Pragmatic Trials in Genomic Medicine: The Integrating Pharmacogenetics In Clinical Care (I-PICC) Study

Building Capacity for Pragmatic Trials of Digital Technology in Primary Care

Frontline primary care teams face important challenges in seeking to transform the quality of care delivered to patients and to reduce clerical burden for clinicians. Digital technologies using artificial intelligence hold substantial promise to aid in this transformation. Both pragmatic clinical trials and implementation science are key tools to successfully introduce, evaluate, and sustain innovations in real-world primary care practices. Previous articles in this thematic series have provided an in-depth overview of pragmatic trials and implementation science. This paper demonstrates and provides a framework for how these concepts, together with digital transformation, can be used to solve many of the challenges facing primary care. This framework is conceived as the collaboration of frontline primary care teams with innovators in academic institutions and industry through pragmatic trials and implementation science.

Implementation of pharmacogenomics testing for precision medicine

Great strides have been made in the past decade to lower barriers to clinical pharmacogenomics implementation. Nevertheless, PGx consultation prior to prescribing therapeutics is not yet mainstream. This review addresses the current climate surrounding PGx implementation, focusing primarily on strategies for implementation at academic institutions, particularly at The University of Chicago, and provides an up-to-date guide of resources supporting the development of PGx programs. Remaining challenges and recent strategies for overcoming these challenges to implementation are discussed.

Advancing pharmacogenetic testing in a tertiary hospital: a retrospective analysis after 10 years of activity

The field of pharmacogenetics (PGx) holds great promise in advancing personalized medicine by adapting treatments based on individual genetic profiles. Despite its benefits, there are still economic, ethical and institutional barriers that hinder its implementation in our healthcare environment. A retrospective analysis approach of anonymized data sourced from electronic health records was performed, encompassing a diverse patient population and evaluating key parameters such as prescribing patterns and test results, to assess the impact of pharmacogenetic testing. A head-to-head comparison with previously published activity results within the same pharmacogenetic laboratory was also conducted to contrast the progress made after 10 years. The analysis revealed significant utilization of pharmacogenetic testing in daily clinical practice, with 1,145 pharmacogenetic tests performed over a 1-year period and showing a 35% growth rate increase over time. Of the 17 different medical departments that sought PGx tests, the Oncology department accounted for the highest number, representing 58.47% of all genotyped patients. A total of 1,000 PGx tests were requested for individuals susceptible to receive a dose modification based on genotype, and 76 individuals received a genotype-guided dose adjustment. This study presents a comprehensive descriptive analysis of real-world data obtained from a public tertiary hospital laboratory specialized in pharmacogenetic testing, and presents data that strongly endorse the integration of pharmacogenetic testing into everyday clinical practice.

Evaluating the prospective utility of pharmacogenetics reporting among Canadian Armed Forces personnel receiving pharmacotherapy: a preliminary assessment towards precision psychiatric care

Pharmacological interventions for treating posttraumatic stress disorder in Canadian Armed Forces (CAF) members and Veterans often achieve modest results. The field of pharmacogenetics, or the study of how genes influence an individual's response to different medications, offers insight into how prior knowledge of gene-drug interactions may potentially improve the trial-and-error process of drug selection in pharmacotherapy, thereby improving treatment effects and remission rates. Given the relative recency of pharmacogenetics testing and sparse research in military samples, we used pharmacogenetics testing in a small pilot group (n=23) of CAF members and Veterans who were already engaged in pharmacotherapy for a service-related mental health condition to better understand the associated opportunities and challenges of pharmacogenetics testing in this population. Our preliminary evaluation involved: (1) reporting the prevalence of pharmacogenetics testing 'bin' status according to participants' reports ('green', 'yellow' or 'red'; intending to signal 'go', 'caution' or 'stop', regarding the potential for gene-drug interactions); (2) calculating the percentage of currently prescribed psychotropic medications that were assessed and included in the reports; (3) evaluating whether prescribers used pharmacogenetics testing information according to clinical notes and (4) collecting informal feedback from participating psychiatrists. While pharmacogenetics testing appeared to provide valuable information for a number of clients, a major limitation was the number of commonly prescribed medications not included in the reports.

Analysis of the characteristics and the degree of pragmatism exhibited by pragmatic-labelled trials of antineoplastic treatments

BACKGROUND

Pragmatic clinical trials (PCTs) are designed to reflect how an investigational treatment would be applied in clinical practice. As such, unlike their explanatory counterparts, they measure therapeutic effectiveness and are capable of generating high-quality real-world evidence. However, the conduct of PCTs remains extremely rare. The scarcity of such studies has contributed to the emergence of the efficacy-effectiveness gap and has led to calls for launching more of them, including in the field of oncology. This analysis aimed to identify self-labelled pragmatic trials of antineoplastic interventions and to evaluate whether their use of this label was justified.

METHODS

We searched PubMed® and Embase® for publications corresponding with studies that investigated antitumor therapies and that were tagged as pragmatic in their titles, abstracts and/or index terms. Subsequently, we consulted all available source documents for the included trials and extracted relevant information from them. The data collected were then used to appraise the degree of pragmatism displayed by the PCTs with the help of the validated PRECIS-2 tool.

RESULTS

The literature search returned 803 unique records, of which 46 were retained upon conclusion of the screening process. This ultimately resulted in the identification of 42 distinct trials that carried the 'pragmatic' label. These studies examined eight different categories of neoplasms and were mostly randomized, open-label, multicentric, single-country trials sponsored by non-commercial parties. On a scale of one (very explanatory) to five (very pragmatic), the median PCT had a PRECIS-2 score per domain of 3.13 (interquartile range: 2.57-3.53). The most and least pragmatic studies in the sample had a score of 4.44 and 1.57, respectively. Only a minority of trials were described in sufficient detail to allow them to be graded across all domains of the PRECIS-2 instrument. Many of the studies examined also had features that arguably precluded them from being pragmatic altogether, such as being monocentric or placebo-controlled in nature.

CONCLUSION

PCTs of antineoplastic treatments are generally no more pragmatic than they are explanatory.

Performance of EHR classifiers for patient eligibility in a clinical trial of precision screening

BACKGROUND

Validated computable eligibility criteria use real-world data and facilitate the conduct of clinical trials. The Genomic Medicine at VA (GenoVA) Study is a pragmatic trial of polygenic risk score testing enrolling patients without known diagnoses of 6 common diseases: atrial fibrillation, coronary artery disease, type 2 diabetes, breast cancer, colorectal cancer, and prostate cancer. We describe the validation of computable disease classifiers as eligibility criteria and their performance in the first 16 months of trial enrollment.

METHODS

We identified well-performing published computable classifiers for the 6 target diseases and validated these in the target population using blinded physician review. If needed, classifiers were refined and then underwent a subsequent round of blinded review until true positive and true negative rates ≥80% were achieved. The optimized classifiers were then implemented as pre-screening exclusion criteria; telephone screens enabled an assessment of their real-world negative predictive value (NPV-RW).

RESULTS

Published classifiers for type 2 diabetes and breast and prostate cancer achieved desired performance in blinded chart review without modification; the classifier for atrial fibrillation required two rounds of refinement before achieving desired performance. Among the 1077 potential participants screened in the first 16 months of enrollment, NPV-RW of the classifiers ranged from 98.4% for coronary artery disease to 99.9% for colorectal cancer. Performance did not differ by gender or race/ethnicity.

CONCLUSIONS

Computable disease classifiers can serve as efficient and accurate pre-screening classifiers for clinical trials, although performance will depend on the trial objectives and diseases under study.

A Cost-Consequence Analysis of Preemptive SLCO1B1 Testing for Statin Myopathy Risk Compared to Usual Care

