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REVIEW PAPER
Personalized medicine in rheumatology
Submission date: 2016-07-22
Final revision date: 2016-08-10
Acceptance date: 2016-08-12
Online publication date: 2016-10-05
Publication date: 2016-08-31
Reumatologia 2016;54(4):177-186
KEYWORDS
TOPICS
ABSTRACT
In the era of the 21st century, rheumatoid arthritis (RA) is still poorly characterized. Rheumatoid
arthritis is a common but heterogeneous disease, not only in the course and clinical symptoms, but also in the clinical response to treatment. Now it is known that early, correct diagnosis and starting treatment with disease-modifying drugs (DMARDs), of which methotrexate (MTX) remains the gold standard in the treatment of RA, is crucial in order to prevent joint destruction, functional disability and an unfavourable disease outcome. Early diagnosis of rheumatoid arthritis is significant in so much as the primary treatment can be started better. Pharmacogenetic and pharmacogenomic studies, which help determine the genetic profile of individual patients, may bring us closer to personalized medicine. Further studies on RA should allow for the identification of disease-specific genes at the stage when their tolerance by the organism is still preserved (before auto-aggression develops).
arthritis is a common but heterogeneous disease, not only in the course and clinical symptoms, but also in the clinical response to treatment. Now it is known that early, correct diagnosis and starting treatment with disease-modifying drugs (DMARDs), of which methotrexate (MTX) remains the gold standard in the treatment of RA, is crucial in order to prevent joint destruction, functional disability and an unfavourable disease outcome. Early diagnosis of rheumatoid arthritis is significant in so much as the primary treatment can be started better. Pharmacogenetic and pharmacogenomic studies, which help determine the genetic profile of individual patients, may bring us closer to personalized medicine. Further studies on RA should allow for the identification of disease-specific genes at the stage when their tolerance by the organism is still preserved (before auto-aggression develops).
REFERENCES (44)
2.
Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med 2001; 7: 201-204.
3.
Ginsburg GS, McCarthy JJ. Personalized medicine: revolutionizing drug Discovery and patient care. Trends in Biotechnology 2001; 19: 491-496.
4.
Hamburg MA, Collins FS. The path to Personalized Medicine. N Engl J Med 2010; 10: 1-4.
6.
Gremese E, Salaffi F, Bosselo SA, et al. Very early rheumatoid arthritis as a predictor of remission: a multicentre real life prospective study. Ann Rheum Dis 2013; 72: 858-862.
7.
Smolen JS, Landewé R, Breedveld FC, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2013 update. Ann Rheum Dis 2014; 73: 492-509.
8.
Burmaster G, Lanas A, Biasucci L, et al. The appropriate use of non-steroidal anti-inflammatory drugs In rheumatic disease: opinions of multidisciplinary European expert panel. Ann Rheum Dis 2011; 70: 818-822.
9.
Smolen JS, Landewé R, Breedveld FC, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs. Ann Rheum Dis 2010; 69: 631-637.
10.
Avińa-Zubieta JA, Abrahamowicz M, De Vera MA, et al. Immediate and past cumulative effects of oral glucocorticoids on the risk of acute myocardial infarction in rheumatoid arthritis: a population-based study. Rheumatology 2013; 52: 68-75.
11.
Whittle SL, Colebatch AN, Buchbinder R, et al. Multinational evidence-based recommendations for pain management by pharmacotherapy in inflammatory arthritis: integrating systematic literature research and expert opinion of a broad panel of rheumatologists in the 3e Initiative. Rheumatology 2012; 51: 1416-1425.
12.
Salliot C, Van der Heijde D. Long term safety of Methotrexate monotherapy in Rheumatoid Arthritis patients: A Systematic Literature Research. Ann Rheum Dis 2009; 68: 1100-1104.
13.
Visser K, Katchamart W, Loza E, et al. Multinational evidence-based recommendations for the use of methotrexate In rheumatic disorders with a focus on rheumatoid arthritis: integrating systematic literature research and expert opinion of a broad international panel of rheumatologists In the 3E. Ann Rheum Dis 2009; 68: 1086-1093.
14.
Szekanecz Z, Mesko B, Poliska SZ, et el. Pharmacogenetics and pharmacogenomics in rheumatology. Immunol Res 2013; 56: 325-333.
15.
Kooloos WM, Huizinga TWJ, Guchelaar HJ, et al. Pharmacogenetics in Treatment of Rheumatoid Arthritis. Current Pharmaceutical Design 2010; 16: 164-175.
16.
Giannopoulou EG, Elemento O, Ivashkiv LB. Use of RNA sequencing to evaluate rheumatic disease patients. Arthritis Res Ther 2015; 17: 167.
17.
Mohan C, Assassi S. Biomarkers in rheumatic diseases: how can they facilitate diagnosis and assessment of disease activity? BMJ 2015; 351: h5079.
18.
