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BackgroundNo immunomodulatory drug has ever demonstrated its efficacy in primary Sjögren’s Syndrome (pSS). Drug repurposing, or drug repositioning, refers to the use in another disease of an existing drug, originally approved or evaluated in a different disease.ObjectivesThe objective of our study was to repurpose existing therapeutic drugs in pSS using a transcriptomic approach.MethodsWe generated pSS transcriptomic signatures from peripheral blood samples of patients with pSS compared to healthy controls in two cohorts (ASSESS and a Norwegian cohort) and data mined available pSS transcriptomic signatures in public databases. We compared each disease signature to transcriptomic signatures, obtained from the biological action of 2837 drugs, 2160 knock-in and 3799 knock-down genes, available in the Connectivity Map database. A median similarity score with regard to disease signatures was computed for each candidate drug/gene. Drugs and genes were selected if p│80│. If this score is sufficiently high and statistically significant (>80, pFigure 1.Methods of drug-repurposing transcriptomic analysis (adapted from Toro-Dominguez et al, Arthritis Res Ther 2017;19:54)Results1091 peripheral blood transcriptomes were analyzed from 6 independent studies (906 patients with pSS and 185 healthy controls). Our analysis identified 11 transcriptomic drug signatures significantly associated with pSS signature. We identified 72 transcriptomic knock-in (11) or knock-down (61) gene signatures significantly associated with that of pSS, including 21 with a negative similarity score (Table 1).Table 1.Knock-down and knock-in genes significantly associated with the pSS transcriptomic signaturesType ofexperimentSimilarity scoreGenesNumber of genesKnock-in+IFNG, DUSP28, IFNB1, LYN, BCL2L2, TNFRSF1A, CD40, BCL10, NLK, ZNF39810-SLC52A2111Knock-down+SLC25A14, GOLIM4, DTYMK, DCXR, RRM2, IMPA1, CLTB, F12, CAB39, ID1, ISOC1, UBAP1, HIGD2A, UFD1L, SOD2, BTG1, PRKCI, HIST2H2BE, NISCH, TEAD4, MTX2, TYK2, GTF2B, NDUFS7, NNT, ACADSB, GSTP1, HOMER2, SORBS3, PCK2, PHB2, PDXK, TES, TM9SF2, TBX2, HOXA6, KIF2C, MED1, NR2F6, CD14, BECN141-TM9SF3, E2F3, PRMT3, KD, PKN2, SUCLA2, CD44, GRN, SP3, ATP5S, MYCBP2, TRAF7, POLA2, ADRB2, PSMG1, PPP2R3C, PMAIP1, ETFA, ANKRD37, SPECC1L2061Type I and II interferons were highly ranked (similarity score >99), and their overexpression mimicked the disease signature. CD40 appeared also as a very relevant target (similarity score = 98.8). Three drugs had a significant negative similarity score: ampicillin (-88.69, p=0.0019), amylocaine (-88.28, p=0.0026), and droxinostat (-85.42, p=0.0027). Droxinostat is a HDAC inhibitor. HDAC activity has been shown to be an essential element of the coactivation system for IFN-induced gene regulation and the IFN-induced innate immune response.ConclusionThis first drug repositioning transcriptomic approach in Sjögren’s syndrome confirms the interest of targeting interferons and identifies histone deacetylases as potential therapeutic targets.AcknowledgementsInvestigators of the ASSESS cohort: Emmanuelle Dernis, Valerie Devauchelle-Pensec, Philippe Dieude, Jean-Jacques Dubost, Anne-Laure Fauchais, Vincent Goeb, Eric Hachulla, Pierre Yves Hatron, Claire Larroche, Véronique Le Guern, Jacques Morel, Aleth Perdriger, Carinne Salliot, Stephanie Rist, Alain Saraux, Jean Sibilia, Olivier Vittecoq, Gaétane Nocturne, Philippe Ravaud, Raphaèle SerorCentre de Ressources Biologiques de l’Hôpital Bichat: Sarah TubianaJohan G. Brun for contributing to the Norwegian cohort.Funding SourcesThis work was supported by the Innovative Medicines Initiative 2 Joint Undertaking (IMI 2 JU) (NECESSITY grant 806975). The Joint Undertaking received support from the European Union’s Horizon 2020 Research and Innovation Program and from the European Federation of Pharmaceutical Industries and Associations. This work was also supported by R01 AR065953 Beth the NIH, United States. The contents are the sole responsibility of the authors and do not necessarily the official views of the NIH.JEG received an unrestricted grant from Bristol-Myer-Squibbs to do the transcriptomic analysis of the ASSESS and Norwegian cohorts. JEG received a grant from Geneviève Garnier (Association Française du Syndrome de Gougerot-Sjögren et des syndromes secs).The ASSESS cohort is promoted by the French Society of Rheumatology and received two research grants from the French Society of Rheumatology.The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Disclosure of InterestsRenaud FELTEN: None declared, Tao Ye: None declared, Cédric Schleiss: None declared, Benno Schwikowski: None declared, Jean Sibilia: None declared, Fanny Monneaux: None declared, Hélène Dumortier: None declared, Roland Jonsson: None declared, Christopher Lessard: None declared, Wan Fai Ng: None declared, Tsutomu Takeuchi: None declared, Xavier Mariette: None declared, Jacques-Eric Gottenberg Grant/research support from: JEG received an unrestricted grant from Bristol-Myer-Squibbs to do the transcriptomic analysis of the ASSESS and Norwegian cohorts.

BackgroundHyperuricemia (HU) is a metabolic abnormality associated with psoriasis (PsO) and psoriatic arthritis (PsA)1. The prevalence of HU is 2–13% in general population, 19–20% in PsO patients (pts), and 27–32% in PsA pts1,2. Pts with PsO/PsA are at significantly increased risk of HU and development of gout1. The pathogenic role of chronic HU in the development and maintenance of PsA is based on epidemiological, clinical, and fundamental arguments and hence does not appear fortuitous. These processes can influence each other3. Moreover, PsA with HU has been shown to be more peripheral, destructive, and challenging to treat4.ObjectivesTo evaluate the impact of HU on PsA in terms of clinical presentation, severity, comorbidities, and response to secukinumab (SEC) over 1-year.MethodsThis post hoc analysis included pooled data from PsA pts enrolled in the FUTURE 2–5 and MAXIMISE phase 3 trials. Pts were stratified into 2 groups based on baseline (BL) serum uric acid (SUA) level (HU: ≥360 µmol/L; without HU: ResultsOverall, 2504 PsA pts were included in the analysis, of which 822 (32.8%) had HU (62 [2.5%] with gout; 49 [2.0%] treated with ULT). At BL, pts with HU were mostly male (76.0% vs 34.2%) and had a higher body mass index (30.9 vs 28.3 kg/m2) with more comorbidities, such as hypertension (43.8% vs 31.3%), compared to pts without HU. A higher proportion of pts with HU had dactylitis (34.5% vs 25.9%), and PsO (48.3% vs 36.3%) with a greater mean PASI score (13.6 vs 10.2), compared to pts without HU (Table 1). The proportion of pts achieving ACR50, resolution of enthesitis/dactylitis, and mean change in HAQ-DI score were comparable up to Week 52 irrespective of BL HU status. The PASI90 response rate was higher in pts without HU with SEC 150 mg (with and without load) and similar in SEC 300 mg group irrespective of BL HU status (Figure 1).Table 1.Demographics and baseline characteristicsParameters, mean ± SD unless specifiedWith hyperuricemia (N=822)Without hyperuricemia (N=1682)Age (Years)48.5 ± 12.4148.3 ± 12.19Gender (Male), n (%)625 (76.0)576 (34.2)Weight (kg)92.71 ± 18.6279.59 ± 17.55BMI (kg/m2)30.90 ± 5.8628.33 ± 5.91History of hypertension, n (%)360 (43.8)526 (31.3)History of diabetes mellitus, n (%)85 (10.3)144 (8.6)TJC20.6 ± 15.5221.3 ± 16.25SJC10.9 ± 9.3110.8 ± 9.13Enthesitis, n (%)412 (50.1)852 (50.7)Dactylitis, n (%)284 (34.5)436 (25.9)Evidence of current psoriasis; n (%)397 (48.3)611 (36.3)Mean PASI score*13.61 ± 11.0310.16 ± 9.13TNFi naїve, n (%)477 (58.0)938 (55.8)MTX use at randomization, n (%)321 (39.1)685 (40.7)Serum uric acid (µmol/L)420.7 ± 57.11274.9 ± 51.98CRP (mg/L)11.6 ± 18.6610.7 ± 23.36*not collected in MAXMISEBMI, body mass index; CRP, C-reactive protein; MTX, methotrexate; SJC, swollen joint count; TJC, tender joint count; TNFi, tumor necrosis factor inhibitorConclusionIn this pooled analysis of SEC PsA studies, pts with HU reported a higher prevalence of hypertension, with more clinical dactylitis, and more PsO, with higher PASI score compared to pts without HU. Efficacy across all musculoskeletal manifestations was similar with SEC 150 and 300 mg; while PASI90 response rate was slightly better in patients without HU with SEC 150 mg, and similar with SEC 300 mg irrespective of HU status, at 1-year.References[1]Tripolino C, et al. Front Med. 2021;8:737573[2]AlJohani R, et al. J Rheumatol. 2018;45(2):213–7[3]Felten R, et al. Clin Rheumatol. 2020;39:1405–13[4]Widawski L, et al. Clin Rheumatol. 2022. https://doi.org/10.1007/s10067-022-06061-xDisclosure of InterestsRenaud FELTEN Consultant of: Novartis (Advisory board), Laura Widawski: None declared, Lionel Spielmann: None declared, Corine Gaillez Shareholder of: Novartis, Employee of: Novartis, Weibin Bao Shareholder of: Novartis, Employee of: Novartis, Jacques-Eric Gottenberg Consultant of: Novartis (Advisory board), Pierre-Marie Duret: None declared, Laurent Messer: None declared

BackgroundThe low prevalence of systemic autoimmune diseases and the diversity of their clinical manifestations make complex to conduct randomised clinical trials to assess the potential efficacy of targeted treatments.ObjectivesTo assess the tolerance and efficacy of targeted therapies prescribed off-label in refractory autoimmune diseases.MethodsThe TATA registry (TArgeted Therapy in Autoimmune Diseases) is a prospective, observational, national and independent cohort follow-up. The inclusion criteria in the registry are as follows: age > 18 years; rare systemic autoimmune disease (systemic lupus erythematosus, Sjögren’s syndrome, systemic scleroderma, inflammatory myopathy, vasculitis) or other refractory rheumatism treated with off-label drugs started after 1st January 2019.ResultsHundred (100) patients (79 females) were enrolled. The median age was 52.5 years [49;56], the median disease duration before enrolment was 5 years [3;7]. The targeted therapies at enrolment were as follows: JAK/STAT inhibitors (44%), anti-IL6R (22%), anti-IL12/23, anti-IL23 and anti-IL17 (9%), anti-BAFF (5%), abatacept (5%), other targeted treatments (9%), and combination of targeted treatments (6%). 73% of patients were receiving corticosteroid therapy at enrolment (median dose 10 mg/day). The current median follow-up time is 9 months [8;10].Safety: 11 serious infections (incidence rate of 14.8 /100 patient-years) and 1 cancer (1.3 cancers/100 patient-years) were observed. Two patients died from severe COVID-19 (2.7 deaths/100 patient-years).Efficacy: The targeted treatment was considered effective by the clinician in 56% of patients and allowed in responders a median reduction of oral corticosteroids of 15 [9-21] mg/day.ConclusionThese initial results of the TATA registry confirm the diversity of targeted treatments prescribed off-label in refractory autoimmune diseases and their corticosteroid-sparing effect when effective. Tolerance was acceptable in these refractory patients with a long history of treatment with immunosuppressive drugs.References[1]B. Terrier et al., Safety and efficacy of rituximab in systemic lupus erythematosus: results from 136 patients from the French AutoImmunity and Rituximab registry. Arthritis Rheum 62, 2458-2466 (2010).[2]J. E. Gottenberg et al., Efficacy of rituximab in systemic manifestations of primary Sjogren’s syndrome: results in 78 patients of the AutoImmune and Rituximab registry. Ann Rheum Dis 72, 1026-1031 (2013).[3]J. E. Gottenberg et al., Risk factors for severe infections in patients with rheumatoid arthritis treated with rituximab in the autoimmunity and rituximab registry. Arthritis Rheum 62, 2625-2632 (2010).[4]F. R. S. S. S. C. I. consortium, contributors, Severity of COVID-19 and survival in patients with rheumatic and inflammatory diseases: data from the French RMD COVID-19 cohort of 694 patients. Ann Rheum Dis, (2020).[5]R. Felten et al., B-cell targeted therapy is associated with severe COVID-19 among patients with inflammatory arthritides: a 1-year multicentre study in 1116 successive patients receiving intravenous biologics. Ann Rheum Dis 81, 143-145 (2022).[6]D. J. Wallace et al., Baricitinib for systemic lupus erythematosus: a double-blind, randomised, placebo-controlled, phase 2 trial. Lancet 392, 222-231 (2018).[7]J. J. Paik et al., Study of Tofacitinib in Refractory Dermatomyositis: An Open-Label Pilot Study of Ten Patients. Arthritis Rheumatol 73, 858-865 (2021).[8]S. Cole et al., Integrative analysis reveals CD38 as a therapeutic target for plasma cell-rich pre-disease and established rheumatoid arthritis and systemic lupus erythematosus. Arthritis Res Ther 20, 85 (2018).[9]S. J. Bowman et al., Safety and efficacy of subcutaneous ianalumab (VAY736) in patients with primary Sjogren’s syndrome: a randomised, double-blind, placebo-controlled, phase 2b dose-finding trial. Lancet 399, 161-171 (2022).AcknowledgementsFrench networks (FAI2R, CRI, IMIDIATE, SFR, SNFMI) focused on rare systemic autoimmune diseases contributed this work by the contribution of network-affiliated physicians.Disclosure of InterestsJacques-Eric Gottenberg Consultant of: Abbvie, BMS, Gilead, Galapagos, Novartis, Lilly Roche Chugai, Sanofi, Janssen, Pfizer, Grant/research support from: BMS.Lilly and Pfizer for this register (with no access to data)., Aurore Chaudier: None declared, Yves Allenbach: None declared, Arsene Mekinian: None declared, Zahir Amoura: None declared, Patrice cacoub: None declared, Divi Cornec: None declared, Eric Hachulla: None declared, Pierre Quartier: None declared, isabelle melki: None declared, Christophe Richez: None declared, Raphaèle Seror: None declared, Benjamin Terrier: None declared, Valerie Devauchelle-Pensec: None declared, Julien Henry: None declared, MARC GATFOSSE: None declared, LAURENCE BOUILLET: None declared, Emeline GAIGNEUX: None declared, Vincent ANDRE: None declared, Gildas BAULIER: None declared, Aurélie SAUNIER: None declared, Marie Desmurs: None declared, Antoine POULET: None declared, Mathieu ETE: None declared, Marie-Elise Truchetet: None declared, Martin Michaud: None declared, Claire Larroche: None declared, AZEDDINE DELLAL: None declared, Amelie LEURS: None declared, Sebastien Ottaviani: None declared, Hubert NIELLY: None declared, Guillaume VIAL: None declared, Roland JAUSSAUD: None declared, Benedicte ROUVIERE: None declared, Pierre-Yves JEANDEL: None declared, Aurelien GUFFROY: None declared, Anne-Sophie Korganow: None declared, Mathieu JOUVRAY: None declared, alain meyer: None declared, Emmanuel Chatelus: None declared, Christelle Sordet: None declared, Renaud FELTEN: None declared, Jean Sibilia: None declared, Samira AHMED YAHIA: None declared, Jean François Kleinmann: None declared, Xavier Mariette Consultant of: BMS, Galapagos, GSK, Janssen, Novartis, Pfizer, Sanofi, UCB

BackgroundVaccination against SARS-CoV-2 is effective in preventing severe forms of COVID-19, but there remain concerns about a reduced vaccine response in patients suffering from inflammatory arthritides who are treated by immunosuppressive therapies.ObjectivesWe analysed the impact of bDMARDs on the humoral anti-SARS-CoV-2 vaccine response of patients followed in day hospitals.MethodsWe studied the vaccine response after a complete vaccine regimen followed in day hospital in 5 French hospitals and treated with an intravenous bDMARD between September 2019 and August 2021. After obtaining their informed consent, we included patients with an anti-SARS-CoV-2 serology. They were considered non-responders if the antibody level detected was inferior to the threshold of positivity of the kit used.Results205 patients were included (148 females/57 males). The median age was 64 years (Interquartile Range [IQR] 56-71). 25 were treated with tocilizumab (TCZ), 36 with abatacept (ABA), 53 with infliximab (IFX) and 91 with rituximab (RTX). When considering both patients after a complete vaccination schema (2 doses, or 1 dose in case of prior COVID-19) and those with 1 booster dose, 34 patients (16.6%) were non-responders (2 [5.9%] treated by IFX, none treated by TCZ, 9 [26.5%] treated by ABA and 23 [67.7%] treated by RTX). In multivariate analysis, the only characteristics that significantly and independently differed between responders and non-responders were last bDMARD and corticosteroid therapy at the time of 1st vaccination (Table 1). In RTX-treated patients, the delay from last infusion