There is a well-validated association between SLCO1B1 (rs4149056) and statin-associated muscle symptoms (SAMS). Preemptive SLCO1B1 pharmacogenetic (PGx) testing may diminish the incidence of SAMS by identifying individuals with increased genetic risk before statin initiation. Despite its potential clinical application, the cost implications of SLCO1B1 testing are largely unknown. We conducted a cost-consequence analysis of preemptive SLCO1B1 testing (PGx+) versus usual care (PGx-) among Veteran patients enrolled in the Integrating Pharmacogenetics in Clinical Care (I-PICC) Study. The assessment was conducted using a health system perspective and 12-month time horizon. Incremental costs of SLCO1B1 testing and downstream medical care were estimated using data from the U.S. Department of Veterans Affairs' Managerial Cost Accounting System. A decision analytic model was also developed to model 1-month cost and SAMS-related outcomes in a hypothetical cohort of 10,000 Veteran patients, where all patients were initiated on simvastatin. Over 12 months, 13.5% of PGx+ (26/193) and 11.2% of PGx- (24/215) participants in the I-PICC Study were prescribed Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline-concordant statins (Δ2.9%, 95% CI -4.0% to 10.0%). Differences in mean per-patient costs for lipid therapy prescriptions, including statins, for PGx+ compared to PGx- participants were not statistically significant (Δ USD 9.53, 95% CI -0.86 to 22.80 USD). Differences in per-patient costs attributable to the intervention, including PGx testing, lipid-lowering prescriptions, SAMS, laboratory and imaging expenses, and primary care and cardiology services, were also non-significant (Δ- USD 1004, 95% CI -2684 to 1009 USD). In the hypothetical cohort, SLCO1B1-informed statin therapy averted 109 myalgias and 3 myopathies at 1-month follow up. Fewer statin discontinuations (78 vs. 109) were also observed, but the SLCO1B1 testing strategy was 96 USD more costly per patient compared to no testing (124 vs. 28 USD). The implementation of SLCO1B1 testing resulted in small, non-significant increases in the proportion of patients receiving CPIC-concordant statin prescriptions within a real-world primary care context, diminished the incidence of SAMS, and reduced statin discontinuations in a hypothetical cohort of 10,000 patients. Despite these effects, SLCO1B1 testing administered as a standalone test did not result in lower per-patient health care costs at 1 month or over 1 year of treatment. The inclusion of SLCO1B1, among other well-validated pharmacogenes, into preemptive panel-based testing strategies may provide a better balance of clinical benefit and cost.

ProjectFlow: a configurable workflow management application for point of care research

Objective

To best meet our point-of-care research (POC-R) needs, we developed ProjectFlow, a configurable, clinical research workflow management application. In this article, we describe ProjectFlow and how it is used to manage study processes for the Diuretic Comparison Project (DCP) and the Research Precision Oncology Program (RePOP).

Materials and methods

The Veterans Health Administration (VHA) is the largest integrated health care system in the United States. ProjectFlow is a flexible web-based workflow management tool specifically created to facilitate conduct of our clinical research initiatives within the VHA. The application was developed using the Grails web framework and allows researchers to create custom workflows using Business Process Model and Notation.

Results

As of January 2021, ProjectFlow has facilitated management of study recruitment, enrollment, randomization, and drug orders for over 10 000 patients for the DCP clinical trial. It has also helped us evaluate over 3800 patients for recruitment and enroll over 370 of them into RePOP for use in data sharing partnerships and predictive analytics aimed at optimizing cancer treatment in the VHA.

Discussion

The POC-R study design embeds research processes within day-to-day clinical care and leverages longitudinal electronic health record (EHR) data for study recruitment, monitoring, and outcome reporting. Software that allows flexibility in study workflow creation and integrates with enterprise EHR systems is critical to the success of POC-R.

Conclusions

We developed a flexible web-based informatics solution called ProjectFlow that supports custom research workflow configuration and has ability to integrate data from existing VHA EHR systems.

Moving Pharmacogenetics Into Practice: It's All About the Evidence!

The evidence for pharmacogenetics has grown rapidly in recent decades. However, the strength of evidence required for the clinical implementation of pharmacogenetics is highly debated. Therefore, the purpose of this review is to summarize different perspectives on the evidence required for the clinical implementation of pharmacogenetics. First, we present two patient cases that demonstrate how knowledge of pharmacogenetic evidence affected their care. Then we summarize resources that curate pharmacogenetic evidence, types of evidence (with an emphasis on randomized controlled trials [RCT]) and their limitations, and different perspectives from implementers, clinicians, and patients. We compare pharmacogenetics to a historical example (i.e., the evidence required for the clinical implementation of pharmacokinetics/therapeutic drug monitoring), and we provide future perspectives on the evidence for pharmacogenetic panels and the need for more education in addition to evidence. Although there are differences in the interpretation of pharmacogenetic evidence across resources, efforts for standardization are underway. Survey data illustrate the value of pharmacogenetic testing from the patient perspective, with their providers seen as key to ensuring maximum benefit from test results. However, clinicians and practice guidelines from medical societies often rely on RCT data to guide treatment decisions, which are not always feasible or ethical in pharmacogenetics. Thus, recognition of other types of evidence to support pharmacogenetic implementation is needed. Among pharmacogenetic implementers, consistent evidence of pharmacogenetic associations is deemed most critical. Ultimately, moving pharmacogenetics into practice will require consideration of multiple stakeholder perspectives, keeping particularly attuned to the voice of the ultimate stakeholder-the patient.

Effect of Pharmacogenetic Testing for Statin Myopathy Risk vs Usual Care on Blood Cholesterol: A Randomized Clinical Trial

IMPORTANCE

Nonadherence to statin guidelines is common. The solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotype is associated with simvastatin myopathy risk and is proposed for clinical implementation. The unintended harms of using pharmacogenetic information to guide pharmacotherapy remain a concern for some stakeholders.

OBJECTIVE

To determine the impact of delivering SLCO1B1 pharmacogenetic results to physicians on the effectiveness of atherosclerotic cardiovascular disease (ASCVD) prevention (measured by low-density lipoprotein cholesterol [LDL-C] levels) and concordance with prescribing guidelines for statin safety and effectiveness.

DESIGN, SETTING, AND PARTICIPANTS

This randomized clinical trial was performed from December 2015 to July 2019 at 8 primary care practices in the Veterans Affairs Boston Healthcare System. Participants included statin-naive patients with elevated ASCVD risk. Data analysis was performed from October 2019 to September 2020.

INTERVENTIONS

SLCO1B1 genotyping and results reporting to primary care physicians at baseline (intervention group) vs after 1 year (control group).

MAIN OUTCOMES AND MEASURES

The primary outcome was the 1-year change in LDL-C level. The secondary outcomes were 1-year concordance with American College of Cardiology-American Heart Association and Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for statin therapy and statin-associated muscle symptoms (SAMS).

RESULTS

Among 408 patients (mean [SD] age, 64.1 [7.8] years; 25 women [6.1%]), 193 were randomized to the intervention group and 215 were randomized to the control group. Overall, 120 participants (29%) had a SLCO1B1 genotype indicating increased simvastatin myopathy risk. Physicians offered statin therapy to 65 participants (33.7%) in the intervention group and 69 participants (32.1%) in the control group. Compared with patients whose physicians did not know their SLCO1B1 results at baseline, patients whose physicians received the results had noninferior reductions in LDL-C at 12 months (mean [SE] change in LDL-C, -1.1 [1.2] mg/dL in the intervention group and -2.2 [1.3] mg/dL in the control group; difference, -1.1 mg/dL; 90% CI, -4.1 to 1.8 mg/dL; P < .001 for noninferiority margin of 10 mg/dL). The proportion of patients with American College of Cardiology-American Heart Association guideline-concordant statin prescriptions in the intervention group was noninferior to that in the control group (12 patients [6.2%] vs 14 patients [6.5%]; difference, -0.003; 90% CI, -0.038 to 0.032; P < .001 for noninferiority margin of 15%). All patients in both groups were concordant with CPIC guidelines for safe statin prescribing. Physicians documented 2 and 3 cases of SAMS in the intervention and control groups, respectively, none of which was associated with a CPIC guideline-discordant prescription. Among patients with a decreased or poor SLCO1B1 transporter function genotype, simvastatin was prescribed to 1 patient in the control group but none in the intervention group.

CONCLUSIONS AND RELEVANCE

Clinical testing and reporting of SLCO1B1 results for statin myopathy risk did not result in poorer ASCVD prevention in a routine primary care setting and may have been associated with physicians avoiding simvastatin prescriptions for patients at genetic risk for SAMS. Such an absence of harm should reassure stakeholders contemplating the clinical use of available pharmacogenetic results.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02871934.

Pharmacogenetics of Statin-Induced Myotoxicity

Statins, a class of lipid-lowering medications, have been a keystone treatment in cardiovascular health. However, adverse effects associated with statin use impact patient adherence, leading to statin discontinuation. Statin-induced myotoxicity (SIM) is one of the most common adverse effects, prevalent across all ages, genders, and ethnicities. Although certain demographic cohorts carry a higher risk, the impaired quality of life attributed to SIM is significant. The pathogenesis of SIM remains to be fully elucidated, but it is clear that SIM is multifactorial. These factors include drug-drug interactions, renal or liver dysfunction, and genetics. Genetic-inferred risk for SIM was first reported by a landmark genome-wide association study, which reported a higher risk of SIM with a polymorphism in the SLCO1B1 gene. Since then, research associating genetic factors with SIM has expanded widely and has become one of the foci in the field of pharmacogenomics. This review provides an update on the genetic risk factors associated with SIM.