Henry M. Dunnenberger

Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers.

Although the field of pharmacogenetics has existed for decades, practioners have been slow to implement pharmacogenetic testing in clinical care. Numerous publications describe the barriers to clinical implementation of pharmacogenetics. Recently, several freely available resources have been developed to help address these barriers. In this review, we discuss current programs that use preemptive genotyping to optimize the pharmacotherapy of patients. Array-based preemptive testing includes a large number of relevant pharmacogenes that impact multiple high-risk drugs. Using a preemptive approach allows genotyping results to be available prior to any prescribing decision so that genomic variation may be considered as an inherent patient characteristic in the planning of therapy. This review describes the common elements among programs that have implemented preemptive genotyping and highlights key processes for implementation, including clinical decision support.

Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

Introduction:Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes.Materials and methods:Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts.Results:Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms.Discussion:The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.Genet Med 19 2, 215–223.

PharmGKB summary: Methotrexate pathway

Methotrexate is a folate analog that is used in the treatment of cancers (e.g. acute lymphoblastic leukemia, non-Hodgkin lymphoma, osteosarcoma, and colon cancer) and autoimmune diseases (e.g. rheumatoid arthritis, Crohn’s disease, and psoriasis). In the treatment of autoimmune diseases, methotrexate is usually administrated orally or subcutaneously, whereas in the cancer treatment, it can be given orally, intramuscularly, as intrathecal injections, or as intravenous infusions (up to 12 g/m2) [1-3]. The pharmacokinetics and pharmacodynamics of methotrexate show large interpatient variability regardless of the route of administration or disease being treated [4-6]. The goal of this study is to provide an introduction to methotrexate pharmacogenomics, showing the candidate genes in the PharmGKB methotrexate pathway (Fig. 1), important variants (Tables ​(Tables11 and ​and2),2), discussing key knowledge, and pointing to more in-depth resources.

Developing knowledge resources to support precision medicine: Principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.

Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update

CYP2D6 and CYP2C19 polymorphisms affect the exposure, efficacy and safety of tricyclic antidepressants (TCAs), with some drugs being affected by CYP2D6 only (e.g., nortriptyline and desipramine) and others by both polymorphic enzymes (e.g., amitriptyline, clomipramine, doxepin, imipramine, and trimipramine). Evidence is presented for CYP2D6 and CYP2C19 genotype-directed dosing of TCAs. This document is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Tricyclic Antidepressants.

Clinical Pharmacogenetics Implementation Consortium Guideline for HLA Genotype and Use of Carbamazepine and Oxcarbazepine: 2017 Update

The variant allele HLA‐B*15:02 is strongly associated with greater risk of Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) in patients treated with carbamazepine or oxcarbazepine. The variant allele HLA‐A*31:01 is associated with greater risk of maculopapular exanthema, drug reaction with eosinophilia and systemic symptoms, and SJS/TEN in patients treated with carbamazepine. We summarize evidence from the published literature supporting these associations and provide recommendations for carbamazepine and oxcarbazepine use based on HLA genotypes.

Pharmacogenomics Implementation: Considerations for Selecting a Reference Laboratory

One of the initial steps for implementing pharmacogenomics into routine patient care is selecting an appropriate clinical laboratory to perform the testing. With the rapid advances in genotyping technologies, many clinical laboratories are now performing pharmacogenomic testing. Selection of a reference laboratory depends on whether a particular genotype assay is already performed by an internal health care organization laboratory or only available externally. Other factors for consideration are coverage of genomic variants important for the patient population, technical support, and cost. In some instances, the decision to select a particular reference laboratory may be the responsibility of the clinician who is recommending genomic interrogation. Only limited guidance is available that describes the laboratory characteristics to consider when selecting a reference laboratory. We provide practical considerations for selecting a clinical laboratory for pharmacogenomic testing broadly categorized into four domains: pharmacogene and variant selection; logistics; reporting of results; and test costs along with reimbursement.

Value of Personalized Medicine

recurrence (HR, 3.53 for recurrent lobar ICH vs 4.23 for recurrent nonlobar ICH). Vidale et al mention systemic diseases (including chronic kidney disease) as relevant contributors to ICH incidence and recurrence. We found no association between medical history of chronic kidney disease and ICH recurrence after adjusting for BP measurements (HR for lobar ICH, 1.35 [95% CI, 0.55-3.30]; HR for nonlobar ICH, 1.29 [95% CI, 0.72-2.32]). These findings may indicate lack of an independent effect of renal function abnormality on risk of ICH recurrence, once systemic pressure elevation is accounted for. We do agree, however, that chronic kidney disease and other comorbidities remain important factors to be incorporated in clinical decision making for individual patients. We agree with Vidale et al that the potential role of BP variability and circadian rhythms in determining risk of ICH recurrence is of great interest, but definitive evidence on this topic is lacking. Although additional investigation is warranted, widespread use of continuous BP monitoring to investigate the adequacy and temporal dynamics of BP control is currently not part of ICH secondary prevention guidelines, given unclear benefits in reducing risk of ICH recurrence.4,5 Furthermore, continuous BP monitoring for all survivors of ICH (outside of dedicated randomized trials) would currently be unfeasible in all but the highest-resource settings.

Patient perspectives following pharmacogenomics results disclosure in an integrated health system

AIM To assess patient perceptions and utilization of pharmacogenomics (PGx) testing in an integrated community health system. METHODS Fifty-seven patients completed an online survey assessing their experiences with PGx testing offered through two methods: a designated PGx clinic or direct access in-home testing. RESULTS The majority of participants perceived PGx testing as helpful in their healthcare and reported understanding their results. Some had concerns about privacy and discrimination; most lacked familiarity with the Genetic Information Nondiscrimination Act. There were no significant differences in views between participants tested through either model. CONCLUSION Participants reported value in both methods of PGx testing. Patient experiences, understanding and result utilization will play an important role in informing future development and implementation of PGx programs.

Genetic Contributions and Personalized Medicine

Chronic diseases can be attributed to lifestyle choices, environmental exposures, and genetics. Genomic alterations can increase the risk of developing a chronic condition, and genetic susceptibility can be exacerbated by lifestyle or environment. Numerous medications are available to treat chronic disorders, and even when adhering to best practices, multiple treatment strategies may exist. Polymorphisms in genes encoding drug-metabolizing enzymes, transporters, and targets can influence drug response; therefore gene-based drug-prescribing strategies may identify medications that are more likely to result in a good response. Pharmacogenomics is the study of how variations in genes encoding pharmacokinetic and pharmacodynamic proteins affect pharmacotherapy outcomes. There is a growing body of evidence demonstrating a correlation between genetic polymorphisms and aberrant efficacy, adverse reactions, and dosage requirements. For certain gene-drug interactions, the evidence is sufficiently strong to warrant clinical implementation. Models are being developed exploring how to integrate genomic medicine into routine clinical practice. Methods are needed to discretely curate genomic alterations in electronic medical records, with dissemination of clinical decision support to remind clinicians of important results. Future studies will need to investigate the impact and cost-effectiveness of implementing personalized medicine into patient care.