Evaluation of Web Service Based Querying of Pharmacogenomics (PGx) Clinical Guidelines Using MyVariant.info, PharmGKB and HGVS Nomenclature

Every year, hundreds of thousands of patients are affected by treatment failure or adverse drug reactions, many of which could be revented by pharmacogenomic testing. To address these deficiencies in care, clinics require automated clinical decision support through computer based systems, which provide clinicians with patient-specific ecommendations. The primary knowledge needed for clinical pharmacogneomics is currently being developed through textual and unstructured guidelines. In this thesis, it is evaluated whether a web service can annotate clinically relevant genetic variants with guideline information using web services and identify areas of challenge. The proposed tool displays a formal representation of pharmacogenomic guideline information through a web service and existing resources. It enables the annotation of variant call format (VCF) files with clinical guideline information from the Pharmacogenomic Knowledge Base (PharmGKB) and Clinical Pharmacogenetics Implementation Consortium (CPIC). The applicability of the web service to nnotate clinically relevant variants with pharmacogenomics guideline information is evaluated by translating five guidelines to a web service workflow and executing the process to annotate publically available genomes. The workflow finds genetic variants covered in CPIC guidelines and influenced drugs. The results show that the web service could be used to annotate in real time clinically relevant variants with up-to-date pharmacogenomics guideline information, although several challenges such as translating variants into star allele nomenclature and the absence of a unique haplotype nomenclature remain before the clinical implementation of this approach and the use on other drugs

User considerations in assessing pharmacogenomic tests and their clinical support tools

Pharmacogenomic (PGx) testing is gaining recognition from physicians, pharmacists and patients as a tool for evidence-based medication management. However, seemingly similar PGx testing panels (and PGx-based decision support tools) can diverge in their technological specifications, as well as the genetic factors that determine test specificity and sensitivity, and hence offer different values for users. Reluctance to embrace PGx testing is often the result of unfamiliarity with PGx technology, a lack of knowledge about the availability of curated guidelines/evidence for drug dosing recommendations, and an absence of wide-spread institutional implementation efforts and educational support. Demystifying an often confusing and variable PGx marketplace can lead to greater acceptance of PGx as a standard-of-care practice that improves drug outcomes and provides a lifetime value for patients. Here, we highlight the key underlying factors of a PGx test that should be considered, and discuss the current progress of PGx implementation

MEDICAL BENEFIT OF PREEMPTIVE REPORTING OF PHARMACOGENOMIC INFORMATION FROM WHOLE EXOME SEQUENCING

The effectiveness of utilizing individual patient’s whole exome sequencing (WES) genetic information to influence patient care by predicting and preventing possible adverse drug reactions (ADRs) is investigated. Pharmacogenomically relevant variants from WES studies collected into two databases were analyzed to determine the expected medical benefit of reporting the incidental findings when WES studies are performed. One dataset was from UNC Chapel Hill as part of the NCGENES project database; the other was from Columbia University Medical Center’s Whole Exome Research Database. The frequency of possible drug exposure for individuals in the US population was approximated by using data gathered from a database of outpatient drug prescribing, www.imshealth.org using the new prescription number data for 2014. Results were calculated to determine the aggregate number needed to screen (ANNS) to determine need for a change in medical management (different drug prescribing, monitoring, etc). The NCGENES data utilized in this analysis included data from 672 individuals’ genomes. The projected ANNS for this data set was 54.02. The calculated mean ANNS for the simulated data was 54.11, with a 95% confidence interval from 53.94 to 54.30 using Monte Carlo simulation (based on 1000 simulated points). CUMC data utilized in this analysis included data from 2,983 individuals’ genomes. The projected ANNS for this data set was 46.15. Using Monte Carlo simulation, the calculated mean ANNS was 46.00 and the projected 95% confidence interval was from 45.93 to 46.08. Monte Carlo simulation of the error in these values was used to compute a 95% confidence interval, because of the complexity of estimating errors in these calculations. Based on this analysis, the pharmacogenomically relevant impact of using WES based screening is in the range of 46 to 52 persons needed to screen when using incidental findings to dictate an expected change in management. A model implementation plane for incorporating pharmacogenomic information into patient care within a healthcare system is provided and discussed.Doctor of Public Healt

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

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

Precision Medicine

This colligated Special Issue of Pharmaceutics on Precision Medicine: Applied Concepts of Pharmacogenomics in Patients with Various Diseases and Polypharmacy offers to the reader a series of articles that describe the concept of Precision Medicine, discuss its implementation process and limitations, demonstrate its value by illustrating some clinical cases, and open the door to new and more sophisticated techniques and applications

Designing a Web-Based, Participatory Education Program Curriculum on Clinical Applications of Whole Genome Sequencing Utilizing Lessons Learned from Previous Participatory Genomics Courses

Background: Until recently, human genetics has primarily been used for research or targeted clinical testing. With the decreased cost and ease of access to genetic testing, there has been an expansion of the application of genetics to health and chronic disease states. The scalability of genetic testing has ushered in the era of Precision Medicine with integration of predictive modeling and genomics into health care. Historically, clinical genetics was limited geneticists supported by genetic counselors. However, with the massive expansion in access to genomic service, this prior model will not be able to meet the growing demand as genetics will now play a role in all areas of medicine. To integrate genetic services across the health care system, critical educational gaps will need to be addressed. Participatory genomic educational courses have been gaining popularity within genetics education because they include the opportunity for participants to undergo genetic testing and integrated applied learning modules. Test2Learn is a participatory education platform designed to teach adult learners about pharmacogenomics which was shown to increase the engagement of the learners by integrating the participatory element. Test2Learn has now been expanded to teach pharmacists, nurses, and primary care residents about pharmacogenomics and key precision medicine concepts. In the most recent iteration presented in this thesis, Test2Lean has been expanded to provide education of whole genome sequencing (WGS) to a broad array of clinicians and key opinion leaders. Objective: Create an online, participatory WGS and Precision Medicine educational program delivered in the Test2Learn platform that is scalable for different populations. Design: Develop a participatory educational program integrating the use of WGS data for adult, educated learners utilizing lessons learned from previous participatory genomics courses offered on the Test2Learn platform. Assessment: Analyze pre- and post- program surveys to gather data to enhance the development of the Mellon Whole Genome Sequencing (MWGS) course. Conclusion: Preliminary data in pilot programs shows that participation in the Test2Learn course significantly increased participants genetic knowledge and comfort level discussing genetic related issues with patients. Analysis of these results and current literature enhanced the creation of a novel, participatory WGS education program

Configuring an implementation model for multi-drug pharmacogenomic testing in the NHS

Backgrounds Pharmacogenomic testing can improve patient outcomes through safer and more efficient dose and drug selection. Implementation of multi-drug pharmacogenomic testing in clinical care has been fragmented internationally and is largely absent within the NHS. The aim of this thesis was to develop and refine a programme theory using behaviour science for the implementation of multi-drug pharmacogenomic testing within an NHS context. Methods Underpinned by behavioural science, the research programme comprised three empirical studies. The first study modelled the impact of multi-drug pharmacogenomic testing in UK primary care, by estimating the occurrence of actionable drug gene interactions in daily practice, using first prescription volumes for 56 PGx drugs and phenotype frequency data. The second study involved a systematic review and narrative synthesis of the barriers and enablers to implementing multi-drug pharmacogenomic testing, using the TDF to map factors affecting prescriber, pharmacist, and patient behaviours. Finally, the third study was a qualitative exploration of the real-world implementation of multi-drug pharmacogenomic testing in the NHS, conducted using a case study methodology. Results Over 20% of all new prescriptions annually issued for 56 medicines in UK primary care had an actionable drug-gene interaction according to guidelines from the Dutch Pharmacogenetic Working Group and/or the Clinical Pharmacogenetics Implementation Consortium. A multi-drug pharmacogenomic testing programme which constitutes testing genetic variants in four genes (CYP2C19, CYP2D6, SLCO1B1, HLA-B) would cover more than 95% of the potential drug-gene interactions occurring in UK primary care. The systematic review found barriers to the implementation of multi-drug pharmacogenomic testing can be organised around four themes influencing behaviours of prescribers, pharmacists and patients. These are: IT infrastructure, Effort, Rewards and Unknown Territory. Barriers were most consistently mapped to TDF domains: memory, attention and decision-making processes, environmental context and resources, and belief about consequences. Pharmacists played a vital role in PGx testing implementation model and enabled prescribers to order and deliver PGx testing for patients. Empirical data using a case study methodology of real-world implementation of multi-drug pharmacogenomic testing, found pharmacists were key drivers for PGx testing implementation model within an NHS context. Training to prepare health professionals to deliver and utilise PGx testing in clinical decision making, should focus on skills development and managing expectations of both patients and health professionals of what PGx testing can provide. Conclusions These three studies advance the understanding of implementing multi-drug pharmacogenomic testing by converging implementation science and genomic medicine. The modelling study provides researchers and policy makers with new knowledge to design a minimum drug-gene panel for a PGx testing panel relevant to the UK population. The multi-drug PGx testing implementation configuration informed by the systematic review and case study requires further modelling and feasibility testing to optimise before implementation across NHS settings. Keywords: pharmacogenomics, personalised medicine, implementatio