THU0063 DIFFERENTIAL PHARMACODYNAMIC EFFECTS OF ABATACEPT AND ADALIMUMAB ON THE SERUM PROTEOME OF PATIENTS WITH RA USING THE SOMASCAN® PLATFORM. (2nd June 2020)

• Record Type: Journal Article
• Authors: Galbraith, D.; Caliskan, M.; Jabado, O.; Hu, S.; Fleischmann, R.; Weinblatt, M.; Connolly, S.; Maldonado, M. A.; Gao, S.
• Is Part Of: Annals of the rheumatic diseases. Volume 79(2020)Supplement 1
• Page Start: 244
• Page End: 244
• Publication Date: 2020-06-02
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2020-eular.1452
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Abatacept (ABA) versus adaliMumab (ADA) comP arison in bioLogic-naïvE RA subjects with background MTX (AMPLE) was a Phase IIIb clinical trial to compare the safety, efficacy and radiographic outcomes of ABA vs ADA in patients with RA who exhibited an inadequate response to MTX and who were naïve to biologic DMARDs. 1 While both therapies demonstrated similar efficacy across multiple outcomes, their mechanisms of action (MoAs) are quite different; ABA is a T-cell co-stimulation modulator and ADA is a TNFα inhibitor. Previous transcriptomic analysis of the whole blood samples showed differential pharmacodynamic (PD) effects between the treatments. 1–3 Objectives: To expand our understanding of differential PD changes in the serum proteome over time in patients treated with ABA or ADA in AMPLE using a novel proteomic platform. Methods: Serum was available from 440 patients in AMPLE at four time points (Days 1, 85, 365 and 729). Serum samples from the patients in AMPLE and 123 healthy individuals with matching demographics were subjected to proteomic quantification by a highly multiplexed DNA aptamer technology with wide dynamic ranges (SomaLogic SomaScan ® platform). 4 A linear model analysis was used to identify protein abundance changes over time and changes specific to treatment. Other covariates included in the model were country of origin, ethnicity and sex. Additionally, patient effect was adjusted for as a random factor. Results: Both treatments exhibited a significant PD effect on serum proteome over the course of the 2-year trial, with 73 proteins modulated by ABA and 125 by ADA. There were large overlaps between the two treatments, including proteins associated with RA, such as C-X-C motif chemokine ligand 13 (CXCL13), matrix metalloproteinase-3 (MMP3) and serum amyloid A1/A2 (SAA1/2). Changes in the levels of these proteins may be indicative of general improvement of the disease. The proteins modulated by the treatments were enriched in the G-protein coupled receptor (GPCR) signalling and innate immunity pathways. Among the proteins that exhibited significantly different PD effects between the treatments were CRP, CC chemokine ligand 17 (CCL17) and β-defensin 112 (Figure). While patients showed marked improvement in their symptoms after 2 years of treatment, the overall serum proteomic profiles of the patients were still different from those of a normal healthy population. Conclusion: The SomaScan ® platform provides a robust method for quantifying the PD change in a broad portion of the serum proteome in clinical trials. In AMPLE, abatacept was more selective than adalimumab in modulating protein biomarkers in patients with RA, though there was large overlap in proteins modulated by both treatments. The treatment-specific changes may reflect the different MoAs leading to similar clinical outcomes. While patients in both groups benefited from treatments, their serum proteome remained notably different from that of a healthy population. Further analysis by responder status may provide additional links between the treatment responses and proteomic changes. Proteomic approaches as described in our study could contribute to clinical trials and help shape treatment strategies for patients with RA. References: [1]Schiff M, et al. Ann Rheum Dis 2014;73 :86–94. [2]Bandyopadhyay S, et al. Arthritis Rheum 2014;66 :Abstract 1520. [3]Sokolove J, et al. Ann Rheum Dis 2016;75 :709–14. [4]Gold L, et al. PLoS One 2010;5 :e15004. Disclosure of Interests: David Galbraith Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Minal Caliskan Employee of: Bristol-Myers Squibb, Omar Jabado Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Sarah Hu Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Roy Fleischmann Grant/research support from: AbbVie, Akros, Amgen, AstraZeneca, Bristol-Myers Squibb, Boehringer, IngelhCentrexion, Eli Lilly, EMD Serono, Genentech, Gilead, Janssen, Merck, Nektar, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Roche, Samsung, Sandoz, Sanofi Genzyme, Selecta, Taiho, UCB, Consultant of: AbbVie, ACEA, Amgen, Bristol-Myers Squibb, Eli Lilly, Gilead, GlaxoSmithKline, Novartis, Pfizer, Sanofi Genzyme, UCB, Michael Weinblatt Grant/research support from: Amgen, Bristol-Myers Squibb, Crescendo, Lily, Sanofi/Regeneron, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Crescendo, Gilead, Horizon, Lily, Pfizer, Roche, Sean Connolly Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Michael A Maldonado Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Sheng Gao Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb