriah®, Yescarta®, Zynteglo®, Zolgensma® and Strimvelis®, and coverage with evidence development for Kymriah® and Yescarta®. Targeted testing with OncotypeDX® was granted value-based reimbursement through initial coverage with evidence development. The main barriers and disincentives to PM financing and reimbursement were the lack of strong links between stakeholders and the lack of demonstrable benefit and value of PM.

CONCLUSIONS

Public-private financing agreements and performance-based reimbursement models could help facilitate the development and uptake of PM interventions with proven clinical benefit.

Perspectives on the Clinical Use of Pharmacogenetic Testing in Late-Life Mental Healthcare: A Survey of the American Association of Geriatric Psychiatry Membership

OBJECTIVE

To assess perspectives on pharmacogenetic (PGx) testing among members of the American Association of Geriatric Psychiatry (AAGP).

DESIGN

Cross-sectional survey.

PARTICIPANTS

Members of the AAGP.

MEASUREMENTS

Anonymous web-based survey consisting of 41 items covering experiences, indications, barriers, facilitators and ethical, legal and social implications for PGx testing.

RESULTS

A total of 124 surveys were completed (response rate = 13%). Most respondents (60%) had used PGx testing but an equal proportion (58%) was uncertain about the clinical usefulness of PGx testing in late-life mental health. Despite self-reported confidence in the ability to order and interpret PGx testing, 60% of respondents felt there was not enough clinical evidence for them to use PGx testing in their practice. This was compounded by uncertainties related to their ethical obligation and legal liability when interpreting and using (or not using) PGx testing results. Respondents strongly affirmed that clinical and legal guidelines for PGx testing in older adults are needed and would be helpful.

CONCLUSION

The findings suggest additional PGx research and physician education in late-life mental healthcare settings is required to reconcile uncertainties related to the clinical efficacy and ethico-legal aspects of PGx testing as well as address current knowledge barriers to testing uptake. These efforts would be further facilitated by the development of clinical practice guidelines to ensure equitable access to testing and standardized implementation of PGx-informed prescribing in older adults.

An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology

Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.

The Value of Pharmacogenetics to Reduce Drug-Related Toxicity in Cancer Patients

Many anticancer drugs cause adverse drug reactions (ADRs) that negatively impact safety and reduce quality of life. The typical narrow therapeutic range and exposure-response relationships described for anticancer drugs make precision dosing critical to ensure safe and effective drug exposure. Germline mutations in pharmacogenes contribute to inter-patient variability in pharmacokinetics and pharmacodynamics of anticancer drugs. Patients carrying reduced-activity or loss-of-function alleles are at increased risk for ADRs. Pretreatment genotyping offers a proactive approach to identify these high-risk patients, administer an individualized dose, and minimize the risk of ADRs. In the field of oncology, the most well-studied gene-drug pairs for which pharmacogenetic dosing recommendations have been published to improve safety are DPYD-fluoropyrimidines, TPMT/NUDT15-thiopurines, and UGT1A1-irinotecan. Despite the presence of these guidelines, the scientific evidence showing the benefits of pharmacogenetic testing (e.g., improved safety and cost-effectiveness) and the development of efficient multi-gene genotyping panels, routine pretreatment testing for these gene-drug pairs has not been implemented widely in the clinic. Important considerations required for widespread clinical implementation include pharmacogenetic education of physicians, availability or allocation of institutional resources to build an efficient clinical infrastructure, international standardization of guidelines, uniform adoption of guidelines by regulatory agencies leading to genotyping requirements in drug labels, and development of cohesive reimbursement policies for pretreatment genotyping. Without clinical implementation, the potential of pharmacogenetics to improve patient safety remains unfulfilled.

Gene-drug pairings for antidepressants and antipsychotics: level of evidence and clinical application

Substantial inter-individual discrepancies exist in both therapeutic effectiveness and adverse effects of antidepressant and antipsychotic medications, which can, in part, be explained by genetic variation. Here, we searched the Pharmacogenomics Knowledge Base for gene-antidepressant and gene-antipsychotic pairs with the highest level of evidence. We then extracted and compared the associated prescribing recommendations for these pairs developed by the Clinical Pharmacogenomics Implementation Consortium, the Dutch Pharmacogenetics Working Group or approved product labels in the US, Canada, Europe, and Asia. Finally, we highlight key economical, educational, regulatory, and ethical issues that, if not appropriately considered, can hinder the implementation of these recommendations in clinical practice. Our review indicates that evidence-based guidelines are available to assist with the implementation of pharmacogenetic-guided antidepressant and antipsychotic prescribing, although the maximum impact of these guidelines on patient care will not be realized until key barriers are minimized or eliminated.

Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice

Pharmacogenomics (PGx) studies the use of genetic data to optimize drug therapy. Numerous clinical centers have commenced implementing pharmacogenetic tests in clinical routines. Next-generation sequencing (NGS) technologies are emerging as a more comprehensive and time- and cost-effective approach in PGx. This review presents the main considerations for applying NGS in guiding drug treatment in clinical practice. It discusses both the advantages and the challenges of implementing NGS-based tests in PGx. Moreover, the limitations of each NGS platform are revealed, and the solutions for setting up and management of these technologies in clinical practice are addressed.

Strategies and settings of clinical pharmacogenetic implementation: a scoping review of pharmacogenetics programs

Pharmacogenetic (PGx) literature has shown beneficial outcomes in safety, efficacy and cost when evidence-based gene-drug decision making is incorporated into clinical practice. PGx programs with successfully implemented clinical services have been published in a variety of settings including academic health centers and community practice. The primary objective was to systematically scope the literature to characterize the current trends, extent, range and nature of clinical PGx programs. Forty articles representing 19 clinical PGx programs were included in analysis. Most programs are in urban, academic institutions. Education, governance and workflow were commonly described while billing/reimbursement and consent were not. This review provides an overview of current PGx models that can be used as a reference for institutions beginning the implementation process.

Clinical Utility of Pharmacogenomic Data Collected by a Health-System Biobank to Predict and Prevent Adverse Drug Events

INTRODUCTION

Medication-related harm represents a significant issue for patient safety and quality of care. One strategy to avoid preventable adverse drug events is to utilize patient-specific factors such as pharmacogenomics (PGx) to individualize therapy.

OBJECTIVE

We measured the number of patients enrolled in a health-system biobank with actionable PGx results who received relevant medications and assessed the incidence of adverse drug events (ADEs) that might have been prevented had the PGx results been used to inform prescribing.

METHODS

Patients with actionable PGx results in the following four genes with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines were identified: HLA-A*31:01, HLA-B*15:02, TPMT, and VKORC1. The patients who received interacting medications (carbamazepine, oxcarbazepine, thiopurines, or warfarin) were identified, and electronic health records were reviewed to determine the incidence of potentially preventable ADEs.

RESULTS

Of 36,424 patients with PGx results, 2327 (6.4%) were HLA-A*31:01 positive; 3543 (9.7%) were HLA-B*15:02 positive; 2893 (7.9%) were TPMT intermediate metabolizers; and 4249 (11.7%) were homozygous for the VKORC1 c.1639 G>A variant. Among patients positive for one of the HLA variants who received carbamazepine or oxcarbazepine (n = 92), four (4.3%) experienced a rash that warranted drug discontinuation. Among the TPMT intermediate metabolizers who received a thiopurine (n = 56), 11 (19.6%) experienced severe myelosuppression that warranted drug discontinuation. Among patients homozygous for the VKORC1 c.1639 G>A variant who received warfarin (n = 379), 85 (22.4%) experienced active bleeding and/or international normalized ratio (INR) > 5 that warranted drug discontinuation or dose reduction.

CONCLUSION

Patients with actionable PGx results from a health-system biobank who received relevant medications experienced predictable ADEs. These ADEs may have been prevented if the patients' PGx results were available in the electronic health record with clinical decision support prior to prescribing.