Michelle Whirl-Carrillo

All Publications

• Pharmacogenomics Clinical Annotation Tool (PharmCAT). Clinical pharmacology and therapeutics Sangkuhl, K., Whirl-Carrillo, M., Whaley, R. M., Woon, M., Lavertu, A., Altman, R. B., Carter, L., Verma, A., Ritchie, M. D., Klein, T. E. 2019

  Abstract

  Pharmacogenomics (PGx) decision support and return of results is an active area of precision medicine. One challenge of implementing PGx is extracting genomic variants and assigning haplotypes in order to apply prescribing recommendations and information from CPIC, FDA, PharmGKB, etc. PharmCAT (1) extracts variants specified in guidelines from a genetic dataset derived from sequencing or genotyping technologies; (2) infers haplotypes and diplotypes; and (3) generates a report containing genotype/diplotype-based annotations and guideline recommendations. We describe PharmCAT and a pilot validation project comparing results for 1000 Genomes sequences of Coriell samples with corresponding Genetic Testing Reference Materials Coordination Program (GeT-RM) sample characterization. PharmCAT was highly concordant with the GeT-RM data. PharmCAT is available in GitHub to evaluate, test and report results back to the community. As precision medicine becomes more prevalent, our ability to consistently, accurately, and clearly define and report PGx annotations and prescribing recommendations is critical. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/cpt.1568

  View details for PubMedID 31306493

• Standardized Biogeographic Grouping System for Annotating Populations in Pharmacogenetic Research CLINICAL PHARMACOLOGY & THERAPEUTICS Huddart, R., Fohner, A. E., Whirl-Carrillo, M., Wojcik, G. L., Gignoux, C. R., Popejoy, A. B., Bustamante, C. D., Altman, R. B., Klein, T. E. 2019; 105 (5): 1256–62

  View details for DOI 10.1002/cpt.1322

  View details for Web of Science ID 000466750900030

• Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genetics in medicine Caudle, K. E., Dunnenberger, H. M., Freimuth, R. R., Peterson, J. F., Burlison, J. D., Whirl-Carrillo, M., Scott, S. A., Rehm, H. L., Williams, M. S., Klein, T. E., Relling, M. V., Hoffman, J. M. 2016

  Abstract

  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.Genet Med advance online publication 21 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.87.

  View details for DOI 10.1038/gim.2016.87

  View details for PubMedID 27441996

• Enabling the curation of your pharmacogenetic study. Clinical pharmacology & therapeutics MCDONAGH, E. M., Whirl-Carrillo, M., Altman, R. B., Klein, T. E. 2015; 97 (2): 116-119

  Abstract

  As pharmacogenomics becomes integrated into clinical practice, curation of published studies becomes increasingly important. At the Pharmacogenomics Knowledgebase (PharmGKB; www.pharmgkb.org), pharmacogenetic associations reported in published articles are manually curated and evaluated. Standard terminologies are used, making findings uniform and unambiguous. Lack of information, clarity, or standards in the original report can make it difficult or impossible to curate. We provide 10 rules to help authors ensure that their results are accurately captured and integrated.

  View details for DOI 10.1002/cpt.15

  View details for PubMedID 25670512

• Pharmacogenomics Knowledge for Personalized Medicine CLINICAL PHARMACOLOGY & THERAPEUTICS Whirl-Carrillo, M., MCDONAGH, E. M., Hebert, J. M., Gong, L., Sangkuhl, K., Thorn, C. F., Altman, R. B., Klein, T. E. 2012; 92 (4): 414-417

  Abstract

  The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene-drug associations and genotype-phenotype relationships. Curators assign levels of evidence to variant-drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine-implementation projects.

  View details for DOI 10.1038/clpt.2012.96

  View details for Web of Science ID 000309017000009

  View details for PubMedID 22992668

  View details for PubMedCentralID PMC3660037

• Phased Whole-Genome Genetic Risk in a Family Quartet Using a Major Allele Reference Sequence PLOS GENETICS Dewey, F. E., Chen, R., Cordero, S. P., Ormond, K. E., Caleshu, C., Karczewski, K. J., Whirl-Carrillo, M., Wheeler, M. T., Dudley, J. T., Byrnes, J. K., Cornejo, O. E., Knowles, J. W., Woon, M., Sangkuhl, K., Gong, L., Thorn, C. F., Hebert, J. M., Capriotti, E., David, S. P., Pavlovic, A., West, A., Thakuria, J. V., Ball, M. P., Zaranek, A. W., Rehm, H. L., Church, G. M., West, J. S., Bustamante, C. D., Snyder, M., Altman, R. B., Klein, T. E., Butte, A. J., Ashley, E. A. 2011; 7 (9)

  Abstract

  Whole-genome sequencing harbors unprecedented potential for characterization of individual and family genetic variation. Here, we develop a novel synthetic human reference sequence that is ethnically concordant and use it for the analysis of genomes from a nuclear family with history of familial thrombophilia. We demonstrate that the use of the major allele reference sequence results in improved genotype accuracy for disease-associated variant loci. We infer recombination sites to the lowest median resolution demonstrated to date (< 1,000 base pairs). We use family inheritance state analysis to control sequencing error and inform family-wide haplotype phasing, allowing quantification of genome-wide compound heterozygosity. We develop a sequence-based methodology for Human Leukocyte Antigen typing that contributes to disease risk prediction. Finally, we advance methods for analysis of disease and pharmacogenomic risk across the coding and non-coding genome that incorporate phased variant data. We show these methods are capable of identifying multigenic risk for inherited thrombophilia and informing the appropriate pharmacological therapy. These ethnicity-specific, family-based approaches to interpretation of genetic variation are emblematic of the next generation of genetic risk assessment using whole-genome sequencing.

  View details for DOI 10.1371/journal.pgen.1002280

  View details for PubMedID 21935354

• An XML-based interchange format for genotype-phenotype data HUMAN MUTATION Whirl-Carrillo, M., Woon, M., Thorn, C. E., Klein, T. E., Altman, R. B. 2008; 29 (2): 212-219

  Abstract

  Recent advances in high-throughput genotyping and phenotyping have accelerated the creation of pharmacogenomic data. Consequently, the community requires standard formats to exchange large amounts of diverse information. To facilitate the transfer of pharmacogenomics data between databases and analysis packages, we have created a standard XML (eXtensible Markup Language) schema that describes both genotype and phenotype data as well as associated metadata. The schema accommodates information regarding genes, drugs, diseases, experimental methods, genomic/RNA/protein sequences, subjects, subject groups, and literature. The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org) has used this XML schema for more than 5 years to accept and process submissions containing more than 1,814,139 SNPs on 20,797 subjects using 8,975 assays. Although developed in the context of pharmacogenomics, the schema is of general utility for exchange of genotype and phenotype data. We have written syntactic and semantic validators to check documents using this format. The schema and code for validation is available to the community at http://www.pharmgkb.org/schema/index.html (last accessed: 8 October 2007).

  View details for DOI 10.1002/humu.20662

  View details for Web of Science ID 000253033000002

  View details for PubMedID 17994540

• Mining biochemical information: Lessons taught by the ribosome RNA-A PUBLICATION OF THE RNA SOCIETY Whirl-Carrillo, M., Gabashvili, I. S., Bada, M., Banatao, D. R., Altman, R. B. 2002; 8 (3): 279-289

  Abstract

  The publication of the crystal structures of the ribosome offers an opportunity to retrospectively evaluate the information content of hundreds of qualitative biochemical and biophysical studies of these structures. We assessed the correspondence between more than 2,500 experimental proximity measurements and the distances observed in the ribosomal crystals. Although detailed experimental procedures and protocols are unique in almost each analyzed paper, the data can be grouped into subsets with similar patterns and analyzed in an integrative fashion. We found that, for crosslinking, footprinting, and cleavage data, the corresponding distances observed in crystal structures generally did not exceed the maximum values expected (from the estimated length of the agent and maximal anticipated deviations from the conformations found in crystals). However, the distribution of distances had heavier tails than those typically assumed when building three-dimensional models, and the fraction of incompatible distances was greater than expected. Some of these incompatibilities can be attributed to the experimental methods used. In addition, the accuracy of these procedures appears to be sensitive to the different reactivities, flexibilities, and interactions among the components. These findings demonstrate the necessity of a very careful analysis of data used for structural modeling and consideration of all possible parameters that could potentially influence the quality of measurements. We conclude that experimental proximity measurements can provide useful distance information for structural modeling, but with a broad distribution of inferred distance ranges. We also conclude that development of automated modeling approaches would benefit from better annotations of experimental data for detection and interpretation of their significance.

  View details for DOI 10.1017/S135583820202407X

  View details for Web of Science ID 000175155500002

  View details for PubMedID 12003488

  View details for PubMedCentralID PMC1370250

• Transfer learning enables prediction of CYP2D6 haplotype function. PLoS computational biology McInnes, G., Dalton, R., Sangkuhl, K., Whirl-Carrillo, M., Lee, S., Tsao, P. S., Gaedigk, A., Altman, R. B., Woodahl, E. L. 2020; 16 (11): e1008399

  Abstract

  Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene whose protein product metabolizes more than 20% of clinically used drugs. Genetic variations in CYP2D6 are responsible for interindividual heterogeneity in drug response that can lead to drug toxicity and ineffective treatment, making CYP2D6 one of the most important pharmacogenes. Prediction of CYP2D6 phenotype relies on curation of literature-derived functional studies to assign a functional status to CYP2D6 haplotypes. As the number of large-scale sequencing efforts grows, new haplotypes continue to be discovered, and assignment of function is challenging to maintain. To address this challenge, we have trained a convolutional neural network to predict functional status of CYP2D6 haplotypes, called Hubble.2D6. Hubble.2D6 predicts haplotype function from sequence data and was trained using two pre-training steps with a combination of real and simulated data. We find that Hubble.2D6 predicts CYP2D6 haplotype functional status with 88% accuracy in a held-out test set and explains 47.5% of the variance in in vitro functional data among star alleles with unknown function. Hubble.2D6 may be a useful tool for assigning function to haplotypes with uncurated function, and used for screening individuals who are at risk of being poor metabolizers.

  View details for DOI 10.1371/journal.pcbi.1008399

  View details for PubMedID 33137098

• Pharmacogenetic information in Swiss drug labels - a systematic analysis. The pharmacogenomics journal Jeiziner, C., Suter, K., Wernli, U., Barbarino, J. M., Gong, L., Whirl-Carrillo, M., Klein, T. E., Szucs, T. D., Hersberger, K. E., Meyer Zu Schwabedissen, H. E. 2020

  Abstract

  Implementation of pharmacogenetics (PGx) and individualization of drug therapy is supposed to obviate adverse drug reactions or therapy failure. Health care professionals (HCPs) use drug labels (DLs) as reliable information about drugs. We analyzed the Swiss DLs to give an overview on the currently available PGx instructions. We screened 4306 DLs applying natural language processing focusing on drug metabolism (pharmacokinetics) and we assigned PGx levels following the classification system of PharmGKB. From 5979 hits, 2564 were classified as PGx-relevant affecting 167 substances. 55% (n=93) were classified as "actionable PGx". Frequently, PGx information appeared in the pharmacokinetics section and in DLs of the anatomic group "nervous system". Unstandardized wording, appearance of PGx information in different sections and unclear instructions challenge HCPs to identify and interpret PGx information and translate it into practice. HCPs need harmonization and standardization of PGx information in DLs to personalize drug therapies and tailor pharmaceutical care.

  View details for DOI 10.1038/s41397-020-00195-4

  View details for PubMedID 33070160

• PharmGKB tutorial for pharmacogenomics of drugs potentially used in the context of COVID-19. Clinical pharmacology and therapeutics Huddart, R., Whirl-Carrillo, M., Altman, R. B., Klein, T. E. 2020

  Abstract

  Pharmacogenomics is a key area of precision medicine which is already being implemented in some health systems and may help guide clinicians towards effective therapies for individual patients. Over the last two decades, the Pharmacogenomics Knowledgebase (PharmGKB) has built a unique repository of pharmacogenomic knowledge, including annotations of clinical guideline and regulator-approved drug labels in addition to evidence-based drug pathways and annotations of the scientific literature. All of this knowledge is freely accessible on the PharmGKB website. In the first of a series of PharmGKB tutorials, we introduce the PharmGKB COVID-19 portal and, using examples of drugs found in the portal, demonstrate some of the main features of PharmGKB. This paper is intended as a resource to help users become quickly acquainted with the wealth of information stored in PharmGKB.

  View details for DOI 10.1002/cpt.2067

  View details for PubMedID 32978778

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2C9 and HLA-B Genotypes and Phenytoin Dosing: 2020 Update. Clinical pharmacology and therapeutics Karnes, J. H., Rettie, A. E., Somogyi, A. A., Huddart, R., Fohner, A. E., Formea, C. M., Lee, M. T., Llerena, A., Whirl-Carrillo, M., Klein, T. E., Phillips, E. J., Mintzer, S., Gaedigk, A., Caudle, K. E., Callaghan, J. T. 2020

  Abstract

  Phenytoin is an antiepileptic drug with a narrow therapeutic index and large inter-patient pharmacokinetic variability, partly due to genetic variation in CYP2C9. Furthermore, the variant allele HLA-B*15:02 is associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis in response to phenytoin treatment. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for the use of phenytoin based on CYP2C9 and/or HLA-B genotypes (updates on cpicpgx.org).

  View details for DOI 10.1002/cpt.2008

  View details for PubMedID 32779747

• Recommendations for Clinical Warfarin Genotyping Allele Selection A Report of the Association for Molecular Pathology and the College of American Pathologists JOURNAL OF MOLECULAR DIAGNOSTICS Pratt, V. M., Cavallari, L. H., Del Tredici, A. L., Hachad, H., Ji, Y., Kalman, L. V., Ly, R. C., Moyer, A. M., Scott, S. A., Whirl-Carrillo, M., Weck, K. E. 2020; 22 (7): 847–59

  Abstract

  The goal of the Association for Molecular Pathology (AMP) Clinical Practice Committee's AMP Pharmacogenomics (PGx) Working Group is to define the key attributes of PGx alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations for a minimum panel of variant alleles (tier 1) and an extended panel of variant alleles (tier 2) that will aid clinical laboratories when designing assays for PGx testing. The AMP PGx Working Group considered functional impact of the variants, allele frequencies in multiethnic populations, the availability of reference materials, as well as other technical considerations for PGx testing when developing these recommendations. The ultimate goal is to promote standardization of PGx gene/allele testing across clinical laboratories. These recommendations are not to be interpreted as prescriptive but to provide a reference guide. Of note, a separate article with recommendations for CYP2C9 allele selection was previously developed by the PGx Working Group that can be applied broadly to CYP2C9-related medications. The warfarin allele recommendations in this report incorporate the previous CYP2C9 allele recommendations and additional genes and alleles that are specific to warfarin testing.

  View details for DOI 10.1016/j.jmoldx.2020.04.204

  View details for Web of Science ID 000546090500003

  View details for PubMedID 32380173

• PharmVar GeneFocus: CYP2C19. Clinical pharmacology and therapeutics Botton, M. R., Whirl-Carrillo, M., Del Tredici, A. L., Sangkuhl, K., Cavallari, L. H., Agundez, J. A., Duconge, J., Lee, M. T., Woodahl, E. L., Claudio-Campos, K., Daly, A. K., Klein, T. E., Pratt, V. M., Scott, S. A., Gaedigk, A. 2020

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2C19 gene. CYP2C19 genetic variation impacts the metabolism of many drugs and has been associated with both, efficacy and safety issues for several commonly prescribed medications. This GeneFocus provides a comprehensive overview and summary of CYP2C19 and describes how haplotype information catalogued by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

  View details for DOI 10.1002/cpt.1973

  View details for PubMedID 32602114

• Response to: Unveiling the guidance heterogeneity for genome-informed drug treatment interventions among regulatory bodies and research consortia. Pharmacological research Huddart, R., Li, G., Sangkuhl, K., Thorn, C. F., Whirl-Carrillo, M., Caudle, K. E., Relling, M. V., Klein, T. E. 2020: 104838

  View details for DOI 10.1016/j.phrs.2020.104838

  View details for PubMedID 32407955

• PharmVar and the Landscape of Pharmacogenetic Resources. Clinical pharmacology and therapeutics Gaedigk, A., Whirl-Carrillo, M., Pratt, V. M., Miller, N. A., Klein, T. E. 2019

  View details for DOI 10.1002/cpt.1654

  View details for PubMedID 31758698

• PharmVar GeneReview: CYP2D6. Clinical pharmacology and therapeutics Nofziger, C., Turner, A. J., Sangkuhl, K., Whirl-Carrillo, M., Agundez, J. A., Black, J. L., Dunnenberger, H. M., Ruano, G., Kennedy, M. A., Phillips, M. S., Hachad, H., Klein, T. E., Gaedigk, A. 2019

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus. CYP2D6 genetic variation impacts the metabolism of numerous drugs and thus can impact drug efficacy and safety. This GeneReview provides a comprehensive overview and summary of CYP2D6 genetic variation and describes how the information provided by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

  View details for DOI 10.1002/cpt.1643

  View details for PubMedID 31544239

• Are Randomized Controlled Trials Necessary to Establish the Value of Implementing Pharmacogenomics in the Clinic? CLINICAL PHARMACOLOGY & THERAPEUTICS Huddart, R., Sangkuhl, K., Whirl-Carrillo, M., Klein, T. E. 2019; 106 (2): 284–86

  View details for DOI 10.1002/cpt.1420

  View details for Web of Science ID 000478601100005

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2B6 and Efavirenz-Containing Antiretroviral Therapy CLINICAL PHARMACOLOGY & THERAPEUTICS Desta, Z., Gammal, R. S., Gong, L., Whirl-Carrillo, M., Gaur, A. H., Sukasem, C., Hockings, J., Myers, A., Swart, M., Tyndale, R. F., Masimirembwa, C., Iwuchukwu, O. F., Chirwa, S., Lennox, J., Gaedigk, A., Klein, T. E., Haas, D. W. 2019

  View details for DOI 10.1002/cpt.1477

  View details for Web of Science ID 000474176300001

• Clinical Pharmacogenetics Implementation Consortium Guideline for Thiopurine Dosing Based on TPMT and NUDT15 Genotypes: 2018 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Relling, M., Schwab, M., Whirl-Carrillo, M., Suarez-Kurtz, G., Pui, C., Stein, C. M., Moyer, A. M., Evans, W. E., Klein, T. E., Guillermo Antillon-Klussmann, F., Caudle, K. E., Kato, M., Yeoh, A. J., Schmiegelow, K., Yang, J. J. 2019; 105 (5): 1095–1105

  View details for DOI 10.1002/cpt.1304

  View details for Web of Science ID 000466750900014

• Pharmacogene Variation Consortium Gene Introduction: NUDT15 CLINICAL PHARMACOLOGY & THERAPEUTICS Yang, J. J., Whirl-Carrillo, M., Scott, S. A., Turner, A. J., Schwab, M., Tanaka, Y., Suarez-Kurtz, G., Schaeffeler, E., Klein, T. E., Miller, N. A., Gaedigki, A. 2019; 105 (5): 1091–94

  View details for DOI 10.1002/cpt.1268

  View details for Web of Science ID 000466750900013

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2B6 and Efavirenz-containing Antiretroviral Therapy. Clinical pharmacology and therapeutics Desta, Z., Gammal, R. S., Gong, L., Whirl-Carrillo, M., Gaur, A. H., Sukasem, C., Hockings, J., Myers, A., Swart, M., Tyndale, R., Masimirembwa, C., Iwuchukwu, O. F., Chirwa, S., Lennox, J., Gaedigk, A., Klein, T., Haas, D. W. 2019

  Abstract

  The human immunodeficiency virus (HIV) type-1 non-nucleoside reverse transcriptase inhibitor, efavirenz, is widely used to treat HIV-1 infection. Efavirenz is predominantly metabolized into inactive metabolites by CYP2B6, and patients with certain CYP2B6 genetic variants may be at increased risk for adverse effects, particularly central nervous system toxicity and treatment discontinuation. We summarize the evidence from the literature and provide therapeutic recommendations for efavirenz prescribing based on CYP2B6 genotypes. This article is protected by copyright. All rights reserved.

  View details for PubMedID 31006110

• Are Randomized Controlled Trials Necessary to Establish the Value of Implementing Pharmacogenomics in the Clinic? Clinical pharmacology and therapeutics Huddart, R., Sangkuhl, K., Whirl-Carrillo, M., Klein, T. E. 2019

  View details for PubMedID 30977517

• Standardizing CYP2D6 Genotype to Phenotype Translation: Consensus Recommendations from the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group. Clinical and translational science Caudle, K. E., Sangkuhl, K., Whirl-Carrillo, M., Swen, J. J., Haidar, C. E., Klein, T. E., Gammal, R. S., Relling, M. V., Scott, S. A., Hertz, D. L., Guchelaar, H. J., Gaedigk, A. 2019

  Abstract

  Translating CYP2D6 genotype to metabolizer phenotype is not standardized across clinical laboratories offering pharmacogenetic (PGx) testing and PGx clinical practice guidelines, such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Dutch Pharmacogenetics Working Group (DPWG). The genotype to phenotype translation discordance between laboratories and guidelines can cause discordant cytochrome P450 2D6 (CYP2D6) phenotype assignments and, thus lead to inconsistent therapeutic recommendations and confusion among patients and clinicians. A modified-Delphi method was used to obtain consensus for a uniform system for translating CYP2D6 genotype to phenotype among a panel of international CYP2D6 experts. Experts with diverse involvement in CYP2D6 interpretation (clinicians, researchers, genetic testing laboratorians, and PGx implementers; n = 37) participated in conference calls and surveys. After completion of 7 surveys, a consensus (> 70%) was reached with 82% of the CYP2D6 experts agreeing to the final CYP2D6 genotype to phenotype translation method. Broad adoption of the proposed CYP2D6 genotype to phenotype translation method by guideline developers, such as CPIC and DPWG, and clinical laboratories as well as researchers will result in more consistent interpretation of CYP2D6 genotype.

  View details for DOI 10.1111/cts.12692

  View details for PubMedID 31647186

• Recommendations for Clinical CYP2C9 Genotyping Allele Selection: A Joint Recommendation of the Association for Molecular Pathology and College of American Pathologists. The Journal of molecular diagnostics : JMD Pratt, V. M., Cavallari, L. H., Del Tredici, A. L., Hachad, H., Ji, Y., Moyer, A. M., Scott, S. A., Whirl-Carrillo, M., Weck, K. E. 2019

  Abstract

  The goals of the Association for Molecular Pathology Pharmacogenomics (PGx) Working Group of the Association for Molecular Pathology Clinical Practice Committee are to define the key attributes of PGx alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document provides recommendations for a minimum panel of variant alleles (Tier 1) and an extended panel of variant alleles (Tier 2) that will aid clinical laboratories when designing assays for CYP2C9 testing. The Working Group considered functional impact of the variants, allele frequencies in different populations and ethnicities, the availability of reference materials, as well as other technical considerations for PGx testing when developing these recommendations. Our goal is to promote standardization of testing PGx genes/allele testing across clinical laboratories. These recommendations are not to be interpreted as restrictive but to provide a reference guide. The current document will focus on CYP2C9 testing that can be applied to all CYP2C9-related medications. A separate recommendation on warfarin PGx testing is being developed to include recommendations on CYP2C9 alleles and additional warfarin sensitivity-associated genes/alleles.

  View details for PubMedID 31075510

• The Clinical Pharmacogenetics Implementation Consortium: 10 Years Later. Clinical pharmacology and therapeutics Relling, M. V., Klein, T. E., Gammal, R. S., Whirl-Carrillo, M., Hoffman, J. M., Caudle, K. E. 2019

  Abstract

  In 2009, the Clinical Pharmacogenetics Implementation Consortium (CPIC; www.cpicpgx.org), a shared project between Pharmacogenomics Knowledge Base (PharmGKB, http://www.pharmgkb.org/) and the National Institutes of Health (NIH), was created to provide freely available, evidence-based, peer-reviewed, and updated pharmacogenetic clinical practice guidelines. To date, CPIC has published 23 guidelines (of which 11 have been updated), covering 19 genes and 46 drugs across several therapeutic areas. CPIC also now provides additional resources to facilitate the implementation of pharmacogenetics into routine clinical practice and the electronic health record. Furthermore, since its inception, CPIC's interactions with other resources, databases, websites and genomic communities have grown. This purpose of this paper is to highlight the progress of CPIC over the past 10 years.

  View details for DOI 10.1002/cpt.1651

  View details for PubMedID 31562822

• BIOINFORMATICS: ACCUMULATING AND IMPLEMENTING PHARMACOGENOMICS INFORMATION Whirl-Carrillo, M. JAPANESE SOC STUDY XENOBIOTICS. 2019: S6

  View details for DOI 10.1016/j.dmpk.2018.09.032

  View details for Web of Science ID 000458519400028

• The Evolution of PharmVar. Clinical pharmacology and therapeutics Gaedigk, A., Sangkuhl, K., Whirl-Carrillo, M., Twist, G. P., Klein, T. E., Miller, N. A., PharmVar Steering Committee 2018

  View details for PubMedID 30536702

• Pharmacogene Variation Consortium Gene Introduction: NUDT15. Clinical pharmacology and therapeutics Yang, J. J., Whirl-Carrillo, M., Scott, S. A., Turner, A. J., Schwab, M., Tanaka, Y., Suarez-Kurtz, G., Schaeffeler, E., Klein, T. E., Miller, N. A., Gaedigk, A. 2018

  View details for PubMedID 30515762

• Standardized biogeographic grouping system for annotating populations in pharmacogenetic research. Clinical pharmacology and therapeutics Huddart, R., Fohner, A. E., Whirl-Carrillo, M., Wojcik, G. L., Gignoux, C. R., Popejoy, A. B., Bustamante, C. D., Altman, R. B., Klein, T. E. 2018

  Abstract

  The varying frequencies of pharmacogenetic alleles between populations have important implications for the impact of these alleles in different populations. Current population grouping methods to communicate these patterns are insufficient as they are inconsistent and fail to reflect the global distribution of genetic variability. To facilitate and standardize the reporting of variability in pharmacogenetic allele frequencies, we present seven geographically-defined groups: American, Central/South Asian, East Asian, European, Near Eastern, Oceanian, and Sub-Saharan African, and two admixed groups: African American/Afro-Caribbean and Latino. These nine groups are defined by global autosomal genetic structure and based on data from large-scale sequencing initiatives. We recognize that broadly grouping global populations is an oversimplification of human diversity and does not capture complex social and cultural identity. However, these groups meet a key need in pharmacogenetics research by enabling consistent communication of the scale of variability in global allele frequencies and are now used by PharmGKB. This article is protected by copyright. All rights reserved.

  View details for PubMedID 30506572

• Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. Clinical pharmacology and therapeutics Relling, M. V., Schwab, M., Whirl-Carrillo, M., Suarez-Kurtz, G., Pui, C., Stein, C. M., Moyer, A. M., Evans, W. E., Klein, T. E., Antillon-Klussmann, F. G., Caudle, K. E., Kato, M., Yeoh, A. E., Schmiegelow, K., Yang, J. J. 2018

  Abstract

  TPMT activity exhibits a monogenic co-dominant inheritance and catabolizes thiopurines. TPMT variant alleles are associated with low enzyme activity and pronounced pharmalogic effecs of thiopurines. Loss-of-function alleles in the NUDT15 gene are common in Asians and Hispanics and reduces the degradation of active thiopurine nucleotide metabolites, also predisposing to myelosuppression. We provide recommendations for adjusting starting dosesof azathioprine, mercaptopurine, and thioguanine based on TPMT and NUDT15 genotypes. This article is protected by copyright. All rights reserved.

  View details for PubMedID 30447069

• Essential characteristics of pharmacogenomics study publications. Clinical pharmacology and therapeutics Thorn, C. F., Whirl-Carrillo, M., Hachad, H., Johnson, J. A., McDonagh, E. M., Ratain, M. J., Relling, M. V., Scott, S. A., Altman, R. B., Klein, T. E. 2018

  Abstract

  Pharmacogenomics (PGx) can be seen as a model for biomedical studies: it includes all disease areas of interest, spans in vitro studies to clinical trials, while focusing on the relationships between genes and drugs and the resulting phenotypes. This review will examine different characteristics of PGx study publications and provide examples of excellence in framing PGx questions and reporting their resulting data in a way that maximizes the knowledge that can be built upon them. This article is protected by copyright. All rights reserved.

  View details for PubMedID 30406943

• Standardization can accelerate the adoption of pharmacogenomics: current status and the path forward PHARMACOGENOMICS Caudle, K. E., Keeling, N. J., Klein, T. E., Whirl-Carrillo, M., Pratt, V. M., Hoffman, J. M. 2018; 19 (10): 847–60

  Abstract

  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.

  View details for PubMedID 29914287

• Pharmacogenomics and big genomic data: from lab to clinic and back again. Human molecular genetics Lavertu, A., McInnes, G., Daneshjou, R., Whirl-Carrillo, M., Klein, T. E., Altman, R. B. 2018; 27 (R1): R72–R78

  Abstract

  The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.

  View details for PubMedID 29635477

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and Tamoxifen Therapy CLINICAL PHARMACOLOGY & THERAPEUTICS Goetz, M. P., Sangkuhl, K., Guchelaar, H., Schwab, M., Province, M., Whirl-Carrillo, M., Symmans, W., McLeod, H. L., Ratain, M. J., Zembutsu, H., Gaedigk, A., van Schaik, R. H., Ingle, J. N., Caudle, K. E., Klein, T. E. 2018; 103 (5): 770–77

  Abstract

  Tamoxifen is biotransformed by CYP2D6 to 4-hydroxytamoxifen and 4-hydroxy N-desmethyl tamoxifen (endoxifen), both with greater antiestrogenic potency than the parent drug. Patients with certain CYP2D6 genetic polymorphisms and patients who receive strong CYP2D6 inhibitors exhibit lower endoxifen concentrations and a higher risk of disease recurrence in some studies of tamoxifen adjuvant therapy of early breast cancer. We summarize evidence from the literature and provide therapeutic recommendations for tamoxifen based on CYP2D6 genotype.

  View details for PubMedID 29385237

  View details for PubMedCentralID PMC5931215

• Comparison of the Guidelines of the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group CLINICAL PHARMACOLOGY & THERAPEUTICS Bank, P. D., Caudle, K. E., Swen, J. J., Gammal, R. S., Whirl-Carrillo, M., Klein, T. E., Relling, M. V., Guchelaar, H. 2018; 103 (4): 599–618

  Abstract

  Both the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Dutch Pharmacogenetics Working Group provide therapeutic recommendations for well-known gene-drug pairs. Published recommendations show a high rate of concordance. However, as a result of different guideline development methods used by these two consortia, differences between the published guidelines exist. The aim of this paper is to compare both initiatives and explore these differences, with the objective to achieve harmonization.

  View details for PubMedID 28994452

  View details for PubMedCentralID PMC5723247

• Clinical Pharmacogenetics Implementation Consortium Guideline for HLA Genotype and Use of Carbamazepine and Oxcarbazepine: 2017 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Phillips, E. J., Sukasem, C., Whirl-Carrillo, M., Mueller, D. J., Dunnenberger, H. M., Chantratita, W., Goldspiel, B., Chen, Y., Carleton, B. C., George, A. L., Mushiroda, T., Klein, T., Gammal, R. S., Pirmohamed, M. 2018; 103 (4): 574–81

  Abstract

  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.

  View details for PubMedID 29392710

  View details for PubMedCentralID PMC5847474

• THE PHARMACOGENE VARIATION CONSORTIUM: INCORPORATION OF THE HUMAN CYTOCHROME P450 ALLELE NOMENCLATURE DATABASE. Gaedigk, A., Klein, T. E., Twist, G. P., Whirl-Carrillo, M., Miller, N. A. WILEY. 2018: S67

  View details for Web of Science ID 000424997100241

• The Pharmacogene Variation (PharmVar) Consortium: Incorporation of the Human Cytochrome P450 (CYP) Allele Nomenclature Database CLINICAL PHARMACOLOGY & THERAPEUTICS Gaedigk, A., Ingelman-Sundberg, M., Miller, N. A., Leeder, J., Whirl-Carrillo, M., Klein, T. E., PharmVar Steering Comm 2018; 103 (3): 399–401

  Abstract

  The Human Cytochrome P450 (CYP) Allele Nomenclature Database, a critical resource for the pharmacogenetics and genomics communities, has transitioned to the Pharmacogene Variation (PharmVar) Consortium. In this report we provide a summary of the current database, provide an overview of the PharmVar consortium, and highlight the PharmVar database which will serve as the new home for pharmacogene nomenclature.

  View details for PubMedID 29134625

  View details for PubMedCentralID PMC5836850

• PharmGKB: A worldwide resource for pharmacogenomic information. Wiley interdisciplinary reviews. Systems biology and medicine Barbarino, J. M., Whirl-Carrillo, M., Altman, R. B., Klein, T. E. 2018

  Abstract

  As precision medicine becomes increasingly relevant in healthcare, the field of pharmacogenomics (PGx) also continues to gain prominence in the clinical setting. Leading institutions have begun to implement PGx testing and the amount of published PGx literature increases yearly. The Pharmacogenomics Knowledgebase (PharmGKB; www.pharmgkb.org) is one of the foremost worldwide resources for PGx knowledge, and the organization has been adapting and refocusing its mission along with the current revolution in genomic medicine. The PharmGKB website provides a diverse array of PGx information, from annotations of the primary literature to guidelines for adjusting drug treatment based on genetic information. It is freely available and accessible to everyone from researchers to clinicians to everyday citizens. PharmGKB was found over 17years ago, but continues to be a vital resource for the entire PGx community and the general public. This article is categorized under: Translational, Genomic, and Systems Medicine > Translational Medicine.

  View details for PubMedID 29474005

• Clinical pharmacogenetics implementation consortium (cpic) guideline for pharmacogenetics-guided warfarin dosing: 2017 update. Clinical pharmacology & therapeutics Johnson, J. A., Caudle, K. E., Gong, L., Whirl-Carrillo, M., Stein, C. M., Scott, S. A., Lee, M. T., Gage, B. F., Kimmel, S. E., Perera, M. A., Anderson, J. L., Pirmohamed, M., Klein, T. E., Limdi, N. A., Cavallari, L. H., Wadelius, M. 2017

  Abstract

  This document is an update to the 2011 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2C9 and VKORC1 genotypes and warfarin dosing. Evidence from the published literature is presented for CYP2C9, VKORC1, CYP4F2, and rs12777823 genotype-guided warfarin dosing to achieve a target international normalized ratio of 2-3 when clinical genotype results are available. In addition, this updated guideline incorporates recommendations for adult and pediatric patients that are specific to continental ancestry.

  View details for DOI 10.1002/cpt.668

  View details for PubMedID 28198005

• Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC) GENETICS IN MEDICINE Caudle, K. E., Dunnenberger, H. M., Freimuth, R. R., Peterson, J. F., Burlison, J. D., Whirl-Carrillo, M., Scott, S. A., Rehm, H. L., Williams, M. S., Klein, T. E., Relling, M. V., Hoffman, J. M. 2017; 19 (2): 215-223

  Abstract

  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.Genet Med advance online publication 21 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.87.

  View details for DOI 10.1038/gim.2016.87

  View details for Web of Science ID 000393534200011

• "The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems". Clinical pharmacology & therapeutics Luzum, J. A., Pakyz, R. E., Elsey, A. R., Haidar, C. E., Peterson, J. F., Whirl-Carrillo, M., Handelman, S. K., Palmer, K., Pulley, J. M., Beller, M., Schildcrout, J. S., Field, J. R., Weitzel, K. W., Cooper-DeHoff, R. M., Cavallari, L. H., O'Donnell, P. H., Altman, R. B., Pereira, N., Ratain, M. J., Roden, D. M., Embi, P. J., Sadee, W., Klein, T. E., Johnson, J. A., Relling, M. V., Wang, L., Weinshilboum, R. M., Shuldiner, A. R., Freimuth, R. R. 2017

  Abstract

  Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the NIH Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/cpt.630

  View details for PubMedID 28090649

• PharmGKB summary: ivacaftor pathway, pharmacokinetics/pharmacodynamics PHARMACOGENETICS AND GENOMICS Fohner, A. E., McDonagh, E. M., Clancy, J. P., Carrillo, M. W., Altman, R. B., Klein, T. E. 2017; 27 (1): 39-42

  View details for DOI 10.1097/FPC.0000000000000246

  View details for Web of Science ID 000390851300005

  View details for PubMedCentralID PMC5140711

• Prediction of CYP2D6 phenotype from genotype across world populations GENETICS IN MEDICINE Gaedigk, A., Sangkuhl, K., Whirl-Carrillo, M., Klein, T., Leeder, J. S. 2017; 19 (1): 69-76

  Abstract

  Owing to its highly polymorphic nature and major contribution to the metabolism and bioactivation of numerous clinically used drugs, CYP2D6 is one of the most extensively studied drug-metabolizing enzymes and pharmacogenes. CYP2D6 alleles confer no, decreased, normal, or increased activity and cause a wide range of activity among individuals and between populations. However, there is no standard approach to translate diplotypes into predicted phenotype.We exploited CYP2D6 allele-frequency data that have been compiled for Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines (>60,000 subjects, 173 reports) in order to estimate genotype-predicted phenotype status across major world populations based on activity score (AS) assignments.Allele frequencies vary considerably across the major ethnic groups predicting poor metabolizer status (AS = 0) between 0.4 and 5.4% across world populations. The prevalence of genotypic intermediate (AS = 0.5) and normal (AS = 1, 1.5, or 2) metabolizers ranges between 0.4 and 11% and between 67 and 90%, respectively. Finally, 1 to 21% of subjects (AS >2) are predicted to have ultrarapid metabolizer status.This comprehensive study summarizes allele frequencies, diplotypes, and predicted phenotype across major populations, providing a rich data resource for clinicians and researchers. Challenges of phenotype prediction from genotype data are highlighted and discussed.Genet Med advance online publication 07 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.80.

  View details for DOI 10.1038/gim.2016.80

  View details for Web of Science ID 000391911100010

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 Genotype and Use of Ondansetron and Tropisetron. Clinical pharmacology & therapeutics Bell, G. C., Caudle, K. E., Whirl-Carrillo, M., Gordon, R. J., Hikino, K., Prows, C. A., Gaedigk, A., Agundez, J. A., Sadhasivam, S., Klein, T. E., Schwab, M. 2016

  View details for DOI 10.1002/cpt.598

  View details for PubMedID 28002639

• Evidence and resources to implement pharmacogenetic knowledge for precision medicine AMERICAN JOURNAL OF HEALTH-SYSTEM PHARMACY Caudle, K. E., Gammal, R. S., Whirl-Carrillo, M., Hoffman, J. M., Belling, M. V., Klein, T. E. 2016; 73 (23): 1977-1985

  Abstract

  The current state of pharmacogenetic data curation and dissemination is described, and evidence-based resources for applying pharmacogenetic data in clinical practice are reviewed.Implementation of pharmacogenetics in clinical practice has been relatively slow despite substantial scientific progress in understanding linkages between genetic variation and variability of drug response and effect. One factor that has inhibited the adoption of genetic data to guide medication use is a lack of knowledge of how to translate genetic test results into clinical action based on currently available evidence. Other implementation challenges include controversy over selection of appropriate evidentiary thresholds for routine clinical implementation of pharmacogenetic data and the difficulty of compiling scientific data to support clinical recommendations given that large randomized controlled trials to demonstrate the utility of pharmacogenetic testing are not feasible or are not considered necessary to establish clinical utility. Organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Pharmacogenomics Knowledgebase (PharmGKB) systematically evaluate emerging evidence of pharmacogenomic linkages and publish evidence-based prescribing recommendations to inform clinical practice. Both CPIC and PharmGKB provide online resources that facilitate the interpretation of genetic test results and provide prescribing recommendations for specific gene-drug pairs.Resources provided by organizations such as CPIC and PharmGKB, which use standardized approaches to evaluate the literature and provide clinical guidance for a growing number of gene-drug pairs, are essential for the implementation of pharmacogenetics into routine clinical practice.

  View details for DOI 10.2146/ajhp150977

  View details for Web of Science ID 000389158200020

  View details for PubMedCentralID PMC5117674

• Evidence and resources to implement pharmacogenetic knowledge for precision medicine. American journal of health-system pharmacy Caudle, K. E., Gammal, R. S., Whirl-Carrillo, M., Hoffman, J. M., Relling, M. V., Klein, T. E. 2016; 73 (23): 1977-1985

  Abstract

  The current state of pharmacogenetic data curation and dissemination is described, and evidence-based resources for applying pharmacogenetic data in clinical practice are reviewed.Implementation of pharmacogenetics in clinical practice has been relatively slow despite substantial scientific progress in understanding linkages between genetic variation and variability of drug response and effect. One factor that has inhibited the adoption of genetic data to guide medication use is a lack of knowledge of how to translate genetic test results into clinical action based on currently available evidence. Other implementation challenges include controversy over selection of appropriate evidentiary thresholds for routine clinical implementation of pharmacogenetic data and the difficulty of compiling scientific data to support clinical recommendations given that large randomized controlled trials to demonstrate the utility of pharmacogenetic testing are not feasible or are not considered necessary to establish clinical utility. Organizations such as the Clinical Pharmacogenetics Implementation Consortium (CPIC) and the Pharmacogenomics Knowledgebase (PharmGKB) systematically evaluate emerging evidence of pharmacogenomic linkages and publish evidence-based prescribing recommendations to inform clinical practice. Both CPIC and PharmGKB provide online resources that facilitate the interpretation of genetic test results and provide prescribing recommendations for specific gene-drug pairs.Resources provided by organizations such as CPIC and PharmGKB, which use standardized approaches to evaluate the literature and provide clinical guidance for a growing number of gene-drug pairs, are essential for the implementation of pharmacogenetics into routine clinical practice.

  View details for PubMedID 27864205

• PharmGKB summary: ivacaftor pathway, pharmacokinetics/pharmacodynamics. Pharmacogenetics and genomics Fohner, A. E., McDonagh, E. M., Clancy, J. P., Whirl Carrillo, M., Altman, R. B., Klein, T. E. 2016: -?

  View details for PubMedID 27636560

• Prediction of CYP2D6 phenotype from genotype across world populations. Genetics in medicine Gaedigk, A., Sangkuhl, K., Whirl-Carrillo, M., Klein, T., Leeder, J. S. 2016

  Abstract

  Owing to its highly polymorphic nature and major contribution to the metabolism and bioactivation of numerous clinically used drugs, CYP2D6 is one of the most extensively studied drug-metabolizing enzymes and pharmacogenes. CYP2D6 alleles confer no, decreased, normal, or increased activity and cause a wide range of activity among individuals and between populations. However, there is no standard approach to translate diplotypes into predicted phenotype.We exploited CYP2D6 allele-frequency data that have been compiled for Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines (>60,000 subjects, 173 reports) in order to estimate genotype-predicted phenotype status across major world populations based on activity score (AS) assignments.Allele frequencies vary considerably across the major ethnic groups predicting poor metabolizer status (AS = 0) between 0.4 and 5.4% across world populations. The prevalence of genotypic intermediate (AS = 0.5) and normal (AS = 1, 1.5, or 2) metabolizers ranges between 0.4 and 11% and between 67 and 90%, respectively. Finally, 1 to 21% of subjects (AS >2) are predicted to have ultrarapid metabolizer status.This comprehensive study summarizes allele frequencies, diplotypes, and predicted phenotype across major populations, providing a rich data resource for clinicians and researchers. Challenges of phenotype prediction from genotype data are highlighted and discussed.Genet Med advance online publication 07 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.80.

  View details for DOI 10.1038/gim.2016.80

  View details for PubMedID 27388693

• Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC) JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Hoffman, J. M., Dunnenberger, H. M., Hicks, J. K., Caudle, K. E., Carrillo, M. W., Freimuth, R. R., Williams, M. S., Klein, T. E., Peterson, J. F. 2016; 23 (4): 796-801

  Abstract

  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.

  View details for DOI 10.1093/jamia/ocw027

  View details for Web of Science ID 000383782100019

• Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Journal of the American Medical Informatics Association Hoffman, J. M., Dunnenberger, H. M., Kevin Hicks, J., Caudle, K. E., Whirl Carrillo, M., Freimuth, R. R., Williams, M. S., Klein, T. E., Peterson, J. F. 2016; 23 (4): 796-801

  Abstract

  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.

  View details for DOI 10.1093/jamia/ocw027

  View details for PubMedID 27026620

• Novel Disease-Drug Database Demonstrating Applicability for Pharmacogenomic-Based Prescribing. Clinical pharmacology & therapeutics Whirl-Carrillo, M., Sangkuhl, K., Gong, L., Klein, T. E. 2016

  Abstract

  Significant advances have been made in the clinical implementation of pharmacogenomics in recent years with tools for clinical decision support (CDS) being developed and integrated in the electronic health record (EHR). In this issue, the article by Hussain et al. describes the creation of a disease-drug association tool that enables providers to search by disease indications to receive a list of treatment options marked with pharmacogenomics annotations at the point of prescribing.

  View details for DOI 10.1002/cpt.420

  View details for PubMedID 27367543

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for UGT1A1 and Atazanavir Prescribing. Clinical pharmacology & therapeutics Gammal, R. S., Court, M. H., Haidar, C. E., Iwuchukwu, O. F., Gaur, A. H., Alvarellos, M., Guillemette, C., Lennox, J. L., Whirl-Carrillo, M., Brummel, S. S., Ratain, M. J., Klein, T. E., Schackman, B. R., Caudle, K. E., Haas, D. W. 2016; 99 (4): 363-369

  Abstract

  The antiretroviral protease inhibitor atazanavir inhibits hepatic uridine diphosphate glucuronosyltransferase (UGT) 1A1, thereby preventing the glucuronidation and elimination of bilirubin. Resultant indirect hyperbilirubinemia with jaundice can cause premature discontinuation of atazanavir. Risk for bilirubin-related discontinuation is highest among individuals who carry two UGT1A1 decreased function alleles (UGT1A1*28 or *37). We summarize published literature that supports this association and provide recommendations for atazanavir prescribing when UGT1A1 genotype is known (updates at www.pharmgkb.org).

  View details for DOI 10.1002/cpt.269

  View details for PubMedID 26417955

  View details for PubMedCentralID PMC4785051

• Pharmacogenetic allele nomenclature: International workgroup recommendations for test result reporting. Clinical pharmacology & therapeutics Kalman, L. V., Agúndez, J., Appell, M. L., Black, J. L., Bell, G. C., Boukouvala, S., Bruckner, C., Bruford, E., Caudle, K., Coulthard, S. A., Daly, A. K., Tredici, A. D., den Dunnen, J. T., Drozda, K., Everts, R. E., Flockhart, D., Freimuth, R. R., Gaedigk, A., Hachad, H., Hartshorne, T., Ingelman-Sundberg, M., Klein, T. E., Lauschke, V. M., Maglott, D. R., McLeod, H. L., McMillin, G. A., Meyer, U. A., Müller, D. J., Nickerson, D. A., Oetting, W. S., Pacanowski, M., Pratt, V. M., RELLING, M. V., Roberts, A., Rubinstein, W. S., Sangkuhl, K., Schwab, M., Scott, S. A., Sim, S. C., Thirumaran, R. K., Toji, L. H., Tyndale, R. F., van Schaik, R., Whirl-Carrillo, M., Yeo, K., Zanger, U. M. 2016; 99 (2): 172-185

  Abstract

  This article provides nomenclature recommendations developed by an international workgroup to increase transparency and standardization of pharmacogenetic (PGx) result reporting. Presently, sequence variants identified by PGx tests are described using different nomenclature systems. In addition, PGx analysis may detect different sets of variants for each gene, which can affect interpretation of results. This practice has caused confusion and may thereby impede the adoption of clinical PGx testing. Standardization is critical to move PGx forward.

  View details for DOI 10.1002/cpt.280

  View details for PubMedID 26479518

  View details for PubMedCentralID PMC4724253

• Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data PLOS GENETICS Dewey, F. E., Grove, M. E., Priest, J. R., Waggott, D., Batra, P., Miller, C. L., Wheeler, M., Zia, A., Pan, C., Karzcewski, K. J., Miyake, C., Whirl-Carrillo, M., Klein, T. E., Datta, S., Altman, R. B., Snyder, M., Quertermous, T., Ashley, E. A. 2015; 11 (10)

  Abstract

  High throughput sequencing has facilitated a precipitous drop in the cost of genomic sequencing, prompting predictions of a revolution in medicine via genetic personalization of diagnostic and therapeutic strategies. There are significant barriers to realizing this goal that are related to the difficult task of interpreting personal genetic variation. A comprehensive, widely accessible application for interpretation of whole genome sequence data is needed. Here, we present a series of methods for identification of genetic variants and genotypes with clinical associations, phasing genetic data and using Mendelian inheritance for quality control, and providing predictive genetic information about risk for rare disease phenotypes and response to pharmacological therapy in single individuals and father-mother-child trios. We demonstrate application of these methods for disease and drug response prognostication in whole genome sequence data from twelve unrelated adults, and for disease gene discovery in one father-mother-child trio with apparently simplex congenital ventricular arrhythmia. In doing so we identify clinically actionable inherited disease risk and drug response genotypes in pre-symptomatic individuals. We also nominate a new candidate gene in congenital arrhythmia, ATP2B4, and provide experimental evidence of a regulatory role for variants discovered using this framework.

  View details for DOI 10.1371/journal.pgen.1005496

  View details for Web of Science ID 000364401600008

  View details for PubMedID 26448358

  View details for PubMedCentralID PMC4598191

• A twentieth anniversary tribute to psb. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Hewett, D., Whirl-Carrillo, M., Hunter, L. E., Altman, R. B., Klein, T. E. 2015; 20: 1-7

  Abstract

  PSB brings together top researchers from around the world to exchange research results and address open issues in all aspects of computational biology. PSB 2015 marks the twentieth anniversary of PSB. Reaching a milestone year is an accomplishment well worth celebrating. It is long enough to have seen big changes occur, but recent enough to be relevant for today. As PSB celebrates twenty years of service, we would like to take this opportunity to congratulate the PSB community for your success. We would also like the community to join us in a time of celebration and reflection on this accomplishment.

  View details for PubMedID 25592562

• PharmGKB summary: very important pharmacogene information for CYP4F2 PHARMACOGENETICS AND GENOMICS Alvarellos, M. L., Sangkuhl, K., Daneshjou, R., Whirl-Carrillo, M., Altman, R. B., Klein, T. E. 2015; 25 (1): 41-47

  View details for DOI 10.1097/FPC.0000000000000100

  View details for Web of Science ID 000346632900006

  View details for PubMedID 25370453

  View details for PubMedCentralID PMC4261059

• Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and HLA-B Genotypes and Phenytoin Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Caudle, K. E., Rettie, A. E., Whirl-Carrillo, M., Smith, L. H., Mintzer, S., Lee, M. T., Klein, T. E., Callaghan, J. T. 2014; 96 (5): 542-548

  Abstract

  Phenytoin is a widely used antiepileptic drug with a narrow therapeutic index and large interpatient variability, partly due to genetic variations in the gene encoding cytochrome P450 (CYP)2C9 (CYP2C9). Furthermore, the variant allele HLA-B*15:02, encoding human leukocyte antigen, is associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis in response to phenytoin treatment. We summarize evidence from the published literature supporting these associations and provide recommendations for the use of phenytoin based on CYP2C9 and/or HLA-B genotype (also available on PharmGKB: http://www.pharmgkb.org). The purpose of this guideline is to provide information for the interpretation of HLA-B and/or CYP2C9 genotype tests so that the results can guide dosing and/or use of phenytoin. Detailed guidelines for the use of phenytoin as well as analyses of cost-effectiveness are out of scope. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines are periodically updated at http://www.pharmgkb.org.

  View details for DOI 10.1038/clpt.2014.159

  View details for Web of Science ID 000343632900015

  View details for PubMedCentralID PMC4206662

• Clinical pharmacogenetics implementation consortium guidelines for CYP2C9 and HLA-B genotypes and phenytoin dosing. Clinical pharmacology & therapeutics Caudle, K. E., Rettie, A. E., Whirl-Carrillo, M., Smith, L. H., Mintzer, S., Lee, M. T., Klein, T. E., Callaghan, J. T. 2014; 96 (5): 542-548

  Abstract

  Phenytoin is a widely used antiepileptic drug with a narrow therapeutic index and large interpatient variability, partly due to genetic variations in the gene encoding cytochrome P450 (CYP)2C9 (CYP2C9). Furthermore, the variant allele HLA-B*15:02, encoding human leukocyte antigen, is associated with an increased risk of Stevens-Johnson syndrome and toxic epidermal necrolysis in response to phenytoin treatment. We summarize evidence from the published literature supporting these associations and provide recommendations for the use of phenytoin based on CYP2C9 and/or HLA-B genotype (also available on PharmGKB: http://www.pharmgkb.org). The purpose of this guideline is to provide information for the interpretation of HLA-B and/or CYP2C9 genotype tests so that the results can guide dosing and/or use of phenytoin. Detailed guidelines for the use of phenytoin as well as analyses of cost-effectiveness are out of scope. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines are periodically updated at http://www.pharmgkb.org.

  View details for DOI 10.1038/clpt.2014.159

  View details for PubMedID 25099164

• In silico comparative characterization of pharmacogenomic missense variants BMC GENOMICS Li, B., Seligman, C., Thusberg, J., Miller, J. L., Auer, J., Whirl-Carrillo, M., Capriotti, E., Klein, T. E., Mooney, S. D. 2014; 15

  Abstract

  Missense pharmacogenomic (PGx) variants refer to amino acid substitutions that potentially affect the pharmacokinetic (PK) or pharmacodynamic (PD) response to drug therapies. The PGx variants, as compared to disease-associated variants, have not been investigated as deeply. The ability to computationally predict future PGx variants is desirable; however, it is not clear what data sets should be used or what features are beneficial to this end. Hence we carried out a comparative characterization of PGx variants with annotated neutral and disease variants from UniProt, to test the predictive power of sequence conservation and structural information in discriminating these three groups.126 PGx variants of high quality from PharmGKB were selected and two data sets were created: one set contained 416 variants with structural and sequence information, and, the other set contained 1,265 variants with sequence information only. In terms of sequence conservation, PGx variants are more conserved than neutral variants and much less conserved than disease variants. A weighted random forest was used to strike a more balanced classification for PGx variants. Generally structural features are helpful in discriminating PGx variant from the other two groups, but still classification of PGx from neutral polymorphisms is much less effective than between disease and neutral variants.We found that PGx variants are much more similar to neutral variants than to disease variants in the feature space consisting of residue conservation, neighboring residue conservation, number of neighbors, and protein solvent accessibility. Such similarity poses great difficulty in the classification of PGx variants and polymorphisms.

  View details for DOI 10.1186/1471-2164-15-S4-S4

  View details for Web of Science ID 000337463200004

• Clinical interpretation and implications of whole-genome sequencing. JAMA : the journal of the American Medical Association Dewey, F. E., Grove, M. E., Pan, C., Goldstein, B. A., Bernstein, J. A., Chaib, H., Merker, J. D., Goldfeder, R. L., Enns, G. M., David, S. P., Pakdaman, N., Ormond, K. E., Caleshu, C., Kingham, K., Klein, T. E., Whirl-Carrillo, M., Sakamoto, K., Wheeler, M. T., Butte, A. J., Ford, J. M., Boxer, L., Ioannidis, J. P., Yeung, A. C., Altman, R. B., Assimes, T. L., Snyder, M., Ashley, E. A., Quertermous, T. 2014; 311 (10): 1035-1045

  Abstract

  Whole-genome sequencing (WGS) is increasingly applied in clinical medicine and is expected to uncover clinically significant findings regardless of sequencing indication.To examine coverage and concordance of clinically relevant genetic variation provided by WGS technologies; to quantitate inherited disease risk and pharmacogenomic findings in WGS data and resources required for their discovery and interpretation; and to evaluate clinical action prompted by WGS findings.An exploratory study of 12 adult participants recruited at Stanford University Medical Center who underwent WGS between November 2011 and March 2012. A multidisciplinary team reviewed all potentially reportable genetic findings. Five physicians proposed initial clinical follow-up based on the genetic findings.Genome coverage and sequencing platform concordance in different categories of genetic disease risk, person-hours spent curating candidate disease-risk variants, interpretation agreement between trained curators and disease genetics databases, burden of inherited disease risk and pharmacogenomic findings, and burden and interrater agreement of proposed clinical follow-up.Depending on sequencing platform, 10% to 19% of inherited disease genes were not covered to accepted standards for single nucleotide variant discovery. Genotype concordance was high for previously described single nucleotide genetic variants (99%-100%) but low for small insertion/deletion variants (53%-59%). Curation of 90 to 127 genetic variants in each participant required a median of 54 minutes (range, 5-223 minutes) per genetic variant, resulted in moderate classification agreement between professionals (Gross κ, 0.52; 95% CI, 0.40-0.64), and reclassified 69% of genetic variants cataloged as disease causing in mutation databases to variants of uncertain or lesser significance. Two to 6 personal disease-risk findings were discovered in each participant, including 1 frameshift deletion in the BRCA1 gene implicated in hereditary breast and ovarian cancer. Physician review of sequencing findings prompted consideration of a median of 1 to 3 initial diagnostic tests and referrals per participant, with fair interrater agreement about the suitability of WGS findings for clinical follow-up (Fleiss κ, 0.24; P < 001).In this exploratory study of 12 volunteer adults, the use of WGS was associated with incomplete coverage of inherited disease genes, low reproducibility of detection of genetic variation with the highest potential clinical effects, and uncertainty about clinically reportable findings. In certain cases, WGS will identify clinically actionable genetic variants warranting early medical intervention. These issues should be considered when determining the role of WGS in clinical medicine.

  View details for DOI 10.1001/jama.2014.1717

  View details for PubMedID 24618965

  View details for PubMedCentralID PMC4119063

• Incorporation of Pharmacogenomics into Routine Clinical Practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline Development Process. Current drug metabolism Caudle, K. E., Klein, T. E., Hoffman, J. M., Muller, D. J., Whirl-Carrillo, M., Gong, L., McDonagh, E. M., Sangkuhl, K., Thorn, C. F., Schwab, M., Agundez, J. A., Freimuth, R. R., Huser, V., Lee, M. T., Iwuchukwu, O. F., Crews, K. R., Scott, S. A., Wadelius, M., Swen, J. J., Tyndale, R. F., Stein, C. M., Roden, D., Relling, M. V., Williams, M. S., Johnson, S. G. 2014; 15 (2): 209-217

  Abstract

  The Clinical Pharmacogenetics Implementation Consortium (CPIC) publishes genotype-based drug guidelines to help clinicians understand how available genetic test results could be used to optimize drug therapy. CPIC has focused initially on well-known examples of pharmacogenomic associations that have been implemented in selected clinical settings, publishing nine to date. Each CPIC guideline adheres to a standardized format and includes a standard system for grading levels of evidence linking genotypes to phenotypes and assigning a level of strength to each prescribing recommendation. CPIC guidelines contain the necessary information to help clinicians translate patient-specific diplotypes for each gene into clinical phenotypes or drug dosing groups. This paper reviews the development process of the CPIC guidelines and compares this process to the Institute of Medicine's Standards for Developing Trustworthy Clinical Practice Guidelines.

  View details for PubMedID 24479687

• PharmGKB summary: very important pharmacogene information for the epidermal growth factor receptor. Pharmacogenetics and genomics Hodoglugil, U., Carrillo, M. W., Hebert, J. M., Karachaliou, N., Rosell, R. C., Altman, R. B., Klein, T. E. 2013; 23 (11): 636-642

  View details for DOI 10.1097/FPC.0b013e3283655091

  View details for PubMedID 23962910

• Clinical pharmacogenetics implementation consortium guidelines for hla-B genotype and carbamazepine dosing. Clinical pharmacology & therapeutics Leckband, S. G., Kelsoe, J. R., Dunnenberger, H. M., George, A. L., Tran, E., Berger, R., Müller, D. J., Whirl-Carrillo, M., Caudle, K. E., Pirmohamed, M. 2013; 94 (3): 324-328

  Abstract

  Human leukocyte antigen B (HLA-B) is a gene that encodes a cell surface protein involved in presenting antigens to the immune system. The variant allele HLA-B*15:02 is associated with an increased risk of Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) in response to carbamazepine treatment. We summarize evidence from the published literature supporting this association and provide recommendations for the use of carbamazepine based on HLA-B genotype (also available on PharmGKB: http://www.pharmgkb.org). The purpose of this article is to provide information to allow the interpretation of clinical HLA-B*15:02 genotype tests so that the results can be used to guide the use of carbamazepine. The guideline provides recommendations for the use of carbamazepine when HLA-B*15:02 genotype results are available. Detailed guidelines regarding the selection of alternative therapies, the use of phenotypic tests, when to conduct genotype testing, and cost-effectiveness analyses are beyond the scope of this document. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines are published and updated periodically on the PharmGKB website at (http://www.pharmgkb.org).

  View details for DOI 10.1038/clpt.2013.103

  View details for PubMedID 23695185

• The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Overcoming Challenges of Real-World Implementation CLINICAL PHARMACOLOGY & THERAPEUTICS Shuldiner, A. R., RELLING, M. V., Peterson, J. F., Hicks, J. K., Freimuth, R. R., Sadee, W., Pereira, N. L., Roden, D. M., Johnson, J. A., Klein, T. E. 2013; 94 (2): 207-210

  View details for DOI 10.1038/clpt.2013.59

  View details for Web of Science ID 000322064400018

  View details for PubMedID 23588301

• Challenges in the Pharmacogenomic Annotation of Whole Genomes CLINICAL PHARMACOLOGY & THERAPEUTICS Altman, R. B., Whirl-Carrillo, M., Klein, T. E. 2013; 94 (2): 211-213

  View details for DOI 10.1038/clpt.2013.111

  View details for Web of Science ID 000322064400019

  View details for PubMedID 23708745

• Clinical Pharmacogenetics Implementation Consortium Guidelines for Thiopurine Methyltransferase Genotype and Thiopurine Dosing: 2013 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Relling, M. V., Gardner, E. E., Sandborn, W. J., Schmiegelow, K., Pui, C., Yee, S. W., Stein, C. M., Carrillo, M., Evans, W. E., Hicks, J. K., Schwab, M., Klein, T. E. 2013; 93 (4): 324-325

  View details for DOI 10.1038/clpt.2013.4

  View details for Web of Science ID 000316847400014

  View details for PubMedID 23422873

  View details for PubMedCentralID PMC3604643

• PharmGKB summary: very important pharmacogene information for cytochrome P-450, family 2, subfamily A, polypeptide 6 PHARMACOGENETICS AND GENOMICS McDonagh, E. M., Wassenaar, C., David, S. P., Tyndale, R. F., Altman, R. B., Whirl-Carrillo, M., Klein, T. E. 2012; 22 (9): 695-708

  View details for DOI 10.1097/FPC.0b013e3283540217

  View details for PubMedID 22547082

• PharmGKB summary: phenytoin pathway PHARMACOGENETICS AND GENOMICS Thorn, C. F., Whirl-Carrillo, M., Leeder, J. S., Klein, T. E., Altman, R. B. 2012; 22 (6): 466-470

  View details for DOI 10.1097/FPC.0b013e32834aeedb

  View details for Web of Science ID 000303769700007

  View details for PubMedID 22569204

  View details for PubMedCentralID PMC3349446

• Using ODIN for a PharmGKB revalidation experiment DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION Rinaldi, F., Clematide, S., Garten, Y., Whirl-Carrillo, M., Gong, L., Hebert, J. M., Sangkuhl, K., Thorn, C. F., Klein, T. E., Altman, R. B. 2012

  Abstract

  The need for efficient text-mining tools that support curation of the biomedical literature is ever increasing. In this article, we describe an experiment aimed at verifying whether a text-mining tool capable of extracting meaningful relationships among domain entities can be successfully integrated into the curation workflow of a major biological database. We evaluate in particular (i) the usability of the system's interface, as perceived by users, and (ii) the correlation of the ranking of interactions, as provided by the text-mining system, with the choices of the curators.

  View details for DOI 10.1093/database/bas021

  View details for Web of Science ID 000304924100001

  View details for PubMedID 22529178

  View details for PubMedCentralID PMC3332569

• Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes CELL Chen, R., Mias, G. I., Li-Pook-Than, J., Jiang, L., Lam, H. Y., Chen, R., Miriami, E., Karczewski, K. J., Hariharan, M., Dewey, F. E., Cheng, Y., Clark, M. J., Im, H., Habegger, L., Balasubramanian, S., O'Huallachain, M., Dudley, J. T., Hillenmeyer, S., Haraksingh, R., Sharon, D., Euskirchen, G., Lacroute, P., Bettinger, K., Boyle, A. P., Kasowski, M., Grubert, F., Seki, S., Garcia, M., Whirl-Carrillo, M., Gallardo, M., Blasco, M. A., Greenberg, P. L., Snyder, P., Klein, T. E., Altman, R. B., Butte, A. J., Ashley, E. A., Gerstein, M., Nadeau, K. C., Tang, H., Snyder, M. 2012; 148 (6): 1293-1307

  Abstract

  Personalized medicine is expected to benefit from combining genomic information with regular monitoring of physiological states by multiple high-throughput methods. Here, we present an integrative personal omics profile (iPOP), an analysis that combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. Our iPOP analysis revealed various medical risks, including type 2 diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions. Extremely high-coverage genomic and transcriptomic data, which provide the basis of our iPOP, revealed extensive heteroallelic changes during healthy and diseased states and an unexpected RNA editing mechanism. This study demonstrates that longitudinal iPOP can be used to interpret healthy and diseased states by connecting genomic information with additional dynamic omics activity.

  View details for DOI 10.1016/j.cell.2012.02.009

  View details for PubMedID 22424236

• From pharmacogenomic knowledge acquisition to clinical applications: the PharmGKB as a clinical pharmacogenomic biomarker resource BIOMARKERS IN MEDICINE McDonagh, E. M., Whirl-Carrillo, M., Garten, Y., Altman, R. B., Klein, T. E. 2011; 5 (6): 795-806

  Abstract

  The mission of the Pharmacogenomics Knowledge Base (PharmGKB; www.pharmgkb.org ) is to collect, encode and disseminate knowledge about the impact of human genetic variations on drug responses. It is an important worldwide resource of clinical pharmacogenomic biomarkers available to all. The PharmGKB website has evolved to highlight our knowledge curation and aggregation over our previous emphasis on collecting primary data. This review summarizes the methods we use to drive this expanded scope of 'Knowledge Acquisition to Clinical Applications', the new features available on our website and our future goals.

  View details for DOI 10.2217/BMM.11.94

  View details for Web of Science ID 000298488200009

  View details for PubMedID 22103613

  View details for PubMedCentralID PMC3339046

• Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 Genotypes and Warfarin Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Johnson, J. A., Gong, L., Whirl-Carrillo, M., Gage, B. F., Scott, S. A., Stein, C. M., Anderson, J. L., Kimmel, S. E., Lee, M. T., Pirmohamed, M., Wadelius, M., Klein, T. E., Altman, R. B. 2011; 90 (4): 625-629

  Abstract

  Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the dose required to achieve target anticoagulation. Common genetic variants in the cytochrome P450-2C9 (CYP2C9) and vitamin K-epoxide reductase complex (VKORC1) enzymes, in addition to known nongenetic factors, account for ~50% of warfarin dose variability. The purpose of this article is to assist in the interpretation and use of CYP2C9 and VKORC1 genotype data for estimating therapeutic warfarin dose to achieve an INR of 2-3, should genotype results be available to the clinician. The Clinical Pharmacogenetics Implementation Consortium (CPIC) of the National Institutes of Health Pharmacogenomics Research Network develops peer-reviewed gene-drug guidelines that are published and updated periodically on http://www.pharmgkb.org based on new developments in the field.(1).

  View details for DOI 10.1038/clpt.2011.185

  View details for Web of Science ID 000295119200035

  View details for PubMedID 21900891

  View details for PubMedCentralID PMC3187550

• PharmGKB summary: fluoropyrimidine pathways PHARMACOGENETICS AND GENOMICS Thorn, C. F., Marsh, S., Carrillo, M. W., McLeod, H. L., Klein, T. E., Altman, R. B. 2011; 21 (4): 237-242

  View details for DOI 10.1097/FPC.0b013e32833c6107

  View details for Web of Science ID 000288444500010

  View details for PubMedID 20601926

  View details for PubMedCentralID PMC3098754

• Clinical Pharmacogenetics Implementation Consortium Guidelines for Thiopurine Methyltransferase Genotype and Thiopurine Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Relling, M. V., Gardner, E. E., Sandborn, W. J., Schmiegelow, K., Pui, C., Yee, S. W., Stein, C. M., Carrillo, M., Evans, W. E., Klein, T. E. 2011; 89 (3): 387-391

  Abstract

  Thiopurine methyltransferase (TPMT) activity exhibits monogenic co-dominant inheritance, with ethnic differences in the frequency of occurrence of variant alleles. With conventional thiopurine doses, homozygous TPMT-deficient patients (~1 in 178 to 1 in 3,736 individuals with two nonfunctional TPMT alleles) experience severe myelosuppression, 30-60% of individuals who are heterozygotes (~3-14% of the population) show moderate toxicity, and homozygous wild-type individuals (~86-97% of the population) show lower active thioguanine nucleolides and less myelosuppression. We provide dosing recommendations (updates at http://www.pharmgkb.org) for azathioprine, mercaptopurine (MP), and thioguanine based on TPMT genotype.

  View details for DOI 10.1038/clpt.2010.320

  View details for Web of Science ID 000287439600018

  View details for PubMedID 21270794

  View details for PubMedCentralID PMC3098761

• The BioPAX community standard for pathway data sharing NATURE BIOTECHNOLOGY Demir, E., Cary, M. P., Paley, S., Fukuda, K., Lemer, C., Vastrik, I., Wu, G., D'Eustachio, P., Schaefer, C., Luciano, J., Schacherer, F., Martinez-Flores, I., Hu, Z., Jimenez-Jacinto, V., Joshi-Tope, G., Kandasamy, K., Lopez-Fuentes, A. C., Mi, H., Pichler, E., Rodchenkov, I., Splendiani, A., Tkachev, S., Zucker, J., Gopinath, G., Rajasimha, H., Ramakrishnan, R., Shah, I., Syed, M., Anwar, N., Babur, O., Blinov, M., Brauner, E., Corwin, D., Donaldson, S., Gibbons, F., Goldberg, R., Hornbeck, P., Luna, A., Murray-Rust, P., Neumann, E., Reubenacker, O., Samwald, M., van Iersel, M., Wimalaratne, S., Allen, K., Braun, B., Whirl-Carrillo, M., Cheung, K., Dahlquist, K., Finney, A., Gillespie, M., Glass, E., Gong, L., Haw, R., Honig, M., Hubaut, O., Kane, D., Krupa, S., Kutmon, M., Leonard, J., Marks, D., Merberg, D., Petri, V., Pico, A., Ravenscroft, D., Ren, L., Shah, N., Sunshine, M., Tang, R., Whaley, R., Letovksy, S., Buetow, K. H., Rzhetsky, A., Schachter, V., Sobral, B. S., Dogrusoz, U., McWeeney, S., Aladjem, M., Birney, E., Collado-Vides, J., Goto, S., Hucka, M., Le Novere, N., Maltsev, N., Pandey, A., Thomas, P., Wingender, E., Karp, P. D., Sander, C., Bader, G. D. 2010; 28 (9): 935-942

  Abstract

  Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

  View details for DOI 10.1038/nbt.1666

  View details for Web of Science ID 000281719100019

  View details for PubMedID 20829833

  View details for PubMedCentralID PMC3001121

• Cytochrome P450 2C9-CYP2C9 PHARMACOGENETICS AND GENOMICS Van Booven, D., Marsh, S., McLeod, H., Carrillo, M. W., Sangkuhl, K., Klein, T. E., Altman, R. B. 2010; 20 (4): 277-281

  View details for DOI 10.1097/FPC.0b013e3283349e84

  View details for Web of Science ID 000276373800008

  View details for PubMedID 20150829

  View details for PubMedCentralID PMC3201766

• Taxane pathway PHARMACOGENETICS AND GENOMICS Oshiro, C., Marsh, S., McLeod, H., Carrillo, M. W., Klein, T., Altman, R. 2009; 19 (12): 979-983

  View details for DOI 10.1097/FPC.0b013e3283335277

  View details for Web of Science ID 000272310800008

  View details for PubMedID 21151855

  View details for PubMedCentralID PMC2998989

• The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge NUCLEIC ACIDS RESEARCH Hernandez-Boussard, T., Whirl-Carrillo, M., Hebert, J. M., Gong, L., Owen, R., Gong, M., Gor, W., Liu, F., Truong, C., Whaley, R., Woon, M., Zhou, T., Altman, R. B., Klein, T. E. 2008; 36: D913-D918

  Abstract

  PharmGKB is a knowledge base that captures the relationships between drugs, diseases/phenotypes and genes involved in pharmacokinetics (PK) and pharmacodynamics (PD). This information includes literature annotations, primary data sets, PK and PD pathways, and expert-generated summaries of PK/PD relationships between drugs, diseases/phenotypes and genes. PharmGKB's website is designed to effectively disseminate knowledge to meet the needs of our users. PharmGKB currently has literature annotations documenting the relationship of over 500 drugs, 450 diseases and 600 variant genes. In order to meet the needs of whole genome studies, PharmGKB has added new functionalities, including browsing the variant display by chromosome and cytogenetic locations, allowing the user to view variants not located within a gene. We have developed new infrastructure for handling whole genome data, including increased methods for quality control and tools for comparison across other data sources, such as dbSNP, JSNP and HapMap data. PharmGKB has also added functionality to accept, store, display and query high throughput SNP array data. These changes allow us to capture more structured information on phenotypes for better cataloging and comparison of data. PharmGKB is available at www.pharmgkb.org.

  View details for DOI 10.1093/nar/gkm1009

  View details for Web of Science ID 000252545400160

  View details for PubMedID 18032438

  View details for PubMedCentralID PMC2238877

• Pacific Symposium on Biocomputing--computational approaches for pharmacogenomics. Pharmacogenomics Wilke, R. A., Carrillo, M. W., Ritchie, M. D. 2005; 6 (2): 111-113

  View details for PubMedID 15882130

• A resource to acquire and summarize pharmacogenetics knowledge in the literature 11th World Congress on Medical Informatics Rubin, D. L., Carrillo, M., Woon, M., Conroy, J., Klein, T. E., Altman, R. B. I O S PRESS. 2004: 793–797

  Abstract

  To determine how genetic variations contribute the variations in drug response, we need to know the genes that are related to drugs of interest. But there are no publicly available data-bases of known gene-drug relationships, and it is time-consuming to search the literature for this information. We have developed a resource to support the storage, summarization, and dissemination of key gene-drug interactions of relevance to pharmacogenetics. Extracting all gene-drug relationships from the literature is a daunting task, so we distributed a tool to acquire this knowledge from the scientific community. We also developed a categorization scheme to classify gene-drug relationships according to the type of pharmacogenetic evidence that supports them. Our resource (http://www.pharmgkb.org/home/project-community.jsp) can be queried by gene or drug, and it summarizes gene-drug relationships, categories of evidence, and supporting literature. This resource is growing, containing entries for 138 genes and 215 drugs of pharmacogenetics significance, and is a core component of PharmGKB, a pharmacogenetics knowledge base (http://www.pharmgkb.org).

  View details for Web of Science ID 000226723300159

  View details for PubMedID 15360921

• Ribosomal dynamics inferred from variations in experimental measurements RNA-A PUBLICATION OF THE RNA SOCIETY Gabashvili, I. S., Whirl-Carrillo, M., Bada, M., Banatao, D. R., Altman, R. B. 2003; 9 (11): 1301-1307

  Abstract

  The crystal structures of the ribosome reveal remarkable complexity and provide a starting set of snapshots with which to understand the dynamics of translation. To augment the static crystallographic models with dynamic information present in crosslink, footprint, and cleavage data, we examined 2691 proximity measurements and focused on the subset that was apparently incompatible with >40 published crystal structures. The measurements from this subset generally involve regions of the structure that are functionally conserved and structurally flexible. Local movements in the crystallographic states of the ribosome that would satisfy biochemical proximity measurements show coherent patterns suggesting alternative conformations of the ribosome. Three different types of data obtained for the two subunits display similar "mismatching" patterns, suggesting that the signals are robust and real. In particular, there is an indication of coherent motion in the decoding region within the 30S subunit and central protuberance and surrounding areas of the 50S subunit. Directions of rearrangements fluctuate around the proposed path of tRNA translocation and the plane parallel to the interface of the two subunits. Our results demonstrate that systematic combination and analysis of noisy, apparently incompatible data sources can provide biologically useful signals about structural dynamics.

  View details for Web of Science ID 000186175900001

  View details for PubMedID 14561879

• Scoring functions sensitive to alignment error have a more difficult search: A paradox for threading Symposium held in Honor of William N Lipscomb on Structures and Mechanisms Chang, J., Carrillo, M. W., Waugh, A., Wei, L. P., Altman, R. B. AMER CHEMICAL SOC. 2002: 309–320

  View details for Web of Science ID 000181756700019

• Calculation of the relative geometry of tRNAs in the ribosome from directed hydroxyl-radical probing data RNA-A PUBLICATION OF THE RNA SOCIETY Joseph, S., Whirl, M. L., Kondo, D., Noller, H. F., Altman, R. B. 2000; 6 (2): 220-232

  Abstract

  The many interactions of tRNA with the ribosome are fundamental to protein synthesis. During the peptidyl transferase reaction, the acceptor ends of the aminoacyl and peptidyl tRNAs must be in close proximity to allow peptide bond formation, and their respective anticodons must base pair simultaneously with adjacent trinucleotide codons on the mRNA. The two tRNAs in this state can be arranged in two nonequivalent general configurations called the R and S orientations, many versions of which have been proposed for the geometry of tRNAs in the ribosome. Here, we report the combined use of computational analysis and tethered hydroxyl-radical probing to constrain their arrangement. We used Fe(II) tethered to the 5' end of anticodon stem-loop analogs (ASLs) of tRNA and to the 5' end of deacylated tRNA(Phe) to generate hydroxyl radicals that probe proximal positions in the backbone of adjacent tRNAs in the 70S ribosome. We inferred probe-target distances from the resulting RNA strand cleavage intensities and used these to calculate the mutual arrangement of A-site and P-site tRNAs in the ribosome, using three different structure estimation algorithms. The two tRNAs are constrained to the S configuration with an angle of about 45 degrees between the respective planes of the molecules. The terminal phosphates of 3'CCA are separated by 23 A when using the tRNA crystal conformations, and the anticodon arms of the two tRNAs are sufficiently close to interact with adjacent codons in mRNA.

  View details for Web of Science ID 000085267900007

  View details for PubMedID 10688361

  View details for PubMedCentralID PMC1369908

• Evidence of oxidative stress in mdx mouse muscle: Studies of the pre-necrotic state JOURNAL OF THE NEUROLOGICAL SCIENCES Disatnik, M. H., Dhawan, J., Yu, Y., Beal, M. F., Whirl, M. M., Franco, A. A., Rando, T. A. 1998; 161 (1): 77-84

  Abstract

  Considerable evidence indicates that free radical injury may underlie the pathologic changes in muscular dystrophies from mammalian and avian species. We have investigated the role of oxidative injury in muscle necrosis in mice with a muscular dystrophy due to a defect in the dystrophin gene (the mdx strain). In order to avoid secondary consequences of muscle necrosis, all experiments were done on muscle prior to the onset of the degenerative process (i.e. during the 'pre-necrotic' phase) which lasted up to 20 days of age in the muscles examined. In pre-necrotic mdx muscle, there was an induction of expression of genes encoding antioxidant enzymes, indicative of a cellular response to oxidative stress. In addition, the levels of lipid peroxidation were greater in mdx muscle than in the control. Since the free radical nitric oxide (NO*) has been shown to mediate oxidative injury in various disease states, and because dystrophin has been shown to form a complex with the enzyme nitric oxide synthase, we examined pre-necrotic mdx muscle for evidence of NO*-mediated injury by measuring cellular nitrotyrosine formation. By both immunohistochemical and electrochemical analyses, no evidence of increased nitrotyrosine levels in mdx muscle was detected. Therefore, although no relationship with NO*-mediated toxicity was found, we found evidence of increased oxidative stress preceding the onset of muscle cell death in dystrophin-deficient mice. These results lend support to the hypothesis that free radical-mediated injury may contribute to the pathogenesis of muscular dystrophies.

  View details for Web of Science ID 000077605200013

  View details for PubMedID 9879685

• MUTAGENESIS OF VITAMIN-K-DEPENDENT CARBOXYLASE DEMONSTRATES A CARBOXYL TERMINUS-MEDIATED INTERACTION WITH VITAMIN-K HYDROQUINONE JOURNAL OF BIOLOGICAL CHEMISTRY Roth, D. A., Whirl, M. L., VELAZQUEZESTADES, L. J., Walsh, C. T., Furie, B., Furie, B. C. 1995; 270 (10): 5305-5311

  Abstract

  The gamma-glutamyl carboxylase and vitamin K epoxidase activities of a series of mutants of bovine vitamin K-dependent carboxylase with progressively larger COOH-terminal deletions have been analyzed. The recombinant wild-type (residues 1-758) and mutant protein carboxylases, Cbx 711, Cbx 676, and Cbx 572, representing residues 1-711, 1-676, and 1-572, respectively, were expressed in baculovirus-infected Sf9 cells. Wild-type carboxylase had a Km for the substrate Phe-Leu-Glu-Glu-Leu (FLEEL) of 0.87 mM; the carboxylation of FLEEL was stimulated 2.5-fold by proPT18, the propeptide of prothrombin. Its Km for vitamin K hydroquinone was 23 microM and the specific epoxidase activity of the carboxylase was 938 pmol vitamin KO/30 min/pmol of carboxylase. Cbx 711, which was also stimulated by proPT18, had a Km for FLEEL, a Km for vitamin K hydroquinone, and a specific epoxidase activity that was comparable to the wild-type carboxylase. In contrast Cbx 572 lacked both carboxylase and epoxidase activities. Although Cbx 676 had a normal carboxylase active site in terms of the Km for FLEEL and its stimulation by proPT18, the Km for vitamin K hydroquinone was 540 microM, and the specific epoxidase activity was 97 pmol KO/30 min/pmol of Cbx 676. The catalytic efficiencies of Cbx 676 for glutamate carboxylation and vitamin K epoxidation were decreased 15- and 400-fold, respectively, from wild-type enzyme reflecting the requirement for formation of an activated vitamin K species for carboxylation to occur. These data indicate that the truncation of COOH-terminal segments of the carboxylase had no effect on FLEEL or propeptide recognition, but in the case of Cbx 676, selectively affected the interaction with vitamin K hydroquinone and the generation of epoxidase activity. These data suggest that a vitamin K epoxidase activity domain may reside near the COOH terminus while the carboxylase active site domain resides toward the NH2 terminus.

  View details for Web of Science ID A1995QL58000055

  View details for PubMedID 7890642