Prediction of Effectiveness and Toxicities of Immune Checkpoint Inhibitors Using Real-World Patient Data

PURPOSE

Although immune checkpoint inhibitors (ICIs) have improved outcomes in certain patients with cancer, they can also cause life-threatening immunotoxicities. Predicting immunotoxicity risks alongside response could provide a personalized risk-benefit profile, inform therapeutic decision making, and improve clinical trial cohort selection. We aimed to build a machine learning (ML) framework using routine electronic health record (EHR) data to predict hepatitis, colitis, pneumonitis, and 1-year overall survival.

METHODS

Real-world EHR data of more than 2,200 patients treated with ICI through December 31, 2018, were used to develop predictive models. Using a prediction time point of ICI initiation, a 1-year prediction time window was applied to create binary labels for the four outcomes for each patient. Feature engineering involved aggregating laboratory measurements over appropriate time windows (60-365 days). Patients were randomly partitioned into training (80%) and test (20%) sets. Random forest classifiers were developed using a rigorous model development framework.

RESULTS

The patient cohort had a median age of 63 years and was 61.8% male. Patients predominantly had melanoma (37.8%), lung cancer (27.3%), or genitourinary cancer (16.4%). They were treated with PD-1 (60.4%), PD-L1 (9.0%), and CTLA-4 (19.7%) ICIs. Our models demonstrate reasonably strong performance, with AUCs of 0.739, 0.729, 0.755, and 0.752 for the pneumonitis, hepatitis, colitis, and 1-year overall survival models, respectively. Each model relies on an outcome-specific feature set, though some features are shared among models.

CONCLUSION

To our knowledge, this is the first ML solution that assesses individual ICI risk-benefit profiles based predominantly on routine structured EHR data. As such, use of our ML solution will not require additional data collection or documentation in the clinic.

Accelerated curation of checkpoint inhibitor-induced colitis cases from electronic health records

Objective

Automatically identifying patients at risk of immune checkpoint inhibitor (ICI)-induced colitis allows physicians to improve patientcare. However, predictive models require training data curated from electronic health records (EHR). Our objective is to automatically identify notes documenting ICI-colitis cases to accelerate data curation.

Materials and Methods

We present a data pipeline to automatically identify ICI-colitis from EHR notes, accelerating chart review. The pipeline relies on BERT, a state-of-the-art natural language processing (NLP) model. The first stage of the pipeline segments long notes using keywords identified through a logistic classifier and applies BERT to identify ICI-colitis notes. The next stage uses a second BERT model tuned to identify false positive notes and remove notes that were likely positive for mentioning colitis as a side-effect. The final stage further accelerates curation by highlighting the colitis-relevant portions of notes. Specifically, we use BERT’s attention scores to find high-density regions describing colitis.

Results

The overall pipeline identified colitis notes with 84% precision and reduced the curator note review load by 75%. The segment BERT classifier had a high recall of 0.98, which is crucial to identify the low incidence (<10%) of colitis.

Discussion

Curation from EHR notes is a burdensome task, especially when the curation topic is complicated. Methods described in this work are not only useful for ICI colitis but can also be adapted for other domains.

Conclusion

Our extraction pipeline reduces manual note review load and makes EHR data more accessible for research.

Implementation and evaluation of the effectiveness of dynamic object detection tools for multithreaded and distributed processing of graphical data

Modern monitoring and operational decision-making systems based on image series have become widely used for a wide variety of applications. At the stage of identification of graphic objects, it is established that the found dynamic object belongs to the class of objects of interest based on a comparative analysis of its contours with a given template. The article is the final in a series of articles devoted to theoretical, algorithmic and software components of the video analytics system. The description of the structural components of the video analytics system, the features of the implementation of the dynamic object detection module on video sequences, and the evaluation of the efficiency of the system is presented.

POS1035 LOW RATES OF RADIOGRAPHIC PROGRESSION WITH 2 YEARS OF GUSELKUMAB, A SELECTIVE INHIBITOR OF THE INTERLEUKIN-23p19 SUBUNIT: RESULTS FROM A PHASE 3, RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED STUDY OF BIOLOGIC-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS

Background

In the phase 3 DISCOVER-2 (D2) study, guselkumab (GUS) 100 mg every 4/8 weeks (Q4W/Q8W) significantly improved joint and skin symptoms in patients (pts) with active psoriatic arthritis (PsA); GUS-treated pts had smaller mean changes in radiographic progression vs placebo (PBO) at W24.1 Clinical response rates and favorable safety profile were durable through W100.2, 3

Objectives

To report details of radiographic assessments comprising Reading Session 3 through W100 of D2, including relationships between radiographic changes and measures of clinical outcomes.

Methods

Biologic-naïve adults with active PsA (≥5 swollen + ≥5 tender joint count; CRP ≥0.6 mg/dL) were randomized (1:1:1) to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or PBO with crossover to GUS 100 mg Q4W (PBO→Q4W) at W24, all through W100. Radiographic Reading Session 3 included assessments at W0/24/52/100 (or at discontinuation after W52) from pts ontinuing study treatment at W52; readers were blinded to treatment group and timepoint. Observed mean changes in total PsA-modified van der Heijde-Sharp (vdH-S), joint space narrowing (JSN), and erosion scores were reported. Changes in total vdH-S scores from W0-100 were determined in pts who did and did not achieve clinical response at W100, assessed by ACR20/50/70, low disease activity (LDA) based on Disease Activity in Psoriatic Arthritis score (DAPSA; ≤14) or Psoriatic Arthritis Disease Activity Score (PASDAS; ≤3.2), minimal disease activity (MDA), and normalized Health-Assessment Questionnaire-Disability Index (HAQ-DI) score (<0.5).

Results

Of 739 randomized pts, 664 had evaluable data from Reading Session 3; 629 had evaluable data from W52-100. Mean total baseline vdH-S scores were: Q4W, 28.0; Q8W, 23.9; PBO→Q4W, 25.6. Mean progression of joint damage from W0-24 was numerically lower in GUS- than PBO-treated pts for erosion, JSN, and total vdH-S scores (Table 1), consistent with the results from Reading Session 1.1 Mean changes in radiographic scores from W52-100 indicated low rates of radiographic progression across GUS groups. Among GUS-randomized pts, mean changes in vdH-S score from W0-100 were numerically lower for pts achieving clinical response assessed using ACR20/50/70, DAPSA LDA, PASDAS LDA, MDA, and HAQ-DI vs pts not achieving response at W100 (Figure 1).

Table 1.

Observed erosion, joint space narrowing, and total PsA-modified vdH-S scores through W100 of DISCOVER-2

GUS Q4WGUS Q8WPBO→GUS Q4WBaseline PsA-modified vdH-S score, n221228215Erosion14.2 (23.3)12.0 (21.9)12.1 (21.9)Joint space narrowing13.8 (21.8)11.9 (19.5)13.5 (21.6)Total28.0 (43.6)23.9 (40.4)25.6 (42.4)Mean (SD) change in PsA-modified vdH-S scoreW0-24 N=221W24-52 N=221W52-100 N=211W0-24 N=228W24-52 N=228W52-100 N=216W0-24 N=215W24-52 N=213W52-100 N=202Erosion0.27 (1.91)0.36 (1.77)0.45 (2.90)0.51 (1.96)0.20 (1.24)0.26 (1.75)0.73 (2.20)0.25 (1.85)0.09 (1.98)Joint space narrowing0.21 (1.17)0.21 (1.11)0.30 (1.32)0.17 (0.69)0.12 (0.66)0.20 (0.92)0.39 (1.72)0.09 (1.11)0.04 (1.90)Total0.48 (2.70)0.57 (2.66)0.75 (4.02)0.68 (2.36)0.31 (1.57)0.46 (2.42)1.12 (3.80)0.34 (2.79)0.13 (3.74)

Data presented as mean (SD).

GUS, guselkumab; PBO, placebo; PsA, Psoriatic Arthritis; Q4W, every 4 weeks; Q8W, every 8 weeks; SD, standard deviation; vdH-S, van der Heijde-Sharp; W, week

Conclusion

In biologic-naïve pts with active PsA enriched for greater risk of radiographic progression, GUS 100 mg (Q4W or Q8W) was associated with low rates of radiographic progression through 2 years. Pts achieving clinical response across several global measures of disease activity or normalized physical function at W100 had lower mean changes in total PsA-modified vdH-S scores compared with nonresponders.

References

[1]Mease PJ. Lancet. 2020;395:1126-1136

[2]McInnes IB. Arthritis Rheumatol. 2021;73:604-616

[3]McInnes IB. Innovations in Dermatology. Presentation: March 16-20, 2021

Disclosure of Interests

Philip J Mease Consultant of: AbbVie, Aclaris, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Aclaris, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Alice B Gottlieb Shareholder of: Xbiotech (stock options only), Consultant of: Anaptyps Bio, Avotres Therapeutics, Beiersdorf, Boehringer Ingelheim, Bristol Myers Squibb, Dermavant, Eli Lilly, Incyte, Janssen, LEO Pharma, Novartis, Pfizer, Sun Pharmaceuticals, UCB, Grant/research support from: Boehringer Ingelheim, Janssen, Novartis, Sun Pharmaceuticals, UCB, and Xbiotech, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research and Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research and Development, LLC, Yusang Jiang Employee of: Cytel, Inc., Shihong Sheng Shareholder of: Johnson & Johnson, Employee of: Janssen Research and Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, Frederic Lavie Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Désirée van der Heijde Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, and UCB Pharma, Employee of: Director of Imaging and Rheumatology BV

POS0074 IMMUNOLOGICAL DIFFERENCES BETWEEN PsA PATIENTS WHO ARE TUMOR NECROSIS FACTOR INHIBITOR-NAIVE AND WHO HAVE INADEQUATE RESPONSE TO TUMOR NECROSIS FACTOR INHIBITORS

Background

A better understanding of the immunological differences between psoriatic arthritis (PsA) patients (pts) who are tumor necrosis factor inhibitor (TNFi)-naïve & who have inadequate response to TNFi (TNFi-IR) may guide treatment choices. In DISCOVER-1, benefit of the IL-23p19 subunit inhibitor guselkumab (GUS) every-four-weeks (Q4W) & Q8W vs placebo (PBO) in improving PsA signs & symptoms was seen in adults with active PsA.1 The Ph3b COSMOS study of GUS Q8W vs PBO in TNFi-IR PsA pts corroborated these findings.2

Objectives

Assess baseline (BL) molecular differences between TNFi-naïve & -IR PsA pts & investigate GUS pharmacodynamic (PD) effect on cytokine expression over time in these cohorts.

Methods

Serum samples collected from consenting biomarker substudy pts in DISCOVER-11 (TNFi-naïve [n=101] & -IR [n=17]), DISCOVER-23 (TNFi-naïve [n=150]), & COSMOS2 (TNFi-IR [n=76]) were analyzed for selected serum cytokine levels. TNFi-IR pts in this post-hoc analysis had active PsA & discontinued 1-2 TNFi due to inadequate efficacy; these pts required a TNFi-specific washout period prior to starting GUS. PD effect of GUS Q8W on cytokine levels was assessed. Differential BL cytokine expression, associations between BL cytokine levels & clinical response (Psoriasis [PsO] Area & Severity Index 75% improvement from BL [PASI75] & American College of Rheumatology 20% improvement [ACR20]), & GUS effect on cytokine levels were analyzed with a General linear model & Spearman linear regression.

Results

BL pt demographics, disease characteristics, & conventional synthetic disease-modifying antirheumatic drug (csDMARD) use were comparable between TNFi-naïve (DISCOVER-1 & -2, N=251) & -IR (DISCOVER-1 & COSMOS, N=93) pts, with differences in mean PASI score (8.9 v 12.5), swollen joint count (SJC) (11.7 v 10.3), PsA duration (5.8 v 9.8 yrs), & PsO duration (16.7 v 20.4 yrs; Table 1). BL serum IL-22 & TNFα levels for pooled treatment groups were higher in TNFi-IR than -naïve pts (p<0.05). At W24, GUS reduced IL-22, IL-17A/F, IL-6, C-reactive protein (CRP), & serum amyloid A protein to similar levels in both cohorts (p<0.05; Figure 1). W24 PASI75 responders had higher BL IL-17F levels with GUS in both cohorts (p<0.05) & higher IL-22 levels in TNFi-IR pts only (p<0.05). A trend of upregulated BL IL-22 expression in W24 ACR20 responders was seen for TNFi-IR pts with GUS (p=0.07).

Table 1.

BL demographics, disease characteristics, & drug use in TNFi-naïve & -IR cohorts with available cytokine data in DISCOVER-1&2 & COSMOS.*

TNFi-naïve (N=251)TNFi-IR (N=93)Age [yrs]47.2 (11.3)48.5 (11.1)Female, n (%)132 (52.6)46 (49.5)Body mass index [kg/m2]29.6 (6.1)30.3 (6.4)Median (range) CRP [mg/dL]0.9 (0.0-12.9)1.0 (0.0-13.2)Log2 IL-22 / TNFα [pg/mL]2.0 (1.4) / 1.1 (0.6)2.5 (1.5) / 1.9 (1.2)Log2 IL-17A / F [pg/mL]-0.4 (1.5) / 1.7 (1.5)-0.1 (1.7) / 2.0 (1.6)SJC [0-66]11.7 (7.1)10.3 (8.3)TJC [0-68]20.3 (13.1)20.6 (14.2)PsA duration [yrs]5.8 (5.9)9.8 (8.2)PsO duration [yrs]16.7 (12.8)20.4 (12.0)PsO Body surface area (%)14.8 (18.6)19.1 (21.3)Investigator’s Global Assessment score [0-4]2.3 (0.9)2.3 (1.0)PASI score [0-72]8.9 (10.6)12.5 (12.0)Enthesitis [Y], n (%)160 (63.7)58 (62.4)csDMARD use [Y], n (%)164 (65.3)62 (66.7)Corticosteroid use (Y), n (%)45 (17.9)19 (20.4)Methotrexate use [Y], n (%)136 (54.2)54 (58.1)

Data are mean (SD) unless otherwise noted. *Pts with serum CRP level ≥0.3 mg/dL, SJC ≥3, & TJC ≥3 (to mimic D1 inclusion criteria1). TJC= tender joint count

Conclusion

Elevated BL IL-22 expression & association between BL IL-22 levels & W24 PASI75 response, & a W24 trend for an association between upregulated BL IL-22 & ACR20 response, in TNFi-IR pts seen in this exploratory analysis may suggest increased involvement of the IL-23 pathway in TNFi-IR pts. GUS showed comparable & significant PD effects for TNFi-naïve & -IR pts, consistent with observed clinical responses.

References

[1]Deodhar A, et al. Lancet. 2020;395:1115-25.

[2]Coates LC, et al. Ann Rheum Dis. 2021;80:140-1.

[3]Mease P, et al. Lancet. 2020;395:1126-36.

Disclosure of Interests

Stefan Siebert Speakers bureau: AbbVie, Biogen, GSK, Janssen, Novartis, UCB, Grant/research support from: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, GSK, Janssen, Novartis, and UCB, Laura Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Medac, Novartis, Pfizer and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Georg Schett Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Gilead, Janssen, Novartis, and UCB, Siba P Raychaudhuri Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Warner Chen Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), Sheng Gao Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC (a wholly owned subsidiary of Johnson & Johnson), May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Frederic Lavie Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Elke Theander Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC (a wholly owned subsidiary of Johnson & Johnson), Marlies Neuhold Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC (a wholly owned subsidiary of Johnson & Johnson), Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Aurinia, Bristol Myers Squibb, Celgene, Eli Lilly, GSK, Janssen, MoonLake, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, and UCB

AB0888 Guselkumab Provides Sustained Improvements in Work Productivity and Daily Activity in Patients With Active Psoriatic Arthritis Through 2 Years of DISCOVER-2

Background

Psoriatic arthritis (PsA) impacts patients’ (pts) work productivity (WP) and daily activity.1 DISCOVER-2 (D2), a Phase 3 trial of the selective interleukin-23 p19-subunit inhibitor guselkumab (GUS) in biologic-naïve pts with PsA,2 demonstrated significant improvements in pt-reported WP and daily activity following 1 year (Y) of GUS treatment.3

Objectives

Assess WP and daily activity impairment in D2 pts through 2Y. Estimate indirect savings associated with GUS treatment and assess changes in employment status.

Methods

Pts with active PsA received GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), and impaired overall WP (absenteeism + presenteeism) for pts employed at baseline (EBL) and daily activity for all pts, including those unemployed at baseline (UBL) during the previous week. Mean changes in WPAI-PsA domains were calculated for each multiple imputation (MI) dataset using an analysis of covariance (ANCOVA); the reported LS mean is the average of all MI datasets. Significance was defined as p<0.05. Among pts EBL, potential indirect savings from improved overall WP were estimated using 2020 European Union mean yearly wage estimate (all occupations) combined with LS mean change from BL in WPAI-PsA overall work impairment.4 A shift analysis evaluated proportions of pts employed vs unemployed by treatment group using observed data over time.

Results

Pts EBL comprised 64% of the analysis cohort. Significant improvements in WP in pts EBL and in daily activity among all pts were observed with GUS Q4W/Q8W vs PBO at W24;3 mean improvements in WP and daily activity increased with continued GUS through 2Y (Table 1). Potential annual indirect savings from improved overall WP in pts EBL were €10,826 GUS Q4W, €12,712 GUS Q8W, and €10,948 PBO→ GUS Q4W at 2Y. Shift analysis showed relatively stable employment in pts EBL with GUS up to 2Y (>83% continued to work). Among pts UBL (36% of cohort), the proportion of pts employed increased by >20% through 2Y of GUS (Figure 1).

Table 1.

Model-Based Estimates of Change From BL in WPAI-PsA Domains1

GUS 100mg Q4WGUS 100mg Q8WPBO (W0-24) → GUS 100 mg Q4W (W24-100)VisitW24W100W24W100W24W100Absenteeism, N145147147149162166  LS Mean (95% CI)-3.4 (-6.5, -0.3)-1.8 (-4.5, 0.9)-3.0 (-6.0, 0.1)-4.2 (-6.8,-1.5)-3.0 (-6.0, 0.04)-4.2 (-6.8,-1.6)  Diff vs. PBO-0.4 (-4.6, 3.8)--0.01 (-4.2, 4.2)---Presenteeism, N145147147149162166  LS Mean (95% CI)-20.1 (-23.7, -16.6)-26.3 (-30.1,-22.5)-19.6 (-23.2, -16.1)-28.0 (-31.8, -24.2)-10.5 (-13.9, -7.0)-24.2 (-27.9, -20.5)  Diff vs PBO-9.7* (-14.4, -5.0)--9.2* (-13.9, -4.5)---Work productivity, N145147147149162166  LS Mean (95% CI)-20.1 (-24.1, -16.1)-23.8 (-28.0, -19.6)-19.2 (-23.1, -15.2)-28.0 (-32.1, -23.8)-10.6 (-14.4, -6.8)-24.1 (-28.1, -20.1)  Diff vs PBO-9.5* (-14.8, -4.2)--8.6* (-13.9, -3.3)---Daily Activity, N242242246246245245  LS Mean (95% CI)-20.5 (-23.3, -17.7)-29.2 (-32.2, -26.1)-21.2 (-23.9, -18.4)-28.0 (-31.0, -24.9)-9.9 (-12.6, -7.1)-26.6 (-29.6, -23.6)  Diff vs PBO-10.6* (-14.4, -6.8)--11.3* (-15.1, -7.5)-

1Mean changes in WPAI-PsA domains were calculated for each MI dataset using an ANCOVA; reported LS mean (95% confidence interval [CI]) = average of all MI datasets.

*p<0.002

Conclusion

In GUS-treated bio-naïve PsA pts, robust improvements in WP and daily activity seen at W24 were maintained and increased through 2Y of GUS. Long-term improvements in WP achieved may result in substantial indirect cost savings for GUS-treated pts. Rates of employment remained stable in pts employed and increased in those unemployed at BL.

References

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–83.

[2]Mease PJ, et al. Lancet. 2020;395:1126–36.

[3]Curtis JR et al. EULAR, June 2–5, 2021. POS1026.

[4]OECD (2020). Average wages (indicator). https://data.oecd.org/earnwage/average-wages.htm

Disclosure of Interests

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly and Company, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly and Company, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Iain McInnes Shareholder of: Causeway Therapeutics, and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Bristol-Myers Squibb, Amgen, Eli Lilly and Company, Cabaletta, Compugen, GSK, Gilead, Janssen, Novartis, Pfizer, Sanofi, Roche, and UCB, Grant/research support from: Astra Zeneca, Bristol-Myers Squibb, Amgen, Eli Lilly and Company, GSK, Janssen, Novartis, Roche, and UCB, Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Dafna D Gladman Consultant of: Abbvie, Amgen, BMS, Eli Lilly, Galapagos, Gilead, janssen, Novartis, Pfizer and UCB., Grant/research support from: Abbvie, Amgen, Eli Lilly, Janssen, Pfizer, UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Natalie Shiff Shareholder of: Johnson & Johnson, Abbvie, Gilead, Employee of: Janssen Scientific Affairs, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Immunology Global Medical Affairs, Janssen Pharmaceutical Companies, William Tillett Speakers bureau: Abbvie, Amgen, Eli-Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Consultant of: Abbvie, Amgen, Eli-Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Grant/research support from: Abbvie, Amgen, Eli-Lilly, Janssen, and UCB, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB

POS1030 PAIN RESPONSE IN PSORIATIC ARTHRITIS PATIENTS TREATED WITH GUSELKUMAB IS DRIVEN PREDOMINANTLY BY INFLAMMATION-INDEPENDENT EFFECTS

Background

Although reducing inflammation has been associated with pain improvement, the two do not always correlate. Recent studies have suggested that, in addition to its role in inflammation pathogenesis, IL-23 may be involved in pain regulation in a lymphocyte-independent manner1. Guselkumab (GUS), a fully human monoclonal antibody that selectively inhibits IL-23, has demonstrated safety and efficacy in treating multiple domains of active PsA in the DISCOVER-1&2 (D1&D2) trials2,3.

Objectives

To quantify the role of reducing inflammation on the observed relationship between GUS and pain response in PsA patients (pts) using mediation modelling.

Methods

Pooled data from the D1&D2 studies were analyzed. Pts in D1 had ≥3 swollen and ≥3 tender joints (SJC/TJC) and C-reactive protein (CRP)≥0.3 mg/dL; in D2, pts had ≥5 SJC and ≥5 TJC and CRP≥0.6 mg/dL. 31% of D1 pts received 1-2 prior tumor necrosis factor inhibitors (TNFi); D2 pts were bio-naïve. Pts were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then every 8 weeks (Q8W); or placebo (PBO); PBO pts crossed over to GUS 100 mg Q4W at W24. Pts with history of fibromyalgia were excluded from the analysis. Least square mean changes in pt-reported pain (0-100 VAS) through W52 were estimated with a repeated measures linear mixed model adjusting for known pain determinants. Mediation modelling was performed separately for Q4W & Q8W, W4 & W24, and TNFi-naïve & -experienced (exp) pts. In each model, change in pt-reported pain was the dependent variable; treatment regimen was the independent variable; inflammation, measured by change in SJC or CRP, was the designated mediator; covariates were: age; sex; and baseline (BL) pain score, BMI, SF-36 MCS score, and NSAID use.

Results

Mean (SD) BL pain levels in the GUS Q4W, GUS Q8W, and PBO groups were 60.4 (19.8), 62.0 (20.2), and 61.1 (19.6), respectively. Treatment with GUS was associated with significantly greater pain improvement compared with PBO as early as W4 (ΔQ4W-PBO [95%CI]: -4.9 [-7.6, -2.2]; ΔQ8W-PBO [95%CI]: -5.2 [-7.9, -2.5] (Figure 1). These between-group differences were further enhanced by W24 (ΔQ4W-PBO [95%CI]: -14.6 [-17.6, -11.5]; ΔQ8W-PBO [95%CI]: -14.3 [-17.3, -11.2]); by W52, GUS-randomized pts exhibited an approximate 30-point (̴50%) decrease in pain. Similar results were observed for TNFi-naïve and TNFi-exp pts.

Figure 1.

Mediation analyses demonstrated that the majority of GUS effect on pain at W4 was not attributable to SJC (direct effect), specifically ≤6% was mediated by inflammation as assessed by changes in SJC (indirect effect; Table 1). Similarly, at W24, the indirect effect via SJC improvement represented ≤10% of the GUS treatment effect. No differences were observed between TNFi-naïve and -exp pts at either timepoint.

Consistent results were obtained when using CRP as the mediator variable instead of SJC, whereby ≤2-9%% of GUS effect on pan was mediated by inflammation and 91-98% was direct (Table 1).

Table 1.

Direct (D) Treatment Effect vs. Indirect (IND) Effect via Inflammation Markers on Pain Improvement

MediatorWeekPt GroupEffectGUS Q4WGUS Q8WSJC4AllD96.7%*97.0%*IND†3.3%3%TNFi-NaiveD93.7%*98.5%*IND†6.3%1.5%TNFi-ExpD100%*100%*IND†0%0%24AllD94.8%*92.0%*IND†5.2%*8.0%*TNFi-NaiveD89.6%*90.1%*IND†10.4%*9.9%*TNFi-ExpD99.8%*95.7%*IND†0.2%4.3%CRP4AllD97.6%*95.0%*IND‡2.4%5.0%TNFi-NaiveD98.2%*95.4%*IND‡1.8%4.6%TNFi-ExpD97.6%*95.3%*IND‡2.4%4.7%24AllD97.2%*94.2%*IND‡2.8%5.8%*TNFi-NaiveD98.1%*95.9%*IND‡1.9%4.1%TNFi-ExpD96.5%*91.4%*IND‡3.5%8.6%

*p<0.05; †via SJC; ‡via CRP

Conclusion

GUS induced significant improvement in pt-reported pain as early as W4 of treatment, which was continuously enhanced through W52. While the known mediation effect of SJC and CRP, as markers of inflammation, on pain was confirmed, the majority of GUS’s effect on pain reduction was independent of its effect on these markers, regardless of dosing regimen or prior TNFi experience.

References

[1]Arthritis Res Ther. 2020;22:123

[2]Lancet. 2020;395:1115

[3]Lancet. 2020;395:1126

Disclosure of Interests

Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Enrique Soriano Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Consultant of: AbbVie, Janssen, Novartis, and Roche, Grant/research support from: AbbVie, Janssen, Novartis, Pfizer, Roche, and UCB, Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC and Drexel University College of Medicine, Emmanouil Rampakakis Consultant of: Janssen, Employee of: JSS Medical Research, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Peter Nash Speakers bureau: Janssen, Abbvie, Pfizer, Novartis, Lilly, Gilead, Roche, Sandoz, Celgene, Sun, Boehringer, and Bristol Myers Squibb, Consultant of: Janssen, Abbvie, Pfizer, Novartis, Lilly, Gilead, Roche, Sandoz, Celgene, Sun, Boehringer, and Bristol Myers Squibb, Grant/research support from: Janssen, Abbvie, Pfizer, Novartis, Lilly, Gilead, Roche, Sandoz, Celgene, Sun, Boehringer, and Bristol Myers Squibb, Proton Rahman Speakers bureau: Janssen, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis

POS1070 BASELINE DETERMINANTS OF PAIN RESPONSE IN PATIENTS WITH PSORIATIC ARTHRITIS RECEIVING GUSELKUMAB

Background

Pain in patients (pts) with psoriatic arthritis (PsA) has multifaceted origins; sustained improvement is difficult to achieve.1 Guselkumab (GUS), a fully human monoclonal antibody that selectively inhibits IL-23, is effective in treating multiple domains of PsA including joint, skin, and entheseal symptoms, and also elicits long-lasting improvements in pt-reported pain in the DISCOVER-1&2 trials of pts with active PsA.2

Objectives

These post hoc analyses were conducted to identify determinants of changes in pt-reported pain in PsA pts using pooled data through 1 year of DISCOVER-1&2.

Methods

Enrolled adult pts had active PsA despite standard therapies. DISCOVER-1 pts had ≥3 swollen and ≥3 tender joints and C-reactive protein (CRP) ≥0.3 mg/dL; DISCOVER-2 pts had ≥5 swollen and ≥5 tender joints and CRP ≥0.6 mg/dL. 31% of DISCOVER-1 pts received 1-2 prior tumor necrosis factor inhibitors; DISCOVER-2 pts were biologic-naïve. Pts were randomized 1:1:1 to GUS 100 mg every 4 weeks (wks) (Q4W); GUS 100 mg at W0, W4, then every 8 wks (Q8W); or placebo (PBO); PBO pts crossed over to GUS 100 mg Q4W at W24. Determinants with a statistically important effect (p<0.15) on pain (0-100 mm Visual Analogue Scale) in univariate Repeated Measures Generalized Linear Mixed Effects Models were included in a multivariate model employing backward stepwise selection (Pout=0.1) to identify independent determinants of pain improvement over 24 wks; the model was then tested separately in pts treated with PBO (through W24) and with GUS (through W24 and through W52).

Results

GUS was associated with significantly greater improvement in pain compared to PBO as early as 2 wks post-treatment; there was a significant interaction between treatment group and time, with effect of GUS on pain continuously enhanced through W24. Higher baseline (BL) pain score, worse mental health (assessed with the Short-Form-36 Mental Component Summary [SF-36 MCS] score), and lower fatigue level and lower tender joint count [TJC] were also associated with significantly greater pain improvements at W24, while background use of NSAIDs was a negative predictor of pain improvement (Table 1). Treatment effect on pain was independent of PsA duration, gender, PsA subtype, prior TNFi exposure, BL skin disease, and BL swollen joint count (SJC). Continuous significant improvement from BL in pain with GUS extended through W52 even after adjustment for the identified determinants of pain improvement through W24 (Figure 1). At W52, predictors of change in pain remained significant with the exception of SF-36 MCS score (Table 1). Results did not exclude a small number of enrolled pts with fibromyalgia (FM: nGUS=8; nPBO=4). According to these exploratory findings, medical history of FM was associated with lower pain improvement through W24 (p=0.066); in the models run separately in pts with GUS and PBO, pts with FM treated with GUS had a mean (95% CI) pain improvement (-9.1 [-19.5, 1.2]) while pts treated with PBO had a mean worsening (0.7 [-12.5, 13.9]). Pain improvement through 52 wks was significant regardless of FM: pts with FM had a mean (95% CI) improvement of -14.7

Table 1.

Significant Predictors of Change in Pain (W24 and W52)

BL DeterminantW24W52Estimate (95% CL)Estimate (95% CL)Pain score-0.62 (-0.69:-0.55) ‡-0.75 (-0.83:-0.67) ‡Fatigue-0.38 (-0.50:-0.27) ‡-0.37 (-0.53:-0.22) ‡SF-36 MCS0.20 (0.11:0.30)‡0.11 (-0.02:0.24)TJC0.13 (0.06:0.19) †0.12 (0.04:0.21) †NSAID use (Y vs N)2.29 (0.62:3.96) †2.76 (0.55:4.98) *

* p <0.05; †p <0.01; ‡p ≤0.0001

(-25.9, -3.6) comparable to non-FM pts at W24, while pain improvement in pts with no FM was -22.2 (-24.0, -20.4).

Conclusion

Early significant effects of GUS on pain were enhanced through 1 year. Significant predictors of change in pain were consistent at W24 and W52, with the exception of mental health measures. The impact of mental status on pt-reported pain and the potential for GUS to improve pain in pts with FM warrant further consideration.

References

[1]Gudu T et al. Expert Rev Clin Immunol 2018;14(5):405-17.

[2]Nash P et al. ACR Convergence 2021;Nov 5-9 (Poster 21-1368).

Disclosure of Interests

Peter Nash Grant/research support from: Janssen, Abbvie, Pfizer, Novartis, Lilly, Gilead, Roche, Sandoz, Celgene, Sun, Boehringer, and Bristol Myers Squibb, Christopher T. Ritchlin Consultant of: UCB Pharma, Amgen, AbbVie, Lilly, Pfizer, Novartis, Gilead, Janssen, Grant/research support from: UCB Pharma, AbbVie, Amgen, Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Emmanouil Rampakakis Consultant of: Janssen, Employee of: JSS Medical Research, YoungJa Lee Shareholder of: Johnson & Johnson, Employee of: Janssen Asia Pacific, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Jonathan Sherlock Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Daniel Cua Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Saakshi Khattri Speakers bureau: AbbVie, Eli Lilly, Glenmark, Ichnos Sciences, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Eli Lilly, Glenmark, Ichnos Sciences, Janssen, Novartis, Pfizer, and UCB, Enrique Soriano Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Consultant of: AbbVie, Janssen, Novartis, and Roche, Grant/research support from: AbbVie, Janssen, Novartis, Pfizer, Roche, and UCB, Dennis McGonagle Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB

POS1031 EARLIER CLINICAL RESPONSE PREDICT LOW RATES OF RADIOGRAPHIC PROGRESSION IN BIO-NAIVE ACTIVE PSORIATIC ARTHRITIS PATIENTS RECEIVING GUSELKUMAB TREATMENT

Background

Guselkumab (GUS), an IL-23p19-subunit inhibitor, demonstrated efficacy and a favorable safety profile in patients (pts) with psoriasis (PsO) and psoriatic arthritis (PsA). In the Phase 3, double-blind, placebo (PBO)-controlled DISCOVER-2 study, GUS 100 mg every 4 or 8 weeks (Q4W or Q8W) significantly improved joint and skin symptoms; GUS-treated pts had smaller mean changes in radiographic progression vs. PBO at W24.1 Low rates of radiographic progression were observed through 2 years among GUS-treated pts, regardless of dosing regimen.2,3

Objectives

Determine whether earlier clinical improvement predicts long-term radiographic progression through 2 years in DISCOVER-2.

Methods

DISCOVER-2 included biologic-naïve pts with active PsA (≥5 swollen and ≥5 tender joint counts [SJC/TJC]; CRP ≥0.6 mg/dL) randomized (1:1:1) to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or PBO with crossover to GUS 100 mg Q4W (PBO→Q4W) at W24. For pts randomized to GUS Q4W or Q8W, predictive models (mixed linear) were developed post-hoc to assess the associations of earlier (at W16) improvement in disease activity (DAPSA remission, DAPSA Improvement, DAPSA Improvement more than the median of 20.7 [>20.7]) or skin improvement (PASI90, PASI≤1) with changes in total PsA modified van der Heijde-Sharp [vdH-S] score through W100, after adjusting for known baseline (BL) determinants of radiographic progression (vdH-S score, age, gender, and CRP).

Results

PsA duration, CRP, and SJC at BL weakly correlated with BL vdH-S score. No correlation was seen between BL PASI and BL vdH-S score (Table 1). Greater improvement in DAPSA score (β [95%CI]: -0.03 [-0.04, -0.01]) and improvement >20.7 in DAPSA from BL to W16 was associated with significantly less radiographic progression through W100 after adjusting for BL DAPSA score, vdH-S score, age, gender, and CRP level. Achievement of PASI90, PASI≤1, and DAPSA remission at W16 was associated with numerically less radiographic progression through W100 after adjusting for BL PASI, vdH-S score, age, gender, and CRP (Figure).

Table 1.

Correlation of Select BL Disease Characteristics with BL vdH-S Score Among GUS Randomized Pts

BL DeterminantsSpearman’s correlation coefficientp-valueAge0.27335<.0001CRP0.28181<.0001PASI Score0.030780.5153PsA Duration0.37070<.0001PsO Duration0.20509<.0001SJC (66)0.26321<.0001

Conclusion

In GUS-treated biologic-naïve pts with active PsA, following adjustment for known BL determinants of radiographic progression, earlier (W16) DAPSA improvement was a significant predictor of less radiographic progression through W100; DAPSA remission and skin improvement at W16 each showed a numerical trend toward less radiographic progression through W100.

References

[1]Mease PJ, et al. Lancet. 2020;395:1126–36.

[2]McInnes IB, et al. Arthritis Rheumatol. 2021;73:604-16.

[3]McInnes IB, et al. Arthritis Rheumatol. 2021 Nov 1. doi: 10.1002/art.42010. Online ahead of print.

Disclosure of Interests

Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Alice B Gottlieb Speakers bureau: AnaptsysBio, Avotres Therapeutics, Beiersdorf, Boehringer Ingelheim, Bristol-Myers Squibb, Incyte, GSK, Janssen, LEO Pharma, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical Industries, Inc., UCB, and Dermavant, Consultant of: AnaptsysBio, Avotres Therapeutics, Beiersdorf, Boehringer Ingelheim, Bristol-Myers Squibb, Incyte, GSK, Janssen, LEO Pharma, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical Industries, Inc., UCB, and Dermavant, Grant/research support from: Boehringer Ingelheim, Incyte, Janssen, Novartis, UCB, Xbiotech, and Sun Pharma, Alexis Ogdie Consultant of: Abbvie, Amgen, BMS, Celgene, CorEvitas, Gilead, Happify Health, Janssen, Lilly, Novartis, Pfizer, and UCB, Grant/research support from: University of Pennsylvania from Abbvie, Pfizer and Novartis and to Forward from Amgen, Iain McInnes Shareholder of: Causeway Therapeutics, and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Amgen, Bristol-Myers Squibb, Eli Lilly, Cabaletta, Compugen, GSK, Gilead, Janssen, Novartis, Pfizer, Sanofi, Roche, and UCB, Grant/research support from: Astra Zeneca, Bristol-Myers Squibb, Amgen, Eli Lilly, GSK, Janssen, Novartis, Roche, and UCB, Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC and Drexel University College of Medicine, Emmanouil Rampakakis Consultant of: Janssen, Employee of: JSS Medical Research, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Frederic Lavie Shareholder of: Johnson & Johnson, Employee of: Janssen Cilag Global Medial Affairs, Mitsumasa Kishimoto Speakers bureau: AbbVie, Amgen-Astellas BioPharma, Asahi-Kasei Pharma, Astellas, Ayumi Pharma, BMS, Chugai, Daiichi-Sankyo, Eisai, Eli Lilly, Gilead, Janssen, Kyowa Kirin, Novartis, Pfizer, Tanabe-Mitsubishi, Teijin Pharma, and UCB, Consultant of: AbbVie, Amgen-Astellas BioPharma, Asahi-Kasei Pharma, Astellas, Ayumi Pharma, BMS, Chugai, Daiichi-Sankyo, Eisai, Eli Lilly, Gilead, Janssen, Kyowa Kirin, Novartis, Pfizer, Tanabe-Mitsubishi, Teijin Pharma, and UCB, Proton Rahman Speakers bureau: Janssen, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis

AB0881 Guselkumab Provides Sustained Improvements in Health-Related Quality of Life in Patients With Active Psoriatic Arthritis Through 2 Years of DISCOVER-2

Background

Psoriatic arthritis (PsA), a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis, and skin/nail psoriasis, is associated with reduced health-related quality of life (HRQoL).

Objectives

To assess long-term effect of guselkumab (GUS), a human monoclonal antibody that selectively targets the interleukin (IL)-23p19 subunit, on HRQoL of bio-naïve PsA patients (pts) who participated in the Phase 3 2-year DISCOVER-2 trial.1

Methods

Pts with active PsA despite nonbiologic disease-modifying antirheumatic drugs (DMARDs) and/or nonsteroidal anti-inflammatory drugs (NSAIDs) received GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. HRQoL was assessed using the pt-reported EuroQoL-5 Dimension-5 Level (EQ-5D-5L) questionnaire index and EuroQol Visual Analog Scale (EQ-VAS), widely used and complimentary tools that allow pts to provide a global assessment of their HRQoL. The EQ-5D-5L index assesses mobility, self-care, usual activities, pain/discomfort, and anxiety/depression; an index score is derived ranging from 0 (death) to 1 (perfect health).2 EQ-VAS assesses pt health state on a scale of 0-100, with higher scores indicating better health. Using mixed effects models for repeated measures (MMRM), least squares (LS) mean changes from baseline in the EQ-5D-5L index and EQ-VAS through W100 were assessed. Observed changes from baseline were evaluated; in pts who met treatment failure rules before W24 and in pts who discontinued with missing data after W24, changes from baseline were imputed as 0.

Results

GUS-treated pts achieved greater improvements in pt-reported health status than PBO at both W16 and W24 when evaluated using both the EQ-5D-5L index score and the EQ-VAS. The improvements by GUS in EQ-5D-5L index scores through W24 (0.12 for GUS Q4W/Q8W vs 0.05 for PBO; each nominal p<0.0001) were maintained with continued GUS through 2 years (0.15 for GUS Q4W/Q8W) (Table 1). PBO-treated pts who started GUS at W24 reported comparable improvements in their HRQoL by W52 (0.12), with maintenance though W100 (0.14). Similar results were observed with EQ-VAS (Figure 1). W24 improvements in EQ-VAS scores were greater following GUS treatment (18.2/18.4 GUS Q4W/Q8W) vs PBO (6.8; nominal p<0.0001). EQ-VAS scores continued to improve with GUS through 2 years (25.0/24.6 GUS Q4W/Q8W). Likewise, PBO-treated pts who crossed over to GUS at W24 experienced improvements in HRQoL by W52 (18.8), with maintenance through W100 (21.2).

Table 1.

LS mean change from baseline through W100 in EQ-5D-5L index

GUS 100mg Q4W(W0-100)GUS 100mg Q8W(W0-100)PBO → GUS 100 mg Q4WPBO(W0-24)GUS(W24-100)Week162410016241001624100N243244243247246248244244244LS mean change (95% CI)0.10 (0.09,0.12)0.12 (0.1,0.13)0.15 (0.13,0.16)0.11 (0.1,0.13)0.12 (0.1,0.13)0.15 (0.13,0.17)0.06 (0.04,0.07)0.05 (0.04,0.07)0.14 (0.12,0.16)  Diff vs. PBO0.04 (0.02,0.06)0.06 (0.04,0.09)--0.05 (0.03,0.07)0.06 (0.04,0.08)--------  Nominal p-value<0.0001<0.0001--<0.0001<0.0001--------

CI=Confidence interval; Diff=Difference

Conclusion

In bio-naïve pts with active PsA receiving GUS, earlier improvements (at the first timepoint assessed) in self-reported HRQoL measures were sustained through 2 years.

References

[1]Mease PJ, et al. Lancet. 2020;395:1126–36.

[2]EuroQol Group. 1990;16:199-208.

Disclosure of Interests

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, CorEvitas, Eli Lilly, Janssen, Myriad, Novartis, Pfizer, Sanofi, and UCB, Iain McInnes Shareholder of: Causeway Therapeutics, and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Amgen, Bristol-Myers Squibb, Cabaletta, Compugen, Eli Lilly, Gilead, GSK, Janssen, Novartis, Pfizer, Roche, Sanofi, and UCB, Grant/research support from: Astra Zeneca, Amgen, Bristol-Myers Squibb, Eli Lilly, GSK, Janssen, Novartis, Roche, and UCB, Proton Rahman Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Dafna D Gladman Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Eli Lilly, Janssen, Pfizer, and UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC (a wholly owned subsidiary of Johnson & Johnson), Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC (a wholly owned subsidiary of Johnson & Johnson), Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), Natalie Shiff Shareholder of: AbbVie, Gilead, and Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC (a wholly owned subsidiary of Johnson & Johnson), Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC (a wholly owned subsidiary of Johnson & Johnson), May Shawi Shareholder of: Johnson & Johnson, Employee of: Immunology Global Medical Affairs, Janssen Pharmaceutical Companies (a wholly owned subsidiary of Johnson & Johnson), William Tillett Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Eli Lilly, Janssen, MSD, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Eli Lilly, Janssen, and UCB, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN Pharma, and UCB

POS1015 SAFETY OF GUSELKUMAB IN PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS WHO ARE BIO-NAÏVE OR TNFi-EXPERIENCED: POOLED RESULTS FROM 4 RANDOMIZED CLINICAL TRIALS THROUGH 2 YEARS

Background

Guselkumab (GUS), a selective IL-23p19 subunit inhibitor, demonstrated efficacy and a favorable safety profile in active psoriatic arthritis (PsA) in the Phase (Ph)21, Ph3 (DISCOVER [D]-1&2)2,3, and Ph3b COSMOS4 randomized controlled trials (RCTs).

Objectives

Assess GUS safety through 2 years (Y) in biologic (bio)-naïve and tumor necrosis factor inhibitor (TNFi)-experienced (exp) active PsA patients (pts) pooled across 4 RCTs (Week [W] 56: Ph2 and COSMOS; W60: D1; W112: D2).

Methods

Eligible pts in COSMOS had inadequate response to 1 or 2 prior TNFi; 9% of Ph2 pts and 30% of D1 pts had 1 or 2 prior TNFi; D2 pts were bio-naïve. Incidence rates of adverse events (AEs) are summarized among all treated pts for the placebo (PBO)-controlled (W0-24) and active treatment periods through 2Y (max duration of exposure 100 W) according to actual treatment received, calculated as the number of events per 100 pt-Y of follow-up (PY), along with 95% confidence intervals (CI). Gastrointestinal (GI)-related serious AEs (SAEs) were identified using the Medical Dictionary for Regulatory Activities (MedDRA) system-organ class; major adverse cardiovascular events (MACE; predefined as MI, Stroke, or CV death) and opportunistic infections (OIs) were identified through medical review.

Results

Across the 4 RCTs, 1508 pts with active PsA received GUS 100 mg every 4 weeks (Q4W) or Q8W and were followed for a median of 1.2 Y, representing 2125 PY. In the overall population (N=1554), which includes PBO-treated pts that discontinued study agent prior to W24, 1138 pts were bio-naïve and 416 pts were TNFi-exp. Among all treated pts, the overall GUS safety profile was generally consistent with that of PBO through W24; rates remained low through 2Y of GUS (Table 1). The GUS safety profile was similar to that observed with PBO within the bio-naïve and TNFi-exp cohorts through W24. Incidence rates of AEs were generally consistent between cohorts in GUS-treated pts; whereas, TNFi-exp PBO-treated pts had more SAEs, study agent d/c due to AEs, and serious infections than bio-naïve PBO pts (Figure).

Table 1.

Overall Treatment-emergent AEs

PBO-controlled (W0-24)aThrough up to 2YPBOb(N=517)GUS Q8W (N=664)GUS Q4W (N=373)Combined GUS (N=1037)GUS Q8W (N=664)GUS Q4W (N=373)Combined GUSc(N=1508)Total (median) PY230 (0.5)305 (0.5)172 (0.5)478 (0.5)941 (1.1)645 (2.1)2125 (1.2)Events/100 PY (95% CI)AEs223 (204, 243)233 (216, 250)223 (201, 246)229 (216, 243)164 (156, 172)139 (130, 148)146 (141, 151)SAEs8.7 (5.3, 13)4.9 (2.8, 8.1)5.2 (2.4, 9.9)5.0 (3.2, 7.5)6.4 (4.9, 8.2)4.7 (3.1, 6.6)5.7 (4.7, 6.8)AEs leading to study agent d/c4.4 (2.1, 8.0)3.6 (1.8, 6.5)7.0 (3.6, 12.2)4.8 (3.1, 7.2)2.6 (1.6, 3.8)2.9 (1.8, 4.6)2.7 (2.1, 3.5)Infections59 (50, 70)56 (48, 65)57 (47, 70)57 (50, 64)43 (38, 47)37 (33, 42)39 (36, 42)Serious Infections2.2 (0.71, 5.1)0.33 (0.01, 1.8)1.7 (0.36, 5.1)0.84 (0.23, 2.1)1.7 (0.97, 2.8)0.77 (0.25, 1.8)1.5 (1.0, 2.1)Malignancy0.44 (0.01, 2.4)0.98 (0.20, 2.9)0.00 (0.00, 1.7)0.63 (0.13, 1.8)0.42 (0.12, 1.1)0.00 (0.00, 0.46)0.28 (0.10, 0.61)MACE0.44 (0.01, 2.4)0.33 (0.01, 1.8)0.58 (0.01, 3.2)0.42 (0.05, 1.5)0.21 (0.03, 0.77)0.46 (0.10, 1.4)0.24 (0.08, 0.55)GI-related SAEs1.3 (0.27, 3.8)0.33 (0.01, 1.8)0.00 (0.00, 1.7)0.21 (0.01, 1.2)0.32 (0.07, 0.93)0.46 (0.10, 1.4)0.28 (0.10, 0.61)OIs0.00 (0.00, 1.3)0.00 (0.00, 0.98)0.00 (0.00, 1.7)0.00 (0.00, 0.63)0.21 (0.03, 0.77)0.00 (0.00, 0.46)0.14 (0.03, 0.41)

MedDRA Version 23.1.

a Includes safety follow-up data through 2Y for pts who d/c study agent prior to W24 and did not receive any study agent at or after W24.

b Includes data prior to GUS in PBO pts who switched from PBO to GUS.

c Includes PBO to GUS cross-over at W24.

Conclusion

The favorable GUS safety profile demonstrated through W24 persisted through 2Y across bio-naïve and TNFi-exp pts.

References

[1]Deodhar A, et al. Lancet. 2018;391:2213-2224.

[2]Deodhar A, et al. Lancet. 2020;395:1115-1125.

[3]Mease PJ, et al. Lancet. 2020;395:1126-1136.

[4]Coates LC, et al. ARD. 2021;80:140-141. OP0230.

Disclosure of Interests

Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Wolf-Henning Boehncke Speakers bureau: AbbVie, Almirall, Janssen, Leo, Lilly, Novartis, and UCB, Consultant of: AbbVie, Almirall, Janssen, Leo, Lilly, Novartis, and UCB, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Alice B Gottlieb Consultant of: AnaptsysBio, Avotres Therapeutics, Beiersdorf, Boehringer Ingelheim, Bristol-Myers Squibb, Incyte, GSK, Janssen, LEO Pharma, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical Industries, Inc., UCB, and Dermavant, Grant/research support from: Boehringer Ingelheim, Incyte, Janssen, Novartis, UCB, Xbiotech, and Sun Pharma, Iain McInnes Shareholder of: Causeway Therapeutics and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Amgen, Bristol-Myers Squibb, Eli Lilly, Cabaletta, Compugen, GSK, Gilead, Janssen, Novartis, Pfizer, Sanofi, Roche, and UCB, Grant/research support from: Astra Zeneca, Bristol-Myers Squibb, Amgen, Eli Lilly, GSK, Janssen, Novartis, Roche, and UCB, Marlies Neuhold Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Yanli Wang Consultant of: Janssen, Employee of: IQVIA, Shihong Sheng Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Paul Bergmans Shareholder of: Johnson & Johnson, Employee of: Janssen Biostatistics, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elke Theander Employee of: Janssen Scientific Affairs, LLC, Jenny Yu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Evan Leibowitz Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, Marilise Marrache Shareholder of: Johnson & Johnson, Employee of: Medical Affairs, Janssen Inc., Laura Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Medac, Novartis, Pfizer and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB

POS0969 GENETIC AND MOLECULAR DISTINCTIONS BETWEEN AXIAL PSORIATIC ARTHRITIS AND ANKYLOSING SPONDYLITIS

Background

Psoriatic arthritis (PsA) and ankylosing spondylitis (AS) represent the prototypical spondyloarthritides. PsA patients may also suffer from axial disease (axPsA). Despite overlapping symptoms, axPsA and AS may be distinct disorders with differing clinical manifestations, genetic associations, and radiographic findings.1 These disorders also respond differently to immunomodulatory therapies such as anti-interleukin (IL)-23 inhibitors. While guselkumab, a human monoclonal antibody targeting the IL-23p19 subunit, improved symptoms of axPsA,2 risankizumab, a humanized monoclonal antibody targeting the IL-23p19 subunit, did not show improvement in the primary endpoint of proportion of AS patients achieving an Assessment of SpondyloArthritis International Society 40% (ASAS40) response at week (W) 12.3

Objectives

To understand molecular distinctions between axPsA and AS to differentiate these diseases and guide treatment choice.

Methods

Whole blood and serum samples were collected from consenting patients in the NCT03162796/NCT0315828 studies of guselkumab in PsA and the NCT02437162/NCT02438787 studies of ustekinumab in AS. axPsA patients were investigator-verified as having magnetic resonance imaging- or pelvic x-ray-confirmed sacroiliitis at screening (locally read). Human leukocyte antigen (HLA) genotypes were determined by RNA sequencing, limited to Caucasian patients to reduce genetic variability,4 and select serum cytokine levels were analyzed alongside samples from healthy individuals. Differential prevalence of HLA alleles in axPsA versus AS was determined using a Fisher’s Exact test. Statistical significance of differential baseline serum cytokine expression among axPsA versus non-axPsA versus AS patients, and of guselkumab effect on serum cytokine reduction versus placebo among axPsA and non-axPsA patients, were determined with a generalized linear model performed on log2-transformed data. Biomarker data from guselkumab every-4-weeks and every-8-weeks treatment arms were pooled.

Results

Among the 186/234 Caucasian axPsA/AS patients with available data, 34%/15% were female, 70%/14% used methotrexate at baseline, mean serum C-reactive protein (CRP) levels were 2.8/2.4 mg/dL and mean Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) scores were 6.4/7.5, respectively. Aside from race, baseline demographics and disease characteristics were representative of the overall population. The prevalence of class I HLA allele -B27, -C01, and -C02 carriers was significantly lower in axPsA than AS patients (30.7% versus 92.3%, p<0.001; 5.9% versus 31.6%, p<0.001; and 28.0% versus 62.0%, p<0.001, respectively), while the prevalence of HLA-C06 was significantly higher in axPsA than AS populations (36.0% versus 8.6%, p<0.001). Baseline serum levels of IL-17A and IL-17F were significantly higher in axPsA (N=71) than in AS (N=58) patients (p<0.01 and p<0.001, respectively). Comparable IL-17A/F expression was seen for axPsA and non-axPsA (N=229) patients (both p=not significant). Significant and comparable reductions from baseline in serum IL-17A/F in axPsA and non-axPsA patients were seen with guselkumab treatment (axPsA N=41, non-axPsA N=160) versus placebo (axPsA N=30, non-axPsA N=69) at W4/24 (all p<0.05).

Conclusion

Adults with axPsA and AS exhibit different genetic risk factors and serum IL-17 levels, supporting the concept of distinct disorders. Guselkumab demonstrated significant pharmacodynamic effects in axPsA patients that aligned with such effects in non-axPsA patients, consistent with observed clinical improvement.2

References

[1]Feld et al. Nat Rev Rheumatol. 2018;14(6):363-371.

[2]Mease et al. Lancet Rheumatol. 2021;3(10)E715-E723.

[3]Baeten et al. Ann Rheum Dis. 2018;77(9):1295-1302.

[4]Buchkovich et al. Genome Med. 2017;9(86).

Disclosure of Interests

Arthur Kavanaugh Consultant of: AbbVie, Amgen, BMS, Genentech, Janssen, Eli Lilly, Merck, Novartis, Pfizer and UCB, Xenofon Baraliakos Consultant of: AbbVie, Chugai, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, Roche, and UCB, Grant/research support from: AbbVie, MSD, and Novartis, Sheng Gao Employee of: Janssen Research & Development, LLC, and may own stock or stock options in Johnson & Johnson, Warner Chen Employee of: Janssen Research & Development, LLC, and may own stock or stock options in Johnson & Johnson, Kristen Sweet Employee of: Janssen Research & Development, LLC, and may own stock or stock options in Johnson & Johnson, Soumya D Chakravarty Employee of: Janssen Scientific Affairs, LLC, and may own stock or stock options in Johnson & Johnson, Qingxuan Song Employee of: Janssen Research & Development, LLC, and may own stock or stock options in Johnson & Johnson, May Shawi Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, and may own stock or stock options in Johnson & Johnson, Frank Behrens Speakers bureau: AbbVie, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Chugai, Eli Lilly, Galapagos, Genzyme, Gilead, Janssen, MSD, Novartis, Pfizer, Roche, Sanofi, and UCB, Consultant of: AbbVie, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Chugai, Eli Lilly, Galapagos, Genzyme, Gilead, Janssen, MSD, Novartis, Pfizer, Roche, Sanofi, and UCB, Grant/research support from: Celgene, Chugai, Janssen, Pfizer, and Roche, Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen, research grants from Janssen and Novartis

POS1028 GUSELKUMAB MAINTAINS RESOLUTION OF DACTYLITIS AND ENTHESITIS IN PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS: RESULTS THROUGH 2 YEARS FROM A PHASE 3 STUDY

Background

Guselkumab (GUS), a selective inhibitor of IL-23, significantly improved the diverse manifestations of active psoriatic arthritis (PsA), including dactylitis and enthesitis, in DISCOVER (D)-1 & 2 trials of patients (pts) with active PsA1,2, with maintenance of response rates through 1 year (yr).3,4 Dactylitis and enthesitis, extra-articular manifestations of PsA, can be difficult to treat and cause significant disease burden.5,6

Objectives

To evaluate the ability of GUS to provide long-term resolution of dactylitis and enthesitis in pts with PsA through 2 yrs of D-2.

Methods

D-2 biologic naïve pts with active PsA were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS Q4W. Independent assessors evaluated dactylitis (total score: 0-60) and enthesitis (Leeds Enthesitis Index [LEI]; total score 0-6). These post hoc analyses assessed baseline (BL) frequency and severity of enthesitis in pts with dactylitis and dactylitis frequency in pts with enthesitis. Post BL, changes in dactylitis and LEI scores over time (least squares [LS] mean changes; analysis of covariance [ANCOVA]) and rates of resolution of dactylitis and enthesitis (Chi square correlation test) were determined in pts with these manifestations at BL (missing data imputed as no change/response).

Results

At BL, more D-2 pts had enthesitis (68%) than dactylitis (45%). At BL, 78% of pts with dactylitis vs 61% without (w/o) dactylitis had enthesitis and 51% of pts with enthesitis vs 32% w/o enthesitis had dactylitis. Among pts with enthesitis at BL, a higher percentage of pts with dactylitis (52%) had severe enthesitis (LEI score ≥3) vs pts w/o dactylitis (44%). Among those with the condition at BL, resolution rates of dactylitis (57%, Q4W; 64%, Q8W) and enthesitis (44%, Q4W; 54%, Q8W) at W24 increased through W52 (dactylitis: 74%, Q4W; 78%, Q8W; enthesitis: 57%, Q4W; 61%, Q8W) and were maintained at W100 (dactylitis: 72%, Q4W; 83%, Q8W; enthesitis: 62%, Q4W; 70%, Q8W). Consistent results were observed when evaluating mean changes in dactylitis and LEI scores and in pts who crossed over from PBO to GUS Q4W at W24 (Table 1). In pts with dactylitis and enthesitis at BL, GUS-treated pts showed significant correlations between resolution of enthesitis and dactylitis at W24 (p=0.004), W52 (p<0.001) and W100 (p=0.039), with nearly 90% of pts with enthesitis resolution also achieving dactylitis resolution at W52 and W100 (Figure).