Goulielmos GN, Zervou MI, Myrthianou E, et al. Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients. Gene 2016; 583: 90-101.
19.
Zheng W, Rao S. Knowledge-based analysis of genetic associations of rheumatoid arthritis to inform studies searching for pleiotropic genes: a literature review and network analysis. Arthritis Res Ther 2015; 17: 202.
20.
Maranville JC, Di Rienzo A. Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases. Pharmacogenomics 2014; 15: 1931-1940.
21.
Malik F, Ranganathan P. Methotrexate pharmacogenetics in rheumatoid arthritis: a status report. Pharmacogenomics 2013; 14: 305-314.
22.
Gervasini G. Polymorphisms in methotrexate pathways: what is clinically relevant, what is not, and what is promising. Curr Drug Metab 2009; 10: 547-566.
23.
Zhu H, Deng FY, Mo XB, et al. Pharmacogenetics and pharmacogenomics for rheumatoid arthritis responsiveness to methotrexate treatment: the 2013 update. Pharmacogenomics 2014; 15: 551-566.
24.
Xie X, Zhang D, Chen JW, et al. Pharmacogenomics of biological treatment in rheumatoid arthritis. Expert Opin Biol Ther 2014; 14: 157-164.
25.
Breedveld F. TNF antagonists opened the way to personalized medicine in rheumatoid arthritis. Mol Med 2014; 20: 7-9.
26.
Burska AN, Roget K, Blits M, et al. Gene expression analysis in RA: towards personalized medicine. Pharmacogenomics J 2014; 14: 93-106.
27.
Naranbhai V, Fairfax BP, Makino S, et al. Genomic modulators of gene expression in human neutrophils. Nat Commun 2015; 6: 7545.
28.
Walsh AM, Whitaker JW, Huang CC. Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations. Genome Biol 2016; 17: 79.
29.
Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 2016; 48: 481-487.
30.
Kim TH, Choi SJ, Lee YH, et al. Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets. Joint Bone Spine 2014; 81: 325-330.
31.
Sanayama Y, Ikeda K, Saito Y, et al. Prediction of therapeutic responses to tocilizumab in patients with rheumatoid arthritis: biomarkers identified by analysis of gene expression in peripheral blood mononuclear cells using genome-wide DNA microarray. Arthritis Rheum 2014; 66: 1421-1431.
32.
Hurd PJ, Nelson CJ. Advantages of next-generation sequencing versus the microarray in epigenetic research. Brief Funct Genomic Proteomic 2009; 8: 174-183.
33.
Castro-Villegas C, Pérez-Sánchez C, Escudero A, et al. Circulating miRNAs as potential biomarkers of therapy effectiveness in rheumatoid arthritis patients treated with anti-TNF-. Arthritis Res Ther 2015; 17: 49.
34.
Duroux-Richard I, Jorgensen C, Apparailly F. What do microRNAs mean for rheumatoid arthritis? Arthritis Rheum 2012; 64: 11-20.
35.
Vicente R, Noël D, Pers YM, et al. Deregulation and therapeutic potential of microRNAs in arthritic diseases. Nat Rev Rheumatol 2016; 12: 211-220.
36.
Churov AV, Oleinik EK, Knip M. MicroRNAs in rheumatoid arthritis: altered expression and diagnostic potential. Autoimmun Rev 2015; 14: 1029-1037.
37.
Benson RA, Patakas A, McQueenie R, et al. Arthritis in space and time – to boldly go! FEBS Letters 2011; 585: 3640-3648.
38.
Richardson S, Isaacs J. Novel immunotherapies for rheumatoid arthritis. Clin Med 2013; 13: 391-394.
39.
Isaacs JD, Ferraccioli G. The need for personalised medicine for rheumatoid arthritis. Ann Rheum Dis 2011; 70: 4-7.
40.
Miossec P, Verweij CL, Klareskog L, et al. Biomarkers and personalised medicine in rheumatoid arthritis: a proposal for interactions between academia, industry and regulatory bodies. Ann Rheum Dis 2011; 70: 1713-1718.
41.
Verweij CL. Transcript profiling towards personalized medicine in rheumatoid arthritis. Neth J Med 2009; 67: 364-371.
42.
Burska A, Boissinot M, Ponchel F. Cytokines as biomarkers in rheumatoid arthritis. Mediators Inflamm 2014; 2014: 545493.
43.
Smolen JS, Aletaha D. Forget personalised medicine and focus on abating disease activity. Ann Rheum Dis 2013; 72: 3-6.
44.
Horgan D, Jansen M, Leyens L, et al. An index of barriers for the implementation of personalised medicine and pharmacogenomics in Europe. Public Health Genomics 2014; 17: 287-298.
Copyright: © Narodowy Instytut Geriatrii, Reumatologii i Rehabilitacji w Warszawie. This is an Open Access journal, all articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (https://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
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