Implementation of Pharmacogenomics into Electronic Health Record and Clinical Decision Support

The advent of electronic health records (EHR) and clinical decision support (CDS) has brought numerous changes in the healthcare field and has improved how patients receive care. The field of pharmacogenomics has made many breakthrough discoveries in the last few decades and these new advances have immensely reduced the cost of genetic testing. As advances have been made, researchers have discovered that individuals may respond to a medication differently due to genetic variants. There is a shift in the medical field from a one size fits all model to a personalized medicine model based on genetic information. Institutions have started to incorporate genetic information in their EHR and CDS systems to aid clinicians in the prescribing process. The rate of implementation is uneven among the institutions across the United States. Healthcare institutions have encountered some challenges associated with implementing pharmacogenomic data into CDS and EHR system. These challenges include lack of clinician education about pharmacogenomic data, poor user interface, and lack of resources for additional information for these alerts. If these challenges are overcome, there is great potential for pharmacogenomic CDS systems to help improve patient care and reduce adverse drug events

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development

Background: Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. Methods: We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. Results: Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype. Conclusions: Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping

A Sustainable Future In The Implementation Of Clinical Pharmacogenomics

Purpose: The sustainability of clinical pharmacogenomics requires further study of clinical education on the topic, its effects on clinical workflow, and the responsibilities of different providers for its delivery. Tools from the discipline of implementation science were utilized herein to help achieve the purposes of the three studies. The broad purpose of this dissertation is to advance the work of clinical pharmacogenomic implementation through a more rigorous convergence with implementation science. Methods: Three studies constitute the whole of this dissertation. The first is a scoping review that provides a broad characterization of the methods utilized in available peer-revieliterature focusing on provider use of and experience with using pharmacogenomics in practice or the study setting. The second study used semi-structured in-depth interviews to elicit strategies and perspectives from leadership in current implementation programs using the Consolidated Framework for Implementation Science (CFIR) Process Domain. The third used a cross-sectional quantitative survey with experimental vignettes to explore the potential for pharmacist-physician collaboration using newly developed implementation science outcomes. Results: The scoping review included 25 studies, with many focused on the interactions of providers with clinical decision support systems and adherence to therapeutic recommendations represented. Results from the interviews were extensive but several highlights included a focus on understanding pharmacogenomic use prior to implementation, high-touch informal communication with providers, and the power of the patient case. The survey analysis revealed that the primary care physicians believe that it is more appropriate to deliver clinical pharmacogenomics when a pharmacist is physically located in a clinic and is responsible for managing and modifying a drug therapy based on these results. Conclusion: These three studies further the convergence of implementation science and genomic medicine, with particular focus on pharmacogenomics and the foundational concept of implementation science, sustainability. The scoping review should provide future researchers with a landscape of available and previously used methodologies for interventional pharmacogenomic studies. The interview results will help new implementers of pharmacogenomics steer around avoidable hurdles or make them easier to address. The survey results showcase the potential for pharmacist-physician collaboration in clinical pharmacogenomics

Clinical Utility of Applying PGx and Deprescribing-Based Decision Support in Polypharmacy

Polypharmacy is a necessary and important aspect of drug treatment; however, it becomes a challenge when the medication risks outweigh the benefits for an individual patient. Drug–drug interactions and the introduction of prescribing cascades are common features of polypharmacy, which can lead to ineffectiveness and increased risk of adverse drug reactions (ADR). Genes encoding CYP450 isozymes and other drug-related biomarkers have attracted considerable attention as targets for pharmacogenetic (PGx) testing due to their impact on drug metabolism and response. This Special Issue is devoted to explore the status and initiatives taken to circumvent ineffectiveness and to improve medication safety for polypharmacy patients. Specific areas include drug–drug interactions and consequences thereof in therapeutic management, including PK- and PD-profiling; the application of PGx-based guidance and/or decision tools for drug–gene and drug–drug gene interactions; medication reviews; development and application of deprescribing tools; and drivers and barriers to overcome for successful implementation in the healthcare system

Per Med

AimTo describe the knowledge and attitudes of clinicians participating in a large pharmacogenomics implementation program.Materials & methodsSemi-structured interviews with 15 physicians and nurse practitioners were conducted.ResultsThree categories of themes were identified: preparation and knowledge, pharmacogenomics usage in practice, and future management of genomic variants. Providers expressed an inability to keep up with the rapid pace of evidence generation and indicated strong support for clinical decision support to assist with genotype-tailored therapies. Concerns raised by clinicians included effectively communicating results, long-term responsibility for actionable results and hand-offs with providers outside the implementation program.ConclusionsClinicians identified their own knowledge deficits, workflow integration, and longitudinal responsibility as challenges to successful usage of pharmacogenomics in clinical practice.U01 HL105198/HL/NHLBI NIH HHS/United StatesU01 HG007253/HG/NHGRI NIH HHS/United StatesUL1 TR000445/TR/NCATS NIH HHS/United StatesU01 HL122904/HL/NHLBI NIH HHS/United StatesU19 HL065962/HL/NHLBI NIH HHS/United StatesU47 CI000824/CI/NCPDCID CDC HHS/United StatesU01 HG006378/HG/NHGRI NIH HHS/United States2016-01-01T00:00:00Z26635887PMC4664195vault:1971

Pharmacogenomics

This Special Issue focuses on the current state of pharmacogenomics (PGx) and the extensive translational process, including the identification of functionally important PGx variation; the characterization of PGx haplotypes and metabolizer statuses, their clinical interpretation, clinical decision support, and the incorporation of PGx into clinical care

A Multifactorial Cytochrome P450 2D6 Genotype-Phenotype Prediction Model to Improve Precision of Clinical Pharmacogenomic Tests

BACKGROUND: CYP2D6 is difficult to accurately genotype due to a large number of single nucleotide variants (SNVs), indels, and structural variation such as deletions, duplications, and CYP2D6/CYP2D7 hybrid genes. CYP2D6 targeted genotyping panels are of limited utility; clinically relevant variants that are not genotyped will be missed. Sequencing solves this problem but requires additional tools to address structural variation. The goal of our study was to determine the predictive power of Stargazer, a novel allele-calling program, which combines SNV/indel calls with structural variation identification. METHODS: In a panel of 309 human livers, CYP2D6 diplotypes and activity scores were initially assigned manually using PGRNSeq SNV/indel data and then reassigned after inclusion of Stargazer-derived structural variation data. We determined CYP2D6 activity in human liver microsomes with metoprolol and dextromethorphan as probe substrates. Then, we used linear regression to assess the relationship between activity and activity scores assigned using SNV/indel data alone versus SNV/indel + structural variation data. RESULTS: Without incorporating structural variation data, diplotypes were incorrectly assigned for 67 samples (22%); activity scores were incorrect for 26 samples (8.4%). Structural variants included 23 deletions, 47 duplications, and 39 hybrids. When diplotypes were assigned based on SNV/indel data alone, activity score explained 31% of the variation in CYP2D6 activity with metoprolol (R2 = 0.31, p \u3c 0.001) and 36% with dextromethorphan (R2 = 0.36, p \u3c 0.001). When reassigned with SNV/indel plus structural variation data, this increased to 36% for metoprolol (R2 = 0.36, p \u3c 0.001) and 41% for dextromethorphan (R2 = 0.41, p \u3c 0.001). CONCLUSION: The accuracy of CYP2D6 phenotype prediction can be improved by using a next-generation sequencing approach coupled with a tool such as Stargazer to detect common and rare SNVs and indels as well as structural variation in CYP2D6

The Implementation of Pharmacogenetics in the United Kingdom.

There is considerable inter-individual variability in the effectiveness and safety of pharmaceutical interventions. This phenomenon can be attributed to a multitude of factors; however, it is widely acknowledged that common genetic variation affecting drug absorption or metabolism play a substantial contributory role. This is a concept known as pharmacogenetics. Understanding how common genetic variants influence responses to medications, and using this knowledge to inform prescribing practice, could yield significant advantages for both patients and healthcare systems. Some health services around the world have introduced pharmacogenetics into routine practice, whereas others are less advanced along the implementation pathway. This chapter introduces the field of pharmacogenetics, the existing body of evidence, and discusses barriers to implementation. The chapter will specifically focus on efforts to introduce pharmacogenetics in the NHS, highlighting key challenges related to scale, informatics, and education