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Tippenhauer K, Philips M, Largiadèr C, Sariyar M. Using the PharmCAT tool for Pharmacogenetic clinical decision support. Brief Bioinform 2023; 25:bbad452. [PMID: 38055839 PMCID: PMC10699732 DOI: 10.1093/bib/bbad452] [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: 08/30/2023] [Revised: 10/25/2023] [Accepted: 11/11/2023] [Indexed: 12/08/2023] Open
Abstract
Here, we will provide our insights into the usage of PharmCAT as part of a pharmacogenetic clinical decision support pipeline, which addresses the challenges in mapping clinical dosing guidelines to variants to be extracted from genetic datasets. After a general outline of pharmacogenetics, we describe some features of PharmCAT and how we integrated it into a pharmacogenetic clinical decision support system within a clinical information system. We conclude with promising developments regarding future PharmCAT releases.
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Affiliation(s)
- Kevin Tippenhauer
- Bern University of Applied Sciences, Quellgasse 21, 2500 Biel, Switzerland
| | - Marwin Philips
- Bern University of Applied Sciences, Quellgasse 21, 2500 Biel, Switzerland
| | - Carlo Largiadèr
- Department of Clinical Chemistry at the Bern University Hospital, Bern, Switzerland
| | - Murat Sariyar
- Bern University of Applied Sciences, Quellgasse 21, 2500 Biel, Switzerland
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Li B, Sangkuhl K, Whaley R, Woon M, Keat K, Whirl-Carrillo M, Ritchie MD, Klein TE. Frequencies of pharmacogenomic alleles across biogeographic groups in a large-scale biobank. Am J Hum Genet 2023; 110:1628-1647. [PMID: 37757824 PMCID: PMC10577080 DOI: 10.1016/j.ajhg.2023.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 09/29/2023] Open
Abstract
Pharmacogenomics (PGx) is an integral part of precision medicine and contributes to the maximization of drug efficacy and reduction of adverse drug event risk. Accurate information on PGx allele frequencies improves the implementation of PGx. Nonetheless, curating such information from published allele data is time and resource intensive. The limited number of allelic variants in most studies leads to an underestimation of certain alleles. We applied the Pharmacogenomics Clinical Annotation Tool (PharmCAT) on an integrated 200K UK Biobank genetic dataset (N = 200,044). Based on PharmCAT results, we estimated PGx frequencies (alleles, diplotypes, phenotypes, and activity scores) for 17 pharmacogenes in five biogeographic groups: European, Central/South Asian, East Asian, Afro-Caribbean, and Sub-Saharan African. PGx frequencies were distinct for each biogeographic group. Even biogeographic groups with similar proportions of phenotypes were driven by different sets of dominant PGx alleles. PharmCAT also identified "no-function" alleles that were rare or seldom tested in certain groups by previous studies, e.g., SLCO1B1∗31 in the Afro-Caribbean (3.0%) and Sub-Saharan African (3.9%) groups. Estimated PGx frequencies are disseminated via the PharmGKB (The Pharmacogenomics Knowledgebase: www.pharmgkb.org). We demonstrate that genetic biobanks such as the UK Biobank are a robust resource for estimating PGx frequencies. Improving our understanding of PGx allele and phenotype frequencies provides guidance for future PGx studies and clinical genetic test panel design, and better serves individuals from wider biogeographic backgrounds.
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Affiliation(s)
- Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Katrin Sangkuhl
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Ryan Whaley
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Mark Woon
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Karl Keat
- Genomics and Computational Biology PhD Program, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA; Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Genetics (by courtesy), Stanford University, Stanford, CA 94305, USA; Department of Medicine (BMIR), Stanford University, Stanford, CA 94305, USA.
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Sadee W, Wang D, Hartmann K, Toland AE. Pharmacogenomics: Driving Personalized Medicine. Pharmacol Rev 2023; 75:789-814. [PMID: 36927888 PMCID: PMC10289244 DOI: 10.1124/pharmrev.122.000810] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.
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Affiliation(s)
- Wolfgang Sadee
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Danxin Wang
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Katherine Hartmann
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
| | - Amanda Ewart Toland
- Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus Ohio (W.S., A.E.T.); Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, Florida (D.W.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania (K.H.); Department of Bioengineering and Therapeutic Sciences, Schools of Pharmacy and Medicine, University of California San Francisco, San Francisco, California (W.S.); and Aether Therapeutics, Austin, Texas (W.S.)
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