Teri Klein

All Publications

• The Value of Clinical Pharmacogenomic Guidelines That Recommend Standard of Care Over Genotype-Based Prescribing. Clinical pharmacology and therapeutics Donnelly, R. S., Whirl-Carrillo, M., Klein, T. E., Caudle, K. E. 2024

  View details for DOI 10.1002/cpt.3401

  View details for PubMedID 39115927

• Curating Genetic Associations with Rheumatologic Autoimmune Diseases to Improve Patient Outcomes. Arthritis & rheumatology (Hoboken, N.J.) Bridges, S. L., Shapira, R., Aksentijevich, I., Mack, S. J., Merriman, T. R., Klein, C. J., Bowen, B. M., Klein, T. E. 2024

  View details for DOI 10.1002/art.42943

  View details for PubMedID 38965695

• Frequencies of pharmacogenomic alleles across biogeographic groups in a large-scale biobank. American journal of human genetics Li, B., Sangkuhl, K., Whaley, R., Woon, M., Keat, K., Whirl-Carrillo, M., Ritchie, M. D., Klein, T. E. 2023

  Abstract

  Pharmacogenomics (PGx) is an integral part of precision medicine and contributes to the maximization of drug efficacy and reduction of adverse drug event risk. Accurate information on PGx allele frequencies improves the implementation of PGx. Nonetheless, curating such information from published allele data is time and resource intensive. The limited number of allelic variants in most studies leads to an underestimation of certain alleles. We applied the Pharmacogenomics Clinical Annotation Tool (PharmCAT) on an integrated 200K UK Biobank genetic dataset (N = 200,044). Based on PharmCAT results, we estimated PGx frequencies (alleles, diplotypes, phenotypes, and activity scores) for 17 pharmacogenes in five biogeographic groups: European, Central/South Asian, East Asian, Afro-Caribbean, and Sub-Saharan African. PGx frequencies were distinct for each biogeographic group. Even biogeographic groups with similar proportions of phenotypes were driven by different sets of dominant PGx alleles. PharmCAT also identified "no-function" alleles that were rare or seldom tested in certain groups by previous studies, e.g., SLCO1B1∗31 in the Afro-Caribbean (3.0%) and Sub-Saharan African (3.9%) groups. Estimated PGx frequencies are disseminated via the PharmGKB (The Pharmacogenomics Knowledgebase: www.pharmgkb.org). We demonstrate that genetic biobanks such as the UK Biobank are a robust resource for estimating PGx frequencies. Improving our understanding of PGx allele and phenotype frequencies provides guidance for future PGx studies and clinical genetic test panel design, and better serves individuals from wider biogeographic backgrounds.

  View details for DOI 10.1016/j.ajhg.2023.09.001

  View details for PubMedID 37757824

• PharmVar Tutorial on CYP2D6 Structural Variation Testing and Recommendations on Reporting. Clinical pharmacology and therapeutics Turner, A. J., Nofziger, C., Ramey, B. E., Ly, R. C., Bousman, C. A., Agúndez, J. A., Sangkuhl, K., Whirl-Carrillo, M., Vanoni, S., Dunnenberger, H. M., Ruano, G., Kennedy, M. A., Phillips, M. S., Hachad, H., Klein, T. E., Moyer, A. M., Gaedigk, A. 2023

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the highly polymorphic human CYP2D6 gene locus and a comprehensive summary of structural variation. CYP2D6 contributes to the metabolism of numerous drugs and thus, genetic variation in its gene impacts drug efficacy and safety. To accurately predict a patient's CYP2D6 phenotype, testing must include structural variants including gene deletions, duplications, hybrid genes, and combinations thereof. This tutorial offers a comprehensive overview of CYP2D6 structural variation, terms and definitions, a review of methods suitable for their detection and characterization, and practical examples to address the lack of standards to describe CYP2D6 structural variants or any other pharmacogene. This PharmVar tutorial offers practical guidance on how to detect the many, often complex, structural variants, as well as recommends terms and definitions for clinical and research reporting. Uniform reporting is not only essential for electronic health record-keeping but also for accurate translation of a patient's genotype into phenotype which is typically utilized to guide drug therapy.

  View details for DOI 10.1002/cpt.3044

  View details for PubMedID 37669183

• How to Run the Pharmacogenomics Clinical Annotation Tool (PharmCAT). Clinical pharmacology and therapeutics Li, B., Sangkuhl, K., Keat, K., Whaley, R. M., Woon, M., Verma, S., Dudek, S., Tuteja, S., Verma, A., Whirl-Carrillo, M., Ritchie, M. D., Klein, T. E. 2022

  Abstract

  Pharmacogenomics (PGx) investigates the genetic influence on drug response and is an integral part of precision medicine. While PGx testing is becoming more common in clinical practice and may be reimbursed by Medicare/Medicaid and commercial insurance, interpreting PGx testing results for clinical decision support is still a challenge. The Pharmacogenomics Clinical Annotation Tool (PharmCAT) has been designed to tackle the need for transparent, automatic interpretations of patient genetic data. PharmCAT incorporates a patient's genotypes, annotates PGx information (allele, genotype, and phenotype), and generates a report with PGx guideline recommendations from the Clinical Pharmacogenetics Implementation Consortium (CPIC) and/or the Dutch Pharmacogenetics Working Group (DPWG). PharmCAT has introduced new features in the last two years, including a VCF preprocessor, the inclusion of DPWG guidelines, and functionalities for PGx research. For example, researchers can use the VCF preprocessor to prepare biobank-scale data for PharmCAT. In addition, PharmCAT enables the assessment of novel partial and combination alleles that are composed of known PGx variants and can call CYP2D6 genotypes based on SNPs and INDELs in the input VCF file. This tutorial provides materials and detailed step-by-step instructions for how to use PharmCAT in a versatile way that can be tailored to users' individual needs.

  View details for DOI 10.1002/cpt.2790

  View details for PubMedID 36350094

• PharmGKB, an Integrated Resource of Pharmacogenomic Knowledge. Current protocols Gong, L., Whirl-Carrillo, M., Klein, T. E. 2021; 1 (8): e226

  Abstract

  The Pharmacogenomics Knowledgebase (PharmGKB) is an integrated online knowledge resource for the understanding of how genetic variation contributes to variation in drug response. Our focus includes not only pharmacogenomic information useful for clinical implementation (e.g., drug dosing guidelines and annotated drug labels), but also information to catalyze scientific research and drug discovery (e.g., variant-drug annotations and drug-centered pathways). As of April 2021, the annotated content of PharmGKB spans 715 drugs, 1761 genes, 227 diseases, 165 clinical guidelines, and 784 drug labels. We have manually curated data from more than 9000 published papers to generate the content of PharmGKB. Recently, we have also implemented an automated natural language processing (NLP) tool to broaden our coverage of the pharmacogenomic literature. This article contains a basic protocol describing how to navigate the PharmGKB website to retrieve information on how genes and genetic variations affect drug efficacy and toxicity. It also includes a protocol on how to use PharmGKB to facilitate interpretation of findings for a pharmacogenomic variant genotype or metabolizer phenotype. PharmGKB is freely available at http://www.pharmgkb.org. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Navigating the homepage of PharmGKB and searching by drug Basic Protocol 2: Using PharmGKB to facilitate interpretation of pharmacogenomic variant genotypes or metabolizer phenotypes.

  View details for DOI 10.1002/cpz1.226

  View details for PubMedID 34387941

• An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine. Clinical pharmacology and therapeutics Whirl-Carrillo, M., Huddart, R., Gong, L., Sangkuhl, K., Thorn, C. F., Whaley, R., Klein, T. E. 2021

  Abstract

  Clinical annotations are one of the most popular resources available on the Pharmacogenomics Knowledgebase (PharmGKB). Each clinical annotation summarizes the association between variant-drug pairs, shows relevant findings from the curated literature and is assigned a level of evidence (LOE) to indicate the strength of support for that association. Evidence from the pharmacogenomic literature is curated into PharmGKB as variant annotations, which can be used to create new clinical annotations or added to existing clinical annotations. This means that the same clinical annotation can be worked on by multiple curators over time. As more evidence is curated into PharmGKB, the task of maintaining consistency when assessing all the available evidence and assigning a level of evidence becomes increasingly difficult. To remedy this, a scoring system has been developed to automate LOE assignment to clinical annotations. Variant annotations are scored according to certain attributes including study size, reported p-value and whether the variant annotation supports or fails to find an association. Clinical guidelines or FDA-approved drug labels which give variant-specific prescribing guidance are also scored. The scores of all annotations attached to a clinical annotation are summed together to give a total score for the clinical annotation, which is used to calculate a LOE. Overall, the system increases transparency, consistency and reproducibility in LOE assignment to clinical annotations. In combination with increased standardization of how clinical annotations are written, use of this scoring system helps to ensure that PharmGKB clinical annotations continue to be a robust source of pharmacogenomic information.

  View details for DOI 10.1002/cpt.2350

  View details for PubMedID 34216021

• ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics Miller, D. T., Lee, K., Chung, W. K., Gordon, A. S., Herman, G. E., Klein, T. E., Stewart, D. R., Amendola, L. M., Adelman, K., Bale, S. J., Gollob, M. H., Harrison, S. M., Hershberger, R. E., McKelvey, K., Richards, C. S., Vlangos, C. N., Watson, M. S., Martin, C. L., ACMG Secondary Findings Working Group 2021

  View details for DOI 10.1038/s41436-021-01172-3

  View details for PubMedID 34012068

• Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2021 update: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics Miller, D. T., Lee, K., Gordon, A. S., Amendola, L. M., Adelman, K., Bale, S. J., Chung, W. K., Gollob, M. H., Harrison, S. M., Herman, G. E., Hershberger, R. E., Klein, T. E., McKelvey, K., Richards, C. S., Vlangos, C. N., Stewart, D. R., Watson, M. S., Martin, C. L., ACMG Secondary Findings Working Group 2021

  View details for DOI 10.1038/s41436-021-01171-4

  View details for PubMedID 34012069

• Expanding evidence leads to new pharmacogenomics payer coverage. Genetics in medicine : official journal of the American College of Medical Genetics Empey, P. E., Pratt, V. M., Hoffman, J. M., Caudle, K. E., Klein, T. E. 2021

  View details for DOI 10.1038/s41436-021-01117-w

  View details for PubMedID 33627827

• 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 Nonsteroidal Anti-inflammatory Drugs. Clinical pharmacology and therapeutics Theken, K. N., Lee, C. R., Gong, L., Caudle, K. E., Formea, C. M., Gaedigk, A., Klein, T. E., Agundez, J. A., Grosser, T. 2020

  Abstract

  Nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most commonly used analgesics due to their lack of addictive potential. However, NSAIDs have the potential to cause serious gastrointestinal, renal, and cardiovascular adverse events. CYP2C9 polymorphisms influence metabolism and clearance of several drugs in this class, thereby affecting drug exposure and potentially safety. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for NSAIDs based on CYP2C9 genotype (updates at www.cpicpgx.org).

  View details for DOI 10.1002/cpt.1830

  View details for PubMedID 32189324

• PGxMine: Text mining for curation of PharmGKB. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Lever, J. n., Barbarino, J. M., Gong, L. n., Huddart, R. n., Sangkuhl, K. n., Whaley, R. n., Whirl-Carrillo, M. n., Woon, M. n., Klein, T. E., Altman, R. B. 2020; 25: 611–22

  Abstract

  Precision medicine tailors treatment to individuals personal data including differences in their genome. The Pharmacogenomics Knowledgebase (PharmGKB) provides highly curated information on the effect of genetic variation on drug response and side effects for a wide range of drugs. PharmGKB's scientific curators triage, review and annotate a large number of papers each year but the task is challenging. We present the PGxMine resource, a text-mined resource of pharmacogenomic associations from all accessible published literature to assist in the curation of PharmGKB. We developed a supervised machine learning pipeline to extract associations between a variant (DNA and protein changes, star alleles and dbSNP identifiers) and a chemical. PGxMine covers 452 chemicals and 2,426 variants and contains 19,930 mentions of pharmacogenomic associations across 7,170 papers. An evaluation by PharmGKB curators found that 57 of the top 100 associations not found in PharmGKB led to 83 curatable papers and a further 24 associations would likely lead to curatable papers through citations. The results can be viewed at https://pgxmine.pharmgkb.org/ and code can be downloaded at https://github.com/jakelever/pgxmine.

  View details for PubMedID 31797632

• A Call for Clear and Consistent Communications Regarding the Role of Pharmacogenetics in Antidepressant Pharmacotherapy. Clinical pharmacology and therapeutics Hicks, J. K., Bishop, J. R., Gammal, R. S., Sangkuhl, K., Bousman, C. A., Leeder, J. S., Llerena, A., Mueller, D. J., Ramsey, L. B., Scott, S. A., Skaar, T. C., Caudle, K. E., Klein, T. E., Gaedigk, A. 2019

  View details for DOI 10.1002/cpt.1661

  View details for PubMedID 31664715

• 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

• 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

• 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

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

• 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

• The Case for Pharmacogenetics-Guided Prescribing of Codeine in Children. Clinical pharmacology and therapeutics Gammal, R. S., Caudle, K. E., Quinn, C. T., Wang, W. C., Gaedigk, A., Prows, C. A., Haidar, C. E., Taylor, A. K., Klein, T. E., Sangkuhl, K., Hankins, J. S., Crews, K. R. 2018

  View details for PubMedID 30467830

• PharmCAT: A Pharmacogenomics Clinical Annotation Tool CLINICAL PHARMACOLOGY & THERAPEUTICS Klein, T. E., Ritchie, M. D. 2018; 104 (1): 19–22

  View details for DOI 10.1002/cpt.928

  View details for Web of Science ID 000434960300013

• 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

• 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

• PharmCAT: A Pharmacogenomics Clinical Annotation Tool. Clinical pharmacology and therapeutics Klein, T. E., Ritchie, M. D. 2017

  Abstract

  Implementation of genomic medicine into clinical care continues to increase in prevalence in medical centers worldwide. As defined by the National Human Genome Research Institute, "Genomic medicine is an emerging medical discipline that involves using genomic information about an individual as part of their clinical care." The genomic information utilized falls broadly into two categories: 1) highly penetrant genetic disorders and 2) pharmacogenomics. Herein, we focus on pharmacogenomics, although the Pharmacogenomics Clinical Annotation Tool (PharmCAT) tool could be extended to include other types of genetic variation.

  View details for PubMedID 29194583

• Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics GENETICS IN MEDICINE Kalia, S. S., Adelman, K., Bale, S. J., Chung, W. K., Eng, C., Evans, J. P., Herman, G. E., Hufnagel, S. B., Klein, T. E., Korf, B. R., McKelvey, K. D., Ormond, K. E., Richards, C. S., Vlangos, C. N., Watson, M., Martin, C. L., Miller, D. T. 2017; 19 (2): 249-255

  Abstract

  Disclaimer: These recommendations are designed primarily as an educational resource for medical geneticists and other healthcare providers to help them provide quality medical services. Adherence to these recommendations is completely voluntary and does not necessarily assure a successful medical outcome. These recommendations should not be considered inclusive of all proper procedures and tests or exclusive of other procedures and tests that are reasonably directed toward obtaining the same results. In determining the propriety of any specific procedure or test, the clinician should apply his or her own professional judgment to the specific clinical circumstances presented by the individual patient or specimen. Clinicians are encouraged to document the reasons for the use of a particular procedure or test, whether or not it is in conformance with this statement. Clinicians also are advised to take notice of the date this statement was adopted and to consider other medical and scientific information that becomes available after that date. It also would be prudent to consider whether intellectual property interests may restrict the performance of certain tests and other procedures.To promote standardized reporting of actionable information from clinical genomic sequencing, in 2013, the American College of Medical Genetics and Genomics (ACMG) published a minimum list of genes to be reported as incidental or secondary findings. The goal was to identify and manage risks for selected highly penetrant genetic disorders through established interventions aimed at preventing or significantly reducing morbidity and mortality. The ACMG subsequently established the Secondary Findings Maintenance Working Group to develop a process for curating and updating the list over time. We describe here the new process for accepting and evaluating nominations for updates to the secondary findings list. We also report outcomes from six nominations received in the initial 15 months after the process was implemented. Applying the new process while upholding the core principles of the original policy statement resulted in the addition of four genes and removal of one gene; one gene did not meet criteria for inclusion. The updated secondary findings minimum list includes 59 medically actionable genes recommended for return in clinical genomic sequencing. We discuss future areas of focus, encourage continued input from the medical community, and call for research on the impact of returning genomic secondary findings.Genet Med 19 2, 249-255.

  View details for DOI 10.1038/gim.2016.190

  View details for PubMedID 27854360

• 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

• 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

• Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans BLOOD Daneshjou, R., Gamazon, E. R., Burkley, B., Cavallari, L. H., Johnson, J. A., Klein, T. E., Limdi, N., Hillenmeyer, S., Percha, B., Karczewski, K. J., Langaee, T., Patel, S. R., Bustamante, C. D., Altman, R. B., Perera, M. A. 2014; 124 (14): 2298-2305

  Abstract

  The anticoagulant warfarin has >30 million prescriptions per year in the United States. Doses can vary 20-fold between patients, and incorrect dosing can result in serious adverse events. Variation in warfarin pharmacokinetic and pharmacodynamic genes, such as CYP2C9 and VKORC1, do not fully explain the dose variability in African Americans. To identify additional genetic contributors to warfarin dose, we exome sequenced 103 African Americans on stable doses of warfarin at extremes (≤ 35 and ≥ 49 mg/week). We found an association between lower warfarin dose and a population-specific regulatory variant, rs7856096 (P = 1.82 × 10(-8), minor allele frequency = 20.4%), in the folate homeostasis gene folylpolyglutamate synthase (FPGS). We replicated this association in an independent cohort of 372 African American subjects whose stable warfarin doses represented the full dosing spectrum (P = .046). In a combined cohort, adding rs7856096 to the International Warfarin Pharmacogenetic Consortium pharmacogenetic dosing algorithm resulted in a 5.8 mg/week (P = 3.93 × 10(-5)) decrease in warfarin dose for each allele carried. The variant overlaps functional elements and was associated (P = .01) with FPGS gene expression in lymphoblastoid cell lines derived from combined HapMap African populations (N = 326). Our results provide the first evidence linking genetic variation in folate homeostasis to warfarin response.

  View details for DOI 10.1182/blood-2014-04-568436

  View details for Web of Science ID 000342763900023

  View details for PubMedCentralID PMC4183989

• CYP2D6 Genotype and Adjuvant Tamoxifen: Meta-Analysis of Heterogeneous Study Populations CLINICAL PHARMACOLOGY & THERAPEUTICS Province, M. A., Goetz, M. P., Brauch, H., Flockhare, D. A., Hebert, J. M., Whaley, R., Suman, V. J., Schroth, W., Winter, S., Zembutsu, H., Mushiroda, T., Newman, W. G., Lee, M. M., Ambrosone, C. B., Beckmann, M. W., Choi, J., Dieudonne, A., Fasching, P. A., Ferraldeschi, R., Gong, L., Haschke-Becher, E., Howel, A., Jordan, L. B., Hamann, U., Kiyotani, K., Krippl, P., Lambrechts, D., Latif, A., Langsenlehner, U., Lorizio, W., Neven, P., Nguyen, A. T., Park, B., Purdie, C. A., Quinlan, P., Renner, W., Schmidt, M., Schwab, M., Shin, J., Stingl, J. C., Wegman, P., Wingren, S., Wu, A. H., Ziv, E., ZIRPOLI, G., Thompson, A. M., Jordan, V. C., Nakamura, Y., Altman, R. B., Ames, M. M., Weinshilboum, R. M., Eichelbaum, M., Ingle, J. N., Klein, T. E. 2014; 95 (2): 216-227

  Abstract

  The International Tamoxifen Pharmacogenomics Consortium was established to address the controversy regarding cytochrome P450 2D6 (CYP2D6) status and clinical outcomes in tamoxifen therapy. We performed a meta-analysis on data from 4,973 tamoxifen-treated patients (12 globally distributed sites). Using strict eligibility requirements (postmenopausal women with estrogen receptor-positive breast cancer, receiving 20 mg/day tamoxifen for 5 years, criterion 1); CYP2D6 poor metabolizer status was associated with poorer invasive disease-free survival (IDFS: hazard ratio = 1.25; 95% confidence interval = 1.06, 1.47; P = 0.009). However, CYP2D6 status was not statistically significant when tamoxifen duration, menopausal status, and annual follow-up were not specified (criterion 2, n = 2,443; P = 0.25) or when no exclusions were applied (criterion 3, n = 4,935; P = 0.38). Although CYP2D6 is a strong predictor of IDFS using strict inclusion criteria, because the results are not robust to inclusion criteria (these were not defined a priori), prospective studies are necessary to fully establish the value of CYP2D6 genotyping in tamoxifen therapy.

  View details for DOI 10.1038/clpt.2013.186

  View details for PubMedID 24060820

• Clinical Implementation of Pharmacogenetics: More Than One Gene at a Time CLINICAL PHARMACOLOGY & THERAPEUTICS Johnson, J. A., Klein, T. E., RELLING, M. V. 2013; 93 (5): 384-385

  Abstract

  Tricyclic antidepressant (TCA) clinical pharmacogenetic implementation guidelines for CYP2D6 and CYP2C19 genotypes highlight the importance of both genes. However, studies of the combined impact of the two genes are sparse, limiting the ability to make strong recommendations based on both genes. The warfarin pharmacogenetics literature highlights the strength of a multigenic approach for discovery and clinical implementation. For optimal impact and interpretation, investigators are encouraged to conduct studies in the context of previously well-defined pharmacogenetics markers.

  View details for DOI 10.1038/clpt.2013.7

  View details for Web of Science ID 000317834800013

  View details for PubMedID 23598455

• 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

• Clopidogrel: A Case for Indication-Specific Pharmacogenetics CLINICAL PHARMACOLOGY & THERAPEUTICS Johnson, J. A., Roden, D. M., Lesko, L. J., Ashley, E., Klein, T. E., Shuldiner, A. R. 2012; 91 (5): 774-776

  Abstract

  The CYP2C19*2 loss-of-function allele is associated with reduced generation of active metabolites of clopidogrel. However, meta-analyses have supported or discounted the impact of genotype on adverse cardiovascular outcomes during clopidogrel therapy, depending on studies included in the analysis. Here we review these data and conclude that evidence supports a differential effect of genotype on protection from major adverse cardiovascular outcomes following percutaneous coronary intervention (PCI), but not for other clopidogrel indications.

  View details for DOI 10.1038/clpt.2012.21

  View details for Web of Science ID 000303047400009

  View details for PubMedID 22513313

  View details for PubMedCentralID PMC3382015

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for Codeine Therapy in the Context of Cytochrome P450 2D6 (CYP2D6) Genotype CLINICAL PHARMACOLOGY & THERAPEUTICS Crews, K. R., Gaedigk, A., Dunnenberger, H. M., Klein, T. E., Shen, D. D., Callaghan, J. T., Kharasch, E. D., Skaar, T. C. 2012; 91 (2): 321-326

  Abstract

  Codeine is bioactivated to morphine, a strong opioid agonist, by the hepatic cytochrome P450 2D6 (CYP2D6); hence, the efficacy and safety of codeine as an analgesic are governed by CYP2D6 polymorphisms. Codeine has little therapeutic effect in patients who are CYP2D6 poor metabolizers, whereas the risk of morphine toxicity is higher in ultrarapid metabolizers. The purpose of this guideline (periodically updated at http://www.pharmgkb.org) is to provide information relating to the interpretation of CYP2D6 genotype test results to guide the dosing of codeine.

  View details for DOI 10.1038/clpt.2011.287

  View details for Web of Science ID 000299654000034

  View details for PubMedID 22205192

  View details for PubMedCentralID PMC3289963

• CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network CLINICAL PHARMACOLOGY & THERAPEUTICS Relling, M. V., Klein, T. E. 2011; 89 (3): 464-467

  View details for DOI 10.1038/clpt.2010.279

  View details for Web of Science ID 000287439600030

  View details for PubMedID 21270786

  View details for PubMedCentralID PMC3098762

• Clinical assessment incorporating a personal genome LANCET Ashley, E. A., Butte, A. J., Wheeler, M. T., Chen, R., Klein, T. E., Dewey, F. E., Dudley, J. T., Ormond, K. E., Pavlovic, A., Morgan, A. A., Pushkarev, D., Neff, N. F., Hudgins, L., Gong, L., Hodges, L. M., Berlin, D. S., Thorn, C. F., Sangkuhl, K., Hebert, J. M., Woon, M., Sagreiya, H., Whaley, R., Knowles, J. W., Chou, M. F., Thakuria, J. V., Rosenbaum, A. M., Zaranek, A. W., Church, G. M., Greely, H. T., Quake, S. R., Altman, R. B. 2010; 375 (9725): 1525-1535

  Abstract

  The cost of genomic information has fallen steeply, but the clinical translation of genetic risk estimates remains unclear. We aimed to undertake an integrated analysis of a complete human genome in a clinical context.We assessed a patient with a family history of vascular disease and early sudden death. Clinical assessment included analysis of this patient's full genome sequence, risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome and clinical risk. Disease and risk analysis focused on prediction of genetic risk of variants associated with mendelian disease, recognised drug responses, and pathogenicity for novel variants. We queried disease-specific mutation databases and pharmacogenomics databases to identify genes and mutations with known associations with disease and drug response. We estimated post-test probabilities of disease by applying likelihood ratios derived from integration of multiple common variants to age-appropriate and sex-appropriate pre-test probabilities. We also accounted for gene-environment interactions and conditionally dependent risks.Analysis of 2.6 million single nucleotide polymorphisms and 752 copy number variations showed increased genetic risk for myocardial infarction, type 2 diabetes, and some cancers. We discovered rare variants in three genes that are clinically associated with sudden cardiac death-TMEM43, DSP, and MYBPC3. A variant in LPA was consistent with a family history of coronary artery disease. The patient had a heterozygous null mutation in CYP2C19 suggesting probable clopidogrel resistance, several variants associated with a positive response to lipid-lowering therapy, and variants in CYP4F2 and VKORC1 that suggest he might have a low initial dosing requirement for warfarin. Many variants of uncertain importance were reported.Although challenges remain, our results suggest that whole-genome sequencing can yield useful and clinically relevant information for individual patients.National Institute of General Medical Sciences; National Heart, Lung And Blood Institute; National Human Genome Research Institute; Howard Hughes Medical Institute; National Library of Medicine, Lucile Packard Foundation for Children's Health; Hewlett Packard Foundation; Breetwor Family Foundation.

  View details for Web of Science ID 000277655100025

  View details for PubMedID 20435227

• Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data NEW ENGLAND JOURNAL OF MEDICINE Klein, T. E., Altman, R. B., Eriksson, N., Gage, B. F., Kimmel, S. E., Lee, M. M., Limdi, N. A., Page, D., Roden, D. M., Wagner, M. J., Caldwell, M. D., Johnson, J. A., Chen, Y. T., Wen, M. S., Caraco, Y., Achache, I., Blotnick, S., Muszkat, M., Shin, J. G., Kim, H. S., Suarez-Kurtz, G., Perini, J. A., Silva-Assuncao, E., Anderson, J. L., Horne, B. D., Carlquist, J. F., Caldwell, M. D., Berg, R. L., Burmester, J. K., Goh, B. C., Lee, S. C., Kamali, F., Sconce, E., Daly, A. K., Wu, A. H., Langaee, T. Y., Feng, H., Cavallari, L., Momary, K., Pirmohamed, M., Jorgensen, A., Toh, C. H., Williamson, P., McLeod, H., Evans, J. P., Weck, K. E., Brensinger, C., Nakamura, Y., Mushiroda, T., Veenstra, D., Meckley, L., Rieder, M. J., Rettie, A. E., Wadelius, M., Melhus, H., Stein, C. M., Schwartz, U., Kurnik, D., Deych, E., Lenzini, P., Eby, C., Chen, L. Y., Deloukas, P., Motsinger-Reif, A., Sagreiya, H., Srinivasan, B. S., Lantz, E., Chang, T., Ritchie, M., Lu, L. S., Shin, J. G. 2009; 360 (8): 753-764

  Abstract

  Genetic variability among patients plays an important role in determining the dose of warfarin that should be used when oral anticoagulation is initiated, but practical methods of using genetic information have not been evaluated in a diverse and large population. We developed and used an algorithm for estimating the appropriate warfarin dose that is based on both clinical and genetic data from a broad population base.Clinical and genetic data from 4043 patients were used to create a dose algorithm that was based on clinical variables only and an algorithm in which genetic information was added to the clinical variables. In a validation cohort of 1009 subjects, we evaluated the potential clinical value of each algorithm by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual stable therapeutic dose; we also evaluated other clinically relevant indicators.In the validation cohort, the pharmacogenetic algorithm accurately identified larger proportions of patients who required 21 mg of warfarin or less per week and of those who required 49 mg or more per week to achieve the target international normalized ratio than did the clinical algorithm (49.4% vs. 33.3%, P<0.001, among patients requiring < or = 21 mg per week; and 24.8% vs. 7.2%, P<0.001, among those requiring > or = 49 mg per week).The use of a pharmacogenetic algorithm for estimating the appropriate initial dose of warfarin produces recommendations that are significantly closer to the required stable therapeutic dose than those derived from a clinical algorithm or a fixed-dose approach. The greatest benefits were observed in the 46.2% of the population that required 21 mg or less of warfarin per week or 49 mg or more per week for therapeutic anticoagulation.

  View details for Web of Science ID 000263411300005

  View details for PubMedID 19228618

  View details for PubMedCentralID PMC2722908

• Mutation and polymorphism spectrum in osteogenesis imperfecta type II: implications for genotype-phenotype relationships HUMAN MOLECULAR GENETICS Bodian, D. L., Chan, T., Poon, A., Schwarze, U., Yang, K., Byers, P. H., Kwok, P., Klein, T. E. 2009; 18 (3): 463-471

  Abstract

  Osteogenesis imperfecta (OI), also known as brittle bone disease, is a clinically and genetically heterogeneous disorder primarily characterized by susceptibility to fracture. Although OI generally results from mutations in the type I collagen genes, COL1A1 and COL1A2, the relationship between genotype and phenotype is not yet well understood. To provide additional data for genotype-phenotype analyses and to determine the proportion of mutations in the type I collagen genes among subjects with lethal forms of OI, we sequenced the coding and exon-flanking regions of COL1A1 and COL1A2 in a cohort of 63 subjects with OI type II, the perinatal lethal form of the disease. We identified 61 distinct heterozygous mutations in type I collagen, including five non-synonymous rare variants of unknown significance, of which 43 had not been seen previously. In addition, we found 60 SNPs in COL1A1, of which 17 were not reported previously, and 82 in COL1A2, of which 18 are novel. In three samples without collagen mutations, we found inactivating mutations in CRTAP and LEPRE1, suggesting a frequency of these recessive mutations of approximately 5% in OI type II. A computational model that predicts the outcome of substitutions for glycine within the triple helical domain of collagen alpha1(I) chains predicted lethality with approximately 90% accuracy. The results contribute to the understanding of the etiology of OI by providing data to evaluate and refine current models relating genotype to phenotype and by providing an unbiased indication of the relative frequency of mutations in OI-associated genes.

  View details for DOI 10.1093/hmg/ddn374

  View details for Web of Science ID 000262519300007

  View details for PubMedID 18996919

  View details for PubMedCentralID PMC2638801

• PharmVar GeneFocus: CYP4F2. Clinical pharmacology and therapeutics Zubiaur, P., Rodríguez-Antona, C., Boone, E. C., Daly, A. K., Tsermpini, E. E., Khasawneh, L. Q., Sangkuhl, K., Duconge, J., Botton, M. R., Savieo, J., Nofziger, C., Whirl-Carrillo, M., Klein, T. E., Gaedigk, A. 2024

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) serves as a global repository providing star (*) allele nomenclature for the polymorphic human CYP4F2 gene. CYP4F2 genetic variation impacts the metabolism of vitamin K, which is associated with warfarin dose requirements, and the metabolism of drugs, such as imatinib or fingolimod, and certain endogenous compounds including vitamin E and eicosanoids. This GeneFocus provides a comprehensive overview and summary of CYP4F2 genetic variation including the characterization of 14 novel star alleles, CYP4F2*4 through *17. A description of how haplotype information cataloged by PharmVar is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC) is also provided.

  View details for DOI 10.1002/cpt.3405

  View details for PubMedID 39135485

• PharmVar GeneFocus: CYP2A6. Clinical pharmacology and therapeutics Langlois, A. W., Chenoweth, M. J., Twesigomwe, D., Scantamburlo, G., Whirl-Carrillo, M., Sangkuhl, K., Klein, T. E., Nofziger, C., Tyndale, R. F., Gaedigk, A. 2024

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) provides nomenclature for the human CYP2A gene locus containing the highly polymorphic CYP2A6 gene. CYP2A6 plays a role in the metabolism of nicotine and various drugs. Thus, genetic variation can substantially contribute to the function of this enzyme and associated efficacy and safety. This GeneFocus provides an overview of the clinical significance of CYP2A6, including its genetic variation and function. We also highlight and discuss caveats in the identification and characterization of allelic variation of this complex pharmacogene, a prerequisite for accurate genotype determination and prediction of phenotype status.

  View details for DOI 10.1002/cpt.3387

  View details for PubMedID 39051767

• Clinical Pharmacogenetics Implementation Consortium Guideline (CPIC)for CYP2D6, ADRB1, ADRB2, ADRA2C, GRK4, and GRK5 Genotypes and Beta-Blocker Therapy. Clinical pharmacology and therapeutics Duarte, J. D., Thomas, C. D., Lee, C. R., Huddart, R., Agundez, J. A., Baye, J. F., Gaedigk, A., Klein, T. E., Lanfear, D. E., Monte, A. A., Nagy, M., Schwab, M., Stein, C. M., Uppugunduri, C. R., van Schaik, R. H., Donnelly, R. S., Caudle, K. E., Luzum, J. A. 2024

  Abstract

  Beta-blockers are widely used medications for a variety of indications, including heart failure, myocardial infarction, cardiac arrhythmias, and hypertension. Genetic variability in pharmacokinetic (e.g., CYP2D6) and pharmacodynamic (e.g., ADRB1, ADRB2, ADRA2C, GRK4, GRK5) genes have been studied in relation to beta-blocker exposure and response. We searched and summarized the strength of the evidence linking beta-blocker exposure and response with the six genes listed above. The level of evidence was high for associations between CYP2D6 genetic variation and both metoprolol exposure and heart rate response. Evidence indicates that CYP2D6 poor metabolizers experience clinically significant greater exposure and lower heart rate in response to metoprolol compared with those who are not poor metabolizers. Therefore, we provide therapeutic recommendations regarding genetically predicted CYP2D6 metabolizer status and metoprolol therapy. However, there was insufficient evidence to make therapeutic recommendations for CYP2D6 and other beta-blockers or for any beta-blocker and the other five genes evaluated (updates at www.cpicpgx.org).

  View details for DOI 10.1002/cpt.3351

  View details for PubMedID 38951961

• Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2B6 Genotype and Methadone Therapy. Clinical pharmacology and therapeutics Robinson, K. M., Eum, S., Desta, Z., Tyndale, R. F., Gaedigk, A., Crist, R. C., Haidar, C. E., Myers, A. L., Samer, C. F., Somogyi, A. A., Zubiaur, P., Iwuchukwu, O. F., Whirl-Carrillo, M., Klein, T. E., Caudle, K. E., Donnelly, R. S., Kharasch, E. D. 2024

  Abstract

  Methadone is a mu (μ) opioid receptor agonist used clinically in adults and children to manage opioid use disorder, neonatal abstinence syndrome, and acute and chronic pain. It is typically marketed as a racemic mixture of R- and S-enantiomers. R-methadone has 30-to 50-fold higher analgesic potency than S-methadone, and S-methadone has a greater adverse effect (prolongation) on the cardiac QTc interval. Methadone undergoes stereoselective metabolism. CYP2B6 is the primary enzyme responsible for catalyzing the metabolism of both enantiomers to the inactive metabolites, S- and R-2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (S- and R-EDDP). Genetic variation in the CYP2B6 gene has been investigated in the context of implications for methadone pharmacokinetics, dose, and clinical outcomes. Most CYP2B6 variants result in diminished or loss of CYP2B6 enzyme activity, which can lead to higher plasma methadone concentrations (affecting S- more than R-methadone). However, the data do not consistently indicate that CYP2B6-based metabolic variability has a clinically significant effect on methadone dose, efficacy, or QTc prolongation. Expert analysis of the published literature does not support a change from standard methadone prescribing based on CYP2B6 genotype (updates at www.cpicpgx.org).

  View details for DOI 10.1002/cpt.3338

  View details for PubMedID 38863207

• Generating Clinical-Grade Gene-Disease Validity Classifications Through the ClinGen Data Platforms. Annual review of biomedical data science Wright, M. W., Thaxton, C. L., Nelson, T., DiStefano, M. T., Savatt, J. M., Brush, M. H., Cheung, G., Mandell, M. E., Wulf, B., Ward, T. J., Goehringer, S., O'Neill, T., Weller, P., Preston, C. G., Keseler, I. M., Goldstein, J. L., Strande, N. T., McGlaughon, J., Azzariti, D. R., Cordova, I., Dziadzio, H., Babb, L., Riehle, K., Milosavljevic, A., Martin, C. L., Rehm, H. L., Plon, S. E., Berg, J. S., Riggs, E. R., Klein, T. E. 2024

  Abstract

  Clinical genetic laboratories must have access to clinically validated biomedical data for precision medicine. A lack of accessibility, normalized structure, and consistency in evaluation complicates interpretation of disease causality, resulting in confusion in assessing the clinical validity of genes and genetic variants for diagnosis. A key goal of the Clinical Genome Resource (ClinGen) is to fill the knowledge gap concerning the strength of evidence supporting the role of a gene in a monogenic disease, which is achieved through a process known as Gene-Disease Validity curation. Here we review the work of ClinGen in developing a curation infrastructure that supports the standardization, harmonization, and dissemination of Gene-Disease Validity data through the creation of frameworks and the utilization of common data standards. This infrastructure is based on several applications, including the ClinGen GeneTracker, Gene Curation Interface, Data Exchange, GeneGraph, and website.

  View details for DOI 10.1146/annurev-biodatasci-102423-112456

  View details for PubMedID 38663031

• Knowledge and attitudes on implementing cardiovascular pharmacogenomic testing. Clinical and translational science Russell, C., Campion, M., Grove, M. E., Matsuda, K., Klein, T. E., Ashley, E., Naik, H., Wheeler, M. T., Scott, S. A. 2024; 17 (3): e13737

  Abstract

  Pharmacogenomics has the potential to inform drug dosing and selection, reduce adverse events, and improve medication efficacy; however, provider knowledge of pharmacogenomic testing varies across provider types and specialties. Given that many actionable pharmacogenomic genes are implicated in cardiovascular medication response variability, this study aimed to evaluate cardiology providers' knowledge and attitudes on implementing clinical pharmacogenomic testing. Sixty-one providers responded to an online survey, including pharmacists (46%), physicians (31%), genetic counselors (15%), and nurses (8%). Most respondents (94%) reported previous genetics education; however, only 52% felt their genetics education prepared them to order a clinical pharmacogenomic test. In addition, most respondents (66%) were familiar with pharmacogenomics, with genetic counselors being most likely to be familiar (p < 0.001). Only 15% of respondents had previously ordered a clinical pharmacogenomic test and a total of 36% indicated they are likely to order a pharmacogenomic test in the future; however, the vast majority of respondents (89%) were interested in pharmacogenomic testing being incorporated into diagnostic cardiovascular genetic tests. Moreover, 84% of providers preferred pharmacogenomic panel testing compared to 16% who preferred single gene testing. Half of the providers reported being comfortable discussing pharmacogenomic results with their patients, but the majority (60%) expressed discomfort with the logistics of test ordering. Reported barriers to implementation included uncertainty about the clinical utility and difficulty choosing an appropriate test. Taken together, cardiology providers have moderate familiarity with pharmacogenomics and limited experience with test ordering; however, they are interested in incorporating pharmacogenomics into diagnostic genetic tests and ordering pharmacogenomic panels.

  View details for DOI 10.1111/cts.13737

  View details for PubMedID 38421234

• CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods GENOME BIOLOGY Jain, S., Bakolitsa, C., Brenner, S. E., Radivojac, P., Moult, J., Repo, S., Hoskins, R. A., Andreoletti, G., Barsky, D., Chellapan, A., Chu, H., Dabbiru, N., Kollipara, N. K., Ly, M., Neumann, A. J., Pal, L. R., Odell, E., Pandey, G., Peters-Petrulewicz, R. C., Srinivasan, R., Yee, S. F., Yeleswarapu, S., Zuhl, M., Adebali, O., Patra, A., Beer, M. A., Hosur, R., Peng, J., Bernard, B. M., Berry, M., Dong, S., Boyle, A. P., Adhikari, A., Chen, J., Hu, Z., Wang, R., Wang, Y., Miller, M., Wang, Y., Bromberg, Y., Turina, P., Capriotti, E., Han, J. J., Ozturk, K., Carter, H., Babbi, G., Bovo, S., Di Lena, P., Martelli, P., Savojardo, C., Casadio, R., Cline, M. S., De Baets, G., Bonache, S., Diez, O., Gutierrez-Enriquez, S., Fernandez, A., Montalban, G., Ootes, L., Ozkan, S., Padilla, N., Riera, C., De la Cruz, X., Diekhans, M., Huwe, P. J., Wei, Q., Xu, Q., Dunbrack, R. L., Gotea, V., Elnitski, L., Margolin, G., Fariselli, P., Kulakovskiy, I. V., Makeev, V. J., Penzar, D. D., Vorontsov, I. E., Favorov, A. V., Forman, J. R., Hasenahuer, M., Fornasari, M. S., Parisi, G., Avsec, Z., Celik, M. H., Thi Yen Duong Nguyen, Gagneur, J., Shi, F., Edwards, M. D., Guo, Y., Tian, K., Zeng, H., Gifford, D. K., Goke, J., Zaucha, J., Gough, J., Ritchie, G. S., Frankish, A., Mudge, J. M., Harrow, J., Young, E. L., Yu, Y., Huff, C. D., Murakami, K., Nagai, Y., Imanishi, T., Mungall, C. J., Jacobsen, J. B., Kim, D., Jeong, C., Jones, D. T., Li, M., Guthrie, V., Bhattacharya, R., Chen, Y., Douville, C., Fan, J., Kim, D., Masica, D., Niknafs, N., Sengupta, S., Tokheim, C., Turner, T. N., Yeo, H., Karchin, R., Shin, S., Welch, R., Keles, S., Li, Y., Kellis, M., Corbi-Verge, C., Strokach, A. V., Kim, P. M., Klein, T. E., Mohan, R., Sinnott-Armstrong, N. A., Wainberg, M., Kundaje, A., Gonzaludo, N., Mak, A. Y., Chhibber, A., Lam, H. K., Dahary, D., Fishilevich, S., Lancet, D., Lee, I., Bachman, B., Katsonis, P., Lua, R. C., Wilson, S. J., Lichtarge, O., Bhat, R. R., Sundaram, L., Viswanath, V., Bellazzi, R., Nicora, G., Rizzo, E., Limongelli, I., Mezlini, A. M., Chang, R., Kim, S., Lai, C., O'Connor, R., Topper, S., van den Akker, J., Zhou, A. Y., Zimmer, A. D., Mishne, G., Bergquist, T. R., Breese, M. R., Guerrero, R. F., Jiang, Y., Kiga, N., Li, B., Mort, M., Pagel, K. A., Pejaver, V., Stamboulian, M. H., Thusberg, J., Mooney, S. D., Teerakulkittipong, N., Cao, C., Kundu, K., Yin, Y., Yu, C., Kleyman, M., Lin, C., Stackpole, M., Mount, S. M., Eraslan, G., Mueller, N. S., Naito, T., Rao, A. R., Azaria, J. R., Brodie, A., Ofran, Y., Garg, A., Pal, D., Hawkins-Hooker, A., Kenlay, H., Reid, J., Mucaki, E. J., Rogan, P. K., Schwarz, J. M., Searls, D. B., Lee, G., Seok, C., Kramer, A., Shah, S., Huang, C. V., Kirsch, J. F., Shatsky, M., Cao, Y., Chen, H., Karimi, M., Moronfoye, O., Sun, Y., Shen, Y., Shigeta, R., Ford, C. T., Nodzak, C., Uppal, A., Shi, X., Joseph, T., Kotte, S., Rana, S., Rao, A., Saipradeep, V. G., Sivadasan, N., Sunderam, U., Stanke, M., Su, A., Adzhubey, I., Jordan, D. M., Sunyaev, S., Rousseau, F., Schymkowitz, J., Van Durme, J., Tavtigian, S. V., Carraro, M., Giollo, M., Tosatto, S. E., Adato, O., Carmel, L., Cohen, N. E., Fenesh, I., Holtzer, I., Juven-Gershon, T., Unger, R., Niroula, A., Olatubosun, A., Valiaho, J., Yang, Y., Vihinen, M., Wahl, M. E., Chang, B., Chong, K., Hu, I., Sun, R., Wu, W., Xia, X., Zee, B. C., Wang, M. H., Wang, M., Wu, C., Lu, Y., Chen, K., Yang, Y., Yates, C. M., Kreimer, A., Yan, Z., Yosef, N., Zhao, H., Wei, Z., Yao, Z., Zhou, F., Folkman, L., Zhou, Y., Daneshjou, R., Altman, R. B., Inoue, F., Ahituv, N., Arkin, A. P., Lovisa, F., Bonvini, P., Bowdin, S., Gianni, S., Mantuano, E., Minicozzi, V., Novak, L., Pasquo, A., Pastore, A., Petrosino, M., Puglisi, R., Toto, A., Veneziano, L., Chiaraluce, R., Ball, M. P., Bobe, J. R., Church, G. M., Consalvi, V., Mort, M., Cooper, D. N., Buckley, B. A., Sheridan, M. B., Cutting, G. R., Scaini, M., Cygan, K. J., Fredericks, A. M., Glidden, D. T., Neil, C., Rhine, C. L., Fairbrother, W. G., Alontaga, A. Y., Fenton, A. W., Matreyek, K. A., Starita, L. M., Fowler, D. M., Loescher, B., Franke, A., Adamson, S. I., Graveley, B. R., Gray, J. W., Malloy, M. J., Kane, J. P., Kousi, M., Katsanis, N., Schubach, M., Kircher, M., Tang, P. F., Kwok, P., Lathrop, R. H., Clark, W. T., Yu, G. K., LeBowitz, J. H., Benedicenti, F., Bettella, E., Bigoni, S., Cesca, F., Mammi, I., Marino-Bus-Ije, C., Milani, D., Peron, A., Polli, R., Sartori, S., Stanzial, F., Ioldo, I., Turolla, L., Aspromonte, M. C., Bellini, M., Leonardi, E., Liu, X., Marshall, C., McCombie, W., Elefanti, L., Menin, C., Meyn, M., Murgia, A., Nadeau, K. Y., Neuhausen, S. L., Nussbaum, R. L., Pirooznia, M., Potash, J. B., Dimster-Denk, D. F., Rine, J. D., Sanford, J. R., Snyder, M., Tavtigian, S. V., Cole, A. G., Sun, S., Verby, M. W., Weile, J., Roth, F. P., Tewhey, R., Sabeti, P. C., Campagna, J., Refaat, M. M., Wojciak, J., Grubb, S., Schmitt, N., Shendure, J., Spurdle, A. B., Stavropoulos, D. J., Walton, N. A., Zandi, P. P., Ziv, E., Burke, W., Chen, F., Carr, L. R., Martinez, S., Paik, J., Harris-Wai, J., Yarborough, M., Fullerton, S. M., Koenig, B. A., McInnes, G., Shigaki, D., Chandonia, J., Furutsuki, M., Kasak, L., Yu, C., Chen, R., Cline, M. S., Pandey, G., Friedberg, I., Getz, G. A., Cong, Q., Kinch, L. N., Zhang, J., Grishin, N. V., Voskanian, A., Kann, M. G., Clark, W. T., Tran, E., Ioannidis, N. M., Hunter, J. M., Udani, R., Cai, B., Morgan, A. A., Sokolov, A., Stuart, J. M., Tavtigian, S. V., Minervini, G., Monzon, A. M., Batzoglou, S., Butte, A. J., Church, G. M., Greenblatt, M. S., Hart, R. K., Hernandez, R., Hubbard, T. P., Kahn, S., O'Donnell-Luria, A., Ng, P. C., Shon, J., Tavtigian, S. V., Veltman, J., Zook, J. M., Critical Assessment Genome 2024; 25 (1): 53

  Abstract

  The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

  View details for DOI 10.1186/s13059-023-03113-6

  View details for Web of Science ID 001184832400002

  View details for PubMedID 38389099

  View details for PubMedCentralID PMC10882881

• From sample to star alleles: a long-read pharmacogenomics pipeline powered by Twist target enrichment and PacBio HiFi sequencing Holt, S., Harting, J., Han, T., Arbiza, L., Kingan, S., Souppe, A., Zhang, S., Baybayan, P., Lambert, C., Ferrao, H., Li, B., Sangkuhl, K., Woon, M., Whaley, R., Whirl-Carrillo, M., Yang, Y., Klein, T., Scott, S., Gonzaludo, N. SPRINGERNATURE. 2024: 701-702

  View details for Web of Science ID 001147414903359

• Investigation of genomic and transcriptomic risk factors in clopidogrel response in African Americans. medRxiv : the preprint server for health sciences Yang, G., Alarcon, C., Chanfreau, C., Lee, N. H., Friedman, P., Nutescu, E., Tuck, M., O'Brien, T., Gong, L., Klein, T. E., Chang, K. M., Tsao, P. S., Meltzer, D. O., Tuteja, S., Perera, M. A. 2023

  Abstract

  Clopidogrel, an anti-platelet drug, used to prevent thrombosis after percutaneous coronary intervention. Clopidogrel resistance results in recurring ischemic episodes, with African Americans suffering disproportionately. The aim of this study was to identify biomarkers of clopidogrel resistance in African American patients. We conducted a genome-wide association study, including local ancestry adjustment, in 141 African Americans on clopidogrel to identify associations with high on-treatment platelet reactivity (HTPR). We validated genome-wide and suggestive hits in an independent cohort of African American clopidogrel patients (N = 823) from the Million Veteran's Program (MVP) along with in vitro functional follow up. We performed differential gene expression (DGE) analysis in whole blood with functional follow-up in MEG-01 cells. We identified rs7807369, within thrombospondin 7A (THSD7A), as significantly associated with increasing risk of HTPR (p = 4.56 × 10-9). Higher THSD7A expression was associated with HTPR in an independent gene expression cohort of clopidogrel treated patients (p = 0.004) and supported by increased gene expression on THSD7A in primary human endothelial cells carrying the risk haplotype. Two SNPs (rs1149515 and rs191786) were validated in the MVP cohort. DGE analysis identified an association with decreased LAIR1 expression to HTPR. LAIR1 knockdown in a MEG-01 cells resulted in increased expression of SYK and AKT1, suggesting an inhibitory role of LAIR1 in the Glycoprotein VI pathway. Notably, the CYP2C19 variants showed no association with clopidogrel response in the discovery or MVP cohorts. In summary, these finding suggest that other variants outside of CYP2C19 star alleles play an important role in clopidogrel response in African Americans.

  View details for DOI 10.1101/2023.12.05.23299140

  View details for PubMedID 38106031

  View details for PubMedCentralID PMC10723512

• Highly Scalable Pharmacogenomic Panel Testing with Hybrid Capture and Long-Read Sequencing Gonzaludo, N., Kingan, S., Harting, J., Baybayan, P., Han, T., Arbiza, L., Li, B., Sangkuhl, K., Woon, M., Whaley, R., Whirl-Carrillo, M., Yang, Y., Klein, T. E., Hammond, N., Scott, S. A. LIPPINCOTT WILLIAMS & WILKINS. 2023: 183

  View details for Web of Science ID 001084789600005

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for <i>CYP2D6</i>, <i>CYP2C19</i>, <i>CYP2B6</i>, <i>SLC6A4</i>, and <i>HTR2A</i> Genotypes and Serotonin Reuptake Inhibitor Antidepressants Bousman, C. A., Stevenson, J. M., Ramsey, L. B., Sangkuhl, K., Hicks, J., Strawn, J. R., Singh, A. B., Ruano, G., Mueller, D. J., Tsermpini, E., Brown, J. T., Bell, G. C., Leeder, J., Gaedigk, A., Scott, S. A., Klein, T. E., Caudle, K. E., Bishop, J. R. LIPPINCOTT WILLIAMS & WILKINS. 2023: 181-183

  View details for Web of Science ID 001084789600004

• PharmGKB summary: disulfiram pathway. Pharmacogenetics and genomics Gonzalez-Suarez, A. D., Thorn, C. F., Whirl-Carrillo, M., Klein, T. E. 2023

  View details for DOI 10.1097/FPC.0000000000000509

  View details for PubMedID 37728645

• ACMG SF v3.2 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics Miller, D. T., Lee, K., Abul-Husn, N. S., Amendola, L. M., Brothers, K., Chung, W. K., Gollob, M. H., Gordon, A. S., Harrison, S. M., Hershberger, R. E., Klein, T. E., Richards, C. S., Stewart, D. R., Martin, C. L., ACMG Secondary Findings Working Group. Electronic address: documents@acmg.net,, 2023: 100866

  View details for DOI 10.1016/j.gim.2023.100866

  View details for PubMedID 37347242

• The Role of Epigenomics in Mapping Potential Precursors for Foot and Ankle Tendinopathy: A Systematic Review. Foot & ankle specialist Williams, S., Ligas, C., Oloff, L., Klein, T. E. 2023: 19386400231170967

  Abstract

  Tendinopathy of the foot and ankle is a common clinical problem for which the exact etiology is poorly understood. The field of epigenetics has been a recent focus of this investigation. The purpose of this article was to review the genomic advances in foot and ankle tendinopathy that could potentially be used to stratify disease risk and create preventative or therapeutic agents. A multi-database search of PubMed, Cochrane, Google Scholar, and clinicaltrials.gov from January 1, 2000 to July 1, 2022 was performed. A total of 18 articles met inclusion and exclusion criteria for this review. The majority of such research utilized case-control candidate gene association to identify different genetic risk factors associated with chronic tendinopathy. Polymorphisms in collagen genes COL5A1, COL27A1, and COL1A1 were noted at a significantly higher frequency in Achilles tendinopathy versus control groups. Other allelic variations that were observed at an increased incidence in Achilles tendinopathy were TNC and CASP8. The extracellular matrix (ECM) demonstrated macroscopic changes in Achilles tendinopathy, including an increase in aggrecan and biglycan mRNA expression, and increased expression of multiple matrix metalloproteinases. Cytokine expression was also influenced in pathology and aberrantly demonstrated dynamic response to mechanical load. The pathologic accumulation of ECM proteins and cytokine expression alters the adaptive response normal tendon has to physiologic stress, further propagating the risk for tendinopathy. By identifying and understanding the epigenetic mediators that lead to tendinopathy, therapeutic agents can be developed to target the exact underlying etiology and minimize side effects.Level of Evidence: Level IV: Systematic Review of Level II-IV Studies.

  View details for DOI 10.1177/19386400231170967

  View details for PubMedID 37165881

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A Genotypes and Serotonin Reuptake Inhibitor Antidepressants. Clinical pharmacology and therapeutics Bousman, C. A., Stevenson, J. M., Ramsey, L. B., Sangkuhl, K., Hicks, J. K., Strawn, J. R., Singh, A. B., Ruaño, G., Mueller, D. J., Tsermpini, E. E., Brown, J. T., Bell, G. C., Leeder, J. S., Gaedigk, A., Scott, S. A., Klein, T. E., Caudle, K. E., Bishop, J. R. 2023

  Abstract

  Serotonin reuptake inhibitor antidepressants, including selective serotonin reuptake inhibitors (SSRIs; i.e., citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline), serotonin and norepinephrine reuptake inhibitors (SNRIs; i.e., desvenlafaxine, duloxetine, levomilnacipran, milnacipran, and venlafaxine), and serotonin modulators with SSRI-like properties (i.e., vilazodone and vortioxetine) are primary pharmacologic treatments for major depressive and anxiety disorders. Genetic variation in CYP2D6, CYP2C19, and CYP2B6 influences the metabolism of many of these antidepressants, which may potentially affect dosing, efficacy, and tolerability. In addition, the pharmacodynamic genes SLC6A4 (serotonin transporter) and HTR2A (serotonin-2A receptor) have been examined in relation to efficacy and side effect profiles of these drugs. This guideline updates and expands the 2015 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 and CYP2C19 genotypes and SSRI dosing and summarizes the impact of CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A genotypes on antidepressant dosing, efficacy, and tolerability. We provide recommendations for using CYP2D6, CYP2C19 and CYP2B6 genotype results to help inform prescribing these antidepressants and describe the existing data for SLC6A4 and HTR2A which do not support their clinical use in antidepressant prescribing.

  View details for DOI 10.1002/cpt.2903

  View details for PubMedID 37032427

• The Role of Global and Local Ancestry on Clopidogrel Response in African Americans Yang, G., Alarcon, C., Friedman, P., Gong, L., Klein, T., O'Brien, T., Nutescu, E. A., Tuck, M., Meltzer, D., Perera, M. A., Altman, R. B., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2023: 221-232

  Abstract

  Pharmacogenomics has long lacked dedicated studies in African Americans, resulting in a lack of indepth data in this populations. The ACCOuNT consortium has collected a cohort of 167 African American patients on steady state clopidogrel with the goal of discovering population specific variation that may contribute to the response of this anti-platelet agent. Here we analyze the role of both global and local ancestry on the clinical phenotypes of P2Y12 reaction units (PRU) and high on-treatment platelet reactivity (HTPR) in this cohort. We found that local ancestry at the TSS of three genes, IRS-1, ABCB1 and KDR were nominally associated with PRU, and local ancestry-adjusted SNP association identified variants in ITGA2 associated to increased PRU. These finding help to explain the variability in drug response seen in African Americans, especially as few studies on genes outside of CYP2C19 has been conducted in this population.

  View details for Web of Science ID 001229095800021

  View details for PubMedID 36540979

  View details for PubMedCentralID PMC9782753

• Evaluating the frequency and the impact of pharmacogenetic alleles in an ancestrally diverse Biobank population. Journal of translational medicine Verma, S. S., Keat, K., Li, B., Hoffecker, G., Risman, M., Sangkuhl, K., Whirl-Carrillo, M., Dudek, S., Verma, A., Klein, T. E., Ritchie, M. D., Tuteja, S. 2022; 20 (1): 550

  Abstract

  Pharmacogenomics (PGx) aims to utilize a patient's genetic data to enable safer and more effective prescribing of medications. The Clinical Pharmacogenetics Implementation Consortium (CPIC) provides guidelines with strong evidence for 24 genes that affect 72 medications. Despite strong evidence linking PGx alleles to drug response, there is a large gap in the implementation and return of actionable pharmacogenetic findings to patients in standard clinical practice. In this study, we evaluated opportunities for genetically guided medication prescribing in a diverse health system and determined the frequencies of actionable PGx alleles in an ancestrally diverse biobank population.A retrospective analysis of the Penn Medicine electronic health records (EHRs), which includes ~ 3.3 million patients between 2012 and 2020, provides a snapshot of the trends in prescriptions for drugs with genotype-based prescribing guidelines ('CPIC level A or B') in the Penn Medicine health system. The Penn Medicine BioBank (PMBB) consists of a diverse group of 43,359 participants whose EHRs are linked to genome-wide SNP array and whole exome sequencing (WES) data. We used the Pharmacogenomics Clinical Annotation Tool (PharmCAT), to annotate PGx alleles from PMBB variant call format (VCF) files and identify samples with actionable PGx alleles.We identified ~ 316.000 unique patients that were prescribed at least 2 drugs with CPIC Level A or B guidelines. Genetic analysis in PMBB identified that 98.9% of participants carry one or more PGx actionable alleles where treatment modification would be recommended. After linking the genetic data with prescription data from the EHR, 14.2% of participants (n = 6157) were prescribed medications that could be impacted by their genotype (as indicated by their PharmCAT report). For example, 856 participants received clopidogrel who carried CYP2C19 reduced function alleles, placing them at increased risk for major adverse cardiovascular events. When we stratified by genetic ancestry, we found disparities in PGx allele frequencies and clinical burden. Clopidogrel users of Asian ancestry in PMBB had significantly higher rates of CYP2C19 actionable alleles than European ancestry users of clopidrogrel (p < 0.0001, OR = 3.68).Clinically actionable PGx alleles are highly prevalent in our health system and many patients were prescribed medications that could be affected by PGx alleles. These results illustrate the potential utility of preemptive genotyping for tailoring of medications and implementation of PGx into routine clinical care.

  View details for DOI 10.1186/s12967-022-03745-5

  View details for PubMedID 36443877

  View details for PubMedCentralID 3098762

• Expanded Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Medication Use in the Context of G6PD Genotype. Clinical pharmacology and therapeutics Gammal, R. S., Pirmohamed, M., Somogyi, A. A., Morris, S. A., Formea, C. M., Elchynski, A. L., Oshikoya, K. A., McLeod, H. L., Haidar, C. E., Whirl-Carrillo, M., Klein, T. E., Caudle, K. E., Relling, M. V. 2022

  Abstract

  Glucose-6-phosphate dehydrogenase (G6PD) deficiency is associated with development of acute hemolytic anemia in the setting of oxidative stress, which can be caused by medication exposure. Regulatory agencies worldwide warn against the use of certain medications in G6PD deficient persons, but in many cases, this information is conflicting, and the clinical evidence is sparse. This guideline provides information on using G6PD genotype as part of the diagnosis of G6PD deficiency and classifies medications that have been previously implicated as unsafe in G6PD deficient individuals by one or more sources. We classify these medications as high, medium, or low-to-no risk based on a systematic review of the published evidence of the gene-drug associations and regulatory warnings. In patients with G6PD deficiency, high risk medications should be avoided, medium risk medications should be used with caution, and low-to-no risk medications can be used with standard precautions, without regard to G6PD phenotype. This new document replaces the prior Clinical Pharmacogenetics Implementation Consortium guideline for rasburicase therapy in the context of G6PD genotype (updates at www.cpicpgx.org).

  View details for DOI 10.1002/cpt.2735

  View details for PubMedID 36049896

• PharmVar GeneFocus: SLCO1B1. Clinical pharmacology and therapeutics Ramsey, L. B., Gong, L., Lee, S., Wagner, J. B., Zhou, X., Sangkuhl, K., Adams, S. M., Straka, R. J., Empey, P. E., Boone, E. C., Klein, T. E., Niemi, M., Gaedigk, A. 2022

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) is now providing star (*) allele nomenclature for the highly polymorphic human SLCO1B1 gene encoding the organic anion transporting polypeptide 1B1 (OATP1B1) drug transporter. Genetic variation within the SLCO1B1 gene locus impacts drug transport, which can lead to altered pharmacokinetic profiles of several commonly prescribed drugs. Variable OATP1B1 function is of particular importance regarding hepatic uptake of statins and the risk of statin-associated musculoskeletal symptoms (SAMS). To introduce this important drug transporter gene into the PharmVar database and serve as a unified reference of haplotype variation moving forward, an international group of gene experts has performed an extensive review of all published SLCO1B1 star alleles. Previously published star alleles were self-assigned by authors and only loosely followed the star nomenclature system which was first developed for Cytochrome P450 genes. This nomenclature system has been standardized by PharmVar and is now applied to other important pharmacogenes such as SLCO1B1. In addition, data from the 1000 Genomes Project and investigator-submitted data were utilized to confirm existing haplotypes, fill knowledge gaps and/or define novel star alleles. The PharmVar-developed SLCO1B1 nomenclature has been incorporated by the Clinical Pharmacogenetics Implementation Consortium (CPIC) 2022 guideline on statin-associated musculoskeletal symptoms.

  View details for DOI 10.1002/cpt.2705

  View details for PubMedID 35797228

• PharmGKB summary: acyclovir/ganciclovir pathway. Pharmacogenetics and genomics Maillard, M., Gong, L., Nishii, R., Yang, J. J., Whirl-Carrillo, M., Klein, T. E. 2022; 32 (5): 201-208

  View details for DOI 10.1097/FPC.0000000000000474

  View details for PubMedID 35665708

• ACMG SF v3.1 list for reporting of secondary findings in clinical exome and genome sequencing: A policy statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics Miller, D. T., Lee, K., Abul-Husn, N. S., Amendola, L. M., Brothers, K., Chung, W. K., Gollob, M. H., Gordon, A. S., Harrison, S. M., Hershberger, R. E., Klein, T. E., Richards, C. S., Stewart, D. R., Martin, C. L., ACMG Secondary Findings Working Group. Electronic address: documents@acmg.net,, 2022; 24 (7): 1407-1414

  View details for DOI 10.1016/j.gim.2022.04.006

  View details for PubMedID 35802134

• Apixaban Concentrations in Routine Clinical Care of Older Adults With Nonvalvular Atrial Fibrillation. JACC. Advances Thomas, A., Fang, M. C., Kogan, S., Hubbard, C. C., Friedman, P. N., Gong, L., Klein, T. E., Nutescu, E. A., O'Brien, T. J., Tuck, M., Perera, M. A., Schwartz, J. B. 2022; 1 (2)

  Abstract

  Direct-acting oral anticoagulants are first-line agents for prevention of stroke in patients with nonvalvular atrial fibrillation (NVAF), but data are limited for the oldest patients, and with reduced dosing.To determine steady-state apixaban peak and trough concentrations during routine care of older adults with NVAF, compare concentrations to clinical trial concentrations, and explore factors associated with concentrations.A cross-sectional study of medically stable older adults with NVAF (≥75 years or ≥70 years if Black) receiving apixaban. Peak (2-4.4 hours post-dose) and trough (before next dose) concentrations were determined by anti-Xa activity calibrated chromogenic assay. Patient characteristics associated with concentrations were determined by multivariate modeling.The median age of patients (n = 115) was 80 (interquartile range: 77-84) years. The cohort comprised 46 women and 69 men; of which 98 are White, 11 Black, and 6 Asian. With 5 mg twice daily per labelling (n = 88), peak concentrations were higher in women: 248 ± 105 vs 174 ± 67 ng/mL in men (P < 0.001) and exceeded expected 95% range in 6 of 30 vs 0 of 55 men (P = 0.002). With 2.5 mg twice daily per label (n = 11), concentrations were <5 mg twice daily (peak: 136 ± 87 vs 201 ± 90 ng/mL, P = 0.026; trough: 65 ± 28 vs 109 ± 56 ng/mL, P < 0.001), but not different than 2.5 mg twice daily without reduction criteria (n = 13; peak: 132 ± 88; trough: 65 ± 31 ng/mL). Covariates associated with concentrations included sex, number of daily medications, and creatinine clearance.Older women had higher than expected peak apixaban concentrations, and 2.5 mg twice daily produced lower concentrations than standard dosing. Factors not currently included in dosing recommendations affected concentrations. The impact of apixaban concentrations on outcomes needs evaluation.

  View details for DOI 10.1016/j.jacadv.2022.100039

  View details for PubMedID 37961076

  View details for PubMedCentralID PMC10643025

• The Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for SLCO1B1, ABCG2, and CYP2C9 and statin-associated musculoskeletal symptoms. Clinical pharmacology and therapeutics Cooper-DeHoff, R. M., Niemi, M., Ramsey, L. B., Luzum, J. A., Tarkiainen, E. K., Straka, R. J., Gong, L., Tuteja, S., Wilke, R. A., Wadelius, M., Larson, E. A., Roden, D. M., Klein, T. E., Yee, S. W., Krauss, R. M., Turner, R. M., Palaniappan, L., Gaedigk, A., Giacomini, K. M., Caudle, K. E., Voora, D. 2022

  Abstract

  Statins reduce cholesterol, prevent cardiovascular disease, and are among the most commonly prescribed medications in the world. Statin-associated musculoskeletal symptoms (SAMS) impact statin adherence and ultimately can impede the long-term effectiveness of statin therapy. There are several identified pharmacogenetic variants that impact statin disposition and adverse events during statin therapy. SLCO1B1 encodes a transporter (SLCO1B1; alternative names include OATP1B1 or OATP-C) that facilitates the hepatic uptake of all statins. ABCG2 encodes an efflux transporter (BCRP) that modulates the absorption and disposition of rosuvastatin. CYP2C9 encodes a Phase-I drug metabolizing enzyme responsible for the oxidation of some statins. Genetic variation in each of these genes alters systemic exposure to statins (i.e., simvastatin, rosuvastatin, pravastatin, pitavastatin, atorvastatin, fluvastatin, lovastatin), which can increase the risk for SAMS. We summarize the literature supporting these associations and provide therapeutic recommendations for statins based on SLCO1B1, ABCG2, and CYP2C9 genotype with the goal of improving the overall safety, adherence and effectiveness of statin therapy. This document replaces the 2012 and 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for SLCO1B1 and simvastatin-induced myopathy.

  View details for DOI 10.1002/cpt.2557

  View details for PubMedID 35152405

• Clinical pharmacogenomic testing and reporting: A technical standard of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics Tayeh, M. K., Gaedigk, A., Goetz, M. P., Klein, T. E., Lyon, E., McMillin, G. A., Rentas, S., Shinawi, M., Pratt, V. M., Scott, S. A., ACMG Laboratory Quality Assurance Committee. Electronic address: documents@acmg.net,, 2022

  Abstract

  Pharmacogenomic testing interrogates germline sequence variants implicated in interindividual drug response variability to infer a drug response phenotype and to guide medication management for certain drugs. Specifically, discrete aspects of pharmacokinetics, such as drug metabolism, and pharmacodynamics, as well as drug sensitivity, can be predicted by genes that code for proteins involved in these pathways. Pharmacogenomics is unique and differs from inherited disease genetics because the drug response phenotype can be drug-dependent and is often unrecognized until an unexpected drug reaction occurs or a patient fails to respond to a medication. Genes and variants with sufficiently high levels of evidence and consensus may be included in a clinical pharmacogenomic test; however, result interpretation and phenotype prediction can be challenging for some genes and medications. This document provides a resource for laboratories to develop and implement clinical pharmacogenomic testing by summarizing publicly available resources and detailing best practices for pharmacogenomic nomenclature, testing, result interpretation, and reporting.

  View details for DOI 10.1016/j.gim.2021.12.009

  View details for PubMedID 35177334

• PharmGKB summary: heparin-induced thrombocytopenia pathway, adverse drug reaction. Pharmacogenetics and genomics Miller, E., Norwood, C., Giles, J. B., Huddart, R., Karnes, J. H., Whirl-Carrillo, M., Klein, T. E. 1800

  View details for DOI 10.1097/FPC.0000000000000465

  View details for PubMedID 35102073

• An Investigation of the Knowledge Overlap between Pharmacogenomics and Disease Genetics. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Li, B., Whirl-Carrillo, M., Wright, M. W., Babb, L., Rehm, H. L., Klein, T. E. 2022; 27: 385-396

  Abstract

  Precision medicine faces many challenges, including the gap of knowledge between disease genetics and pharmacogenomics (PGx). Disease genetics interprets the pathogenicity of genetic variants for diagnostic purposes, while PGx investigates the genetic influences on drug responses. Ideally, the quality of health care would be improved from the point of disease diagnosis to drug prescribing if PGx is integrated with disease genetics in clinical care. However, PGx genes or variants are usually not reported as a secondary finding even if they are included in a clinical genetic test for diagnostic purposes. This happens even though the detection of PGx variants can provide valuable drug prescribing recommendations. One underlying reason is the lack of systematic classification of the knowledge overlap between PGx and disease genetics. Here, we address this issue by analyzing gene and genetic variant annotations from multiple expert-curated knowledge databases, including PharmGKB, CPIC, ClinGen and ClinVar. We further classified genes based on the strength of evidence supporting a gene's pathogenic role or PGx effect as well as the level of clinical actionability of a gene. Twenty-six genes were found to have pathogenic variation associated with germline diseases as well as strong evidence for a PGx association. These genes were classified into four sub-categories based on the distinct connection between the gene's pathogenic role and PGx effect. Moreover, we have also found thirteen RYR1 genetic variants that were annotated as pathogenic and at the same time whose PGx effect was supported by a preponderance of evidence and given drug prescribing recommendations. Overall, we identified a nontrivial number of gene and genetic variant overlaps between disease genetics and PGx, which laid out a foundation for combining PGx and disease genetics to improve clinical care from disease diagnoses to drug prescribing and adherence.

  View details for PubMedID 34890165

• PharmVar GeneFocus: CYP3A5. Clinical pharmacology and therapeutics Rodriguez-Antona, C., Savieo, J. L., Lauschke, V. M., Sangkuhl, K., Drögemöller, B. I., Wang, D., van Schaik, R. H., Gilep, A. A., Peter, A. P., Boone, E. C., Ramey, B. E., Klein, T. E., Whirl-Carrillo, M., Pratt, V. M., Gaedigk, A. 2022

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) catalogs star (*) allele nomenclature for the polymorphic human CYP3A5 gene. Genetic variation within the CYP3A5 gene locus impacts the metabolism of several clinically important drugs, including the immunosuppressants tacrolimus, sirolimus, cyclosporine, and the benzodiazepine midazolam. Variable CYP3A5 activity is of clinical importance regarding tacrolimus metabolism. This GeneFocus provides a CYP3A5 gene summary with a focus on aspects regarding standardized nomenclature. In addition, this review also summarizes recent changes and updates including the retirement of several allelic variants and provides an overview of how PharmVar CYP3A5 star allele nomenclature is utilized by the Pharmacogenomics Knowledgebase (PharmGKB) and the Clinical Pharmacogenetics Implementation Consortium (CPIC).

  View details for DOI 10.1002/cpt.2563

  View details for PubMedID 35202484

• Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update. Clinical pharmacology and therapeutics Lee, C. R., Luzum, J. A., Sangkuhl, K., Gammal, R. S., Sabatine, M. S., Stein, C. M., Kisor, D. F., Limdi, N. A., Lee, Y. M., Scott, S. A., Hulot, J. S., Roden, D. M., Gaedigk, A., Caudle, K. E., Klein, T. E., Johnson, J. A., Shuldiner, A. R. 2022

  Abstract

  CYP2C19 catalyzes the bioactivation of the antiplatelet prodrug clopidogrel, and CYP2C19 genotype impacts clopidogrel active metabolite formation. CYP2C19 intermediate and poor metabolizers who receive clopidogrel experience reduced platelet inhibition and increased risk for major adverse cardiovascular and cerebrovascular events. This guideline is an update to the 2013 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for the use of clopidogrel based on CYP2C19 genotype and includes expanded indications for CYP2C19 genotype-guided antiplatelet therapy, increased strength of recommendation for CYP2C19 intermediate metabolizers, updated CYP2C19 genotype to phenotype translation, and evidence from an expanded literature review (updates at www.cpicpgx.org).

  View details for DOI 10.1002/cpt.2526

  View details for PubMedID 35034351

• Correction to: ACMG SF v3.0 list for reporting of secondary findings in clinical exome and genome sequencing: a policy statement of the American College of Medical Genetics and Genomics (ACMG). Genetics in medicine : official journal of the American College of Medical Genetics Miller, D. T., Lee, K., Chung, W. K., Gordon, A. S., Herman, G. E., Klein, T. E., Stewart, D. R., Amendola, L. M., Adelman, K., Bale, S. J., Gollob, M. H., Harrison, S. M., Hershberger, R. E., McKelvey, K., Richards, C. S., Vlangos, C. N., Watson, M. S., Martin, C. L., ACMG Secondary Findings Working Group 2021

  View details for DOI 10.1038/s41436-021-01278-8

  View details for PubMedID 34345026

• PharmVar GeneFocus: CYP2C9. Clinical pharmacology and therapeutics Sangkuhl, K., Claudio-Campos, K., Cavallari, L. H., Agundez, J., Whirl-Carrillo, M., Duconge, J., Del Tredici, A. L., Wadelius, M., Botton, M. R., Woodahl, E. L., Scott, S. A., Klein, T. E., Pratt, V. M., Daly, A. K., Gaedigk, A. 2021

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2C9 gene.Genetic variation within the CYP2C9 gene locusimpacts the metabolism or bioactivation of many clinically important drugs including NSAIDs, phenytoin, anti-diabetic agents and angiotensin receptor blocker. Variable CYP2C9 activity is of particular importance regarding efficacy and safety of warfarin and siponimod as indicated in their package inserts. This GeneFocus provides a comprehensive overview and summary of CYP2C9 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.2333

  View details for PubMedID 34109627

• PharmVar: A Global Resource and Repository for Pharmacogene Variation. Clinical pharmacology and therapeutics Gaedigk, A., Casey, S. T., Whirl-Carrillo, M., Miller, N. A., Klein, T. E. 2021

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) was founded in 2017 to provide the clinical and research communities a repository and standardized nomenclature of genes contributing to variability in drug metabolism and response. Over the past four years, PharmVar has provided the research and clinical pharmacogenetics/genomics communities with essential information for flagship pharmacogenes with well-established drug-gene relationships and published clinical guidelines such as CYP2C9, CYP2C19 and CYP2D6. In this perspective we highlight recent milestones and standardization efforts.

  View details for DOI 10.1002/cpt.2321

  View details for PubMedID 34091888

• Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for the use of aminoglycosides based on MT-RNR1 genotype. Clinical pharmacology and therapeutics McDermott, J. H., Wolf, J., Hoshitsuki, K., Huddart, R., Caudle, K. E., Whirl-Carrillo, M., Steyger, P. S., Smith, R. J., Cody, N., Rodriguez-Antona, C., Klein, T. E., Newman, W. G. 2021

  Abstract

  Aminoglycosides are widely used antibiotics with notable side effects such as nephrotoxicity, vestibulotoxicity and sensorineural hearing loss (cochleotoxicity). MT-RNR1 is a gene that encodes the 12s rRNA subunit and is the mitochondrial homologue of the prokaryotic 16s rRNA. Some MT-RNR1 variants (i.e., m.1095T>C; m.1494C>T; m.1555A>G) more closely resemble the bacterial 16s rRNA subunit and result in increased risk of aminoglycoside-induced hearing loss. Use of aminoglycosides should be avoided in individuals with an MT-RNR1 variant associated with an increased risk of aminoglycoside-induced hearing loss unless the high risk of permanent hearing loss is outweighed by the severity of infection and safe or effective alternative therapies are not available. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for the use of aminoglycosides based on MT-RNR1 genotype (updates at https://cpicpgx.org/guidelines/ and www.pharmgkb.org).

  View details for DOI 10.1002/cpt.2309

  View details for PubMedID 34032273

• Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6, OPRM1, and COMT genotype and select opioid therapy. Clinical pharmacology and therapeutics Crews, K. R., Monte, A. A., Huddart, R. n., Caudle, K. E., Kharasch, E. D., Gaedigk, A. n., Dunnenberger, H. M., Leeder, J. S., Callaghan, J. T., Samer, C. F., Klein, T. E., Haidar, C. E., Van Driest, S. L., Ruano, G. n., Sangkuhl, K. n., Cavallari, L. H., Müller, D. J., Prows, C. A., Nagy, M. n., Somogyi, A. A., Skaar, T. C. 2021

  Abstract

  Opioids are mainly used to treat both acute and chronic pain. Several opioids are metabolized to some extent by CYP2D6 (codeine, tramadol, hydrocodone, oxycodone and methadone). Polymorphisms in CYP2D6 have been studied for an association with the clinical effect and safety of these drugs. Other genes which have been studied for their association with opioid clinical effect or adverse events include OPRM1 (mu receptor) and COMT (catechol-O-methyltransferase). This guideline updates and expands the 2014 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6 genotype and codeine therapy and includes a summation of the evidence describing the impact of CYP2D6, OPRM1 and COMT on opioid analgesia and adverse events. We provide therapeutic recommendations for the use of CYP2D6 genotype results for prescribing codeine and tramadol and describe the limited and/or weak data for CYP2D6 and hydrocodone, oxycodone and methadone and for OPRM1 and COMT for clinical use.

  View details for DOI 10.1002/cpt.2149

  View details for PubMedID 33387367

• PharmVar GeneFocus: CYP2B6. Clinical pharmacology and therapeutics Desta, Z. n., El-Boraie, A. n., Gong, L. n., Somogyi, A. A., Lauschke, V. M., Dandara, C. n., Klein, K. n., Miller, N. n., Klein, T. E., Tyndale, R. F., Whirl-Carrillo, M. n., Gaedigk, A. n. 2021

  Abstract

  The Pharmacogene Variation Consortium (PharmVar) catalogues star (*) allele nomenclature for the polymorphic human CYP2B6 gene. Genetic variation within the CYP2B6 gene locus impacts the metabolism or bioactivation of clinically important drugs. Of particular importance are efficacy and safety concerns regarding: efavirenz, which is used for the treatment of HIV type-1 infection; methadone, a mainstay in the treatment of opioid use disorder and as an analgesic; ketamine, used as an antidepressant and analgesic; and bupropion, which is prescribed to treat depression and for smoking cessation. This GeneFocus provides a comprehensive overview and summary of CYP2B6 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.2166

  View details for PubMedID 33448339

• Verifying Nomenclature of DNA Variants in Submitted Manuscripts: Guidance for Journals. Human mutation Higgins, J., Dalgleish, R., den Dunnen, J. T., Barsh, G., Freeman, P. J., Cooper, D. N., Cullinan, S., Davies, K. E., Dorkins, H., Gong, L., Imoto, I., Klein, T. E., Korf, B., Misra, A., Paalman, M. H., Ratzel, S., Reichardt, J. K., Rehm, H. L., Tokunaga, K., Weck, K. E., Cutting, G. R. 2020

  Abstract

  Documenting variation in our genomes is important for research and clinical care. Accuracy in the description of DNA variants is therefore essential. To address this issue, the Human Variome Project convened a committee to evaluate the feasibility of requiring authors to verify that all variants submitted for publication complied with a widely accepted standard for description. After a pilot study at two journals, the committee agreed that requiring authors to verify that variants complied with Human Genome Variation Society nomenclature is a reasonable step toward standardizing the worldwide inventory of human variation. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/humu.24144

  View details for PubMedID 33252176

• Pharmacogenetics at Scale: An Analysis of the UK Biobank. Clinical pharmacology and therapeutics McInnes, G., Lavertu, A., Sangkuhl, K., Klein, T. E., Whirl-Carrillo, M., Altman, R. B. 2020

  Abstract

  Pharmacogenetics (PGx) studies the influence of genetic variation on drug response. Clinically actionable associations inform guidelines created by the Clinical Pharmacogenetics Implementation Consortium (CPIC), but the broad impact of genetic variation on entire populations is not well-understood. We analyzed PGx allele and phenotype frequencies for 487,409 participants in the U.K. Biobank, the largest PGx study to date. For fourteen CPIC pharmacogenes known to influence human drug response, we find that 99.5% of individuals may have an atypical response to at least one drug; on average they may have an atypical response to 10.3 drugs. Nearly 24% of participants have been prescribed a drug for which they are predicted to have an atypical response. Non-European populations carry a greater frequency of variants that are predicted to be functionally deleterious; many of these are not captured by current PGx allele definitions. Strategies for detecting and interpreting rare variation will be critical for enabling broad application of pharmacogenetics.

  View details for DOI 10.1002/cpt.2122

  View details for PubMedID 33237584

• 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

• 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

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2C19 and Proton Pump Inhibitor Dosing. Clinical pharmacology and therapeutics Lima, J. J., Thomas, C. D., Barbarino, J., Desta, Z., Van Driest, S. L., El Rouby, N., Johnson, J. A., Cavallari, L. H., Shakhnovich, V., Thacker, D. L., Scott, S. A., Schwab, M., Uppugunduri, C. R., Formea, C. M., Franciosi, J. P., Sangkuhl, K., Gaedigk, A., Klein, T. E., Gammal, R. S., Furuta, T. 2020

  Abstract

  Proton pump inhibitors (PPIs) are widely used for acid suppression in the treatment and prevention of many conditions including gastroesophageal reflux disease, gastric and duodenal ulcers, erosive esophagitis, H. pylori infection, and pathological hypersecretory conditions. Most PPIs are metabolized primarily by CYP2C19 into inactive metabolites, and CYP2C19 genotype has been linked to PPI exposure, efficacy, and adverse effects. We summarize the evidence from the literature and provide therapeutic recommendations for PPI prescribing based on CYP2C19 genotype (updates at www.cpicpgx.org). The potential benefits of using CYP2C19 genotype data to guide PPI therapy include 1) identifying patients with genotypes predictive of lower plasma exposure and prescribing them a higher dose that will increase the likelihood of efficacy; and 2) identifying patients on chronic therapy with genotypes predictive of higher plasma exposure and prescribing them a decreased dose to minimize the risk of toxicity that is associated with long-term PPI use, particularly at higher plasma concentrations.

  View details for DOI 10.1002/cpt.2015

  View details for PubMedID 32770672

• 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

• PharmGKB summary: lamotrigine pathway, pharmacokinetics and pharmacodynamics. Pharmacogenetics and genomics Mitra-Ghosh, T., Callisto, S. P., Lamba, J. K., Remmel, R. P., Birnbaum, A. K., Barbarino, J. M., Klein, T. E., Altman, R. B. 2020

  View details for DOI 10.1097/FPC.0000000000000397

  View details for PubMedID 32187155

• Genome-wide association study of platelet reactivity and cardiovascular response in patients treated with clopidogrel: a study by the International Clopidogrel Pharmacogenomics Consortium (ICPC). Clinical pharmacology and therapeutics Verma, S. S., Bergmeijer, T. O., Gong, L. n., Reny, J. L., Lewis, J. P., Mitchell, B. D., Alexopoulos, D. n., Aradi, D. n., Altman, R. B., Bliden, K. n., Bradford, Y. n., Campo, G. n., Chang, K. n., Cleator, J. H., Déry, J. P., Dridi, N. P., Fernandez-Cadenas, I. n., Fontana, P. n., Gawaz, M. n., Geisler, T. n., Gensini, G. F., Giusti, B. n., Gurbel, P. A., Hochholzer, W. n., Holmvang, L. n., Kim, E. Y., Kim, H. S., Marcucci, R. n., Montaner, J. n., Backman, J. D., Pakyz, R. E., Roden, D. M., Schaeffeler, E. n., Schwab, M. n., Shin, J. G., Siller-Matula, J. M., Ten Berg, J. M., Trenk, D. n., Valgimigli, M. n., Wallace, J. n., Wen, M. S., Kubo, M. n., Lee, M. T., Whaley, R. n., Winter, S. n., Klein, T. E., Shuldiner, A. R., Ritchie, M. D. 2020

  Abstract

  Antiplatelet response to clopidogrel shows wide variation, and poor response is correlated with adverse clinical outcomes. CYP2C19 loss-of-function alleles play an important role in this response, but account for only a small proportion of variability in response to clopidogrel. An aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify other genetic determinants of clopidogrel pharmacodynamics and clinical response. A genome-wide association study (GWAS) was performed using DNA from 2,750 European ancestry individuals, using adenosine diphosphate (ADP) induced platelet reactivity and major cardiovascular and cerebrovascular events as outcome parameters. GWAS for platelet reactivity revealed a strong signal for CYP2C19*2 (p-value=1.67e-33). After correction for CYP2C19*2 no other SNP reached genome-wide significance. GWAS for a combined clinical endpoint of cardiovascular death, myocardial infarction, or stroke (5.0% event rate) or a combined endpoint of cardiovascular death or myocardial infarction (4.7% event rate) showed no significant results, although in coronary artery disease, percutaneous coronary intervention, and acute coronary syndrome subgroups, mutations in SCOS5P1, CDC42BPA and CTRAC1 showed genome-wide significance (lowest p-values: 1.07e-09, 4.53e-08 and 2.60e-10, respectively). CYP2C19*2 is the strongest genetic determinant of on-clopidogrel platelet reactivity. We identified three novel associations in clinical outcome subgroups, suggestive for each of these outcomes.

  View details for DOI 10.1002/cpt.1911

  View details for PubMedID 32472697

• Review and Consensus on Pharmacogenomic Testing in Psychiatry. Pharmacopsychiatry Bousman, C. A., Bengesser, S. A., Aitchison, K. J., Amare, A. T., Aschauer, H. n., Baune, B. T., Asl, B. B., Bishop, J. R., Burmeister, M. n., Chaumette, B. n., Chen, L. S., Cordner, Z. A., Deckert, J. n., Degenhardt, F. n., DeLisi, L. E., Folkersen, L. n., Kennedy, J. L., Klein, T. E., McClay, J. L., McMahon, F. J., Musil, R. n., Saccone, N. L., Sangkuhl, K. n., Stowe, R. M., Tan, E. C., Tiwari, A. K., Zai, C. C., Zai, G. n., Zhang, J. n., Gaedigk, A. n., Müller, D. J. 2020

  Abstract

  The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

  View details for DOI 10.1055/a-1288-1061

  View details for PubMedID 33147643

• Effect of CYP3A4*22 and PPAR-α Genetic Variants on Platelet Reactivity in Patients Treated with Clopidogrel and Lipid-Lowering Drugs Undergoing Elective Percutaneous Coronary Intervention. Genes Bergmeijer, T. O., Yasmina, A. n., Vos, G. J., Janssen, P. W., Hackeng, C. M., Kelder, J. C., Verma, S. S., Ritchie, M. D., Gong, L. n., Klein, T. E., Icpc Investigators, n. n., de Boer, A. n., Klungel, O. H., Ten Berg, J. M., Deneer, V. H. 2020; 11 (9)

  Abstract

  This study aims to determine whether genetic variants that influence CYP3A4 expression are associated with platelet reactivity in clopidogrel-treated patients undergoing elective percutaneous coronary intervention (PCI), and to evaluate the influence of statin/fibrate co-medication on these associations. A study cohort was used containing 1124 consecutive elective PCI patients in whom CYP3A4*22 and PPAR-α (G209A and A208G) SNPs were genotyped and the VerifyNow P2Y12 platelet reactivity test was performed. Minor allele frequencies were 0.4% for CYP3A4*22/*22, 6.8% for PPAR-α G209A AA, and 7.0% for PPAR-α A208G GG. CYP3A4*22 was not associated with platelet reactivity. The PPAR-α genetic variants were significantly associated with platelet reactivity (G209A AA: -24.6 PRU [-44.7, -4.6], p = 0.016; A208G GG: -24.6 PRU [-44.3, -4.8], p = 0.015). Validation of these PPAR-α results in two external cohorts, containing 716 and 882 patients, respectively, showed the same direction of effect, although not statistically significant. Subsequently, meta-analysis of all three cohorts showed statistical significance of both variants in statin/fibrate users (p = 0.04 for PPAR-a G209A and p = 0.03 for A208G), with no difference in statin/fibrate non-users. In conclusion, PPAR-α G209A and A208G were associated with lower platelet reactivity in patients undergoing elective PCI who were treated with clopidogrel and statin/fibrate co-medication. Further research is necessary to confirm these findings.

  View details for DOI 10.3390/genes11091068

  View details for PubMedID 32932966

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

• PGxMine: Text mining for curation of PharmGKB Lever, J., Barbarino, J. M., Gong, L., Huddart, R., Sangkuhl, K., Whaley, R., Whirl-Carrillo, M., Woon, M., Klein, T. E., Altman, R. B., Altman, R. B., Dunker, A. K., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2020: 611-622

  View details for Web of Science ID 000702064500054

• PharmGKB summary: very important pharmacogene information for CACNA1S. Pharmacogenetics and genomics Sangkuhl, K., Dirksen, R. T., Alvarellos, M. L., Altman, R. B., Klein, T. E. 2019

  View details for DOI 10.1097/FPC.0000000000000393

  View details for PubMedID 31851124

• PharmGKB summary: sertraline pathway, pharmacokinetics. Pharmacogenetics and genomics Huddart, R., Hicks, J. K., Ramsey, L. B., Strawn, J. R., Smith, D. M., Bobonis Babilonia, M., Altman, R. B., Klein, T. E. 2019

  View details for DOI 10.1097/FPC.0000000000000392

  View details for PubMedID 31851125

• 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

• Pharmacogenomic Polygenic Response Score Predicts Ischemic Events and Cardiovascular Mortality in Clopidogrel-Treated Patients. European heart journal. Cardiovascular pharmacotherapy Lewis, J. P., Backman, J. D., Reny, J., Bergmeijer, T. O., Mitchell, B. D., Ritchie, M. D., Dery, J., Pakyz, R. E., Gong, L., Ryan, K., Kim, E., Aradi, D., Fernandez-Cadenas, I., Lee, M. T., Whaley, R. M., Montaner, J., Gensini, G. F., Cleator, J. H., Chang, K., Holmvang, L., Hochholzer, W., Roden, D. M., Winter, S., Altman, R., Alexopoulos, D., Kim, H., Gawaz, M., Bliden, K., Valgimigli, M., Marcucci, R., Campo, G., Schaeffeler, E., Dridi, N. P., Wen, M., Shin, J. G., Fontana, P., Giusti, B., Geisler, T., Kubo, M., Trenk, D., Siller-Matula, J. M., Ten Berg, J. M., Gurbel, P. A., Schwab, M., Klein, T. E., Shuldiner, A. R. 2019

  Abstract

  AIMS: Clopidogrel is prescribed for the prevention of atherothrombotic events. While investigations have identified genetic determinants of inter-individual variability in on-treatment platelet inhibition (e.g. CYP2C19*2), evidence that these variants have clinical utility to predict major adverse cardiovascular events remains controversial.METHODS AND RESULTS: We assessed the impact of 31 candidate gene polymorphisms on ADP-stimulated platelet reactivity in 3,391 clopidogrel-treated coronary artery disease patients of the International Clopidogrel Pharmacogenomics Consortium (ICPC). The influence of these polymorphisms on cardiovascular events (CVE) was tested in 2,134 ICPC patients (N=129 events) in whom clinical event data were available. Several variants were associated with on-treatment ADP-stimulated platelet reactivity (CYP2C19*2, P=8.8x10-54; CES1 G143E, P=1.3x10-16; CYP2C19*17, P=9.5x10-10; CYP2B6 1294+53C>T, P=3.0x10-4; CYP2B6 516G>T, P=1.0x10-3; CYP2C9*2, P=1.2x10-3; and CYP2C9*3, P=1.5x10-3). While no individual variant was associated with CVEs, generation of a pharmacogenomic polygenic response score (PgxRS) revealed that patients who carried a greater number of alleles that associated with increased on-treatment platelet reactivity were more likely to experience CVEs (beta=0.17, SE 0.06, P=0.01) and cardiovascular-related death (beta=0.43, SE 0.16, P=0.007). Patients who carried 8 or more risk alleles were significantly more likely to experience CVEs (OR=1.78, 95%CI 1.14-2.76, P=0.01) and cardiovascular death (OR=4.39, 95%CI 1.35-14.27, P=0.01) compared to patients who carried 6 or fewer of these alleles.CONCLUSION: Several polymorphisms impact clopidogrel response and PgxRS is a predictor of cardiovascular outcomes. Additional investigations that identify novel determinants of clopidogrel response and validating polygenic models may facilitate future precision medicine strategies.

  View details for DOI 10.1093/ehjcvp/pvz045

  View details for PubMedID 31504375

• PharmGKB summary: methylphenidate pathway, pharmacokinetics/pharmacodynamics PHARMACOGENETICS AND GENOMICS Stevens, T., Sangkuhl, K., Brown, J. T., Altman, R. B., Klein, T. E. 2019; 29 (6): 136–54

  View details for DOI 10.1097/FPC.0000000000000376

  View details for Web of Science ID 000474099000003

• Considerations for pharmacogenomic testing in a health system GENETICS IN MEDICINE Gammal, R. S., Caudle, K. E., Klein, T. E., Relling, M. V. 2019; 21 (8): 1886–87

  View details for DOI 10.1038/s41436-018-0421-x

  View details for Web of Science ID 000478784800032

• 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 Cytochrome P450 (CYP)2D6 Genotype and Atomoxetine Therapy CLINICAL PHARMACOLOGY & THERAPEUTICS Brown, J. T., Bishop, J. R., Sangkuhl, K., Nurmi, E. L., Mueller, D. J., Dinh, J. C., Gaedigk, A., Klein, T. E., Caudle, K. E., McCracken, J. T., de Leon, J., Leeder, J. 2019; 106 (1): 94–102

  View details for DOI 10.1002/cpt.1409

  View details for Web of Science ID 000474029300025

• PharmGKB summary: Ondansetron and tropisetron pathways, pharmacokinetics and pharmacodynamics PHARMACOGENETICS AND GENOMICS Huddart, R., Altman, R. B., Klein, T. E. 2019; 29 (4): 91–97

  View details for DOI 10.1097/FPC.0000000000000369

  View details for Web of Science ID 000466783000004

• The Case for Pharmacogenetics-Guided Prescribing of Codeine in Children CLINICAL PHARMACOLOGY & THERAPEUTICS Gammal, R. S., Caudle, K. E., Quinn, C. T., Wang, W. C., Gaedigk, A., Prows, C. A., Haidar, C. E., Taylor, A. K., Klein, T. E., Sangkuhl, K., Hankins, J. S., Crews, K. R. 2019; 105 (6): 1300–1302

  View details for DOI 10.1002/cpt.1260

  View details for Web of Science ID 000467751900008

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for the Use of Potent Volatile Anesthetic Agents and Succinylcholine in the Context of RYR1 or CACNA1S Genotypes CLINICAL PHARMACOLOGY & THERAPEUTICS Gonsalves, S. G., Dirksen, R. T., Sangkuhl, K., Pulk, R., Alvarellos, M., Vo, T., Hikino, K., Roden, D., Klein, T. E., Poler, S., Patel, S., Caudle, K. E., Gordon, R., Brandom, B., Biesecker, L. G. 2019; 105 (6): 1338–44

  View details for DOI 10.1002/cpt.1319

  View details for Web of Science ID 000467751900017

• 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

• The ACCOuNT Consortium: A Model for the Discovery, Translation, and Implementation of Precision Medicine in African Americans CTS-CLINICAL AND TRANSLATIONAL SCIENCE Friedman, P. N., Shaazuddin, M., Gong, L., Grossman, R. L., Harralson, A. F., Klein, T. E., Lee, N. H., Miller, D. C., Nutescu, E. A., O'Brien, T. J., O'Donnell, P. H., O'Leary, K. J., Tuck, M., Meltzer, D. O., Perera, M. A. 2019; 12 (3): 209–17

  View details for DOI 10.1111/cts.12608

  View details for Web of Science ID 000467589700002

• 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

• PharmGKB summary: methylphenidate pathway, pharmacokinetics/pharmacodynamics. Pharmacogenetics and genomics Stevens, T., Sangkuhl, K., Brown, J. T., Altman, R. B., Klein, T. E. 2019

  View details for PubMedID 30950912

• Pharmacogenomics in dermatology: tools for understanding gene-drug associations. Seminars in cutaneous medicine and surgery Daneshjou, R., Huddart, R., Klein, T. E., Altman, R. B. 2019; 38 (1): E19–E24

  Abstract

  Pharmacogenomics aims to associate human genetic variability with differences in drug phenotypes in order to tailor drug treatment to individual patients. The massive amount of genetic data generated from large cohorts of patients with variable drug phenotypes have led to advances in this field. Understanding the application of pharmacogenomics in dermatology could inform clinical practice and provide insight for future research. The Pharmacogenomics Knowledge Base and the Clinical Pharmacogenetics Implementation Consortium are among the resources to help clinicians and researchers navigate the many gene-drug associations that have already been discovered. The implementation of clinical pharmacogenomics within health care systems remains an area of ongoing development. This review provides an introduction to the field of pharmacogenomics and to current pharmacogenomics resources using examples of gene-drug associations relevant to the field of dermatology.

  View details for DOI 10.12788/j.sder.2019.009

  View details for PubMedID 31051019

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 Genotype and Atomoxetine Therapy. Clinical pharmacology and therapeutics Brown, J. T., Bishop, J. R., Sangkuhl, K., Nurmi, E. L., Mueller, D. J., Dinh, J. C., Gaedigk, A., Klein, T. E., Caudle, K. E., McCracken, J. T., de Leon, J., Steven Leeder, J. 2019

  Abstract

  Atomoxetine is a non-stimulant medication used to treat attention-deficit/hyperactivity disorder. CYP2D6 polymorphisms influence the metabolism of atomoxetine thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide therapeutic recommendations for atomoxetine based on CYP2D6 genotype (updates at www.cpicpgx.org). This article is protected by copyright. All rights reserved.

