Skip to main content

Abstract

Rheumatoid arthritis is a complex systemic autoimmune disease that is characterized by chronic inflammatory polyarthritis, extra-articular features, and autoantibody formation. Although a targeted therapeutic approach using disease-modifying rheumatic drugs has markedly improved overall patient outcomes, there remain significant delays in accomplishing low disease activity in many patients. Reducing the numbers of patients needed for clinical trials is essential to the future of rheumatoid arthritis medical product development programs. Integration of biomarkers into clinical trials for rheumatoid arthritis may be helpful for early disease detection, patient stratification, and treatment response assessment. This goal has not yet been realized but can be achievable with good basic and applied research, systematic data collection, and data systems that can be used to integrate and share data. Herein, we explore what is currently known regarding biomarkers for rheumatoid arthritis and discuss issues to be addressed as biomarkers are sought for future development programs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aleksey M, Natalya L, Polina O, Peter S, Christian H, Juliane C, Bogdanos DP, Lapin SV, Dirk R (2014) Anti-hnRNP B1 (RA33) autoantibodies are associated with the clinical phenotype in Russian patients with rheumatoid arthritis and systemic sclerosis. J Immunol Res 2014:516593. doi: 10.1155/2014/516593

  2. Aletaha D, Neogi T, Silman AJ et al (2010) Rheumatoid arthritis classification criteria. An American College of Rheumatology/European league against rheumatism collaborative initiative. Arthritis Rheum 62(9):2569–2581

    Article  PubMed  Google Scholar 

  3. Allinson J, Brooks S (2004) Biomarkers in drug development – a CRO perspective. Curr Sep 21(1):15–19

    Google Scholar 

  4. Al-Mughales JA (2015) Immunodiagnostic significance of anti-RA33 autoantibodies in Saudi patients with rheumatoid arthritis. J Immunol Res 2015:604305. doi: 10.1155/2015/604305

  5. Biomarkers Definition Working Group (2001) Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69:89–95

    Article  Google Scholar 

  6. Cameron HJ, Williams BO (1996) Clinical trials in the elderly should we do more? Drugs Aging 9:307–310

    Article  CAS  PubMed  Google Scholar 

  7. Casteleyn L, Dumez B, Jamers A, Van Damme K. Ethics and data protection in human biomarker studies. In: Environmental cancer risk, nutrition and individual susceptibility. ECNIS, 2010. Published by Nofer Institute of Occupational Medicine, Lodz, Poland. Website: http://www.ecnis.org/images/stories/ecnis/documents/reports/Ethics_and_data_protection/ethicsand_data_protection.pdf

  8. Cheng Y, Chen Y, Sun X, Li Y, Huang C, Deng H, Li Z (2014) Identification of potential serum biomarkers for RA by high resolution quantitative proteomic analysis. Inflammation 37(5):1459–1467. doi: 10.1007/s10753-014-9871-8

  9. Collins FS, Varmus (2015) A new initiative on precision medicine. N Engl J Med 372:793–795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Conrad K, Roggenbuck D, Reinhold D, Dörner T (2010) Profiling of rheumatoid arthritis associated autoantibodies. Autoimmun Rev 9(6):431–435. doi: 10.1016/j.autrev.2009.11.017

  11. D’Agostino M, Boers M, Kirwan J et al (2014) Updating the OMERACT filter: implications for imaging and soluble biomarkers. J Rheumatol 41(5):1016–1024

    Article  PubMed  PubMed Central  Google Scholar 

  12. Frueh FW. Personalized medicine: what is it? How will it affect health care? 11th Annual FDA Science Forum. 26 Apr 2005; Washington, DC. Available at: http://www.fda.gov/downloads/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm085716.pdf

  13. Goodsaid F, Frueh F (2007) Biomarker qualification pilot process at the US Food and Drug Administration. AAPS J 9(1):E105–E108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hafström I, Engvall I-L, Rönnelid J, Boonen A, van der Heijde D, Svensson B (2014) Rheumatoid factor and anti-CCP do not predict progressive joint damage in patients with early rheumatoid arthritis treated with prednisolone: a randomized study. BMJ Open 4:7e005246

    Article  Google Scholar 

  15. Hassfeld W, Steiner G, Hartmuth K, Kolarz G, Scherak O, Graninger W, Thumb N, Smolen J (1989) Demonstration of a new antinuclear antibody (anti-RA 33) that is highly specific for rheumatoid arthritis. Arthritis Rheum 32:1515–1520. PMID:2597207

    Google Scholar 

  16. HIPAA Privacy Rule. Other requirements relating to uses and disclosures of protected health information. 45 CFR § 164.514, 2013.

