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Zhang C, Guo W, Cheng Y, Chen W, Yang X, Dai R, Yan M, Li Q. WITHDRAWN: Genetic polymorphisms of pharmacogenomic VIP variants in the Wa population from southwest China. Drug Metab Pharmacokinet 2018. [DOI: 10.1016/j.dmpk.2018.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Jin T, Zhao R, Shi X, He N, He X, Ouyang Y, Wang H, Wang B, Kang L, Yuan D. Genetic polymorphisms study of pharmacogenomic VIP variants in Han ethnic of China's Shaanxi province. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2016; 46:27-35. [PMID: 27414743 DOI: 10.1016/j.etap.2016.06.026] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 04/21/2016] [Accepted: 06/26/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND Multiple factors include genetic and non-genetic interactions induce to different drug response among different individuals. Lots of researches proved that different frequencies of genetic variants exists different ethnic groups. The aim of this study was to screen Han volunteers in Shaanxi for VIP gene polymorphisms. MATERIALS AND METHODS We genotyped 80 Very Important Pharmacogenes (VIP) (selected from the PharmGKB database) in 192 unrelated, healthy Han ethnic adults from Shaanxi, the northwest of China, and then analyzed genotyping data wtih Structure and F-statistics (Fst) analysis. RESULTS We compared our data with 15 other populations (Deng, Kyrgyz, Tajik, Uygur and 11 HapMap populations), and found the frequency distribution of Han population in Shaanxi is most similar with CHB. Also, Structure and Fst showed that Shaanxi Han has a closest genetic background with CHB. CONCLUSIONS Our study have supplemented the Han Chinese data related to pharmacogenomics and illustrated differences in genotypic frequencies of selected VIP variants' among the Han population and 15 other populations.
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Affiliation(s)
- Tianbo Jin
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China; Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; National Engineering Research Center for Miniaturized Detection Systems, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Ruimin Zhao
- Otorhinolaryngological, Head and Neck Surgery Department, School of Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Xugang Shi
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China; Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; National Engineering Research Center for Miniaturized Detection Systems, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Na He
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China; Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China
| | - Xue He
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China; Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China
| | - Yongri Ouyang
- National Engineering Research Center for Miniaturized Detection Systems, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Hong Wang
- National Engineering Research Center for Miniaturized Detection Systems, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Bo Wang
- National Engineering Research Center for Miniaturized Detection Systems, School of Life Sciences, Northwest University, Xi'an, Shaanxi 710069, China
| | - Longli Kang
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China; Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China
| | - Dongya Yuan
- Key Laboratory for Molecular Genetic Mechanisms and Intervention Research on High Altitude Disease of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China; Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang 712082, China; Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, Shaanxi 712082, China.
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3
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Jin T, Shi X, Wang L, Wang H, Feng T, Kang L. Genetic polymorphisms of pharmacogenomic VIP variants in the Mongol of Northwestern China. BMC Genet 2016; 17:70. [PMID: 27233804 PMCID: PMC4884435 DOI: 10.1186/s12863-016-0379-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 05/22/2016] [Indexed: 11/23/2022] Open
Abstract
Background Within a population, the differences of pharmacogenomic variant frequencies may produce diversities in drug efficacy, safety, and the risk associated with adverse drug reactions. With the development of pharmacogenomics, widespread genetic research on drug metabolism has been conducted on major populations, but less is known about minorities. Results In this study, we recruited 100 unrelated, healthy Mongol adults from Xinjiang and genotyped 85 VIP variants from the PharmGKB database. We compared our data with eleven populations listed in 1000 genomes project and HapMap database. We used χ2 tests to identify significantly different loci between these populations. We downloaded SNP allele frequencies from the ALlele FREquency Database to observe the global genetic variation distribution for these specific loci. And then we used Structure software to perform the genetic structure analysis of 12 populations. Conclusions Our results demonstrated that different polymorphic allele frequencies exist between different nationalities,and indicated Mongol is most similar to Chinese populations, followed by JPT. This information on the Mongol population complements the existing pharmacogenomic data and provides a theoretical basis for screening and therapy in the different ethnic groups within Xinjiang.
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Affiliation(s)
- Tianbo Jin
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, 712082, China. .,Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, 712082, Shaanxi, China. .,National Engineering Research Center for Miniaturized Detection Systems, Xi'an, 710069, China. .,School of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, China.
| | - Xugang Shi
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, 712082, China.,Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, 712082, Shaanxi, China
| | - Li Wang
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, 712082, China.,Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, 712082, Shaanxi, China
| | - Huijuan Wang
- National Engineering Research Center for Miniaturized Detection Systems, Xi'an, 710069, China.,School of Life Sciences, Northwest University, Xi'an, Shaanxi, 710069, China
| | - Tian Feng
- National Engineering Research Center for Miniaturized Detection Systems, Xi'an, 710069, China
| | - Longli Kang
- Key Laboratory of High Altitude Environment and Genes Related to Diseases of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, Xianyang, 712082, China. .,Key Laboratory for Basic Life Science Research of Tibet Autonomous Region, School of Medicine, Xizang Minzu University, #6 East Wenhui Road, Xianyang, 712082, Shaanxi, China.
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Mahurkar S, Moldovan M, Suppiah V, Sorosina M, Clarelli F, Liberatore G, Malhotra S, Montalban X, Antigüedad A, Krupa M, Jokubaitis VG, McKay FC, Gatt PN, Fabis-Pedrini MJ, Martinelli V, Comi G, Lechner-Scott J, Kermode AG, Slee M, Taylor BV, Vandenbroeck K, Comabella M, Boneschi FM, King C. Response to interferon-beta treatment in multiple sclerosis patients: a genome-wide association study. THE PHARMACOGENOMICS JOURNAL 2016; 17:312-318. [PMID: 27001119 DOI: 10.1038/tpj.2016.20] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Revised: 11/04/2015] [Accepted: 02/12/2016] [Indexed: 01/23/2023]
Abstract
Up to 50% of multiple sclerosis (MS) patients do not respond to interferon-beta (IFN-β) treatment and determination of response requires lengthy clinical follow-up of up to 2 years. Response predictive genetic markers would significantly improve disease management. We aimed to identify IFN-β treatment response genetic marker(s) by performing a two-stage genome-wide association study (GWAS). The GWAS was carried out using data from 151 Australian MS patients from the ANZgene/WTCCC2 MS susceptibility GWAS (responder (R)=51, intermediate responders=24 and non-responders (NR)=76). Of the single-nucleotide polymorphisms (SNP) that were validated in an independent group of 479 IFN-β-treated MS patients from Australia, Spain and Italy (R=273 and NR=206), eight showed evidence of association with treatment response. Among the replicated associations, the strongest was observed for FHIT (Fragile Histidine Triad; combined P-value 6.74 × 10-6) and followed by variants in GAPVD1 (GTPase activating protein and VPS9 domains 1; combined P-value 5.83 × 10-5) and near ZNF697 (combined P-value 8.15 × 10-5).
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Affiliation(s)
- S Mahurkar
- School of Pharmacy and Medical Sciences and Sansom Institute for Health Research, University of South Australia, Frome Road, Adelaide, South Australia, Australia
| | - M Moldovan
- South Australian Health &Medical Research Institute and Sansom Institute for Health Research, University of South Australia, Adelaide, South Australia, Australia.,Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia
| | - V Suppiah
- School of Pharmacy and Medical Sciences and Sansom Institute for Health Research, University of South Australia, Frome Road, Adelaide, South Australia, Australia
| | - M Sorosina
- Laboratory of Genetics of Complex Neurological Disorders, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - F Clarelli
- Laboratory of Genetics of Complex Neurological Disorders, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - G Liberatore
- Laboratory of Genetics of Complex Neurological Disorders, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy.,Department of Neurology and Neurorehabilitation, San Raffaele Scientific Institute, Milan, Italy
| | - S Malhotra
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Institut de Receca Vall d'Hebron (VHIR), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - X Montalban
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Institut de Receca Vall d'Hebron (VHIR), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - A Antigüedad
- Servicio de Neurología, Basurto Hospital, Bilbao, Spain
| | - M Krupa
- Flinders University and Medical Centre, Adelaide, South Australia, Australia
| | - V G Jokubaitis
- Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Parkville, Victoria, Australia
| | - F C McKay
- Centre for Immunology and Allergy Research, Westmead Millennium Institute, University of Sydney, Sydney, New South Wales, Australia
| | - P N Gatt
- Centre for Immunology and Allergy Research, Westmead Millennium Institute, University of Sydney, Sydney, New South Wales, Australia
| | - M J Fabis-Pedrini
- Western Australian Neuroscience Research Institute, Centre for Neuromuscular and Neurological Disorders, University of WA, Nedlands, Western Australia, Australia
| | - V Martinelli
- Laboratory of Genetics of Complex Neurological Disorders, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - G Comi
- Laboratory of Genetics of Complex Neurological Disorders, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy.,Department of Neurology and Neurorehabilitation, San Raffaele Scientific Institute, Milan, Italy
| | - J Lechner-Scott
- Hunter Medical Research Institute, The University of Newcastle, Newcastle, New South Wales, Australia
| | - A G Kermode
- Western Australian Neuroscience Research Institute, Centre for Neuromuscular and Neurological Disorders, University of WA, Nedlands, Western Australia, Australia.,Institute of Immunology and Infectious Diseases, Murdoch University, Western Australia, Australia
| | - M Slee
- Flinders University and Medical Centre, Adelaide, South Australia, Australia
| | - B V Taylor
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - K Vandenbroeck
- Neurogenomiks Group, Universidad del País Vasco (UPV/EHU), Leioa, Spain.,Achucarro Basque Center for Neuroscience, Zamudio, Spain.,Ikerbasque, Basque Foundation of Science, Bilbao, Spain
| | - M Comabella
- Servei de Neurologia-Neuroimmunologia, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Institut de Receca Vall d'Hebron (VHIR), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - F M Boneschi
- Laboratory of Genetics of Complex Neurological Disorders, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy.,Department of Neurology and Neurorehabilitation, San Raffaele Scientific Institute, Milan, Italy
| | | | - C King
- School of Pharmacy and Medical Sciences and Sansom Institute for Health Research, University of South Australia, Frome Road, Adelaide, South Australia, Australia
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5
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The genome as pharmacopeia: Association of genetic dose with phenotypic response. Biochem Pharmacol 2015; 94:229-40. [DOI: 10.1016/j.bcp.2015.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 02/12/2015] [Accepted: 02/12/2015] [Indexed: 11/21/2022]
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Yunus Z, Liu L, Wang H, Zhang L, Li X, Geng T, Kang L, Jin T, Chen C. Genetic polymorphisms of pharmacogenomic VIP variants in the Kyrgyz population from northwest China. Gene 2013; 529:88-93. [PMID: 23954225 DOI: 10.1016/j.gene.2013.07.078] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Revised: 07/18/2013] [Accepted: 07/23/2013] [Indexed: 11/29/2022]
Abstract
Pharmacogenomic variant information is well known for major human populations; however, this information is less commonly studied in minorities. In the present study, we genotyped 85 very important pharmacogenetic (VIP) variants (selected from the PharmGKB database) in the Kyrgyz population and compared our data with other four major human populations including Han Chinese in Beijing, China (CHB), the Japanese in Tokyo, Japan (JPT), a northern and western Europe population (CEU), and the Yoruba in Ibadan, Nigeria (YRI). There were 13, 12 and 16 of the selected VIP variant genotype frequencies in the Kyrgyz which differed from those of the CHB, JPT and CEU, respectively (p<0.005). In the YRI, there were 32 different variants, compared to the Kyrgyz (p<0.005). Genotype frequencies of ADH1B, AHR, CYP3A5, PTGS2, VDR, and VKORC1 in the Kyrgyz differed widely from those in the four populations. Haplotype analyses also showed differences among the Kyrgyz and the other four populations. Our results complement the information provided by the database of pharmacogenomics on Kyrgyz. We provide a theoretical basis for safer drug administration and individualized treatment plans for the Kyrgyz. We also provide a template for the study of pharmacogenomics in various ethnic minority groups in China.
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Affiliation(s)
- Zulfiya Yunus
- School of Life Sciences, Northwest University, Xi'an 710069, China; National Engineering Research Center for Miniaturized Detection Systems, Xi'an 710069, China; College of Life Sciences and Technology, Xinjiang University, Urumqi 830046, China
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Godman B, Finlayson AE, Cheema PK, Zebedin-Brandl E, Gutiérrez-Ibarluzea I, Jones J, Malmström RE, Asola E, Baumgärtel C, Bennie M, Bishop I, Bucsics A, Campbell S, Diogene E, Ferrario A, Fürst J, Garuoliene K, Gomes M, Harris K, Haycox A, Herholz H, Hviding K, Jan S, Kalaba M, Kvalheim C, Laius O, Lööv SA, Malinowska K, Martin A, McCullagh L, Nilsson F, Paterson K, Schwabe U, Selke G, Sermet C, Simoens S, Tomek D, Vlahovic-Palcevski V, Voncina L, Wladysiuk M, van Woerkom M, Wong-Rieger D, Zara C, Ali R, Gustafsson LL. Personalizing health care: feasibility and future implications. BMC Med 2013; 11:179. [PMID: 23941275 PMCID: PMC3750765 DOI: 10.1186/1741-7015-11-179] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2013] [Accepted: 07/09/2013] [Indexed: 01/11/2023] Open
Abstract
Considerable variety in how patients respond to treatments, driven by differences in their geno- and/ or phenotypes, calls for a more tailored approach. This is already happening, and will accelerate with developments in personalized medicine. However, its promise has not always translated into improvements in patient care due to the complexities involved. There are also concerns that advice for tests has been reversed, current tests can be costly, there is fragmentation of funding of care, and companies may seek high prices for new targeted drugs. There is a need to integrate current knowledge from a payer's perspective to provide future guidance. Multiple findings including general considerations; influence of pharmacogenomics on response and toxicity of drug therapies; value of biomarker tests; limitations and costs of tests; and potentially high acquisition costs of new targeted therapies help to give guidance on potential ways forward for all stakeholder groups. Overall, personalized medicine has the potential to revolutionize care. However, current challenges and concerns need to be addressed to enhance its uptake and funding to benefit patients.
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Affiliation(s)
- Brian Godman
- Department of Laboratory Medicine, Division of Clinical Pharmacology, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86, Stockholm, Sweden
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
- National Institute for Science and Technology on Innovation on Neglected Diseases, Centre for Technological Development in Health, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
| | - Alexander E Finlayson
- King’s Centre for Global Health, Global Health Offices, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK
| | - Parneet K Cheema
- Sunnybrook Odette Cancer Centre, 2075 Bayview Avenue, Toronto, ON, Canada
| | - Eva Zebedin-Brandl
- Hauptverband der Österreichischen Sozialversicherungsträger, 21 Kundmanngasse, AT-1031, Wien, Austria
- Institute of Pharmacology and Toxicology, Department for Biomedical Sciences, University of Vienna, Vienna, Austria
| | - Inaki Gutiérrez-Ibarluzea
- Osteba Basque Office for HTA, Ministry of Health of the Basque Country, Donostia-San Sebastian 1, 01010, Vitoria-Gasteiz, Basque Country, Spain
| | - Jan Jones
- NHS Tayside, Kings Cross, Dundee DD3 8EA, UK
| | - Rickard E Malmström
- Department of Medicine, Clinical Pharmacology Unit, Karolinska Institutet, Karolinska University Hospital Solna, SE-17176, Stockholm, Sweden
| | - Elina Asola
- Pharmaceutical Pricing Board, Ministry of Social Affairs and Health, PO Box 33, FI-00023 Government, Helsinki, Finland
| | | | - Marion Bennie
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
- Public Health & Intelligence Strategic Business Unit, NHS National Services Scotland, Edinburgh EH12 9EB, UK
| | - Iain Bishop
- Public Health & Intelligence Strategic Business Unit, NHS National Services Scotland, Edinburgh EH12 9EB, UK
| | - Anna Bucsics
- Hauptverband der Österreichischen Sozialversicherungsträger, 21 Kundmanngasse, AT-1031, Wien, Austria
| | - Stephen Campbell
- Centre for Primary Care, Institute of Population Health, University of Manchester, Manchester M13 9PL, UK
- NIHR Greater Manchester Primary Care Patient Safety Translational Research Centre, Manchester M13 9PL, UK
| | - Eduardo Diogene
- Unitat de Coordinació i Estratègia del Medicament, Direcció Adjunta d'Afers Assistencials, Catalan Institute of Health, Barcelona, Spain
| | - Alessandra Ferrario
- London School of Economics and Political Science, LSE Health, Houghton Street, London WC2A 2AE, UK
| | - Jurij Fürst
- Health Insurance Institute, Miklosiceva 24, SI-1507, Ljubljana, Slovenia
| | - Kristina Garuoliene
- Medicines Reimbursement Department, National Health Insurance Fund, Europas a. 1, Vilnius, Lithuania
| | - Miguel Gomes
- INFARMED, Parque da Saúde de Lisboa, Avenida do Brasil 53, 1749-004, Lisbon, Portugal
| | - Katharine Harris
- King’s Centre for Global Health, Global Health Offices, Weston Education Centre, Cutcombe Road, London SE5 9RJ, UK
| | - Alan Haycox
- Liverpool Health Economics Centre, University of Liverpool, Chatham Street, Liverpool L69 7ZH, UK
| | - Harald Herholz
- Kassenärztliche Vereinigung Hessen, 15 Georg Voigt Strasse, DE-60325, Frankfurt am Main, Germany
| | - Krystyna Hviding
- Norwegian Medicines Agency, Sven Oftedals vei 8, 0950, Oslo, Norway
| | - Saira Jan
- Clinical Programs, Pharmacy Management, Horizon Blue Cross Blue Shield of New Jersey, Newark, USA
| | - Marija Kalaba
- Republic Institute for Health Insurance, Jovana Marinovica 2, 11000, Belgrade, Serbia
| | | | - Ott Laius
- State Agency of Medicines, Nooruse 1, 50411, Tartu, Estonia
| | - Sven-Ake Lööv
- Department of Healthcare Development, Stockholm County Council, Stockholm, Sweden
| | - Kamila Malinowska
- HTA Consulting, Starowiślna Street, 17/3, 31-038, Cracow, Poland
- Public Health School, The Medical Centre of Postgraduate Education, Kleczewska Street, 61/63, 01-813, Warsaw, Poland
| | - Andrew Martin
- NHS Greater Manchester Commissioning Support Unit, Salford, Manchester, UK
| | - Laura McCullagh
- National Centre for Pharmacoeconomics, St James's Hospital, Dublin 8, Ireland
| | - Fredrik Nilsson
- Dental and Pharmaceuticals Benefits Agency (TLV), PO Box 22520 Flemingatan 7, SE-104, Stockholm, Sweden
| | | | - Ulrich Schwabe
- University of Heidelberg, Institute of Pharmacology, D-69120, Heidelberg, Germany
| | - Gisbert Selke
- Wissenschaftliches Institut der AOK (WIDO), Rosenthaler Straße 31, 10178, Berlin, Germany
| | | | - Steven Simoens
- KU Leuven Department of Pharmaceutical and Pharmacological Sciences, 3000, Leuven, Belgium
| | - Dominik Tomek
- Faculty of Pharmacy, Comenius University and Faculty of Medicine, Slovak Medical University, Bratislava, Slovakia
| | - Vera Vlahovic-Palcevski
- Unit for Clinical Pharmacology, University Hospital Rijeka, Krešimirova 42, 51000, Rijeka, Croatia
| | - Luka Voncina
- Ministry of Health, Republic of Croatia, Ksaver 200a, Zagreb, Croatia
| | | | - Menno van Woerkom
- Dutch Institute for Rational Use of Medicines, 3527 GV, Utrecht, Netherlands
| | - Durhane Wong-Rieger
- Institute for Optimizing Health Outcomes, 151 Bloor Street West, Suite 600, Toronto, ON M5S 1S4, Canada
| | - Corrine Zara
- Barcelona Health Region, Catalan Health Service, Esteve Terrades 30, 08023, Barcelona, Spain
| | - Raghib Ali
- INDOX Cancer Research Network, Cancer Epidemiology Unit, University of Oxford, Oxford, UK
| | - Lars L Gustafsson
- Department of Laboratory Medicine, Division of Clinical Pharmacology, Karolinska Institutet, Karolinska University Hospital Huddinge, SE-141 86, Stockholm, Sweden
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Xu R, Wang Q. A semi-supervised approach to extract pharmacogenomics-specific drug-gene pairs from biomedical literature for personalized medicine. J Biomed Inform 2013; 46:585-93. [PMID: 23570835 DOI: 10.1016/j.jbi.2013.04.001] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Revised: 03/15/2013] [Accepted: 04/01/2013] [Indexed: 12/14/2022]
Abstract
Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx) is to identify genetic variants that may affect drug efficacy and toxicity. The availability of a comprehensive and accurate PGx-specific drug-gene relationship knowledge base is important for personalized medicine. However, building a large-scale PGx-specific drug-gene knowledge base is a difficult task. In this study, we developed a bootstrapping, semi-supervised learning approach to iteratively extract and rank drug-gene pairs according to their relevance to drug pharmacogenomics. Starting with a single PGx-specific seed pair and 20 million MEDLINE abstracts, the extraction algorithm achieved a precision of 0.219, recall of 0.368 and F1 of 0.274 after two iterations, a significant improvement over the results of using non-PGx-specific seeds (precision: 0.011, recall: 0.018, and F1: 0.014) or co-occurrence (precision: 0.015, recall: 1.000, and F1: 0.030). After the extraction step, the ranking algorithm further improved the precision from 0.219 to 0.561 for top ranked pairs. By comparing to a dictionary-based approach with PGx-specific gene lexicon as input, we showed that the bootstrapping approach has better performance in terms of both precision and F1 (precision: 0.251 vs. 0.152, recall: 0.396 vs. 0.856 and F1: 0.292 vs. 0.254). By integrative analysis using a large drug adverse event database, we have shown that the extracted drug-gene pairs strongly correlate with drug adverse events. In conclusion, we developed a novel semi-supervised bootstrapping approach for effective PGx-specific drug-gene pair extraction from large number of MEDLINE articles with minimal human input.
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Affiliation(s)
- Rong Xu
- Medical Informatics Division, Case Western Reserve University, OH, USA.
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Moen M, Lamba J. Assessment of healthcare students' views on pharmacogenomics at the University of Minnesota. Pharmacogenomics 2013; 13:1537-45. [PMID: 23057552 DOI: 10.2217/pgs.12.139] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM The aim was to determine if the University of Minnesota (MN, USA) healthcare students' perceived value of pharmacogenomics matches their self-observed comfort and education in pharmacogenomics. MATERIALS & METHODS A 24-question, anonymous, online survey was distributed to all pharmacy, nursing and medical students enrolled at the University of Minnesota. RESULTS Among healthcare students, 70.6% agreed or strongly agreed that pharmacogenomics should be an important part of their curriculum; however, only 11.1% agreed or strongly agreed that it actually is. Only 29.7% of students reported taking a genetics course that specifically addressed the applications of genetics in pharmacy, and those students were more likely to feel comfortable interpreting information from a pharmacogenetics test, answering questions on pharmacogenomics, educating patients on risks and benefits of testing, and were comfortable that they knew which medications required pharmacogenomics testing. CONCLUSION Healthcare students consider pharmacogenomics to be an important area of clinical practice; yet generally express it has not been an important part of their curriculum. Education emphasizing medical applications of pharmacogenomics can increase student comfort level in pharmacogenomics practice.
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Affiliation(s)
- Meg Moen
- College of Pharmacy, University of Minnesota, MN, USA
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10
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Waldman SA, Terzic A. Translational medicine individualizes healthcare discovery, development and delivery. Biomark Med 2013; 7:1-3. [DOI: 10.2217/bmm.13.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Affiliation(s)
- Scott A Waldman
- Jefferson Institute for Individualized Medicine, Department of Pharmacology & Experimental Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Thomas Jefferson University, Philadelphia, PA, USA
| | - Andre Terzic
- Mayo Clinic Center for Regenerative Medicine, Divisions of Cardiovascular Diseases & Clinical Pharmacology, Departments of Medicine, Molecular Pharmacology & Experimental Therapeutics & Medical Genetics, Mayo Clinic, Rochester, MN, USA
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11
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Gurwitz D. High-Quality Phenomics are Crucial for Informative Omics Studies. Drug Dev Res 2012. [DOI: 10.1002/ddr.21025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- David Gurwitz
- Department of Human Molecular Genetics and Biochemistry; Sackler Faculty of Medicine; Tel-Aviv University; Tel-Aviv; 69978; Israel
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12
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Abstract
Early genome-wide association studies (GWAS) using relatively small samples have identified both rare and common genetic variants with large impact on severe adverse drug reactions, dosing, and efficacy. Here we outline the challenges and recent successes of the GWAS approach in disease genetics and the ways in which these can be applied to pharmacogenomics for biological discovery, determination of heritability, and personalized treatment. We highlight that the genetic architecture of drug efficacy reflects a complex trait yet that of adverse drug reactions more closely mirrors the architecture of Mendelian diseases and how this difference affects future study design. Given that multiple layers of biological data are increasingly available on large samples from biorepositories linked to electronic medical records, GWAS will remain a key component of the systems biology approach to uncovering small to moderate genetic determinants of drug response; these discoveries should move us closer to a personalized approach to health care.
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Affiliation(s)
- Kaixin Zhou
- Medical Research Institute, University of Dundee, Scotland, United Kingdom DD1 9SY.
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13
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Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM, Basford MA, Cowan JD, Xu H, Ramirez AH, Crawford DC, Ritchie MD, Peterson JF, Masys DR, Wilke RA, Roden DM. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Clin Pharmacol Ther 2012; 92:235-42. [PMID: 22739144 DOI: 10.1038/clpt.2012.66] [Citation(s) in RCA: 154] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Routine integration of genotype data into drug decision making could improve patient safety, particularly if many relevant genetic variants can be assayed simultaneously before prescribing the target drug. The frequency of opportunities for pharmacogenetic prescribing and the potential adverse events (AEs) mitigated are unknown. We examined the frequency with which 56 medications with known outcomes influenced by variant alleles were prescribed in a cohort of 52,942 medical home patients at Vanderbilt University Medical Center (VUMC). Within a 5-year window, we estimated that 64.8% (95% confidence interval (CI): 64.4-65.2%) of individuals were exposed to at least one medication with an established pharmacogenetic association. Using previously published results for six medications with severe, well-characterized, genetically linked AEs, we estimated that 383 events (95% CI, 212-552) could have been prevented with an effective preemptive genotyping program. Our results suggest that multiplexed, preemptive genotyping may represent an efficient alternative approach to current single-use ("reactive") methods and may also improve safety.
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Affiliation(s)
- J S Schildcrout
- Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA
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14
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Clinical Pharmacology & Therapeutics 2011: Year in Review. Clin Pharmacol Ther 2012. [DOI: 10.1038/clpt.2011.353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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15
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Affiliation(s)
- Scott A Waldman
- Department of Pharmacology and Experimental Therapeutics, Division of Clinical Pharmacology, Department of Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
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16
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A common 5'-UTR variant in MATE2-K is associated with poor response to metformin. Clin Pharmacol Ther 2011; 90:674-84. [PMID: 21956618 DOI: 10.1038/clpt.2011.165] [Citation(s) in RCA: 108] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Multidrug and toxin extrusion 2 (MATE2-K (SLC47A2)), a polyspecific organic cation exporter, facilitates the renal elimination of the antidiabetes drug metformin. In this study, we characterized genetic variants of MATE2-K, determined their association with metformin response, and elucidated their impact by means of a comparative protein structure model. Four nonsynonymous variants and four variants in the MATE2-K basal promoter region were identified from ethnically diverse populations. Two nonsynonymous variants-c.485C>T and c.1177G>A-were shown to be associated with significantly lower metformin uptake and reduction in protein expression levels. MATE2-K basal promoter haplotypes containing the most common variant, g.-130G>A (>26% allele frequency), were associated with a significant increase in luciferase activities and reduced binding to the transcriptional repressor myeloid zinc finger 1 (MZF-1). Patients with diabetes who were homozygous for g.-130A had a significantly poorer response to metformin treatment, assessed as relative change in glycated hemoglobin (HbA1c) (-0.027 (-0.076, 0.033)), as compared with carriers of the reference allele, g.-130G (-0.15 (-0.17, -0.13)) (P=0.002). Our study showed that MATE2-K plays a role in the antidiabetes response to metformin.
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Pirmohamed M. Pharmacogenetics: past, present and future. Drug Discov Today 2011; 16:852-61. [PMID: 21884816 DOI: 10.1016/j.drudis.2011.08.006] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2011] [Accepted: 08/16/2011] [Indexed: 12/15/2022]
Abstract
The subject area of pharmacogenetics, also known as pharmacogenomics, has a long history. Research in this area has led to fundamental discoveries, which have helped our understanding of the reasons why individuals differ in the way they handle drugs, and ultimately in the way they respond to drugs, either in terms of efficacy or toxicity. However, not much of this knowledge has been translated into clinical practice, most drug-gene associations that have some evidence of clinical validity have not progressed to clinical settings. Advances in genomics since 2000, including the ready availability of data on the variability of the human genome, have provided us with unprecedented opportunities to understand variability in drug responses, and the opportunity to incorporate this into patient care. This is only likely to occur with a systematic approach that evaluates and overcomes the different translational gaps in taking a biomarker from discovery to clinical practice. In this article, I explore the history of pharmacogenetics, appraise the current state of research in this area, and finish off with suggestions for progressing in the field in the future.
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Affiliation(s)
- Munir Pirmohamed
- The Wolfson Centre for Personalised Medicine, Department of Pharmacology, University of Liverpool, 1-5 Brownlow Street, Liverpool L693GL, UK.
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