Table 1.

LS mean change from baseline over time in dactylitis and LEI scores in pts with manifestation at baseline

GUS 100 mg Q4WGUS 100 mg Q8WPBO → GUS 100 mg Q4WDactylitis score (0-60)Pts, N12111199W24a-5.9 (-6.7, -5.0)-6.0 (-6.8, -5.1)-4.0 (-5.0, -3.1)W52a-6.5 (-7.2, -5.8)-7.2 (-7.9, -6.5)-6.9 (-7.6, -6.2)W100a-6.5 (-7.1, -5.8)-7.5 (-8.1, -6.8)-6.9 (-7.6, -6.2)LEI score (1-6)Pts, N170158178W24a-1.5 (-1.8, -1.3)-1.6 (-1.8, -1.4)-1.0 (-1.3, -0.8)W52a-1.8 (-2.0, -1.6)-1.9 (-2.1, -1.7)-2.0 (-2.2, -1.8)W100a-1.9 (-2.1, -1.7)-2.1 (-2.3, -1.8)-2.1 (-2.3, -1.9)

aResults are LS mean change (95% confidence interval [CI]); LS mean change determined by ANCOVA; missing data was imputed as no change for pts who discontinued treatment and using multiple imputation for remaining missing data

GUS, guselkumab; LEI, Leeds Enthesitis Index; LS, least squares; PBO, placebo; pts, patients; Q4W, every 4 weeks; Q8W, every 8 weeks; W, week

Conclusion

Pts with PsA often present with concurrent enthesitis and dactylitis, both of which can be recalcitrant to treatment. GUS resolved enthesitis and dactylitis in substantial proportions of pts through W100. GUS-treated pts who achieved enthesitis resolution were more likely to achieve dactylitis resolution and vice versa.

References

[1]Deodhar A et al. Lancet. 2020;395:1115

[2]Mease PJ et al. Lancet. 2020;395:1126

[3]Ritchlin C et al. RMD Open. 2021;7(1):e001457

[4]McInnes IB et al. Arthritis Rheumatol. 2021;73:604

[5]Kaeley GS et al. Semin Arthritis Rheum. 2018;48:35

[6]McGonagle D et al. Nat Rev Rheumatol. 2019;15:113

Disclosure of Interests

Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis, Iain McInnes Consultant of: AbbVie, Bristol Myers Squibb, Boehringer Ingelheim, Celgene, Eli Lilly, Gilead, Janssen, Novartis, and UCB, Grant/research support from: AstraZeneca, Bristol Myers Squibb, Celgene, Janssen, Eli Lilly, Novartis, and UCB, Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, GlaxoSmithKline, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Eli Lilly, GlaxoSmithKline, Novartis, Pfizer, and UCB, Georg Schett Speakers bureau: Abbvie, Janssen, and Novartis, Philip J Mease Speakers bureau: AbbVie, Aclaris, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, Grant/research support from: AbbVie, Aclaris, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Inmagene, Janssen, Novartis, Pfizer, SUN Pharma, and UCB, May Shawi Shareholder of: Janssen Global Services, LLC, Employee of: Janssen Global Services, LLC, Daniel Cua Shareholder of: Janssen Research & Development, LLC, Employee of: Janssen Research & Development, LLC, Jonathan Sherlock Shareholder of: Janssen Research & Development, LLC, Employee of: Janssen Research & Development, LLC, Alexa Kollmeier Shareholder of: Janssen Research & Development, LLC, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Janssen Research & Development, LLC, Employee of: Janssen Research & Development, LLC, Yusang Jiang Consultant of: Janssen, Employee of: Cytel Inc, Shihong Sheng Shareholder of: Janssen Research & Development, LLC, Employee of: Janssen Research & Development, LLC, Christopher T. Ritchlin Consultant of: AbbVie, Amgen, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, and UCB Pharma, Grant/research support from: AbbVie, Amgen, and UCB Pharma, Dennis McGonagle Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB

Overcoming barriers in academic-industry partnerships to improve predictive modeling in immuno-oncology

e13581

Background: In the past decade, immunotherapies have revolutionized oncology practice by prolonging patient survival in previously rapidly fatal cancers. However, severe immune toxicities present a challenge, affecting ̃20% and up to 50% of patients on immune monotherapies and combination immunotherapies, respectively. Oncologists must balance toxicity risk with potential efficacy, and pharmaceutical companies have a vested interest in selecting patients with the highest benefit–risk ratio during trial enrollment. Predictive toxicity–efficacy modeling has the potential to guide trial subject selection and clinical care, yet there remains a need for predictive models that can be practically implemented in these settings. Methods: A common academic–industry contract data–transfer framework—wherein academic medical institutions and industry counterparts act in isolation—creates barriers to development of high-quality algorithms with practical applications. In this framework, 1) academic medical institutions provide patient data as a “data dump;” these data are static and cannot be refined—reducing opportunities for quality control; 2) predictive model outcomes may include artifacts that are not identified; 3) manual curation of patient data is required to accurately replicate the model in real-world settings; and 4) lack of clinician participation reduces the potential clinical applications of models and reciprocal benefit to the academic institution. We outline a more contemporary, engaged approach to unite strengths of both partners to achieve a common goal. Results: In 2019, Vanderbilt University Medical Center and GE Healthcare partnered with the goal of enabling safer and more precise immunotherapy use. As part of this work, we formulated a recipe for academic–industry partnerships that offers unique advantages over a static contract framework. In our iterative, interactive approach, 1) clinical and curation experts meet with industry modelers to dynamically refine deidentified data sets by resolving discrepancies in data from different sources (e.g., manually curated vs. structured data); 2) clinical experts iteratively review outputs of predictive models to identify potential artifacts and refine final models; 3) expert curators iterate with in-house machine-learning experts to create algorithms to automate curation of natural language elements from the identified EHR data; and 4) clinical and industry stakeholders participate in regular meetings with modelers to ensure clinical and trial utility of the modeling approach. Conclusions: Compared to data transfer-only relationships, this partnership framework offers an opportunity to develop more informed, higher quality immunotherapy models with clinical and industry applications.

POS0930 SAFETY AND EFFICACY OF IXEKIZUMAB TREATMENT IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS: 3-YEAR CLINICAL TRIAL RESULTS FROM THE COAST PROGRAMME

Background

Ixekizumab (IXE) has demonstrated efficacy at week (wk) 16 which was maintained through 2 years (yrs) and was associated with a consistent safety profile in patients (pts) with r- and nr-axSpA, who are bDMARD-naïve and TNFi-experienced.1-3

Objectives

To report safety and efficacy from the COAST programme at 3 yrs: 1 yr of the originating studies (COAST-V/W/X) and 2 yrs of COAST-Y.

Methods

COAST-Y (NCT03129100) is the phase 3, long-term extension study of the 3 originating studies COAST-V/W/X. Pts continued with the dose received at the end of the originating trial at week (wk) 52: either with 80 mg IXE every 4 wks (Q4W) or every 2 wks (Q2W). Pts assigned to adalimumab (ADA) or placebo (PBO) were re-randomised to IXE Q4W or Q2W at wk 16 in COAST-V and -W. Pts who received PBO for 52 wks in COAST-X were switched to IXE Q4W to continue in COAST-Y. Starting at wk 116 (wk 64 of COAST-Y), pts receiving IXE Q4W could have their dose escalated to Q2W. This analysis focused on pts receiving ≥1 dose of IXE Q4W, observed data while on IXE Q2W dose escalation are excluded. Continuous data are summarised as observed. Safety data while on IXE were analysed for pts who received ≥1 dose of IXE; observed data while on PBO or ADA are excluded.

Results

A total of 932 pts received ≥1 dose of IXE, 414 received ≥1 dose of IXE Q4W, and 562/932 (60%) pts completed 3 yrs of follow-up (PBO→IXE Q4W, 63/119 (53%); ADA→IXE Q4W, 29/44 (66%); and IXE Q4W→IXE Q4W, 114/251(45%)). Through 3 yrs, the most frequently reported treatment-emergent adverse events were infections [incidence rate (IR) 25.7/100 patient years (PY)] and injection site reactions [IR 7.4/100 PY]; the majority of which were mild/moderate in severity. Serious adverse events were reported at an IR of 4.8/100 PY, of which osteoarthritis was the most frequent at 0.4/100 PY. A total of 3 deaths were reported among all pts who received ≥1 dose of IXE [IR 0.1/100 PY]. For all pts, baseline disease activity (Ankylosing Spondylitis Disease Activity Score; ASDAS) was high (see Table 1). The 3 yr mean (SD) change from baseline (observed) in ASDAS among bDMARD-naïve pts with r-axSpA, TNFi-experienced pts with r-axSpA, and bDMARD-naïve pts with nr-axSpA is presented in the Table 1. A consistent disease control through 3 yrs was confirmed across additional efficacy endpoints (Table 1).

Table 1.

Baseline demographics and disease activity characteristics through 3 yrs. Data presented as mean (SD) unless otherwise specified.

COAST-VCOAST-WCOAST-XPBO (N=87)ADA (N=90)IXE Q4W (N=81)PBO (N=104)IXE Q4W (N=114)PBO (N=105)IXE Q4W (N=96)Age43 (12)42 (11)41 (12)47 (13)47 (13)40 (12)41 (15)Male, n (%)71 (83)73 (81)68 (84)87 (84)91 (80)44 (42)50 (52)Symptom duration (years)16.6 (10.1)15.6 (9.3)15.8 (11.2)19.9 (11.6)18.8 (11.6)10.1 (8.3)11.3 (10.7)HLA-B27, n (%)76 (89)82 (91)75 (93)86 (83)91 (80)77 (74)71 (75)ASDAS3.9 (0.7)3.7 (0.8)3.7 (0.7)4.1 (0.8)4.2 (0.9)3.8 (0.9)3.8 (0.8)BASDAI6.8 (1.2)6.7 (1.5)6.8 (1.3)7.3 (1.3)7.5 (1.3)7.2 (1.5)7.0 (1.5)3 years (observed)PBO→ADA→IXEPBO→IXEIXEIXE Q4WIXE Q4WQ4W→IXE Q4WQ4W→PBO→Q4W→N=42N=44IXE Q4WN=46IXE Q4WIXE Q4WIXE Q4WN=81N=114N=31N=56ASDAS CFB-1.9 (0.9)-1.5 (0.9)-1.9 (0.9)-1.6 (1.0)-1.7 (1.0)1.8 (1.0)-1.7 (1.4)ASDAS LDA, n (%)13/24 (54)21/29 (72)33/44 (75)7/20 (35)16/41 (39)13/19 (68)19/29 (66)BASDAI CFB-3.9 (1.9)-3.5 (2.3)-4.0 (2.2)-3.7 (1.7)-3.4 (2.2)-4.4 (2.1)-3.4 (2.7)BASDAI50, n (%)15/24 (63)18/29 (62)31/44 (71)9/20 (45)20/41 (49)12/19 (63)16/29 (55)ASAS40, n (%)13/24 (54)18/29 (62)30/44 (68)10/20 (50)23/41 (56)15/19 (79)17/29 (59)

Conclusion

This analysis of a subset of pts in COAST-Y demonstrated that the safety profile is consistent with the established safety profile, with no new safety signals observed. IXE Q4W was efficacious (observed data) in all patients studied who remained on the treatment through 3 yrs.

References

[1]Dougados et al. Ann Rheum Dis 2020;79.

[2]Deodhar et al. Lancet 2020; 395.

[3]Braun et al. Ann Rheum Dis, 2021; 80: supp 1

Figure 1.

Observed mean CFB in ASDAS for pts treated with IXE Q4W in COAST-V. At wk 16, PBO pts received IXE Q4W.

Acknowledgements

The authors thank So Young Park, PhD, of Eli Lilly and Company for statistical review, and Edel Hughes, PhD, of Eli Lilly and Company for writing and process support.

Disclosure of Interests

Atul Deodhar Speakers bureau: AbbVie, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly and Company, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Galapagos, Glaxo Smith & Kline, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Eli Lilly and Company, Glaxo Smith & Kline, Novartis, Pfizer, UCB, Denis Poddubnyy Speakers bureau: AbbVie, Bristol-Myers Squibb, Eli Lilly and Company, MSD, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Biocad, Eli Lilly and Company, Gilead, GlaxoSmithKline, Janssen, MSD, Novartis, Pfizer, Samsung Bioepis, and UCB, Grant/research support from: AbbVie, Eli Lilly and Company, MSD, Novartis, and Pfizer, Proton Rahman Speakers bureau: Abbott, AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Novartis, and Pfizer., Grant/research support from: Janssen, Novartis, Rebecca Bolce Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Soyi Liu Leage Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Andris Kronbergs Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Caroline Johnson Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Ann Leung Employee of: Employee of Syneos Health, and a contractor for Eli Lilly and Company, Désirée van der Heijde Consultant of: AbbVie, Bayer, BMS, Cyxone, Eisai, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Eli Lilly and Company, Novartis, Pfizer, UCB Pharma, and Director of Imaging Rheumatology BV.

POS1067 DOMAINS CONTRIBUTING TO MINIMAL DISEASE ACTIVITY ACHIEVEMENT IN PATIENTS WITH PSORIATIC ARTHRITIS RECEIVING GUSELKUMAB

Background

Despite effective treatments, a minority of psoriatic arthritis (PsA) patients (pts) realize sustained minimal disease activity (MDA).1 Pt-driven domains of MDA are less frequently achieved, potentially arising from comorbid conditions.1

Objectives

Identify domains contributing to and factors influencing MDA achievement in the 2-year Phase 3 DISCOVER-2 trial.

Methods

Randomized and treated adults (N=739) had active PsA, were biologic/JAK inhibitor-naive, and had swollen and tender joint counts (SJC/TJC) each ≥5 and C-reactive protein ≥0.6 mg/dL. Pts with medical history of fibromyalgia (FM) were not excluded. Pts were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then every 8 weeks (Q8W); or placebo (PBO); PBO pts crossed over to GUS 100 mg Q4W at W24. MDA requires fulfillment of ≥5/7 criteria: TJC ≤1, SJC ≤1, Psoriasis Area and Severity Index (PASI) score ≤1, Pt Pain score ≤15, Pt global disease activity (PtGA) score ≤20, Health Assessment Questionnaire – Disability Index (HAQ-DI) score ≤0.5, and ≤1 tender entheses. A longitudinal trajectory of achieving each MDA criterion through W100 was derived (nonresponder imputation [NRI]). Time to achieve was estimated via Kaplan-Meier survival curve for scores deriving from native scales and those normalized to a 0-66 scale (corresponding to SJC). Multivariate regression models for time to achievement (cox proportional hazard) and achievement (logistic regression) of MDA at W100 identified predictors of response.

Results

Among 492 GUS pts, continuous improvement across all MDA domains was shown through proportions of pts achieving criteria at W24 & W100 (NRI): SJC (45% & 65%), TJC (16% & 34%), PASI (71% & 72%), Pt Pain (23% & 37%), PtGA (29% & 45%), HAQ-DI (34% & 44%), entheseal points (75% & 80%). Times to achieve minimal SJC, PASI, and enthesitis with GUS were significantly faster than for PtGA, Pt Pain, TJC, and HAQ-DI for native-scale scores; when normalized, PtGA, Pt Pain, and HAQ-DI were achieved less often (Figure 1). Higher baseline (BL) Pt Pain score and lower BL Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue score (worse fatigue) were significant predictors of longer time; lower BMI was associated with shorter time to achieve Pt Pain ≤15. Results for achieving Pt Pain ≤15 at W100 were similar. Worse baseline fatigue and PtGA were significant predictors of longer time to PtGA ≤20; worse fatigue also predicted non-achievement of PtGA ≤20 at W100. For time to achieve HAQ-DI ≤0.5, significant BL negative predictors were higher age and BL HAQ-DI score, which were also significant predictors of HAQ-DI ≤0.5 non-achievement at W100. Although seen in only a small number of pts, a significant impact of FM history and suicidal ideation/behavior on Pt Pain ≤15 and HAQ-DI ≤0.5, respectively, was observed (Table 1).

Table 1.

Predictors of Time to Achievement and Achievement of Recalcitrant MDA Domains at Week 100 in GUS-randomized Pts (N=492)

Time to achievementIndependent BL VariablesPt Pain ≤15PtGA ≤20HAQ-DI ≤0.5HR (95% CI)HR (95% CI)HR (95% CI)Age-0.98 (0.97-0.99)†HAQ-DI-0.26 (0.19-0.36)‡PtGA VAS-0.99 (0.98-1.00)*-Pain VAS0.99 (0.98-1.00)†-FACIT-Fatigue§1.02 (1.00-1.03)*1.02 (1.01-1.04)‡-BMI0.98 (0.96-1.00)*FM (N=8)0.70 (0.55-0.90)†--Achievement at W100OR (95% CL)∥OR (95% CL)∥OR (95% CL)∥Age--0.98 (0.96: 1.00)*HAQ-DI--0.13 (0.08: 0.20)‡Pain VAS0.98 (0.97: 1.00)†--FACIT-Fatigue§1.02 (1.00: 1.05)*1.05 (1.03: 1.07)‡-BMI0.97 (0.94: 1.00)*--Suicidal Ideation/Behaviour (N=9)--0.16 (0.03: 0.86)*FM (N=8)0.59 (0.40: 0.86)†--

HR Hazard Ratio CI Confidence Interval OR Odds Ratio CL Confidence Limits

*p <0.05; †p <0.01; ‡p ≤0.0001

§Higher score indicates less fatigue

∥Wald CL

Conclusion

GUS provided continuous improvement in each MDA domain through W100. BL domain score, as well as age, fatigue, and BMI, were significant determinants of MDA achievement in recalcitrant pt-driven domains (Pt Pain, PtGA, HAQ-DI). The impact of FM and mental health status merits further evaluation.

References

[1]Rahman et al. BMJ Open 2017;7(8): e016619

Disclosure of Interests

Laura Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, Janssen, Medac, Novartis, Pfizer and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Emmanouil Rampakakis Consultant of: Janssen, Employee of: JSS Medical Research, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC,, Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, Iain McInnes Shareholder of: Causeway Therapeutics, and Evelo Compugen, Consultant of: Astra Zeneca, AbbVie, Bristol-Myers Squibb, Amgen, Eli Lilly and Company, Cabaletta, Compugen, GSK, Gilead, Janssen, Novartis, Pfizer, Sanofi, Roche, and UCB, Grant/research support from: Astra Zeneca, Bristol-Myers Squibb, Amgen, Eli Lilly and Company, GSK, Janssen, Novartis, Roche, and UCB, Lai-Shan Tam Consultant of: Janssen, Pfizer, Sanofi, AbbVie, Boehringer Ingelheim, and Lilly, Grant/research support from: Amgen, Boehringer Ingelheim, Janssen, GSK, Novartis and Pfizer

AB0357 INCIDENCE AND DETERMINANTS OF INFECTION IN RHEUMATOID ARTHRITIS PATIENTS TREATED WITH GOLIMUMAB IN REAL-WORLD PRACTICE

Background

Although biologic use in rheumatoid arthritis (RA) has a well-characterized infections risk factor, most studies evaluating this association were conducted on first-generation anti-tumor necrosis factor (TNFi) agents or in early years post-drug development (early 2000).

Objectives

To (i) characterize the long-term incidence of infection in a real-world cohort of RA patients treated with subcutaneous golimumab (GLM) in Canadian routine care; (ii) assess the impact of infections on GLM retention, and (iii) explore factors associated with the risk of infection.

Methods

BioTRAC registry was a prospective, multicenter study that collected real-world clinical, laboratory, safety and patient-reported data from TNFi naïve patients or treated with biologics for a period of <6 months before enrolment. This post-hoc analysis included patients with RA who initiated GLM treatment. The incidence density rates (IDR) of total serious (SI) and non-serious (NSI) infections were calculated for the overall follow-up (90 months) period as well as by 6-month interval. Time to first infection and time to treatment discontinuation were assessed with the Kaplan-Meier estimator of the survival function. Determinants of infection over time or within the first 6 months were explored using generalized estimating equation models and logistic regression, respectively.

Results

530 patients were included with a mean (SD) age of 57.7 (13.0) years and disease duration of 8.0 (8.3) years. Of these, 404 (76.2%) were females, 74 (14.0%) were treated with ≤15mg/week MTX, 280 (52.8%) with >15mg/week MTX, while 173 (32.6%) were not on MTX. In terms of corticosteroids (CS), 72 (13.6%) were treated with ≤5mg/day, 63 (11.9%) with >5mg/day, and 391 (73.8%) were not on CS. Diabetes (4.5%), pulmonary disease (8.9%), and renal disease (18.5%) were present.

Over a mean follow-up duration of 27.0 months, the IDR for total infections, NSI, and SI was 35.10 events/100 PYs, 32.90 events/100 PYs, and 2.23 events/100 PYs. Median estimated time to first infection was 52.9 months (SI: 84.9 months; NSI: 55.1 months) (Table 1). The incidence of total infections was 44.0, 37.3, 35.1, 29.4, 31.1, 35.7, 19.3 and 7.4 events/100 PYs at 0-6 months, 6-12 months, 12-24 months, 24-36 months, 36-48 months, 48-60 months, 60-72 months, 72-84 months, respectively and no infections between 84-90 months. In terms of determinants, no significant associations were identified for the incidence of infections within the first 6 months. However, presence of pulmonary disease was identified as a significant determinant of total infections (OR [95%CI]: 2.19 [1.36-3.52]) and NSI (2.22 [1.35-3.66]) over time, while higher age (1.08 [1.00-1.26]) and high (≥5 mg/day) CS dose (7.25 [1.12-46.80]) were associated with significantly higher odds of SI. Incidence of SI (6.48 [1.16-36.13]), but not NSI, was associated with significantly higher odds of GLM discontinuation; additional predictors of discontinuation were increased baseline CDAI (1.06 [1.04-1.08]) and use of concomitant MTX at low dose (0.52 [0.30-0.91]) or high dose (0.71 [0.49-1.04]).

Table 1.

Incidence Density Rate (IDR) by Infection Type.

Infection TypeIDR (Events/100 PYs)Median Time to 1st Infection (months)Total Infections35.152.9Non-serious Infections32.984.9Serious Infections2.255.1

Conclusion

The infection rates reported with GLM in this cohort are low compared to the rates reported in earlier registry studies with TNFi. Changes in the characteristics of patients starting TNFi (lower disease activity, shorter disease duration, less exposure to CS) in recent years may explain the decreased risk of infection. Compared to the available literature, treatment with GLM was associated with relatively low infection rate. Most infections occurred during the first 6 months of treatment and decreased thereafter. Presence of pulmonary disease, higher age, and higher CS dose were identified as significant predictors of infections. SIs, but not NSIs, were associated with significantly higher odds of treatment discontinuation-TNFi.

Disclosure of Interests

Louis Bessette Speakers bureau: Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Lilly, Novartis, Gilead, Sandoz, Fresenius Kabi, Teva, Consultant of: Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Lilly, Novartis, Gilead, Sandoz, Fresenius Kabi, Teva, Grant/research support from: Amgen, BMS, Janssen, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Lilly, Novartis, Gilead, Sandoz, Fresenius Kabi, Teva, Proton Rahman Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, John Kelsall: None declared, Jane Purvis: None declared, Emmanouil Rampakakis Consultant of: Janssen, Allen Lehman Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Meagan Rachich Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Francois Nantel Shareholder of: Johnson & Johnson, Employee of: Retiree from Janssen Pharmaceutical Companies of Johnson & Johnson, Marilise Marrache Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Odalis Asin Milan Shareholder of: Johnson & Johnson, Employee of: Employees of Janssen Pharmaceutical Companies of Johnson & Johnson

OP0025 FACTORS ASSOCIATED WITH FATIGUE AND ITS IMPROVEMENT – A PRINCIPAL COMPONENT ANALYSIS OF PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS FROM GUSELKUMAB PHASE 3 TRIALS

Background

Fatigue, one of the top 3 patient (pt)-reported symptoms of psoriatic arthritis (PsA) and a recent PsA outcome domain,1 causes impaired health-related quality-of-life, diminished productivity, and disability.1-3 Although the origins of fatigue are multifactorial, inflammation is hypothesized to play an important role.4 In pts with active PsA, treatment with guselkumab (GUS) led to clinically meaningful and sustained improvements in fatigue through 1 year in DISCOVER-1 (D1) and DISCOVER-2 (D2).5

Objectives

To identify 1) factors associated with fatigue and 2) factors associated with change in fatigue among pts with PsA treated with GUS.

Methods

In the Phase 3 D1 (N=381, biologic-naïve and tumor necrosis factor inhibitor-experienced) and D2 (N=739, biologic-naïve) studies, pts with active PsA despite standard therapies and/or biologic disease-modifying antirheumatic drugs were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at W0, W4, then Q8W; or placebo (PBO) with crossover to GUS 100 mg Q4W at W24. The pt-reported Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatigue) scale measured fatigue (scored 0-52). In these post-hoc analyses of D1 and D2 pts, a principal component analysis (PCA) was performed using W0 data to identify the underlying baseline factors associated with fatigue. Additionally, linear regression analyses were performed to identify covariates associated with change in fatigue from W0 to W24.

Results

In 1120 pts (mean age 47 yrs, mean disease duration 5.9 yrs, 48% female), mean FACIT-Fatigue scores at baseline ranged from 29.1 to 31.4 (vs 43.6 for the general US population).5 PCA showed that 62% of the variability in fatigue could be explained by 3 components (Figure 1). The first component, explaining 34% of variability in fatigue, largely comprised systemic disease activity and function measures such as pain, pt global assessment of disease activity (PtGDA), physician’s global assessment of disease activity, and Health Assessment Questionnaire-Disability Index (HAQ-DI). The second component, explaining 16% of variability, comprised joint manifestations including swollen joint count (SJC) and tender joint count (TJC). Skin involvement as assessed by Psoriasis Area and Severity Index (PASI) and systemic inflammation (C-reactive protein [CRP]) could explain 12% of the variability in fatigue (Figure 1 and Table 1). In a multivariate linear regression analysis, after adjusting for effects from other variables, improvement in CRP, physical function (HAQ-DI), PtGDA, and PASI score were significantly associated with fatigue improvement in GUS-treated pts at W24 (all p<0.001).

Table 1.

PCA of Pts With Active PsA in D1+D2 (N=1120; Pooled W0 data): Factor Loading Estimates by Covariates

Component1 Systemic Disease Activity and FunctionComponent 2 Joint ManifestationsComponent 3 Skin Involvement and InflammationPsA disease duration, yr0.100.140.25PASI total score (0-72)0.220.230.74CRP, mg/dL0.36-0.130.55HAQ-DI score (0-3)0.73-0.09-0.19Pain (0-10 VAS)0.83-0.35-0.13PtGDA (0-10 VAS)0.82-0.36-0.16Physician global assessment of disease activity (0-10 VAS)0.65-0.180.23SJC (0-66)0.500.74-0.12TJC (0-68)0.540.70-0.18

VAS=Visual Analog Scale.

Conclusion

Among pts with PsA, measures of systemic disease activity and function, followed by joint manifestations, and skin involvement/inflammation accounted for 62% of the variability in fatigue. The large residual effect (38%) that was unexplained by the current model suggests the need for further research to identify additional factors (eg, distinct molecular pathways) contributing to the fatigue reported by PsA pts.

References

[1]Leung YY, et al. J Rheumatol (Suppl). 2020;96:46-9.

[2]Gudu T, et al. Joint Bone Spine. 2016;83:439-43.

[3]Husted JA, et al. Ann Rheum Dis. 2009;68:1553-8.

[4]Krajewska-Włodarczyk M, et al. Reumatologia. 2017;55:125-30.

[5]Rahman P, et al. Arthritis Res Ther. 2021;23:190.

Disclosure of Interests

Proton Rahman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: Janssen and Novartis, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Consultant of: AbbVie, Aclaris, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Inmagene, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, Sun Pharma, and UCB, Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Aurinia, Bristol Myers Squibb, Celgene, Eli Lilly, GlaxoSmithKline, Janssen, MoonLake, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Eli Lilly, GlaxoSmithKline, Novartis, Pfizer, and UCB, Arthur Kavanaugh Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Genentech, Janssen, Merck, Novartis, Pfizer and UCB, Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Yan Liu Shareholder of: 3 Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Pharmaceutical Companies of Johnson & Johnson, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC.

OP0034 IXEKIZUMAB IMPROVES SIGNS, SYMPTOMS, AND QUALITY OF LIFE IN PATIENTS WITH AXIAL SpA IRRESPECTIVE OF DISEASE DURATION: RESULTS FROM THE COAST-V, COAST-W AND COAST-X TRIALS

Background

Ixekizumab (IXE), a high-affinity monoclonal antibody selectively targeting interleukin-17A,1 has demonstrated superior efficacy to placebo (PBO) in the treatment of patients (pts) with radiographic axial spondyloarthritis (r-axSpA) (COAST-V [NCT02696785]; -W [NCT02696798]), and non-radiographic axial spondyloarthritis (nr-axSpA) (COAST-X [NCT02757352]).

Objectives

Assess treatment response to IXE categorised by disease duration since symptom onset (<5 years (yrs), ≥5yrs) in pts with r-axSpA and nr-axSpA up to 52 Weeks (Wks).

Methods

Pts fulfilled ASAS classification criteria for r-axSpA or nr-axSpA and were randomised to receive 80mg subcutaneous IXE every 2Wks or 4Wks, or PBO (16Wks COAST-V/W; 52Wks COAST-X). Data were summarized by disease duration and treatment in eligible intent-to-treat (ITT) pts. Wk16 treatment comparisons were conducted using Cochran-Haenszel-Mantel test and ANCOVA. Missing data were handled using non-responder imputation and modified baseline observation carried forward, for categorical and continuous endpoints, respectively.

Results

Table 1 presents pt demographics and baseline characteristics. Data is from pooled IXE pts. In pts with r-axSpA and <5yrs symptom duration, ASAS40 response was achieved by 51.5% at Wk16 and 60.6% at Wk52, compared to 36.9% at Wk16, significantly different to PBO (p<0.001), and 40.5% at Wk52, in pts with ≥5yrs symptom duration. In pts with nr-axSpA and <5yrs symptom duration, ASAS40 response was achieved by 42.5% at Wk16, significantly different to PBO (p=0.012), and 54.8% at Wk52, compared to 36.0% at Wk16 and 41.4% at Wk52 in pts with ≥5yrs symptom duration (Figure 1). In pts with r-axSpA and <5yrs symptom duration, ASDAS LDA <2.1 response was achieved by 39.4% at Wk16 and 48.5% at Wk52, compared to 27.5% at Wk16, significantly different to PBO (p<0.001), and 35.6% at Wk52 in pts with ≥5yrs symptom duration. In pts with nr-axSpA and <5yrs symptom duration, ASDAS LDA <2.1 response was achieved by 32.9% at Wk16, significantly different to PBO (p=0.003), and 49.3% at Wk52, compared to 23.9% at Wk16 and 36.9% at Wk52 in pts with ≥5yrs symptom duration. At Wk16, in pts with r-axSpA pts and <5yrs symptom duration, the Change from Baseline (CFB) in SF-36 Physical Component Summary (PCS) Score (LSM±SE) was 7.91 (±1.52), compared to 6.81 (±0.40) in pts with ≥5yrs symptom duration. In pts with nr-axSpA and <5yrs symptom duration, this score was 8.95 (±0.95) compared to 7.07 (±0.73) in pts with ≥5yrs symptom duration; both were significantly different to PBO (r-axSpA: p<0.001; nr-axSpA: p=0.037).

Table 1.

Patient demographics and Baseline Characteristics

Pts with r-axSpAPts with nr-axSpA<5yrs (Ns=33)≥5yrs (Ns=306)<5yrs (Ns=73)≥5yrs (Ns=111)Age (years)33.1 (8.15)45.1 (12.12)31.9 (10.07)46.4 (11.86)Male, n (%)26 (78.8)245 (80.1)40 (54.8)53 (47.7)Female, n (%)7 (21.2)61 (19.9)33 (45.2)58 (52.3)Age at axSpA onset (years)30.4 (8.29)27.0 (9.06)29.9 (10.22)29.5 (8.76)ASDAS score, mean (SD)3.87 (0.79)4.03 (0.82)3.79 (0.80)3.85 (0.78)BASDAI score, mean (SD)7.16 (1.64)7.25 (1.35)7.00 (1.33)7.30 (1.26)SF-36 PCS, mean (SD)34.02 (7.73)32.86 (7.63)32.84 (7.86)32.57 (6.97)

Abbreviations: IXE=ixekizumab, n=number of pts in specified category, Ns=number of pts in each subgroup, SD=standard deviation.

Figure 1.

ASAS40 Response Rates for patients with r-axSpA (COAST-V/W) and nr-axSpA (COAST-X) Symptom Duration <5 and ≥5 years up to Week 52, ITT, NRI: Significantly greater response of IXE versus PBO at Week 16 denotated by * (p≤0.05), *** (p≤0.001). Abbreviations: PBO, placebo; IXE, Ixekizumab; NRI, nonresponder imputation; ITT, Intent-to-Treat (population), ASAS, Assessment of Spondyloarthritis International Society.

Conclusion

Efficacy response to the therapy with IXE was observed in both subgroups based on disease duration (<5 and ≥5yrs) with more robust responses in the <5 years subgroup.

References

[1]Paller AS, Br J Dermatol. 2020;183(2):231-241.

Disclosure of Interests

Victoria Navarro-Compán Speakers bureau: AbbVie, Janssen, Eli Lilly and Company, Novartis, Pfizer and UCB, Paid instructor for: AbbVie, Janssen, Eli Lilly and Company, Novartis, Pfizer and UCB, Consultant of: AbbVie, Eli Lilly and Company, Novartis, Pfizer and UCB, Grant/research support from: ASAS: Research grant 2018 and 2021.

Novartis: Research grant 2021 (Payment to institution), John D Reveille Speakers bureau: Eli Lilly and Company and UCB Pharma, Consultant of: UCB Pharma, Grant/research support from: Eli Lilly and Company, Proton Rahman Speakers bureau: Abbott, AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, and Company Janssen, Novartis, and Pfizer., Paid instructor for: Advisory Board: Abbott, AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Novartis, and Pfizer., Grant/research support from: Janssen and Novartis, José Maldonado-Cocco Speakers bureau: Pfizer, Merck Sharp Dohme, Wyeth, Sanofi Aventis, Novartis, Bristol Myers Squibb, Roche, Shering-Plough, Abbvie, UCB, Gilead., Consultant of: Pfizer, Merck Sharp Dohme, Wyeth, Sanofi Aventis, Novartis, Bristol Myers Squibb, Roche, Shering-Plough, Abbvie, UCB, Gilead., Grant/research support from: Pfizer, Merck Sharp Dohme, Wyeth, Sanofi Aventis, Novartis, Bristol Myers Squibb, Roche, Shering-Plough, Abbvie, UCB, Gilead., Marina Magrey Speakers bureau: Novartis, Abbvie, Eli Lilly and Company., Consultant of: Novartis, Eli Lilly and Comapny, Pfizer, Janssen, UCB Pharma, Abbvie, BMS., Rebecca Bolce Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, David Sandoval Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, So Young Park Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Andris Kronbergs Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Martin Rudwaleit Speakers bureau: Abbvie, BMS, Boehringer Ingelheim, Chugai, Eli Lilly and Company, Novartis, Pfizer, UCB., Paid instructor for: Abbvie, Eli Lilly, Novartis, UCB., Consultant of: UCB, Grant/research support from: Galapagos, UCB, Novartis.

OP0017 RECAPTURE RATES WITH IXEKIZUMAB AFTER WITHDRAWAL OF THERAPY IN PATIENTS WITH AXIAL SPONDYLOARTHRITIS: RESULTS AT WEEK 104 FROM A RANDOMIZED PLACEBO-CONTROLLED WITHDRAWAL STUDY

Background

COAST-Y is the first study to evaluate the effect of continuing vs withdrawing an IL-17A antagonist, Ixekizumab (IXE) on the maintenance of disease control in patients (pts) with ankylosing spondylitis and non-radiographic axial spondyloarthritis through 104 Weeks (wks).

Objectives

Here, we describe the final results of pts re-randomized to either placebo (PBO; IXE Withdrawal) or IXE, who experienced flare, and recaptured response before or after open label retreatment during COAST-Y.

Methods

COAST-Y (NCT03129100) is a Phase 3, long-term extension study that included a double-blind, PBO-controlled, randomized withdrawal-retreatment period (RWP). Eligible pts who completed an originating study (COAST-V, -W, or -X) entered a 24-Week (Wk) lead-in period and received 80 mg IXE every 2 (Q2W) or 4 wks (Q4W) (the treatment regimen at the end of the originating study); pts receiving PBO at the end of COAST-X were assigned to IXE Q4W in COAST-Y. Pts who achieved remission (Ankylosing Spondylitis Disease Activity Score (ASDAS) <1.3 (inactive disease; ID) at least once at Wk 16 or 20, and <2.1 (low disease activity; LDA) at both visits) were randomized 2:1 at Wk 24 to continue IXE (as per lead-in period) or withdrawn to PBO. Pts who subsequently experienced flare (ASDAS ≥2.1 at 2 consecutive visits or ASDAS >3.5 at any visit) were switched to open label IXE Q2W or Q4W at the next visit (same as lead-in period). Time to first flare was analyzed using the Kaplan-Meier method with treatment comparison performed using log-rank test. The observed proportion of pts who recaptured ASDAS LDA and ID were summarized for pts who experienced flare and were retreated with open label IXE.

Results

A total of 155 pts met the criteria for remission and entered the RWP (PBO [IXE withdrawal], N=53; IXE Q4W, N=48; IXE Q2W, N=54) and 138 completed Wk 104. At Wk 104, significantly more pts in the combined IXE group (75.5%, p<0.001, IXE Q4W: 75.0%, p<0.001; IXE Q2W: 75.9%, p<0.001) remained flare free through Wk 104 vs PBO (Figure 1). Notably, 35.8% of pts on PBO (IXE Withdrawal) never experienced flare. Of the PBO pts who experienced flare and were retreated during Wk 24-104 (N=28), 4 recaptured LDA before switching to open label IXE retreatment, while 23 recaptured LDA and 19 met ID after switching (Table 1). Of the continuously treated IXE pts (N=13), 7 recaptured LDA before switching to open label IXE retreatment, while 5 recaptured LDA and 4 met ID after.

Figure 1.

The proportion (%) of patients who remained flare free through 104 weeks. ‡p<0.001, †p<0.01, *p<0.05 vs PBO (IXE Withdrawal).

Table 1.

Recapture of first treatment response before or after switching to open label IXE through 104 weeks among placebo (ixekizumab withdrawal)-treated patients who experienced a flare and retreated

Total patients who flared and were switched to open-label ixekizumab retreatmentPlacebo (ixekizumab withdrawal)(N=28)ASDAS disease activity statusLDAIDRecaptured response before open label ixekizumab retreatment41Recaptured response with open label ixekizumab retreatment (≤16 weeks)2314Recaptured response with open label ixekizumab retreatment (>16 weeks)05Total patients who recaptured response at week 10427/28 (96%)20/28 (71%)

Data are presented as n, (%) for the total row and n only for all other rows. In each column, the denominator is 28. ASDAS, Ankylosing Spondylitis Disease Activity Score; ID, inactive disease; LDA, low disease activity including ID; N, number of patients in the analysis population.

Conclusion

Pts continuously treated with IXE were less likely to experience flare vs pts on PBO (IXE withdrawal). The vast majority of pts withdrawn from IXE to PBO recaptured at least LDA and over half met ID with IXE retreatment. This may provide support for pts who require interruption in therapy.

Acknowledgements

This study was sponsored by Eli Lilly and Company. Medical writing services were provided by Edel Hughes, PhD and Sumeet Sood, PhD of Eli Lilly and Company, and was funded by Eli Lilly and Company.

Disclosure of Interests

Robert B.M. Landewé Consultant of: Rheumatology Consultancy BV, AbbVie, UCB, Pfizer, Eli Lilly and Company, Novartis, and Celgene, Denis Poddubnyy Speakers bureau: AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Merck Sharp & Dohme, Novartis, Pfizer, Roche, and UCB Pharma, Consultant of: AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Merck Sharp & Dohme, Novartis, Pfizer, Roche, and UCB Pharma, Grant/research support from: AbbVie, Eli Lilly and Company, Merck Sharp & Dohme, Novartis, and Pfizer, Proton Rahman Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Merck, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Merck, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, Merck, Novartis, Pfizer, and UCB, Rebecca Bolce Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Soyi Liu Leage Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Jeffrey Lisse Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Ann Leung: None declared, So Young Park Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Lianne S. Gensler Consultant of: AbbVie, Eli Lilly and Company, Grant/research support from: Novartis, Pfizer, and UCB.

Systems Approach to Integrating Preclinical Apolipoprotein E-Knockout Investigations Reveals Novel Etiologic Pathways and Master Atherosclerosis Network in Humans

OBJECTIVE

Animal models of atherosclerosis are used extensively to interrogate molecular mechanisms in serial fashion. We tested whether a novel systems biology approach to integration of preclinical data identifies novel pathways and regulators in human disease. Approach and Results: Of 716 articles published in ATVB from 1995 to 2019 using the apolipoprotein E knockout mouse to study atherosclerosis, data were extracted from 360 unique studies in which a gene was experimentally perturbed to impact plaque size or composition and analyzed using Ingenuity Pathway Analysis software. TREM1 (triggering receptor expressed on myeloid cells) signaling and LXR/RXR (liver X receptor/retinoid X receptor) activation were identified as the top atherosclerosis-associated pathways in mice (both P<1.93×10-4, TREM1 implicated early and LXR/RXR in late atherogenesis). The top upstream regulatory network in mice (sc-58125, a COX2 inhibitor) linked 64.0% of the genes into a single network. The pathways and networks identified in mice were interrogated by testing for associations between the genetically predicted gene expression of each mouse pathway-identified human homolog with clinical atherosclerosis in a cohort of 88 660 human subjects. Homologous human pathways and networks were significantly enriched for gene-atherosclerosis associations (empirical P<0.01 for TREM1 and LXR/RXR pathways and COX2 network). This included 12(60.0%) TREM1 pathway genes, 15(53.6%) LXR/RXR pathway genes, and 67(49.3%) COX2 network genes. Mouse analyses predicted, and human study validated, the strong association of COX2 expression (PTGS2) with increased likelihood of atherosclerosis (odds ratio, 1.68 per SD of genetically predicted gene expression; P=1.07×10-6).

CONCLUSIONS

PRESCIANT (Preclinical Science Integration and Translation) leverages published preclinical investigations to identify high-confidence pathways, networks, and regulators of human disease.

Semi-Automated Data Curation from Biomedical Literature

Data curation is a bottleneck for many informatics pipelines. A specific example of this is aggregating data from preclinical studies to identify novel genetic pathways for atherosclerosis in humans. This requires extracting data from published mouse studies such as the perturbed gene and its impact on lesion sizes and plaque inflammation, which is non-trivial. Curation efforts are resource-heavy, with curators manually extracting data from hundreds of publications. In this work, we describe the development of a semi-automated curation tool to accelerate data extraction. We use natural language processing (NLP) methods to auto-populate a web-based form which is then reviewed by a curator. We conducted a controlled user study to evaluate the curation tool. Our NLP model has a 70% accuracy on categorical fields and our curation tool accelerates task completion time by 49% compared to manual curation.

AB0527 CORRELATIONS BETWEEN REDUCTIONS IN FATIGUE SEVERITY AND IMPROVEMENTS IN PHYSICAL FUNCTION AND CLINICAL RESPONSE IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM THE PHASE 3 DISCOVER PROGRAM

Background:

In patients (pts) with psoriatic arthritis (PsA), fatigue is a major driver of perceived impact of disease and has been identified as an important domain to be assessed in clinical trials.1,2 The association between fatigue and other PsA domains (eg, physical function) or clinical response is not well understood.

Objectives:

Fatigue was measured with the Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue questionnaire in the pivotal DISCOVER-1 and DISCOVER-2 Phase 3 studies of guselkumab (GUS) vs placebo (PBO). This post hoc analysis explores the correlation between FACIT-Fatigue and physical function and clinical response in the DISCOVER program.

Methods:

This analysis used pooled data from pts (N=1120) treated with GUS or PBO. In DISCOVER-1 and DISCOVER-2, 381 pts with active PsA (swollen joint count [SJC] ≥3, tender joint count [TJC] ≥3, C-reactive protein [CRP] ≥0.3 mg/dL) and 739 pts with active PsA (SJC ≥5, TJC ≥5, CRP ≥0.6 mg/dL) and inadequate response to standard therapies, respectively, were randomized 1:1:1 to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or PBO. PBO pts switched to GUS 100 mg Q4W at W24. The FACIT-Fatigue questionnaire has 13 items that assess self-reported fatigue/tiredness over the last 7 days. Items are scored from 0 (very much fatigued) to 4 (not at all fatigued). FACIT-Fatigue response was defined as an increase of ≥4 points from baseline. Physical function was evaluated with the Health Assessment Questionnaire-Disability Index (HAQ-DI). HAQ-DI response was defined as a decrease of ≥0.35 points from baseline. Clinical response was defined as achievement of ≥20% improvement in the American College of Rheumatology (ACR 20) criteria. Relationships between FACIT-Fatigue and HAQ-DI at W8/16/24 were assessed by Pearson correlation coefficients. Mean changes in HAQ-DI scores at W8/16/24 were summarized in FACIT-Fatigue responders and nonresponders. A logistic regression model was applied to estimate odds ratios (ORs) for achievement of HAQ-DI and ACR 20 response by FACIT-Fatigue response status at each visit. A multiple linear regression model was used to evaluate the association between FACIT-Fatigue and HAQ-DI at W24 after adjusting for SJC, TJC, CRP, and pt assessment of pain.

Results:

FACIT-Fatigue and HAQ-DI scores and changes from baseline were negatively correlated at W8, W16, and W24 (Table 1). Mean changes in HAQ-DI were −0.31, −0.43, and −0.48 at W8, W16, and W24, respectively, in FACIT-Fatigue responders and −0.06, −0.07, and −0.09, respectively, in FACIT-Fatigue nonresponders. FACIT-Fatigue responders were significantly more likely than nonresponders to achieve HAQ-DI and ACR 20 response (OR [95% CI] at W8, 2.8 [2.2-3.7] and 2.4 [1.9-3.1]; at W16, 3.6 [2.8-4.7] and 2.9 [2.3-3.7]; and at W24, 4.4 [3.4-5.7] and 3.2 [2.5-4.2], respectively; Figure 1). Correlations between FACIT-Fatigue and HAQ-DI remained significant after adjusting for SJC, TJC, CRP, and pt assessment of pain.

Conclusion:

In pts with PsA, fatigue response is a clinically meaningful predictor of improvements in physical function and achievement of ACR 20 response, reinforcing the importance of assessing fatigue in PsA disease management.

References:

[1]Leung YY et al. J Rheumatol. 2020;96:46-9.

[2]Gudu T et al. Joint Bone Spine. 2016;83:439-43.

Table 1.

Correlation* of FACIT-Fatigue and HAQ-DI

VisitHAQ-DI and FACIT Fatigue ScoresChanges From Baseline in HAQ-DI and FACIT-Fatigue ScoresW8−0.61 (p<0.0001)−0.42 (p<0.0001)W16−0.60 (p<0.0001)−0.47 (p<0.0001)W24−0.62 (p<0.0001)−0.50 (p<0.0001)

*Determined by Pearson correlation coefficient; p-values derived from hypothesis tests of correlation ρ=0 (ie, no correlation).

Figure 1

Disclosure of Interests:

Arthur Kavanaugh Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Genentech, Janssen, Eli Lilly, Merck, Novartis, Pfizer and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Genentech, Janssen, Eli Lilly, Merck, Novartis, Pfizer and UCB, Yan Liu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, GSK, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Philip J Mease Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB; and personal fees from Boehringer Ingelheim and GlaxoSmithKline, Philip Helliwell Consultant of: Galapagos, Janssen, Novartis, Grant/research support from: Abbvie, Janssen, Pfizer, Laure Gossec Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Biogen, Celgene, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, UCB, Grant/research support from: Amgen, Galapagos, Janssen, Eli Lilly, Pfizer, Sandoz, Sanofi, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS1048 IN PHASE-3 TRIALS DISCOVER 1 & 2, GUSELKUMAB REDUCED FATIGUE OVER 52 WEEKS IN PATIENTS WITH PSORIATIC ARTHRITIS AND DEMONSTRATED INDEPENDENT TREATMENT EFFECTS ON FATIGUE AFTER ADJUSTMENT FOR CLINICAL RESPONSE (ACR20)

Background:

DISCOVER 1 & 2 are phase-3 trials of guselkumab (GUS, an IL-23 inhibitor) in patients with psoriatic arthritis (PsA). In both trials, treatment with GUS led to significantly more improvement than placebo (PBO) in the primary endpoint (American College of Rheumatology 20% improvement criteria [ACR20]) and in other measures of arthritis and psoriasis at week (w) 24,1,2 and these improvements were maintained through 1 year of active treatment.3,4

Objectives:

To evaluate the effect of GUS on fatigue in DISCOVER 1 & 2 using the patient reported outcome (PRO) FACIT-Fatigue, which has demonstrated content validity and strong psychometric properties in clinical trials.5

Methods:

DISCOVER 1 & 2 enrolled patients with active PsA, despite non-biologic DMARDS or NSAIDS, who were biologic naïve except ~30% of patients in DISCOVER 1 who had received 1-2 TNFi. Patients were randomized (1:1:1) in a blinded fashion to subcutaneous GUS 100 mg at w0, w4, then every (q) 8w; GUS 100 mg q4w; or matching PBO. At w24, PBO patients were switched to GUS q4w. Concomitant treatment with select non-biologic DMARDS, oral corticosteroids, and NSAIDs was allowed. The FACIT-Fatigue is a 13-item PRO assessing fatigue and its impact on daily activities and function over the past 7 days, total score ranging from 0 to 52, higher score denoting less fatigue. A change of ≥4 points is considered clinically meaningful.5 The change from baseline in FACIT-Fatigue presented below is based on observed data. Mediation analysis6 was applied to the treatment effect of GUS on FACIT-Fatigue to estimate the natural direct and indirect effects, after adjusting for ACR20 response (Table 1).

Results:

At baseline in DISCOVER 1 & 2, the mean FACIT-fatigue scores (SD) were 30.4 (10.4) and 29.7 (9.7), respectively, indicating that patients with PsA experienced fatigue worse than the general population. At w24 in the DISCOVER trials, treatment with GUS led to significant improvements in FACIT-Fatigue scores compared with PBO, as early as w16 in DISCOVER 1 and w8 in DISCOVER 2. Improvements in fatigue were similar between GUS q4w and q8w doses, and the improvements at w24 were maintained through w52 (Figure 1). After a switch to GUS q4w at w24, PBO patients achieved FACIT-Fatigue scores that were comparable to those of GUS patients (Figure 1). 54%-63% of GUS patients compared with 35%-46% of PBO patients achieved clinically meaningful improvement (≥4 points) in FACIT-Fatigue at w24 (P≤0.003). At w52, 61%-70% of both GUS and PBO to GUS groups reached this improvement. As evaluated by mediation analysis at w24, GUS had independent positive treatment effects on fatigue (12%-36% in the q8w GUS dosing group and 69%-70% in the q4w GUS group) after adjustment for ACR20 response (Table 1).

Conclusion:

In 2 phase-3 trials, GUS treatment improved fatigue when compared to PBO during PBO-controlled periods and maintained improvements through 1 year of active treatment. Substantial proportions of those effects were independent of the effects on ACR20, especially for the q4W dosing group.

References:

[1]Deodhar et al. Lancet 2020;395:1115

[2]Mease et al. Lancet 2020;395:1126

[3]Ritchlin et al. EULAR20. SAT0397

[4]McInnes et al. EULAR20. SAT0402

[5]Cella et al. J Patient-Reported Outcomes 2019;3:30

[6]Valeri et al. Psychologic Meth 2013;18:137

Table 1.

Mediation Analysis: Guselkumab Has Direct Effects and Indirect Effects (Mediated through ACR20) on Fatigue in PsA

EffectGUS 100 mg q8w vs. PBO (95% CI)GUS 100 mg q4w vs. PBO (95% CI)DISCOVER-1Total Effect3.1 (1.0, 5.2)(p<0.02)3.8 (1.9, 5.4)(p<0.02)% Direct Effect11.7%68.5%% Indirect effect mediated by ACR2088.3%31.5%DISCOVER-2Total Effect4.0 (2.4, 5.5)(p<0.02)3.6 (2.1, 5.0)(p<0.02)% Direct Effect36.3%69.7%% Indirect effect mediated by ACR2063.7%30.3%

ACR, American College of Rheumatology; CI, confidence interval; GUS, guselkumab; PBO, placebo; PsA, psoriatic arthritis; q4W, every 4 weeks; q8W, every 8 weeks

Disclosure of Interests:

Proton Rahman Speakers bureau: Received speakers fees from Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Grant/research support from: Received grant/research support from Janssen and Novartis, consultation fees from Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer, Philip Helliwell Consultant of: Consultation fees paid to charity (AbbVie, Amgen, Pfizer, UCB) or himself (Celgene, Galapagos), Grant/research support from: Received grants/research support paid to charity (AbbVie, Janssen, Novartis), Atul Deodhar Speakers bureau: Received speakers fees from AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Consultant of: Received consultation fees from AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Grant/research support from: Received grant/research support from AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Alexa Kollmeier Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Bei Zhou Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Xiwu Lin Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Chenglong Han Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Philip J Mease Speakers bureau: Received speakers fees from Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB, Consultant of: Received consultation fees from Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB, Grant/research support from: Received grant/research support from Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB.

POS1027 EFFICACY AND SAFETY OF GUSELKUMAB, A MONOCLONAL ANTIBODY SPECIFIC TO THE p19-SUBUNIT OF INTERLEUKIN-23, THROUGH 2 YEARS: RESULTS FROM A PHASE 3, RANDOMIZED, DOUBLE-BLIND, PLACEBO-CONTROLLED STUDY CONDUCTED IN BIOLOGIC-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS

Background:

Guselkumab (GUS), a selective IL-23 inhibitor dosed every 4 or 8 weeks (Q4W or Q8W), demonstrated efficacy for joint and skin symptoms, inhibition of structural damage progression (Q4W), and safety vs. placebo (PBO) through Week 24 (W24) of the Ph3, double-blind, PBO-controlled trial in biologic-naïve pts with PsA (DISCOVER-2).1 Favorable benefit-risk was also seen through 1 year.2

Objectives:

To assess GUS efficacy and safety through 2 years.

Methods:

Biologic-naïve adults with active PsA (≥5 swollen joint count [SJC] + ≥5 tender joint count [TJC]; CRP ≥0.6 mg/dL) were randomized (1:1:1) to GUS 100 mg Q4W; GUS 100 mg at W0, W4, Q8W; or PBO with crossover to GUS 100 mg Q4W (PBO→Q4W) at W24. Clinical efficacy (ACR/PASI/IGA/HAQ-DI) was assessed in the modified intention to treat (mITT) population through W100 with missing data imputation (nonresponse for categorical endpoints; no change/multiple imputation for continuous endpoints). Observed PsA-modified van der Heijde Sharp (vdH-S) scores derived from blinded radiographic images collected at W0, W24, W52, W100 (or at discontinuation [d/c]) and adverse events (AEs) through W112 were collected.

Results:

712/739 (96%) randomized pts continued study agent at W24; 687/739 (93%) continued at W52; 652/739 (88%) completed W100. ACR20 response rates in the mITT population continued to increase after W24, and at W100 were 76% for Q4W and 74% for Q8W (Figure 1). Similar response patterns were seen for ACR50/70, HAQ-DI and PASI90/100 (Table 1), and IGA0/1 and PASI75 response rates were consistent through W100 in pts randomized to Q4W and Q8W; W100 data for PBO→Q4W pts were consistent with pts treated with Q4W and Q8W (Table 1). GUS improvements in SF-36 PCS/MCS at W52 also persisted through W100 (data not shown). Low rates of radiographic progression (as measured by PsA-modified vdH-S scores) were observed during W52-100 for Q4W (n=227; 0.75) and Q8W (n=232; 0.46). In the PBO→Q4W group (n=228), radiographic progression was 1.12 during W0-24 (while on PBO), 0.51 during W24-100 (while on Q4W), and 0.13 during W52-100. Through W112, the incidences of AEs, serious AEs (SAEs), AEs leading to d/c, infections, serious infections, and injection site reactions were generally consistent with the PBO-controlled period and through 1 year. Of the pts in the Q4W (n=245), Q8W (n=248), and PBO→Q4W (n=238) groups, 9%, 9% and 7% had ≥1 SAE; 2%, 3% and 3% had ≥1 serious infection; 2 Q8W pts (fungal esophagitis, disseminated herpes zoster) and 1 PBO→Q4W pt (listeria meningitis) had opportunistic infections; 1 PBO→Q4W pt died (road traffic accident); 1 PBO-randomized pt had IBD; no pt had anaphylactic or serum sickness reaction, or active TB.

Conclusion:

In biologic-naïve PsA pts, GUS improvements in joint and skin symptoms, physical function, and low rates of radiographic progression persisted through 2 years. GUS safety in PsA through 2 years was comparable with safety at 6 months and 1 year, similar between Q4W and Q8W, and consistent with GUS safety in psoriasis.

References:

[1]Mease PJ. Lancet. 2020 Apr 4;395(10230):1126-1136. [2] McInnes IB. Arthritis Rheumatol. 2020 Oct 11. doi: 10.1002/art.41553.

Table 1.

Efficacy Through W100 (NRI)

Data are %GUS Q4WGUS Q8WPBO→GUS Q4WW24W52W100W24W52W100W24W52W100Analysis set, n245248246 ACR 50334656324855144148 ACR 7013263519283641830BL HAQ-DI ≥0.35, n228228236Improvement ≥0.35a565963505864314856BL ≥3% BSA psoriasis + IGA ≥2, n184176183 IGA0/1696362715855196367 PASI75788783798682238380PASI90617774697470107277PASI10045585945535335261

BL, Baseline; BSA, Body surface area; HAQ-DI, Health assessment questionnaire disability index; IGA, Investigator global assessment; NRI, nonresponder imputation; PASI, Psoriasis area and severity index. a≥0.35 improvement among pts with HAQ-DI ≥0.35 at BL.

Disclosure of Interests:

Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis, Alice B Gottlieb Consultant of: Avotres Therapeutics, Beiersdorf, Boehringer Ingelheim, Bristol-Myers Squibb Co, Incyte, Janssen, LEO Pharma, Eli Lilly, Novartis, Sun Pharmaceutical Industries Inc, UCB, and Xbiotech, Grant/research support from: Boehringer Ingelheim, Incyte, Janssen, Novartis, Sun Pharmaceuticals Industries Inc, UCB, and Xbiotech, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research and Development, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Shihong Sheng Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Yusang Jiang Employee of: Cytel, Inc. providing statistical support (funded by Janssen), May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, Soumya D Chakravarty Shareholder of: Johnson & Johnson, Employee of: Janssen Scientific Affairs, LLC, Désirée van der Heijde Paid instructor for: Director of Imaging and Rheumatology BV, Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, Bayer, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, and UCB, Philip J Mease Speakers bureau: Boehringer Ingelheim and GlaxoSmithKline, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB.

AB0528 COMPARABLE SAFETY PROFILE OF GUSELKUMAB IN PSORIATIC ARTHRITIS AND PSORIASIS: RESULTS FROM PHASE 3 TRIALS THROUGH 1 YEAR

Background:

DISCOVER 1&2 (PsA) and VOYAGE 1&2 (PsO) are Phase 3 trials of guselkumab (GUS).

Objectives:

Compare safety results through up to 1yr of GUS in PsA and PsO pts.

Methods:

In DISCOVER, 1120 pts with active PsA despite standard therapy were treated. Most pts were biologic-naïve; ~30% in DISCOVER 1 had previous exposure to 1-2 TNFi. Concomitant MTX (57%), oral corticosteroids (17%), and NSAIDs (64%) were permitted. Pts were randomized to SC GUS 100mg at W0, W4, then Q8W; GUS 100mg Q4W; or PBO. At W24, PBO patients were switched to GUS 100mg Q4W. In VOYAGE, in which concomitant MTX use was prohibited, 1245 pts with moderate to severe PsO were treated and randomized to SC GUS 100 mg at W0, W4, W12, then Q8W; or PBO at W0, W4, W12, with crossover to GUS at W16, W20, then Q8W. AEs and laboratory parameters, analyzed by National Cancer Institute-Common Terminology Criteria for AEs [NCI-CTCAE] toxicity grades, were summarized through the PBO-controlled periods and 1yr.

Results:

Safety profiles were generally consistent across the GUS PsO and PsA clinical programs (Table 1). Time-adjusted incidence rates for numbers of AEs, serious AEs, serious infections, malignancy, MACE and AEs leading to d/c were generally similar between PsO and PsA. No cases of anaphylaxis or opportunistic infections were reported. Proportions of pts with decreased neutrophil counts and elevations in hepatic transaminases were slightly higher in PsA vs PsO. These abnormalities were mostly of NCI-CTCAE Grade 1 or 2 (<LLN-1000/mm3 for neutrophils; <5.0 x ULN for AST/ ALT), generally transient, required no medical interventions, resolved spontaneously, and did not lead to interruption or d/c of treatment. Through 1yr, proportions of pts with ALT/AST elevations in PsA trials were slightly higher for GUS Q4W than Q8W and in pts with vs without baseline MTX use.

Conclusion:

The GUS safety profile was generally consistent in PsA and PsO GUS-treated pts through 1yr of the DISCOVER and VOYAGE trials.

Table 1.

Treatment-Emergent AEs During PBO-controlled Period and Through 1Yr: VOYAGE & DISCOVER Trials

Pooled VOYAGE 1&2Pooled DISCOVER 1&2Time PeriodW0-16Through 1YrW0-24bThrough 1Yr(N=)PBO(422)GUS Q8W(823)Combined GUSa(1221)PBOc(372)GUS Q8W(375)GUS Q4W (373)GUS Q8W(375)GUS Q4W (373)Combined GUS† (1100)Total pt-yrs of follow-up128255974173173172384385973Incidence/100 pt-yrs (95% CI)dAEs317 (287,349)330 (308,353)259 (249, 270)219 (198,243)256 (232,281)221 (200, 245)218 (203,233)177 (164,191)191 (182, 199)SAEs5 (2, 10)6 (4, 10)6 (5, 8)9 (5, 15)4 (2, 8)5 (2, 10)6 (4, 9)4 (2, 7)6 (4, 7)AEs leading to study agent d/c3 (0.9, 8)4 (2, 8)2 (2, 4)4 (2, 8)3 (1, 7)7 (4, 12)2 (1, 4)4 (2, 6)3 (2, 5)Infections86 (71, 104)98 (86, 111)98 (92, 104)58 (48, 71)58 (47, 71)63 (51, 76)58 (50, 66)53 (46, 61)55 (50, 60)Serious Infections0. 8 (0, 4)0.4 (0, 2)1 (0.5, 2)4 (2, 8)0.6 (0, 3)2 (0.4, 5)2 (0.6, 3)1 (0, 2)2 (0.9, 3)All Malignancy0 (0, 2)0.4 (0, 2)1 (0.4, 2)0.6 (0, 3)1 (0, 4)0 (0, 2)0.5 (0, 2)0 (0, 0. 8)0 (0, 1)MACE0 (0, 2)0.4 (0, 2)0.4 (0, 1)0.6 (0, 3)0 (0, 2)0.6 (0, 3)0 (0, 0.8)0.3 (0, 1.4)0.1 (0, 0.6)% pts with ≥1 injection site rxn3.14.55.00.31.31.11.62.41.7

aPlacebo crossover pts were included in the combined GUS column after crossover to GUS

bFor all pts who d/c study treatment early with the last dose of PBO/GUS prior to W24 and who did not receive any PBO/GUS at or after Wk24, all data including the final safety follow-up visit collected through 1yr were included

cFor pts in PBO group who switched to GUS due to cross-over or inadvertently, only data prior to first administration of GUS were included.

dCI based on an exact method assuming observed number of events follows a Poisson distribution

Disclosure of Interests:

Alice B Gottlieb Consultant of: Anaptyps Bio, Avotres Therapeutics, Beiersdorf, Boehringer Ingelheim, Bristol-Myers-Squibb, Eli Lilly, Janssen, LEO Pharma, Novartis, Sun Pharmaceuticals, UCB, and Xbiotech, Grant/research support from: Boehringer Ingelheim, Janssen, Novartis, Sun Pharmaceuticals, UCB, and Xbiotech, Joseph F. Merola Consultant of: AbbVie, Arena, Biogen, BMS, Dermavant, Eli Lilly, Janssen, Novartis, Pfizer, Sun Pharma, UCB, April Armstrong Consultant of: AbbVie, Janssen, Lilly, Leo, Novartis, UCB, Ortho Dermatologics, Dermira, KHK, Sanofi, Regeneron, Sun Pharma, BMS, Dermavant, and Modernizing Medicine, Richard Langley Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Celgene, Eli Lilly, Janssen, LEO Pharma, Merck, Novartis, Pizer, Sun Pharmaceutical, and UCB Pharma, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Celgene, Eli Lilly, Janssen, LEO Pharma, Merck, Novartis, Pizer, Sun Pharmaceutical, and UCB Pharma, Mark Lebwohl Consultant of: Aditum Bio, Allergan, Almirall, Arcutis, Inc., Avotres Therapeutics, BirchBioMed Inc., BMD skincare, Boehringer-Ingelheim, Bristol-Myers Squibb, Cara Therapeutics, Castle Biosciences, Corrona, Dermavant Sciences, Evelo, Evommune, Facilitate International Dermatologic Education, Foundation for Research and Education in Dermatology, Inozyme Pharma, Kyowa Kirin, LEO Pharma, Meiji Seika Pharma, Menlo, Mitsubishi, Neuroderm, Pfizer, Promius/Dr. Reddy’s Laboratories, Serono, Theravance, and Verrica., Grant/research support from: Abbvie, Amgen, Arcutis, Boehringer Ingelheim, Dermavant, Eli Lilly, Evommune, Incyte, Janssen, Leo Pharmaceutucals, Ortho Dermatologics, Pfizer, and UCB, Christopher E.M. Griffiths Speakers bureau: AbbVie, Amgen, Almirall, BMS, Boehringer Ingelheim Celgene, Janssen, LEO Pharma, Lilly, Novartis, Pfizer, Sun Pharma, UCB Pharma., Consultant of: AbbVie, Amgen, Almirall, BMS, Boehringer Ingelheim Celgene, Janssen, LEO Pharma, Lilly, Novartis, Pfizer, Sun Pharma, UCB Pharma., Grant/research support from: AbbVie, Amgen, Almirall, BMS, Boehringer Ingelheim Celgene, Janssen, LEO Pharma, Lilly, Novartis, Pfizer, Sun Pharma, UCB Pharma., May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Ya-Wen Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Miwa Izutsu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Paraneedharan Ramachandran Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Shihong Sheng Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Yin You Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Megan Miller Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Christopher T. Ritchlin Consultant of: AbbVie, Amgen, Gilead, Janssen, Lilly, Novartis, Pfizer, and UCB Pharma, Grant/research support from: AbbVie, Amgen, and UCB Pharma, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, and Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS0406 miRNAs DEREGULATED IN RESPONSE TO IL17A INHIBITORS IN PSORIATIC ARTHRITIS REGULATE GENE PRODUCTS IN Rho-GTPase PATHWAYS

Background:

Using transcriptomic data at initiation of therapy, we recently identified differentially expressed genes (DEGs) that separated IL-17Ai response from non-response1. Integration of cell-type-specific DEGs with protein-protein interactions (PPIs) and further comprehensive pathway enrichment analysis revealed Rho GTPase signaling pathway exhibited a strong signal specific to IL-17Ai response and particularly the genes, RAC1 and ROCKs.

Objectives:

To characterize microRNA (miR) profiles among IL-17Ai responders and non-responders, as it relates to RHO GTPase pathway.

Methods:

We interrogated 20 psoriatic arthritis (PsA) patients initiating IL-17Ai. Patients achieving at least low disease activity according to the Disease Activity Index for PsA (DAPSA) at three months were classified as responders. There were seven responders (35%) and thirteen non-responders (65%) in the IL-17Ai group, with biologic treatment naïve (bio-naïve) and previously-exposed (bio-exposed) patients exhibiting a 50% (4/8) and a 25% (3/12) response rate, respectively. For the miR analysis, CD4 positive T cells were isolated from peripheral blood mononuclear cells using DynabeadsTM CD4 beads (ThermoFisher). Total RNA was extracted from the CD4+ T cells using Lexogen’s Split RNA Extraction Kit (D-Mark Biosciences). Libraries were prepared from 200ng total RNA with the NEXTFLEX Small RNA-Seq Kit v3 with UDIs (Bioo Scientific) and sequenced on the Illumina NovaSeq 6000. Raw sequencing fastq data assessed the quality using FastQC. The miRDeep2 was used to trim the adapter, align and quantify human mature miRs from miRbase (Release 22). The abundance of miRs was converted to read counts per million, normalized and limma R package was used to identify pre- and post-differentially expressed miRs.

Results:

We obtained 2,889 miRs. After removing miRs with low reads in >90% of samples, 1902 high quality miRs remained for further analysis. Using mirDIP v4.1 we identified gene targets for differential miRs, and focused on recently identified DEGs related to RHO GTPase pathway. The miRs on the left of the figure 1 are those deregulated in pre-treatment, and the miRs on the right of the figure 1 show the post-treatment deregulated miRs. hsa-miR-3691-5p and hsa-miR-3161 represent the miRs that were deregulated in both conditions. The red highlighted nodes represent the most connected miRs and genes; thus, representing miRs that are the most RHO-pathway centric regulators (hsa-miR-495-3p, 16-5p, 129-5p, 520h, 520g-3p), and genes representing the most strongly regulated RHO-pathway gene products (ROCK1, RHOQ, PFN2, TAOK1, DYNC1L12, MAPRE1, PAFAH1B1, ARHGAP5, MAPK1, CALM1, DIAPH2, PKN2, ITSN1).

Conclusion:

Pre- and post-treatment differential miRs related to IL-17Ai response regulate multiple genes from RHO GTPase pathway.

References:

[1]Rahmati S, O’Rielly DD, Li Q, Codner D, Dohey A, Jenkins K, Jurisica I, Gladman DD, Chandran V, Rahman P. Rho-GTPase pathways may differentiate treatment response to TNF-alpha and IL-17A inhibitors in psoriatic arthritis. Sci Rep. 2020 Dec 10;10(1):21703.

Figure 1.

Disclosure of Interests:

Proton Rahman Speakers bureau: AbbVie, Amgen, BMS, Celgene, Eli Lily, Janssen, Merck, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, BMS, Celgene, Eli Lily, Janssen, Merck, Novartis, Pfizer, UCB, Grant/research support from: Janssen, Novartis, Quan Li: None declared, Dianne Codner: None declared, Darren O’Rielly: None declared, Amanda Dohey: None declared, Kari Jenkins: None declared, Dafna D Gladman Speakers bureau: AbbVie, Amgen, BMS, Eli Lily, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, BMS, Eli Lily, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Amgen, Eli Lily, Janssen, Novartis, Pfizer, UCB, Vinod Chandran Speakers bureau: AbbVie, Amgen, BMS, Eli Lily, Janssen, Novartis, Pfizer, UCB, Paid instructor for: AbbVie, Amgen, BMS, Eli Lily, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Amgen, Eli Lily, Employee of: Spousal Employment Eli Lilly, Igor Jurisica: None declared

POS1028 PATIENT CHARACTERISTICS & CLINICAL FEATURES ASSOCIATE WITH HEALTH-RELATED QUALITY OF LIFE IN BIO-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS THROUGH WEEK 24 OF THE DISCOVER-2 STUDY

Background:

Psoriatic arthritis (PsA) is a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis, & skin/nail psoriasis. Patients (pts) with PsA often experience reduced health-related quality of life (HRQoL) due to these features.

Objectives:

Using EuroQoL-5 dimension-5 level (EQ-5D-5L) questionnaire index & visual analog scale (EQ-VAS) scores, we assessed HRQoL in pts with PsA & its association with pt characteristics & clinical features of PsA, including fatigue.

Methods:

The Phase 3 DISCOVER-2 trial evaluated guselkumab (GUS), a human monoclonal antibody targeting the IL-23p19-subunit, in bio-naïve adults with active PsA (swollen joint count [SJC] ≥5, tender joint count [TJC] ≥5, C-reactive protein [CRP] ≥0.6 mg/dL) despite standard therapies.1 Pts were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at Week 0 (W0), W4, then Q8W; or placebo (PBO). EQ-5D-5L index assesses mobility, self-care, usual activities, pain/discomfort, & anxiety/depression. EQ-VAS assesses pt health state. Spearman correlation testing was used to evaluate relationships between baseline (BL) pt characteristics & PsA clinical features & BL EQ-5D-5L index & EQ-VAS scores (Figure 1). Employing absolute observed scores at both W0 & W24, univariate linear regression was used to assess the association between EQ-5D-5L index & EQ-VAS scores & pt characteristics/PsA clinical features. Variables with p<0.20 in the univariate analysis were included in a multivariate analysis employing mixed-effect model for repeated measures (MMRM), controlling for all other variables; resulting p values <0.05 were considered statistically significant. Least-squares (LS) mean changes in EQ-5D-5L index & EQ-VAS were assessed at W24 using MMRM.

Results:

Among 738 pts, BL EQ-5D-5L index & EQ-VAS scores were moderately to strongly correlated (ie, ≥0.4) with BL pt-reported pain (0-10 VAS), physical function (Health Assessment Questionnaire-Disability Index [HAQ-DI]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F] scale), & 36-item Short Form Health Survey (SF-36) physical & mental component summary (PCS & MCS) scores & weakly correlated with other variables (Figure 1). Based on univariate analyses (p<0.20) & evaluation of collinearity between variables, attributes at W0 & W24 included in the multivariate models were age, sex, CRP, FACIT-F, pain, psoriasis area & severity index (PASI) score, TJC, SJC, enthesitis, & dactylitis. In the final model, CRP, FACIT-F, pain, PASI score, & the presence of dactylitis were significantly associated with EQ-5D-5L index & EQ-VAS scores. A higher TJC was significantly associated with a worse EQ-5D-5L index score. A higher SJC was significantly associated with a worse EQ-VAS score (Table 1). For reference, in the GUS Q4W (N=244), GUS Q8W (N=246), & PBO (N=244) groups, the LS mean changes from baseline at W24 were 0.12, 0.12, & 0.05, respectively, for EQ-5D-5L index & 18.1, 18.4, & 6.8, respectively, for EQ-VAS.

Conclusion:

Joint & skin symptoms, dactylitis, fatigue, pain, & elevated levels of CRP were significantly associated with reduced HRQoL (measured by EQ-5D-5L index & EQ-VAS) in bio-naïve pts with active PsA. Treatment of multiple PsA domains may help optimize HRQoL. Improvement across clinical domains1 & in HRQoL has been observed in GUS-treated pts with PsA.

References:

[1]Mease P, et al. Lancet 2020;395:1126-36.

Table 1.

Multivariate analysis of pt characteristics/clinical features & EQ-5D-5L index & EQ-VAS scores at W0 & W24

ParameterEQ-5D-5L IndexEQ-VASEstimatep valueEstimatep valueAge (y)-0.00010.690.060.12Female-0.0030.531.110.20CRP (mg/dL)-0.005<0.001-0.510.007FACIT-F (0-52)0.007<0.0010.57<0.001Pain (0-10)-0.02<0.001-3.47<0.001PASI (0-72)-0.0010.03-0.17<0.001SJC (0-66)-0.0010.21-0.170.02TJC (0-68)-0.0010.04-0.040.41Dactylitis (Y/N)0.010.021.740.49Enthesitis (Y/N)-0.0040.33-0.980.22

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, and UCB, Dafna D Gladman Consultant of: Abbvie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Abbvie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, MSD, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS0200 CLINICAL CHARACTERISTICS & OUTCOMES ASSOCIATE WITH WORK PRODUCTIVITY IN BIO-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS THROUGH WEEK 24 OF THE DISCOVER-2 STUDY

Background:

Psoriatic arthritis (PsA), a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis & skin/nail psoriasis, causes impaired physical function, disability & loss of work productivity.

Objectives:

Evaluate associations between PsA clinical characteristics & outcomes including fatigue & work productivity using Work Productivity & Activity Impairment Questionnaire: PsA (WPAI-PsA).

Methods:

The Phase 3 DISCOVER-2 trial assessed guselkumab (GUS), an anti-IL-23p19 subunit monoclonal antibody, in bio-naïve adults with active PsA (swollen joint count [SJC] ≥5 & tender joint count [TJC] ≥5, C-reactive protein [CRP] ≥0.6 mg/dL) despite standard therapies.1 Patients (Pts) were randomized 1:1:1 to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), productivity loss (absenteeism+presenteeism), & daily activity during the previous week. Spearman correlation testing evaluated relationships between pt demographics & disease characteristics of PsA & WPAI domain scores based on observed values at baseline. Univariate linear regression assessed associations between WPAI & these variables based on observed data at W0 & at W24. Variables with p<0.10 were included in a multivariate analysis employing a mixed-effects model for repeated measures, controlling for all other variables; resulting p-values <0.05 were considered statistically significant.

Results:

As reported elsewhere,2 least-squares mean % changes from baseline at W24 were -3.8/-19.5/-20.0/-20.5 for GUS Q4W, -3.1/-19.4/-19.7/-21.5 for GUS Q8W, & -3.5/-10.2/-10.9/-10.3 for PBO for absenteeism, presenteeism, absenteeism+presenteeism, & daily activity impairment, respectively. Among 738 pts, WPAI domain scores were moderately to strongly correlated (ie, ≥0.4) with pt-reported pain (0-10 visual analog scale), physical function (Health Assessment Questionnaire Disability Index [HAQ-DI]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F] scale) & 36-Item Short Form Health Survey (SF-36) Physical Component Summary (PCS) score, but weakly correlated with other variables (Figure 1). Based on univariate analyses & evaluation of collinearity between variables, attributes included in multivariate models were age, body mass index (BMI), gender, CRP, FACIT-F, pain, Psoriasis Area Severity Index (PASI), TJC, SJC, enthesitis & dactylitis. In final model, CRP, FACIT-F, & pain were statistically significantly associated with all WPAI domains (Table 1). Presence of enthesitis & higher PASI score were significantly associated with higher loss of work productivity & activity outside work.

Conclusion:

In PsA pts, extra-articular symptoms, fatigue, pain & elevated CRP were significantly associated with WPAI-assessed work & activity impairment. Treating all major clinical manifestations of PsA is needed to help pts improve work & activity impairment. GUS effectively treats all major clinical manifestations1 & improves work & activity impairment in PsA.2

References:

[1]Mease P. Lancet 2020;395:1126-36.

[2]Curtis J. ACR 2020; Poster 0332.

Table 1.

Multivariate analysis of clinical characteristics/outcomes & WPAI domains at W0 & W24

ParameterAbsenteeismaPresenteeismaProductivity LossaActivity ImpairmentbEstimatep-valueEstimatep-valueEstimatep-valueEstimatep-valueAge-0.050.42-0.27<0.001-0.28<0.001-0.060.17Female0.910.46-1.540.22-1.740.202.380.02CRP0.730.040.970.011.010.010.89<0.001FACIT-F-0.31<0.001-0.67<0.001-0.73<0.001-0.75<0.001Pain1.03<0.0014.15<0.0014.25<0.0014.02<0.001PASI0.060.360.160.020.140.050.150.003SJC0.080.48-0.050.61-0.050.660.030.75TJC-0.100.130.110.090.090.190.100.04Dactylitis (Y/N)-1.100.392.470.052.580.050.540.57Enthesitis (Y/N)1.520.202.380.042.990.012.400.01

aPts working at baseline

bAll pts in study

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Eli Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Eli Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, and UCB, Dafna D Gladman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Feifei Yang Shareholder of: Janssen, Employee of: Janssen, Steve Peterson Shareholder of: Janssen, Employee of: Janssen, Prasheen Agarwal Shareholder of: Janssen, Employee of: Janssen, Alexa Kollmeier Shareholder of: Janssen, Employee of: Janssen, Elizabeth C Hsia Shareholder of: Janssen, Employee of: Janssen, Chenglong Han Shareholder of: Janssen, Employee of: Janssen, May Shawi Shareholder of: Janssen, Employee of: Janssen, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, MSD, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, and Novartis, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis

AB0538 POOLED SAFETY RESULTS FROM TWO PHASE-3 TRIALS OF GUSELKUMAB IN PATIENTS WITH PSORIATIC ARTHRITIS THROUGH 1 YEAR

Background:

DISCOVER 1 & 2, two double-blind, phase-3, psoriatic arthritis (PsA) trials of guselkumab (GUS, an IL-23 inhibitor), demonstrated significant improvement with GUS vs placebo (PBO) in signs and symptoms of PsA, with good tolerability, at week (w) 24 during the PBO-controlled period.1,2 Beyond w24, all patients (pts) switched to GUS. Continued treatment maintained efficacy through w52.3,4

Objectives:

To describe pooled safety results from the DISCOVER 1 & 2 trials through 1-year of GUS treatment.

Methods:

Adults with active PsA (DISCOVER 1: ≥3 tender/swollen joints and C-Reactive protein [CRP] ≥0.3 mg/dL; DISCOVER 2: ≥5 tender/swollen joints and CRP ≥0.6 mg/dL) were randomized to subcutaneous GUS 100 mg at w0, w4, then every 8 w (q8w); GUS 100 mg q4w; or PBO. At w24, PBO pts switched to GUS 100 mg q4w. Pts were biologic naive except ~30% pts in DISCOVER 1. Safety was reported through w60 in DISCOVER 1 and through w52 in DISCOVER 2.

Results:

Baseline characteristics were similar between treatment groups in the pooled studies. Through w24 and 1 year, numbers of pts per 100 patient years with ≥1 event were similar among treatment groups for adverse events (AEs), serious AEs, infections, serious infections, and discontinuations due to AE (Table 1). At 1 year, there were no cases of active tuberculosis, opportunistic infections (including candida), or inflammatory bowel disease in GUS-treated pts; 2 deaths in PBO pts; and low incidences that were similar across treatment groups for malignancy, major adverse cardiac events, and injection-site reactions. Incidence of anti-GUS antibodies was 4.5%, and most were not neutralizing. Mild elevations in serum hepatic transaminases and decreases in neutrophil counts were consistent at 1 year with the results at w24 (Table 1).

Conclusion:

GUS regimens of q8w and q4w were well tolerated in PsA pts through 1 year of treatment in the phase-3 DISCOVER trials, consistent with the w24 results. No meaningful differences between incidences of AEs were reported in the q8w and q4w groups. The safety profile of GUS in PsA pts is generally comparable with the previously established safety profile of GUS.

References:

[1]Deodhar A et al. Lancet. 2020;395:1115

[2]Mease P et al. Lancet. 2020;395:1126

[3]Ritchlin C et al. EULAR 2020 # SAT0397

[4]McInnes I et al. EULAR 2020 # SAT0402

Table 1.

Number of Patients with AEs per 100 PY and Incidence of AEs of Interest

Time Period24 Weeks1 Year*Treatment GroupPBOGUS SC 100 mgPBO to GUS‡GUS SC 100 mgDosing ScheduleMatchingq8wq4wGUSCombined†q4wq8wq4wGUSCombined‡ N3723753737483523753731100Total PY Follow-Up173173172346204384385589Patients with AEs per 100 PY, n (95% CI)≥1 AE143 (123, 166)148 (127, 171)154 (132, 178)151 (136, 167)92 (77, 108)114 (100, 130)115 (101, 131)109 (100, 117)≥1 Serious AE7.1 (3.7, 12)4.1 (1.6, 8.4)4.7 (2.0, 9.3)4.4 (2.5, 7.3)7.0 (3.8, 11.8)4.8 (2.9, 7.6)4.0 (2.2, 6.6)4.9 (3.6, 6.6)≥1 Infection50 (39, 62)47 (37, 59)52 (42, 65)49 (42, 58)39 (31, 49)41 (34, 48)38 (31, 45)39 (35, 44)≥1 Serious Infection1.7 (0.4, 5.1)0.6 (0.0, 3.2)1.8 (0.4, 5.1)1.2 (0.3, 3.0)2.5 (0.8, 5.8)1.3 (0.4, 3.1)0.8 (0.2, 2.3)1.3 (0.7, 2.3)Discontinued due to AE4.1 (1.6, 8.4)2.9 (1.0, 6.8)4.7 (2.0, 9.3)3.8 (2.0, 6.5)3.5 (1.4, 7.1)2.1 (0.9, 4.1)2.6 (1.3, 4.8)2.6 (1.7, 3.8)AEs of Interest§, n (%)Death2 (0.5)0000000Malignancy1 (0.3)2 (0.5)02 (0.3)1 (0.3)2 (0.5)03 (0.3)Major Adverse Cardiac Events1 (0.3)01 (0.3)1 (0.1)001 (0.3)1 (0.1)Opportunistic Infections00000000Tuberculosis00000000Inflammatory Bowel Disease1 (0.3)0000000Injection-Site Reaction1 (0.3)5 (1.3)4 (1.1)9 (1.2)4 (1.1)6 (1.6)9 (2.4)19 (1.7)Anti-GUS Antibody+-6/373 (1.6)9/371 (2.4)15/744 (2.0)14/350 (4.0)18/373 (4.8)17/371 (4.6)49/1094 (4.5)

*Through w60 for DISCOVER 1 and w52 for DISCOVER 2; †Combined GUS q8w and q4w; ‡For patients who switched from PBO to GUS, only data on and after first GUS administration were included in this group; §PBO N=370.

AE, adverse event; CI, confidence interval; GUS, guselkumab; PBO, placebo; PY, patient year; q4w, every 4 weeks; q8w, every 8 weeks; SC, subcutaneous; w, week

Disclosure of Interests:

Christopher T. Ritchlin Grant/research support from: Received grant/research support from UCB Pharma, AbbVie, Amgen, consultation fees from UCB Pharma, Amgen, AbbVie, Lilly, Pfizer, Novartis, Gilead, Janssen, Proton Rahman Speakers bureau: Received speakers fees from Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Grant/research support from: Received grant/research support from Janssen and Novartis, consultation fees from Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Philip Helliwell Consultant of: Consultation fees paid to charity (AbbVie, Amgen, Pfizer, UCB) or himself (Celgene, Galapagos), Grant/research support from: Received grants/research support paid to charity (AbbVie, Janssen, Novartis), Wolf-Henning Boehncke Consultant of: Received consultation fees from Janssen, Grant/research support from: Received grant/research support from Janssen Research & Development, LLC, Iain McInnes Consultant of: Received consultation fees from AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Received grant/research support from Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Alice B Gottlieb Speakers bureau: Received speakers fees from Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB, Consultant of: Received consultation fees from Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB, Grant/research support from: Received grant/research support from Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB, Shelly Kafka Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Alexa Kollmeier Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Xie L Xu Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, May Shawi Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Shihong Sheng Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Prasheen Agarwal Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Bei Zhou Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Paraneedharan Ramachandran Shareholder of: Shareholder of Johnson & Johnson, Employee of: Employee of Janssen Research & Development, LLC, Philip J Mease Speakers bureau: Received speakers fees from Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Consultant of: Received consultation fees from Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB, Grant/research support from: Received grant/research support from Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB.

POS0909 IXEKIZUMAB IMPROVES SIGNS, SYMPTOMS AND QUALITY OF LIFE OF ANKYLOSING SPONDYLITIS IN PATIENTS IRRESPECTIVE OF HLA-B27 STATUS: POOLED RESULTS FROM THE COAST-V AND COAST-W TRIALS

Background:

Human leukocyte antigen-B27 (HLA-B27) is found in most patients (pts) with ankylosing spondylitis (AS). Although a subset of pts with AS are HLA-B27 negative (NEG), ensuring treatment efficacy in this subpopulation is important.

Objectives:

This analysis evaluated the efficacy of ixekizumab (IXE), a high-affinity monoclonal antibody selectively targeting interleukin-17A, in AS pts who are either HLA-B27 positive (POS) or NEG.

Methods:

COAST-V (NCT02696785) and COAST-W (NCT02696798) were 2 phase 3, multicenter, randomized, double-blind, PBO-controlled trials investigating the efficacy of 80-mg IXE administered every 4 weeks (Q4W) and every 2 weeks (Q2W) in pts naïve to biological disease-modifying anti-rheumatic drugs (bDMARDs; COAST-V) and in those who were inadequate responders or intolerant to 1 or 2 tumor necrosis factor inhibitors (TNFi; COAST-W). Only data from pts randomized at baseline to PBO, IXE Q4W or Q2W in both trials who met per protocol eligibility criteria were integrated and stratified based on HLA-B27 status (POS, NEG) for an ad hoc subgroup analysis. Efficacy was assessed using the Assessment of Spondyloarthritis International Society 40 (ASAS40), Bath Ankylosing Spondylitis Disease Activity Index 50 (BASDAI50) and Short Form 36 Physical Component Scores (SF-36 PCS). Missing data were imputed by non-responder imputation (NRI) for binary measure and by modified baseline observation carried forward (mBOCF). Data from PBO was reported up to Week 16 and for pooled IXE Q4W and Q2W up to Week 52.

Results:

This analysis includes pts with AS who are HLA-B27 POS (N=453; PBO=155, IXE=298) or NEG (N=62, PBO=21, IXE=41). Overall, more pts were male (82.6%, POS; 77.4%, NEG), and for IXE treated pts, the duration of disease since r-axSpA diagnosis was 10.3 (SD=9.1) and 6.9 (SD=5.6) years, among POS and NEG pts respectively (Table 1). Among IXE treated patients, the mean age of HLA-B27 NEG pts was approximately 7-8 years older than HLA-B27 POS pts. At Week 16, 39.6% (n=118/298) of HLA-B27 POS and 29.3% (n=12/41) of HLA-B27 NEG pts achieved ASAS40, and 34.6% (n=103) of HLA-B27 POS and 17.1% (n=7) of HLA-B27 NEG pts achieved BASDAI50; improvements were seen as early as Week 1 and sustained or improved up to Week 52. The mean baseline SF-36 PCS was 33.2 (SD=7.63) for HLA-B27 POS and 31.4 (SD=7.67) for HLA-B27 NEG pts. At Week 16, the mean change from baseline in SF-36 PCS was 7.3 (SD 7.6) for HLA-B27 POS and 3.7 (SD=6.83) for HLA-B27 NEG pts. Improvements were sustained to Week 52 (Figure 1).

Conclusion:

IXE improves signs, symptoms, patient-reported outcomes and health-related quality of life in HLA-B27 POS and NEG pts with AS, however the HLA-B27 POS pts have a faster and more robust response than HLA-B27 NEG pts.

Table 1.

Baseline Characteristics by HLA-B27 Positive and Negative Status

Positive HLA-B27Negative HLA-B27PBO (N=155)IXE (N=298)PBO (N=21)IXE (N=41)Male, n (%)133 (85.8)241 (80.9)18 (85.7)30 (73.2)Age of r-axSpA onset (yr), mean (SD)25.2 (9.28)26.3 (8.38)35.4 (8.43)‡34.6 (10.32)‡Duration of symptoms since r-axSpA onset (yr)18.7 (11.82)17.1 (10.38)16.0 (9.69)15.9 (12.40)C-Reactive protein (mg/L), mean (SD)16.7 (22.43)16.9 (25.50)18.1 (21.69)13.7 (14.38)ASDAS score, mean (SD)4.0 (0.78)4.0 (0.83)4.3 (0.76)4.1 (0.73)BASDAI score, mean (SD)7.1 (1.25)7.2 (1.37)7.6 (1.31)*7.4 (1.41)SF-36 PCS, mean (SD)33.7 (7.50)33.2 (7.63)34.1 (8.28)31.4 (7.67)

Abbreviations: ASDAS, Assessment of Spondyloarthritis International Society 40; BASDAI, Bath Ankylosing Spondylitis Disease Activity Index; HLA-B27, Human leukocyte antigen-B27; IXE, pooled ixekizumab every 2 weeks and every 4 weeks; N, total number of patients in group; n, number of patients in category/subgroup; PBO, placebo; r-axSpA, axial spondyloarthritis; SD, standard deviation; SF-36 PCS, Short Form 36 Physical Component Scores; yr, years.

p-value: vs. HLA-B27 positive: *≤0.05,‡<0.0001

Figure 1.

Comparisons done using t-test and chi-square test for continuous and categorical variables.

Disclosure of Interests:

John D Reveille Consultant of: UCB (Union Chimique Belge), Eli Lilly, Novartis, and Pfizer, Grant/research support from: Janssen 2018; Eli Lilly 2020, Proton Rahman Speakers bureau: Abbott, AbbVie, Amgen, Bristol-Meyers Squibb, Celgene, Eli Lilly, Janssen, Novartis, and Pfizer, Consultant of: Abbot, AbbVie, Amgen, Bristol-Meyers Squibb, Celgene, Eli Lilly, Janssen, Novartis, and Pfizer, Grant/research support from: Janssen and Novartis, David Sandoval Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Talia Muram Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Rebecca Bolce Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, So Young Park Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Martin Rudwaleit Speakers bureau: AbbVie, Bristol-Meyers Squibb, Chugai, Janssen, Eli Lilly, MSD (Merck Sharp and Dohme), Novartis, Pfizer, UCD (Union Chimique Belge), Consultant of: AbbVie, Janssen, Eli Lilly, Novartis, Pfizer, UCB (Union Chimique Belge)

AB0557 ACHIEVING TREATMENT TARGETS IN PSORIATIC ARTHRITIS WITH APREMILAST IN CANADIAN PRACTICE: REAL WORLD RESULTS FROM APPRAISE

Background:

Real-world evidence on achieving treatment targets with apremilast (APR) in patients (pts) with PsA is limited. In the phase 3 PALACE trials, pts reached remission (REM)/low disease activity (LDA) targets at 52 wks most frequently when early APR treatment was initiated and pts were in moderate disease activity, as measured by Clinical Disease Activity Index for PsA (cDAPSA) score. In APPRAISE, we assessed APR effectiveness/tolerability in pts with PsA in routine clinical practice in Canada.

Objectives:

This interim efficacy analysis focused on the available data on APR effectiveness measuring rate of achieving cDAPSA REM or LDA at 12 mos and Pt Acceptable Symptom Status (PASS) results.

Methods:

The prospective, multicenter, observational APPRAISE study assessed APR effectiveness/tolerability in adults with active PsA in routine clinical care enrolled from July 2018-March 2020. Pts were followed from treatment initiation to 12 mos, with visits suggested every 4 mos. The primary effectiveness endpoint was the rate of achieving at least LDA (cDAPSA <14) at 12 mos. Pt-reported outcome measures were assessed. Data reported are as observed in pts continuing APR treatment.

Results:

In total, 101 pts were enrolled in APPRAISE. Mean age was 52 yrs; 56% were women. Mean (SD) PsA duration at baseline (BL) was 6 (8) yrs. Oligoarticular disease (≤4 joint involvement) was most common (41%), followed by polyarticular (35%). Most pts (92%) received prior conventional DMARDs and 17% received prior biologic therapy; concomitant MTX was reported in 41% at BL. By 12 mos, 41/101 enrolled pts discontinued, 35 reached 12 mos follow-up (4 mos: n=92; 8 mos: n=61), and 25 have yet to reach 12 mos. The majority (92%) of discontinuations due to lack/loss of effectiveness or AEs occurred within 4-8 mos. AEs were primarily GI related early in treatment. The proportion of pts with continued APR achieving cDAPSA REM/LDA treatment targets increased significantly over time (Figure 1). Significant reductions were seen over 12 mos in swollen/tender joint counts and plaque psoriasis, with reduced mean (SD) body surface area of −4% (9%) (Table 1). Prevalence of dactylitis/enthesitis at BL, 4, 8, and 12 mos was 17%/33%, 9%/24%, 5%/19%, and 0%/21%, respectively. Pain assessment (VAS) significantly improved over time. The proportion of pts achieving PASS with continued APR increased significantly over 12 mos (BL: 27%; 12 mos: 65%) (Figure 1). COVID restrictions impacted in-office assessment visits, necessitating reliance on virtual visits.

Conclusion:

Pts with PsA receiving APR were assessed at regular intervals in routine clinical care in Canada. This interim analysis revealed a greater number of pts receiving APR (66%) who completed the 12-mo follow-up achieved REM or LDA, as measured by cDAPSA over 12 mos. A majority of pts (65%) reported satisfaction with their disease state, as measured by PASS. No new safety signals were observed.

Table 1.

Change in Clinical Parameters and Pt-Reported Outcomes From BL to 4, 8, and 12 Mos

Outcome Measure*, Mean (SD)BL (n = 101)4 Mos (n = 92)8 Mos (n = 61)12 Mos (n = 35)Disease/Clinical Parameters Tender joint count (0-68)7.5 (6.7)−2.5 (6.3)*−3.9 (5.2)*−2.2 (6.4) Swollen joint count (0-66)5.4 (5.4)−3.0 (4.5)*−3.1 (4.3)*−3.1 (4.4)* PhGA42.9 (18.8)−19.0 (24.6)*−24.2 (24.2)*−21.2 (26.3)* Body surface area, %3.1 (6.1)−2.2 (6.0)*−2.7 (7.5)*−4.2 (9.1)* cDAPSA22.2 (13.3)−7.9 (12.1)*−10.1 (13.5)*−6.9 (12.0)*Pt-Reported Outcomes PtGA, mm50.0 (24.6)−10.2 (27.5)*−9.1 (31.9)*−3.6 (39.7) Pain, mm48.3 (25.3)−9.5 (26.2)*−12.2 (28.7)*−7.3 (26.0) HAQ-DI0.9 (0.7)−0.13 (0.5)*−0.15 (0.6)−0.1 (0.7)

*Denotes significant change from BL (P<0.05) from paired-sample t-tests; note that mean change from BL may be greater than the mean BL value when improvements of large magnitude, for pts with relatively elevated BL values, are observed in samples with lower n’s. HAQ-DI = Health Assessment Questionnaire-Disability Index; PhGA = Physician’s Global Assessment; PtGA = Patient’s Global Assessment.

Acknowledgements:

This study was funded by Celgene and Amgen Inc. Writing support was funded by Amgen Inc. and provided by Kristin Carlin, RPh, MBA, of Peloton Advantage, LLC, an OPEN Health company.

Disclosure of Interests:

Vinod Chandran Consultant of: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Louis Bessette Consultant of: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Janssen, Merck, Pfizer, Sanofi, Novartis, UCB, Grant/research support from: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Janssen, Merck, Pfizer, Sanofi, Novartis, UCB, Carter Thorne Speakers bureau: AbbVie, Amgen, Celgene Corporation, Eli Lilly, Medexus/Medac, Merck, Novartis, Pfizer, Sandoz, Sanofi, Consultant of: Centocor, Medexus/Medac, Merck, Grant/research support from: Novartis, Pfizer, Maqbool Sheriff Speakers bureau: AbbVie, Celgene Corporation, Eli Lilly, Janssen, Merck, Pfizer, Sandoz, Proton Rahman Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, UCB, Dafna D Gladman Consultant of: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Sabeen Anwar Consultant of: AbbVie, Amgen Inc., BMS, Novartis, and Pfizer, Grant/research support from: AbbVie, Amgen Inc., Eli Lilly, Janssen, and Pfizer, Jennifer Jelley Employee of: Amgen Canada Inc., Anne-Julie Gaudreau Employee of: Amgen Canada Inc., Manprit Chohan Employee of: Amgen Canada Inc., John S. Sampalis Employee of: JSS Medical Research.

POS1026 GUSELKUMAB PROVIDES SUSTAINED IMPROVEMENTS IN WORK PRODUCTIVITY AND NON-WORK ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS THROUGH 1 YEAR OF A PHASE 3 TRIAL

Background:

DISCOVER-2 was a Phase 3 trial of the first-in-class anti-IL-23-specific mAb guselkumab (GUS) in patients (pts) with psoriatic arthritis (PsA). PsA impacts patients’ productivity at work and in daily activity.1

Objectives:

To evaluate the effect of GUS on work productivity and daily activity in DISCOVER-2 through 1 year using the Work Productivity and Activity Impairment Questionnaire: PsA (WPAI- PsA).

Methods:

Bio-naïve adults with active PsA despite nonbiologic DMARDs &/or NSAIDs received subcutaneous GUS 100 mg every 4 weeks (Q4W); GUS 100 mg W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), impaired overall work productivity (absenteeism + presenteeism), and daily activity during the previous week. A shift analysis evaluated proportions of pts employed vs unemployed (regardless of desire to work) over time. Among pts working at baseline, least-squares (LS) mean changes from baseline in WPAI-PsA domains were determined using a mixed-effects model for repeated measures analysis, whereby mean changes in WPAI-PsA domains were calculated for each multiple imputation (MI) dataset using an analysis of covariance (ANCOVA); the reported LSmean is the average of all MI datasets. Also, among pts employed at baseline, indirect savings from improved overall work productivity were estimated using 2020 EU mean yearly wage estimate (all occupations).2

Results:

In pts working at baseline, significant improvement in work productivity and non-work activity vs PBO was observed at W24. Productivity gains seen with GUS at W24 continued to improve through 1 year (Table 1). Shift analysis showed relatively stable employment in pts employed at baseline (62% of shift analysis cohort) through 1 year of GUS (>91% continued to work when assessed at W16, W24, and W52 [data not shown]). For those unemployed at baseline (38% of cohort), the proportion of pts working increased by ~10% following 1 year of GUS (Figure 1). Potential yearly indirect savings from improved overall work productivity were: €7409 GUS Q4W and €7039 GUS Q8W vs €4075 PBO at W24 and were €8520 GUS Q4W, €9632 GUS Q8W, and €6668 PBO→GUS Q4W at W52.

Conclusion:

Improvement in work productivity and non-work activity was greater with GUS vs PBO among pts with active PsA through W52. Improvements demonstrated may result in reduction in PsA costs associated with work productivity.

References:

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–83.

[2]OECD (2020). Average wages (indicator). https://data.oecd.org/earnwage/average-wages.htm

Table 1.

Model-based estimates of LSmean changea (95% CI) from baseline in WPAI-PsA domains among pts working at baseline and with an observed change through W24 (N=474) and W52 (N=475)

Change from baselineGUS 100mg Q4WGUS 100mg Q8WPBO(W0-24)PBO → GUS 100 mg Q4W (W24-52)VisitW24W52W24W52W24W52Absenteeism, N145145147147162163LSmean-3.4 (-6.5,-0.3)-4.1 (-6.8,-1.5)-3.0 (-6.0,0.1)-4.0 (-6.6,-1.3)-3.0 (-6.0, 0.04)-3.0 (-5.5,-0.4)Diff vs. PBO-0.4 (-4.6,3.8)-0.01 (-4.2, 4.2)Presenteeism, N145145147147162163LSmean-20.1 (-23.7,-16.6)-22.4 (-26.3,-18.6)-19.6 (-23.2,-16.1)-25.7 (-29.5,-21.8)-10.5 (-13.9,-7.0)-18.5 (-22.2,-14.7)Diff vs PBO-9.7* (-14.4,-5.0)-9.2* (-13.9,-4.5)Work productivity, N145145147147162163LSmean-20.1 (-24.1,-16.1)-22.6 (-26.8,-18.3)-19.2 (-23.1,-15.2)-25.9 (-30.0,-21.7)-10.6 (-14.4,-6.8)-17.6 (-21.7,-13.6)Diff vs PBO-9.5* (-14.8,-4.2)-8.6* (-13.9,-3.3)Non-work Activity, N242242246246245245LSmean-20.5 (-23.3,-17.7)-25.7 (-28.6,-22.7)-21.2 (-23.9,-18.4)-25.4 (-28.4,-22.5)-9.9 (-12.6,-7.1)-22.3 (-25.3,-19.4)Diff vs PBO-10.6* (-14.4,-6.8)-11.3* (-15.1,-7.5)

CI=Confidence interval

a. LSmean for each MI dataset is calculated based on an ANCOVA model for the change from baseline at W24/W52. The combined LSmean, which is the average of the LSmean, taken over all the MI datasets, is presented.

*p<0.05

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, and UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, and UCB, Philip J Mease Speakers bureau: Boehringer Ingelheim and GlaxoSmithKline, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS0405 GENETIC VARIANTS WITHIN PSORIATIC ARTHRITIS (PsA)-WEIGHTED GENES FBXL19 AND HLA-B*39 MAY SERVE AS A POTENTIAL LINK BETWEEN PsA AND OBESITY

Background:

PsA patients have been observed to have a higher body mass index (BMI) compared to individuals with a similar disease (e.g., rheumatoid arthritis) or healthy controls1. Approximately 45% of PsA patients are considered obese with BMI’s exceeding 30 kg/m2, and these patients have more severe articular disease and lower response to therapy2. A recent mendelian randomization study noted that higher BMI leads to higher risk of psoriasis when using genetic variants as instrumental variables for BMI3.

Objectives:

To determine if known PsA-weighted genetic variants are overrepresented in an obese population.

Methods:

696 samples were identified from a previous genetic study in obesity where each patient was systematically examined with BMI and other related anthropometric measures were recorded. No patients had psoriasis, inflammatory arthritis or any extra-articular manifestations of spondyloarthritis. Samples were genotyped using a PsA-weighted single nucleotide polymorphism (SNP) panel, representing genetic variants associated with PsA. The cohort consisted of 73% female with an average age of 49 years ± 15. The average BMI of the group was 35± 8 kg/m2, ranging from 17 to 67 kg/m2. The PsA SNP panel consists of 42 SNPs associated with PsA including IL23R, 5q31, PTPN22, TNFAIP3, HLA-C, TNFRSF9, LCE3A, ADAMTS9-MAGI1, HLA-B, IL13, MICA, IL12B, ZNF8816A, TRAF3IP2, KIR2DS2, FBXL19, REL, IL23R, IL23A, TNIP1, and TYK2. DNA (10ng/uL) was used to prepare a PCR, followed by SAP, and extension reaction with Agena iPLEX Pro kit using Agena MassARRAY. Quantitative trait analysis was performed to obtain the association between BMI and genotype of the 42 SNPs using a linear regression model. Bonferroni correction was used to adjust for multiple comparisons. The factors of age, gender, smoking and height also have been adjusted in the analysis (Table 1).

Table 1.

Regression analysis between BMI and 2 significant SNPs

SNPCHRAllele 1Allele 2BETASETP (Genotype Only)P (adjusted with factors)*rs31313826TC2.2270.72073.170.0015965.40E-05rs1078200116GA1.5840.43473.6440.000290.0007524Genotypes of FBXL19 and HLA-B*3905 SNPs with mean BMISNPrs3131382rs10782001GenotypesTTTCCCGGGAAASample Counts69957575324246Genotype Frequency0.00880.150.840.110.500.38Mean BMI47.1736.7335.4637.2936.3634.43SD16.076.647.127.687.406.83

*Adjusted for age, sex, height, and smoking

Results:

Linear regression analysis with and without clinical factors for the two significant SNPs are presented in Table 1. Genotypes of two SNPs (rs10782001 and rs3131382) showed a difference with BMI (Table 1). The rs10782001 variant is within FBXL19 and the average BMI in the presence of GG genotype was 37.2 vs 34.3 for the AA genotype (p=0.0007). The rs3131382 variant is within HLA-B*39:05 and the average BMI with TT genotype was 47.1 vs 35.4 for the CC genotype (p=0.00005). Both SNPs maintained significance after correction for multiple testing (p<0.001).

Conclusion:

Homozygotes for the minor allele of SNPs within HLA-B*39 and FBXL19 have shown to have an increased BMI, suggesting a potential genetic link between these genes and PsA and obesity. Interestingly, it has been recently noted that miR-26 suppresses adipocyte progenitor differentiation and fat production by targeting FBXL19, leading to possible biologic possibility regarding the link between PsA-weighted genetic variants and obesity4.

References:

[1]Bhole VM et al., Rheumatology (Oxford). 2012 Mar;51(3):552-6.

[2]Klingberg E et al., Arthritis Res Ther. 2019;21(1):17.

[3]Budu-Aggrey A et al., PLoS Med. 2019 Jan 31;16(1):e1002739.

[4]Acharya A et al., Fbxl19. Genes Dev. 2019;33(19-20):1367-1380. doi:10.1101/gad.328955.119

Disclosure of Interests:

Cassidy Welsh: None declared, Tanya Burry: None declared, Quan Li: None declared, Amanda Dohey: None declared, Dianne Codner: None declared, Vinod Chandran Speakers bureau: AbbVie, Amgen, BMS, Eli Lily, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, BMS, Eli Lily, Janssen, Novartis, Pfizer, UCB, Grant/research support from: AbbVie, Amgen, Eli Lily, Employee of: Spousal Employment Eli Lilly, Dafna D Gladman Speakers bureau: AbbVie, Amgen, BMS, Eli Lily, Galapagos, Gilead, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, BMS, Eli Lily, Galapagos, Gilead, Janssen, Novartis, Pfizer, UC, Grant/research support from: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Darren O’Rielly: None declared, Proton Rahman Speakers bureau: Amgen, Abbott, AbbVie, BMS, Celgene, Eli Lily, Janssen, Novartis, Pfizer, Merck, UCB, Consultant of: Amgen, Abbott, AbbVie, BMS, Celgene, Eli Lily, Janssen, Novartis, Pfizer, Merck, UCB, Grant/research support from: Janssen, Novartis

POS0901 IXEKIZUMAB SHOWS A DISTINCT PATTERN OF PAIN IMPROVEMENT BEYOND INFLAMMATION IN RADIOGRAPHIC AXIAL SPONDYLOARTHRITIS

Background:

The efficacy of ixekizumab (IXE) in biologic-naïve patients with radiographic axial spondyloarthritis (r-axSpA) has been previously presented using traditional axSpA outcome measures, such as BASDAI and ASAS.

Objectives:

In patients with active r-axSpA, to assess the analgesic efficacy of IXE as it relates to patient-reported and objective measures of inflammation.

Methods:

The Phase III COAST-V (NCT02696785) multi-center, randomized, double-blind, placebo (PBO)-controlled and active reference arm with adalimumab (ADA) trial investigated the efficacy of IXE in 341 patients (pts) with active r-axSpA for 52 weeks (W). Pts were initially randomized to IXEQ4W, IXEQ2W, PBO, and ADAQ2W. At W16, pts assigned to PBO and ADA were re-randomized to IXEQ2W or Q4W. Changes in spinal pain at night (SP-N) and spinal pain were measured at each study visit and analysed while controlling for CRP levels or mean of BASDAI questions 5 & 6 (Q5: Duration and Q6: Intensity of morning stiffness). Observed data analyses are presented for each group stratified by treatment arm and compared to PBO. In the initial analysis, pts were categorized into 2 sub-groups defined as “Sustained” and “Fluctuating” depending on: CRP <5 mg/L W4-16 vs. CRP ≥5 mg/L at any point beyond W4 between weeks 4-16 respectively. In a second analysis, pts were categorized based on BASDAI Q5/6 improvement: “Sustained” if ≥2-pt improvement W12-16 vs. “Fluctuating” if <2-pt improvement at any point beyond W12 between W12-16.

Results:

Between W0 and W16, pts treated (tx) with IXEQ4W experienced greater reduction in SP-N than pts tx with ADA, in both CRP sustained and fluctuating groups (Fig 1a). Pts in the IXEQ4W and ADA arms showed different trajectories of pain improvement in the CRP fluctuating groups. For the pts with a fluctuating CRP ≥5 mg/L, pts in IXEQ4W arm demonstrated a greater reduction in SP-N compared to pts in PBO arm (p < .001) at W16, whereas pts in ADA arm did not experience a reduction in SP-N compared to PBO (p = .416). For the pts with a sustained CRP <5mg/L, IXEQ4W and ADA treatments both significantly demonstrated reduction in SP-N compared to PBO at W16 (IXEQ4W: p = .002; ADA: p = .02), with IXEQ4W treatment showing a greater level of reduction (Fig 1a). The pts randomized to ADA and re-randomized to IXEQ2W or Q4W (ADA/IXE) experienced further improvement in SP-N. This effect was sustained over the 52-wk period (Fig 1b). The same pattern of improvement in SP-N was observed when controlling for the BASDAI Q5/6; the SP-N improvement was greater in pts with a sustained BASDAI Q5/6 compared to pts with a fluctuating BASDAI Q5/6, regardless of treatment (Table 1). In the fluctuating BASDAI Q5/6, for pts in ADA/IXE arm, further reduction of both spinal pain and SP-N were observed (Table 1).

Table 1.

Change in Pain Outcome at baseline, week 16 and week 52 by Inflammation Status as assessed by BASDAI or CRP levels for patients receiving placebo (PBO), adalimumab (ADA), and ixekizumab every 4 weeks (IXE Q4W)

Change from baselinePBOADAIXEQ4WbaselineWeek 16(as observed)Week 52PBO/IXE(as observed)baselineWeek 16(as observed)Week 52ADA/IXE(as observed)baselineWeek 16(as observed)Week 52(as observed)Spinal painBASDAI Q5/6 sustained7.54-3.33-4.657.21-4.07-4.57.4-4.52-4.94BASDAI Q5/6 fluctuating7.37-1.32-2.826.76-1.2-2.246.97-1.3-2.52CRP sustained7-1.53-1.936.83-2.9-3.67.23-3.57-4.21CRP fluctuating7.51-1.96-3.637.28-2-2.897.24-2.91-3.93Spinal pain at nightBASDAI Q5/6 sustained7.12-3.21-4.617.26-4.63-4.927.12-4.73-4.91BASDAI Q5/6 fluctuating7.05-1.15-2.886.6-1.2-2.497.03-2.17-2.92CRP sustained7.2-1.87-2.276.76-3.2-3.856.89-3.8-4.12CRP fluctuating7.04-1.69-3.67.24-2.21-3.397.35-3.68-4.38

Conclusion:

IXE reduced SP-N and spinal pain irrespective of CRP or morning stiffness. Additionally, pts treated with ADA re-randomized to IXE experienced a further reduction in SP-N and spinal pain. Collectively, these results support the additive benefits of IXE in reducing pain above measurable effects on inflammation.

Acknowledgements:

The authors would like to thank Eglantine Julle-Daniere for writing and editorial contributions

Disclosure of Interests:

Kurt de Vlam Speakers bureau: Eli Lilly, Novartis, Pfizer, Paid instructor for: Celgene, Amgen, Consultant of: Elil Lillyn Novartis, UCB, Galapagos, Sandoz, Pfizer, Grant/research support from: Celgene, Gaia Gallo Shareholder of: Eli Lilly, Employee of: Eli Lilly, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sun, UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Janssen, Novartis, Pfizer, Sun, UCB, Proton Rahman Speakers bureau: AbbVie, Amgen, BMS, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, BMS, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, UCB, Grant/research support from: Janssen, Novartis, Venkatesh Krishnan Shareholder of: Eli Lilly, Employee of: Eli Lilly, David Sandoval Shareholder of: Eli Lilly, Employee of: Eli Lilly, Chen-Yen Lin Shareholder of: Eli Lilly, Employee of: Eli Lilly, Rebecca Bolce Shareholder of: Eli Lilly, Employee of: Eli Lilly, Philip G Conaghan Consultant of: personal fees from: AbbVie, AstraZeneca, BMS, Eli Lilly, EMD Serono, Flexion Therapeutics, Galapagos, Gilead, Novartis, Pfizer

Clinical and genomic predictors of brain metastases (BM) in non-small cell lung cancer (NSCLC): An AACR Project GENIE analysis

2032

Background: 30-50% of patients with non-early NSCLC will eventually develop BM, with a median survival of less than one year from BM diagnosis. There are no widely accepted clinical risk models for development of BM in patients without them at baseline. We predicted the binary risk of BM using clinical and genetic factors from a large multi-institutional cohort. Methods: Stage II-IV NSCLC patients from the AACR Project GENIE Biopharma Consortium dataset were eligible. This consisted of 4 academic institutions who curated clinical data of patients who had somatic next-generation tumor sequencing (NGS) between 2015-2017. We excluded patients who had BM at baseline, died within 30 days of NSCLC diagnosis, or did not undergo brain imaging. Covariates included demographics, anticancer therapies (received up to 90 days prior to BM development and within 5 years from NSCLC diagnosis), and NGS data; radiotherapy (RT) data were not available. NGS features included mutations and copy number alterations. These features were restricted to those classified as oncogenic by OncoKB. Univariate feature selection with Fisher’s test (p<.1) was performed on medication and genetic features. We compared 5 different machine learning models for prediction: random forest (RF), support vector machine (SVM), lasso regression, ridge regression, and an ensemble classifier. We split our data into training and test sets. 10-fold cross-validation was done on the training set for parameter tuning. The area under the receiver-operating curve (AUC) is reported on the test set. Results: 956 patients were included, 192 (20%) in the test set. Univariate features associated with BM were treatment with etoposide, Asian race, presence of bone metastases at NSCLC diagnosis, mutations in TP53 and EGFR, amplifications of ERBB2 and EGFR, and deletions of RB1, CDKN2A and CDKN2B. Univariate features inversely associated with BM were older age, treatment with nivolumab, vinorelbine, alectinib, pembrolizumab, atezolizumab, and gemcitabine, as well as mutations in NOTCH1 and KRAS. Ridge regression had the best AUC, 0.73 (Table). Conclusions: We achieved reasonable prediction performance using commonly obtained clinical and genomic information in non-early NSCLC. The biologic role of the associated alterations deserves further scrutiny; this study replicates similar findings for EGFR and KRAS in a much smaller cohort. Certain subsets of NSCLC patients may benefit from increased surveillance for BM and transition to drug therapies known to effectively cross the blood-brain barrier, e.g., nivolumab and alectinib. Inclusion of additional covariates, e.g., brain RT, may further improve model performance.[Table: see text]

Amplifying Domain Expertise in Clinical Data Pipelines

Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts' interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

OP0230 EFFICACY AND SAFETY OF TILDRAKIZUMAB, A HIGH-AFFINITY ANTI–INTERLEUKIN-23P19 MONOCLONAL ANTIBODY, IN PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS IN A RANDOMISED, DOUBLE-BLIND, PLACEBO-CONTROLLED, MULTIPLE-DOSE, PHASE 2B STUDY

Background:

Tildrakizumab (TIL), a high-affinity anti–interleukin-23p19 monoclonal antibody, is approved to treat moderate to severe plaque psoriasis and is under investigation for treatment of psoriatic arthritis (PsA).1

Objectives:

To evaluate efficacy and safety of TIL up to week (W)52 in a randomised, double-blind, placebo-controlled, multiple-dose, phase 2b study in PsA (NCT02980692).

Methods:

Patients (pts) ≥18 years with active PsA2were randomised 1:1:1:1:1 to TIL 200 mg every 4 weeks (Q4W) to W52, TIL 200 mg Q12W to W52, TIL 100 mg Q12W to W52, TIL 20 mg Q12W until W24 then TIL 200 mg Q12W to W52, or placebo (PBO) Q4W until W24 then TIL 200 mg Q12W to W52. Efficacy assessments included ACR20/50/70, 75%/90%/100% improvement in Psoriasis Area and Severity Index (PASI), proportion of pts with residual minimal disease activity (MDA) response; and mean change from baseline (BL) in HAQ-DI, Leeds Dactylitis Index (LDI, pts with BL LDI ≥1), and Leeds Enthesitis Index (LEI, pts with BL LEI ≥1) to W52. Treatment-emergent adverse events (TEAEs) were monitored.

Results:

Of 500 pts screened, 391 were randomised and received ≥1 dose of drug. Proportions of ACR20/50/70 responders were superior with TIL vs PBO through W24; after W24 rates of responses further increased for TIL 20→200 mg Q12W and PBO→200 mg Q12W through W52 (Figure 1, 2). Other efficacy results are shown in Table. Overall from BL→W24/W25→W52, 50.4%/39.9% and 2.3%/1.0% of pts experienced a TEAE and serious AE, respectively. From BL→W24, 1 case of pyelonephritis and urinary tract infection was reported in the TIL 100 mg Q12W arm and 1 case of chronic tonsillitis was reported in the TIL 20 mg→200 mg Q12W arm. During W25→W52, 1 malignancy (intraductal proliferative breast lesion) was reported with TIL 20 mg→200 mg Q12W. No deaths or major adverse cardiac events occurred.

Table.

W52 clinical efficacy

TIL 200 mg Q4Wn=78TIL 200 mg Q12Wn=79TIL 100 mg Q12Wn=77TIL 20→200 mg Q12Wn=78PBO→TIL 200 mg Q12Wn=79HAQ-DI, BLa1.0 ± 0.61.0 ±0.61.0 ± 0.71.1 ± 0.61.2 ± 0.6 W52b−0.5 ± 0.5−0.5 ± 0.6−0.5 ± 0.6−0.5 ± 0.5−0.5 ± 0.5LEI, BLa,c1.9 ± 2.01.5 ± 1.92.2 ± 2.12.2 ± 2.01.5 ± 1.9 W52b−1.3 ± 1.9−1.0 ± 1.6−1.7 ± 2.1−1.2 ± 1.8−1.2 ± 1.8LDI, BLa,d32.8 ± 32.961.3 ± 73.593.8 ± 146.571.4 ± 118.599.6 ± 170.7 W52b,d−21.4 ± 37.1−42.1 ± 76.7−41.6 ± 89.3−56.5 ± 123.4−81.5 ± 173.0 BL, W52 mediand21.8, 7.428.3, 3.232.1, 20.028.6, 034.0, 5.6MDAe56.964.445.047.142.0PASI 100e54.044.443.947.535.0PASI 90e72.080.658.555.050.0PASI 75e82.094.482.975.067.5

aBL mean ± SD.bMean change from BL ± SD.cPts with BL LEI ≥1 will be presented at EULAR.dPts with BL LDI ≥1 (n = 27, 21, 21, 19, 25) using nonresponder imputation.e% at W52

Missing data not imputed.

SD, standard deviation.

Conclusion:

TIL was well tolerated and improved joint and skin manifestations of PsA through W52.

References:

[1]Reich, et al.Lancet2017;390:276−88.

[2]Taylor, et al.Arthritis Rheum2006;54:2665–73.

Disclosure of Interests:

Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Saima Chohan Employee of: Partner/physician at Arizona Arthritis and Rheumatology Associates, Ferran J García Fructuoso Grant/research support from: AbbVie, Eli Lilly, Gedeon Richter, MedImmune, Nichi-Iko, Pfizer, Sanofi-Aventis, Takeda, and UCB, Consultant of: AbbVie, Eli Lilly, Gedeon Richter, MedImmune, Nichi-Iko, Pfizer, Sanofi-Aventis, Takeda, and UCB, Speakers bureau: AbbVie, Eli Lilly, Gedeon Richter, MedImmune, Nichi-Iko, Pfizer, Sanofi-Aventis, Takeda, and UCB, Alice B Gottlieb Grant/research support from:: Research grants, consultation fees, or speaker honoraria for lectures from: Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB., Consultant of:: Research grants, consultation fees, or speaker honoraria for lectures from: Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB., Speakers bureau:: Research grants, consultation fees, or speaker honoraria for lectures from: Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB., Michael E Luggen Grant/research support from: AbbVie; Amgen; Eli Lilly; Genentech; Nichi-Iko; Novartis; Pfizer; R-Pharm; and Sun Pharmaceutical Industries, Inc., Consultant of: AbbVie; Amgen; Eli Lilly; Genentech; Nichi-Iko; Novartis; Pfizer; R-Pharm; and Sun Pharmaceutical Industries, Inc., Speakers bureau: AbbVie; Amgen; Eli Lilly; Genentech; Nichi-Iko; Novartis; Pfizer; R-Pharm; and Sun Pharmaceutical Industries, Inc., Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Siba P Raychaudhuri Grant/research support from: AbbVie; Janssen; Novartis, Pfizer; Sun Pharmaceutical Industries, Inc, Consultant of: Amgen; Eli Lilly; Janssen; Novartis and Pfizer, Richard C Chou Consultant of: Sun Pharmaceutical Industries, Inc, Alan M Mendelsohn Shareholder of: Johnson and Johnson, Employee of: Sun Pharmaceutical Industries, Inc, Stephen Rozzo Employee of: Sun Pharmaceutical Industries, Inc, Ana-Maria Orbai Grant/research support from: Abbvie, Eli Lilly and Company, Celgene, Novartis, Janssen, Horizon, Consultant of: Eli Lilly; Janssen; Novartis; Pfizer; UCB. Ana-Maria Orbai was a private consultant or advisor for Sun Pharmaceutical Industries, Inc, not in her capacity as a Johns Hopkins faculty member and was not compensated for this service.

OP0050 ADALIMUMAB INTRODUCTION VERSUS METHOTREXATE DOSE ESCALATION IN PATIENTS WITH INADEQUATELY CONTROLLED PSORIATIC ARTHRITIS: RESULTS FROM RANDOMIZED PHASE 4 CONTROL STUDY

Background:

Methotrexate (MTX) is often used as first-line therapy for patients (pts) with psoriatic arthritis (PsA) despite limited efficacy and data on appropriate dosage. Minimal Disease Activity (MDA) is suggested as an optimal treat-to-target outcome. Biologic disease-modifying antirheumatic drugs (bDMARDs) have demonstrated improved outcomes (including MDA rates) over MTX. However, more data are needed to define the optimal timing of bDMARD initiation and characterize efficacy of MTX dose escalation, to achieve optimal outcomes.

Objectives:

To compare achievement of MDA between adding adalimumab (ADA) vs escalating MTX dose in PsA pts with inadequate disease control after initial MTX therapy.

Methods:

The open-label, 2-part CONTROL study enrolled bDMARD-naive adult pts with active PsA (not in MDA at screening and ≥3 tender and ≥3 swollen joints) despite MTX 15 mg every wk (ew) for ≥4 wks. Pts were randomized to ADA 40 mg every other wk + MTX 15 mg (ADA+MTX) or escalated MTX to 20–25 mg ew or highest tolerable dose during 16-wk part 1 (Fig 1). The primary endpoint was achievement of MDA, defined as fulfilling ≥5 of the 7 criteria: tender joint count 68 (TJC68) ≤1, swollen joint count 66 (SJC66) ≤1, Psoriasis Area Severity Index (PASI) ≤1 or body surface area (BSA) ≤3%, pt’s pain (visual analogue scale [VAS] 0–100) ≤15, Pt’s Global Assessment of disease activity (PtGA) VAS ≤20, Health Assessment Questionnaire Disability Index (HAQ-DI) ≤0.5 and tender entheseal points (0–8) ≤1. Key secondary efficacy endpoints were achievement of ACR20 and PASI75 and change from baseline in HAQ-DI and Leeds Enthesitis Index (LEI) at wk 16.

Results:

Overall, 246 pts were randomized; 245 received treatment (ADA+MTX, n=123; escalated MTX, n=122); 117 (95%) pts and 110 (90%) pts, respectively, completed part 1. Baseline characteristics were similar between groups (Table). During part 1, the average dose of MTX was 21.8 mg/wk (55% on oral MTX) in the escalated MTX group. Significantly higher proportion of pts in ADA+MTX (42%) vs escalated MTX (13%) group achieved MDA at wk 16 (non-responder imputation [NRI]; difference [95% CI] 28% [18%–39%];P<0.001;Fig 2). Observed case analysis confirmed the NRI analysis. Lower MDA rates at wk 16 were observed in the escalated MTX arm regardless of prior MTX duration (Fig 2). Significant improvements in key secondary endpoints were also observed with ADA+MTX vs escalated MTX (allP<0.05;Fig 2). In part 1, the proportion of patients with adverse events was similar between groups (ADA+MTX, 62% vs escalated MTX, 57%); no opportunistic infections, tuberculosis, malignancies, or deaths were reported during part 1.

Conclusion:

A significantly higher proportion of pts achieved MDA at wk 16 after introducing ADA compared with escalating MTX dose; higher rates were observed regardless of prior MTX duration. Significantly higher responses in musculoskeletal, skin, and quality of life measures were observed with ADA+MTX vs escalated MTX. No new safety signals with ADA were identified in this pt population.

Table 1.

Baseline Demographics

Characteristics, mean (SD)ADA+MTXn=123Escalated MTXn=122Female, n (%)64 (52.0)59 (48.4)Age, y51.4 (12.2)48.8 (12.7)BSA >3%, n (%)74 (60.2)78 (63.9)Pt pain63.7 (19.5)62.3 (20.9)PtGA65.0 (19.9)62.9 (20.9)HAQ-DI1.2 (0.6)1.2 (0.7)LEI + plantar count3.5 (2.1)3.5 (2.1)

Disclosure of Interests:

Laura C Coates: None declared, William Tillett Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, UCB, Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, UCB, Maria Antonietta D’Agostino Consultant of: AbbVie, BMS, Novartis, and Roche, Speakers bureau: AbbVie, BMS, Novartis, and Roche, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Frank Behrens Grant/research support from: Pfizer, Janssen, Chugai, Celgene, Lilly and Roche, Consultant of: Pfizer, AbbVie, Sanofi, Lilly, Novartis, Genzyme, Boehringer, Janssen, MSD, Celgene, Roche and Chugai, Philip G Conaghan Consultant of: AbbVie, BMS, Eli Lilly, EMD Serono, Flexion Therapeutics, Galapagos, GSK, Novartis, Pfizer, Speakers bureau: AbbVie, Eli Lilly, Novartis, Pfizer, Erin McDearmon-Blondell Shareholder of: AbbVie, Employee of: AbbVie, Xianwei Bu Shareholder of: AbbVie, Employee of: AbbVie, Liang Chen Shareholder of: AbbVie, Employee of: AbbVie, Mudra Kapoor Shareholder of: AbbVie, Employee of: AbbVie, Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau

THU0396 IMPACT OF IXEKIZUMAB ON WORK PRODUCTIVITY IN PATIENTS WITH ANKYLOSING SPONDYLITIS: RESULTS FROM THE COAST-V AND COAST-W TRIALS AT 52 WEEKS

Background:

Patients with ankylosing spondylitis (AS) are burdened with decreased work productivity.1Ixekizumab (IXE), a high-affinity monoclonal antibody selectively targeting interleukin-17A, has been shown to improve disease signs and symptoms in 2 phase 3 trials assessing patients with active AS.2, 3

Objectives:

This study investigated the effect of IXE treatment for 52 weeks on work productivity and activity impairment as measured by absenteeism, presenteeism, overall work impairment, and activity impairment in patients with active AS.

Methods:

COAST-V (NCT02696785) and COAST-W (NCT02696798) were phase 3, multicenter, randomized, double-blind, placebo (PBO)-controlled (COAST-V active-controlled with adalimumab) trials investigating the efficacy of IXE every 4 weeks (Q4W) and every 2 weeks (Q2W) in 341 patients with active AS naïve to biologic disease-modifying antirheumatic drugs (bDMARDs; COAST-V) and in 316 patients who were inadequate responders or intolerant to 1 or 2 tumor necrosis factor inhibitors (TNFi; COAST-W). Patients receiving PBO were switched to IXE Q4W or Q2W at Week 16; patients receiving adalimumab (ADA) were switched to IXE Q4W or Q2W at Week 20. Data for IXE Q4W and Q2W were combined for PBO/IXE and ADA/IXE groups. Changes from baseline in work productivity were measured for those reporting full- or part-time work at Weeks 16 and 52 with the Work Productivity and Activity Impairment (WPAI) Questionnaire for Spondyloarthritis.

Results:

Compared to bDMARD-naïve patients (COAST-V), TNFi-experienced patients (COAST-W) were slightly older, had longer disease duration, reported less paid employment, and had greater scores for impaired work productivity, signifying more severe baseline disease. At Week 16, bDMARD-naïve patients treated with IXE Q4W or Q2W had significant improvements in activity impairment compared to placebo (p<0.01); TNFi-experienced patients treated with IXE Q4W or Q2W had significant improvements in presenteeism (p<0.05) and overall work impairment (p<0.05; Figure). TNFi-experienced patients treated with IXE Q2W also had significant improvement in activity impairment at Week 16 (p<0.05; Figure). Improvements were sustained through Week 52 (Figure).

Conclusion:

Both bDMARD-naïve and TNFi-experienced patients with AS receiving IXE had greater improvements in aspects of work productivity compared to placebo. Improvements were sustained through Week 52.

References:

[1]Boonen, van der Linden. (2006).J Rheumatol Suppl.78:4-11.

[2]Van der Heijde, et al. (2018)Lancet. 392(10163):2441-51.

[3]Deodhar, et al. (2019)Arthritis Rheumatol.71(4):599-611.

Disclosure of Interests:

Helena Marzo-Ortega Grant/research support from: Janssen, Novartis, Consultant of: Abbvie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Speakers bureau: Abbvie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Takeda, UCB, Philip J Mease Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Janssen, Eli Lilly, Novartis, Pfizer, Sun Pharma, UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Galapagos, Gilead, Novartis, Pfizer, Sun Pharma, UCB Pharma, Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Genentech, Janssen, Novartis, Pfizer, UCB Pharma, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Victoria Navarro-Compán Consultant of: Abbvie, Lilly, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, MSD, Lilly, Novartis, Pfizer, UCB, Vibeke Strand Consultant of: AbbVie, Amgen, Biogen, Celltrion, Consortium of Rheumatology Researchers of North America, Crescendo Bioscience, Eli Lilly, Genentech/Roche, GlaxoSmithKline, Hospira, Janssen, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Sanofi, UCB, Maxime Dougados Grant/research support from: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Consultant of: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Speakers bureau: AbbVie, Eli Lilly, Merck, Novartis, Pfizer and UCB Pharma, Bernard Combe Grant/research support from: Novartis, Pfizer, Roche-Chugai, Consultant of: AbbVie; Gilead Sciences, Inc.; Janssen; Eli Lilly and Company; Pfizer; Roche-Chugai; Sanofi, Speakers bureau: Bristol-Myers Squibb; Gilead Sciences, Inc.; Eli Lilly and Company; Merck Sharp & Dohme; Pfizer; Roche-Chugai; UCB, James Cheng-Chung Wei Grant/research support from: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Chugai, Eisai, Janssen, Novartis, Pfizer Inc, Sanofi-Aventis, UCB Pharma, Xenofon Baraliakos Grant/research support from: Grant/research support from: AbbVie, BMS, Celgene, Chugai, Merck, Novartis, Pfizer, UCB and Werfen, Consultant of: AbbVie, BMS, Celgene, Chugai, Merck, Novartis, Pfizer, UCB and Werfen, Speakers bureau: AbbVie, BMS, Celgene, Chugai, Merck, Novartis, Pfizer, UCB and Werfen, Theresa Hunter Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, David Sandoval Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Xiaoqi Li Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Baojin Zhu Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Louis Bessette Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Roche, Sanofi, UCB Pharma, Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, Merck, Novartis, Pfizer, Sanofi, Atul Deodhar Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB

Figure.

Changes from baseline in Overall Work Impairment in A) bDMARD-naïve (COAST-V) and B) TNFi-experienced (COAST-W) patients and Activity Impairment in C) bDMARD-naïve and D) TNFi-experienced patients.

SAT0638-HPR CHARACTERIZING THE IMPACT OF AXIAL SPONDYLOARTHRITIS ON DAILY LIFE: GENDER AND PATIENT-REPORTED OUTCOMES ASSOCIATED WITH FUNCTIONAL LIMITATION IN CANADA. RESULTS FROM THE IMAS SURVEY

Background:

Understanding the most limiting daily activities reported by patients is important for a holistic healthcare approach.

Objectives:

To evaluate the degree of functional limitation on daily activities and its association with PROs in Canada.

Methods:

The International Map of Axial Spondyloarthritis (IMAS) is a cross-sectional online survey of non-selected patients with self-reported axSpA conducted in 22 countries and endorsed by the Axial Spondyloarthritis International Federation. IMAS captures the patients’ perspective of the burden of axSpA. The Canadian adaptation included a review of the survey by an advisory board of axSpA patients and a national steering committee composed of the Canadian Spondylitis Association, rheumatologists and axSpA patients. Canadian participants were recruited from 2018 to 2019. Socio-demographics variables, BASDAI and mental health (GHQ-12) data were collected. Degree of functional limitation in 18 daily activities was evaluated using a 3-point Likert scale. Bivariate analysis was performed to assess activities associated with poorer BASDAI and mental health status.

Table 1.

BASDAI and mental health (GHQ-12)- impact on daily activities (N = 542)

BASDAIMean ± SDGHQ-12Mean ± SDLow limitationMedium + High Limitationp-valueLow limitationMedium + High Limitationp-valueDressing / undressing5.5 ± 2.06.4 ± 4.4<.001*4.4 ± 3.85.7 ±4.0.007*Washing / personal grooming5.6 ±2.06.3 ± 2.0.002*4.7 ± 4.15.6 ± 4.1.099Taking a bath / shower5.6 ±2.06.6 ±1.8<.001*4.2 ± 4.05.9 ± 4.0.002*Tying shoe laces5.4 ± 2.06.1 ± 2.0.005*4.3 ± 3.85.2 ± 4.1.044*Walking / getting around the house5.5 ± 2.06.4 ± 1.9<.001*4.2 ± 3.95.6 ± 4.1.005*Stair climbing5.1 ±1.96.3 ±1.8<.001*3.4 ± 3.45.4 ± 4.0<.001*Lying down / getting up from bed5.2 ± 2.06.3 ± 1.9<.001*3.6 ± 3.65.5 ± 4.1<.001*Going to the toilet5.4 ± 2.06.7 ± 1.9<.001*4.3 ± 4.25.6 ± 3.9.024*Shopping5.6 ± 1.86.2 ± 1.9.003*4.1 ± 3.75.3 ± 4.1.025*Cooking5.6 ± 1.96.3 ± 1.8.008*3.7 ± 3.65.8 ±4.2<.001*Eating5.9 ± 2.16.9 ± 1.9.024*5.0 ± 4.45.8 ± 3.9.282Housework / cleaning4.9 ± 2.06.0 ± 1.8<.001*3.7 ± 3.64.8 ± 4.0.021*Walking down the street5.4 ± 1.96.1 ± 2.0.005*4.4 ± 3.85.1 ± 4.1.228Using public transportation5.6 ± 1.96.1 ± 1.9.1804.4 ± 4.05.3 ± 4.0.155Driving5.5 ± 2.06.1 ± 2.1.021*4.2 ± 3.95.3 ± 4.2.050Doing physical exercise4.7 ± 2.15.8 ± 1.9<.001*3.4 ± 3.74.7 ± 4.0.002*Engaging in intimate relations5.2 ± 1.96.0 ±1.9<.0014.0 ± 3.85.1 ± 4.0.015*Caring for children / grandchildren5.2 ± 1.96.0 ± 2.0.0033.7 ± 3.85.2 ± 4.1.005

* p≤.05

Results:

542 axSpA patients participated. Mean age was 44.3±13.9 years and 63% were female. Mean BASDAI was 5.3±2.1, mean GHQ-12 score was 4.0±3.8 and 50% were on biologics. 94% reported ≥1 limitation in daily activities, of which physical exercise (30%), house cleaning (22%), intimacy (21%) and stair climbing (21%) were most commonly severely impacted (high limitation). Women reported significantly higher limitations in house cleaning, stair climbing, driving, moving around the house and caring for young children (p<.05 for all activities vs men). Compared with low limitation, medium–high limitation in most activities was significantly associated with higher disease activity and worsened mental health for the overall population (Table 1).

Conclusion:

Canadian axSpA patients, particularly women, are limited in daily life activities beyond those captured by other validated scales. Strong association between functional limitation, disease activity and mental health emphasizes the need for holistic evaluation of axSpA patients.

Disclosure of Interests:

Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Marco Garrido-Cumbrera: None declared, Sherry Rohekar: None declared, Michael Mallinson: None declared, Gerald Major: None declared, Algis Jovaisas: None declared, Nigil Haroon: None declared, Wendy Gerhart: None declared, Artur J. deBrum Fernandes: None declared, Martin Cohen: None declared, Jon Chan: None declared, Patrick Leclerc Employee of: Novartis, Julie Schneiderman Employee of: Novartis, Robert Inman: None declared

SAT0414 THE PERFORMANCE OF A MULTI-MARKER GENETIC TEST TO IDENTIFY PATIENTS WITH PSORIATIC ARTHRITIS AMONG PSORIASIS PATIENTS

Background:

Improved understanding of the complex genetic architecture of psoriatic arthritis (PsA) along with the reduction in the cost of genetic testing provide an opportunity to assess the application of genetic testing for PsA diagnosis in clinical setting.

Objectives:

The study aimed to assess the performance of the multi-SNP genetic test in predicting a clinical diagnosis of PsA by a rheumatologist among psoriasis patients with musculoskeletal symptoms.

Methods:

328 patients with psoriasis and musculoskeletal symptoms that were referred to a rapid access clinic for suspected PsA were enrolled. Patients with a prior diagnosis of PsA were excluded. A rheumatologist evaluated all patients and classified them as “PsA” or “not PsA”. All patients who were classified as not PsA at baseline were reassessed 1 year later to determine whether they have developed PsA. We tested 2 outcomes: 1) diagnosis of PsA at baseline; and 2) diagnosis of PsA at baseline or at 1 year. A custom multi-SNP genetic assay was genotyped on a MassARRAY system (Agena Biosciences). The custom PsA weighted genetic panel included 42 variants in or near 20 genes based on genome-wide significance in PsA studies. We tested the ability of each genetic maker individually to predict PsA using logistic regression models adjusted for age and sex. Machine-learning methods including logistic regression, naïve bayes and random forest were used to identify the optimal prediction model. Age and sex were included in the prediction models.

Results:

78 patients were classified as PsA (PsA-baseline) and the remaining 250 patients as not PsA. After 1 year, 17 additional patients developed PsA resulting in 95 patients with PsA at 1 year (PsA-1 year). The association between the tested SNPs and PsA is shown in Table 1. 5 SNPs located at LCE3A (rs10888503), TNIP1 (rs146571698) and IL-23R (rs4655683, rs2201841 and rs12044149) were associated with PsA-baseline or PsA-1 year (all p<0.05). Of the three machine-learning methods used, logistic regression was found to have the best prediction properties (highest AUC). Overall, the performance of prediction models to classify patients as PsA was modest (see Table 2). The AUC, sensitivity and specificity of the models to predict PsAat baselinewere: 0.62, 0.15, 0.97, respectively andat 1 year: 0.62, 0.15, 0.94.

Table 1.

The association between (selected top markers)

ChromSNPGenePsA diagnosis at baseline (N=78)PsA diagnosis at 1 year (N=95)OR95% CIP valueOR95% CIP value1rs10888503LCE3A1.781.21, 2.620.0031.521.06, 2.170.026rs12191877HLA-C0.520.31, 0.890.0170.630.39, 1.010.0576rs2894207HLA-C*60.570.35, 0.930.0250.620.39, 0.990.0456rs13214872HLA-C0.570.34, 0.960.0350.650.41, 1.050.0786rs12189871HLA-C0.550.30, 1.010.0550.680.40, 1.170.1696rs4406273HLA-C0.570.32, 1.020.0570.670.40, 1.120.1225rs146571698TNIP12.030.97, 4.240.0612.071.02, 4.180.0441rs4655683IL-23R1.420.97, 2.080.0691.521.06, 2.170.0221rs2201841IL-23R0.720.50, 1.050.0870.680.48, 0.960.0541rs12044149IL-23R1.340.88,2.060.1671.601.07, 2.390.021Table 2.

Performance of the genetic assay in predicting PsA

2A. Prediction of PsA at baseline (N=78)MethodTPTNFPFNAccuracySensitivitySpecificityAUCLogistic Regression122428660.770.150.970.62Naïve Bayes2720347510.700.350.810.61Random Forest1423713640.770.180.950.562B. Prediction of PsA at 1 year (N=95)MethodTPTNFPFNAccuracySensitivitySpecificityAUCLogistic Regression1521815800.710.150.940.62Naïve Bayes2120330740.680.220.870.62Random Forest1620429790.670.170.880.55

Conclusion:

Despite the association of several genetic markers with PsA, genetic testing has marginal effect on predicting a diagnosis of PsA among patients with psoriasis and musculoskeletal symptoms.

Disclosure of Interests:

Lihi Eder Grant/research support from: Abbvie, Lily, Janssen, Amgen, Novartis, Consultant of: Janssen, Speakers bureau: Abbvie, Lily, Janssen, Amgen, Novartis, Quan Li: None declared, Dana Jerome: None declared, Chandra Farrer: None declared, Tanya Burry: None declared, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer

AB0756 GUSELKUMAB IMPROVED WORK PRODUCTIVITY AND DAILY ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM A PHASE 3 TRIAL

Background:

DISCOVER 2 (DISC 2) is a Phase 3 trial of anti-IL-23-specific mAb guselkumab (GUS) in psoriatic arthritis (PsA) pts, who experience impaired physical function, resulting in disability, work productivity loss, and economic consequences.1

Objectives:

To evaluate the effect of GUS on impaired work productivity and daily activity in DISC 2 using the Work Productivity and Activity Impairment Questionnaire: Psoriatic Arthritis (WPAI-PsA).

Methods:

Bio-naïve adults with active PsA despite nonbiologic DMARDs &/or NSAIDs received subcutaneous GUS 100 mg every (q) 4 weeks (W); GUS 100 mg W0, W4, q8W; or placebo (PBO). WPAI-PsA assesses, due to PsA over the previous week, work time missed (absenteeism), impairment while working (presenteeism), and impaired overall work productivity (absenteeism + presenteeism) and daily activity. Percentage change from baseline was analyzed for WPAI-PsA domains using mixed-effect model repeated measure (MMRM). Indirect savings from improved overall work productivity were estimated with 2018 US mean yearly wage estimate (all occupations).2

Results:

At Week 24, impaired overall work productivity and daily activity were improved 20-22% in GUS-treated and 10-11% in PBO-treated pts (Table). Potential yearly indirect savings from improved overall work productivity was $10,242 with GUS q8W and $10,404 with GUS q4W vs $5,648 with PBO; $4,594 and $4,756 difference, respectively.

Conclusion:

Improvement in overall work productivity and daily activity was greater with GUS versus PBO among pts with moderate-to-severe PsA, resulting in potential annual incremental economic gains.

References:

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–283.

[2]US Bureau of Labor Statistics. May 2018 National Occupational Employment and Wage Estimates United States.https://www.bls.gov/oes/current/oes_nat.htm#00-000

Table.

Model-based estimates of mean change from baseline in WPAI-PsA domains

% change from baselinePBOGUS 100 mg q8WGUS 100 mg q4WW16W24W16W24W16W24Work time missed (absenteeism), n155152141145145143LSMean-4.6 (-7.2,-1.9)-3.5 (-6.4,-0.6)-3.5 (-6.2,-0.7)-3.1 (-6.1,-0.1)-4.7 (-7.4,-2.0)-3.8 (-6.8,-0.8)LSMean diff1.1 (-2.6,-4.8)*0.4 (-3.7,4.5)*-0.2 (-3.9,3.5)*-0.3 (-4.4,3.8)*Impairment while working (presenteeism), n131130125129133130LSMean-10.3 (-13.9,-6.7)-10.2 (-13.7,-6.7)-16.1 (-19.7,-12.4)-19.4 (-22.9,-15.9)-15.1 (-18.7,-11.5)-19.5 (-23.0,-16.0)LSMean diff-5.8 (-10.8,-0.8)†-9.2 (-14.0,-4.4)‡-4.8 (-9.7,0.1)*-9.3 (-14.1,-4.5)‡Overall work productivity impairment (absenteeism + presenteeism), n131130125129133130LSMean-11.2 (-15.0,-7.5)-10.9 (-14.6,-7.1)-15.9 (-19.7,-12.2)-19.7 (-23.4,-16.0)-15.8 (-19.5,-12.1)-20.0 (-23.7,-16.3)LSMean diff-4.7 (-9.9,0.5)*-8.8 (-14.0,-3.7)‡-4.6 (-9.7,0.5)*-9.2 (-14.3,-4.0)‡Daily activity impairment, n244244247246243245LSMean-10.6 (-13.3,-7.9)-10.3 (-13.1,-7.6)-17.1 (-19.8,-14.4)-21.5 (-24.2,-18.7)-17.0 (-19.7,-14.3)-20.5 (-23.2,-17.7)LSMean diff-6.5 (-10.2,-2.8)‡-11.1 (-15.0,-7.4)‡-6.5 (-10.2,-2.7)‡-10.2 (-14.0,-6.4)‡

Data are % (95% CI)

*p>0.05, †p<0.05,‡p<0.001

LSmeans, p values based on MMRM

LSmean diffs, p values vs PBO

Acknowledgments:

None

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Iain McInnes Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, William Tillett Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, UCB, Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, UCB, Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Bei Zhou Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Steve Peterson Employee of: Janssen Research & Development, LLC, Chenglong Han Employee of: Janssen Research & Development, LLC

FRI0359 INTEGRATED SAFETY RESULTS OF TWO PHASE-3 TRIALS OF GUSELKUMAB IN PATIENTS WITH PSORIATIC ARTHRITIS THROUGH THE PLACEBO-CONTROLLED PERIODS

Background:

DISCOVER 1 & 2 are phase 3 psoriatic arthritis (PsA) trials investigating guselkumab (GUS), an IL-23 inhibitor that specifically binds the IL-23p19 subunit. In both studies, GUS showed significant improvement vs placebo (PBO) through week (W) 24 in the PBO-controlled period.1,2

Objectives:

To present integrated safety results of DISC 1 & 2 through the PBO-controlled periods.

Methods:

Adult patients (pts) with active PsA despite standard therapy were enrolled. All pts were biologic-naïve, except ~30% in DISC 1 with previous exposure to 1-2 TNF inhibitors. Pts were randomized to SC GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or PBO. Adverse events (AEs) and lab results were analyzed from pooled data.

Results:

The rates of pts experiencing ≥1 AE, serious AE, infection, serious infection, and discontinuation due to an AE were similar between GUS and PBO (Table 1). There were 2 deaths, 3 malignancies, 2 Major Adverse Cardiac Events (MACE), and no opportunistic infections (treatment group not shown to prevent unblinding). Among the AEs reported by ≥5% pts in any group (Table 1), nasopharyngitis and elevated serum hepatic aminotransferases were more common with GUS vs PBO. Laboratory ALT and AST elevations were mostly mild, transient, and not associated with significant bilirubin elevation. There was a trend to decreased neutrophil count (mostly Grade 1, transient, and not associated with infection) with GUS vs PBO (Table 2). Low rates of injection-site reactions were seen with GUS vs PBO. Anti-drug antibody development was also low (Table 1).

Table 1.

Patient Reported AEs, n (%)

GUS100 mgQ8WGUS100 mgQ4WPBON375373372≥1 AE182 (48.5)182 (48.8)176 (47.3)≥1 Serious AE7 (1.9)8 (2.1)12 (3.2)Discontinuation due to AE5 (1.3)8 (2.1)7 (1.9)≥1 Infection73 (19.5)80 (21.4)77 (20.7)≥1 Serious infection1 (0.3)3 (0.8)3 (0.8)≥1 Opportunistic Infection (including Candida)000Active Tuberculosis000≥1 Injection-site reaction5 (1.3)4 (1.1)1 (0.3)Anti-GUS antibody +, n/N (%)6/373 (1.6)9/371 (2.4)--AEs* reported by ≥5% of patients in any treatment groupNasopharyngitis26 (6.9)19 (5.1)17 (4.6)Upper respiratory tract infection13 (3.5)23 (6.2)17 (4.6)Increased ALT23 (6.1)28 (7.5)14 (3.8)Increased AST23 (6.1)14 (3.8)9 (2.4)

*Medical Dictionary for Regulatory Activities (MedDRA) preferred term

Table 2.

Lab Results*

GUS100 mgQ8WGUS100 mgQ4WPBON373371370ALT Increased (%)Grade 128.235.030.121.12.71.43-40.81.10.8Neutrophil Count Decreased (%)Grade 15.65.93.221.61.60.83-400.30.3

*NCI toxicity grade

ALT=Alanine aminotransferase

Conclusion:

GUS was safe and well tolerated through the PBO-controlled period in 2 randomized, phase 3 trials of patients with active PsA. There were no meaningful safety differences between the Q8W and Q4W groups, no significant safety issues identified when comparing GUS to PBO, and no safety signals with regards to infections, malignancy, and MACE. The safety profile of GUS Q4W and Q8W in PsA pts was generally consistent with that in the Phase 3 trials of GUS Q8W for psoriasis.3,4

References:

[1]Deodhar et al. ACR 2019 (#807). Arth Rheum 2019;71 S10:1386

[2]Mease et al. ACR 2019 (#L13). Arth Rheum 2019;71 S10:5247

[3]Blauvelt et al. J Am Acad Derm 2017;76:405

[4]Reich et al. J Am Acad Derm 2017;76:418

Acknowledgments:

None

Disclosure of Interests:

Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Christopher T. Ritchlin Grant/research support from: UCB Pharma, AbbVie, Amgen, Consultant of: UCB Pharma, Amgen, AbbVie, Lilly, Pfizer, Novartis, Gilead, Janssen, Philip Helliwell: None declared, Wolf-Henning Boehncke Grant/research support from: Janssen Research & Development, LLC, Consultant of: Janssen, Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Alice B Gottlieb Grant/research support from:: Research grants, consultation fees, or speaker honoraria for lectures from: Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB., Consultant of:: Research grants, consultation fees, or speaker honoraria for lectures from: Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB., Speakers bureau:: Research grants, consultation fees, or speaker honoraria for lectures from: Pfizer, AbbVie, BMS, Lilly, MSD, Novartis, Roche, Sanofi, Sandoz, Nordic, Celltrion and UCB., Shelly Kafka Employee of: Janssen Scientific Affairs, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Shihong Sheng Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Bei Zhou Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Paraneedharan Ramachandran Employee of: Janssen Research & Development, LLC, Iain McInnes Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB

SAT0629-HPR FACTORS ASSOCIATED WITH USE OF BIOLOGICAL THERAPIES FOR AXIAL SPONDYLOARTHRITIS IN CANADA. RESULTS FROM THE IMAS SURVEY

Background:

Biologics have revolutionized the treatment of axial spondyloarthritis (axSpA). However, there is limited knowledge about factors associated with their use in Canada.

Objectives:

To evaluate sociodemographic, healthcare and patient-reported outcomes (PROs) associated with the use of biologics in Canadian axSpA patients.

Methods:

The International Map of Axial Spondyloarthritis (IMAS) is a cross-sectional online survey of non-selected patients with self-reported axSpA, conducted in 21 countries and endorsed by the Axial Spondyloarthritis International Federation (ASIF). IMAS captures the patients’ perspective of the burden of axSpA. The Canadian adaptation included a review of the survey by an advisory board of axSpA patients and a national steering committee composed of the Canadian Spondylitis Association, rheumatologists and patients. Participants were recruited between August 2018 and February 2019. Sociodemographic and healthcare-related variables, as well as PROs (disease activity [BASDAI, 0–10], spinal stiffness [3–12], functional limitation [0–54] and psychological distress [GHQ-12]) were collected. Respondents were divided in 2 groups 1) biologic and 2) NSAIDs or no treatment, based on reported pharmacologic treatments. Statistical analyses were performed to assess associations between variables and biologic use (bivariate) and the relative weight of these associations (multivariate).

Results:

542 axSpA patients were recruited. Mean age was 44.3±13.9 years, 63.1% were female, 66.4% married and 81.0% educated to university/college level. 22.8% of patients lived >50 km from their rheumatologist. Mean BASDAI was 5.3±2.1 and mean GHQ-12 score (mental health) was 4.0±3.8. Nearly half (49.6%) were currently on a biologic. Reported incidence of side effects was lower for patients having biologics (42.5%) vs. a NSAIDs (53.7%) as part of their treatment armamentarium. Only 15.7% of patients had discontinued biologic therapy, the main reasons for withdrawal being physician recommendation (50%), side effects (50%) and personal choice (34%). Variables associated with biologic use included: membership of a patient support group (p<0.001), non-manual work (p=0.008), higher income level (p=0.039), having a combination of public and private insurance schemes (p<0.001) and diagnosis by a rheumatologist (p<0.001). Associated PROs were spinal stiffness (p=0.011) and diagnostic delay (p=0.030). In the multivariate analysis, all variables except income and diagnostic delay were associated with biologic use (Table 1).

Table 1.

Analysis of sociodemographic and clinical variables in relation to pharmacologic treatment

VariableUnivariate linear regressionMultivariate stepwise linear regressionB95% CIB95% CIIncome level0.0011.000–1.000NANAEmployment—manual worker–0.7610.266–0.822–0.8380.228–0.820Member of a patient support group0.9371.797–3.6281.1161.754–5.309Health insurance scheme—combination0.2091.162–1.3070.2151.132–1.357Diagnostic delay0.0090.993–1.026NANASpinal Stiffness (3–12)0.0991.022–1.1930.2201.090–1.424Diagnosed by rheumatologist0.5351.412–2.0670.3351.041–1.877

B, B coefficient; NA, [not applicable]

Conclusion:

Canadian axSpA patients with greater social status, disease awareness, and insurance options are more likely to receive biologic therapy. Furthermore, Canadian physicians are more inclined to prescribe biologics to patients with increased spinal stiffness.

Disclosure of Interests:

Robert Inman: None declared, Marco Garrido-Cumbrera: None declared, Jon Chan: None declared, Martin Cohen: None declared, Artur J. deBrum Fernandes: None declared, Wendy Gerhart: None declared, Nigil Haroon: None declared, Algis Jovaisas: None declared, Gerald Major: None declared, Michael Mallinson: None declared, Sherry Rohekar: None declared, Patrick Leclerc Employee of: Novartis, Julie Schneiderman Employee of: Novartis, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer

SAT0359 THE ASSOCIATION BETWEEN IMAGING SUB-PHENOTYPES OF PSORIATIC ARTHRITIS AND GENE EXPRESSION PROFILES

Background:

Heterogeneity is a hallmark of psoriatic arthritis (PsA), which is reflected in diverse clinical, imaging and molecular features that may reflect disease course and response to treatment. We hypothesized that specific molecular pathways underlie the various manifestations of PsA.

Objectives:

To create a model for accurate and biologically meaningful sub-phenotyping of PsA using imaging and molecular data. Specifically, we aimed to identify imaging sub-phenotypes in patients with PsA and determine their association with whole blood mRNA expression markers.

Methods:

55 patients with PsA ready to initiate treatment for active disease were prospectively recruited. An ultrasound assessment of the extent of musculoskeletal inflammation in 64 joints, 34 tendons and 16 entheses was performed. Sonographic inflammation (in greyscale and Doppler) of the following domains was graded for: a) synovitis; b) peri-tendonitis; c) tenosynovitis; and d) enthesitis. A global inflammatory score was calculated for each tissue domain. Peripheral blood was profiled with RNAseq, and gene expression data were obtained. Analyses were performed in two stages: 1) Unsupervised cluster analysis was performed using hierarchial and k-means to define imaging sub-phenotypes in PsA that reflected the predominant tissue involved; 2) Principal component analysis with ellipses was used to determine the association between imaging-defined clusters and peripheral blood gene expression profile.

Results:

The patients could be divided into 3 groups based on unsupervised hierarchical and k-means clustering of images indicating the predominant involved tissue (Figure 1): 1) Enthesitis predominant (N=13 [24%]); 2) Peri-tendonitis predominant (N=11 [20%]); 3); Synovitis predominant (N=31 [56%]). Patients in the synovitis predominant group had more nail involvement, while those in the peri-tendonitis group had the highest number of clinically active joints (Table 1). Unsupervised clustering of gene expression data identified three clusters that partially overlapped with the imaging clustering (Figure 2). Overall, 344 genes were differentially expressed (p<0.05) in two of the three comparisons between the imaging clusters.

Table 1.

Clinical Features by Imaging Clustering

VariableEnthesitis predominantcluster(N=13)Peritendonitis predominant cluster(N=11)Synovitis predominant cluster(N=31)Age (years)47 (14)49 (16)45 (20)Sex: Female8 (61.5%)5 (45.5%)15 (48.4%)PsA duration (years)1.2 (1.5)1.6 (11.5)0.8 (3.7)BMI29.4 (6.8)25 (8.1)26.1 (8.4)Nail lesions3 (23.1%)5 (45.5%)17 (54.8%)PASI1.2 (2.7)1.2 (3.2)2.8 (7.8)Tender joint count6 (9)11 (5)3 (6)Swollen joint count2 (6)10 (7)3 (6)Dactylitis3 (23.1%)4 (36.4%)7 (22.6%)Enthesitis count3 (3)1 (4)0 (2)Enthesitis12 (92.3%)7 (63.6%)15 (48.4%)hsCRP2.9 (8.8)8.5 (21.5)3.6 (9.4)

Median (IQ range) and frequencies (%)

Bolded=Statistically different between the 3 groups (p<0.05)

Conclusion:

We identified three different imaging clusters based on the predominant tissue involved in patients with active PsA. Distinct gene expression profiles may underlie these imaging clusters seen in PsA.

Acknowledgments:

The study was supported by a Discovery Grant from the National Psoriasis Foundation.

Disclosure of Interests:

Lihi Eder Grant/research support from: Abbvie, Lily, Janssen, Amgen, Novartis, Consultant of: Janssen, Speakers bureau: Abbvie, Lily, Janssen, Amgen, Novartis, Quan Li: None declared, Sara Rahmati: None declared, Iris Eshed: None declared, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Igor Jurisica Grant/research support from: IBM, Vinod Chandran Grant/research support from: Abbvie, Celgene, Consultant of: Abbvie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lily, Janssen, Novartis, Pfizer, UCB, Employee of: Spouse employed by Eli Lily

SAT0421 GUSELKUMAB DEMONSTRATED AN INDEPENDENT TREATMENT EFFECT ON FATIGUE AFTER ADJUSTMENT FOR CLINICAL RESPONSE (ACR20) IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM PHASE-3 TRIALS DISCOVER 1 & 2

Background:

DISCOVER 1 and 2 are phase-3 trials of guselkumab (GUS, a monoclonal antibody that specifically binds the p19-subunit of IL-23) in patients with psoriatic arthritis (PsA). In both trials, treatment with GUS led to significantly more improvement than placebo (PBO) in the primary endpoint (ACR20) as well as in other measures of arthritis and psoriasis at week (W) 24.1,2

Objectives:

To evaluate the effect of GUS on fatigue in DISC 1 & 2 using the patient reported outcome (PRO) FACIT-Fatigue, which has demonstrated content validity and strong psychometric properties in clinical trials.3

Methods:

DISC 1 & 2 enrolled patients with active PsA, despite nonbiologic DMARDS and/or NSAIDS, who were mostly biologic naïve except for ~30% of patients in DISC 1 who had received 1-2 TNFi. Patients were randomized (1:1:1) in a blinded fashion to subcutaneous GUS 100 mg at W0 and W4 then every (q) 8W, to GUS 100 mg q4W, or to matching PBO. Concomitant treatment with select non-biologic DMARDS, oral corticosteroids, and NSAIDs was allowed. The FACIT-Fatigue is a 13-item PRO instrument assessing fatigue and its impact on daily activities and function over the past seven days, with a total score ranging from 0 to 52, higher score denoting less fatigue. A change of ≥4 points is identified as clinically meaningful.3Change from baseline in FACIT-Fatigue was analyzed using MMRM (Figure). Independence of treatment effect on FACIT-Fatigue from effect on ACR20 was assessed using Mediation Analysis4(Table) to estimate the natural direct effect (NDE) and natural indirect effect (NIE) mediated by ACR20 response.

Results:

At baseline in DISC 1 & 2, the mean FACIT-fatigue scores (SD) were 30.4 (10.4) and 29.7 (9.7), respectively, indicating moderate to severe fatigue. In both DISCOVER 1 & 2 trials, treatment with GUS led to improvements in FACIT-Fatigue scores compared with PBO as early as W8 (Figure). 54%-63% of GUS patients compared with 35%-46% of PBO patients achieved clinically meaningful improvement (≥4 points) in FACIT-Fatigue (P≤0.003). Mediation analysis revealed that the independent treatment effects on fatigue after adjustment for ACR20 response (Natural Direct Effect [NDE], Table) were 12-36% in the q8W GUS dosing group and 69% -70% in the q4W GUS group.

Conclusion:

In 2 phase-3 trials, treatment with GUS of patients with active PsA led to significant improvements compared to PBO in fatigue, including substantial effects on FACIT-Fatigue that were independent of the effects on ACR 20, especially for the q4W dosing group.

References:

[1]Deodhar et al. ACR 2019. Abstract #807. Arthr Rheumatol. 2019;71 S10: 1386

[2]Mease et al. ACR 2019. Abstract # L13. Arthr Rheumatol. 2019;71 S10:5247

[3]Cella et al. Journal of Patient-Reported Outcomes. 2019;3:30

[4]Valeri et al. Psychologic Meth. 2013;18:137

Table.

Mediation Analysis of the Effect of ACR 20 Response on Change from Baseline in FACIT-Fatigue Score at Week 24

EffectGUS 100 mg q8W vs. PBOEstimate (95% CI)GUS 100 mg q4W vs. PBOEstimate (95% CI)DISCOVER 1NDE0.36 (-1.7, 2.4)2.60 (0.6, 4.5)*NIE2.75 (1.4, 4.3)*1.20 (0.3, 2.3)*Total Effect3.12 (1.0, 5.2)*3.79 (1.9, 5.4)*Proportion Independent11.7%68.5%Proportion Mediated88.3%31.5%DISCOVER 2NDE1.44 (-0.1, 3.0)2.49 (1.0, 4.1)*NIE2.53 (1.6, 3.6)*1.09 (0.4, 1.9)*Total Effect3.97 (2.4, 5.5)*3.58 (2.1, 5.0)*Proportion Independent36.3%69.7%Proportion Mediated63.7%30.3%

*P vs placebo<0.02

NDE=Natural Direct Effect (effect on FACIT-F beyond effect on ACR20), NIE=Natural Indirect Effect (effect on FACIT-F mediated by ACR20)

Mediation analysis4used linear and logistics regression models with Bootstrapping method

Acknowledgments:

None

Disclosure of Interests:

Philip Helliwell: None declared, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Atul Deodhar Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Bei Zhou Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Xiwu Lin Employee of: Janssen Research & Development, LLC, Chenglong Han Employee of: Janssen Research & Development, LLC, Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau

THU0384 IMPACT OF IXEKIZUMAB ON WORK PRODUCTIVITY IN NON-RADIOGRAPHIC AXIAL SPONDYLOARTHRITIS PATIENTS: RESULTS FROM THE COAST-X TRIAL AT 52 WEEKS

Background:

Patients with non-radiographic axial spondyloarthritis (nr-axSpA) experience impairments in health-related quality of life comparable to those seen in ankylosing spondylitis, including impacts on work productivity. Ixekizumab (IXE) is a high-affinity monoclonal antibody that selectively targets interleukin-17A and effectively treats axial spondyloarthritis.1,2,3

Objectives:

This analysis evaluated the effect of IXE treatment for 52 weeks on work productivity and activity impairment as measured by absenteeism, presenteeism, overall work impairment, and activity impairment in patients with active nr-axSpA.

Methods:

COAST-X (NCT02757352) was a phase 3, multicenter, randomized, double-blind, placebo-controlled, parallel-group outpatient study investigating the efficacy and safety of 80 mg IXE every 2 weeks (Q2W) and every 4 weeks (Q4W) compared to placebo (PBO) in 303 patients naïve to biologic disease-modifying anti-rheumatic drugs with active nr-axSpA during a 52-week treatment period. From Weeks 16 through 44, if patients’ disease activity required escalation of treatment at investigator discretion, patients were switched to open-label IXE Q2W or subsequent tumor necrosis factor inhibitor treatment. Analysis was performed for the intent-to-treat population, which included data up to the time of biologic switching. Patients who switched to open-label IXE were considered non-responders. Changes from baseline in work productivity were measured for patients reporting full- or part-time work at Weeks 16 and 52 with the Work Productivity and Activity Impairment (WPAI) Questionnaire for Spondyloarthritis and analyzed with an analysis of covariance model including treatment, geographic region, screening magnetic resonance imaging and C-reactive protein level status, and baseline value as factors. Missing data was imputed using the modified baseline observation carried forward.

Results:

A majority of patients (63.5–65.7%) reported part-time or full-time paid work at baseline, with baseline scores for presenteeism and overall work activity slightly higher for patients in the PBO arm (p<0.05). Patients treated with IXE Q4W had significantly greater improvement than PBO in activity impairment at Weeks 16 (p=0.003) and 52 (p=0.004), presenteeism at Weeks 16 (p=0.007) and 52 (p=0.003), and overall work impairment at Weeks 16 (p=0.014) and 52 (p=0.005; Figure). Patients treated with IXE Q2W had significantly greater improvement than PBO in activity impairment at Weeks 16 (p=0.007) and 52 (p=0.006; Figure). Patients treated with either IXE regimen had numeric improvements in all WPAI measures compared to those receiving PBO at Weeks 16 and 52 (Figure).

Conclusion:

Patients with nr-axSpA treated with either IXE regimen had significant improvements in activity impairment compared to PBO. Patients receiving IXE Q4W also had significant improvements in presenteeism and overall work impairment.

References:

[1]Sieper, et al. (2016)Clin Exp Rheumatol.34(6):975-83.

[2]Van der Heijde, et al. (2018)Lancet. 392(10163):2441-51.

[3]Deodhar, et al. (2019)Arthritis Rheumatol.71(4):599-611.

Figure.

Changes from baseline in A) Absenteeism, B) Presenteeism, C) Overall Work Impairment, and D) Activity Impairment.

Disclosure of Interests:

Atul Deodhar Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myer Squibb (BMS), Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Philip J Mease Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Janssen, Eli Lilly, Novartis, Pfizer, Sun Pharma, UCB Pharma, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Janssen, Eli Lilly, Galapagos, Gilead, Novartis, Pfizer, Sun Pharma, UCB Pharma, Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Genentech, Janssen, Novartis, Pfizer, UCB Pharma, Lianne S. Gensler Grant/research support from: Pfizer, Novartis, UCB, Consultant of: AbbVie, Eli Lilly, GSK, Novartis, UCB, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, Victoria Navarro-Compán Consultant of: Abbvie, Lilly, Novartis, Pfizer, UCB, Speakers bureau: AbbVie, MSD, Lilly, Novartis, Pfizer, UCB, Helena Marzo-Ortega Grant/research support from: Janssen, Novartis, Consultant of: Abbvie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Speakers bureau: Abbvie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Takeda, UCB, Theresa Hunter Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, David Sandoval Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Andris Kronbergs Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Baojin Zhu Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Ann Leung: None declared, Vibeke Strand Consultant of: AbbVie, Amgen, Biogen, Celltrion, Consortium of Rheumatology Researchers of North America, Crescendo Bioscience, Eli Lilly, Genentech/Roche, GlaxoSmithKline, Hospira, Janssen, Merck, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Sanofi, UCB

OP0305 RHO-GTPASE PATHWAYS MAY DIFFERENTIATE RESPONDER AND NON-RESPONDERS TO TUMOUR NECROSIS FACTOR INHIBITOR (TNFI) AND INTERLEUKIN-17A INHIBITOR (IL-17AI) THERAPY IN PSORIATIC ARTHRITIS (PSA)

Background:

In PsA there is a pressing need to develop a coherent strategy for identifying initial and subsequent biologic responders. PsA patients present substantial heterogeneity in response to biologics, and molecular subtyping will help to identify the right patient for the right treatment.

Objectives:

To identify transcript profiles (biomarkers) that will select TNFi and IL-17Ai responders in PsA using baseline CD4+ cells; and elucidate novel signaling pathways relevant to biologic disease modifying antirheumatic drug (bDMARD) response using a systems biology approach.

Methods:

Consenting patients initiating TNFi agents (20 patients) or IL-17Ai agents (20 patients) with moderate-to-severe PsA were assessed with a comprehensive standardized protocol at baseline and at 3 months. Responder to bDMARDs was defined by Disease Activity index for PSoriatic Arthritis (DAPSA) score of less than 14 (low disease activity). Global transcript profiling was performed on all patients prior to initiation of and 3 months post bDMARDs. We mapped RNA-seq reads to the hg19 reference genome using STAR and quantified transcripts with Cufflinks. The transcripts per million (TPM) values were log-transformed for statistical analyses

Results:

The demographics of PsA patients for both treatment groups are presented (Table 1). Differentially expressed genes (DEGs) were identified using the limma tool for TNFi and IL-17Ai responders and non-responders (Figure 1) as well as DEGs that differentiated TNFi from IL-17Ai response and non-response. Integration of differential gene expression data with tissue-specific protein-protein interactions (IID version 2018-11) identified 117 and 132 DEGs between responders and non-responders to TNFi and IL-17Ai treatments, respectively. Comprehensive pathway enrichment analysis of these genes using pathDIP (v 4.1) revealed 576 (out of 5380) pathways enriched in 117 DEGs between responders and non-responders to TNFi, and 125 pathways enriched in 132 DEGs between responders and non-responders to IL-17Ai. Interestingly, while these two gene lists share only 17 genes, they have 79 enriched pathways in common suggesting potential effect of two different treatments on similar pathways but through different pathway members (Figure 2). Moreover, it suggests potential importance of the 17 shared genes in association with these pathways. Most of these pathways are related to innate and adaptive immune system, and to “osteoclast differentiation”. Among 46 pathways specific to response to IL-17Ai, multiple Rho-GTPase-related pathways were identified. It has been shown that experimental inhibition of ROCK2, a target of Rho-GTPase family is effective in psoriatic disease through regulation of IL-17/23/10, but not IL-6 and TNFα.

Table 1.

Demographics of psoriatic arthritis (PsA) patients.

IL-17AiTNFiNumber of PsA patients2020Gender (% female)55%75%Mean Age (Years)55.9 (9.5)56.8 (7.9)Mean Disease Duration (Years)10.4 (6.9)7.3 (7.9)Mean DAPSA baseline38.8 (17.5)45.6 (28.9)Responders (%) (DAPSA <14)35%65%Biologic naïve (%)40%60%Figure 1.

PCA plots illustrating that DEGs of CD4+ cells can differentiate responders from non-responders when treated with:A.IL-17Ai; andB.TNFi.

Figure 2.

Over-represented terms in significantly enriched pathways considering DEGs between:A.IL-17i responders and non-responders (125 pathways);B.TNFi responders and non-responders (576 pathways).

Conclusion:

Integration of cell-specific transcriptomic data with protein networks represents a very promising strategy for identifying biologic responders and pathways involved in predicting response that may have identified the Rho-GTP pathway as a potential marker to guide the choice of biologic agents for individual patients.

Disclosure of Interests:

Sara Rahmati: None declared, Darren O’Rielly: None declared, Quan Li: None declared, Dianne Codner: None declared, Amanda Dohey: None declared, Kari Jenkins: None declared, Igor Jurisica Grant/research support from: IBM, Dafna D Gladman Grant/research support from: AbbVie, Amgen Inc., BMS, Celgene Corporation, Janssen, Novartis, Pfizer, UCB – grant/research support, Consultant of: AbbVie, Amgen Inc., BMS, Celgene Corporation, Janssen, Novartis, Pfizer, UCB – consultant, Vinod Chandran Grant/research support from: Abbvie, Celgene, Consultant of: Abbvie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lily, Janssen, Novartis, Pfizer, UCB, Employee of: Spouse employed by Eli Lily, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer