Literature DB >> 24295560

Pharmacogenomics of biological treatment in rheumatoid arthritis.

Xi Xie1, David Zhang, Jin-wei Chen, Jing Tian, Guang-hui Ling, Fen Li.   

Abstract

INTRODUCTION: Rheumatoid arthritis (RA) demonstrates a high heterogeneity in clinical responses to treatment. Although the efficacy of biological therapy has undoubtedly been established, the response differs considerably between individuals. This variability between individuals has aroused the research for biomarkers predictive of treatment response. Pharmacogenomics underlying individual responses to drugs is rapidly developed and has the potential of realizing the personalized therapy in RA. This review will summarize the pharmacogenomics of biological therapies approved for clinical RA treatment. AREAS COVERED: The pharmacogenomics underlies individual responses to biological drugs in RA. Current studies on pharmacogenomics of biological therapy in RA are presented. EXPERT OPINION: The personalized treatment in RA according to pharmacogenomics is promising, but the available pharmacogenomic data on biological treatment in RA are not adequate and consistent and still require further larger sample studies to corroborate.

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Year:  2013        PMID: 24295560     DOI: 10.1517/14712598.2014.859672

Source DB:  PubMed          Journal:  Expert Opin Biol Ther        ISSN: 1471-2598            Impact factor:   4.388


  3 in total

1.  Genetics: a new interpretation of genetic studies in RA.

Authors:  Michel Neidhart; Emmanuel Karouzakis
Journal:  Nat Rev Rheumatol       Date:  2014-02-25       Impact factor: 20.543

Review 2.  Personalized medicine in rheumatology.

Authors:  Anna Kłak; Agnieszka Paradowska-Gorycka; Brygida Kwiatkowska; Filip Raciborski
Journal:  Reumatologia       Date:  2016-10-05

3.  The Elevated Secreted Immunoglobulin D Enhanced the Activation of Peripheral Blood Mononuclear Cells in Rheumatoid Arthritis.

Authors:  Yujing Wu; Wensheng Chen; Hengshi Chen; Lingling Zhang; Yan Chang; Shangxue Yan; Xing Dai; Yang Ma; Qiong Huang; Wei Wei
Journal:  PLoS One       Date:  2016-01-27       Impact factor: 3.240

  3 in total

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