{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T14:16:39Z","timestamp":1792678599781,"version":"4.5.0"},"reference-count":43,"publisher":"BMJ","issue":"3","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"unspecified","delay-in-days":34,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009857","name":"Regeneron Pharmaceuticals","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009857","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004339","name":"Sanofi","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004339","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["RMD Open"],"accepted":{"date-parts":[[2025,7,9]]},"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>To identify blood-based predictive and pharmacodynamic biomarkers at different timepoints in patients with active rheumatoid arthritis (RA) treated with anti-interleukin-6 receptor (anti-IL-6R) and anti-tumour necrosis factor-\u03b1 (anti-TNF-\u03b1).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>This study used blood samples from the MONARCH trial (<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"clintrialgov\" xlink:href=\"NCT02332590\">NCT02332590<\/jats:ext-link>), a randomised, double-blind, phase III trial that compared the safety and efficacy of sarilumab (anti-IL-6R) and adalimumab (anti-TNF-\u03b1) monotherapy in patients with RA who were intolerant\/inadequate responders to methotrexate. The study evaluated predictive biomarkers to anti-IL-6R and anti-TNF-\u03b1 treatments at baseline and week 2 and pharmacodynamic biomarkers at week 2 and week 24 using Olink proteomics analysis (n=804 serum samples from 268 patients). Change in gene expression levels (n=522 peripheral blood samples from 261 patients) by both treatments was assessed using RNA sequencing analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Serum biomarkers most predictive to anti-IL-6R were different from those of anti-TNF-\u03b1; predictive biomarkers for anti-IL-6R were correlated with innate immune activation and synovial inflammation, while predictive biomarkers for anti-TNF-\u03b1 seemed to be more T-cell and neutrophil-related. For baseline predictive biomarkers, we had to focus on relative prediction as the absolute prediction performance of single and combination biomarkers using cross-validation was limited. Additionally, the pharmacodynamic effects of anti-IL-6R and anti-TNF-\u03b1 on biomarkers as well as pathway signatures were distinct.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The unbiased analysis of serum proteins identified biomarkers most predictive of anti-IL-6R and anti-TNF-\u03b1 at different timepoints that could explain the difference in the response rate in patients with RA. Further, both biomarker and pathway results highlighted a differentiated mode of action of both treatments.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1136\/rmdopen-2025-005556","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T18:40:12Z","timestamp":1754332812000},"page":"e005556","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":7,"title":["Deciphering differential biomarkers for anti-interleukin-6 receptor and anti-tumour necrosis factor-\u03b1 treatment response in rheumatoid arthritis by multiomics 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Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5177-8378","authenticated-orcid":false,"given":"Anne","family":"Remaury","sequence":"additional","affiliation":[{"name":"Sanofi R&D, Translational Sciences, Chilly-Mazarin, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rapha\u00ebl","family":"Brard","sequence":"additional","affiliation":[{"name":"Sanofi R&D, Biostatistics, Industriepark Hoechst, Frankfurt am Main, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Frau","sequence":"additional","affiliation":[{"name":"Sanofi R&D, Digital and Data Science, Industriepark Hoechst, Frankfurt am Main, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2728-5073","authenticated-orcid":false,"given":"Emilie","family":"Gerard","sequence":"additional","affiliation":[{"name":"Sanofi, Randomised Clinical Trials and Post Hoc 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