  View details for PubMedID 30801677

• PharmGKB summary: Ondansetron and tropisetron pathways, pharmacokinetics and pharmacodynamics. Pharmacogenetics and genomics Huddart, R., Altman, R. B., Klein, T. E. 2019

  View details for PubMedID 30672837

• Considerations for pharmacogenomic testing in a health system. Genetics in medicine : official journal of the American College of Medical Genetics Gammal, R. S., Caudle, K. E., Klein, T. E., Relling, M. V. 2019

  View details for PubMedID 30631112

• The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression. Journal of neural transmission (Vienna, Austria : 1996) Amare, A. T., Schubert, K. O., Tekola-Ayele, F., Hsu, Y., Sangkuhl, K., Jenkins, G., Whaley, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V., Brockmoller, J., Chen, C., Domschke, K., Hall-Flavin, D. K., Hong, C., Illi, A., Ji, Y., Kampman, O., Kinoshita, T., Leinonen, E., Liou, Y., Mushiroda, T., Nonen, S., Skime, M. K., Wang, L., Kato, M., Liu, Y., Praphanphoj, V., Stingl, J. C., Bobo, W. V., Tsai, S., Kubo, M., Klein, T. E., Weinshilboum, R. M., Biernacka, J. M., Baune, B. T. 2019

  Abstract

  Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N=865), and in a replication cohort (STAR*D, N=1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

  View details for PubMedID 30610379

• The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression JOURNAL OF NEURAL TRANSMISSION Amare, A. T., Schubert, K., Tekola-Ayele, F., Hsu, Y., Sangkuhl, K., Jenkins, G., Whaley, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V., Brockmoeller, J., Chen, C., Domschke, K., Hall-Flavin, D. K., Hong, C., Illi, A., Ji, Y., Kampman, O., Kinoshita, T., Leinonen, E., Liou, Y., Mushiroda, T., Nonen, S., Skime, M. K., Wang, L., Kato, M., Liu, Y., Praphanphoj, V., Stingl, J. C., Bobo, W. V., Tsai, S., Kubo, M., Klein, T. E., Weinshilboum, R. M., Biernacka, J. M., Baune, B. T. 2019; 126 (1): 35–45

  View details for DOI 10.1007/s00702-018-01966-x

  View details for Web of Science ID 000458148200005

• The Evolution of PharmVar CLINICAL PHARMACOLOGY & THERAPEUTICS Gaedigk, A., Sangkuhl, K., Whirl-Carrillo, M., Twist, G. P., Klein, T. E., Miller, N. A., PharmVar Steering Comm 2019; 105 (1): 29–32

  View details for DOI 10.1002/cpt.1275

  View details for Web of Science ID 000454618200006

• 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. n., Whirl-Carrillo, M. n., 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. n. 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

• Essential Characteristics of Pharmacogenomics Study Publications CLINICAL PHARMACOLOGY & 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. 2019; 105 (1): 86–91

  View details for DOI 10.1002/cpt.1279

  View details for Web of Science ID 000454618200017

• The ACCOuNT Consortium: a model for the discovery, translation and implementation of precision medicine in African Americans. Clinical and translational science Friedman, P. N., Shaazuddin, M., Gong, L., Grossman, R. L., Harralson, A. F., Klein, T. E., Lee, N. H., Miller, D. C., Nutescu, E. A., O'Brien, T. J., O'Donnell, P. H., O'Leary, K. J., Tuck, M., Meltzer, D. O., Perera, M. A. 2018

  Abstract

  The majority of pharmacogenomic studies have been conducted on European ancestry populations, thereby excluding minority populations and impeding the discovery and translation of African American specific genetic variation into precision medicine. Without accounting for variants found in African Americans, clinical recommendations based solely on genetic biomarkers found in European populations could result in misclassification of drug response in African Americans patients. To address these challenges, we formed the Transdisciplinary Collaborative Center (TCC), ACCOuNT (African American Cardiovascular Pharmacogenetic Consortium), to discover novel genetic variants in African Americans related to clinically actionable cardiovascular phenotypes and to incorporate African American-specific sequence variations into clinical recommendations at the point of care. The TCC consists of two research projects focused on discovery and translation of genetic findings and four cores that support the projects. In addition, the largest repository of pharmacogenomic information on African Americans is being established as well as lasting infrastructure that can be utilized to spur continued research in this understudied population. This article is protected by copyright. All rights reserved.

  View details for PubMedID 30592548

• 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

• Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for the use of potent volatile anesthetic agents and succinylcholine in the context of RYR1 or CACNA1S genotypes. Clinical pharmacology and therapeutics Gonsalves, S. G., Dirksen, R. T., Sangkuhl, K., Pulk, R., Alvarellos, M., Vo, T., Hikino, K., Roden, D., Klein, T., Mark Poler, S., Patel, S., Caudle, K. E., Gordon, R., Brandom, B., Biesecker, L. G. 2018

  Abstract

  The identification in a patient of one of the 50 variants in the RYR1 or CACNA1S genes reviewed here should lead to a presumption of malignant hyperthermia susceptibility. Malignant hyperthermia susceptibility can lead to life-threatening reactions to potent volatile anesthetic agents or succinylcholine. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for the use of these agents in patients with these RYR1 or CACNA1S variants (updates at https://cpicpgx.org/guidelines/ and www.pharmgkb.org). This article is protected by copyright. All rights reserved.

  View details for PubMedID 30499100

• 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

• PharmGKB summary: oxycodone pathway, pharmacokinetics PHARMACOGENETICS AND GENOMICS Huddart, R., Clarke, M., Altman, R. B., Klein, T. E. 2018; 28 (10): 230–37

  View details for PubMedID 30222708

• Pharmacogenomics Knowledge for Personalized Medicine Klein, T., Ritchie, M. NATURE PUBLISHING GROUP. 2018: 30–31

  View details for Web of Science ID 000489312600065

• PharmGKB summary: clozapine pathway, pharmacokinetics PHARMACOGENETICS AND GENOMICS Thorn, C. F., Mueller, D. J., Altman, R. B., Klein, T. E. 2018; 28 (9): 214–22

  View details for PubMedID 30134346

• Substitutions for arginine at position 780 in triple helical domain of the alpha 1 (I) chain alter folding of the type I procollagen molecule and cause osteogenesis imperfecta PLOS ONE Makareeva, E., Sun, G., Mirigian, L. S., Mertz, E. L., Vera, J. C., Espinoza, N. A., Yang, K., Chen, D., Klein, T. E., Byers, P. H., Leikin, S. 2018; 13 (7): e0200264

  Abstract

  OI is a clinically and genetically heterogeneous disorder characterized by bone fragility. More than 90% of patients are heterozygous for mutations in type I collagen genes, COL1A1 and COL1A2, and a common mutation is substitution for an obligatory glycine in the triple helical Gly-X-Y repeats. Few non-glycine substitutions in the triple helical domain have been reported; most result in Y-position substitutions of arginine by cysteine. Here, we investigated leucine and cysteine substitutions for one Y-position arginine, p.Arg958 (Arg780 in the triple helical domain) of proα1(I) chains that cause mild OI. We compared their effects with two substitutions for glycine located in close proximity. Like substitutions for glycine, those for arginine reduced the denaturation temperature of the whole molecule and caused asymmetric posttranslational overmodification of the chains. Circular dichroism and increased susceptibility to cleavage by MMP1, MMP2 and catalytic domain of MMP1 revealed significant destabilization of the triple helix near the collagenase cleavage site. On a cellular level, we observed slower triple helix folding and intracellular collagen retention, which disturbed the Endoplasmic Reticulum function and affected matrix deposition. Molecular dynamic modeling suggested that Arg780 substitutions disrupt the triple helix structure and folding by eliminating hydrogen bonds of arginine side chains, in addition to preventing HSP47 binding. The pathogenic effects of these non-glycine substitutions in bone are probably caused mostly by procollagen misfolding and its downstream effects.

  View details for PubMedID 29990383

• 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; 10 (4)

  View details for DOI 10.1002/wsbm.1417

  View details for Web of Science ID 000435287900002

• 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

• Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects CLINICAL PHARMACOLOGY & THERAPEUTICS Volpi, S., Bult, C. J., Chisholm, R. L., Deverka, P. A., Ginsburg, G. S., Jacob, H. J., Kasapi, M., McLeod, H. L., Roden, D. M., Williams, M. S., Green, E. D., Rodriguez, L., Aronson, S., Cavallari, L. H., Denny, J. C., Dressler, L. G., Johnson, J. A., Klein, T. E., Leeder, J., Piquette-Miller, M., Perera, M., Rasmussen-Torvik, L. J., Rehm, H. L., Ritchie, M. D., Skaar, T. C., Wagle, N., Weinshilboum, R., Weitzel, K. W., Wildin, R., Wilson, J., Manolio, T. A., Relling, M. V. 2018; 103 (5): 778–86

  Abstract

  Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.

  View details for PubMedID 29460415

  View details for PubMedCentralID PMC5902434

• 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

  View details for DOI 10.1093/hmg/ddy116

  View details for Web of Science ID 000431884200012

• PharmGKB summary: atazanavir pathway, pharmacokinetics/pharmacodynamics PHARMACOGENETICS AND GENOMICS Alvarellos, M., Guillemette, C., Altman, R. B., Klein, T. E. 2018; 28 (5): 127–37

  View details for PubMedID 29517518

  View details for PubMedCentralID PMC5910198

• PharmGKB summary: clobazam pathway, pharmacokinetics PHARMACOGENETICS AND GENOMICS Huddart, R., Leeder, J., Altman, R. B., Klein, T. E. 2018; 28 (4): 110–15

  View details for PubMedID 29517622

• 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

• Genome-wide and candidate gene approaches of clopidogrel efficacy using pharmacodynamic and clinical end points-Rationale and design of the International Clopidogrel Pharmacogenomics Consortium (ICPC) AMERICAN HEART JOURNAL Bergmeijer, T. O., Reny, J., Pakyz, R. E., Gong, L., Lewis, J. P., Kim, E., Aradi, D., Fernandez-Cadenas, I., Horenstein, R. B., Lee, M., Whaley, R. M., Montaner, J., Franco Gensini, G., Cleator, J. H., Chang, K., Holmvang, L., Hochholzer, W., Roden, D. M., Winter, S., Altman, R. B., Alexopoulos, D., Kim, H., Dery, J., Gawaz, M., Bliden, K., Valgimigli, M., Marcucci, R., Campo, G., Schaeffeler, E., Dridi, N. P., Wen, M., Shin, J., Simon, T., Fontana, P., Giusti, B., Geisler, T., Kubo, M., Trenk, D., Siller-Matula, J. M., ten Berg, J. M., Gurbel, P. A., Hulot, J., Mitchell, B. D., Schwab, M., Ritchie, M., Klein, T. E., Shuldiner, A. R., ICPC Investigators 2018; 198: 152–59

  Abstract

  The P2Y12 receptor inhibitor clopidogrel is widely used in patients with acute coronary syndrome, percutaneous coronary intervention, or ischemic stroke. Platelet inhibition by clopidogrel shows wide interpatient variability, and high on-treatment platelet reactivity is a risk factor for atherothrombotic events, particularly in high-risk populations. CYP2C19 polymorphism plays an important role in this variability, but heritability estimates suggest that additional genetic variants remain unidentified. The aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify genetic determinants of clopidogrel pharmacodynamics and clinical response.Based on the data published on www.clinicaltrials.gov, clopidogrel intervention studies containing genetic and platelet function data were identified for participation. Lead investigators were invited to share DNA samples, platelet function test results, patient characteristics, and cardiovascular outcomes to perform candidate gene and genome-wide studies.In total, 17 study sites from 13 countries participate in the ICPC, contributing individual patient data from 8,829 patients. Available adenosine diphosphate-stimulated platelet function tests included vasodilator-stimulated phosphoprotein assay, light transmittance aggregometry, and the VerifyNow P2Y12 assay. A proof-of-principle analysis based on genotype data provided by each group showed a strong and consistent association between CYP2C19*2 and platelet reactivity (P value=5.1 × 10-40).The ICPC aims to identify new loci influencing clopidogrel efficacy by using state-of-the-art genetic approaches in a large cohort of clopidogrel-treated patients to better understand the genetic basis of on-treatment response variability.

  View details for PubMedID 29653637

• Association of the Polygenic Scores for Personality Traits and Response to Selective Serotonin Reuptake Inhibitors in Patients with Major Depressive Disorder FRONTIERS IN PSYCHIATRY Amare, A. T., Schubert, K., Tekola-Ayele, F., Hsu, Y., Sangkuhl, K., Jenkins, G., Whaley, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V., Brockmoeller, J., Chen, C., Domschke, K., Hall-Flavin, D. K., Hong, C., Illi, A., Ji, Y., Kampman, O., Kinoshita, T., Leinonen, E., Liou, Y., Mushiroda, T., Nonen, S., Skime, M. K., Wang, L., Kato, M., Liu, Y., Praphanphoj, V., Stingl, J. C., Bobo, W. V., Tsai, S., Kubo, M., Klein, T. E., Weinshilboum, R. M., Biernacka, J. M., Baune, B. T. 2018; 9: 65

  Abstract

  Studies reported a strong genetic correlation between the Big Five personality traits and major depressive disorder (MDD). Moreover, personality traits are thought to be associated with response to antidepressants treatment that might partly be mediated by genetic factors. In this study, we examined whether polygenic scores (PGSs) derived from the Big Five personality traits predict treatment response and remission in patients with MDD who were prescribed selective serotonin reuptake inhibitors (SSRIs). In addition, we performed meta-analyses of genome-wide association studies (GWASs) on these traits to identify genetic variants underpinning the cross-trait polygenic association. The PGS analysis was performed using data from two cohorts: the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS, n = 529) and the International SSRI Pharmacogenomics Consortium (ISPC, n = 865). The cross-trait GWAS meta-analyses were conducted by combining GWAS summary statistics on SSRIs treatment outcome and on the personality traits. The results showed that the PGS for openness and neuroticism were associated with SSRIs treatment outcomes at p < 0.05 across PT thresholds in both cohorts. A significant association was also found between the PGS for conscientiousness and SSRIs treatment response in the PGRN-AMPS sample. In the cross-trait GWAS meta-analyses, we identified eight loci associated with (a) SSRIs response and conscientiousness near YEATS4 gene and (b) SSRI remission and neuroticism eight loci near PRAG1, MSRA, XKR6, ELAVL2, PLXNC1, PLEKHM1, and BRUNOL4 genes. An assessment of a polygenic load for personality traits may assist in conjunction with clinical data to predict whether MDD patients might respond favorably to SSRIs.

  View details for PubMedID 29559929

• 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

• Response to "Impact of CYP3A4 Genotype on Voriconazole Exposure: New Insights Into the Contribution of CYP3A4(star)22 to Metabolism of Voriconazole" CLINICAL PHARMACOLOGY & THERAPEUTICS Walsh, T. J., Moriyama, B., Penzak, S. R., Klein, T. E., Caudle, K. E. 2018; 103 (2): 187

  View details for PubMedID 28786218

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing: 2017 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Amstutz, U., Henricks, L. M., Offer, S. M., Barbarino, J., Schellens, J. M., Swen, J. J., Klein, T. E., McLeod, H. L., Caudle, K. E., Diasio, R. B., Schwab, M. 2018; 103 (2): 210–16

  Abstract

  The purpose of this guideline is to provide information for the interpretation of clinical dihydropyrimidine dehydrogenase (DPYD) genotype tests so that the results can be used to guide dosing of fluoropyrimidines (5-fluorouracil and capecitabine). Detailed guidelines for the use of fluoropyrimidines, their clinical pharmacology, as well as analyses of cost-effectiveness are beyond the scope of this document. The Clinical Pharmacogenetics Implementation Consortium (CPIC® ) guidelines consider the situation of patients for which genotype data are already available (updates available at https://cpicpgx.org/guidelines/guideline-for-fluoropyrimidines-and-dpyd/).

  View details for PubMedID 29152729

  View details for PubMedCentralID PMC5760397

• New Pharmacogenomics Research Network: An Open Community Catalyzing Research and Translation in Precision Medicine CLINICAL PHARMACOLOGY & THERAPEUTICS Relling, M. V., Krauss, R. M., Roden, D. M., Klein, T. E., Fowler, D. M., Terada, N., Lin, L., Riel-Mehan, M., Do, T. P., Kubo, M., Yee, S. W., Johnson, G. T., Giacomini, K. M. 2017; 102 (6): 897–902

  Abstract

  The goal of pharmacogenomics research is to discover genetic polymorphisms that underlie variation in drug response. Increasingly, pharmacogenomics research involves large numbers of patients and the application of new technologies and methodologies to enable discovery. The Pharmacogenomics Research Network (PGRN) has become a community-driven network of investigators spanning scientific and clinical disciplines. Here, we highlight the activities and types of resources that enable PGRN members to enhance and drive basic and translational research in pharmacogenomics.

  View details for PubMedID 28795399

  View details for PubMedCentralID PMC5706548

• PharmGKB summary: very important pharmacogene information for ABCG2 PHARMACOGENETICS AND GENOMICS Fohner, A. E., Brackman, D. J., Giacomini, K. M., Altman, R. B., Klein, T. E. 2017; 27 (11): 420–27

  View details for PubMedID 28858993

  View details for PubMedCentralID PMC5788016

• Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges HUMAN MUTATION Daneshjou, R., Wang, Y., Bromberg, Y., Bovo, S., Martelli, P. L., Babbi, G., Di Lena, P., Casadio, R., Edwards, M., Gifford, D., Jones, D. T., Sundaram, L., Bhat, R., Li, X., Pal, L. R., Kundu, K., Yin, Y., Moult, J., Jiang, Y., Pejaver, V., Pagel, K. A., Li, B., Mooney, S. D., Radivojac, P., Shah, S., Carraro, M., Gasparini, A., Leonardi, E., Giollo, M., Ferrari, C., Tosatto, S. E., Bachar, E., Azaria, J. R., Ofran, Y., Unger, R., Niroula, A., Vihinen, M., Chang, B., Wang, M. H., Franke, A., Petersen, B., Pirooznia, M., Zandi, P., McCombie, R., Potash, J. B., Altman, R. B., Klein, T. E., Hoskins, R. A., Repo, S., Brenner, S. E., Morgan, A. A. 2017; 38 (9): 1182–92

  Abstract

  Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

  View details for DOI 10.1002/humu.23280

  View details for Web of Science ID 000407861100014

  View details for PubMedID 28634997

  View details for PubMedCentralID PMC5600620

• PharmGKB summary: pazopanib pathway, pharmacokinetics PHARMACOGENETICS AND GENOMICS Thorn, C. F., Sharma, M. R., Altman, R. B., Klein, T. E. 2017; 27 (8): 307–12

  View details for PubMedID 28678138

  View details for PubMedCentralID PMC5862561

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP2C19 and Voriconazole Therapy CLINICAL PHARMACOLOGY & THERAPEUTICS Moriyama, B., Obeng, A., Barbarino, J., Penzak, S. R., Henning, S. A., Scott, S. A., Agundez, J. G., Wingard, J. R., McLeod, H. L., Klein, T. E., Cross, S. J., Caudle, K. E., Walsh, T. J. 2017; 102 (1): 45–51

  View details for DOI 10.1002/cpt.583

  View details for Web of Science ID 000403285000008

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP2C19 and Voriconazole Therapy. Clinical pharmacology and therapeutics Moriyama, B., Obeng, A. O., Barbarino, J., Penzak, S. R., Henning, S. A., Scott, S. A., Agúndez, J., Wingard, J. R., McLeod, H. L., Klein, T. E., Cross, S. J., Caudle, K. E., Walsh, T. J. 2017; 102 (1): 45-51

  Abstract

  Voriconazole, a triazole antifungal agent, demonstrates wide interpatient variability in serum concentrations, due in part to variant CYP2C19 alleles. Individuals who are CYP2C19 ultrarapid metabolizers have decreased trough voriconazole concentrations, delaying achievement of target blood concentrations; whereas poor metabolizers have increased trough concentrations and are at increased risk of adverse drug events. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for the use of voriconazole for treatment based on CYP2C19 genotype (updates at https://cpicpgx.org/guidelines/ and www.pharmgkb.org).

  View details for DOI 10.1002/cpt.583

  View details for PubMedID 27981572

  View details for PubMedCentralID PMC5474211

• PharmGKB summary: sorafenib pathways PHARMACOGENETICS AND GENOMICS Gong, L., Giacomini, M. M., Giacomini, C., Maitland, M. L., Altman, R. B., Klein, T. E. 2017; 27 (6): 240-246

  View details for DOI 10.1097/FPC.0000000000000279

  View details for Web of Science ID 000400664400006

  View details for PubMedID 28362716

• PharmGKB summary: voriconazole pathway, pharmacokinetics PHARMACOGENETICS AND GENOMICS Barbarino, J. M., Obeng, A. O., Klein, T. E., Altman, R. B. 2017; 27 (5): 201-209

  View details for DOI 10.1097/FPC.0000000000000276

  View details for Web of Science ID 000398829200005

  View details for PubMedID 28277330

• Response to "Pharmacogenetics of Voriconazole: CYP2C19 but Also CYP3A4 Need to Be Genotyped" - The Role of CYP3A4 and CYP3A5 Polymorphisms in Clinical Pharmacokinetics of Voriconazole. Clinical pharmacology & therapeutics Walsh, T. J., Moriyama, B., Penzak, S. R., Klein, T. E., Caudle, K. E. 2017

  View details for DOI 10.1002/cpt.681

  View details for PubMedID 28455946

• 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

• PharmGKB summary: Macrolide antibiotic pathway, pharmacokinetics/pharmacodynamics. Pharmacogenetics and genomics Fohner, A. E., Sparreboom, A., Altman, R. B., Klein, T. E. 2017

  View details for DOI 10.1097/FPC.0000000000000270

  View details for PubMedID 28146011

  View details for PubMedCentralID PMC5346035

• "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

• Clinical pharmacogenetics implementation consortium guideline (CPIC) for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants: 2016 update. Clinical pharmacology & therapeutics Hicks, J. K., Sangkuhl, K., Swen, J. J., Ellingrod, V. L., Müller, D. J., Shimoda, K., Bishop, J. R., Kharasch, E. D., Skaar, T. C., Gaedigk, A., Dunnenberger, H. M., Klein, T. E., Caudle, K. E., Stingl, J. C. 2016

  View details for DOI 10.1002/cpt.597

  View details for PubMedID 27997040

• PharmGKB summary: very important pharmacogene information for MT-RNR1. Pharmacogenetics and genomics Barbarino, J. M., McGregor, T. L., Altman, R. B., Klein, T. E. 2016; 26 (12): 558-567

  View details for PubMedID 27654872

• 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

• 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

• The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Translational psychiatry Biernacka, J. M., Sangkuhl, K., Jenkins, G., WHALEY, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V., Brockmöller, J., Chen, C. H., Domschke, K., Hall-Flavin, D. K., Hong, C. J., Illi, A., Ji, Y., Kampman, O., Kinoshita, T., Leinonen, E., Liou, Y. J., Mushiroda, T., Nonen, S., Skime, M. K., Wang, L., Baune, B. T., Kato, M., Liu, Y. L., Praphanphoj, V., Stingl, J. C., Tsai, S. J., Kubo, M., Klein, T. E., Weinshilboum, R. 2016; 6 (11)

  View details for DOI 10.1038/tp.2016.187

  View details for PubMedID 27801898

  View details for PubMedCentralID PMC5314112

• 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

• PharmGKB summary: isoniazid pathway, pharmacokinetics. Pharmacogenetics and genomics Klein, D. J., Boukouvala, S., McDonagh, E. M., Shuldiner, S. R., Laurieri, N., Thorn, C. F., Altman, R. B., Klein, T. E. 2016; 26 (9): 436-444

  View details for DOI 10.1097/FPC.0000000000000232

  View details for PubMedID 27232112

  View details for PubMedCentralID PMC4970941

• Population-specific single-nucleotide polymorphism confers increased risk of venous thromboembolism in African Americans. Molecular genetics & genomic medicine Daneshjou, R., Cavallari, L. H., Weeke, P. E., Karczewski, K. J., Drozda, K., Perera, M. A., Johnson, J. A., Klein, T. E., Bustamante, C. D., Roden, D. M., Shaffer, C., Denny, J. C., Zehnder, J. L., Altman, R. B. 2016; 4 (5): 513-520

  Abstract

  African Americans have a higher incidence of venous thromboembolism (VTE) than European descent individuals. However, the typical genetic risk factors in populations of European descent are nearly absent in African Americans, and population-specific genetic factors influencing the higher VTE rate are not well characterized.We performed a candidate gene analysis on an exome-sequenced African American family with recurrent VTE and identified a variant in Protein S (PROS1) V510M (rs138925964). We assessed the population impact of PROS1 V510M using a multicenter African American cohort of 306 cases with VTE compared to 370 controls. Additionally, we compared our case cohort to a background population cohort of 2203 African Americans in the NHLBI GO Exome Sequencing Project (ESP).In the African American family with recurrent VTE, we found prior laboratories for our cases indicating low free Protein S levels, providing functional support for PROS1 V510M as the causative mutation. Additionally, this variant was significantly enriched in the VTE cases of our multicenter case-control study (Fisher's Exact Test, P = 0.0041, OR = 4.62, 95% CI: 1.51-15.20; allele frequencies - cases: 2.45%, controls: 0.54%). Similarly, PROS1 V510M was also enriched in our VTE case cohort compared to African Americans in the ESP cohort (Fisher's Exact Test, P = 0.010, OR = 2.28, 95% CI: 1.26-4.10).We found a variant, PROS1 V510M, in an African American family with VTE and clinical laboratory abnormalities in Protein S. Additionally, we found that this variant conferred increased risk of VTE in a case-control study of African Americans. In the ESP cohort, the variant is nearly absent in ESP European descent subjects (n = 3, allele frequency: 0.03%). Additionally, in 1000 Genomes Phase 3 data, the variant only appears in African descent populations. Thus, PROS1 V510M is a population-specific genetic risk factor for VTE in African Americans.

  View details for DOI 10.1002/mgg3.226

  View details for PubMedID 27652279

• 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

• 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

• 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

• PharmGKB summary: very important pharmacogene information for RYR1 PHARMACOGENETICS AND GENOMICS Alvarellos, M. L., Krauss, R. M., Wilke, R. A., Altman, R. B., Klein, T. E. 2016; 26 (3): 138-144

  View details for DOI 10.1097/FPC.0000000000000198

  View details for Web of Science ID 000373526700005

  View details for PubMedID 26709912

  View details for PubMedCentralID PMC4738161

• 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

• Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for human leukocyte antigen B (HLA-B) genotype and allopurinol dosing: 2015 update CLINICAL PHARMACOLOGY & THERAPEUTICS Saito, Y., Stamp, L. K., Caudle, K. E., Hershfield, M. S., MCDONAGH, E. M., Callaghan, J. T., Tassaneeyakul, W., Mushiroda, T., Kamatani, N., Goldspiel, B. R., Phillips, E. J., Klein, T. E., Lee, M. T. 2016; 99 (1): 36-37

  Abstract

  The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B*58:01 Genotype and Allopurinol Dosing was originally published in February 2013. We reviewed the recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplemental Material and included additional resources for applying CPIC guidelines into the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

  View details for DOI 10.1002/cpt.161

  View details for Web of Science ID 000368803000020

  View details for PubMedCentralID PMC4675696

• Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for human leukocyte antigen B (HLA-B) genotype and allopurinol dosing: 2015 update. Clinical pharmacology and therapeutics Saito, Y., Stamp, L. K., Caudle, K. E., Hershfield, M. S., McDonagh, E. M., Callaghan, J. T., Tassaneeyakul, W., Mushiroda, T., Kamatani, N., Goldspiel, B. R., Phillips, E. J., Klein, T. E., Lee, M. T. 2016; 99 (1): 36-7

  Abstract

  The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B*58:01 Genotype and Allopurinol Dosing was originally published in February 2013. We reviewed the recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplemental Material and included additional resources for applying CPIC guidelines into the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

  View details for DOI 10.1002/cpt.161

  View details for PubMedID 26094938

  View details for PubMedCentralID PMC4675696

• PharmGKB summary: succinylcholine pathway, pharmacokinetics/pharmacodynamics PHARMACOGENETICS AND GENOMICS Alvarellos, M. L., McDonagh, E. M., Patel, S., McLeod, H. L., Altman, R. B., Klein, T. E. 2015; 25 (12): 622-630

  View details for DOI 10.1097/FPC.0000000000000170

  View details for Web of Science ID 000364626100006

  View details for PubMedID 26398623

  View details for PubMedCentralID PMC4631707

• 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 PubMedID 26448358

• 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

• PharmGKB summary: peginterferon-alpha pathway PHARMACOGENETICS AND GENOMICS Shuldiner, S. R., Gong, L., Muir, A. J., Altman, R. B., Klein, T. E. 2015; 25 (9): 465-474

  View details for DOI 10.1097/FPC.0000000000000158

  View details for Web of Science ID 000359645700006

  View details for PubMedID 26111151

• Genome-wide association study of warfarin maintenance dose in a Brazilian sample. Pharmacogenomics Parra, E. J., Botton, M. R., Perini, J. A., Krithika, S., Bourgeois, S., Johnson, T. A., Tsunoda, T., Pirmohamed, M., Wadelius, M., Limdi, N. A., Cavallari, L. H., Burmester, J. K., Rettie, A. E., Klein, T. E., Johnson, J. A., Hutz, M. H., Suarez-Kurtz, G. 2015; 16 (11): 1253-63

  Abstract

  Extreme discordant phenotype and genome-wide association (GWA) approaches were combined to explore the role of genetic variants on warfarin dose requirement in Brazilians.Patients receiving low (≤ 20 mg/week; n = 180) or high stable warfarin doses (≥ 42.5 mg/week; n = 187) were genotyped with Affymetrix Axiom(®) Biobank arrays. Imputation was carried out using data from the combined 1000 Genomes project.Genome-wide signals (p ≤ 5 × 10(-8)) were identified in the well-known VKORC1 (lead SNP, rs749671; OR: 20.4; p = 1.08 × 10(-33)) and CYP2C9 (lead SNP, rs9332238, OR: 6.8 and p = 4.4 × 10(-13)) regions. The rs9332238 polymorphism is in virtually perfect LD with CYP2C9*2 (rs1799853) and CYP2C9*3 (rs1057910). No other genome-wide significant regions were identified in the study.We confirmed the important role of VKORC1 and CYP2C9 polymorphisms in warfarin dose. Original submitted 14 January 2015; Revision submitted 26 May 2015.

  View details for DOI 10.2217/PGS.15.73

  View details for PubMedID 26265036

  View details for PubMedCentralID PMC4573240

• PharmGKB summary: pathways of acetaminophen metabolism at the therapeutic versus toxic doses PHARMACOGENETICS AND GENOMICS Mazaleuskaya, L. L., Sangkuhl, K., Thorn, C. F., FitzGerald, G. A., Altman, R. B., Klein, T. E. 2015; 25 (8): 416-426

  View details for DOI 10.1097/FPC.0000000000000150

  View details for Web of Science ID 000357993500007

  View details for PubMedCentralID PMC4498995

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors. Clinical pharmacology and therapeutics Hicks, J. K., Bishop, J. R., Sangkuhl, K., Müller, D. J., Ji, Y., Leckband, S. G., Leeder, J. S., Graham, R. L., Chiulli, D. L., LLerena, A., Skaar, T. C., Scott, S. A., Stingl, J. C., Klein, T. E., Caudle, K. E., Gaedigk, A. 2015; 98 (2): 127-34

  Abstract

  Selective serotonin reuptake inhibitors (SSRIs) are primary treatment options for major depressive and anxiety disorders. CYP2D6 and CYP2C19 polymorphisms can influence the metabolism of SSRIs, thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide dosing recommendations for fluvoxamine, paroxetine, citalopram, escitalopram, and sertraline based on CYP2D6 and/or CYP2C19 genotype (updates at www.pharmgkb.org).

  View details for DOI 10.1002/cpt.147

  View details for PubMedID 25974703

  View details for PubMedCentralID PMC4512908

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for CYP2D6 and CYP2C19 Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors CLINICAL PHARMACOLOGY & THERAPEUTICS Hicks, J. K., Bishop, J. R., Sangkuhl, K., Mueller, D. J., Ji, Y., Leckband, S. G., Leeder, J. S., GRAHAM, R. L., Chiulli, D. L., Llerena, A., Skaar, T. C., Scott, S. A., Stingl, J. C., Klein, T. E., Caudle, K. E., Gaedigk, A. 2015; 98 (1): 127-134

  Abstract

  Selective serotonin reuptake inhibitors (SSRIs) are primary treatment options for major depressive and anxiety disorders. CYP2D6 and CYP2C19 polymorphisms can influence the metabolism of SSRIs, thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide dosing recommendations for fluvoxamine, paroxetine, citalopram, escitalopram, and sertraline based on CYP2D6 and/or CYP2C19 genotype (updates at www.pharmgkb.org).

  View details for DOI 10.1002/cpt.147

  View details for Web of Science ID 000358503400005

  View details for PubMedCentralID PMC4512908

• PharmGKB summary: pathways of acetaminophen metabolism at the therapeutic versus toxic doses. Pharmacogenetics and genomics Mazaleuskaya, L. L., Sangkuhl, K., Thorn, C. F., FitzGerald, G. A., Altman, R. B., Klein, T. E. 2015; 25 (8): 416-26

  View details for DOI 10.1097/FPC.0000000000000150

  View details for PubMedID 26049587

  View details for PubMedCentralID PMC4498995

• PharmGKB summary: Efavirenz pathway, pharmacokinetics PHARMACOGENETICS AND GENOMICS McDonagh, E. M., Lau, J. L., Alvarellos, M. L., Altman, R. B., Klein, T. E. 2015; 25 (7): 363-376

  View details for DOI 10.1097/FPC.0000000000000145

  View details for Web of Science ID 000356370900005

  View details for PubMedID 25966836

  View details for PubMedCentralID PMC4461466

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing. Clinical pharmacology & therapeutics Birdwell, K. A., Decker, B., Barbarino, J. M., Peterson, J. F., Stein, C. M., Sadee, W., Wang, D., Vinks, A. A., He, Y., Swen, J. J., Leeder, J. S., van Schaik, R., Thummel, K. E., Klein, T. E., Caudle, K. E., MacPhee, I. 2015; 98 (1): 19-24

  Abstract

  Tacrolimus is the mainstay immunosuppressant drug used after solid organ and hematopoietic stem cell transplantation. Individuals who express CYP3A5 (extensive and intermediate metabolizers) generally have decreased dose-adjusted trough concentrations of tacrolimus as compared with those who are CYP3A5 nonexpressers (poor metabolizers), possibly delaying achievement of target blood concentrations. We summarize evidence from the published literature supporting this association and provide dosing recommendations for tacrolimus based on CYP3A5 genotype when known (updates at www.pharmgkb.org).

  View details for DOI 10.1002/cpt.113

  View details for PubMedID 25801146

  View details for PubMedCentralID PMC4481158

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for CYP3A5 Genotype and Tacrolimus Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Birdwell, K. A., Decker, B., Barbarino, J. M., Peterson, J. F., Stein, C. M., Sadee, W., Wang, D., Vinks, A. A., He, Y., Swen, J. J., Leeder, J. S., van Schaik, R. H., Thummel, K. E., Klein, T. E., Caudle, K. E., MacPhee, I. A. 2015; 98 (1): 19-24

  Abstract

  Tacrolimus is the mainstay immunosuppressant drug used after solid organ and hematopoietic stem cell transplantation. Individuals who express CYP3A5 (extensive and intermediate metabolizers) generally have decreased dose-adjusted trough concentrations of tacrolimus as compared with those who are CYP3A5 nonexpressers (poor metabolizers), possibly delaying achievement of target blood concentrations. We summarize evidence from the published literature supporting this association and provide dosing recommendations for tacrolimus based on CYP3A5 genotype when known (updates at www.pharmgkb.org).

  View details for DOI 10.1002/cpt.113

  View details for Web of Science ID 000358502900017

  View details for PubMedCentralID PMC4481158

• ClinGen - The Clinical Genome Resource NEW ENGLAND JOURNAL OF MEDICINE Rehm, H. L., Berg, J. S., Brooks, L. D., Bustamante, C. D., Evans, J. P., Landrum, M. J., Ledbetter, D. H., Maglott, D. R., Martin, C. L., Nussbaum, R. L., Plon, S. E., Ramos, E. M., Sherry, S. T., Watson, M. S. 2015; 372 (23): 2235-2242

  View details for DOI 10.1056/NEJMsr1406261

  View details for PubMedID 26014595

• Evidence for Clinical Implementation of Pharmacogenomics in Cardiac Drugs MAYO CLINIC PROCEEDINGS Kaufman, A. L., Spitz, J., Jacobs, M., Sorrentino, M., Yuen, S., Danahey, K., Saner, D., Klein, T. E., Altman, R. B., Ratain, M. J., O'Donnell, P. H. 2015; 90 (6): 716-729

  Abstract

  To comprehensively assess the pharmacogenomic evidence of routinely used drugs for clinical utility.Between January 2, 2011, and May 31, 2013, we assessed 71 drugs by identifying all drug/genetic variant combinations with published clinical pharmacogenomic evidence. Literature supporting each drug/variant pair was assessed for study design and methods, outcomes, statistical significance, and clinical relevance. Proposed clinical summaries were formally scored using a modified AGREE (Appraisal of Guidelines for Research and Evaluation) II instrument, including recommendation for or against guideline implementation.Positive pharmacogenomic findings were identified for 51 of 71 cardiovascular drugs (71.8%), representing 884 unique drug/variant pairs from 597 publications. After analysis for quality and clinical relevance, 92 drug/variant pairs were proposed for translation into clinical summaries, encompassing 23 drugs (32.4% of drugs reviewed). All were recommended for clinical implementation using AGREE II, with mean ± SD overall quality scores of 5.18±0.91 (of 7.0; range, 3.67-7.0). Drug guidelines had highest mean ± SD scores in AGREE II domain 1 (Scope) (91.9±6.1 of 100) and moderate but still robust mean ± SD scores in domain 3 (Rigor) (73.1±11.1), domain 4 (Clarity) (67.8±12.5), and domain 5 (Applicability) (65.8±10.0). Clopidogrel (CYP2C19), metoprolol (CYP2D6), simvastatin (rs4149056), dabigatran (rs2244613), hydralazine (rs1799983, rs1799998), and warfarin (CYP2C9/VKORC1) were distinguished by the highest scores. Seven of the 9 most commonly prescribed drugs warranted translation guidelines summarizing clinical pharmacogenomic information.Considerable clinically actionable pharmacogenomic information for cardiovascular drugs exists, supporting the idea that consideration of such information when prescribing is warranted.

  View details for DOI 10.1016/j.mayocp.2015.03.016

  View details for Web of Science ID 000355557900008

  View details for PubMedID 26046407

  View details for PubMedCentralID PMC4475352

• The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response TRANSLATIONAL PSYCHIATRY Biernacka, J. M., Sangkuhl, K., Jenkins, G., WHALEY, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V., Brockmoeller, J., Chen, C. H., Domschke, K., Hall-Flavin, D. K., Hong, C. J., Illi, A., Ji, Y., Kampman, O., Kinoshita, T., Leinonen, E., Liou, Y. J., Mushiroda, T., Nonen, S., Skime, M. K., Wang, L., Baune, B. T., Kato, M., Liu, Y. L., Praphanphoj, V., Stingl, J. C., Tsai, S. J., Kubo, M., Klein, T. E., Weinshilboum, R. 2015; 5

  Abstract

  Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. The top association result in the meta-analysis of response represents SNPs 5′ upstream of the neuregulin-1 gene, NRG1 (P = 1.20E - 06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.

  View details for DOI 10.1038/tp.2015.47

  View details for Web of Science ID 000367655600002

  View details for PubMedCentralID PMC4462610

• The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. Translational psychiatry Biernacka, J. M., Sangkuhl, K., Jenkins, G., Whaley, R. M., Barman, P., Batzler, A., Altman, R. B., Arolt, V., Brockmöller, J., Chen, C. H., Domschke, K., Hall-Flavin, D. K., Hong, C. J., Illi, A., Ji, Y., Kampman, O., Kinoshita, T., Leinonen, E., Liou, Y. J., Mushiroda, T., Nonen, S., Skime, M. K., Wang, L., Baune, B. T., Kato, M., Liu, Y. L., Praphanphoj, V., Stingl, J. C., Tsai, S. J., Kubo, M., Klein, T. E., Weinshilboum, R. 2015; 5: e553

  Abstract

  Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. The top association result in the meta-analysis of response represents SNPs 5′ upstream of the neuregulin-1 gene, NRG1 (P = 1.20E - 06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.

  View details for DOI 10.1038/tp.2015.47

  View details for PubMedID 25897834

  View details for PubMedCentralID PMC4462610

• PharmGKB summary: very important pharmacogene information for human leukocyte antigen B. Pharmacogenetics and genomics Barbarino, J. M., Kroetz, D. L., Klein, T. E., Altman, R. B. 2015; 25 (4): 205-221

  View details for DOI 10.1097/FPC.0000000000000118

  View details for PubMedID 25647431

  View details for PubMedCentralID PMC4356642

• PharmGKB summary: very important pharmacogene information for CFTR. Pharmacogenetics and genomics McDonagh, E. M., Clancy, J. P., Altman, R. B., Klein, T. E. 2015; 25 (3): 149-156

  View details for DOI 10.1097/FPC.0000000000000112

  View details for PubMedID 25514096

  View details for PubMedCentralID PMC4336773

• 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

• INTERACTIVE GENOTYPE-BASED DOSING GUIDELINES. Whirl-Carrillo, M., Whaley, R. M., Caudle, K. E., Relling, M. V., Altman, R. B., Klein, T. E. WILEY-BLACKWELL. 2015: S61

  View details for Web of Science ID 000348730500184

• PharmGKB summary: ibuprofen pathways PHARMACOGENETICS AND GENOMICS Mazaleuskaya, L. L., Theken, K. N., Gong, L., Thorn, C. F., FitzGerald, G. A., Altman, R. B., Klein, T. E. 2015; 25 (2): 96-106

  View details for DOI 10.1097/FPC.0000000000000113

  View details for Web of Science ID 000347393200006

  View details for PubMedID 25502615

  View details for PubMedCentralID PMC4355401

• 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

• Genome-wide association study of warfarin maintenance dose in a Brazilian sample PHARMACOGENOMICS Parra, E. J., Botton, M. R., Perini, J. A., Krithika, S., Bourgeois, S., Johnson, T. A., Tsunoda, T., Pirmohamed, M., Wadelius, M., Limdi, N. A., Cavallari, L. H., Burmester, J. K., Rettie, A. E., Klein, T. E., Johnson, J. A., Hutz, M. H., Suarez-Kurtz, G. 2015; 16 (11): 1253-1263

  Abstract

  Extreme discordant phenotype and genome-wide association (GWA) approaches were combined to explore the role of genetic variants on warfarin dose requirement in Brazilians.Patients receiving low (≤ 20 mg/week; n = 180) or high stable warfarin doses (≥ 42.5 mg/week; n = 187) were genotyped with Affymetrix Axiom(®) Biobank arrays. Imputation was carried out using data from the combined 1000 Genomes project.Genome-wide signals (p ≤ 5 × 10(-8)) were identified in the well-known VKORC1 (lead SNP, rs749671; OR: 20.4; p = 1.08 × 10(-33)) and CYP2C9 (lead SNP, rs9332238, OR: 6.8 and p = 4.4 × 10(-13)) regions. The rs9332238 polymorphism is in virtually perfect LD with CYP2C9*2 (rs1799853) and CYP2C9*3 (rs1057910). No other genome-wide significant regions were identified in the study.We confirmed the important role of VKORC1 and CYP2C9 polymorphisms in warfarin dose. Original submitted 14 January 2015; Revision submitted 26 May 2015.

  View details for DOI 10.2217/pgs.15.73

  View details for Web of Science ID 000361142000005

  View details for PubMedCentralID PMC4573240

• Evidence synthesis and guideline development in genomic medicine: current status and future prospects GENETICS IN MEDICINE Schully, S. D., Lam, T. K., Dotson, W. D., Chang, C. Q., Aronson, N., Birkeland, M. L., Brewster, S. J., Boccia, S., Buchanan, A. H., Calonge, N., Calzone, K., Djulbegovic, B., Goddard, K. A., Klein, R. D., Klein, T. E., Lau, J., Long, R., Lyman, G. H., Morgan, R. L., Palmer, C. G., Ling, M. V., Rubinstein, W. S., Swen, J. J., Terry, S. F., Williams, M. S., Khoury, M. J. 2015; 17 (1): 63-67

  Abstract

  With the accelerated implementation of genomic medicine, health-care providers will depend heavily on professional guidelines and recommendations. Because genomics affects many diseases across the life span, no single professional group covers the entirety of this rapidly developing field.To pursue a discussion of the minimal elements needed to develop evidence-based guidelines in genomics, the Centers for Disease Control and Prevention and the National Cancer Institute jointly held a workshop to engage representatives from 35 organizations with interest in genomics (13 of which make recommendations). The workshop explored methods used in evidence synthesis and guideline development and initiated a dialogue to compare these methods and to assess whether they are consistent with the Institute of Medicine report "Clinical Practice Guidelines We Can Trust."The participating organizations that develop guidelines or recommendations all had policies to manage guideline development and group membership, and processes to address conflicts of interests. However, there was wide variation in the reliance on external reviews, regular updating of recommendations, and use of systematic reviews to assess the strength of scientific evidence.Ongoing efforts are required to establish criteria for guideline development in genomic medicine as proposed by the Institute of Medicine.

  View details for DOI 10.1038/gim.2014.69

  View details for PubMedID 24946156

• A TWENTIETH ANNIVERSARY TRIBUTE TO PSB Hewett, D., Whirl-Carrillo, M., Hunter, L. E., Altman, R. B., Klein, T. E., Altman, R. B., Dunker, A. K., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2015: 1–7

  View details for Web of Science ID 000461835500001

• 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: gemcitabine pathway PHARMACOGENETICS AND GENOMICS Alvarellos, M. L., Lamba, J., Sangkuhl, K., Thorn, C. F., Wang, L., Klein, D. J., Altman, R. B., Klein, T. E. 2014; 24 (11): 564-574

  View details for DOI 10.1097/FPC.0000000000000086

  View details for Web of Science ID 000343666300003

  View details for PubMedCentralID PMC4189987

• 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

• 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

• PharmGKB summary: gemcitabine pathway. Pharmacogenetics and genomics Alvarellos, M. L., Lamba, J., Sangkuhl, K., Thorn, C. F., Wang, L., Klein, D. J., Altman, R. B., Klein, T. E. 2014; 24 (11): 564-574

  View details for DOI 10.1097/FPC.0000000000000086

  View details for PubMedID 25162786

• Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans. Blood Daneshjou, R., Gamazon, E. R., Burkley, B., Cavallari, L. H., Johnson, J. A., Klein, T. E., Limdi, N., Hillenmeyer, S., Percha, B., Karczewski, K. J., Langaee, T., Patel, S. R., Bustamante, C. D., Altman, R. B., Perera, M. A. 2014; 124 (14): 2298-2305

  Abstract

  The anticoagulant warfarin has >30 million prescriptions per year in the United States. Doses can vary 20-fold between patients, and incorrect dosing can result in serious adverse events. Variation in warfarin pharmacokinetic and pharmacodynamic genes, such as CYP2C9 and VKORC1, do not fully explain the dose variability in African Americans. To identify additional genetic contributors to warfarin dose, we exome sequenced 103 African Americans on stable doses of warfarin at extremes (≤ 35 and ≥ 49 mg/week). We found an association between lower warfarin dose and a population-specific regulatory variant, rs7856096 (P = 1.82 × 10(-8), minor allele frequency = 20.4%), in the folate homeostasis gene folylpolyglutamate synthase (FPGS). We replicated this association in an independent cohort of 372 African American subjects whose stable warfarin doses represented the full dosing spectrum (P = .046). In a combined cohort, adding rs7856096 to the International Warfarin Pharmacogenetic Consortium pharmacogenetic dosing algorithm resulted in a 5.8 mg/week (P = 3.93 × 10(-5)) decrease in warfarin dose for each allele carried. The variant overlaps functional elements and was associated (P = .01) with FPGS gene expression in lymphoblastoid cell lines derived from combined HapMap African populations (N = 326). Our results provide the first evidence linking genetic variation in folate homeostasis to warfarin response.

  View details for DOI 10.1182/blood-2014-04-568436

  View details for PubMedID 25079360

• The clinical pharmacogenetics implementation consortium (CPIC): facilitating the adoption of pharmacogenetics into routine clinical practice and the electronic health record. Caudle, K. E., Hoffman, J. M., Whirl-Carrillo, M., Haidar, C. E., Crews, K. R., Klein, T. E., Relling, M. V. WILEY-BLACKWELL. 2014: E251–E252

  View details for Web of Science ID 000342916800229

• The clinical pharmacogenetics implementation consortium guideline for SLCO1B1 and simvastatin-induced myopathy: 2014 update. Clinical pharmacology and therapeutics Ramsey, L. B., Johnson, S. G., Caudle, K. E., Haidar, C. E., Voora, D., Wilke, R. A., Maxwell, W. D., McLeod, H. L., Krauss, R. M., Roden, D. M., Feng, Q., Cooper-DeHoff, R. M., Gong, L., Klein, T. E., Wadelius, M., Niemi, M. 2014; 96 (4): 423-8

  Abstract

  Simvastatin is among the most commonly used prescription medications for cholesterol reduction. A single coding single-nucleotide polymorphism, rs4149056T>C, in SLCO1B1 increases systemic exposure to simvastatin and the risk of muscle toxicity. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for simvastatin based on SLCO1B1 genotype. This article is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium guideline for SLCO1B1 and simvastatin-induced myopathy.

  View details for DOI 10.1038/clpt.2014.125

  View details for PubMedID 24918167

  View details for PubMedCentralID PMC4169720

• The Clinical Pharmacogenetics Implementation Consortium Guideline for SLCO1B1 and Simvastatin-Induced Myopathy: 2014 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Ramsey, L. B., Johnson, S. G., Caudle, K. E., Haidar, C. E., Voora, D., Wilke, R. A., MAXWELL, W. D., McLeod, L., Krauss, R. M., Roden, D. M., Feng, Q., Cooper-DeHoff, R. M., Gong, L., Klein, T. E., Wadelius, M., Niemi, M. 2014; 96 (4): 423-428

  Abstract

  Simvastatin is among the most commonly used prescription medications for cholesterol reduction. A single coding single-nucleotide polymorphism, rs4149056T>C, in SLCO1B1 increases systemic exposure to simvastatin and the risk of muscle toxicity. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for simvastatin based on SLCO1B1 genotype. This article is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium guideline for SLCO1B1 and simvastatin-induced myopathy.

  View details for DOI 10.1038/clpt.2014.125

  View details for Web of Science ID 000342675400019

  View details for PubMedCentralID PMC4169720

• PharmGKB summary: uric acid-lowering drugs pathway, pharmacodynamics. Pharmacogenetics and genomics McDonagh, E. M., Thorn, C. F., Callaghan, J. T., Altman, R. B., Klein, T. E. 2014; 24 (9): 464-476

  View details for DOI 10.1097/FPC.0000000000000058

  View details for PubMedID 24915143

• PharmGKB summary: very important pharmacogene information for N-acetyltransferase 2. Pharmacogenetics and genomics McDonagh, E. M., Boukouvala, S., Aklillu, E., Hein, D. W., Altman, R. B., Klein, T. E. 2014; 24 (8): 409-425

  View details for DOI 10.1097/FPC.0000000000000062

  View details for PubMedID 24892773

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for Rasburicase Therapy in the Context of G6PD Deficiency Genotype. Clinical pharmacology & therapeutics RELLING, M. V., MCDONAGH, E. M., Chang, T., Caudle, K. E., McLeod, H. L., Haidar, C. E., Klein, T., Luzzatto, L. 2014; 96 (2): 169-174

  Abstract

  Glucose-6-phosphate dehydrogenase (G6PD) deficiency is associated with development of acute hemolytic anemia (AHA) induced by a number of drugs. We provide guidance as to which G6PD genotypes are associated with G6PD deficiency in males and females. Rasburicase is contraindicated in G6PD-deficient patients due to the risk of AHA and possibly methemoglobinemia. Unless preemptive genotyping has established a positive diagnosis of G6PD deficiency, quantitative enzyme assay remains the mainstay of screening prior to rasburicase use. The purpose of this article is to help interpret the results of clinical G6PD genotype tests so that they can guide the use of rasburicase. Detailed guidelines on other aspects of the use of rasburicase, including analyses of cost-effectiveness, are beyond the scope of this document. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines are published and updated periodically on https://www.pharmgkb.org/page/cpic to reflect new developments in the field.Clinical Pharmacology & Therapeutics (2014); advance online publication 11 June 2014. doi:10.1038/clpt.2014.97.

  View details for DOI 10.1038/clpt.2014.97

  View details for PubMedID 24787449

• Interpreting the CYP2D6 results from the International Tamoxifen Pharmacogenetics Consortium. Clinical pharmacology & therapeutics Province, M. A., Altman, R. B., Klein, T. E. 2014; 96 (2): 144-146

  View details for DOI 10.1038/clpt.2014.100

  View details for PubMedID 25056393

  View details for PubMedCentralID PMC4147833

• PharmGKB summary: tramadol pathway PHARMACOGENETICS AND GENOMICS Gong, L., Stamer, U. M., Tzvetkov, M. V., Altman, R. B., Klein, T. E. 2014; 24 (7): 374-380

  View details for DOI 10.1097/FPC.0000000000000057

  View details for Web of Science ID 000337727000007

  View details for PubMedID 24849324

  View details for PubMedCentralID PMC4100774

• PharmGKB summary: very important pharmacogene information for SLC22A1. Pharmacogenetics and genomics Goswami, S., Gong, L., Giacomini, K., Altman, R. B., Klein, T. E. 2014; 24 (6): 324-328

  View details for DOI 10.1097/FPC.0000000000000048

  View details for PubMedID 24681965

  View details for PubMedCentralID PMC4035531

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for Ivacaftor Therapy in the Context of CFTR Genotype. Clinical pharmacology & therapeutics Clancy, J. P., Johnson, S. G., Yee, S. W., MCDONAGH, E. M., Caudle, K. E., Klein, T. E., Cannavo, M., Giacomini, K. M. 2014; 95 (6): 592-597

  Abstract

  Cystic fibrosis (CF) is a life-shortening disease arising as a consequence of mutations within the CFTR gene. Novel therapeutics for CF are emerging that target CF transmembrane conductance regulator protein (CFTR) defects resulting from specific CFTR variants. Ivacaftor is a drug that potentiates CFTR gating function and is specifically indicated for CF patients with a particular CFTR variant, G551D-CFTR (rs75527207). Here, we provide therapeutic recommendations for ivacaftor based on preemptive CFTR genotype results.

  View details for DOI 10.1038/clpt.2014.54

  View details for PubMedID 24598717

  View details for PubMedCentralID PMC4026598

• 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

• PharmGKB summary: abacavir pathway. Pharmacogenetics and genomics Barbarino, J. M., Kroetz, D. L., Altman, R. B., Klein, T. E. 2014; 24 (5): 276-282

  View details for DOI 10.1097/FPC.0000000000000040

  View details for PubMedID 24625462

• Clinical Pharmacogenetics Implementation Consortium Guidelines for HLA-B Genotype and Abacavir Dosing: 2014 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Martin, M. A., Hoffman, J. M., Freimuth, R. R., Klein, T. E., Dong, B. J., Pirmohamed, M., Hicks, J. K., Wilkinson, M. R., Haas, D. W., Kroetz, D. L. 2014; 95 (5): 499-500

  Abstract

  The Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for HLA-B Genotype and Abacavir Dosing were originally published in April 2012. We reviewed recent literature and concluded that none of the evidence would change the therapeutic recommendations in the original guideline; therefore, the original publication remains clinically current. However, we have updated the Supplementary Material online and included additional resources for applying CPIC guidelines to the electronic health record. Up-to-date information can be found at PharmGKB (http://www.pharmgkb.org).

  View details for DOI 10.1038/clpt.2014.38

  View details for Web of Science ID 000334759900020

  View details for PubMedID 24561393

  View details for PubMedCentralID PMC3994233

• Genotype-Guided Dosing of Vitamin K Antagonists NEW ENGLAND JOURNAL OF MEDICINE Daneshjou, R., Klein, T. E., Altman, R. B. 2014; 370 (18): 1762–63

  View details for Web of Science ID 000335405200021

  View details for PubMedID 24804303

• Genotype-guided dosing of vitamin K antagonists. New England journal of medicine Daneshjou, R., Klein, T. E., Altman, R. B. 2014; 370 (18): 1762-1763

  View details for DOI 10.1056/NEJMc1402521#SA4

  View details for PubMedID 24785217

• Clinical Pharmacogenetics Implementation Consortium Guidelines for Cytochrome P450 2D6 Genotype and Codeine Therapy: 2014 Update. Clinical pharmacology & therapeutics Crews, K. R., Gaedigk, A., Dunnenberger, H. M., Leeder, J. S., Klein, T. E., Caudle, K. E., Haidar, C. E., Shen, D. D., Callaghan, J. T., Sadhasivam, S., Prows, C. A., Kharasch, E. D., Skaar, T. C. 2014; 95 (4): 376-382

  Abstract

  Codeine is bioactivated to morphine, a strong opioid agonist, by the hepatic cytochrome P450 2D6 (CYP2D6); hence, the efficacy and safety of codeine are governed by CYP2D6 activity. Polymorphisms are a major cause of CYP2D6 variability. We summarize evidence from the literature supporting this association and provide therapeutic recommendations for codeine based on CYP2D6 genotype. This document is an update to the 2012 Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for CYP2D6 genotype and codeine therapy.

  View details for DOI 10.1038/clpt.2013.254

  View details for PubMedID 24458010

  View details for PubMedCentralID PMC3975212

• Clinical interpretation and implications of whole-genome sequencing. JAMA 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

• 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

• PharmGKB summary: very important pharmacogene information for UGT1A1 PHARMACOGENETICS AND GENOMICS Barbarino, J. M., Haidar, C. E., Klein, T. E., Altman, R. B. 2014; 24 (3): 177-183

  View details for DOI 10.1097/FPC.0000000000000024

  View details for Web of Science ID 000331209100006

  View details for PubMedID 24492252

  View details for PubMedCentralID PMC4091838

• PharmGKB summary: ifosfamide pathways, pharmacokinetics and pharmacodynamics. Pharmacogenetics and genomics Lowenberg, D., Thorn, C. F., Desta, Z., Flockhart, D. A., Altman, R. B., Klein, T. E. 2014; 24 (2): 133-138

  View details for DOI 10.1097/FPC.0000000000000019

  View details for PubMedID 24401834

• Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines for IFNL3 (IL28B) Genotype and PEG Interferon-alpha-Based Regimens CLINICAL PHARMACOLOGY & THERAPEUTICS Muir, A. J., Gong, L., Johnson, S. G., Lee, M. T., Williams, M. S., Klein, T. E., Caudle, K. E., Nelson, D. R. 2014; 95 (2): 141-146

  Abstract

  Pegylated interferon-α (PEG-IFN-α or PEG-IFN 2a and 2b)- and ribavirin (RBV)-based regimens are the mainstay for treatment of hepatitis C virus (HCV) genotype 1. IFNL3 (IL28B) genotype is the strongest baseline predictor of response to PEG-IFN-α and RBV therapy in previously untreated patients and can be used by patients and clinicians as part of the shared decision-making process for initiating treatment for HCV infection. We provide information regarding the clinical use of PEG-IFN-α- and RBV-containing regimens based on IFNL3 genotype.

  View details for DOI 10.1038/clpt.2013.203

  View details for Web of Science ID 000330151100018

  View details for PubMedID 24096968

  View details for PubMedCentralID PMC3904555

• 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

• Path-scan: a reporting tool for identifying clinically actionable variants. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Daneshjou, R., Zappala, Z., Kukurba, K., Boyle, S. M., Ormond, K. E., Klein, T. E., Snyder, M., Bustamante, C. D., Altman, R. B., Montgomery, S. B. 2014; 19: 229-240

  Abstract

  The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.

  View details for PubMedID 24297550

• Re: CYP2D6 genotyping and the use of tamoxifen in breast cancer. Journal of the National Cancer Institute Province, M. A., Klein, T. E. 2014; 106 (2): djt379

  View details for PubMedID 24408655

  View details for PubMedCentralID PMC4067108

• PATH-SCAN: A REPORTING TOOL FOR IDENTIFYING CLINICALLY ACTIONABLE VARIANTS Daneshjou, R., Zappala, Z., Kukurba, K., Boyle, S. M., Ormond, K. E., Klein, T. E., Snyder, M., Bustamante, C. D., Altman, R. B., Montgomery, S. B., Altman, R. B., Dunker, A. K., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2014: 229–40

  View details for Web of Science ID 000461865200022

• PharmGKB summary: mycophenolic acid pathway PHARMACOGENETICS AND GENOMICS Lamba, V., Sangkuhl, K., Sanghavi, K., Fish, A., Altman, R. B., Klein, T. E. 2014; 24 (1): 73-79

  View details for DOI 10.1097/FPC.0000000000000010

  View details for Web of Science ID 000328629800009

  View details for PubMedID 24220207

• PharmGKB summary: venlafaxine pathway PHARMACOGENETICS AND GENOMICS Sangkuhl, K., Stingl, J. C., Turpeinen, M., Altman, R. B., Klein, T. E. 2014; 24 (1): 62-72

  View details for DOI 10.1097/FPC.0000000000000003

  View details for Web of Science ID 000328629800008

  View details for PubMedID 24128936

• PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 8 PHARMACOGENETICS AND GENOMICS Aquilante, C. L., Niemi, M., Gong, L., Altman, R. B., Klein, T. E. 2013; 23 (12): 721-728

  View details for DOI 10.1097/FPC.0b013e3283653b27

  View details for Web of Science ID 000326971400009

  View details for PubMedID 23962911

• Clinical Pharmacogenetics Implementation Consortium Guidelines for Dihydropyrimidine Dehydrogenase Genotype and Fluoropyrimidine Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Caudle, K. E., Thorn, C. F., Klein, T. E., Swen, J. J., McLeod, H. L., Diasio, R. B., Schwab, M. 2013; 94 (6): 640-645

  Abstract

  The fluoropyrimidines are the mainstay chemotherapeutic agents for the treatment of many types of cancers. Detoxifying metabolism of fluoropyrimidines requires dihydropyrimidine dehydrogenase (DPD, encoded by the DPYD gene), and reduced or absent activity of this enzyme can result in severe, and sometimes fatal, toxicity. We summarize evidence from the published literature supporting this association and provide dosing recommendations for fluoropyrimidines based on DPYD genotype (updates at http://www.pharmgkb.org).

  View details for DOI 10.1038/clpt.2013.172

  View details for Web of Science ID 000327168400020

  View details for PubMedID 23988873

  View details for PubMedCentralID PMC3831181

• 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

• PharmGKB summary: tamoxifen pathway, pharmacokinetics. Pharmacogenetics and genomics Klein, D. J., Thorn, C. F., Desta, Z., Flockhart, D. A., Altman, R. B., Klein, T. E. 2013; 23 (11): 643-647

  View details for DOI 10.1097/FPC.0b013e3283656bc1

  View details for PubMedID 23962908

• PharmGKB summary: cyclosporine and tacrolimus pathways PHARMACOGENETICS AND GENOMICS Barbarino, J. M., Staatz, C. E., Venkataramanan, R., Klein, T. E., Altman, R. B. 2013; 23 (10): 563-585

  View details for DOI 10.1097/FPC.0b013e328364db84

  View details for Web of Science ID 000324527600007

  View details for PubMedID 23922006

  View details for PubMedCentralID PMC4119065

• PharmGKB summary: methylene blue pathway PHARMACOGENETICS AND GENOMICS McDonagh, E. M., Bautista, J. M., Youngster, I., Altman, R. B., Klein, T. E. 2013; 23 (9): 498-508

  View details for DOI 10.1097/FPC.0b013e32836498f4

  View details for Web of Science ID 000323220200007

  View details for PubMedID 23913015

• Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C19 Genotype and Clopidogrel Therapy: 2013 Update CLINICAL PHARMACOLOGY & THERAPEUTICS Scott, S. A., Sangkuhl, K., Stein, C. M., Hulot, J., Mega, J. L., Roden, D. M., Klein, T. E., Sabatine, M. S., Johnson, J. A., Shuldiner, A. R. 2013; 94 (3): 317-323

  View details for DOI 10.1038/clpt.2013.105

  View details for Web of Science ID 000323686900016

  View details for PubMedID 23698643

• Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study. Lancet Perera, M. A., Cavallari, L. H., Limdi, N. A., Gamazon, E. R., Konkashbaev, A., Daneshjou, R., Pluzhnikov, A., Crawford, D. C., Wang, J., Liu, N., Tatonetti, N., Bourgeois, S., Takahashi, H., Bradford, Y., Burkley, B. M., Desnick, R. J., Halperin, J. L., Khalifa, S. I., Langaee, T. Y., Lubitz, S. A., Nutescu, E. A., Oetjens, M., Shahin, M. H., Patel, S. R., Sagreiya, H., Tector, M., Weck, K. E., Rieder, M. J., Scott, S. A., Wu, A. H., Burmester, J. K., Wadelius, M., Deloukas, P., Wagner, M. J., Mushiroda, T., Kubo, M., Roden, D. M., Cox, N. J., Altman, R. B., Klein, T. E., Nakamura, Y., Johnson, J. A. 2013; 382 (9894): 790-796

  Abstract

  BACKGROUND: VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. METHODS: We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 -1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10(-8) in the discovery cohort and p<0·0038 in the replication cohort. FINDINGS: The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10(-8)). This association was confirmed in the replication cohort (p=5·04×10(-5)); analysis of the two cohorts together produced a p value of 4·5×10(-12). Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). INTERPRETATION: A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. FUNDING: National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.

  View details for DOI 10.1016/S0140-6736(13)60681-9

  View details for PubMedID 23755828

• 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 PubMedID 23708745

• 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

• PharmGKB summary: diuretics pathway, pharmacodynamics PHARMACOGENETICS AND GENOMICS Thorn, C. F., Ellison, D. H., Turner, S. T., Altman, R. B., Klein, T. E. 2013; 23 (8): 449-453

  View details for DOI 10.1097/FPC.0b013e3283636822

  View details for Web of Science ID 000323226500009

  View details for PubMedID 23788015

• Pharmacogenetics of warfarin: challenges and opportunities JOURNAL OF HUMAN GENETICS Lee, M. T., Klein, T. E. 2013; 58 (6): 334-338

  Abstract

  Since the introduction in the 1950s, warfarin has become the commonly used oral anticoagulant for the prevention of thromboembolism in patients with deep vein thrombosis, atrial fibrillation or prosthetic heart valve replacement. Warfarin is highly efficacious; however, achieving the desired anticoagulation is difficult because of its narrow therapeutic window and highly variable dose response among individuals. Bleeding is often associated with overdose of warfarin. There is overwhelming evidence that an individual's warfarin maintenance is associated with clinical factors and genetic variations, most notably polymorphisms in cytochrome P450 2C9 and vitamin K epoxide reductase subunit 1. Numerous dose-prediction algorithms incorporating both genetic and clinical factors have been developed and tested clinically. However, results from major clinical trials are not available yet. This review aims to provide an overview of the field of warfarin which includes information about the drug, genetics of warfarin dose requirements, dosing algorithms developed and the challenges for the clinical implementation of warfarin pharmacogenetics.Journal of Human Genetics advance online publication, 9 May 2013; doi:10.1038/jhg.2013.40.

  View details for DOI 10.1038/jhg.2013.40

  View details for Web of Science ID 000320843600006

  View details for PubMedID 23657428

• Pathway analysis of genome-wide data improves warfarin dose prediction BMC GENOMICS Daneshjou, R., Tatonetti, N. P., Karczewski, K. J., Sagreiya, H., Bourgeois, S., Drozda, K., Burmester, J. K., Tsunoda, T., Nakamura, Y., Kubo, M., Tector, M., Limdi, N. A., Cavallari, L. H., Perera, M., Johnson, J. A., Klein, T. E., Altman, R. B. 2013; 14

  Abstract

  Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

  View details for DOI 10.1186/1471-2164-14-S3-S11

  View details for Web of Science ID 000319869500011

  View details for PubMedID 23819817

• Valproic acid pathway: pharmacokinetics and pharmacodynamics PHARMACOGENETICS AND GENOMICS Ghodke-Puranik, Y., Thorn, C. F., Lamba, J. K., Leeder, J. S., Song, W., Birnbaum, A. K., Altman, R. B., Klein, T. E. 2013; 23 (4): 236-241

  View details for DOI 10.1097/FPC.0b013e32835ea0b2

  View details for Web of Science ID 000316109700008

  View details for PubMedID 23407051

• 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

• Nomenclature for alleles of the thiopurine methyltransferase gene PHARMACOGENETICS AND GENOMICS Appell, M. L., Berg, J., Duley, J., Evans, W. E., Kennedy, M. A., Lennard, L., Marinaki, T., McLeod, H. L., Relling, M. V., Schaeffeler, E., Schwab, M., Weinshilboum, R., Yeoh, A. E., McDonagh, E. M., Hebert, J. M., Klein, T. E., Coulthard, S. A. 2013; 23 (4): 242-248

  Abstract

  The drug-metabolizing enzyme thiopurine methyltransferase (TPMT) has become one of the best examples of pharmacogenomics to be translated into routine clinical practice. TPMT metabolizes the thiopurines 6-mercaptopurine, 6-thioguanine, and azathioprine, drugs that are widely used for treatment of acute leukemias, inflammatory bowel diseases, and other disorders of immune regulation. Since the discovery of genetic polymorphisms in the TPMT gene, many sequence variants that cause a decreased enzyme activity have been identified and characterized. Increasingly, to optimize dose, pretreatment determination of TPMT status before commencing thiopurine therapy is now routine in many countries. Novel TPMT sequence variants are currently numbered sequentially using PubMed as a source of information; however, this has caused some problems as exemplified by two instances in which authors' articles appeared on PubMed at the same time, resulting in the same allele numbers given to different polymorphisms. Hence, there is an urgent need to establish an order and consensus to the numbering of known and novel TPMT sequence variants. To address this problem, a TPMT nomenclature committee was formed in 2010, to define the nomenclature and numbering of novel variants for the TPMT gene. A website (http://www.imh.liu.se/tpmtalleles) serves as a platform for this work. Researchers are encouraged to submit novel TPMT alleles to the committee for designation and reservation of unique allele numbers. The committee has decided to renumber two alleles: nucleotide position 106 (G>A) from TPMT*24 to TPMT*30 and position 611 (T>C, rs79901429) from TPMT*28 to TPMT*31. Nomenclature for all other known alleles remains unchanged.

  View details for DOI 10.1097/FPC.0b013e32835f1cc0

  View details for Web of Science ID 000316109700009

  View details for PubMedID 23407052

• The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Overcoming Challenges of Real-World Implementation. Clinical pharmacology and therapeutics Shuldiner, A. R., Relling, M. V., Peterson, J. F., Hicks, K., Freimuth, R. R., Sadee, W., Pereira, N. L., Roden, D. M., Johnson, J. A., Klein, T. E. 2013

  View details for DOI 10.1038/clpt.2013.59

  View details for PubMedID 23588301

• Clinical Pharmacogenetics Implementation Consortium Guidelines for Human Leukocyte Antigen-B Genotype and Allopurinol Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Hershfield, M. S., Callaghan, J. T., Tassaneeyakul, W., Mushiroda, T., Thorn, C. F., Klein, T. E., Lee, M. T. 2013; 93 (2): 153-158

  Abstract

  Allopurinol is the most commonly used drug for the treatment of hyperuricemia and gout. However, allopurinol is also one of the most common causes of severe cutaneous adverse reactions (SCARs), which include drug hypersensitivity syndrome, Stevens–Johnson syndrome, and toxic epidermal necrolysis. A variant allele of the human leukocyte antigen (HLA)-B, HLA-B*58:01, associates strongly with allopurinolinduced SCAR. We have summarized the evidence from the published literature and developed peer-reviewed guidelines for allopurinol use based on HLA-B genotype.

  View details for DOI 10.1038/clpt.2012.209

  View details for Web of Science ID 000314139100016

  View details for PubMedID 23232549

  View details for PubMedCentralID PMC3564416

• EXPRESSION QUANTITATIVE TRAIT LOCI ANALYSIS OF STABLE WARFARIN DOSE IDENTIFIES NOVEL ASSOCIATIONS: FINDING SIGNAL WITHIN THE NOISE Annual Meeting of the American-Society-for-Clinical-Pharmacology-and-Therapeutics (ASCPT) Gamazon, E. R., Daneshjou, R., Cavallari, L. H., Limdi, N. A., Wadelius, M., Johnson, J. A., Klein, T. E., Scott, S., Tsunoda, T., Deloukas, P., Altman, R., Cox, N., Perera, M. A. NATURE PUBLISHING GROUP. 2013: S27–S27

  View details for Web of Science ID 000314138700072

• Improving data and knowledge management to better integrate health care and research. Journal of internal medicine Cases, M. n., Furlong, L. I., Albanell, J. n., Altman, R. B., Bellazzi, R. n., Boyer, S. n., Brand, A. n., Brookes, A. J., Brunak, S. n., Clark, T. W., Gea, J. n., Ghazal, P. n., Graf, N. n., Guigó, R. n., Klein, T. E., López-Bigas, N. n., Maojo, V. n., Mons, B. n., Musen, M. n., Oliveira, J. L., Rowe, A. n., Ruch, P. n., Shabo, A. n., Shortliffe, E. H., Valencia, A. n., van der Lei, J. n., Mayer, M. A., Sanz, F. n. 2013

  View details for DOI 10.1111/joim.12105

  View details for PubMedID 23808970

• Governmental and Academic Efforts to Advance the Field of Pharmacogenomics PHARMACOGENOMICS: CHALLENGES AND OPPORTUNITIES IN THERAPEUTIC IMPLEMENTATION Cavallari, L. H., Klein, T. E., Huang, S., Lam, Y. W., Cavallari, L. H. 2013: 63–88

  View details for DOI 10.1016/B978-0-12-391918-2.00003-2

  View details for Web of Science ID 000323772400004

• PharmGKB: the Pharmacogenomics Knowledge Base. Methods in molecular biology (Clifton, N.J.) Thorn, C. F., Klein, T. E., Altman, R. B. 2013; 1015: 311-320

  Abstract

  The Pharmacogenomics Knowledge Base, PharmGKB, is an interactive tool for researchers investigating how genetic variation affects drug response. The PharmGKB Web site, http://www.pharmgkb.org , displays genotype, molecular, and clinical knowledge integrated into pathway representations and Very Important Pharmacogene (VIP) summaries with links to additional external resources. Users can search and browse the knowledgebase by genes, variants, drugs, diseases, and pathways. Registration is free to the entire research community, but subject to agreement to use for research purposes only and not to redistribute. Registered users can access and download data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

  View details for DOI 10.1007/978-1-62703-435-7_20

  View details for PubMedID 23824865

• Pathway analysis of genome-wide data improves warfarin dose prediction. BMC genomics Daneshjou, R., Tatonetti, N. P., Karczewski, K. J., Sagreiya, H., Bourgeois, S., Drozda, K., Burmester, J. K., Tsunoda, T., Nakamura, Y., Kubo, M., Tector, M., Limdi, N. A., Cavallari, L. H., Perera, M., Johnson, J. A., Klein, T. E., Altman, R. B. 2013; 14: S11-?

  Abstract

  Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.

  View details for DOI 10.1186/1471-2164-14-S3-S11

  View details for PubMedID 23819817

• PharmGKB summary: zidovudine pathway PHARMACOGENETICS AND GENOMICS Ghodke, Y., Anderson, P. L., Sangkuhl, K., Lamba, J., Altman, R. B., Klein, T. E. 2012; 22 (12): 891-894

  View details for DOI 10.1097/FPC.0b013e32835879a8

  View details for Web of Science ID 000311031800008

  View details for PubMedID 22960662

• Metformin pathways: pharmacokinetics and pharmacodynamics PHARMACOGENETICS AND GENOMICS Gong, L., Goswami, S., Giacomini, K. M., Altman, R. B., Klein, T. E. 2012; 22 (11): 820-827

  View details for DOI 10.1097/FPC.0b013e3283559b22

  View details for Web of Science ID 000309977100008

  View details for PubMedID 22722338

  View details for PubMedCentralID PMC3651676

• Very important pharmacogene summary for VDR PHARMACOGENETICS AND GENOMICS Poon, A. H., Gong, L., Brasch-Andersen, C., Litonjua, A. A., Raby, B. A., Hamid, Q., Laprise, C., Weiss, S. T., Altman, R. B., Klein, T. E. 2012; 22 (10): 758-763

  View details for DOI 10.1097/FPC.0b013e328354455c

  View details for Web of Science ID 000309115000007

  View details for PubMedID 22588316

  View details for PubMedCentralID PMC3678550

• Implementing Personalized Medicine: Development of a Cost-Effective Customized Pharmacogenetics Genotyping Array CLINICAL PHARMACOLOGY & THERAPEUTICS Johnson, J. A., Burkley, B. M., Langaee, T. Y., Clare-Salzler, M. J., Klein, T. E., Altman, R. B. 2012; 92 (4): 437-439

  Abstract

  Although there is increasing evidence to support the implementation of pharmacogenetics in certain clinical scenarios, the adoption of this approach has been limited. The advent of preemptive and inexpensive testing of critical pharmacogenetic variants may overcome barriers to adoption. We describe the design of a customized array built for the personalized-medicine programs of the University of Florida and Stanford University. We selected key variants for the array using the clinical annotations of the Pharmacogenomics Knowledgebase (PharmGKB), and we included variants in drug metabolism and transporter genes along with other pharmacogenetically important variants.

  View details for DOI 10.1038/clpt.2012.125

  View details for Web of Science ID 000309017000017

  View details for PubMedID 22910441

  View details for PubMedCentralID PMC3454443

• 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: very important pharmacogene information for GSTT1 PHARMACOGENETICS AND GENOMICS Thorn, C. F., Ji, Y., Weinshilboum, R. M., Altman, R. B., Klein, T. E. 2012; 22 (8): 646-651

  View details for DOI 10.1097/FPC.0b013e3283527c02

  View details for Web of Science ID 000306483500009

  View details for PubMedID 22643671

  View details for PubMedCentralID PMC3395771

• PharmGKB summary: very important pharmacogene information for CYP3A5 PHARMACOGENETICS AND GENOMICS Lamba, J., Hebert, J. M., Schuetz, E. G., Klein, T. E., Altman, R. B. 2012; 22 (7): 555-558

  View details for DOI 10.1097/FPC.0b013e328351d47f

  View details for Web of Science ID 000305429900009

  View details for PubMedID 22407409

• The Clinical Pharmacogenomics Implementation Consortium: CPIC Guideline for SLCO1B1 and Simvastatin-Induced Myopathy CLINICAL PHARMACOLOGY & THERAPEUTICS Wilke, R. A., Ramsey, L. B., Johnson, S. G., MAXWELL, W. D., McLeod, H. L., Voora, D., Krauss, R. M., Roden, D. M., Feng, Q., Cooper-DeHoff, R. M., Gong, L., Klein, T. E., Wadelius, M., Niemi, M. 2012; 92 (1): 112-117

  Abstract

  Cholesterol reduction from statin therapy has been one of the greatest public health successes in modern medicine. Simvastatin is among the most commonly used prescription medications. A non-synonymous coding single-nucleotide polymorphism (SNP), rs4149056, in SLCO1B1 markedly increases systemic exposure to simvastatin and the risk of muscle toxicity. This guideline explores the relationship between rs4149056 (c.521T>C, p.V174A) and clinical outcome for all statins. The strength of the evidence is high for myopathy with simvastatin. We limit our recommendations accordingly.

  View details for DOI 10.1038/clpt.2012.57

  View details for Web of Science ID 000305589800019

  View details for PubMedID 22617227

  View details for PubMedCentralID PMC3384438

• 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

• PharmGKB summary: caffeine pathway PHARMACOGENETICS AND GENOMICS Thorn, C. F., Aklillu, E., McDonagh, E. M., Klein, T. E., Altman, R. B. 2012; 22 (5): 389-395

  View details for DOI 10.1097/FPC.0b013e3283505d5e

  View details for Web of Science ID 000302783800008

  View details for PubMedID 22293536

  View details for PubMedCentralID PMC3381939

• 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

• Celecoxib pathways: pharmacokinetics and pharmacodynamics PHARMACOGENETICS AND GENOMICS Gong, L., Thorn, C. F., Bertagnolli, M. M., Grosser, T., Altman, R. B., Klein, T. E. 2012; 22 (4): 310-318

  View details for DOI 10.1097/FPC.0b013e32834f94cb

  View details for Web of Science ID 000301537400010

  View details for PubMedID 22336956

  View details for PubMedCentralID PMC3303994

• Clinical Pharmacogenetics Implementation Consortium Guidelines for HLA-B Genotype and Abacavir Dosing CLINICAL PHARMACOLOGY & THERAPEUTICS Martin, M. A., Klein, T. E., Dong, B. J., Pirmohamed, M., Haas, D. W., Kroetz, D. L. 2012; 91 (4): 734-738

  Abstract

  Human leukocyte antigen B (HLA-B) is responsible for presenting peptides to immune cells and plays a critical role in normal immune recognition of pathogens. A variant allele, HLA-B*57:01, is associated with increased risk of a hypersensitivity reaction to the anti-HIV drug abacavir. In the absence of genetic prescreening, hypersensitivity affects ~6% of patients and can be life-threatening with repeated dosing. We provide recommendations (updated periodically at http://www.pharmkgb.org) for the use of abacavir based on HLA-B genotype.

  View details for DOI 10.1038/clpt.2011.355

  View details for Web of Science ID 000301891800026

  View details for PubMedID 22378157

  View details for PubMedCentralID PMC3374459

• 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

• PharmGKB summary: very important pharmacogene information for G6PD PHARMACOGENETICS AND GENOMICS McDonagh, E. M., Thorn, C. F., Bautista, J. M., Youngster, I., Altman, R. B., Klein, T. E. 2012; 22 (3): 219-228

  View details for DOI 10.1097/FPC.0b013e32834eb313

  View details for Web of Science ID 000300409800008

  View details for PubMedID 22237549

  View details for PubMedCentralID PMC3382019

• PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 19 PHARMACOGENETICS AND GENOMICS Scott, S. A., Sangkuhl, K., Shuldiner, A. R., Hulot, J., Thorn, C. F., Altman, R. B., Klein, T. E. 2012; 22 (2): 159-165

  View details for DOI 10.1097/FPC.0b013e32834d4962

  View details for Web of Science ID 000299310600008

  View details for PubMedID 22027650

  View details for PubMedCentralID PMC3349992

• PharmGKB summary: very important pharmacogene information for CYP1A2 PHARMACOGENETICS AND GENOMICS Thorn, C. F., Aklillu, E., Klein, T. E., Altman, R. B. 2012; 22 (1): 73-77

  View details for DOI 10.1097/FPC.0b013e32834c6efd

  View details for Web of Science ID 000298249500009

  View details for PubMedID 21989077

  View details for PubMedCentralID PMC3346273

• SYSTEMS PHARMACOGENOMICS-BRIDGING THE GAP Ritchie, M., Cox, N., Cheng, C., Weiss, S., Klein, T., Altman, R., P-STAR, PGRN Syst Biol Grp, PharmGKB, Altman, R. B., Dunker, A. K., Hunter, L., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2012: 442

  View details for Web of Science ID 000407150800044

• PharmGKB summary: carbamazepine pathway PHARMACOGENETICS AND GENOMICS Thorn, C. F., Leckband, S. G., Kelsoe, J., Leeder, J. S., Mueller, D. J., Klein, T. E., Altman, R. B. 2011; 21 (12): 906-910

  View details for DOI 10.1097/FPC.0b013e328348c6f2

  View details for Web of Science ID 000296799900016

  View details for PubMedID 21738081

  View details for PubMedCentralID PMC3349991

• 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

• PharmGKB summary: citalopram pharmacokinetics pathway PHARMACOGENETICS AND GENOMICS Sangkuhl, K., Klein, T. E., Altman, R. B. 2011; 21 (11): 769-772

  View details for DOI 10.1097/FPC.0b013e328346063f

  View details for Web of Science ID 000296146400010

  View details for PubMedID 21546862

  View details for PubMedCentralID PMC3349993

• PharmGKB summary: methotrexate pathway PHARMACOGENETICS AND GENOMICS Mikkelsen, T. S., Thorn, C. F., Yang, J. J., Ulrich, C. M., French, D., Zaza, G., Dunnenberger, H. M., Marsh, S., McLeod, H. L., Giacomini, K., Becker, M. L., Gaedigk, R., Leeder, J. S., Kager, L., Relling, M. V., Evans, W., Klein, T. E., Altman, R. B. 2011; 21 (10): 679-686

  View details for DOI 10.1097/FPC.0b013e328343dd93

  View details for Web of Science ID 000294808900008

  View details for PubMedID 21317831

  View details for PubMedCentralID PMC3139712

• Pharmacogenetics: Call to Action CLINICAL PHARMACOLOGY & THERAPEUTICS Relling, M. V., Guchelaar, H. J., Roden, D. M., Klein, T. E. 2011; 90 (4): 507-507

  View details for DOI 10.1038/clpt.2011.172

  View details for Web of Science ID 000295119200019

  View details for PubMedID 21881564

• 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: very important pharmacogene information for PTGS2 PHARMACOGENETICS AND GENOMICS Thorn, C. F., Grosser, T., Klein, T. E., Altman, R. B. 2011; 21 (9): 607-613

  View details for DOI 10.1097/FPC.0b013e3283415515

  View details for Web of Science ID 000293731200012

  View details for PubMedID 21063235

  View details for PubMedCentralID PMC3141084

• 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

• Platelet aggregation pathway PHARMACOGENETICS AND GENOMICS Sangkuhl, K., Shuldiner, A. R., Klein, T. E., Altman, R. B. 2011; 21 (8): 516-521

  View details for DOI 10.1097/FPC.0b013e3283406323

  View details for Web of Science ID 000292634200009

  View details for PubMedID 20938371

  View details for PubMedCentralID PMC3134593

• Clinical Pharmacogenetics Implementation Consortium Guidelines for Cytochrome P450-2C19 (CYP2C19) Genotype and Clopidogrel Therapy CLINICAL PHARMACOLOGY & THERAPEUTICS Scott, S. A., Sangkuhl, K., Gardner, E. E., Stein, C. M., Hulot, J., Johnson, J. A., Roden, D. M., Klein, T. E., Shuldiner, A. R. 2011; 90 (2): 328-332

  View details for DOI 10.1038/clpt.2011.132

  View details for Web of Science ID 000292974900028

  View details for PubMedID 21716271

  View details for PubMedCentralID PMC3234301

• Doxorubicin pathways: pharmacodynamics and adverse effects PHARMACOGENETICS AND GENOMICS Thorn, C. F., Oshiro, C., Marsh, S., Hernandez-Boussard, T., McLeod, H., Klein, T. E., Altman, R. B. 2011; 21 (7): 440-446

  View details for DOI 10.1097/FPC.0b013e32833ffb56

  View details for Web of Science ID 000291633300011

  View details for PubMedID 21048526

  View details for PubMedCentralID PMC3116111

• PharmGKB summary: dopamine receptor D2 PHARMACOGENETICS AND GENOMICS Mi, H., Thomas, P. D., Ring, H. Z., Jiang, R., Sangkuhl, K., Klein, T. E., Altman, R. B. 2011; 21 (6): 350-356

  View details for DOI 10.1097/FPC.0b013e32833ee605

  View details for Web of Science ID 000290431200007

  View details for PubMedID 20736885

  View details for PubMedCentralID PMC3091980

• PharmGKB summary: cytochrome P450, family 2, subfamily J, polypeptide 2: CYP2J2 PHARMACOGENETICS AND GENOMICS Berlin, D. S., Sangkuhl, K., Klein, T. E., Altman, R. B. 2011; 21 (5): 308-311

  View details for DOI 10.1097/FPC.0b013e32833d1011

  View details for Web of Science ID 000289460200009

  View details for PubMedID 20739908

  View details for PubMedCentralID PMC3086341

• 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

• Very important pharmacogene summary: ABCB1 (MDR1, P-glycoprotein) PHARMACOGENETICS AND GENOMICS Hodges, L. M., Markova, S. M., Chinn, L. W., Gow, J. M., Kroetz, D. L., Klein, T. E., Altman, R. B. 2011; 21 (3): 152-161

  View details for DOI 10.1097/FPC.0b013e3283385a1c

  View details for Web of Science ID 000286971900007

  View details for PubMedID 20216335

  View details for PubMedCentralID PMC3098758

• 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

• PharmGKB: very important pharmacogene - HMGCR PHARMACOGENETICS AND GENOMICS Medina, M. W., Sangkuhl, K., Klein, T. E., Altman, R. B. 2011; 21 (2): 98-101

  View details for DOI 10.1097/FPC.0b013e328336c81b

  View details for Web of Science ID 000286096000006

  View details for PubMedID 20084049

  View details for PubMedCentralID PMC3098759

• Bisphosphonates pathway PHARMACOGENETICS AND GENOMICS Gong, L., Altman, R. B., Klein, T. E. 2011; 21 (1): 50-53

  View details for DOI 10.1097/FPC.0b013e328335729c

  View details for Web of Science ID 000285331700007

  View details for PubMedID 20023594

  View details for PubMedCentralID PMC3086066

• Molecular dynamics simulations of the full triple helical region of collagen type I provide an atomic scale view of the protein's regional heterogeneity. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Bodian, D. L., Radmer, R. J., Holbert, S., Klein, T. E. 2011: 193-204

  Abstract

  Collagen is a ubiquitous extracellular matrix protein. Its biological functions, including maintenance of the structural integrity of tissues, depend on its multiscale, hierarchical structure. Three elongated, twisted peptide chains of > 1000 amino acids each assemble into trimeric proteins characterized by the defining triple helical domain. The trimers associate into fibrils, which pack into fibers. We conducted a 10 ns molecular dynamics simulation of the full-length triple helical domain, which was made computationally feasible by segmenting the protein into overlapping fragments. The calculation included ~1.8 million atoms, including solvent, and took approximately 11 months using the CPUs of over a quarter of a million computers. Specialized analysis protocols and a relational database were developed to process the large amounts of data, which are publicly available. The simulated structures exhibit heterogeneity in the triple helical domain consistent with experimental results but at higher resolution. The structures serve as the foundation for studies of higher order forms of the protein and for modeling the effects of disease-associated mutations.

  View details for PubMedID 21121047

• KCNH2 pharmacogenomics summary PHARMACOGENETICS AND GENOMICS Oshiro, C., Thorn, C. F., Roden, D. M., Klein, T. E., Altman, R. B. 2010; 20 (12): 775-777

  View details for DOI 10.1097/FPC.0b013e3283349e9c

  View details for Web of Science ID 000284148300006

  View details for PubMedID 20150828

  View details for PubMedCentralID PMC3086352

• SLC19A1 pharmacogenomics summary PHARMACOGENETICS AND GENOMICS Yee, S. W., Gong, L., Badagnani, I., Giacomini, K. M., Klein, T. E., Altman, R. B. 2010; 20 (11): 708-715

  View details for DOI 10.1097/FPC.0b013e32833eca92

  View details for Web of Science ID 000282965100006

  View details for PubMedID 20811316

  View details for PubMedCentralID PMC2956130

• VKORC1 Pharmacogenomics Summary PHARMACOGENETICS AND GENOMICS Owen, R. P., Gong, L., Sagreiya, H., Klein, T. E., Altman, R. B. 2010; 20 (10): 642-644

  View details for DOI 10.1097/FPC.0b013e32833433b6

  View details for Web of Science ID 000281830900010

  View details for PubMedID 19940803

  View details for PubMedCentralID PMC3086043

• Thiopurine pathway PHARMACOGENETICS AND GENOMICS Zaza, G., Cheok, M., Krynetskaia, N., Thorn, C., Stocco, G., Hebert, J. M., McLeod, H., Weinshilboum, R. M., Relling, M. V., Evans, W. E., Klein, T. E., Altman, R. B. 2010; 20 (9): 573-574

  View details for DOI 10.1097/FPC.0b013e328334338f

  View details for Web of Science ID 000281295500008

  View details for PubMedID 19952870

  View details for PubMedCentralID PMC3098750

• PharmGKB summary: very important pharmacogene information for CYP2B6 PHARMACOGENETICS AND GENOMICS Thorn, C. F., Lamba, J. K., Lamba, V., Klein, T. E., Altman, R. B. 2010; 20 (8): 520-523

  View details for DOI 10.1097/FPC.0b013e32833947c2

  View details for Web of Science ID 000279865400007

  View details for PubMedID 20648701

  View details for PubMedCentralID PMC3086041

• Clopidogrel pathway PHARMACOGENETICS AND GENOMICS Sangkuhl, K., Klein, T. E., Altman, R. B. 2010; 20 (7): 463-465

  View details for DOI 10.1097/FPC.0b013e3283385420

  View details for Web of Science ID 000278879400009

  View details for PubMedID 20440227

  View details for PubMedCentralID PMC3086847

• Very important pharmacogene summary: thiopurine S-methyltransferase PHARMACOGENETICS AND GENOMICS Wang, L., Pelleymounter, L., Weinshilboum, R., Johnson, J. A., Hebert, J. M., Altman, R. B., Klein, T. E. 2010; 20 (6): 401-405

  View details for DOI 10.1097/FPC.0b013e3283352860

  View details for Web of Science ID 000277594800007

  View details for PubMedID 20154640

  View details for PubMedCentralID PMC3086840

• Challenges in the clinical application of whole-genome sequencing LANCET Ormond, K. E., Wheeler, M. T., Hudgins, L., Klein, T. E., Butte, A. J., Altman, R. B., Ashley, E. A., Greely, H. T. 2010; 375 (9727): 1749-1751

  View details for DOI 10.1016/S0140-6736(10)60599-5

  View details for Web of Science ID 000277890200036

  View details for PubMedID 20434765

• Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups BLOOD Limdi, N. A., Wadelius, M., Cavallari, L., Eriksson, N., Crawford, D. C., Lee, M. M., Chen, C., Motsinger-Reif, A., Sagreiya, H., Liu, N., Wu, A. H., Gage, B. F., Jorgensen, A., Pirmohamed, M., Shin, J., Suarez-Kurtz, G., Kimmel, S. E., Johnson, J. A., Klein, T. E., Wagner, M. J. 2010; 115 (18): 3827-3834

  Abstract

  Warfarin-dosing algorithms incorporating CYP2C9 and VKORC1 -1639G>A improve dose prediction compared with algorithms based solely on clinical and demographic factors. However, these algorithms better capture dose variability among whites than Asians or blacks. Herein, we evaluate whether other VKORC1 polymorphisms and haplotypes explain additional variation in warfarin dose beyond that explained by VKORC1 -1639G>A among Asians (n = 1103), blacks (n = 670), and whites (n = 3113). Participants were recruited from 11 countries as part of the International Warfarin Pharmacogenetics Consortium effort. Evaluation of the effects of individual VKORC1 single nucleotide polymorphisms (SNPs) and haplotypes on warfarin dose used both univariate and multi variable linear regression. VKORC1 -1639G>A and 1173C>T individually explained the greatest variance in dose in all 3 racial groups. Incorporation of additional VKORC1 SNPs or haplotypes did not further improve dose prediction. VKORC1 explained greater variability in dose among whites than blacks and Asians. Differences in the percentage of variance in dose explained by VKORC1 across race were largely accounted for by the frequency of the -1639A (or 1173T) allele. Thus, clinicians should recognize that, although at a population level, the contribution of VKORC1 toward dose requirements is higher in whites than in nonwhites; genotype predicts similar dose requirements across racial groups.

  View details for DOI 10.1182/blood-2009-12-255992

  View details for Web of Science ID 000277335900027

  View details for PubMedID 20203262

  View details for PubMedCentralID PMC2865873

• Vascular endothelial growth factor pathway PHARMACOGENETICS AND GENOMICS Maitland, M. L., Lou, X. J., Ramirez, J., Desai, A. A., Berlin, D. S., McLeod, H. L., Weichselbaum, R. R., Ratain, M. J., Altman, R. B., Klein, T. E. 2010; 20 (5): 346-349

  View details for DOI 10.1097/FPC.0b013e3283364ed7

  View details for Web of Science ID 000276704800009

  View details for PubMedID 20124951

  View details for PubMedCentralID PMC3086058

• 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

• Pharmacogenomics and bioinformatics: PharmGKB PHARMACOGENOMICS Thorn, C. F., Klein, T. E., Altman, R. B. 2010; 11 (4): 501-505

  Abstract

  The NIH initiated the PharmGKB in April 2000. The primary mission was to create a repository of primary data, tools to track associations between genes and drugs, and to catalog the location and frequency of genetic variations known to impact drug response. Over the past 10 years, new technologies have shifted research from candidate gene pharmacogenetics to phenotype-based pharmacogenomics with a consequent explosion of data. PharmGKB has refocused on curating knowledge rather than housing primary genotype and phenotype data, and now, captures more complex relationships between genes, variants, drugs, diseases and pathways. Going forward, the challenges are to provide the tools and knowledge to plan and interpret genome-wide pharmacogenomics studies, predict gene-drug relationships based on shared mechanisms and support data-sharing consortia investigating clinical applications of pharmacogenomics.

  View details for DOI 10.2217/PGS.10.15

  View details for Web of Science ID 000276769300008

  View details for PubMedID 20350130

  View details for PubMedCentralID PMC3098752

• DNATwist: A Web-Based Tool for Teaching Middle and High School Students About Pharmacogenomics CLINICAL PHARMACOLOGY & THERAPEUTICS Berlin, D. S., Person, M. G., Mittal, A., Oppezzo, M. A., Chin, D. B., Starr, B., Klein, T. E., Schwartz, D. L., Altman, R. B. 2010; 87 (4): 393-395

  Abstract

  DNATwist is a Web-based learning tool (available at http://www.dnatwist.org) that explains pharmacogenomics concepts to middle- and high-school students. Its features include (i) a focus on drug responses of interest to teenagers (e.g., alcohol intolerance), (ii) reusable graphical interfaces that reduce extension costs, and (iii) explanations of molecular and cellular drug responses. In testing, students found the tool and topic understandable and engaging. The tool is being modified for use at the Tech Museum of Innovation in California.

  View details for DOI 10.1038/clpt.2009.303

  View details for Web of Science ID 000276506900009

  View details for PubMedID 20305671

  View details for PubMedCentralID PMC3098756

• PharmGKB very important pharmacogene: SLCO1B1 PHARMACOGENETICS AND GENOMICS Oshiro, C., Mangravite, L., Klein, T., Altman, R. 2010; 20 (3): 211-216

  View details for DOI 10.1097/FPC.0b013e328333b99c

  View details for Web of Science ID 000275061200007

  View details for PubMedID 19952871

  View details for PubMedCentralID PMC3086841

• PharmGKB summary: very important pharmacogene information for angiotensin-converting enzyme PHARMACOGENETICS AND GENOMICS Thorn, C. F., Klein, T. E., Altman, R. B. 2010; 20 (2): 143-146

  View details for DOI 10.1097/FPC.0b013e3283339bf3

  View details for Web of Science ID 000274306700011

  View details for PubMedID 19898265

  View details for PubMedCentralID PMC3098760

• Very important pharmacogene summary ADRB2 PHARMACOGENETICS AND GENOMICS Litonjua, A. A., Gong, L., Duan, Q. L., Shin, J., Moore, M. J., Weiss, S. T., Johnson, J. A., Klein, T. E., Altman, R. B. 2010; 20 (1): 64-69

  View details for DOI 10.1097/FPC.0b013e328333dae6

  View details for Web of Science ID 000273307600008

  View details for PubMedID 19927042

  View details for PubMedCentralID PMC3098753

• Adjuvant Tamoxifen Treatment Outcome According to Cytochrome P450 2D6 (CYP2D6) Phenotype in Early Stage Breast Cancer: Findings from the International Tamoxifen Pharmacogenomics Consortium Goetz, M. P., Berry, D. A., Klein, T. E. AMER ASSOC CANCER RESEARCH. 2009: 492S–493S

  View details for Web of Science ID 000272920700040

• 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

• Selective serotonin reuptake inhibitors pathway PHARMACOGENETICS AND GENOMICS Sangkuhl, K., Klein, T. E., Altman, R. B. 2009; 19 (11): 907-909

  View details for DOI 10.1097/FPC.0b013e32833132cb

  View details for Web of Science ID 000271602800010

  View details for PubMedID 19741567

  View details for PubMedCentralID PMC2896866

• Codeine and morphine pathway PHARMACOGENETICS AND GENOMICS Thorn, C. F., Klein, T. E., Altman, R. B. 2009; 19 (7): 556-558

  View details for DOI 10.1097/FPC.01b013e32832e0eac

  View details for Web of Science ID 000267619000009

  View details for PubMedID 19512957

• Clinically Available Pharmacogenomics Tests CLINICAL PHARMACOLOGY & THERAPEUTICS Flockhart, D. A., Skaar, T., Berlin, D. S., Klein, T. E., Nguyen, A. T. 2009; 86 (1): 109-113

  Abstract

  The development of robust and clinically valuable pharmacogenomic tests has been anticipated to be one of the first tangible results of the Human Genome Project. Despite both obvious and unanticipated obstacles, a number of tests have now become available in various practice settings. Lessons can be learned from examination of these tests, the evidence that has catalyzed their use, their value to prescribers, and their merit as tools for personalizing therapeutics.

  View details for DOI 10.1038/clpt.2009.39

  View details for Web of Science ID 000267225200021

  View details for PubMedID 19369936

  View details for PubMedCentralID PMC2730436

• Platinum pathway PHARMACOGENETICS AND GENOMICS Marsh, S., McLeod, H., Dolan, E., Shukla, S. J., Rabik, C. A., Gong, L., Hernandez-Boussard, T., Lou, X. J., Klein, T. E., Altman, R. B. 2009; 19 (7): 563-564

  View details for DOI 10.1097/FPC.0b013e32832e0ed7

  View details for Web of Science ID 000267619000011

  View details for PubMedID 19525887

• Antiestrogen pathway (aromatase inhibitor) PHARMACOGENETICS AND GENOMICS Desta, Z., Nguyen, A., Flockhart, D., Skaar, T., Fletcher, R., Weinshilboum, R., Berlin, D. S., Klein, T. E., Altman, R. B. 2009; 19 (7): 554-555

  View details for DOI 10.1097/FPC.0b013e32832e0ec1

  View details for Web of Science ID 000267619000008

  View details for PubMedID 19512956

  View details for PubMedCentralID PMC2756763

• Cytochrome P450 2D6 PHARMACOGENETICS AND GENOMICS Owen, R. P., Sangkuhl, K., Klein, T. E., Altman, R. B. 2009; 19 (7): 559-562

  View details for DOI 10.1097/FPC.0b013e32832e0e97

  View details for Web of Science ID 000267619000010

  View details for PubMedID 19512959

• Etoposide pathway PHARMACOGENETICS AND GENOMICS Yang, J., Bogni, A., Schuetz, E. G., Ratain, M., Dolan, M. E., McLeod, H., Gong, L., Thorn, C., Relling, M. V., Klein, T. E., Altman, R. B. 2009; 19 (7): 552-553

  View details for DOI 10.1097/FPC.0b013e32832e0e7f

  View details for Web of Science ID 000267619000007

  View details for PubMedID 19512958

• Warfarin Pharmacogenetics NEW ENGLAND JOURNAL OF MEDICINE Garcia, D. A., Hylek, E. 2009; 360 (23): 2474-2474

  View details for Web of Science ID 000266590500018

  View details for PubMedID 19494226

• Warfarin Pharmacogenetics Reply NEW ENGLAND JOURNAL OF MEDICINE Klein, T. E., Kimmel, S. E., Johnson, J. A. 2009; 360 (23): 2475

  View details for Web of Science ID 000266590500020

• Very important pharmacogene summary: sulfotransferase 1A1 PHARMACOGENETICS AND GENOMICS Hildebrandt, M., Adjei, A., Weinshilbou, R., Johnson, J. A., Berlin, D. S., Klein, T. E., Altman, R. B. 2009; 19 (6): 404-406

  View details for DOI 10.1097/FPC.0b013e32832e042e

  View details for Web of Science ID 000266575500002

  View details for PubMedID 19451861

• COLdb, a Database Linking Genetic Data to Molecular Function in Fibrillar Collagens HUMAN MUTATION Bodian, D. L., Klein, T. E. 2009; 30 (6): 946-951

  Abstract

  Fibrillar collagens are ubiquitous proteins essential for the structural integrity of bones, skin, blood vessels, and other tissues. Mutations in collagen genes result in disorders including osteogenesis imperfecta, chondrodysplasias, and Ehlers-Danlos syndromes, but the molecular basis for the heterogeneity of clinical phenotypes is not well understood. A more complete understanding of the relationship between sequence and phenotype requires synthesis of multiple facets of collagen structure and function. To facilitate such an analysis, we developed COLdb, a freely available database integrating collagen biological and physicochemical properties with known variants. A Web-based, interactive, graphical user interface displays the data as annotations on the collagen protein sequences. Collagen gene-level data are provided as custom tracks for display in the UCSC genome browser. COLdb currently includes 35,582 data points spanning collagen types I, II, and III, and, importantly, users can add their own data to the display. The database is the first comprehensive integration of disparate functional information on the three major fibrillar collagens, and the first electronic collection of mutations in the COL2A1 gene.

  View details for DOI 10.1002/humu.20978

  View details for Web of Science ID 000267635100012

  View details for PubMedID 19370761

• New feature: pathways and important genes from PharmGKB PHARMACOGENETICS AND GENOMICS Elchelbaum, M., Altman, R. B., Ratain, M., Klein, T. E. 2009; 19 (6): 403-403

  View details for DOI 10.1097/FPC.0b013e32832b16ba

  View details for Web of Science ID 000266575500001

• New feature: pathways and important genes from PharmGKB. Pharmacogenetics and genomics Eichelbaum, M. n., Altman, R. B., Ratain, M. n., Klein, T. E. 2009; 19 (6): 403

  View details for PubMedID 20161212

  View details for PubMedCentralID PMC2715563

• PharmGKB: an integrated resource of pharmacogenomic data and knowledge. Current protocols in bioinformatics / editoral board, Andreas D. Baxevanis ... [et al.] Gong, L., Owen, R. P., Gor, W., Altman, R. B., Klein, T. E. 2008; Chapter 14: Unit14 7-?

  Abstract

  The PharmGKB is a publicly available online resource that aims to facilitate understanding how genetic variation contributes to variation in drug response. It is not only a repository of pharmacogenomics primary data, but it also provides fully curated knowledge including drug pathways, annotated pharmacogene summaries, and relationships among genes, drugs, and diseases. This unit describes how to navigate the PharmGKB Web site to retrieve detailed information on genes and important variants, as well as their relationship to drugs and diseases. It also includes protocols on our drug-centered pathway, annotated pharmacogene summaries, and our Web services for downloading the underlying data. Workflow on how to use PharmGKB to facilitate design of the pharmacogenomic study is also described in this unit.

  View details for DOI 10.1002/0471250953.bi1407s23

  View details for PubMedID 18819074

• Predicting the clinical lethality of osteogenesis imperfecta from collagen glycine mutations BIOCHEMISTRY Bodian, D. L., Madhan, B., Brodsky, B., Klein, T. E. 2008; 47 (19): 5424-5432

  Abstract

  Osteogenesis imperfecta (OI), or brittle bone disease, often results from missense mutation of one of the conserved glycine residues present in the repeating Gly-X-Y sequence characterizing the triple-helical region of type I collagen. A composite model was developed for predicting the clinical lethality resulting from glycine mutations in the alpha1 chain of type I collagen. The lethality of mutations in which bulky amino acids are substituted for glycine is predicted by their position relative to the N-terminal end of the triple helix. The effect of a Gly --> Ser mutation is modeled by the relative thermostability of the Gly-X-Y triplet on the carboxy side of the triplet containing the substitution. This model also predicts the lethality of Gly --> Ser and Gly --> Cys mutations in the alpha2 chain of type I collagen. The model was validated with an independent test set of six novel Gly --> Ser mutations. The hypothesis derived from the model of an asymmetric interaction between a Gly --> Ser mutation and its neighboring residues was tested experimentally using collagen-like peptides. Consistent with the prediction, a significant decrease in stability, calorimetric enthalpy, and folding time was observed for a peptide with a low-stability triplet C-terminal to the mutation compared to a similar peptide with the low-stability triplet on the N-terminal side. The computational and experimental results together relate the position-specific effects of Gly --> Ser mutations to the local structural stability of collagen and lend insight into the etiology of OI.

  View details for DOI 10.1021/bi800026k

  View details for Web of Science ID 000255547600018

  View details for PubMedID 18412368

• PharmGKB and the international warfarin pharmacogenetlics consortium: The changing role for pharmacogenomic databases and single-drug pharmacogenetics HUMAN MUTATION Owen, R. P., Altman, R. B., Klein, T. E. 2008; 29 (4): 456-460

  Abstract

  PharmGKB, the pharmacogenetics and pharmacogenomics knowledge base (www.pharmgkb.org) is a publicly available online resource dedicated to the dissemination of how genetic variation leads to variation in drug responses. The goals of PharmGKB are to describe relationships between genes, drugs, and diseases, and to generate knowledge to catalyze pharmacogenetic and pharmacogenomic research. PharmGKB delivers knowledge in the form of curated literature annotations, drug pathway diagrams, and very important pharmacogene (VIP) summaries. Recently, PharmGKB has embraced a new role--broker of pharmacogenomic data for data sharing consortia. In particular, we have helped create the International Warfarin Pharmacogenetics Consortium (IWPC), which is devoted to pooling genotype and phenotype data relevant to the anticoagulant warfarin. PharmGKB has embraced the challenge of continuing to maintain its original mission while taking an active role in the formation of pharmacogenetic consortia.

  View details for DOI 10.1002/humu.20731

  View details for Web of Science ID 000254800400002

  View details for PubMedID 18330919

• Natural variation in four human collagen genes across an ethnically diverse population GENOMICS Chan, T., Poon, A., Basu, A., Addleman, N. R., Chen, J., Phong, A., Byers, P. H., Klein, T. E., Kwok, P. 2008; 91 (4): 307-314

  Abstract

  Collagens are members of one of the most important families of structural proteins in higher organisms. There are 28 types of collagens encoded by 43 genes in humans that fall into several different functional protein classes. Mutations in the major fibrillar collagen genes lead to osteogenesis imperfecta (COL1A1 and COL1A2 encoding the chains of Type I collagen), chondrodysplasias (COL2A1 encoding the chains of Type II collagen), and vascular Ehlers-Danlos syndrome (COL3A1 encoding the chains of Type III collagen). Over the past 2 decades, mutations in these collagen genes have been catalogued, in hopes of understanding the molecular etiology of diseases caused by these mutations, characterizing the genotype-phenotype relationships, and developing robust models predicting the molecular and clinical outcomes. To achieve these goals better, it is necessary to understand the natural patterns of variation in collagen genes in human populations. We screened exons, flanking intronic regions, and conserved noncoding regions for variations in COL1A1, COL1A2, COL2A1, and COL3A1 in 48 individuals from each of four ethnically diverse populations. We identified 459 single-nucleotide polymorphisms (SNPs), more than half of which were novel and not found in public databases. Of the 52 SNPs found in coding regions, 15 caused amino acid substitutions while 37 did not. Although the four collagens have similar gene and protein structures, they have different molecular evolutionary characteristics. For example, COL1A1 appears to have been under substantially stronger negative selection than the rest. Phylogenetic analysis also suggests that the four genes have very different evolutionary histories among the different ethnic groups. Our observations suggest that the study of collagen mutations and their relationships with disease phenotypes should be performed in the context of the genetic background of the subjects.

  View details for DOI 10.1016/j.ygeno.2007.12.008

  View details for Web of Science ID 000255386300001

  View details for PubMedID 18272325

  View details for PubMedCentralID PMC2737816

• 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

• 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

• PharmGKB: UNDERSTANDING THE EFFECTS OF INDIVIDUAL GENETIC VARIANTS DRUG METABOLISM REVIEWS Sangkuhl, K., Berlin, D. S., Altman, R. B., Klein, T. E. 2008; 40 (4): 539-551

  Abstract

  The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB: http://www.pharmgkb.org) is devoted to disseminating primary data and knowledge in pharmacogenetics and pharmacogenomics. We are annotating the genes that are most important for drug response and present this information in the form of Very Important Pharmacogene (VIP) summaries, pathway diagrams, and curated literature. The PharmGKB currently contains information on over 500 drugs, 500 diseases, and 700 genes with genotyped variants. New features focus on capturing the phenotypic consequences of individual genetic variants. These features link variant genotypes to phenotypes, increase the breadth of pharmacogenomics literature curated, and visualize single-nucleotide polymorphisms on a gene's three-dimensional protein structure.

  View details for DOI 10.1080/03602530802413338

  View details for Web of Science ID 000260325500002

  View details for PubMedID 18949600

  View details for PubMedCentralID PMC2677552

• Folding and misfolding of the collagen triple helix: Markov analysis of molecular dynamics simulations BIOPHYSICAL JOURNAL Park, S., Klein, T. E., Pande, V. S. 2007; 93 (12): 4108-4115

  Abstract

  Folding and misfolding of the collagen triple helix are studied through molecular dynamics simulations of two collagenlike peptides, [(POG)(10)](3) and [(POG)(4)POA(POG)(5)](3), which are models for wild-type and mutant collagen, respectively. To extract long time dynamics from short trajectories, we employ Markov state models. By analyzing thermodynamic and kinetic quantities calculated from the Markov state models, we examine folding mechanisms of the collagen triple helix and consequences of glycine mutations. We find that the C-to-N zipping of the collagen triple helix must be initiated by a nucleation event consisting of formation of three stable hydrogen bonds, and that zipping through a glycine mutation site requires a renucleation event which also consists of formation of three stable hydrogen bonds. Our results also suggest that slow kinetics, rather than free energy differences, is mainly responsible for the stability of the collagen triple helix.

  View details for DOI 10.1529/biophysj.107.108100

  View details for Web of Science ID 000251298100006

  View details for PubMedID 17766343

  View details for PubMedCentralID PMC2098736

• The education potential of the pharmacogenetics and pharmacogenomics knowledge base (PharmGKB) CLINICAL PHARMACOLOGY & THERAPEUTICS Owen, R. P., Klein, T. E., Altman, R. B. 2007; 82 (4): 472-475

  Abstract

  The pharmacogenetics and pharmacogenomics knowledge base (PharmGKB, http://www.pharmgkb.org) is a publicly available internet resource dedicated to the integration, annotation, and aggregation of pharmacogenomic knowledge. PharmGKB is a repository for pharmacogenetic and pharmacogenomic data, and curators provide integrated knowledge in terms of gene summaries, pathways, and annotated literature. Although PharmGKB is primarily directed toward catalyzing new research, it also has utility as a source of information for education about pharmacogenomics.

  View details for DOI 10.1038/sj.clpt.6100332

  View details for Web of Science ID 000249636500024

  View details for PubMedID 17713470

• The Pharmacogenetics Research Network: From SNP discovery to clinical drug response CLINICAL PHARMACOLOGY & THERAPEUTICS Giacomini, K. M., Brett, C. M., Altman, R. B., Benowitz, N. L., Dolan, M. E., Flockhart, D. A., Johnson, J. A., Hayes, D. F., Klein, T., Krauss, R. M., Kroetz, D. L., McLeod, H. L., Nguyen, A. T., Ratain, M. J., RELLING, M. V., Reus, V., Roden, D. M., Schaefer, C. A., Shuldiner, A. R., Skaar, T., Tantisira, K., Tyndale, R. F., Wang, L., Weinshilboum, R. M., Weiss, S. T., Zineh, I. 2007; 81 (3): 328-345

  Abstract

  The NIH Pharmacogenetics Research Network (PGRN) is a collaborative group of investigators with a wide range of research interests, but all attempting to correlate drug response with genetic variation. Several research groups concentrate on drugs used to treat specific medical disorders (asthma, depression, cardiovascular disease, addiction of nicotine, and cancer), whereas others are focused on specific groups of proteins that interact with drugs (membrane transporters and phase II drug-metabolizing enzymes). The diverse scientific information is stored and annotated in a publicly accessible knowledge base, the Pharmacogenetics and Pharmacogenomics Knowledge base (PharmGKB). This report highlights selected achievements and scientific approaches as well as hypotheses about future directions of each of the groups within the PGRN. Seven major topics are included: informatics (PharmGKB), cardiovascular, pulmonary, addiction, cancer, transport, and metabolism.

  View details for DOI 10.1038/sj.clpt.6100087

  View details for Web of Science ID 000244850300011

  View details for PubMedID 17339863

• Biomedical informatics training at Stanford in the 21st century JOURNAL OF BIOMEDICAL INFORMATICS Altman, R. B., Klein, T. E. 2007; 40 (1): 55-58

  Abstract

  The Stanford Biomedical Informatics training program began with a focus on clinical informatics, and has now evolved into a general program of biomedical informatics training, including clinical informatics, bioinformatics and imaging informatics. The program offers PhD, MS, distance MS, certificate programs, and is now affiliated with an undergraduate major in biomedical computation. Current dynamics include (1) increased activity in informatics within other training programs in biology and the information sciences (2) increased desire among informatics students to gain laboratory experience, (3) increased demand for computational collaboration among biomedical researchers, and (4) interaction with the newly formed Department of Bioengineering at Stanford University. The core focus on research training-the development and application of novel informatics methods for biomedical research-keeps the program centered in the midst of this period of growth and diversification.

  View details for DOI 10.1016/j.jbi.2006.02.005

  View details for Web of Science ID 000243216000007

  View details for PubMedID 16564233

• The PharmGKB: integration, aggregation, and annotation of pharmacogenomic data and knowledge CLINICAL PHARMACOLOGY & THERAPEUTICS Hodge, A. E., Altman, R. B., Klein, T. E. 2007; 81 (1): 21-24

  Abstract

  The Pharmacogenetics and Pharmacogenomics Knowledge Base, PharmGKB (http://www.pharmgkb.org), curates pharmacogenetic and pharmacogenomic information to generate knowledge concerning the relationships among genes, drugs, and diseases, and the effects of gene variation on these relationships. PharmGKB curators collect information on genotype-phenotype relationships both from the literature and from the deposition of primary research data into our database. Their goal is to catalyze pharmacogenetic and pharmacogenomic research.

  View details for DOI 10.1038/sj.clpt.6100048

  View details for Web of Science ID 000242874200010

  View details for PubMedID 17185992

• Integrating large-scale genotype and phenotype data OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY Hernandez-Boussard, T., Woon, M., Klein, T. E., Altman, R. B. 2006; 10 (4): 545-554

  Abstract

  With the completion of the Human Genome Project, a new emphasis is focusing on the sequence variation and the resulting phenotype. The number of data available from genomic studies addressing this relationship is rapidly growing. In order to analyze these data as a whole, they need to be integrated, aggregated and annotated in a timely manner. The Pharmacogenetics and Pharmacogenomics Knowledge Base PharmGKB; () assembles and disseminates these data and their associated metadata that are needed for unambiguous identification and replication. Assembling these data in a timely manner is challenging, and the scalability of these data produce major challenges for a knowledge base such as PharmGKB. However, it is only through rapid global meta-annotation of these data that we will understand the relationship between specific genotype(s) and the related phenotype. PharmGKB has confronted these challenges, and these experiences and solutions can benefit all genome communities.

  View details for Web of Science ID 000243893500009

  View details for PubMedID 17233563

• Pharmacogenomics: The relevance of emerging genotyping technologies. MLO: medical laboratory observer Hernandez-Boussard, T., Klein, T. E., Altman, R. B. 2006; 38 (3): 24-?

  View details for PubMedID 16610446

• Triple helical structure and stabilization of collagen-like molecules with 4(R)-hydroxyproline in the Xaa position BIOPHYSICAL JOURNAL Radmer, R. J., Klein, T. E. 2006; 90 (2): 578-588

  Abstract

  In this study, we examine the relationships between the structure and stability of five related collagen-like molecules that have hydroxyproline residues occupying positions not observed in vertebrate collagen. Two of the molecules contain valine or threonine and form stable triple helices in water. Three of the molecules contain allo-threonine (an enantiomer of threonine), serine, or alanine, and are not stable. Using molecular dynamics simulation methods, we examine possible explanations for the stability difference, including considering the possibility that differences in solvent shielding of the essential interchain hydrogen bonds may result in differences in stability. By comparing the structures of threonine- and allo-threonine-containing molecules in six polar and nonpolar solvation conditions, we find that solvent shielding is not an adequate explanation for the stability difference. A closer examination of the peptides shows that the structures of the unstable molecules are looser, having weaker intermolecular hydrogen bonds. The weakened hydrogen bonds result from extended Yaa residue Psi-angles that prevent optimal geometry. The Phi-Psi-maps of the relevant residues suggest that each residue's most favorable Psi-angle determines the corresponding collagen-like molecule's stability. Additionally, we propose that these molecules illustrate a more general feature of triple-helical structures: interchain hydrogen bonds are always longer and weaker than ideal, so they are sensitive to relatively small changes in molecular structure. This sensitivity to small changes may explain why large stability differences often result from seemingly small changes in residue sequence.

  View details for DOI 10.1529/biophysj.105.065276

  View details for Web of Science ID 000234252100018

  View details for PubMedID 16258051

  View details for PubMedCentralID PMC1367062

• A new set of molecular mechanics parameters for hydroxyproline and its use in molecular dynamics simulations of collagen-like peptides JOURNAL OF COMPUTATIONAL CHEMISTRY Park, S., Radmer, R. J., Klein, T. E., Pande, V. S. 2005; 26 (15): 1612-1616

  Abstract

  Recently, the importance of proline ring pucker conformations in collagen has been suggested in the context of hydroxylation of prolines. The previous molecular mechanics parameters for hydroxyproline, however, do not reproduce the correct pucker preference. We have developed a new set of parameters that reproduces the correct pucker preference. Our molecular dynamics simulations of proline and hydroxyproline monomers as well as collagen-like peptides, using the new parameters, support the theory that the role of hydroxylation in collagen is to stabilize the triple helix by adjusting to the right pucker conformation (and thus the right phi angle) in the Y position.

  View details for DOI 10.1002/jcc.20301

  View details for Web of Science ID 000232570300006

  View details for PubMedID 16170799

• A statistical approach to scanning the biomedical literature for pharmacogenetics knowledge JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION Rubin, D. L., Thorn, C. F., Klein, T. E., Altman, R. B. 2005; 12 (2): 121-129

  Abstract

  Biomedical databases summarize current scientific knowledge, but they generally require years of laborious curation effort to build, focusing on identifying pertinent literature and data in the voluminous biomedical literature. It is difficult to manually extract useful information embedded in the large volumes of literature, and automated intelligent text analysis tools are becoming increasingly essential to assist in these curation activities. The goal of the authors was to develop an automated method to identify articles in Medline citations that contain pharmacogenetics data pertaining to gene-drug relationships.The authors built and evaluated several candidate statistical models that characterize pharmacogenetics articles in terms of word usage and the profile of Medical Subject Headings (MeSH) used in those articles. The best-performing model was used to scan the entire Medline article database (11 million articles) to identify candidate pharmacogenetics articles.A sampling of the articles identified from scanning Medline was reviewed by a pharmacologist to assess the precision of the method. The authors' approach identified 4,892 pharmacogenetics articles in the literature with 92% precision. Their automated method took a fraction of the time to acquire these articles compared with the time expected to be taken to accumulate them manually. The authors have built a Web resource (http://pharmdemo.stanford.edu/pharmdb/main.spy) to provide access to their results.A statistical classification approach can screen the primary literature to pharmacogenetics articles with high precision. Such methods may assist curators in acquiring pertinent literature in building biomedical databases.

  View details for DOI 10.1197/jamia.M1640

  View details for Web of Science ID 000227842000003

  View details for PubMedID 15561790

  View details for PubMedCentralID PMC551544

• PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base. Methods in molecular biology (Clifton, N.J.) Thorn, C. F., Klein, T. E., Altman, R. B. 2005; 311: 179-191

  Abstract

  The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation effects drug response. The PharmGKB web site, www.pharmgkb.org, displays genotype, molecular, and clinical primary data integrated with literature, pathway representations, protocol information, and links to additional external resources. Users can search and browse the knowledge base by genes, drugs, diseases, and pathways. Registration is free to the entire research community but subject to an agreement to respect the rights and privacy of the individuals whose information is contained within the database. Registered users can access and download primary data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

  View details for PubMedID 16100408

• An "omics" view of drug development DRUG DEVELOPMENT RESEARCH Altman, R. B., Rubin, D. L., Klein, T. E. 2004; 62 (2): 81-85

  View details for DOI 10.1002/ddr.10370

  View details for Web of Science ID 000225497400003

• Severity of osteogenesis imperfecta and structure of a collagen-like peptide modeling a lethal mutation site BIOCHEMISTRY Radmer, R. J., Klein, T. E. 2004; 43 (18): 5314-5323

  Abstract

  We show that there are correlations between the severities of osteogenesis imperfecta (OI) phenotypes and changes in the residues near the mutation site. Our results show the correlations between the severity of various forms of the inherited disease OI and alteration of residues near the site of OI causing mutations. Among our many observed correlations are particularly striking ones between the presence of nearby proline residues and lethal mutations, and the presence of nearby alanines residues and nonlethal mutations. We investigated the possibility that these correlations have a structural basis using molecular dynamics simulations of collagen-like molecules designed to mimic the site of a lethal OI mutation in collagen type I. Our significant finding is that interchain hydrogen bonding is greatly affected by variations in residue type. We found that the strength of hydrogen bond networks between backbone atoms on different chains depends on the local residue sequence and is weaker in proline-rich regions of the molecule. We also found that an alanine at a site near an OI mutation causes less structural disruption than a proline, and that residue side chains also form interchain hydrogen bonds with frequencies that are dependent on residue type. For example, arginine side chains form strong hydrogen bonds with the backbone of the subsequent peptide chain, while lysine and glutamine less frequently form similar hydrogen bonds. This decrease in the observed hydrogen bond frequency correlates with a decrease in the experimentally determined thermal stability. We contrasted general structural properties of model collagen peptides with and without the mutation to examine the effect of the single-point mutation on the surrounding residues.

  View details for DOI 10.1021/bi035676w

  View details for Web of Science ID 000221343200021

  View details for PubMedID 15122897

• PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base PHARMACOGENOMICS JOURNAL Klein, T. E., Altman, R. B. 2004; 4 (1): 1-1

  View details for DOI 10.1038/sj.tpj.6500230

  View details for Web of Science ID 000220143500001

  View details for PubMedID 14735107

• 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

• Microenvironment analysis and identification of magnesium binding sites in RNA NUCLEIC ACIDS RESEARCH Banatao, D. R., Altman, R. B., Klein, T. E. 2003; 31 (15): 4450-4460

  Abstract

  Interactions with magnesium (Mg2+) ions are essential for RNA folding and function. The locations and function of bound Mg2+ ions are difficult to characterize both experimentally and computationally. In particular, the P456 domain of the Tetrahymena thermophila group I intron, and a 58 nt 23s rRNA from Escherichia coli have been important systems for studying the role of Mg2+ binding in RNA, but characteristics of all the binding sites remain unclear. We therefore investigated the Mg2+ binding capabilities of these RNA systems using a computational approach to identify and further characterize their Mg2+ binding sites. The approach is based on the FEATURE algorithm, reported previously for microenvironment analysis of protein functional sites. We have determined novel physicochemical descriptions of site-bound and diffusely bound Mg2+ ions in RNA that are useful for prediction. Electrostatic calculations using the Non-Linear Poisson Boltzmann (NLPB) equation provided further evidence for the locations of site-bound ions. We confirmed the locations of experimentally determined sites and further differentiated between classes of ion binding. We also identified potentially important, high scoring sites in the group I intron that are not currently annotated as Mg2+ binding sites. We note their potential function and believe they deserve experimental follow-up.

  View details for DOI 10.1093/nar/gkg471

  View details for Web of Science ID 000184532900029

  View details for PubMedID 12888505

  View details for PubMedCentralID PMC169872

• Analysis of mutations in the COLIA1 gene with second-order rule induction INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE Hewett, R., Leuchner, J., Mooney, S. D., Klein, T. E. 2003; 17 (5): 721-740

  View details for Web of Science ID 000185117000004

• WebFEATURE: an interactive web tool for identifying and visualizing functional sites on macromolecular structures NUCLEIC ACIDS RESEARCH Liang, M. P., Banatao, D. R., Klein, T. E., Brutlag, D. L., Altman, R. B. 2003; 31 (13): 3324-3327

  Abstract

  WebFEATURE (http://feature.stanford.edu/webfeature/) is a web-accessible structural analysis tool that allows users to scan query structures for functional sites in both proteins and nucleic acids. WebFEATURE is the public interface to the scanning algorithm of the FEATURE package, a supervised learning algorithm for creating and identifying 3D, physicochemical motifs in molecular structures. Given an input structure or Protein Data Bank identifier (PDB ID), and a statistical model of a functional site, WebFEATURE will return rank-scored 'hits' in 3D space that identify regions in the structure where similar distributions of physicochemical properties occur relative to the site model. Users can visualize and interactively manipulate scored hits and the query structure in web browsers that support the Chime plug-in. Alternatively, results can be downloaded and visualized through other freely available molecular modeling tools, like RasMol, PyMOL and Chimera. A major application of WebFEATURE is in rapid annotation of function to structures in the context of structural genomics.

  View details for DOI 10.1093/nar/gkg553

  View details for Web of Science ID 000183832900010

  View details for PubMedID 12824318

  View details for PubMedCentralID PMC168960

• A functional analysis of disease-associated mutations in the androgen receptor gene NUCLEIC ACIDS RESEARCH Mooney, S. D., Klein, T. E., Altman, R. B., Trifiro, M. A., GOTTLIEB, B. 2003; 31 (8)

  Abstract

  Mutations in the androgen receptor (AR) are associated with a variety of diseases including androgen insensitivity syndrome and prostate cancer, but the way in which these mutations cause disease is poorly understood. We present a method for distinguishing likely disease-causing mutations from mutations that are merely associated with disease but have no causal role. Our method uses a measure of nucleotide conservation, and we find that conservation often correlates with severity of the clinical phenotype. Further, by only including mutations whose pathogenicity has been proven experimentally, this correlation is enhanced in the case of prostate cancer-associated mutations. Our method provides a means for assessing the significance of single nucleotide polymorphisms (SNPs) and cancer-associated mutations.

  View details for DOI 10.1093/nar/gng042

  View details for Web of Science ID 000182161400002

  View details for PubMedID 12682377

  View details for PubMedCentralID PMC153754

• Indexing pharmacogenetic knowledge on the World Wide Web PHARMACOGENETICS Altman, R. B., Flockhart, D. A., Sherry, S. T., Oliver, D. E., Rubin, D. L., Klein, T. E. 2003; 13 (1): 3-5

  View details for Web of Science ID 000180584000002

  View details for PubMedID 12544507

• A personalized and automated dbSNP surveillance system 2nd International Computational Systems Bioinformatics Conference Liu, S., Lin, S., Woon, M., Klein, T. E., Altman, R. B. IEEE COMPUTER SOC. 2003: 132–136

  Abstract

  The development of high throughput techniques and large-scale studies in the biological sciences has given rise to an explosive growth in both the volume and types of data available to researchers. A surveillance system that monitors data repositories and reports changes helps manage the data overload. We developed a dbSNP surveillance system (URL: http://www.pharmgkb.org/do/serve?id=tools.surveillance.dbsnp) that performs surveillance on the dbSNP database and alerts users to new information. The system is notable because it is personalized and fully automated. Each registered user has a list of genes to follow and receives notification of new entries concerning these genes. The system integrates data from dbSNP, LocusLink, PharmGKB, and Genbank to position SNPs on reference sequences and classify SNPs into categories such as synonymous and non-synonymous SNPs. The system uses data warehousing, object model-based data integration, object-oriented programming, and a platform-neutral data access mechanism.

  View details for Web of Science ID 000188997700026

  View details for PubMedID 16452787

• Structural models of osteogenesis imperfecta-associated variants in the COL1A1 gene MOLECULAR & CELLULAR PROTEOMICS Mooney, S. D., Klein, T. E. 2002; 1 (11): 868-875

  Abstract

  Osteogenesis imperfecta (OI) is a genetic disease in which the most common mutations result in substitutions for glycine residues in the triple helical domain of the chains of type I collagen. Currently there is no way to use sequence information to predict the clinical OI phenotype. However, structural models coupled with biophysical and machine learning methods may be able to predict sequences that, when mutated, would be associated with more severe forms of OI. To build appropriate structural models, we have applied a high throughput molecular dynamic approach. Homotrimeric peptides covering 57 positions in which mutations are associated with OI were simulated both with and without mutations. Our models revealed structural differences that occur with different substituting amino acids. When mutations were introduced, we observed a decrease in helix stability, as caused by fewer main chain backbone hydrogen bonds, and an increase in main chain root mean square deviation and specifically bound water molecules.

  View details for DOI 10.1074/mcp.M200064-MCP200

  View details for Web of Science ID 000181516000002

  View details for PubMedID 12488462

• Conformational preferences of substituted prolines in the collagen triple helix BIOPOLYMERS Mooney, S. D., Kollman, P. A., Klein, T. E. 2002; 64 (2): 63-71

  Abstract

  Researchers have recently questioned the role hydroxylated prolines play in stabilizing the collagen triple helix. To address these issues, we have developed new molecular mechanics parameters for the simulation of peptides containing 4(R)-fluoroproline (Flp), 4(R)-hydroxyproline (Hyp), and 4(R)-aminoproline (Amp). Simulations of peptides based on these parameters can be used to determine the components that stabilize hydroxyproline over proline in the triple helix. The dihedrals F-C-C-N, O-C-C-N, and N-C-C-N were built using a N-beta-ethyl amide model. One nanosecond simulations were performed on the trimers [(Pro-Pro-Gly)(10)](3), [(Pro-Hyp-Gly)(10)](3), [(Pro-Amp-Gly)(10)](3), [(Pro-Amp(1+)-Gly)(10)](3), and [(Pro-Flp-Gly)(10)](3) in explicit solvent. The results of our simulations suggest that pyrrolidine ring conformation is mediated by the strength of the gauche effect and classical electrostatic interactions.

  View details for DOI 10.1002/bip.10123

  View details for Web of Science ID 000175596100002

  View details for PubMedID 11979516

• A multidomain TIGR/olfactomedin protein family with conserved structural similarity in the N-terminal region and conserved motifs in the C-terminal region MOLECULAR & CELLULAR PROTEOMICS Green, M. L., Klein, T. E. 2002; 1 (5): 394-403

  Abstract

  Based on the similarity between the TIGR (trabecular-meshwork inducible glucocorticoid response) (also known as myocilin) and olfactomedin protein families identified throughout the length of the TIGR protein, we have identified more distantly related proteins to determine the elements essential to the function/structure of the TIGR and olfactomedin proteins. Using a sequence walk method and the Shotgun program, we have identified a family including 31 olfactomedin domain-containing sequences. Multiple sequence alignments and secondary structure analyses were used to identify conserved sequence elements. Pairwise identity in the olfactomedin domain ranges from 8 to 64%, with an average pairwise identity of 24%. The N-terminal regions of the proteins fall into two subgroups, one including the TIGR and olfactomedin families and another group of apparently unrelated domains. The TIGR and olfactomedin sequences display conserved motifs including a residual leucine zipper region and maintain a similar secondary structure throughout the N-terminal region. The correlation between conserved elements and disease-associated mutations and apparent polymorphisms in human TIGR was also examined to evaluate the apparent importance of conserved residues to the function/structure of TIGR. Several residues have been identified as essential to the function and/or structure of the human TIGR protein based on their degree of conservation across the family and their implication in the pathogenesis of primary open-angle glaucoma. Additionally, we have identified a group of chitinase sequences containing several of the highly conserved motifs present in the C-terminal region of the olfactomedin domain-containing sequences.

  View details for DOI 10.1074/mcp.200023-MCP200

  View details for Web of Science ID 000181515400006

  View details for PubMedID 12118081

• PharmGKB: The Pharmacogenetics Knowledge Base NUCLEIC ACIDS RESEARCH Hewett, M., Oliver, D. E., Rubin, D. L., Easton, K. L., Stuart, J. M., Altman, R. B., Klein, T. E. 2002; 30 (1): 163-165

  Abstract

  The Pharmacogenetics Knowledge Base (PharmGKB; http://www.pharmgkb.org/) contains genomic, phenotype and clinical information collected from ongoing pharmacogenetic studies. Tools to browse, query, download, submit, edit and process the information are available to registered research network members. A subset of the tools is publicly available. PharmGKB currently contains over 150 genes under study, 14 Coriell populations and a large ontology of pharmacogenetics concepts. The pharmacogenetic concepts and the experimental data are interconnected by a set of relations to form a knowledge base of information for pharmacogenetic researchers. The information in PharmGKB, and its associated tools for processing that information, are tailored for leading-edge pharmacogenetics research. The PharmGKB project was initiated in April 2000 and the first version of the knowledge base went online in February 2001.

  View details for Web of Science ID 000173077100041

  View details for PubMedID 11752281

  View details for PubMedCentralID PMC99138

• The functional importance of disease-associated mutation BMC BIOINFORMATICS Mooney, S. D., Klein, T. E. 2002; 3

  Abstract

  For many years, scientists believed that point mutations in genes are the genetic switches for somatic and inherited diseases such as cystic fibrosis, phenylketonuria and cancer. Some of these mutations likely alter a protein's function in a manner that is deleterious, and they should occur in functionally important regions of the protein products of genes. Here we show that disease-associated mutations occur in regions of genes that are conserved, and can identify likely disease-causing mutations.To show this, we have determined conservation patterns for 6185 non-synonymous and heritable disease-associated mutations in 231 genes. We define a parameter, the conservation ratio, as the ratio of average negative entropy of analyzable positions with reported mutations to that of every analyzable position in the gene sequence. We found that 84.0% of the 231 genes have conservation ratios less than one. 139 genes had eleven or more analyzable mutations and 88.0% of those had conservation ratios less than one.These results indicate that phylogenetic information is a powerful tool for the study of disease-associated mutations. Our alignments and analysis has been made available as part of the database at http://cancer.stanford.edu/mut-paper/. Within this dataset, each position is annotated with the analysis, so the most likely disease-causing mutations can be identified.

  View details for Web of Science ID 000181476800024

  View details for PubMedID 12220483

• Automating data acquisition into ontologies from pharmacogenetics relational data sources using declarative object definitions and XML. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Rubin, D. L., Hewett, M., Oliver, D. E., Klein, T. E., Altman, R. B. 2002: 88-99

  Abstract

  Ontologies are useful for organizing large numbers of concepts having complex relationships, such as the breadth of genetic and clinical knowledge in pharmacogenomics. But because ontologies change and knowledge evolves, it is time consuming to maintain stable mappings to external data sources that are in relational format. We propose a method for interfacing ontology models with data acquisition from external relational data sources. This method uses a declarative interface between the ontology and the data source, and this interface is modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML, and data integrity is checked by validating the XML submission with an XML schema. We have implemented this approach in PharmGKB (http://www.pharmgkb.org/), a pharmacogenetics knowledge base. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence information. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis.

  View details for PubMedID 11928521

• Ontology development for a pharmacogenetics knowledge base. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Oliver, D. E., Rubin, D. L., Stuart, J. M., Hewett, M., Klein, T. E., Altman, R. B. 2002: 65-76

  Abstract

  Research directed toward discovering how genetic factors influence a patient's response to drugs requires coordination of data produced from laboratory experiments, computational methods, and clinical studies. A public repository of pharmacogenetic data should accelerate progress in the field of pharmacogenetics by organizing and disseminating public datasets. We are developing a pharmacogenetics knowledge base (PharmGKB) to support the storage and retrieval of both experimental data and conceptual knowledge. PharmGKB is an Internet-based resource that integrates complex biological, pharmacological, and clinical data in such a way that researchers can submit their data and users can retrieve information to investigate genotype-phenotype correlations. Successful management of the names, meaning, and organization of concepts used within the system is crucial. We have selected a frame-based knowledge-representation system for development of an ontology of concepts and relationships that represent the domain and that permit storage of experimental data. Preliminary experience shows that the ontology we have developed for gene-sequence data allows us to accept, store, and query data submissions.

  View details for PubMedID 11928517

• Challenges for biomedical informatics and pharmacogenomics ANNUAL REVIEW OF PHARMACOLOGY AND TOXICOLOGY Altman, R. B., Klein, T. E. 2002; 42: 113-133

  Abstract

  Pharmacogenomics requires the integration and analysis of genomic, molecular, cellular, and clinical data, and it thus offers a remarkable set of challenges to biomedical informatics. These include infrastructural challenges such as the creation of data models and databases for storing these data, the integration of these data with external databases, the extraction of information from natural language text, and the protection of databases with sensitive information. There are also scientific challenges in creating tools to support gene expression analysis, three-dimensional structural analysis, and comparative genomic analysis. In this review, we summarize the current uses of informatics within pharmacogenomics and show how the technical challenges that remain for biomedical informatics are typical of those that will be confronted in the postgenomic era.

  View details for Web of Science ID 000174038800007

  View details for PubMedID 11807167

• Similarities and differences between the TIGR and olfactomedin proteins. Green, M. L., Do, H., Polansky, J. R., Nguyen, T. D., Klein, T. E. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2001: S656–S656

  View details for Web of Science ID 000168392103495

• Computed free energy differences between point mutations in a collagen-like peptide BIOPOLYMERS Mooney, S. D., Huang, C. C., Kollman, P. A., Klein, T. E. 2001; 58 (3): 347-353

  Abstract

  We studied the results of mutating alanine --> glycine at three positions of a collagen-like peptide in an effort to develop a computational method for predicting the energetic and structural effects of a single point genetic mutation in collagen, which is associated with the clinical diagnosis of Osteogenesis Imperfecta (OI). The differences in free energy of denaturation were calculated between the collagen-like peptides [(POG)(4)(POA)(POG)(4)](3) and [(POG)(10)](3) (POG: proline-hydroxyproline-glycine).* Our computational results, which suggest significant destabilization of the collagen-like triple-helix upon the glycine --> alanine mutations, correlate very well with the experimental free energies of denaturation. The robustness of our collagen-like peptide model is shown by its reproduction of experimental results with both different simulation paths and different lengths of the model peptide. The individual free energy for each alanine --> glycine mutation (and the reverse free energy, glycine --> alanine mutation) in the collagen-like peptide has been calculated. We find that the first alanine introduced into the triple helix causes a very large destabilization of the helix, but the last alanine introduced into the same position of an adjacent chain causes a very small change in the peptide stability. Thus, our results demonstrate that each mutation does not contribute equally to the free energy. We find that the sum of the calculated individual residues' free energy can accurately model the experimental free energy for the whole peptide.

  View details for Web of Science ID 000166698400012

  View details for PubMedID 11169394

• ViewFeature: integrated feature analysis and visualization. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Banatao, D. R., Huang, C. C., Babbitt, P. C., Altman, R. B., Klein, T. E. 2001: 240-250

  Abstract

  Visualization interfaces for high performance computing systems pose special problems due to the complexity and volume of data these systems manipulate. In the post-genomic era, scientists must be able to quickly gain insight into structure-function problems, and require flexible computing environments to quickly create interfaces that link the relevant tools. Feature, a program for analyzing protein sites, takes a set of 3-dimensional structures and creates statistical models of sites of structural or functional significance. Until now, Feature has provided no support for visualization, which can make understanding its results difficult. We have developed an extension to the molecular visualization program Chimera that integrates Feature's statistical models and site predictions with 3-dimensional structures viewed in Chimera. We call this extension ViewFeature, and it is designed to help users understand the structural Features that define a site of interest. We applied ViewFeature in an analysis of the enolase superfamily; a functionally distinct class of proteins that share a common fold, the alpha/beta barrel, in order to gain a more complete understanding of the conserved physical properties of this superfamily. In particular, we wanted to define the structural determinants that distinguish the enolase superfamily active site scaffold from other alpha/beta barrel superfamilies and particularly from other metal-binding alpha/beta barrel proteins. Through the use of ViewFeature, we have found that the C-terminal domain of the enolase superfamily does not differ at the scaffold level from metal-binding alpha/beta barrels. We are, however, able to differentiate between the metal-binding sites of alpha/beta barrels and those of other metal-binding proteins. We describe the overall architectural Features of enolases in a radius of 10 Angstroms around the active site.

  View details for PubMedID 11262944

• Integrating genotype and phenotype information: an overview of the PharmGKB project. Pharmacogenetics Research Network and Knowledge Base. pharmacogenomics journal Klein, T. E., Chang, J. T., Cho, M. K., Easton, K. L., FERGERSON, R., Hewett, M., Lin, Z., Liu, Y., Liu, S., Oliver, D. E., Rubin, D. L., SHAFA, F., Stuart, J. M., Altman, R. B. 2001; 1 (3): 167-170

  View details for PubMedID 11908751

• Integrated tools for structural and sequence alignment and analysis. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Huang, C. C., Novak, W. R., Babbitt, P. C., Jewett, A. I., Ferrin, T. E., Klein, T. E. 2000: 230-241

  Abstract

  We have developed new computational methods for displaying and analyzing members of protein superfamilies. These methods (MinRMS, AlignPlot and MSFviewer) integrate sequence and structural information and are implemented as separate but cooperating programs to our Chimera molecular modeling system. Integration of multiple sequence alignment information and three-dimensional structural representations enable researchers to generate hypotheses about the sequence-structure relationship. Structural superpositions can be generated and easily tuned to identify similarities around important characteristics such as active sites or ligand binding sites. Information related to the release of Chimera, MinRMS, AlignPlot and MSFviewer can be obtained at http:¿www.cgl.ucsf.edu/chimera.

  View details for PubMedID 10902172

• Macromolecular docking of a three-body system: The recognition of human growth hormone by its receptor PROTEIN SCIENCE Hendrix, D. K., Klien, T. E., Kuntz, I. D. 1999; 8 (5): 1010-1022

  Abstract

  Human growth hormone (hGH) binds to its receptor (hGHr) in a three-body interaction: one molecule of the hormone and two identical monomers of the receptor form a trimer. Curiously, the hormone-receptor interactions in the trimer are not equivalent and the formation of the complex occurs in a specific kinetic order (Cunningham BC, Ultsch M, De Vos AM, Mulkerrin MG, Clauser KR, Wells JA, 1991, Science 254:821-825). In this paper, we model the recognition of hGH to the hGHr using shape complementarity of the three-dimensional structures and macromolecular docking to explore possible binding modes between the receptor and hormone. The method, reported previously (Hendrix DK, Kuntz ID, 1998, Pacific symposium on biocomputing 1998, pp 1234-1244), is based upon matching complementary-shaped strategic sites on the molecular surface. We modify the procedure to examine three-body systems. We find that the order of binding seen experimentally is also essential to our model. We explore the use of mutational data available for hGH to guide our model. In addition to docking hGH to the hGHr, we further test our methodology by successfully reproducing 16 macromolecular complexes from X-ray crystal structures, including enzyme-inhibitor, antibody-antigen, protein dimer, and protein-DNA complexes.

  View details for Web of Science ID 000080109000008

  View details for PubMedID 10338012

  View details for PubMedCentralID PMC2144328

• Computational investigations of structural changes resulting from point mutations in a collagen-like peptide BIOPOLYMERS Klein, T. E., Huang, C. C. 1999; 49 (2): 167-183

  Abstract

  The results of 0.5-1.0 ns molecular dynamics simulations of the collagen-like peptides [(POG)4(POA)(POG)4]3 and [(POG)9]3 (POG: proline-hydroxyproline-glycine) are presented. All simulations were performed using the AMBER-94 molecular mechanical force field with a shell of TIP3P waters surrounding the peptides. The initial geometries for the collagen-like peptides included an x-ray crystallographic structure, a computer-generated structure, a [(POG)9]3 structure modeled from the x-ray structure, and the x-ray structure with crystallographic waters replaced with a shell of modeled TIP3P waters. We examined the molecular dynamics peptide residue rms deviation fluctuations, dihedral angles, molecular and chain end-to-end distances, helical parameters, and peptide-peptide and peptide-solvent hydrogen-bonding patterns. Our molecular dynamics simulations of [(POG)4(POA)(POG)4]3 show average structures and internal coordinates similar to the x-ray crystallographic structure. Our results demonstrate that molecular dynamics can be used to reproduce the experimental structures of collagen-like peptides. We have demonstrated the feasibility of using the AMBER-94 molecular mechanical force field, which was parameterized to model nucleic acids and globular proteins, for fibril proteins. We provide a new interpretation of peptide-solvent hydrogen bonding and a peptide-peptide hydrogen bonding pattern not previously reported in x-ray studies. Last, we report on the differences; in particular with respect to main-chain dihedral angles and hydrogen bonding, between the native and mutant collagen-like peptides.

  View details for Web of Science ID 000078270000005

  View details for PubMedID 10070265

• Quantitative structure-activity relationships of 2,4-diamino-5-(2-X-benzyl)pyrimidines versus bacterial and avian dihydrofolate reductase JOURNAL OF MEDICINAL CHEMISTRY Selassie, C. D., Gan, W. X., Kallander, L. S., Klein, T. E. 1998; 41 (22): 4261-4272

  Abstract

  Quantitative structure-activity relationships (QSAR) have been formulated for a set of 15 2,4-diamino-5-(2-X-benzyl)pyrimidines versus dihydrofolate reductase from Lactobacillus casei and chicken liver. QSARs were also developed for comprehensive data sets containing mono-, di-, and trisubstituted benzyl derivatives. Particular emphasis was placed on the role played by ortho substituents in the overall binding process and subsequent inhibition of the catalytic process in both the prokaryotic and eucaryotic DHFRs. Comparisons between the two QSARs reveal subtle differences at specific positions which can be optimized to design more selective antibacterial agents.

  View details for Web of Science ID 000076676100012

  View details for PubMedID 9784101

• The object technology framework: an object-oriented interface to molecular data and its application to collagen. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Huang, C. C., Couch, G. S., Pettersen, E. F., Ferrin, T. E., Howard, A. E., Klein, T. E. 1998: 349-361

  Abstract

  We describe the Object Technology Framework (OTF) software system developed at the University of California, San Francisco Computer Graphics Laboratory for creating C+2 classes that facilitate rapid biomolecular application development and the application of the OTF to collagen modeling. C+2 class libraries for accessing and manipulating data from standard scientific data sources can be generated using the program genlib and its class library toolkit Molecule, thereby facilitating development of new applications. Use of the OTF for generating ideal collagen structural models (gencollagen) is described. The source code for the OTF is freely available at http:/(/)www.cgl.ucsf.edu/off/ to interested application developers.

  View details for PubMedID 9697195

• CONIC - A FAST RENDERER FOR SPACE-FILLING MOLECULES WITH SHADOWS JOURNAL OF MOLECULAR GRAPHICS Huang, C. C., Pettersen, E. F., Klein, T. E., Ferrin, T. E., LANGRIDGE, R. 1991; 9 (4): 230-?

  Abstract

  We present an algorithm for generating images of molecules represented as a set of intersecting opaque spheres. Both perspective and shadows are computed to provide realistic visual cues. Compared to existing programs for generating similar images, our algorithm is both more accurate and several times faster. We present in detail the mathematics used in picture generation, along with examples of the computed images.

  View details for Web of Science ID A1991GW91400004

  View details for PubMedID 1772848

• SEPARATION OF ELECTRONIC AND HYDROPHOBIC EFFECTS FOR THE PAPAIN HYDROLYSIS OF SUBSTITUTED N-BENZOYLGLYCINE ESTERS BIOCHIMICA ET BIOPHYSICA ACTA Compadre, C. M., Hansch, C., Klein, T. E., PETRIDOUFISCHER, J., Selassie, C. D., Smith, R. N., Steinmetz, W., Yang, C. Z., Yang, G. Z. 1991; 1079 (1): 43-52

  Abstract

  The role of hydrophobic and electronic effects on the kinetic constants kcat and Km for the papain hydrolysis of a series of 22 substituted N-benzoylglycine pyridyl esters was investigated. The series studied comprises a wide variety of substituents on the N-benzoyl ring, with about a 300,000-fold range in their hydrophobicities, and 2.1-fold range in their electronic Hammet constants (sigma). It was found that the variation in the log kcat and log 1/Km constants could be explained by the following quantitative-structure activity relationships (QSAR): log 1/Km = 0.40 pi 4 + 4.40 and log 1/kcat = 0.45 sigma + 0.18. The substituent constant, pi 4, is the hydrophobic parameter for the 4-N-benzoyl substituents. QSAR analysis of two smaller sets of glycine phenyl and methyl esters produced similar results. A clear separation of the substituent effects indicates that in the case of these particular esters, acylation appears to be the rate limiting catalytic step.

  View details for Web of Science ID A1991GD60600007

  View details for PubMedID 1888764

• QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS AND MOLECULAR GRAPHICS IN EVALUATION OF ENZYME LIGAND INTERACTIONS METHODS IN ENZYMOLOGY Hansch, C., Klein, T. E. 1991; 202: 512-543

  View details for Web of Science ID A1991GV01300024

  View details for PubMedID 1784187

• THE STRUCTURE-ACTIVITY RELATIONSHIP OF THE PAPAIN HYDROLYSIS OF N-BENZOYLGLYCINE ESTERS BIOCHIMICA ET BIOPHYSICA ACTA Compadre, C. M., Hansch, C., Klein, T. E., LANGRIDGE, R. 1990; 1038 (2): 158-163

  Abstract

  The relationship between structure and the Michaelis-Menten constants (Km) for the papain hydrolysis of a series of 37 N-benzoylglycine esters was investigated. The series studied comprises a wide range of aromatic and aliphatic esters with a 5000-fold variation in their Km constants and essentially constant kcat values. It was found that the variation in the Km constants could be rationalized by the following quantitative structure-activity relationship (QSAR): log 1/Km = 8.13F + 0.33Z + 1.27II3' + 1.95. In this equation F is the field inductive parameter, II3' is the hydrophobic constant for the more lipophilic of the two possible meta substituents and Z is the Van der Waals distance from oxygen through the end of the molecule, in the direction of the 4 position of the aromatic ester moiety.

  View details for Web of Science ID A1990DB89000004

  View details for PubMedID 2331480

• A REAL-TIME MALLEABLE MOLECULAR-SURFACE JOURNAL OF MOLECULAR GRAPHICS Klein, T. E., Huang, C. C., Pettersen, E. F., Couch, G. S., Ferrin, T. E., LANGRIDGE, R. 1990; 8 (1): 16-?

  Abstract

  We describe a method for generating a molecular surface using a parametric patch representation. Unlike previous methods, this algorithm generates a parametric patch surface which is smooth and G continuous and manipulable in real-time. Crucial to our approach is the creation of a net of approximately equilateral triangles from which we generate the control points used as the basis for describing the surface. We present in detail the method used for generating the triangular net and accompanying control points, along with examples of the resulting surfaces.

  View details for Web of Science ID A1990CW84400003

  View details for PubMedID 2268622

• QSAR AND MOLECULAR GRAPHICS IN DRUG DESIGN ALFRED BENZON SYMP ON FRONTIERS IN DRUG RESEARCH : CRYSTALLOGRAPHIC AND COMPUTATIONAL METHODS Hansch, C., Klein, T. MUNKSGAARD. 1990: 327–338

  View details for Web of Science ID A1990BT39X00020

• ON THE STRUCTURE SELECTIVITY PROBLEM IN DRUG DESIGN - A COMPARATIVE-STUDY OF BENZYLPYRIMIDINE INHIBITION OF VERTEBRATE AND BACTERIAL DIHYDROFOLATE-REDUCTASE VIA MOLECULAR GRAPHICS AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS JOURNAL OF MEDICINAL CHEMISTRY Selassie, C. D., Fang, Z. X., Li, R. L., Hansch, C., Debnath, G., Klein, T. E., LANGRIDGE, R., KAUFMAN, B. T. 1989; 32 (8): 1895-1905

  Abstract

  Quantitative structure-activity relationships (QSAR) have been derived for the action of 68 5-(substituted benzyl)-2,4-diaminopyrimidines on dihydrofolate reductase (DHFR) from Lactobacillus casei and chicken liver. The QSAR are analyzed with respect to the stereographics models of the active sites of the enzymes and found to be in good agreement. Using these QSAR equations, we have attempted to design new trimethoprim-type antifolates having higher selectivity for the bacterial enzyme. The general problem of developing selective inhibitors is discussed.

  View details for Web of Science ID A1989AH18700035

  View details for PubMedID 2502631

• INHIBITION OF CARBONIC-ANHYDRASE BY SUBSTITUTED BENZENESULFONAMIDES - A REINVESTIGATION BY QSAR AND MOLECULAR GRAPHICS ANALYSIS QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS Carotti, A., RAGUSEO, C., Campagna, F., LANGRIDGE, R., Klein, T. E. 1989; 8 (1): 1-10

  View details for Web of Science ID A1989U961300001

• The structure selectivity problem in drug design with respect to antifolates. Progress in clinical and biological research Dias Selassie, C., Li, R. L., Hansch, C. H., Klein, T., LANGRIDGE, R., KAUFMAN, B. T., FREISHEIM, J., KHWAJA, T. 1989; 291: 341-344

  View details for PubMedID 2498904

• QSAR ANALYSIS OF THE SUBTILISIN HYDROLYSIS OF X-PHENYL HIPPURATES .2. A STUDY OF SUBTILISIN BPN' CHEMICO-BIOLOGICAL INTERACTIONS Carotti, A., RAGUSEO, C., Klein, T. E., LANGRIDGE, R., Hansch, C. 1988; 67 (3-4): 171-?

  Abstract

  The hydrolysis of 30 substituted phenyl hippurates (X-C6H4OCOCH2NHCOC6H5) by subtilisin BPN' was studied and from the results the following quantitative structure-activity relationship was derived: log 1/Km = 0.39 sigma + 0.16 B5.4 + 0.29 pi'3 + 3.58. In this expression Km is the Michaelis constant, sigma is the Hammett constant, B5.4 is the sterimol steric parameter of X in the 4-position and pi'3 is the hydrophobic parameter for the more hydrophobic of the two possible meta substituents. The other meta substitutent is assigned a pi value of 0. This mathematical model is qualitatively compared with a molecular graphics model constructed from the X-ray crystallographic coordinates of subtilisin BPN'. The results with subtilisin BPN' are compared with our earlier study of similar substrates with Carlsberg subtilisin.

  View details for Web of Science ID A1988R186900001

  View details for PubMedID 3056624

• CONCANAVALIN X-PHENYL BETA-D-GLUCOPYRANOSIDE INTERACTIONS - A MOLECULAR GRAPHICS-QSAR ANALYSIS FARMACO-EDIZIONE SCIENTIFICA Recanatini, M., Klein, T. E., LANGRIDGE, R., Hansch, C. 1987; 42 (12): 879-891

  Abstract

  Poretz and Goldstein showed that X-phenyl beta-D-glucopyranosides prevent the agglutination of concanavalin A with polysaccharides and derived inhibition constants for the process. Using their data the binding of 25 glucosides to concanavalin is now shown to be correlated with the molar refractivity of the substituents on the phenyl ring. This is interpreted to mean that it is the bulk of the substituents and not their hydrophobicity which prevents the union of concanavalin and the polysaccharide. These results are similar to those found for other haptens preventing antibody-antigen interaction.

  View details for Web of Science ID A1987M184000002

  View details for PubMedID 3449391

• CHYMOTRYPSIN HYDROLYSIS OF X-PHENYL HIPPURATES - A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP AND MOLECULAR GRAPHICS ANALYSIS JOURNAL OF BIOLOGICAL CHEMISTRY Morgenstern, L., Recanatini, M., Klein, T. E., Steinmetz, W., Yang, C. Z., LANGRIDGE, R., Hansch, C. 1987; 262 (22): 10767-10772

  Abstract

  The hydrolysis of a set of 28 X-phenyl hippurates by chymotrypsin was investigated. From the derived Km and kcat values a quantitative structure-activity relationship was developed. This equation shows that para substituents correlated by sigma- display only an electronic effect on the formation of the ES complex whereas meta hydrophobic substituents show a hydrophobic interaction correlated by pi in addition to their electronic effect. Meta polar substituents avoid contact with the enzyme and show only electronic effects on Km. Using the x-ray crystallographic coordinates for chymotrypsin and computer graphics, a model was constructed which is used to interpret the quantitative structure-activity relationship. As with a number of previously reported examples, we have found that when polar substituents have the option of binding to hydrophobic space or remaining in the aqueous phase they follow the latter possibility.

  View details for Web of Science ID A1987J441600057

  View details for PubMedID 3611088

• PHOTOAFFINITY ANALOGS OF METHOTREXATE AS FOLATE ANTAGONIST BINDING PROBES .1. PHOTOAFFINITY-LABELING OF MURINE L1210 DIHYDROFOLATE-REDUCTASE AND AMINO-ACID-SEQUENCE OF THE BINDING REGION BIOCHEMISTRY Price, E. M., Smith, P. L., Klein, T. E., FREISHEIM, J. H. 1987; 26 (15): 4751-4756

  Abstract

  N alpha-(4-Amino-4-deoxy-10-methylpteroyl)-N epsilon-(4-azido-5- [125I]iodosalicylyl)-L-lysine, a photoaffinity analogue of methotrexate, is only 2-fold less potent than methotrexate in the inhibition of murine L1210 dihydrofolate reductase. Irradiation of the enzyme in the presence of an equimolar concentration of the 125I-labeled analogue ultimately leads to an 8% incorporation of the photoprobe. A 100-fold molar excess of methotrexate essentially blocks this incorporation. Cyanogen bromide digestion of the labeled enzyme, followed by high-pressure liquid chromatography purification of the generated peptides, indicates that greater than 85% of the total radioactivity is incorporated into a single cyanogen bromide peptide. Sequence analysis revealed this peptide to be residues 53-111, with a majority of the radioactivity centered around residues 63-65 (Lys-Asn-Arg). These data demonstrate that the photoaffinity analogue specifically binds to dihydrofolate reductase and covalently modifies the enzyme following irradiation and is therefore a photolabeling agent useful for probing the inhibitor binding domain of the enzyme.

  View details for Web of Science ID A1987J480800023

  View details for PubMedID 3663623

• MOLECULAR GRAPHICS AND QSAR IN THE STUDY OF ENZYME LIGAND INTERACTIONS - ON THE DEFINITION OF BIORECEPTORS ACCOUNTS OF CHEMICAL RESEARCH Hansch, C., Klein, T. E. 1986; 19 (12): 392-400

  View details for Web of Science ID A1986F631800003

• A QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP AND MOLECULAR GRAPHICS ANALYSIS OF HYDROPHOBIC EFFECTS IN THE INTERACTIONS OF INHIBITORS WITH ALCOHOL-DEHYDROGENASE JOURNAL OF MEDICINAL CHEMISTRY Hansch, C., Klein, T., MCCLARIN, J., LANGRIDGE, R., Cornell, N. W. 1986; 29 (5): 615-620

  Abstract

  An analysis of the inhibition constants of pyrazoles, phenylacetamides, formylbenzylamines, and acetamides acting on liver alcohol dehydrogenase (ADH) yields quantitative structure-activity relationships (QSAR) having a linear dependency on octanol-water partition coefficients (log P). The average coefficient and standard deviation with the log P term for six different QSAR is 0.96 (+/- 0.14). This suggests complete desolvation of the substituents (directly comparable to partitioning into octanol) on binding to the enzyme. Study of a molecular graphics model of ADH constructed from the X-ray crystallographic coordinates shows that the substituents are engulfed in a long hydrophobic channel which is so narrow that water of solvation must be removed from them in the binding process.

  View details for Web of Science ID A1986C179100005

  View details for PubMedID 2939242

• INHIBITION OF CHICKEN LIVER DIHYDROFOLATE-REDUCTASE BY 5-(SUBSTITUTED BENZYL)-2,4-DIAMINOPYRIMIDINES - A QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP AND GRAPHICS ANALYSIS JOURNAL OF MEDICINAL CHEMISTRY Selassie, C. D., Fang, Z. X., Li, R. L., Hansch, C., Klein, T., LANGRIDGE, R., KAUFMAN, B. T. 1986; 29 (5): 621-626

  Abstract

  The inhibition of chicken liver dihydrofolate reductase by a series of substituted benzylpyrimidines has been investigated. From the inhibition constants a quantitative structure-activity relationship has been formulated. This mathematical model is compared with molecular graphics models constructed from the X-ray crystallographic coordinates of trimethoprim and 5-(3,4-dimethoxy-4-isopropenylbenzyl)-2,4- diaminopyrimidine bound to the enzyme. There is good correspondence between the two types of models.

  View details for Web of Science ID A1986C179100006

  View details for PubMedID 3701780

• QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS AND MOLECULAR GRAPHICS IN LIGAND RECEPTOR INTERACTIONS - AMIDINE INHIBITION OF TRYPSIN MOLECULAR PHARMACOLOGY Recanatini, M., Klein, T., Yang, C. Z., MCCLARIN, J., LANGRIDGE, R., Hansch, C. 1986; 29 (4): 436-446

  Abstract

  Quantitative structure-activity relationships have been formulated for four sets of amidine inhibitors of trypsin. The quantitative results from these equations are compared with qualitative models constructed from the X-ray crystallographic coordinates of a benzamidine bound to trypsin. The good agreement between the mathematical and graphics models provides further support for the use of substituent constants and regression analysis in the study of enzyme-ligand interactions.

  View details for Web of Science ID A1986A898800015

  View details for PubMedID 3702862

• COMPUTER-ASSISTED DRUG RECEPTOR MAPPING ANALYSIS ACS SYMPOSIUM SERIES Klein, T. E., Huang, C., Ferrin, T. E., LANGRIDGE, R., Hansch, C. 1986; 306: 147-158

  View details for Web of Science ID A1986C630300013

• A QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP AND MOLECULAR GRAPHICS STUDY OF CARBONIC-ANHYDRASE INHIBITORS MOLECULAR PHARMACOLOGY Hansch, C., MCCLARIN, J., Klein, T., LANGRIDGE, R. 1985; 27 (5): 493-498

  Abstract

  A quantitative structure-activity relationship (QSAR) (log K = 1.55 alpha + 0.64 log P - 2.07I1 - 3.28I2 + 6.94) has been formulated for the binding of a set of substituted benzenesulfonamides to human carbonic anhydrase. The binding constant (K) are from the studies of King and Burgen [Proc. R. Soc. Lond. B. 193:107-125 (1976)], sigma is the Hammett electronic substituent constant, P is the octanol/water partition coefficient, and I1 and I2 are indicator variables for meta and ortho substituents, respectively. The negative coefficients with the indicator variables suggest steric hindrance by these substituents in contrast to para substituents. Qualitative features of the QSAR are correlated with a color stereomolecular graphics model of the enzyme-inhibitor complex which was constructed from the X-ray crystallographic coordinates of the enzyme.

  View details for Web of Science ID A1985AHM7100001

  View details for PubMedID 3990676