POS0930 SECUKINUMAB IN PATIENTS WITH PSORIATIC ARTHRITIS AND AXIAL MANIFESTATIONS: PREDICTORS OF RESPONSE FROM THE DOUBLE-BLIND, RANDOMISED, PHASE 3B MAXIMISE TRIAL
Authors: X. Baraliakos, E. Pournara, L. Gossec, H. Marzo-Ortega, P. J. Mease, R. White, E. O’brien, B. Schulz, L. C. Coates
Journal: Annals of the Rheumatic Diseases (2021)

Background: Psoriatic arthritis (PsA) is a heterogeneous disease with variability of response to different therapeutic modalities. 1 Identifying potential demographic and disease characteristics as predictors of treatment response may define personalised treatment optimisation strategies. 2–3 Objectives: This post-hoc exploratory analysis of the MAXIMISE trial 4 investigated the differential treatment effect of demographics and baseline characteristics as predictive factors in biologic naïve patients with active PsA and symptoms of active spinal disease. Methods: The full analysis set (FAS) comprised of all patients from the randomised set assigned to study treatment, fulfilling the predefined clinical criteria for active axial disease and for whom Assessment of SpondyloArthritis International Society (ASAS) 20 data were available at Week 12. The research hypothesis was that the odds ratio associated with the effect of treatment on ASAS20 responder status at Week 12 would be different depending on 12 pre-specified predictor variables. A logistic regression model was initially fitted to the FAS that included 12 pre-specified covariates. A second logistic regression model was then fitted to the FAS that allowed for all 12 pre-specified variables to interact with treatment. 5 The log-likelihood of the two fitted models were compared using a likelihood ratio test at a pre-specified significance level of 20% (i.e. P -value ≤0.20) to test whether any of the predefined variables interacted with treatment. If the above test was statistically significant at the 20% level of statistical significance the variables of the second model were formally examined to determine whether the overall effect of treatment is not applicable. Three forest plots were produced, one for each treatment group. Hypothesis tests were employed to determine the strength of evidence for each individual variable. Results: The likelihood ratio test provided evidence against the assumption that the overall effect of treatment is applicable to all patients ( P -value = 0.08). Notably, the odds of being an ASAS20 responder if nail dystrophy is present at baseline were 3 times greater in the secukinumab 150 mg group and 5 times greater in the 300 mg group compared with placebo (interaction P -value = 0.029). Although males fare worse than females in the placebo group, in the secukinumab 150 mg and 300 mg treatment groups the odds of being a responder were similar to females (interaction P -value = 0.039). Current smokers were less likely to be ASAS20 responders compared to never smokers regardless of treatment group (interaction P -value = 0.589) (Figure 1). Age, CRP level, Berlin MRI spine/SIJ score, time since first axial signs, number of swollen joints, new bone formation and BMI did not show a differential treatment effect on ASAS20 responses. Conclusion: Of the 12 baseline variables of a unique population of 473 PsA patients with active axial disease diagnosed by clinical criteria, our analyses showed evidence of a differential treatment effect most notably for nail dystrophy suggesting that the presence of nail dystrophy may predict a better response to secukinumab in PsA patients with axial manifestations. References: [1]Coates LC, Helliwell PS. Clin Med (Lond). 2017;17(1):65–70. [2]Watson DS, et al. BMJ . 2019;364:l886. [3]Hügle M, et al. Rheumatol Adv Pract. 2020;4(1):rkaa005. [4]Baraliakos X, et al. Ann Rheum Dis. Published Online First: 17 Dec 2020. doi:10.1136/annrheumdis-2020-218808. [5]Peto R, et al. Br J Cancer. 1977;35(1):1–39. Figure 1. Forest plots of the adjusted odds ratio by treatment using interaction modelDisclosure of Interests: Xenofon Baraliakos Speakers bureau: AbbVie, BMS, Celgene, Chugai, MSD, Novartis, Pfizer, and UCB., Consultant of: AbbVie, BMS, Celgene, Chugai, Galapagos, Gilead, MSD, Novartis, Pfizer, and UCB., Grant/research support from: AbbVie, and Novartis., Effie Pournara Shareholder of: Novartis, Employee of: Novartis, Laure Gossec Consultant of: AbbVie, Amgen, BMS, Biogen, Celgene, Gilead, Janssen, Lilly, Novartis, Pfizer, Samsung Bioepis, Sanofi-Aventis, and UCB., Grant/research support from: Amgen, Lilly, Janssen, Pfizer, Sandoz, Sanofi, and Galapagos., Helena Marzo-Ortega Consultant of: Janssen, Novartis, AbbVie, Celgene, Lilly, Pfizer, Takeda and UCB., Grant/research support from: Janssen, Novartis, AbbVie, Celgene, Lilly, Pfizer, Takeda and UCB., Philip J Mease Speakers bureau: AbbVie, Amgen, Genentech, Janssen, Lilly, Merck, Novartis, Pfizer and UCB., Consultant of: AbbVie, Amgen, BMS, Boehringer Ingelheim, Galapagos, Celgene, Genentech, Gilead, Janssen, Lilly, Novartis, Pfizer, SUN Pharma, and UCB., Grant/research support from: AbbVie, Amgen, BMS, Celgene, Galapagos, Genentech, Gilead, Janssen, Lilly, Merck, Novartis, Pfizer, SUN, and UCB., Roisin White Shareholder of: Novartis, Employee of: Novartis, Eamonn O’Brien Shareholder of: Novartis, Employee of: Novartis, Barbara Schulz Employee of: Novartis, Laura C Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Gilead, Eli Lilly, Janssen, Medac, Novartis, Pfizer, and UCB., Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB., Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Pfizer, and Novartis.