to 1st vaccine dose was significantly shorter in non-responders (median 4.3 IQR [2.9-6.1] months in non-responders versus 8.4 IQR [5.7-14.5] in responders, p=0.0007). Median survival, i.e. achieving a vaccine response in 50% of RTX-treated subjects, was achieved after 277 days (95CI [209-310]) in patients vaccinated with 2 or 3 doses (Figure 1). In ABA-treated patients, the delay from last infusion to 1st vaccine dose was not different between non-responders and responders.Table 1.Patients’ characteristics and comparisons between responders and non-responders.All patients (n=205)Responders (n=171)Non responders (n=34)Univariate p valueMultivariatep valueAge (median [IQR]), in years64 [56-71]64 [54-70]69 [57-75.5]0.070.40Female sex, n (%)148 (72.2)125 [73.1)23 (67.7)0.53Inflammatory arthritides, n (%)0.16**0.24 Rheumatoid Arthritis156 (78.0)128 (74.9)28 (82.4)0.51 Spondyloarthritis33 (16.1)31 (18.1)2 (5.9)0.12 Others*16 (7.8)12 (5.9)4 (1.9)0.31Last bDMARDs at time of 1st vaccination, n (%)0.0004ABA/RTX versus IFX/TCZ < 0.00010.00024 Infliximab53 (25.9)51 (29.8)2 (5.9) Tocilizumab25 (12.2)25 (14.6)0 Abatacept36 (17.6)27 (15.8)9 (26.5) Rituximab91 (44.4)68 (39.8)23 (67.7)Associated treatments at time of 1st vaccination CsDMARDs, n (%)126 (61.5)107 (62.6)19(55.9)0.56 Methotrexate91 (44.4)78 (45.6)13 (38.2)0.46 Median dose in users (mg /week) [IQR]15 [10-17.5]13.8 [10-15.6]15 [13.8-20]0.07 Corticosteroids, n (%)25 (12.2)19 (11.1)6 (17.6)0.29 Median dose (mg /day) [IQR]0 [0-0]0 [0-0]0 [0-2]0.0350.016Previous COVID-19 infection, n (%)23 (11.2)21 (12.3)2 (5.9)0.38Type of vaccine, n (%)0.62 Pfizer169 (82.4)142 (83.0)27 (79.4)0.62 Moderna14 (68.3)11 (6.4)3 (8.8)0.71 Astra-Zeneca17 (8.3)15 (8.8)2 (5.9)0.74 Janssen5 (2.4)3 (1.8)2 (5.9)0.19Vaccination, n (%) Complete167 (81.5)141 (82.5)28 (16.8)0.47 Complete + 1 booster dose56 (27.3)43 (25.1)13 (38.2)0.14Figure 1.Cumulative seropositive rate according to the interval (days) between the last course of rituximab administration and vaccinationConclusionABA and RTX alter the anti-SARS-CoV-2 vaccine response and were associated with nearly all vaccine non-responses in the present study. Corticosteroid therapy was associated with a lower vaccine response regardless of its indication or the concomitant use of bMARD.Disclosure of InterestsNone declared

BackgroundThe Fibrosis-4 (FIB4) score, including age, transaminases and platelets, can detect severe fibrosis (F3-F4) in patients with Non Alcoolic Steato Hepatitis (NASH) and could be of interest in the follow-up of patients with RA. Indeed, platelets contribute to the pathophysiology of RA, transaminases are used in the liver monitoring of our treatments. In addition, retrospective data suggested the association between FIB4 and mortality in RA (1).ObjectivesWe aimed to evaluate the value of the FIB4 score as a prognostic factor in RA in the prospective ESPOIR cohort.MethodsPatients of the ESPOIR cohort diagnosed with RA according to ACR-EULAR criteria were included in our analysis. The formula for the FIB-4 score is as follows: [Age (years) × ASAT (U/L)] / [Platelet count (10^9/L) × ALT (U/L)1/2]. The analyses were based on linear mixed-effects models with a random effect on the subject to account for repeated measures throughout time.Results633 of the 813 patients included met the ACR/EULAR criteria for RA and had a calculable FIB4 score. Median FIB4 was 0.75 IQR (0.53-0.99) and 61 patients (9.6%) had a high FIB4 score at baseline. Baseline FIB4 was significantly higher in patients with a chronic alcohol consumption (p=0.021) or viral hepatitis (pTable 1.Associations of FIB4 score with DAS28 and modified Sharp score evolutions in multivariate analysesVariableVariables included in modelp-valueDAS28TimeAge0.97Baseline number of swollen jointsBaseline Rheumatoid Factor0.51evolution over timeBaseline ACPA (presence)0.97Baseline CRPBaseline modified Sharp score > 00.15Baseline FIB40.26Modified Sharp scoreTime0.052Age0.0005Baseline number of swollen joints0.38Baseline Rheumatoid Factor0.61evolution over timeBaseline ACPA (presence)0.012Baseline CRP0.84Baseline FIB40.25ConclusionOur study was the first to evaluate the value of FIB4 in a prospective cohort of RA patients. The present prospective cohort study with a 10-year follow-up did not find a prognostic role of FIB4 in RA, in contrast to previous retrospective studies. Reassuringly, FIB4 score was not increased by DMARD treatment after 10 years of follow-up, confirming the absence of long-term DMARD-related hepatotoxicity.References[1]Seung Up Kim, Beom Kyung Kim, Jun Yong Park, Do Young Kim, Sang Hoon Ahn, Yong-Beom Park, Kwang-Hyub Han & Sang-Won Lee (2018): Fibrosis-4 index at diagnosis can predict all-cause mortality in patients with rheumatoid arthritis: A retrospective monocentric study, Modern Rheumatology, DOI: 10.1080/14397595.2018.1558760Figure 1.Impact of baseline FIB4 score on DAS28, HAQ and total modified-Sharp score over time.AcknowledgementsAn unrestricted grant from Merck Sharp and Dohme (MSD) was allocated for the first 5 years. Two additional grants from INSERM were obtained to support part of the biological database. The French Society of Rheumatology, Pfizer, Abbvie, Lilly, Sanofi also supported the ESPOIR cohort study.We also wish to thank Nathalie Rincheval (CHU Montpellier and EA 2415) who did expert monitoring and data management and all the investigators who recruited and followed the patients (F. Berenbaum, Paris-Saint Antoine, MC. Boissier, Paris-Bobigny, A. Cantagrel, Toulouse, B. Combe, Montpellier, M. Dougados, Paris-Cochin, P. Fardellone et P. Boumier Amiens, B. Fautrel, Paris-La Pitié, RM. Flipo, Lille, Ph. Goupille, Tours, F. Liote, Paris- Lariboisière, O. Vittecoq, Rouen, X. Mariette, Paris Bicetre, P. Dieude, Paris Bichat, A. Saraux, Brest, T. Schaeverbeke, Bordeaux, J. Sibilia, Strasbourg) V. Devauchelle and C Lukas for expert X-ray reading.Disclosure of InterestsNone declared.

Background:Several large randomised controlled trials (RCTs) in primary Sjögren’s syndrome (pSS) failed to demonstrate drug efficacy.1-4 Many of these trials used ESSDAI as primary endpoint, showing large but similar response rates in active treatment and placebo groups.1,3,4 Given the heterogeneous nature of pSS, there is need for a composite endpoint including multiple clinically relevant parameters.Objectives:To develop and validate the Composite of Relevant Endpoints in Sjögren’s Syndrome (CRESS).Methods:A multidisciplinary team of pSS experts selected clinically relevant items and measurements to include in the CRESS. Definition of response of CRESS items was based on clinical relevance, previously defined minimal clinically important improvement (MCII) and data of the single-centre ASAP-III (abatacept) trial.1 CRESS was validated in three independent RCTs: TRACTISS (rituximab) trial2, multi-centre abatacept trial3 and ETAP (tocilizumab) trial4. CRESS response rates were assessed at the primary endpoint visit of all four trials.Results:Five complementary items were selected to form CRESS: systemic disease activity, patient-reported symptoms, tear gland, salivary gland and serological item. Definition of response per item is presented in Table 1. Total CRESS response was defined as response on ≥3 of 5 items. Since not all trials have ocular staining score or salivary gland ultrasonography (SGUS) available, the concise CRESS (cCRESS) was developed simultaneously, leaving Schirmer’s test and unstimulated whole saliva flow for the tear and salivary gland items, respectively. In the ASAP-III trial, CRESS response rates were 24/40 (60%) for abatacept vs. 7/39 (18%) for placebo at week 24 (pTable 1.CRESS items and definition of responseItemsMeasurementsDefinition of responseSystemic disease activityClinESSDAIScorePatient-reported symptomsESSPRIDecrease of ≥1 point or ≥15%Tear gland*Schirmer/OSS**-If abnormal Schirmer (≤5 mm) at baseline: increase of ≥5 mm in Schirmer-Or if abnormal OSS (≥3 points) at baseline: decrease ≥2 points in OSS-Or if both Schirmer/OSS normal scores at baseline: no change to abnormal in bothSalivary gland*UWS/SGUSIncrease of ≥25% in UWS (or if score is 0 at baseline, any increase)Or decrease of ≥25% in total Hocevar score (SGUS)SerologicalRF/IgGDecrease of ≥25% in RFOr decrease of ≥10% in IgGCRESS responderResponder on ≥3 of 5 itemsOcular Staining Score (OSS), Unstimulated whole salivary flow (UWS), Salivary gland ultrasonography (SGUS), Rheumatoid factor (RF), Immunoglobuline G (IgG)*Concise CRESS (cCRESS): CRESS without OSS and SGUS, leaving Schirmer and UWS for tear and salivary gland items, respectively**Mean of both eyesIn the external validation trials, cCRESS response rates for TRACTISS were: 33/67 (49%) rituximab vs. 20/66 (30%) placebo at week 48 (p=0.026). CRESS response rates (without SGUS) for the multi-centre abatacept trial were: 41/92 (45%) abatacept vs. 30/95 (32%) placebo at week 24 (p=0.067). cCRESS response rates (without rheumatoid factor) for ETAP were: 10/55 (18%) tocilizumab vs. 13/55 (24%) placebo at week 24 (p=0.482) (Figure 1A-D). Compared to ESSDAI MCII of ≥3 points decrease, CRESS was able to approximately halve placebo response rates in RCTs with high baseline ESSDAI scores (>5) (Figures 1E-H).Conclusion:CRESS shows lower placebo response rates compared to ESSDAI MCII, which is crucial for demonstrating treatment efficacy. With the CRESS, higher response rates in abatacept and rituximab treated patients compared to placebo were found in RCTs which previously showed negative primary endpoint results. CRESS confirmed that no differences were found for almost all outcome measures between tocilizumab and placebo,4 with low response rates. The CRESS is a well-balanced, feasible, composite endpoint for use in clinical trials in pSS patients.References:[1]Van Nimwegen 2020;9913(19):1–11[2]Bowman 2017;69(7):1440–50[3]Baer (doi:218599)[4]Felten (doi:21846)Acknowledgements:The authors would like to acknowledge all contributors of the included trials.Disclosure of Interests:Suzanne Arends: None declared, Liseth de Wolff: None declared, Jolien F. van Nimwegen Speakers bureau: Bristol Myers Squibb, Consultant of: Bristol Myers Squibb, Gwenny M. Verstappen: None declared, Jelle Vehof: None declared, Michele Bombardieri Consultant of: MedImmune, GlaxoSmithKline, Grant/research support from: MedImmune, Simon J. Bowman Consultant of: AstraZenecea/MedImmune, Bristol Myers Squibb, Celgene, Eli Lilly, Glenmark, GlaxoSmithKline, MTPharma, Novartis, Ono, Pfizer, Takeda, UCB, XTLBio, Elena Pontarini: None declared, Alan Baer Consultant of: Bristol Myers Squibb, Sanofi, VielaBio, Novartis, Marleen Nys Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Jacques-Eric Gottenberg Grant/research support from: Bristol Myers Squibb, Pfizer, Renaud FELTEN: None declared, Neelanjana Ray Shareholder of: Bristol Myers Squibb, Employee of: Bristol Myers Squibb, Arjan Vissink: None declared, Frans G.M. Kroese Speakers bureau: Bristol Myers Squibb, Roche and Janssen-Cilag, Consultant of: Bristol Myers Squibb, Grant/research support from: Unrestricted grants from Bristol Myers Squibb, Hendrika Bootsma Speakers bureau: Bristol Myers Squibb and Novartis, Consultant of: Bristol Myers Squibb, Roche, Novartis, Medimmune, Union Chimique Belge, Grant/research support from: Unrestricted grants from Bristol Myers Squibb and Roche