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PLoS One. 2016 Sep 16;11(9):e0162866. doi: 10.1371/journal.pone.0162866. eCollection 2016.

A European Spectrum of Pharmacogenomic Biomarkers: Implications for Clinical Pharmacogenomics.

Author information

1
Erasmus University Medical Center, Faculty of Medicine, Department of Bioinformatics, Rotterdam, the Netherlands.
2
University of Malta, Faculty of Medicine and Surgery, Department of Physiology and Biochemistry, Msida, Malta.
3
University of Patras School of Health Sciences, Department of Pharmacy, Patras, Greece.
4
Center for Proteomic and Genomic Research, Observatory, Cape Town, South Africa.
5
King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
6
University of Debrecen, Debrecen, Hungary.
7
Boğaziçi University, Istanbul, Turkey.
8
University of Kiel, Institute for Experimental and Clinical Pharmacology, Kiel, Germany.
9
University of Malta, Department of Applied Biomedical Science, Faculty of Health Sciences, Msida, Malta.
10
University of Rome "Tor Vergata", Department of Biomedicine and Prevention, Rome, Italy.
11
University Hospital Centre, Zagreb, Croatia.
12
Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland.
13
University of Santiago de Compostela, Santiago, Spain.
14
University of Cyprus, Molecular Medicine Research Center, Department of Biological Sciences, Nicosia, Cyprus.
15
University of Ljubljana Faculty of Medicine, Ljubljana, Slovenia.
16
University of Malta, Faculty of Medicine, Department of Surgery, Msida, Malta.
17
Department of Human and Medical Genetics, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
18
Comenius University, Faculty of Natural Sciences, Bratislava, Slovakia.
19
Center for Molecular Medicine, Slovak Academy of Sciences, Bratislava, Slovakia.
20
Institute of Biochemistry and Genetics, Ufa Scientific Center, Russian Academy of Sciences, Ufa, Russia.
21
Department of Genetics and Fundamental Medicine, Bashkir State University, Ufa, Russia.
22
University of Athens, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Athens, Greece.
23
Charles University, 2nd Faculty of Medicine and University Hospital Motol, Prague, Czech Republic.
24
Institute of Hereditary Pathology, Ukrainian National Academy of Medical Sciences, Lviv, Ukraine.
25
Erasmus University Medical Center, Department of Clinical Chemistry, Rotterdam, the Netherlands.
26
The Golden Helix Foundation, London, United Kingdom.
27
Institute of Molecular Genetics and Genetic Engineering University of Belgrade, Laboratory of Molecular Biomedicine, Belgrade, Serbia.
28
University of Turin School of Medicine, Turin, Italy.
29
University of Zagreb School of Medicine, Zagreb, Croatia.
30
University of Cagliari, Department of Biomedical Sciences, Cagliari, Italy.
31
RIKEN Institute, Center for Genomic Medicine, Laboratory for International Alliance, Yokohama, Japan.
32
North Carolina State University, Department of Statistics, Raleigh, NC, United States of America.
33
Moffitt Cancer Center, Tampa, FL, United States of America.

Abstract

Pharmacogenomics aims to correlate inter-individual differences of drug efficacy and/or toxicity with the underlying genetic composition, particularly in genes encoding for protein factors and enzymes involved in drug metabolism and transport. In several European populations, particularly in countries with lower income, information related to the prevalence of pharmacogenomic biomarkers is incomplete or lacking. Here, we have implemented the microattribution approach to assess the pharmacogenomic biomarkers allelic spectrum in 18 European populations, mostly from developing European countries, by analyzing 1,931 pharmacogenomics biomarkers in 231 genes. Our data show significant inter-population pharmacogenomic biomarker allele frequency differences, particularly in 7 clinically actionable pharmacogenomic biomarkers in 7 European populations, affecting drug efficacy and/or toxicity of 51 medication treatment modalities. These data also reflect on the differences observed in the prevalence of high-risk genotypes in these populations, as far as common markers in the CYP2C9, CYP2C19, CYP3A5, VKORC1, SLCO1B1 and TPMT pharmacogenes are concerned. Also, our data demonstrate notable differences in predicted genotype-based warfarin dosing among these populations. Our findings can be exploited not only to develop guidelines for medical prioritization, but most importantly to facilitate integration of pharmacogenomics and to support pre-emptive pharmacogenomic testing. This may subsequently contribute towards significant cost-savings in the overall healthcare expenditure in the participating countries, where pharmacogenomics implementation proves to be cost-effective.

PMID:
27636550
PMCID:
PMC5026342
DOI:
10.1371/journal.pone.0162866
[Indexed for MEDLINE]
Free PMC Article

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