Implementation of preemptive testing of a pharmacogenomic panel in clinical practice: Where do we stand?

Adverse drug reactions (ADRs) account for a large proportion of hospitalizations among adults, and are more common in multimorbid patients, worsening clinical outcomes and burdening healthcare resources. Over the past decade, pharmacogenomics has been developed as a practical tool for optimizing treatment outcomes by mitigating the risk of ADRs. Some single-gene reactive tests are already used in clinical practice, including the DPYD test for fluoropyrimidines, which demonstrates how integrating pharmacogenomic data into routine care can improve patient safety in a cost-effective manner. The evolution from reactive single-gene testing to comprehensive preemptive genotyping panels holds great potential for refining drug prescribing practices. Several implementation projects have been conducted to test the feasibility of applying different genetic panels in clinical practice. Recently, the results of a large prospective randomized trial in Europe (Ubiquitous Pharmacogenomics-PREPARE study) have provided the first evidence that prospective application of a preemptive pharmacogenomic test panel in clinical practice, in seven European healthcare systems, is feasible and yielded a 30% reduction in the risk of developing clinically relevant toxicities. Nevertheless, some important questions remain unanswered, and will hopefully be addressed by future dedicated studies. These issues include the cost-effectiveness of applying a preemptive genotyping panel, the role of multiple co-medications, the transferability of currently tested pharmacogenetic guidelines among patients of non-European origin, and the impact of rare pharmacogenetic variants that are not detected by currently used genotyping approaches

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

Information management to enable personalized medicine: stakeholder roles in building clinical decision support

<p>Abstract</p> <p>Background</p> <p>Advances in technology and the scientific understanding of disease processes are presenting new opportunities to improve health through individualized approaches to patient management referred to as personalized medicine. Future health care strategies that deploy genomic technologies and molecular therapies will bring opportunities to prevent, predict, and pre-empt disease processes but will be dependent on knowledge management capabilities for health care providers that are not currently available. A key cornerstone to the potential application of this knowledge will be effective use of electronic health records. In particular, appropriate clinical use of genomic test results and molecularly-targeted therapies present important challenges in patient management that can be effectively addressed using electronic clinical decision support technologies.</p> <p>Discussion</p> <p>Approaches to shaping future health information needs for personalized medicine were undertaken by a work group of the American Health Information Community. A needs assessment for clinical decision support in electronic health record systems to support personalized medical practices was conducted to guide health future development activities. Further, a suggested action plan was developed for government, researchers and research institutions, developers of electronic information tools (including clinical guidelines, and quality measures), and standards development organizations to meet the needs for personalized approaches to medical practice. In this article, we focus these activities on stakeholder organizations as an operational framework to help identify and coordinate needs and opportunities for clinical decision support tools to enable personalized medicine.</p> <p>Summary</p> <p>This perspective addresses conceptual approaches that can be undertaken to develop and apply clinical decision support in electronic health record systems to achieve personalized medical care. In addition, to represent meaningful benefits to personalized decision-making, a comparison of current and future applications of clinical decision support to enable individualized medical treatment plans is presented. If clinical decision support tools are to impact outcomes in a clear and positive manner, their development and deployment must therefore consider the needs of the providers, including specific practice needs, information workflow, and practice environment.</p

Single-patient (n-of-1) trials: a pragmatic clinical decision methodology for patient-centered comparative effectiveness research

AbstractObjectiveTo raise awareness among clinicians and epidemiologists that single-patient (n-of-1) trials are potentially useful for informing personalized treatment decisions for patients with chronic conditions.Study Design and SettingWe reviewed the clinical and statistical literature on methods and applications of single-patient trials and then critically evaluated the needs for further methodological developments.ResultsExisting literature reports application of 2,154 single-patient trials in 108 studies for diverse clinical conditions; various recent commentaries advocate for wider application of such trials in clinical decision making. Preliminary evidence from several recent pilot acceptability studies suggests that single-patient trials have the potential for widespread acceptance by patients and clinicians as an effective modality for increasing the therapeutic precision. Bayesian and adaptive statistical methods hold promise for increasing the informational yield of single-patient trials while reducing participant burden, but are not widely used. Personalized applications of single-patient trials can be enhanced through further development and application of methodologies on adaptive trial design, stopping rules, network meta-analysis, washout methods, and methods for communicating trial findings to patients and clinicians.ConclusionsSingle-patient trials may be poised to emerge as an important part of the methodological armamentarium for comparative effectiveness research and patient-centered outcomes research. By permitting direct estimation of individual treatment effects, they can facilitate finely graded individualized care, enhance therapeutic precision, improve patient outcomes, and reduce costs

EQUITABLE PHARMACOGENETIC TESTING IMPLEMENTATION FOR RURAL AND UNDERSERVED POPULATIONS

Pharmacogenetic testing has potential to transform healthcare, yet implementation strategies have been limited to major academic medical centers serving metropolitan communities and large health systems. In contrast, rural, community-based health systems are slow to implement these advances, threatening to exacerbate existing healthcare disparities for rural populations. A majority of Montanans live in rural areas, with unique challenges in providing access to pharmacogenetics. We have established partnerships with three clinical sites who serve rural, underserved populations including American Indian, pediatric, and low socioeconomic status patients. We conducted a needs assessment for pharmacogenetic testing implementation by interviewing 48 key stakeholders. Interview questions were centered around participants opinions regarding pharmacogenetics and their perceived barriers and facilitators for implementation of testing. A codebook was created by analysis and organization of common themes. Positive opinions on using pharmacogenetics to guide therapy were common. Perceived benefits included reduced time to symptom management, fewer adverse events, and improved adherence. Concerns expressed in similar studies based in larger medical centers were also present, including conflicts with reimbursement and test turnaround time. Unique concerns for vulnerable, underserved populations included equitable access based on socioeconomic status and sensitivity to culture and historical injustices, particularly for tribal people. Participants were enthusiastic about using telehealth to implement pharmacogenetics in these communities. This will provide an innovative strategy for pharmacogenetic testing and consultations. Participants were eager to implement testing in their facilities. Many concerns can be mitigated with a strategic implementation plan targeted for underserved patients. Our model will implement pharmacogenetics using a telehealth delivery model centered at the University of Montana with outreach to rural health systems and providers. This has the potential to expand as new health innovations are translated into practice. Future work in this area will involve assisting partner sites with implementation efforts and measuring clinical outcomes related to testing services. Our study will help overcome the unique challenges in delivering pharmacogenetics to rural and underserved communities and we aim to provide a model for states with similar patient populations. Our goal is to pave the way for equitable access to pharmacogenetics for all

COMMUNITY-BASED PARTICIPATORY RESEARCH TO PARTNER WITH THE CONFEDERATED SALISH AND KOOTENAI TRIBES IN PHARMACOGENETICS RESEARCH

Pharmacogenetics research has advanced our knowledge of the genetic basis of individual drug responses. The aim of pharmacogenetics research is to provide opportunities for the development of strategies aimed at discovering clinically relevant gene-drug pairs. Further benefits stem from the translation of pharmacogenetics research into the clinic to identify patients who are at high risk of adverse drug events. However, American Indian and Alaska Native (AI/AN) populations have not benefited markedly from genetics-guided therapeutics. A key strategy in engaging AI/AN people in pharmacogenetics research has been the implementation of community-based participatory research (CBPR). CBPR is a qualitative research methodology in which a partnership is formed between the research institution and the community under study. CBPR provides a framework for both partners to be involved in all aspects of the research process, from developing research questions to data analysis, and dissemination of research findings. Early in the project, approval was given by the Confederated Salish and Kootenai Tribes (CSKT) through discussions with Tribal Health and Tribal Council to conduct pharmacogenetics research with the CSKT community. Thereafter, a collaborative university-community partnership was established with the CSKT to ensure the community has sufficient knowledge about pharmacogenetics research and to develop culturally-relevant research strategies. We formed an oversight committee, the Community Pharmacogenetics Advisory Council (CPAC), to ensure community involvement. We also held workshops to provide education and bring awareness to the community about pharmacogenetics research. CPAC engagement and education through workshops and research involvement was evaluated through a questionnaire. Seventeen healthcare provider interviews have been conducted, transcribed, and analyzed. The interviews were conducted with Montana healthcare providers to assess their views on the potential benefits and harms of pharmacogenetics research and the feasibility of its future implementation into Tribal Health. In addition, two focus groups have been conducted thus far. CPAC helped design a moderator’s guide and developed recruitment tools for focus groups. These focus group materials were used and will continue to be used to conduct focus groups with enrolled CSKT members who receive their healthcare through Tribal Health to assess their views and perceptions of pharmacogenetics research, its translation into the clinic, and dissemination of results to the broader community. The details of the results of the focus groups and healthcare provider interviews will be described in this study. This collaboration created a CBPR framework that best fits the needs of the community. Engaging CSKT community partners in informal and formal discussions about pharmacogenetics research has aided in identifying priorities of the community and building mutually productive partnership

Bayesian Pharmacokinetic Models for Inference and Optimal Sequential Decision Making with Applications in Personalized Medicine

Patients can react to a drug differently just by virtue of being different people, and this between-patient variability in drug response is an obstacle to optimal treatment. Pharmacokinetic modelling offers one approach to studying drug response, often with covariate focused dose adjustment criteria being reported along side pharmacokinetic modelling. However, excess variation in concentrations continues to be reported despite the use of these criteria, bringing into question optimal dosing strategies for some drugs. This thesis provides methods for creating Bayesian pharmacokinetic models for two purposes: inference into the effects of covariates on concentrations and optimal sequential decision making for dose size. The thesis addresses three objectives: To compare existing approaches to fitting Bayesian models with recent advancements in pursuit of fitting population pharmacokinetic models, to develop a framework for evaluating the benefits of collecting additional information for use in personalization, and to demonstrate how academic personalized medicine researchers can use all data available to them to study effects of clinical variables on pharmacokinetics. To this end, the thesis makes three research contributions. First, a simulation study demonstrating that inferences using popular inference methods in pharmacokinetic research can lead to different and poorer calibrated decisions as compared to newer inference methods. The model presented in the simulation study was developed using a specific parameterization achieved through non-dimensionalization of the differential equation governing the mass transit of the drug and enables more reliable inference by sampling using Hamiltonian Monte Carlo as compared to a standard parameterization. Second, a unified framework for the development and simulation based evaluation of personalization based on pharmacokinetic modelling combined with dynamic treatment regimes. Lastly, a demonstration of how investigators can fit Bayesian pharmacokinetic models with the aim of accurate modelling of pharmacokinetics and exploration of novel variables using data from heterogeneous sources. These contributions provide methodologies do address two central goals of personalized medicine -- identification of factors driving between patient variability in drug response, and selection of an optimal dose -- and can enable a richer set of personalized decisions to be made

Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference

Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI