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Bao S, Yang S, Hua Z, Li J, Zang Y, Li X. Ziprasidone population pharmacokinetics and co-medication effects in Chinese patients. NAUNYN-SCHMIEDEBERG'S ARCHIVES OF PHARMACOLOGY 2024:10.1007/s00210-024-03244-y. [PMID: 38918237 DOI: 10.1007/s00210-024-03244-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024]
Abstract
Ziprasidone is widely used in the treatment of psychiatric disorders. Despite its prevalence, there is a notable lack of population pharmacokinetics (PPK) studies on ziprasidone in serum, both domestically and internationally. This study aimed to comprehensively investigate the various factors influencing the PPK characteristics of Ziprasidone, thereby providing a scientific basis for personalized treatment strategies in clinical settings. This is a retrospective study. A non-linear mixed-effects modeling method was used for data analysis, with the ziprasidone PPK model established using the Phoenix NLME 8.1 software. Model evaluation employed goodness-of-fit plots, visual predictive checks, and Bootstrap methods to ensure reliability and accuracy. To further validate the model's applicability, data from an additional 30 patients meeting the same inclusion criteria but not included in the final model were collected for external validation. Simulations were performed to explore the personalized dosage regimens. This retrospective analysis collected 547 drug concentration data points from 185 psychiatric disorder patients, along with related medical records. The data included detailed demographic information (such as age, gender, weight), dosing regimens, laboratory test results, and concomitant medication details. In the final model, Ka was fixed at 0.5 h-1 based on literature, and the population typical values for ziprasidone clearance (CL) and volume of distribution (V) were 18.74 L/h and 110.24 L, respectively. Co-administration of lorazepam and valproic acid significantly influenced the clearance of ziprasidone. Moreover, the model evaluation indicated good stability and predictive accuracy. A simple to use dosage regimen table was derived based on the results of simulations. This study successfully established and validated a PPK model for ziprasidone in Chinese patients with psychiatric disorders. The model provides a scientific reference for individualized dosing of ziprasidone and holds the potential to optimize treatment strategies, thereby enhancing therapeutic efficacy and safety.
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Affiliation(s)
- Shuang Bao
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China
- Department of Pharmacy, Beijing Anding Hospital, Capital Medical University, No. 5 Ankang Hutong, Xicheng District, Beijing, 100088, China
| | - Siyu Yang
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Zixin Hua
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Jiqian Li
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China
| | - Yannan Zang
- Department of Pharmacy, Beijing Anding Hospital, Capital Medical University, No. 5 Ankang Hutong, Xicheng District, Beijing, 100088, China
| | - Xingang Li
- Department of Pharmacy, Beijing Friendship Hospital, Capital Medical University, No. 95 Yongan Road, Xicheng District, Beijing, 100050, China.
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2
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Lenz C, Narang A, Bousman CA. Pharmacogenomic allele coverage of genome-wide genotyping arrays: a comparative analysis. Pharmacogenet Genomics 2024; 34:130-134. [PMID: 38359167 DOI: 10.1097/fpc.0000000000000523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
The use of genome-wide genotyping arrays in pharmacogenomics (PGx) research and clinical implementation applications is increasing but it is unclear which arrays are best suited for these applications. Here, we conduct a comparative coverage analysis of PGx alleles included on genome-wide genotyping arrays, with an emphasis on alleles in genes with PGx-based prescribing guidelines. Genomic manifest files for seven arrays including the Axiom Precision Medicine Diversity Array (PMDA), Axiom PMDA Plus, Axiom PangenomiX, Axiom PangenomiX Plus, Infinium Global Screening Array, Infinium Global Diversity Array (GDA) and Infinium GDA with enhanced PGx (GDA-PGx) Array, were evaluated for coverage of 523 star alleles across 19 pharmacogenes included in prescribing guidelines developed by the Clinical Pharmacogenetic Implementation Consortium and Dutch Pharmacogenomics Working Group. Specific attention was given to coverage of the Association of Molecular Pathology's Tier 1 and Tier 2 allele sets for CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, NUDT15, TPMT and VKORC1 . Coverage of the examined PGx alleles was highest for the Infinium GDA-PGx (88%), Axiom PangenomiX Plus (77%), Axiom PangenomiX (72%) and Axiom PMDA Plus (70%). Three arrays (Infinium GDA-PGx, Axiom PangenomiX Plus and Axiom PMDA Plus) fully covered the Tier 1 alleles and the Axiom PangenomiX array provided full coverage of Tier 2 alleles. In conclusion, PGx allele coverage varied by gene and array. A superior array for all PGx applications was not identified. Future comparative analyses of genotype data produced by these arrays are needed to determine the robustness of the reported coverage estimates.
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Affiliation(s)
| | | | - Chad A Bousman
- Alberta Children's Hospital Research Institute
- Department of Medical Genetics
- The Mathison Centre for Mental Health Research and Education, Hotchkiss Brain Institute, Cumming School of Medicine
- Departments of Psychiatry
- Physiology and Pharmacology
- Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
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3
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Shriver SP, Adams D, McKelvey BA, McCune JS, Miles D, Pratt VM, Ashcraft K, McLeod HL, Williams H, Fleury ME. Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology. J Clin Oncol 2024:JCO2301748. [PMID: 38386947 DOI: 10.1200/jco.23.01748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/08/2023] [Accepted: 12/12/2023] [Indexed: 02/24/2024] Open
Abstract
Pharmacogenomics (PGx), the study of inherited genomic variation and drug response or safety, is a vital tool in precision medicine. In oncology, testing to identify PGx variants offers patients the opportunity for customized treatments that can minimize adverse effects and maximize the therapeutic benefits of drugs used for cancer treatment and supportive care. Because individuals of shared ancestry share specific genetic variants, PGx factors may contribute to outcome disparities across racial and ethnic categories when genetic ancestry is not taken into account or mischaracterized in PGx research, discovery, and application. Here, we examine how the current scientific understanding of the role of PGx in differential oncology safety and outcomes may be biased toward a greater understanding and more complete clinical implementation of PGx for individuals of European descent compared with other genetic ancestry groups. We discuss the implications of this bias for PGx discovery, access to care, drug labeling, and patient and provider understanding and use of PGx approaches. Testing for somatic genetic variants is now the standard of care in treatment of many solid tumors, but the integration of PGx into oncology care is still lacking despite demonstrated actionable findings from PGx testing, reduction in avoidable toxicity and death, and return on investment from testing. As the field of oncology is poised to expand and integrate germline genetic variant testing, it is vital that PGx discovery and application are equitable for all populations. Recommendations are introduced to address barriers to facilitate effective and equitable PGx application in cancer care.
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Affiliation(s)
| | | | | | - Jeannine S McCune
- City of Hope/Beckman Research Institute Department of Hematologic Malignancies Translational Sciences, Duarte, CA
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Ji Y, Shaaban S. Interrogating Pharmacogenetics Using Next-Generation Sequencing. J Appl Lab Med 2024; 9:50-60. [PMID: 38167765 DOI: 10.1093/jalm/jfad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/18/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Pharmacogenetics or pharmacogenomics (PGx) is the study of the role of inherited or acquired sequence change in drug response. With the rapid evolution of molecular techniques, bioinformatic tools, and increased throughput of functional genomic studies, the discovery of PGx associations and clinical implementation of PGx test results have now moved beyond a handful variants in single pharmacogenes and multi-gene panels that interrogate a few pharmacogenes to whole-exome and whole-genome scales. Although some laboratories have adopted next-generation sequencing (NGS) as a testing platform for PGx and other molecular tests, most clinical laboratories that offer PGx tests still use targeted genotyping approaches. CONTENT This article discusses primarily the technical considerations for clinical laboratories to develop NGS-based PGx tests including whole-genome and whole-exome sequencing analyses and highlights the challenges and opportunities in test design, content selection, bioinformatic pipeline for PGx allele and diplotype assignment, rare variant classification, reporting, and briefly touches a few additional areas that are important for successful clinical implementation of PGx results. SUMMARY The accelerated speed of technology development associated with continuous cost reduction and enhanced ability to interrogate complex genome regions makes it inevitable for most, if not all, clinical laboratories to transition PGx testing to an NGS-based platform in the near future. It is important for laboratories and relevant professional societies to recognize both the potential and limitations of NGS-based PGx profiling, and to work together to develop a standard and consistent practice to maximize the variant or allele detection rate and utility of PGx testing.
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Affiliation(s)
- Yuan Ji
- Department of Pathology, University of Utah, Salt Lake City, UT, United States
- Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, UT, United States
| | - Sherin Shaaban
- Department of Pathology, University of Utah, Salt Lake City, UT, United States
- Molecular Genetics and Genomics, ARUP Laboratories, Salt Lake City, UT, United States
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5
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McCarley SC, Murphy DA, Thompson J, Shovlin CL. Pharmacogenomic Considerations for Anticoagulant Prescription in Patients with Hereditary Haemorrhagic Telangiectasia. J Clin Med 2023; 12:7710. [PMID: 38137783 PMCID: PMC10744266 DOI: 10.3390/jcm12247710] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/10/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023] Open
Abstract
Hereditary haemorrhagic telangiectasia (HHT) is a vascular dysplasia that commonly results in bleeding but with frequent indications for therapeutic anticoagulation. Our aims were to advance the understanding of drug-specific intolerance and evaluate if there was an indication for pharmacogenomic testing. Genes encoding proteins involved in the absorption, distribution, metabolism, and excretion of warfarin, heparin, and direct oral anticoagulants (DOACs) apixaban, rivaroxaban, edoxaban, and dabigatran were identified and examined. Linkage disequilibrium with HHT genes was excluded, before variants within these genes were examined following whole genome sequencing of general and HHT populations. The 44 genes identified included 5/17 actionable pharmacogenes with guidelines. The 76,156 participants in the Genome Aggregation Database v3.1.2 had 28,446 variants, including 9668 missense substitutions and 1076 predicted loss-of-function (frameshift, nonsense, and consensus splice site) variants, i.e., approximately 1 in 7.9 individuals had a missense substitution, and 1 in 71 had a loss-of-function variant. Focusing on the 17 genes relevant to usually preferred DOACs, similar variant profiles were identified in HHT patients. With HHT patients at particular risk of haemorrhage when undergoing anticoagulant treatment, we explore how pre-emptive pharmacogenomic testing, alongside HHT gene testing, may prove beneficial in reducing the risk of bleeding and conclude that HHT patients are well placed to be at the vanguard of personalised prescribing.
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Affiliation(s)
- Sarah C. McCarley
- National Heart and Lung Institute, Imperial College London, London W12 0NN, UK; (S.C.M.); (J.T.)
| | - Daniel A. Murphy
- Pharmacy Department, Imperial College Healthcare NHS Trust, London W2 1NY, UK;
- Social, Genetic and Envionmental Determinants of Health Theme, NIHR Imperial Biomedical Research Centre, London W2 1NY, UK
| | - Jack Thompson
- National Heart and Lung Institute, Imperial College London, London W12 0NN, UK; (S.C.M.); (J.T.)
| | - Claire L. Shovlin
- National Heart and Lung Institute, Imperial College London, London W12 0NN, UK; (S.C.M.); (J.T.)
- Social, Genetic and Envionmental Determinants of Health Theme, NIHR Imperial Biomedical Research Centre, London W2 1NY, UK
- Specialist Medicine, Hammersmith Hospital, Imperial College Healthcare NHS Trust, London W12 0HS, UK
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Paetznick C, Okoro O. The Intersection between Pharmacogenomics and Health Equity: A Case Example. PHARMACY 2023; 11:186. [PMID: 38133461 PMCID: PMC10747429 DOI: 10.3390/pharmacy11060186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/25/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
Pharmacogenomics (PGx) and the study of precision medicine has substantial power to either uplift health equity efforts or further widen the gap of our already existing health disparities. In either occurrence, the medication experience plays an integral role within this intersection on an individual and population level. Examples of this intertwined web are highlighted through a case discussion. With these perspectives in mind, several recommendations for the research and clinical communities are highlighted to promote equitable healthcare with PGx integrated.
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Affiliation(s)
| | - Olihe Okoro
- Department of Pharmacy Practice and Pharmaceutical Sciences, College of Pharmacy, University of Minnesota, Duluth, MN 55812, USA
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Van Heukelom J, Morgan J, Massmann A, Jacobsen K, Petry NJ, Baye JF, Frear S, Schultz A. Evolution of pharmacogenomic services and implementation of a multi-state pharmacogenomics clinic across a large rural healthcare system. Front Pharmacol 2023; 14:1274165. [PMID: 38035031 PMCID: PMC10682150 DOI: 10.3389/fphar.2023.1274165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Introduction: Pharmacogenomics (PGx) aims to maximize drug benefits while minimizing risk of toxicity. Although PGx has proven beneficial in many settings, clinical uptake lags. Lack of clinician confidence and limited availability of PGx testing can deter patients from completing PGx testing. A few novel PGx clinic models have been described as a way to incorporate PGx testing into the standard of care. Background: A PGx clinic was implemented to fill an identified gap in provider availability, confidence, and utilization of PGx across our health system. Through a joint pharmacist and Advanced Practice Provider (APP) collaborative clinic, patients received counseling and PGx medication recommendations both before and after PGx testing. The clinic serves patients both in-person and virtually across four states in the upper Midwest. Results: The majority of patients seen in the PGx clinic during the early months were clinician referred (77%, n = 102) with the remainder being self-referred. Patients were, on average, taking two medications with Clinical Pharmacogenetics Implementation Consortium guidelines. Visits were split almost equally between in-person and virtual visits. Conclusion: Herein, we describe the successful implementation of an interdisciplinary PGx clinic to further enhance our PGx program. Throughout the implementation of the PGx clinic we have learned valuable lessons that may be of interest to other implementors. Clinicians were actively engaged in clinic referrals and early adoption of telemedicine was key to the clinic's early successes.
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Affiliation(s)
- Joel Van Heukelom
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
- Department of Internal Medicine, University of South Dakota School of Medicine, Vermillion, SD, United States
| | - Jennifer Morgan
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
- Department of Medical Genetics, Sanford Health, Sioux Falls, SD, United States
| | - Amanda Massmann
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
- Department of Internal Medicine, University of South Dakota School of Medicine, Vermillion, SD, United States
| | - Kristen Jacobsen
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
| | - Natasha J. Petry
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
- Department of Pharmacy Practice, North Dakota State University, Fargo, ND, United States
| | - Jordan F. Baye
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
- Department of Internal Medicine, University of South Dakota School of Medicine, Vermillion, SD, United States
- Department of Pharmacy Practice, South Dakota State University College of Pharmacy and Allied Health Professions, Brookings, SD, United States
| | - Samantha Frear
- Translational Software, Inc., Mercer Island, WA, United States
| | - April Schultz
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, United States
- Department of Internal Medicine, University of South Dakota School of Medicine, Vermillion, SD, United States
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8
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Scott SA. The Genetic Testing Reference Materials Coordination Program: Over 10 Years of Support for Pharmacogenomic Testing. J Mol Diagn 2023; 25:630-633. [PMID: 37481236 PMCID: PMC10488323 DOI: 10.1016/j.jmoldx.2023.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/06/2023] [Accepted: 07/11/2023] [Indexed: 07/24/2023] Open
Affiliation(s)
- Stuart A Scott
- Department of Pathology, Stanford University, Stanford, California; Clinical Genomics Laboratory, Stanford Medicine, Palo Alto, California.
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Pratt VM, Cavallari LH, Fulmer ML, Gaedigk A, Hachad H, Ji Y, Kalman LV, Ly RC, Moyer AM, Scott SA, van Schaik RHN, Whirl-Carrillo M, Weck KE. CYP3A4 and CYP3A5 Genotyping Recommendations: A Joint Consensus Recommendation of the Association for Molecular Pathology, Clinical Pharmacogenetics Implementation Consortium, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, European Society for Pharmacogenomics and Personalized Therapy, and Pharmacogenomics Knowledgebase. J Mol Diagn 2023; 25:619-629. [PMID: 37419245 PMCID: PMC10565868 DOI: 10.1016/j.jmoldx.2023.06.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/17/2023] [Accepted: 06/01/2023] [Indexed: 07/09/2023] Open
Abstract
The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations for a minimum panel of variant alleles (tier 1) and an extended panel of variant alleles (tier 2) that will aid clinical laboratories when designing assays for PGx testing. The Association for Molecular Pathology PGx Working Group considered functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, and other technical considerations for PGx testing when developing these recommendations. The goal of this Working Group is to promote standardization of PGx gene/allele testing across clinical laboratories. This document will focus on clinical CYP3A4 and CYP3A5 PGx testing that may be applied to all CYP3A4- and CYP3A5-related medications. These recommendations are not to be interpreted as prescriptive but to provide a reference guide.
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Affiliation(s)
- Victoria M Pratt
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana.
| | - Larisa H Cavallari
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pharmacotherapy and Translational Research, Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Florida
| | - Makenzie L Fulmer
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and ARUP Laboratories, University of Utah School of Medicine, Salt Lake City, Utah
| | - Andrea Gaedigk
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute and School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri
| | - Houda Hachad
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Clinical Operations, AccessDx, Houston, Texas
| | - Yuan Ji
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and ARUP Laboratories, University of Utah School of Medicine, Salt Lake City, Utah
| | - Lisa V Kalman
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Reynold C Ly
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ann M Moyer
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Stuart A Scott
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University, Stanford, California; Clinical Genomics Laboratory, Stanford Medicine, Palo Alto, California
| | - Ron H N van Schaik
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Clinical Chemistry/International Federation of Clinical Chemistry and Laboratory Medicine Expert Center Pharmacogenetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Michelle Whirl-Carrillo
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Karen E Weck
- Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, North Carolina; Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
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10
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Gaedigk A, Boone EC, Turner AJ, van Schaik RHN, Chernova D, Wang WY, Broeckel U, Granfield CA, Hodge JC, Ly RC, Lynnes TC, Mitchell MW, Moyer AM, Oliva J, Kalman LV. Characterization of Reference Materials for CYP3A4 and CYP3A5: A (GeT-RM) Collaborative Project. J Mol Diagn 2023; 25:655-664. [PMID: 37354993 DOI: 10.1016/j.jmoldx.2023.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/23/2023] [Accepted: 06/01/2023] [Indexed: 06/26/2023] Open
Abstract
Pharmacogenetic testing for CYP3A4 is increasingly provided by clinical and research laboratories; however, only a limited number of quality control and reference materials are currently available for many of the CYP3A4 variants included in clinical tests. To address this need, the Division of Laboratory Systems, CDC-based Genetic Testing Reference Material Coordination Program (GeT-RM), in collaboration with members of the pharmacogenetic testing and research communities and the Coriell Institute for Medical Research, has characterized 30 DNA samples derived from Coriell cell lines for CYP3A4. Samples were distributed to five volunteer laboratories for genotyping using a variety of commercially available and laboratory-developed tests. Sanger and next-generation sequencing were also utilized by some of the laboratories. Whole-genome sequencing data from the 1000 Genomes Projects were utilized to inform genotype. Twenty CYP3A4 alleles were identified in the 30 samples characterized for CYP3A4: CYP3A4∗4, ∗5, ∗6, ∗7, ∗8, ∗9, ∗10, ∗11, ∗12, ∗15, ∗16, ∗18, ∗19, ∗20, ∗21, ∗22, ∗23, ∗24, ∗35, and a novel allele, CYP3A4∗38. Nineteen additional samples with preexisting data for CYP3A4 or CYP3A5 were re-analyzed to generate comprehensive reference material panels for these genes. These publicly available and well-characterized materials can be used to support the quality assurance and quality control programs of clinical laboratories performing clinical pharmacogenetic testing.
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Affiliation(s)
- Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, Missouri; University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Erin C Boone
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, Missouri
| | - Amy J Turner
- RPRD Diagnostics, Milwaukee, Wisconsin; Section on Genomic Pediatrics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ron H N van Schaik
- Department of Clinical Chemistry/International Federation of Clinical Chemistry and Laboratory Medicine Expert Center Pharmacogenetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Dilyara Chernova
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, Missouri
| | - Wendy Y Wang
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, Missouri
| | - Ulrich Broeckel
- RPRD Diagnostics, Milwaukee, Wisconsin; Section on Genomic Pediatrics, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Caitlin A Granfield
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Jennelle C Hodge
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Reynold C Ly
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ty C Lynnes
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | | | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | - Lisa V Kalman
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia.
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11
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Rakicevic L. DNA and RNA Molecules as a Foundation of Therapy Strategies for Treatment of Cardiovascular Diseases. Pharmaceutics 2023; 15:2141. [PMID: 37631355 PMCID: PMC10459020 DOI: 10.3390/pharmaceutics15082141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/27/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
There has always been a tendency of medicine to take an individualised approach to treating patients, but the most significant advances were achieved through the methods of molecular biology, where the nucleic acids are in the limelight. Decades of research of molecular biology resulted in setting medicine on a completely new platform. The most significant current research is related to the possibilities that DNA and RNA analyses can offer in terms of more precise diagnostics and more subtle stratification of patients in order to identify patients for specific therapy treatments. Additionally, principles of structure and functioning of nucleic acids have become a motive for creating entirely new therapy strategies and an innovative generation of drugs. All this also applies to cardiovascular diseases (CVDs) which are the leading cause of mortality in developed countries. This review considers the most up-to-date achievements related to the use of translatory potential of DNA and RNA in treatment of cardiovascular diseases, and considers the challenges and prospects in this field. The foundations which allow the use of translatory potential are also presented. The first part of this review focuses on the potential of the DNA variants which impact conventional therapies and on the DNA variants which are starting points for designing new pharmacotherapeutics. The second part of this review considers the translatory potential of non-coding RNA molecules which can be used to formulate new generations of therapeutics for CVDs.
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Affiliation(s)
- Ljiljana Rakicevic
- Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Vojvode Stepe 444a, 11042 Belgrade, Serbia
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12
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Shugg T, Ly RC, Osei W, Rowe EJ, Granfield CA, Lynnes TC, Medeiros EB, Hodge JC, Breman AM, Schneider BP, Sahinalp SC, Numanagić I, Salisbury BA, Bray SM, Ratcliff R, Skaar TC. Computational pharmacogenotype extraction from clinical next-generation sequencing. Front Oncol 2023; 13:1199741. [PMID: 37469403 PMCID: PMC10352904 DOI: 10.3389/fonc.2023.1199741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/22/2023] [Indexed: 07/21/2023] Open
Abstract
Background Next-generation sequencing (NGS), including whole genome sequencing (WGS) and whole exome sequencing (WES), is increasingly being used for clinic care. While NGS data have the potential to be repurposed to support clinical pharmacogenomics (PGx), current computational approaches have not been widely validated using clinical data. In this study, we assessed the accuracy of the Aldy computational method to extract PGx genotypes from WGS and WES data for 14 and 13 major pharmacogenes, respectively. Methods Germline DNA was isolated from whole blood samples collected for 264 patients seen at our institutional molecular solid tumor board. DNA was used for panel-based genotyping within our institutional Clinical Laboratory Improvement Amendments- (CLIA-) certified PGx laboratory. DNA was also sent to other CLIA-certified commercial laboratories for clinical WGS or WES. Aldy v3.3 and v4.4 were used to extract PGx genotypes from these NGS data, and results were compared to the panel-based genotyping reference standard that contained 45 star allele-defining variants within CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DPYD, G6PD, NUDT15, SLCO1B1, TPMT, and VKORC1. Results Mean WGS read depth was >30x for all variant regions except for G6PD (average read depth was 29 reads), and mean WES read depth was >30x for all variant regions. For 94 patients with WGS, Aldy v3.3 diplotype calls were concordant with those from the genotyping reference standard in 99.5% of cases when excluding diplotypes with additional major star alleles not tested by targeted genotyping, ambiguous phasing, and CYP2D6 hybrid alleles. Aldy v3.3 identified 15 additional clinically actionable star alleles not covered by genotyping within CYP2B6, CYP2C19, DPYD, SLCO1B1, and NUDT15. Within the WGS cohort, Aldy v4.4 diplotype calls were concordant with those from genotyping in 99.7% of cases. When excluding patients with CYP2D6 copy number variation, all Aldy v4.4 diplotype calls except for one CYP3A4 diplotype call were concordant with genotyping for 161 patients in the WES cohort. Conclusion Aldy v3.3 and v4.4 called diplotypes for major pharmacogenes from clinical WES and WGS data with >99% accuracy. These findings support the use of Aldy to repurpose clinical NGS data to inform clinical PGx.
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Affiliation(s)
- Tyler Shugg
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Reynold C. Ly
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Wilberforce Osei
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elizabeth J. Rowe
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Caitlin A. Granfield
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Ty C. Lynnes
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Elizabeth B. Medeiros
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jennelle C. Hodge
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Amy M. Breman
- Division of Diagnostic Genetics and Genomics, Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Bryan P. Schneider
- Division of Hematology/Oncology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - S. Cenk Sahinalp
- Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, United States
| | - Ibrahim Numanagić
- Department of Computer Science, University of Victoria, Victoria, BC, Canada
| | | | | | | | - Todd C. Skaar
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
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Liu TY, Hsu HY, You YS, Hsieh YW, Lin TC, Peng CW, Huang HY, Chang SS, Tsai FJ. Efficacy of Warfarin Therapy Guided by Pharmacogenetics: A Real-world Investigation Among Han Taiwanese. Clin Ther 2023; 45:662-670. [PMID: 37301690 DOI: 10.1016/j.clinthera.2023.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/01/2023] [Accepted: 04/10/2023] [Indexed: 06/12/2023]
Abstract
PURPOSE The anticoagulation activity of warfarin in populations with CYP2C9, VKORC1, and CYP4F2 variants differs between individuals and is correlated with poor international normalized ratio (INR) control. Pharmacogenetics-guided warfarin dosing has been successfully developed for patients with genetic variations in recent years. However, few real-world data have been used to investigate the INR and warfarin dosage and the time to target INR. This study examined the largest collection of genetic and clinical real-world data related to warfarin to provide further evidence supporting the benefits of pharmacogenetics in clinical outcomes. METHODS We retrieved a total of 69,610 INR-warfarin records after the index date from 2,613 patients in the China Medical University Hospital database between January 2003 and December 2019. Each INR reading was obtained from the latest laboratory data after the hospital visit date. Patients with a history of malignant neoplasms or pregnancy before the index date were excluded, as were patients without data on INR measurements after the fifth day of prescription, genetic information, or gender variables. The primary outcomes were the INR and warfarin dosage during days 7, 14, 28, 56, and 84 after prescription. The secondary outcome was the time required to reach the INR ranges of 1.5 to 3.0 and >4.0. FINDINGS A total of 59,643 INR-warfarin records from 2188 patients were retrieved. The average INR was higher for homozygous carriers of the minor allele at CYP2C9 and VKORC1 during the first 7 days (1.83 [1.03] [CYP2C9*1] and 2.46 [1.44] [CYP2C9*3], P < 0.001; 1.39 [0.36] [rs9923231 G/G], 1.55 [0.79] [rs9923231 G/A], and 1.96 [1.13] [rs9923231 A/A], P < 0.001) than for the wild-type allele. These patients with variants required lower warfarin doses than those with the wild-type allele during the first 28 days. CYP4F2 variant patients seemed to require higher doses of warfarin than those in the wild-type group; however, no significant difference in the average INR was observed (1.95 [1.14] [homozygous V433 carriers], 1.78 [0.98] [heterozygous V433M carriers], and 1.66 [0.91] [homozygous M433 carriers], P = 0.016). IMPLICATIONS Our study indicates that genetic variants in the Han population may enhance warfarin responsiveness, which holds clinical relevance. An increased warfarin dosage was not linked to a shorter time to therapeutic INR between CYP4F2 variant patients and those with a wild-type allele. Assessing CYP2C9 and VKORC1 genetic polymorphisms before initiating warfarin treatment in real-world practice is essential for potentially vulnerable patients and is likely to optimize therapeutic dosing.
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Affiliation(s)
- Ting-Yuan Liu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
| | - Hsing-Yu Hsu
- Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan.
| | - Ying-Shu You
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
| | - Yow-Wen Hsieh
- Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan.
| | - Tzu-Ching Lin
- Department of Pharmacy, China Medical University Hospital, Taichung, Taiwan.
| | - Chun-Wei Peng
- Artificial Intelligence and Data Science, National Chung Hsing University, Taichung, Taiwan.
| | - Hsin-Yi Huang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan.
| | - Shih-Sheng Chang
- Division of Cardiovascular Medicine, China Medical University Hospital, Taichung, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan; School of Chinese Medicine, China Medical University, Taichung, Taiwan; Division of Pediatric Genetics, Children's Hospital of China Medical University, Taichung, Taiwan; Department of Biotechnology and Bioinformatics, Asia University, Taichung, Taiwan.
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14
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Padmanabhan S, du Toit C, Dominiczak AF. Cardiovascular precision medicine - A pharmacogenomic perspective. CAMBRIDGE PRISMS. PRECISION MEDICINE 2023; 1:e28. [PMID: 38550953 PMCID: PMC10953758 DOI: 10.1017/pcm.2023.17] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/24/2023] [Accepted: 06/12/2023] [Indexed: 05/16/2024]
Abstract
Precision medicine envisages the integration of an individual's clinical and biological features obtained from laboratory tests, imaging, high-throughput omics and health records, to drive a personalised approach to diagnosis and treatment with a higher chance of success. As only up to half of patients respond to medication prescribed following the current one-size-fits-all treatment strategy, the need for a more personalised approach is evident. One of the routes to transforming healthcare through precision medicine is pharmacogenomics (PGx). Around 95% of the population is estimated to carry one or more actionable pharmacogenetic variants and over 75% of adults over 50 years old are on a prescription with a known PGx association. Whilst there are compelling examples of pharmacogenomic implementation in clinical practice, the case for cardiovascular PGx is still evolving. In this review, we shall summarise the current status of PGx in cardiovascular diseases and look at the key enablers and barriers to PGx implementation in clinical practice.
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Affiliation(s)
- Sandosh Padmanabhan
- BHF Glasgow Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Clea du Toit
- BHF Glasgow Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Anna F. Dominiczak
- BHF Glasgow Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
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15
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Kilpatrick MC, Givens SK, Watts Alexander CS. What Is Precision Medicine? PHYSICIAN ASSISTANT CLINICS 2023. [DOI: 10.1016/j.cpha.2022.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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16
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Williams GR, Tsongalis GJ, Lewis LD, Barney RE, Cook LJ, Geno KA, Nerenz RD. Potential Impact of Pharmacogenomic Single Nucleotide Variants in a Rural Caucasian Population. J Appl Lab Med 2023; 8:251-263. [PMID: 36611001 PMCID: PMC10539040 DOI: 10.1093/jalm/jfac091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 08/15/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND In the US adverse drug reactions (ADRs) are estimated to cause 100 000 fatalities and cost over $136 billion annually. A patient's genes play a significant role in their response to a drug. Pharmacogenomics aims to optimize drug choice and dose for individual patients by characterizing patients' pharmacologically relevant genes to identify variants of known impact. METHODS DNA was extracted from randomly selected remnant whole blood samples from Caucasian patients with previously performed complete blood counts. Samples were genotyped by mass spectrometry using a customized pharmacogenomics panel. A third-party result interpretation service used genotypic results to predict likely individual responses to frequently prescribed drugs. RESULTS Complete genotypic and phenotypic calls for all tested Cytochrome P450 isoenzymes and other genes were obtained from 152 DNA samples. Of these 152 unique genomic DNA samples, 140 had genetic variants suggesting dose adjustment for at least one drug. Cardiovascular and psychiatry drugs had the highest number of recommendations, which included United States Food and Drug Administration warnings for highly prescribed drugs metabolized by CYP2C19, CYP2C9, CYP2D6, HLA-A, and VKORC1. CONCLUSIONS Risk for each drug:gene pairing primarily depends upon the degree of predicted enzyme impairment or activation, width of the therapeutic window, and whether parent compound or metabolite is pharmacologically active. The resulting metabolic variations range from risk of toxicity to therapeutic failure. Pharmacogenomic profiling likely reduces ADR potential by allowing up front drug/dose selection to fit a patient's unique drug-response profile.
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Affiliation(s)
- Grace R. Williams
- Department of Pathology, Virginia Commonwealth University Health System, Richmond, VA, USA
| | - Gregory J. Tsongalis
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Lionel D. Lewis
- Department of Medicine, The Geisel School of Medicine at Dartmouth and Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Rachael E. Barney
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Leanne J. Cook
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - K. Aaron Geno
- Department of Pathology, Texas Tech University Health Sciences Center El Paso, El Paso, TX, USA
| | - Robert D. Nerenz
- Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
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17
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Levitskaya ES, Kastanayan AA, Leonova GN, Yakovlev AA, Zatonsky SA, Ganenko LA. Warfarin hypersensitivity in the internist's practice: a case report. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2023. [DOI: 10.15829/1728-8800-2023-3392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023] Open
Abstract
Anticoagulants are widely used in clinical practice to reduce the risk of cardiovascular events. However, there are associated clinical conditions that require coagulation system monitoring due to an increased risk of bleeding. In clinical practice, cases of warfarin hypersensitivity due to gene polymorphisms are known. Taking warfarin in such a situation is often manifested by massive bleeding that threatens the patient's life. Sixty seven-years-old female patient was admitted to the internal medicine department of Rostov State Medical University clinic in March 2022. The day before, she noted severe abdominal pain, loose bloody stool. An outpatient examination revealed an increase in international normalized ratio to 9,42, thrombin time >30 sec. There were no signs of primary gastrointestinal pathology. According to anamnesis, on January 21, 2022, the patient underwent mechanical aortic valve replacement, in connection with which warfarin was prescribed at a dose of 2,5 mg/day. After establishing hypocoagulation, warfarin was discontinued. A pharmacogenetic analysis was performed, which established the carriage of polymorphisms of cytochrome P450 system genes, the homozygous mutation 1075A>C (CYP2C9 (*3/*3)), and the heterozygous mutation of the vitamin K reductase gene VKORC1 G(-1639)A (VKORC1 GA). The individual dosage of warfarin was calculated according to International Warfarin Pharmacogenetics Consortium guidelines, which was 9 mg/week. After adjusting the dose of warfarin, the level of international normalized ratio decreased to 3,61, thrombin time to 13 sec. The patient was discharged with recommendations to follow an individual warfarin regimen and monitor coagulation parameters. The presence of hypersensitivity to warfarin is not a reason for its complete withdrawal in situations requiring longterm anticoagulation. In this regard, it is necessary for the doctor to be vigilant when prescribing warfarin to a patient, understanding the causes and methods for diagnosing hypersensitivity to warfarin, and timely correction of its dosage.
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18
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Cicali EJ, Lemke L, Al Alshaykh H, Nguyen K, Cavallari LH, Wiisanen K. How to Implement a Pharmacogenetics Service at your Institution. JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2022; 5:1161-1175. [PMID: 36589694 PMCID: PMC9799247 DOI: 10.1002/jac5.1699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/29/2022] [Indexed: 01/05/2023]
Abstract
The vast majority of patients possess one or more pharmacogenetic variants that can influence optimal medication use. When pharmacogenetic data are used to guide drug choice and dosing, evidence points to improved disease outcomes, fewer adverse effects, and lower healthcare spending. Although its science is well established, clinical use of pharmacogenetic data to guide drug therapy is still in its infancy. Pharmacogenetics essentially involves the intersection of an individual's genetic data with their medications, which makes pharmacists uniquely qualified to provide clinical support and education in this field. In fact, most pharmacogenetics implementations, to date, have been led by pharmacists as leaders or members of a multidisciplinary team or as individual practitioners. A successful large-scale pharmacogenetics implementation requires coordination and synergy among administrators, clinicians, informatics teams, laboratories, and patients. Because clinical implementation of pharmacogenetics is in its early stages, there is an urgent need for guidance and dissemination of shared experiences to provide a framework for clinicians. Many early adopters of pharmacogenetics have explored various strategies among diverse practice settings. This article relies on the experiences of early adopters to provide guidance for critical steps along the pathway to implementation, including strategies to engage stakeholders; evaluate pharmacogenetic evidence; coordinate laboratory testing, results interpretation and their integration into the electronic health record; identify reimbursement avenues; educate providers and patients; and maintain a successful program. Learning from early adopters' published experiences and strategies can allow clinicians leading a new pharmacogenetics implementation to avoid pitfalls and adapt and apply lessons learned by others to their own practice.
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Affiliation(s)
- Emily J Cicali
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Lauren Lemke
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Hana Al Alshaykh
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Khoa Nguyen
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
| | - Larisa H Cavallari
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
| | - Kristin Wiisanen
- Department of Pharmacotherapy and Translational Research, University of Florida, College of Pharmacy, Gainesville, FL, USA
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Fl, USA
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19
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Pratt VM, Cavallari LH, Fulmer ML, Gaedigk A, Hachad H, Ji Y, Kalman LV, Ly RC, Moyer AM, Scott SA, van Schaik RHN, Whirl-Carrillo M, Weck KE. TPMT and NUDT15 Genotyping Recommendations: A Joint Consensus Recommendation of the Association for Molecular Pathology, Clinical Pharmacogenetics Implementation Consortium, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, European Society for Pharmacogenomics and Personalized Therapy, and Pharmacogenomics Knowledgebase. J Mol Diagn 2022; 24:1051-1063. [PMID: 35931343 PMCID: PMC9808500 DOI: 10.1016/j.jmoldx.2022.06.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/04/2022] [Accepted: 06/14/2022] [Indexed: 02/06/2023] Open
Abstract
The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This article provides recommendations for a minimum panel of variant alleles (Tier 1) and an extended panel of variant alleles (Tier 2) that will aid clinical laboratories when designing assays for PGx testing. The Association for Molecular Pathology PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, as well as other technical considerations for PGx testing when developing these recommendations. The ultimate goal of this Working Group is to promote standardization of PGx gene/allele testing across clinical laboratories. This article focuses on clinical TPMT and NUDT15 PGx testing, which may be applied to all thiopurine S-methyltransferase (TPMT) and nudix hydrolase 15 (NUDT15)-related medications. These recommendations are not to be interpreted as prescriptive, but to provide a reference guide.
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Affiliation(s)
- Victoria M Pratt
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana.
| | - Larisa H Cavallari
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida, Gainesville, Florida
| | - Makenzie L Fulmer
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Pathology and ARUP Laboratories, University of Utah School of Medicine, Salt Lake City, Utah
| | - Andrea Gaedigk
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri
| | - Houda Hachad
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Clinical Operations, AccessDx, Houston, Texas
| | - Yuan Ji
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Pathology and ARUP Laboratories, University of Utah School of Medicine, Salt Lake City, Utah
| | - Lisa V Kalman
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Reynold C Ly
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ann M Moyer
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Stuart A Scott
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Pathology, Stanford University, Stanford, California; Clinical Genomics Laboratory, Stanford Health Care, Palo Alto, California
| | - R H N van Schaik
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Clinical Chemistry/International Federation of Clinical Chemistry and Laboratory Medicine Expert Center Pharmacogenetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands; European Society of Pharmacogenomics and Personalized Therapy (ESPT), Milan, Italy; Dutch Pharmacogenetics Working Group (DPWG), The Hague, the Netherlands
| | - Michelle Whirl-Carrillo
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Karen E Weck
- The Pharmacogenomics (PGx) Working Group of the Clinical Practice Committee, Association for Molecular Pathology (AMP), Rockville, Maryland; Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, North Carolina; Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
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20
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Pratt VM, Wang WY, Boone EC, Broeckel U, Cody N, Edelmann L, Gaedigk A, Lynnes TC, Medeiros EB, Moyer AM, Mitchell MW, Scott SA, Starostik P, Turner A, Kalman LV. Characterization of Reference Materials for TPMT and NUDT15: A GeT-RM Collaborative Project. J Mol Diagn 2022; 24:1079-1088. [PMID: 35931342 PMCID: PMC9554816 DOI: 10.1016/j.jmoldx.2022.06.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/09/2022] [Accepted: 06/22/2022] [Indexed: 02/09/2023] Open
Abstract
Pharmacogenetic testing is increasingly provided by clinical and research laboratories; however, only a limited number of quality control and reference materials are currently available for many of the TPMT and NUDT15 variants included in clinical tests. To address this need, the Division of Laboratory Systems, Centers for Disease Control and Prevention-based Genetic Testing Reference Material (GeT-RM) coordination program, in collaboration with members of the pharmacogenetic testing and research communities and the Coriell Institute for Medical Research, has characterized 19 DNA samples derived from Coriell cell lines. DNA samples were distributed to four volunteer testing laboratories for genotyping using a variety of commercially available and laboratory developed tests and/or Sanger sequencing. Of the 12 samples characterized for TPMT, newly identified variants include TPMT∗2, ∗6, ∗12, ∗16, ∗21, ∗24, ∗32, ∗33, and ∗40; for the 7 NUDT15 reference material samples, newly identified variants are NUDT15∗2, ∗3, ∗4, ∗5, ∗6, and ∗9. In addition, a novel haplotype, TPMT∗46, was identified in this study. Preexisting data on an additional 11 Coriell samples, as well as some supplemental testing, were used to create comprehensive reference material panels for TPMT and NUDT15. These publicly available and well-characterized materials can be used to support the quality assurance and quality control programs of clinical laboratories performing clinical pharmacogenetic testing.
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Affiliation(s)
- Victoria M Pratt
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Wendy Y Wang
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri
| | - Erin C Boone
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri
| | - Ulrich Broeckel
- RPRD Diagnostics, Milwaukee, Wisconsin; Department of Pediatrics, Section on Genomic Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Neal Cody
- Sema4, Stamford, Connecticut; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lisa Edelmann
- Sema4, Stamford, Connecticut; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology, and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, Missouri; Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Ty C Lynnes
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Elizabeth B Medeiros
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | - Stuart A Scott
- Department of Pathology, Stanford University, Stanford, California; Clinical Genomics Laboratory, Stanford Healthcare, Palo Alto, California
| | - Petr Starostik
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida College of Medicine, Gainesville, Florida
| | - Amy Turner
- RPRD Diagnostics, Milwaukee, Wisconsin; Department of Pediatrics, Section on Genomic Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Lisa V Kalman
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia.
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21
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Bartos MN, Scott SA, Jabs EW, Naik H. Attitudes on pharmacogenomic results as secondary findings among medical geneticists. Pharmacogenet Genomics 2022; 32:273-280. [PMID: 35916546 DOI: 10.1097/fpc.0000000000000479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Abstract
OBJECTIVES As evidence mounts supporting the utility of pharmacogenomic-guided medication management, incorporating pharmacogenomic genes into secondary finding results from sequencing panels is increasingly under consideration. We studied medical geneticists' attitudes on receiving pharmacogenomic results as secondary finding. METHODS Four focus groups with 16 medical geneticists total were conducted followed by thematic analysis. RESULTS All participants ordered genetic sequencing tests; however, the majority had rarely or never ordered pharmacogenomic tests (10/16) or prescribed medications with established response variability (11/16). In total 81.3% expressed low comfort interpreting pharmacogenomic results without appropriate clinical resources (13/16). The positives of receiving pharmacogenomic results as secondary finding included prevention of adverse drug reactions in adults, grateful information-seeking patients, the ability to rapidly prescribe more effective treatments and appreciation of the recent advances in both pharmacogenomic knowledge and available guidelines. Negatives included laboratory reporting issues, exclusivity of pharmacogenomic results to certain populations, lengthy reports concealing pharmacogenomic results in patient charts and laboratories marketing to individuals without prior pharmacogenomic knowledge or targeting inappropriate populations. The most desirable pharmacogenomic resources included a universal electronic health record clinical decision support tool to assist identifying and implementing pharmacogenomic results, a specialized pharmacist as part of the care team, additional pharmacogenomic training during medical/graduate school, and a succinct interpretation of pharmacogenomic results included on laboratory reports. CONCLUSIONS The majority of participants agreed that adding certain actionable pharmacogenomic genes to the American College of Medical Genetics and Genomics SF list is reasonable; however, this was qualified with a need for additional resources to support implementation.
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Affiliation(s)
- Meghan N Bartos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York
- Department of Genetics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama
| | - Stuart A Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York
- Department of Pathology, Stanford University, Stanford
- Clinical Genomics Laboratory, Stanford Health Care, Palo Alto, California, USA
| | - Ethylin Wang Jabs
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York
| | - Hetanshi Naik
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York
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Koshy L, Vb R, M M, Ben MP, Kishor P, Sudhakaran PR, Abdullakutty J, Venugopal K, Zachariah G, Mohanan PP, Harikrishnan S, G S. Pharmacogenetic variants influence vitamin K anticoagulant dosing in patients with mechanical prosthetic heart valves. Pharmacogenomics 2022; 23:475-485. [PMID: 35608144 DOI: 10.2217/pgs-2022-0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background: Vitamin K antagonists (VKAs) are class I oral anticoagulants that are widely prescribed following surgical heart valve implantation. The objective of this study was to quantify the relative effects of VKORC1, CYP2C9 and CYP4F2 genotypes in predicting VKA dosing. Materials & methods: A total of 506 South Indian patients with mechanical prosthetic heart valves who were prescribed oral VKAs, such as warfarin or acenocoumarol, were genotyped. The discriminatory ability of mutant genotypes to predict dose categories and bleeding events was assessed using regression analysis. Results: The VKORC1 rs9923231, CYP2C9*3 and CYP4F2*3 mutant genotypes significantly influenced VKA-dose requirements and explained 27.47% of the observed dose variation. Conclusion: These results support pharmacogenetic screening for initial VKA dosing among South Indian patients with mechanical prosthetic heart valves.
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Affiliation(s)
- Linda Koshy
- Centre for Advanced Research & Excellence in Heart Failure, Sree Chitra Tirunal Institute for Medical Sciences & Technology, Trivandrum, Kerala, 695011, India
| | - Raghu Vb
- Inter-University Centre for Genomics & Gene Technology, Department of Biotechnology, University of Kerala, Trivandrum, Kerala, 695581, India
| | - Madhuma M
- Centre for Advanced Research & Excellence in Heart Failure, Sree Chitra Tirunal Institute for Medical Sciences & Technology, Trivandrum, Kerala, 695011, India
| | - Midhuna P Ben
- Inter-University Centre for Genomics & Gene Technology, Department of Biotechnology, University of Kerala, Trivandrum, Kerala, 695581, India
| | - Pritam Kishor
- Integrated Science Education & Research Centre, Visva-Bharati, Santineketan, West Bengal, 731235, India
| | - P R Sudhakaran
- Inter-University Centre for Genomics & Gene Technology, Department of Biotechnology, University of Kerala, Trivandrum, Kerala, 695581, India
| | | | - K Venugopal
- Department of Cardiology, Pushpagiri Hospital, Thiruvalla, Pathanamthitta, Kerala, 689101, India
| | - Geevar Zachariah
- Department of Cardiology, Mother Hospital, Thrissur, Kerala, 680012, India
| | - P P Mohanan
- Department of Cardiology, Westfort Hi-Tech Hospital, Thrissur, Kerala, 680002, India
| | - S Harikrishnan
- Department of Cardiology, Sree Chitra Tirunal Institute for Medical Sciences & Technology, Trivandrum, Kerala, 695011, India
| | - Sanjay G
- Department of Cardiology, Sree Chitra Tirunal Institute for Medical Sciences & Technology, Trivandrum, Kerala, 695011, India
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23
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Wen YF, Gaedigk A, Boone EC, Wang WY, Straka RJ. The Identification of Novel CYP2D6 Variants in US Hmong: Results From Genome Sequencing and Clinical Genotyping. Front Pharmacol 2022; 13:867331. [PMID: 35387332 PMCID: PMC8979107 DOI: 10.3389/fphar.2022.867331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 02/21/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: Hmong individuals represent a unique East Asian subpopulation in whom limited information concerning pharmacogenetic variation exists. The objectives of this study were to comprehensively characterize the highly polymorphic CYP2D6 gene in Hmong, estimate allele and phenotype frequencies and to compare results between two testing platforms. Methods: DNA from 48 self-identified Hmong participants were sequenced using a targeted next-generation sequencing (NGS) panel. Star allele calls were made using Astrolabe, manual inspection of NGS variant calls and confirmatory Sanger sequencing. Structural variation was determined by long-range (XL)-PCR and digital droplet PCR (ddPCR). The consensus diplotypes were subsequently translated into phenotype utilizing the activity score system. Clinical grade pharmacogenetic testing was obtained for 12 of the 48 samples enabling an assessment of concordance between the consensus calls and those determined by clinical testing platforms. Results: A total of 13 CYP2D6 alleles were identified. The most common alleles were CYP2D6*10 and its structural arrangements (37.5%, 36/96) and the *5 gene deletion (13.5%, 13/96). Three novel suballeles (*10.007, *36.004, and *75.002) were also identified. Phenotype frequencies were as follows: ultrarapid metabolizers (4.2%, 2/48), normal metabolizers (41.7%, 20/48) and intermediate metabolizers (52.1%, 25/48); none of the 48 participants were predicted to be poor metabolizers. Concordance of diplotype and phenotype calls between the consensus and clinical testing were 66.7 and 50%, respectively. Conclusion: Our study to explore CYP2D6 genotypes in the Hmong population suggests that this subpopulation is unique regarding CYP2D6 allelic variants; also, a higher portion of Hmong participants (50%) are predicted to have an intermediate metabolizer phenotype for CYP2D6 compared to other East Asians which range between 27 and 44%. Results from different testing methods varied considerably. These preliminary findings underscore the importance of thoroughly interrogating unique subpopulations to accurately predict a patient's CYP2D6 metabolizer status.
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Affiliation(s)
- Ya Feng Wen
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Twin Cities, MN, United States
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, MO, United States.,School of Medicine, University of Missouri-Kansas City, Kansas City, MO, United States
| | - Erin C Boone
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, MO, United States
| | - Wendy Y Wang
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Research Institute, Kansas City, MO, United States
| | - Robert J Straka
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Twin Cities, MN, United States
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24
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Tayeh MK, Gaedigk A, Goetz MP, Klein TE, Lyon E, McMillin GA, Rentas S, Shinawi M, Pratt VM, Scott SA. Clinical pharmacogenomic testing and reporting: A technical standard of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2022; 24:759-768. [PMID: 35177334 DOI: 10.1016/j.gim.2021.12.009] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 12/14/2022] Open
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.
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Affiliation(s)
- Marwan K Tayeh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, MO; Department of Pediatrics, UMKC School of Medicine, University of Missouri-Kansas City, Kansas City, MO
| | - Matthew P Goetz
- Department of Pharmacology and Oncology, Mayo Clinic, Rochester, MN
| | - Teri E Klein
- Department of Biomedical Data Science and Department of Medicine, Stanford University, Stanford, CA
| | - Elaine Lyon
- HudsonAlpha Institute for Biotechnology, Huntsville, AL
| | | | - Stefan Rentas
- Department of Pathology, Duke University School of Medicine, Durham, NC
| | - Marwan Shinawi
- Division of Genetics & Genomic Medicine, Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO
| | - Victoria M Pratt
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Stuart A Scott
- Department of Pathology, Stanford University, Stanford, CA; Clinical Genomics Laboratory, Stanford Health Care, Palo Alto, CA
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25
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Klanderman BJ, Koch C, Machini K, Parpattedar SS, Bandyadka S, Lin CF, Hynes E, Lebo MS, Amr SS. Automated Pharmacogenomic Reports for Clinical Genome Sequencing. J Mol Diagn 2022; 24:205-218. [PMID: 35041930 DOI: 10.1016/j.jmoldx.2021.12.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/09/2021] [Accepted: 12/07/2021] [Indexed: 11/26/2022] Open
Abstract
Clinical laboratories offering genome sequencing have the opportunity to return pharmacogenomic findings to patients, providing the added benefit of preemptive testing that could help inform medication selection or dosing throughout the lifespan. Implementation of pharmacogenomic reporting must address several challenges, including inherent limitations in short-read genome sequencing methods, gene and variant selection, standardization of genotype and phenotype nomenclature, and choice of guidelines and drugs to report. An automated pipeline, lmPGX, was developed as an end-to-end solution that produces two versions of a pharmacogenomic report, presenting either Clinical Pharmacogenetics Implementation Consortium or US Food and Drug Administration guidelines for 12 genes. The pipeline was validated for performance using reference samples and pharmacogenetic data from the Genetic Testing Reference Materials Coordination Program. To determine performance and limitations, lmPGX was compared with three additional publicly available pharmacogenomic pipelines. The lmPGX pipeline offers clinical laboratories an opportunity for seamless integration of pharmacogenomic results with genome reporting.
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Affiliation(s)
- Barbara J Klanderman
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Christopher Koch
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Kalotina Machini
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts; Deparment of Pathology, Harvard Medical School, Boston, Massachusetts
| | - Shruti S Parpattedar
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Shruthi Bandyadka
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Chiao-Feng Lin
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
| | - Elizabeth Hynes
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts
| | - Matthew S Lebo
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts; Deparment of Pathology, Harvard Medical School, Boston, Massachusetts; Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.
| | - Sami S Amr
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, Massachusetts; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts; Deparment of Pathology, Harvard Medical School, Boston, Massachusetts.
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26
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Lopes JL, Harris K, Karow MB, Peterson SE, Kluge ML, Kotzer KE, Lopes GS, Larson NB, Bielinski SJ, Scherer SE, Wang L, Weinshilboum RM, Black JL, Moyer AM. Targeted Genotyping in Clinical Pharmacogenomics: What Is Missing? J Mol Diagn 2022; 24:253-261. [PMID: 35041929 PMCID: PMC8961466 DOI: 10.1016/j.jmoldx.2021.11.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 11/09/2021] [Accepted: 11/29/2021] [Indexed: 01/01/2023] Open
Abstract
Clinical pharmacogenomic testing typically uses targeted genotyping, which only detects variants included in the test design and may vary among laboratories. To evaluate the potential patient impact of genotyping compared with sequencing, which can detect common and rare variants, an in silico targeted genotyping panel was developed based on the variants most commonly included in clinical tests and applied to a cohort of 10,030 participants who underwent sequencing for CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, DPYD, SLCO1B1, TPMT, UGT1A1, and VKORC1. The results of in silico targeted genotyping were compared with the clinically reported sequencing results. Of the 10,030 participants, 2780 (28%) had at least one potentially clinically relevant variant/allele identified by sequencing that would not have been detected in a standard targeted genotyping panel. The genes with the largest number of participants with variants only detected by sequencing were SLCO1B1, DPYD, and CYP2D6, which affected 13%, 6.3%, and 3.5% of participants, respectively. DPYD (112 variants) and CYP2D6 (103 variants) had the largest number of unique variants detected only by sequencing. Although targeted genotyping detects most clinically significant pharmacogenomic variants, sequencing-based approaches are necessary to detect rare variants that collectively affect many patients. However, efforts to establish pharmacogenomic variant classification systems and nomenclature to accommodate rare variants will be required to adopt sequencing-based pharmacogenomics.
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Affiliation(s)
- Jaime L. Lopes
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Kimberley Harris
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Mary Beth Karow
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Sandra E. Peterson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Michelle L. Kluge
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Katrina E. Kotzer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Guilherme S. Lopes
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | | | - Steven E. Scherer
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - Richard M. Weinshilboum
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
| | - John L. Black
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Ann M. Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota,Address correspondence to Ann M. Moyer, M.D., Ph.D., Mayo Clinic, 200 First St SW, Rochester, MN 55905.
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27
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Nine-gene pharmacogenomics profile service: The Mayo Clinic experience. THE PHARMACOGENOMICS JOURNAL 2022; 22:69-74. [PMID: 34671112 DOI: 10.1038/s41397-021-00258-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 09/28/2021] [Accepted: 10/06/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE The Pharmacogenomics (PGx) Profile Service was a proof-of-concept project to implement PGx in patient care at Mayo Clinic. METHODS Eighty-two healthy individuals aged 18 and older underwent genotyping of CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, HLA-B*58:01, and VKORC1. A PGx pharmacist was involved in ordering, meeting with patients, interpreting, reviewing, and documenting results. RESULTS Ninety three percent were CYP1A2 rapid metabolizers, 92% CYP3A4 normal metabolizers, and 88% CYP3A5 poor metabolizers; phenotype frequencies for CYP2C19 and CYP2D6 varied. Seventy-three percent had normal functioning SLCO1B1 transporter, 4% carried the HLA-B*58:01 risk variant, and 35% carried VKORC1 and CYP2C9 variants that increased warfarin sensitivity. CONCLUSION Pre-emptive PGx testing offered medication improvement opportunity in 56% of participants for commonly used medications. A collaborative approach involving a PGx pharmacist integrated within a clinical practice with regards to utility of PGx results allowed for implementation of the PGx Profile Service. KEY POINTS The Mayo Clinic PGx (PGx) Profile Service was a proof-of-concept project to utilize PGx testing as another clinical tool to enhance medication selection and decrease serious adverse reactions or medication failures. Over one-half of participants in the pilot using the PGx Profile Service were predicted to benefit from pre-emptive PGx testing to guide pharmacotherapy. PGx pharmacists played a crucial role in the PGx Profile Service by educating participants, identifying medication-gene interactions, and providing evidence-based (CPIC and DPWG) PGx recommendations for past, current, and future medication us.
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28
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The VKORC1 and CYP2C9 gene variants as pharmacogenetic factors in acenocoumarol therapy in Serbian patients - consideration of hypersensitivity and resistance. SRP ARK CELOK LEK 2022. [DOI: 10.2298/sarh211118013r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Introduction/Objective. Coumarin therapy represents one of the best models
for applying pharmacogenetics. The contribution of factors influencing
coumarin therapy can vary significantly between ethnic groups, which
justifies conducting population-specific studies. The aim of this study was
to analyze the influence of the most important genetic factors (VKORC1 and
CYP2C9 genes) that affect coumarin therapy in patients from Serbia.
Methods. A retrospective study involving 207 patients on acenocoumarol
therapy was conducted. Genetic analyses were performed by direct sequencing.
Influence on acenocoumarol dose of variants (VKORC1, CYP2C9*2, CYP2C9*3)
causing hypersensitivity and VKORC1 variants causing resistance to
acenocoumarol were analyzed. Multiple regression analysis was used to design
a mathematical model for predicting individual drug dosage based on
clinical-demographic and genetic data. Results. The study confirmed
significant influence of the analyzed genetic factors on acenocoumarol
maintenance dose. We designed mathematical model for predicting individual
acenocoumarol dose and its unadjusted R2 was 61.8. In the testing cohort,
our model gave R2 value of 42.6 and showed better prediction in comparison
with model given by other authors. In the analyzed patients, nine different
variants in the VKORC1 coding region were found. Among carriers of these
variants 78% were completely resistant, and it was not possible to achieve
therapeutic effect even with high doses of acenocoumarol. Conclusions.
Population-specific model for prediction individual dose of acenocoumarol,
may show advantages over protocols that are used in a generalized manner.
Also, VKORC1 variants which cause coumarin resistance should be considered
when planning therapy.
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29
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Mroz P, Michel S, Allen JD, Meyer T, McGonagle EJ, Carpentier R, Vecchia A, Schlichte A, Bishop JR, Dunnenberger HM, Yohe S, Thyagarajan B, Jacobson PA, Johnson SG. Development and Implementation of In-House Pharmacogenomic Testing Program at a Major Academic Health System. Front Genet 2021; 12:712602. [PMID: 34745204 PMCID: PMC8564018 DOI: 10.3389/fgene.2021.712602] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/16/2021] [Indexed: 12/26/2022] Open
Abstract
Pharmacogenomics (PGx) studies how a person's genes affect the response to medications and is quickly becoming a significant part of precision medicine. The clinical application of PGx principles has consistently been cited as a major opportunity for improving therapeutic outcomes. Several recent studies have demonstrated that most individuals (> 90%) harbor PGx variants that would be clinically actionable if prescribed a medication relevant to that gene. In multiple well-conducted studies, the results of PGx testing have been shown to guide therapy choice and dosing modifications which improve treatment efficacy and reduce the incidence of adverse drug reactions (ADRs). Although the value of PGx testing is evident, its successful implementation in a clinical setting presents a number of challenges to molecular diagnostic laboratories, healthcare systems, providers and patients. Different molecular methods can be applied to identify PGx variants and the design of the assay is therefore extremely important. Once the genotyping results are available the biggest technical challenge lies in turning this complex genetic information into phenotypes and actionable recommendations that a busy clinician can effectively utilize to provide better medical care, in a cost-effective, efficient and reliable manner. In this paper we describe a successful and highly collaborative implementation of the PGx testing program at the University of Minnesota and MHealth Fairview Molecular Diagnostic Laboratory and selected Pharmacies and Clinics. We offer detailed descriptions of the necessary components of the pharmacogenomic testing implementation, the development and technical validation of the in-house SNP based multiplex PCR based assay targeting 20 genes and 48 SNPs as well as a separate CYP2D6 copy number assay along with the process of PGx report design, results of the provider and pharmacists usability studies, and the development of the software tool for genotype-phenotype translation and gene-phenotype-drug CPIC-based recommendations. Finally, we outline the process of developing the clinical workflow that connects the providers with the PGx experts within the Molecular Diagnostic Laboratory and the Pharmacy.
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Affiliation(s)
- Pawel Mroz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Stephen Michel
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Josiah D Allen
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | - Tim Meyer
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
| | - Erin J McGonagle
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | | | | | | | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States.,Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Henry M Dunnenberger
- Mark R Neaman Center for Personalized Medicine Center, NorthShore University HealthSystem, Evanston, IL, United States
| | - Sophia Yohe
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, United States
| | - Pamala A Jacobson
- Department of Experimental and Clinical Pharmacology, University of Minnesota College of Pharmacy, Minneapolis, MN, United States
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, United States
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30
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Abstract
Over the past decade, pharmacogenetic testing has emerged in clinical practice to guide selected cardiovascular therapies. The most common implementation in practice is CYP2C19 genotyping to predict clopidogrel response and assist in selecting antiplatelet therapy after percutaneous coronary intervention. Additional examples include genotyping to guide warfarin dosing and statin prescribing. Increasing evidence exists on outcomes with genotype-guided cardiovascular therapies from multiple randomized controlled trials and observational studies. Pharmacogenetic evidence is accumulating for additional cardiovascular medications. However, data for many of these medications are not yet sufficient to support the use of genotyping for drug prescribing. Ultimately, pharmacogenetics might provide a means to individualize drug regimens for complex diseases such as heart failure, in which the treatment armamentarium includes a growing list of medications shown to reduce morbidity and mortality. However, sophisticated analytical approaches are likely to be necessary to dissect the genetic underpinnings of responses to drug combinations. In this Review, we examine the evidence supporting pharmacogenetic testing in cardiovascular medicine, including that available from several clinical trials. In addition, we describe guidelines that support the use of cardiovascular pharmacogenetics, provide examples of clinical implementation of genotype-guided cardiovascular therapies and discuss opportunities for future growth of the field.
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31
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Pratt VM, Cavallari LH, Del Tredici AL, Gaedigk A, Hachad H, Ji Y, Kalman LV, Ly RC, Moyer AM, Scott SA, van Schaik RHN, Whirl-Carrillo M, Weck KE. Recommendations for Clinical CYP2D6 Genotyping Allele Selection: A Joint Consensus Recommendation of the Association for Molecular Pathology, College of American Pathologists, Dutch Pharmacogenetics Working Group of the Royal Dutch Pharmacists Association, and the European Society for Pharmacogenomics and Personalized Therapy. J Mol Diagn 2021; 23:1047-1064. [PMID: 34118403 DOI: 10.1016/j.jmoldx.2021.05.013] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/11/2021] [Accepted: 05/25/2021] [Indexed: 01/14/2023] Open
Abstract
The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing, and to determine a minimal set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations on a minimal panel of variant alleles (Tier 1) and an extended panel of variant alleles (Tier 2) that will aid clinical laboratories in designing assays for PGx testing. When developing these recommendations, the Association for Molecular Pathology PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, as well as other technical considerations with regard to PGx testing. The ultimate goal of this Working Group is to promote standardization of PGx gene/allele testing across clinical laboratories. This document is focused on clinical CYP2D6 PGx testing that may be applied to all cytochrome P450 2D6-metabolized medications. These recommendations are not meant to be interpreted as prescriptive but to provide a reference guide for clinical laboratories that may be either implementing PGx testing or reviewing and updating their existing platform.
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Affiliation(s)
- Victoria M Pratt
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana.
| | - Larisa H Cavallari
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, Florida
| | - Andria L Del Tredici
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Millennium Health, LLC, San Diego, California
| | - Andrea Gaedigk
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children's Mercy Kansas City, and School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri
| | - Houda Hachad
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; private precision medicine consultancy, Seattle, Washington
| | - Yuan Ji
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and ARUP Laboratories, University of Utah School of Medicine, Salt Lake City, Utah
| | - Lisa V Kalman
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia
| | - Reynold C Ly
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ann M Moyer
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Stuart A Scott
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Stanford University, Stanford, California; Clinical Genomics Program, Stanford Health Care, Palo Alto, California
| | - R H N van Schaik
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Clinical Chemistry/IFCC Expert center Pharmacogenetics, Erasmus MC University Medical Center, Rotterdam, the Netherlands; European Society of Pharmacogenomics and Personalized Therapy
| | - Michelle Whirl-Carrillo
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Biomedical Data Science, Stanford University, Stanford, California
| | - Karen E Weck
- The Pharmacogenomics Working Group of the Clinical Practice Committee, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine and Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
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Pratt VM, Turner A, Broeckel U, Dawson DB, Gaedigk A, Lynnes TC, Medeiros EB, Moyer AM, Requesens D, Vetrini F, Kalman LV. Characterization of Reference Materials with an Association for Molecular Pathology Pharmacogenetics Working Group Tier 2 Status: CYP2C9, CYP2C19, VKORC1, CYP2C Cluster Variant, and GGCX: A GeT-RM Collaborative Project. J Mol Diagn 2021; 23:952-958. [PMID: 34020041 DOI: 10.1016/j.jmoldx.2021.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 03/26/2021] [Accepted: 04/29/2021] [Indexed: 10/21/2022] Open
Abstract
Pharmacogenetic testing is increasingly available from clinical and research laboratories. However, only a limited number of quality control and other reference materials are currently available for many of the variants that are tested. The Association for Molecular Pathology Pharmacogenetic Work Group has published a series of papers recommending alleles for inclusion in clinical testing. Several of the alleles were not considered for tier 1 because of a lack of reference materials. To address this need, the Division of Laboratory Systems, Centers for Disease Control and Prevention-based Genetic Testing Reference Material (GeT-RM) program, in collaboration with members of the pharmacogenetic testing and research communities and the Coriell Institute for Medical Research, has characterized 18 DNA samples derived from Coriell cell lines. DNA samples were distributed to five volunteer testing laboratories for genotyping using three commercially available and laboratory developed tests. Several tier 2 variants, including CYP2C9∗13, CYP2C19∗35, the CYP2C cluster variant (rs12777823), two variants in VKORC1 (rs61742245 and rs72547529) related to warfarin resistance, and two variants in GGCX (rs12714145 and rs11676382) related to clotting factor activation, were identified among these samples. These publicly available materials complement the pharmacogenetic reference materials previously characterized by the GeT-RM program and will support the quality assurance and quality control programs of clinical laboratories that perform pharmacogenetic testing.
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Affiliation(s)
- Victoria M Pratt
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Amy Turner
- RPRD Diagnostics, Milwaukee, Wisconsin; Department of Pediatrics, Section on Genomic Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Ulrich Broeckel
- RPRD Diagnostics, Milwaukee, Wisconsin; Department of Pediatrics, Section on Genomic Pediatrics, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - D Brian Dawson
- Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio; Department of Pathology and Laboratory Medicine, University of Cincinnati, Cincinnati, Ohio; Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Andrea Gaedigk
- Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Ty C Lynnes
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Elizabeth B Medeiros
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Ann M Moyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | | | - Francesco Vetrini
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Lisa V Kalman
- Division of Laboratory Systems, Centers for Disease Control and Prevention, Atlanta, Georgia.
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33
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Loudon E, Scott SA, Rigobello R, Scott ER, Zinberg R, Naik H. Pharmacogenomic education among genetic counseling training programs in North America. J Genet Couns 2021; 30:1500-1508. [PMID: 33882174 DOI: 10.1002/jgc4.1417] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 02/22/2021] [Accepted: 02/25/2021] [Indexed: 11/06/2022]
Abstract
The increasing number of genetic counselors participating directly in clinical pharmacogenomic post-test counseling prompted our evaluation of pharmacogenomic education across genetic counseling training programs in North America. Thirty-one program leadership participants from both the United States (U.S.) and Canada responded to a survey assessing pharmacogenomics education and the role of genetic counselors. Eighty-five percent of respondents agreed pharmacogenomics is currently within the scope of genetic counseling practice, and 96.3% indicated their training programs currently provide education on pharmacogenomics, with the majority reporting < 7 hr of education. Lectures on pharmacogenomics were the most common method for didactics; however, some programs also included practical modalities (e.g., case studies, clinical rotations) and online resources. Barriers to expanding pharmacogenomic education included the constrained timeline of training, and lack of resources and local expertise. Moreover, participants suggested that genetic counselors ideally should be able to order pharmacogenomic tests and counsel patients on pharmacogenomics, including result interpretation, as they believe pharmacogenomics does fall within the scope of practice of genetic counseling. Our novel results also confirm that training program leadership support a pharmacogenomic service delivery model that includes a combined effort between genetic counselors and pharmacists to utilize their synergistic expertise. However, this model likely still necessitates expanding pharmacogenomic didactics in genetic counseling training programs through more practical training and/or by leveraging online pharmacogenomic courses dedicated to supporting clinical implementation.
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Affiliation(s)
- Elizabeth Loudon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stuart A Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Sema4, Stamford, CT, USA.,Clinical Genomics Laboratory, Stanford Health Care, Palo Alto, CA, USA
| | | | - Erick R Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Randi Zinberg
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hetanshi Naik
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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34
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Scott SA, Scott ER, Seki Y, Chen AJ, Wallsten R, Owusu Obeng A, Botton MR, Cody N, Shi H, Zhao G, Brake P, Nicoletti P, Yang Y, Delio M, Shi L, Kornreich R, Schadt EE, Edelmann L. Development and Analytical Validation of a 29 Gene Clinical Pharmacogenetic Genotyping Panel: Multi-Ethnic Allele and Copy Number Variant Detection. Clin Transl Sci 2020; 14:204-213. [PMID: 32931151 PMCID: PMC7877843 DOI: 10.1111/cts.12844] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 06/16/2020] [Indexed: 12/12/2022] Open
Abstract
To develop a novel pharmacogenetic genotyping panel, a multidisciplinary team evaluated available evidence and selected 29 genes implicated in interindividual drug response variability, including 130 sequence variants and additional copy number variants (CNVs). Of the 29 genes, 11 had guidelines published by the Clinical Pharmacogenetics Implementation Consortium. Targeted genotyping and CNV interrogation were accomplished by multiplex single‐base extension using the MassARRAY platform (Agena Biosciences) and multiplex ligation‐dependent probe amplification (MRC Holland), respectively. Analytical validation of the panel was accomplished by a strategic combination of > 500 independent tests performed on 170 unique reference material DNA samples, which included sequence variant and CNV accuracy, reproducibility, and specimen (blood, saliva, and buccal swab) controls. Among the accuracy controls were 32 samples from the 1000 Genomes Project that were selected based on their enrichment of sequence variants included in the pharmacogenetic panel (VarCover.org). Coupled with publicly available samples from the Genetic Testing Reference Materials Coordination Program (GeT‐RM), accuracy validation material was available for the majority (77%) of interrogated sequence variants (100% with average allele frequencies > 0.1%), as well as additional structural alleles with unique copy number signatures (e.g., CYP2D6*5, *13, *36, *68; CYP2B6*29; and CYP2C19*36). Accuracy and reproducibility for both genotyping and copy number were > 99.9%, indicating that the optimized panel platforms were precise and robust. Importantly, multi‐ethnic allele frequencies of the interrogated variants indicate that the vast majority of the general population carries at least one of these clinically relevant pharmacogenetic variants, supporting the implementation of this panel for pharmacogenetic research and/or clinical implementation programs.
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Affiliation(s)
- Stuart A Scott
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erick R Scott
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | - Aniwaa Owusu Obeng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mariana R Botton
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Neal Cody
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | - Paola Nicoletti
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yao Yang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Lisong Shi
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruth Kornreich
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eric E Schadt
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lisa Edelmann
- Sema4, Stamford, Connecticut, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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