    Google Scholar 

  17. Isaacs J, Ferraccioli C (2011) The need for personalized medicine for rheumatoid arthritis. Ann Rheum Dis 70:4–7

    Article  CAS  PubMed  Google Scholar 

  18. Jilani AA, Mackworth-Young CG (2015) The role of citrullinated protein antibodies in predicting erosive disease in rheumatoid arthritis: a systematic literature review and meta-analysis. Int J Rheumatol 2015:728610

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Jónsson T, Steinsson K, Jónsson H, Geirsson AJ, Thorsteinsson J, Valdimarsson H (1998) Combined elevation of IgM and IgA rheumatoid factor has high diagnostic specificity for rheumatoid arthritis. Rheumatol Int 18(3):119–122

    Article  PubMed  Google Scholar 

  20. Kent D, Alsheikh-Ali A, Hayward R (2008) Competing risk and heterogeneity of treatment effect in clinical trials. Trials 22:9–30

    Google Scholar 

  21. Kilani RT, Maksymowych WP, Aitken A et al (2007) Detection of high levels of 2 specific isoforms of 14-3-3 proteins in synovial fluid from patients with joint inflammation. J Rheumatol 34(8):1650–1657

    CAS  PubMed  Google Scholar 

  22. Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR, Tsao A, Stewart DJ, Hicks ME, Erasmus J, Gupta S (2011) The BATTLE trial: personalizing therapy for lung cancer. Cancer Disc 1:44–53

    Article  CAS  Google Scholar 

  23. Landolt-Marticorena C (2015) The need for preclinical biomarkers in systemic autoimmune rheumatic diseases. J Rheumatol 42(2):152–154

    Article  CAS  PubMed  Google Scholar 

  24. Lee Y, Haney D, Alexander C et al (2013) Application of a multi-biomarker disease activity (Vectra® DA) score for assessing rheumatoid arthritis patients with low CRP or fibromyalgia. Ann Rheum Dis 72(suppl 3):A612–A613

    Google Scholar 

  25. Lindstrom T, Robinson W (2010) Biomarkers for rheumatoid arthritis: making it personal. Scand J Clin Lab Invest Suppl 242:79–84. doi: “//dx.doi.org/10.3109/00365513.2010.493406”10.3109/00365513.2010.493406

    Google Scholar 

  26. Maksymowych WP, Fitzgerald O, Wells GA et al (2009) Proposal for levels of evidence schema for validation of a soluble biomarker reflecting damage endpoints in rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis, and recommendations for study design. J Rheumatol 36:1792–1799. doi: 10.3899/jrheum090347

  27. Maksymowych WP1, Marotta A (2014) 14-3-3η: a novel biomarker platform for rheumatoid arthritis. Clin Exp Rheumatol 32(5 Suppl 85):S-35–S-39

    Google Scholar 

  28. Markusse IM, Dirven L, van den Broek M et al (2014) A multibiomarker disease activity score for rheumatoid arthritis predicts radiographic joint damage in the BeSt study. J Rheumatol 41

    Google Scholar 

  29. Nell VPK, Machold KP, Stam TA et al (2005) Autoantibody profiling as early diagnostic and prognostic tool for rheumatoid arthritis. Ann Rheum Dis 64:1731–1736. doi: “//dx.doi.org/10.1136/ard.2005.035691”10.1136/ard.2005.035691

    Google Scholar 

  30. Nielsen SF, Bojesen SE, Schnohr P, Nordestgaard BG (2012) Elevated rheumatoid factor and long term risk of RA: a prospective cohort study. BMJ 345:e5244

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nishimura K, Sugiyama D, Kogata Y et al (2007) Meta-analysis: diagnostic accuracy of anti-CCP antibody and rheumatoid factor for rheumatoid arthritis. Ann Intern Med 146(11):816–817

    Article  Google Scholar 

  32. Peddicord D, Waldo AB, Boutin M, Grande T, Gutierrez L Jr (2010) A proposal to protect privacy of health information while accelerating comparative effectiveness research. Health Aff (Millwood) 29(11):2082–2090

    Article  Google Scholar 

  33. Project Data Sphere Initiative. www.projectdatasphere.org. doi: 10.1634/theoncologist.2014-0431

  34. Rantapää-Dahlqvist S et al. Antibodies against cyclic citrullinated peptide and IgA rheumatoid factor predict the development of rheumatoid arthritis. Arthritis Rheum. 2003;48(10):2741–2749. doi: dx.doi.org/10.1002/art.11223”10.1002/art.11223

    Google Scholar 

  35. Rongchun Shen BM, Xiaojuan Ren BM, Rongrong Jing MM et al (2015) Rheumatoid factor, anti-cyclic citrullinated peptide antibody, C-reactive protein, and erythrocyte sedimentation rate for the clinical diagnosis of rheumatoid arthritis. Lab Med Summer 46:226–22

    Article  Google Scholar 

  36. Romão et al. Old drugs, old problems: where do we stand in prediction of rheumatoid arthritis responsiveness to methotrexate and other syntheticDMARDs?, BMC Medicine 2013;11:17. doi: “//dx.doi.org/10.1186/1741-7015-11-17”10.1186/1741-7015-11-17

    Google Scholar 

  37. Seegobin SD, Ma MHY, Dahanayake C et al (2014) ACPA-positive and ACPA-negative rheumatoid arthritis differ in their requirements for combination DMARDs and corticosteroids: secondary analysis of a randomized controlled trial. Arthritis Res Ther 16(1):R13. doi: 10.1186/ar4439

  38. Segurado OG, Sasso EH (2014) Vectra DA for the objective measurement of disease activity in patients with rheumatoid arthritis. Clin Exp Rheumatol 32(suppl 85):S29–S34

    Google Scholar 

  39. Senolt L, Grassi W, Szodoray P (2014) Laboratory biomarkers or imaging in the diagnostics of Rheumatoid arthritis. BMC Med 12:49. doi: 10.1186/1741-7015-12-49

  40. Shi J, Knevel R, Suwannalai P et al (2011) Autoantibodies recognizing carbamylated proteins are present in sera of patients with rheumatoid arthritis and predict joint damage. Proc Natl Acad Sci 108:17372–17377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Strand, Sokolove (2009) Randomized controlled trial design in rheumatoid arthritis: the past decade. Arthritis Res Ther 11:205

    Article  PubMed  PubMed Central  Google Scholar 

  42. Tufts Center for the Study of Drug Development. The adoption and impact of adaptive trial designs. http://csdd.tufts.edu/reports/white_papers, May 2013

  43. U.S. Food and Drug Administration. Guidance for industry: bioanalytical method validation. Draft. Sept 2013. Available at: Http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM368107.pdf

  44. van der Helm-van Mil AHM, Verpoort KN, Breedveld FC et al (2005) Antibodies to citrullinated proteins and differences in clinical progression of rheumatoid arthritis. Arthritis Res Ther 7:R949–R958

    Article  Google Scholar 

  45. van der Helm-van Mil AHM, Knevel R, Cavet G et al (2013) An evaluation of molecular and clinical remission in rheumatoid arthritis by assessing radiographic progression. Rheumatology 52:839–846

    Article  Google Scholar 

  46. Wahab AA, Mohammad M, Rahma M, Said M (2013) Anti-cyclic citrullinated peptide antibody is a good indicator for the diagnosis of rheumatoid arthritis. Pak J Med Sci 29(3):773–777

    PubMed  PubMed Central  Google Scholar 

  47. Wiley GB, Kelly JA, Gaffney PM (2014) Use of next-generation DNA sequencing to analyze genetic variants in rheumatic disease. Arthritis Res Ther 16(6):490

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Holly Hilton PhD .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Dennis, G.J., Fernandez, G., Iocca, H., Hilton, H. (2017). Biomarkers in Clinical Trials for Rheumatoid Arthritis. In: Mina-Osorio, P. (eds) Next-Generation Therapies and Technologies for Immune-Mediated Inflammatory Diseases. Progress in Inflammation Research. Springer, Cham. https://doi.org/10.1007/978-3-319-42252-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42252-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42251-0

  • Online ISBN: 978-3-319-42252-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics