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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. 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. 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. 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.

Clinical laboratories are increasingly implementing pharmacogenomic (PGx) testing. Although PGx is similar to genetic testing for other indications, there are unique aspects that laboratories should consider. To aid clinical laboratories that are implementing clinical PGx testing by describing characteristics of PGx test design and validation, as well as approaches to reporting. Resources that are useful for clinical laboratories performing PGx testing will be highlighted. The College of American Pathologists formed a workgroup composed of laboratorians with expertise in clinical PGx testing. The workgroup included representatives from the Association for Molecular Pathology and the American College of Medical Genetics and Genomics. The workgroup reviewed pertinent literature, as well as experience from proficiency testing and from members' laboratories. The workgroup recommends that laboratories implementing PGx consider the following concepts: testing platform, test design (ie, selection of pharmacogenes and variants/alleles), use of reference materials during test development and as controls during clinical runs, star allele and standard nomenclature systems, translations from genotype to predicted phenotype, and considerations for result reporting including making medication recommendations. The workgroup provides considerations when using report vendors, emphasizing the clinical laboratory's role and responsibility when implementing such reporting tools from vendors. Clinical laboratories should be familiar with the fundamentals of PGx, ensure that PGx testing meets the applicable regulatory requirements for all aspects of the clinical laboratory testing process, and follow recommendations for standardization of nomenclature and reporting.

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. Resistance to first line treatment is a major hurdle in cancer treatment, that can be overcome with drug combinations. Here, the authors provide a large drug combination screen across cancer cell lines to benchmark crowdsourced methods and to computationally predict drug synergies.

To report the design and implementation of the Right Drug, Right Dose, Right Time—Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

Depending on how many biomarkers are profiled, hundreds, or even thousands, of matched control and disease samples may be needed to satisfy regulatory requirements and demonstrate that a test confers clinical or economic benefits. best practice The recent shift to testing for multiple biomarkers (multiplex profiling) has stimulated impressive innovation in high-throughput technologies for automated parallel profiling of genes, proteins, RNA molecules and metabolites. [...] the US National Cancer Institute's Cancer Human Biobank (caHUB) has established stringent guidelines to ensure that samples from healthy individuals and cancer patients are collected, annotated, stored and analysed under standardized conditions and accompanied by appropriate donor medical information.

Successfully implementing pharmacogenomics into routine clinical practice requires an efficient process to order genetic tests and report the results to clinicians and patients. Lack of standardized approaches and terminology in clinical laboratory processes, ordering of the test and reporting of test results all impede this workflow. Expert groups such as the Association for Molecular Pathology and the Clinical Pharmacogenetics Implementation Consortium have published recommendations for standardizing laboratory genetic testing, reporting and terminology. Other resources such as PharmGKB, ClinVar, ClinGen and PharmVar have established databases of nomenclature for pharmacogenetic alleles and variants. Opportunities remain to develop new standards and further disseminate existing standards which will accelerate the implementation of pharmacogenomics.

The Dutch Pharmacogenetics Working Group (DPWG) aims to facilitate pharmacogenetics implementation in clinical practice by developing evidence-based guidelines to optimize pharmacotherapy based on pharmacogenetic test results. The current guideline describes the gene-drug interaction between CYP2D6 and venlafaxine, mirtazapine and duloxetine. In addition, the interaction between CYP2C19 and mirtazapine and moclobemide is presented. The DPWG identified a gene-drug interaction that requires therapy adjustment for CYP2D6 and venlafaxine. However, as the side effects do not appear to be related to plasma concentrations, it is not possible to offer a substantiated advice for dose reduction. Therefore, the DPWG recommends avoiding venlafaxine for CYP2D6 poor and intermediate metabolisers. Instead, an alternative antidepressant, which is not, or to a lesser extent, metabolized by CYP2D6 is recommended. When it is not possible to avoid venlafaxine and side effects occur, it is recommended to reduce the dose and monitor the effect and side effects or plasma concentrations. No action is required for ultra-rapid metabolisers as kinetic effects are minimal and no clinical effect has been demonstrated. In addition, a gene-drug interaction was identified for CYP2D6 and mirtazapine and CYP2C19 and moclobemide, but no therapy adjustment is required as no effect regarding effectiveness or side effects has been demonstrated for these gene-drug interactions. Finally, no gene-drug interaction and need for therapy adjustment between CYP2C19 and mirtazapine and CYP2D6 and duloxetine were identified. The DPWG classifies CYP2D6 genotyping as being “potentially beneficial” for venlafaxine, indicating that genotyping prior to treatment can be considered on an individual patient basis.

The use of opioids to alleviate pain is complicated by the risk of severe adverse events and the large variability in dose requirements. Pharmacogenetics (PGx) could possibly be used to tailor pain medication based on an individual's genetic background. Many potential genetic markers have been described, and the importance of genetic predisposition in opioid efficacy and toxicity has been demonstrated in knockout mouse models and human twin studies. Such predictors are especially of value for neonates and young children, in whom the assessment of efficacy or side effects is complicated by the inability of the patient to communicate this properly. The current problem is determining which of the many potential candidates to focus on for clinical implementation. We systematically searched publications on PGx for opioids in 5 databases, aiming to identify PGx markers with sufficient robust data and high enough occurrence for potential clinical application. The initial search yielded 4257 unique citations, eventually resulting in 852 relevant articles covering 24 genes. From these genes, we evaluated the evidence and selected the most promising 10 markers: cytochrome P450 family 2 subfamily D member 6 ( ), cytochrome P450 family 3 subfamily A member 4 ( ), cytochrome P450 family 3 subfamily A member 5 ( ), UDP glucuronosyltransferase family 2 member B7 ( ), ATP binding cassette subfamily B member 1 ( ), ATP binding cassette subfamily C member 3 ( ), solute carrier family 22 member 1 ( ), opioid receptor kappa 1 ( ), catechol- -methyltransferase ( ), and potassium voltage-gated channel subfamily J member 6 ( ). Treatment guidelines based on genotype are already available only for . The application of PGx in the management of pain with opioids has the potential to improve therapy. We provide a shortlist of 10 genes that are the most promising markers for clinical use in this context.

The University of Florida and Shands Hospital recently launched a genomic medicine program focused on the clinical implementation of pharmacogenetics called the Personalized Medicine Program. We focus on a pre-emptive, chip-based genotyping approach that is cost effective, while providing experience that will be useful as genomic medicine moves towards genome sequence data for patients becoming available. The Personalized Medicine Program includes a regulatory body that is responsible for ensuring that evidence-based examples are moved to clinical implementation, and relies on clinical decision support tools to provide healthcare providers with guidance on use of the genetic information. The pilot implementation was with -clopidogrel and future plans include expansion to additional pharmacogenetic examples, along with aiding in implementation in other health systems across Florida.