AB0527 PHARMACOGENETICS AND PHARMACODYNAMICS OF RESPONSE TO APREMILAST IN A PHASE 3 CLINICAL STUDY IN SUBJECTS WITH ACTIVE BEHÇET’S DISEASE
Authors: J. Maranville, I. Medvedeva, R. Yang, M. Chen, L. R. Fang, S. Collazo, S. Mccue, P. Schafer
Journal: Annals of the Rheumatic Diseases (2020)

Background: Apremilast (APR), an oral phosphodiesterase 4 (PDE4) inhibitor, modulates inflammatory mediators 1 and has demonstrated efficacy in treating oral ulcers in a phase III Behçet’s syndrome study (BCT-002 [RELIEF]). 2 Objectives: To conduct an exploratory analysis of genetic polymorphisms, plasma biomarkers, and blood leukocytes with clinical response in RELIEF. Methods: Subjects with active Behçet’s disease (BD) were randomized (1:1) to APR 30 mg twice daily or placebo (PBO). The primary clinical efficacy endpoint was the area under the curve for the number of oral ulcers through Week 12 (AUC Wk0-12 ). Among the 207 subjects enrolled, 140 provided consent for DNA genotyping, 116 for plasma biomarker testing, and 96 for leukocyte subset testing. Genotyping was performed on the Illumina Omni2.5 BeadChip (Covance Genomics Laboratory). TNF-α, IL-6, interferon-γ, and IL-17A levels were measured using Simoa Single Molecule Array; IL-8 and IL-23 were measured using the Human DiscoveryMAP multiplex panel (Myriad RBM). Th17, Treg, and CD3 T cells were counted using bisulfite-specific RT-PCR (Epiontis Gmbh). A rank ANCOVA model was used to estimate between-treatment differences (APR vs. PBO) in percent change from baseline for each biomarker/leukocyte subtype over the 12 weeks of treatment. Within each treatment group, the correlation of percent change from baseline at Week 12 in biomarker/leukocyte subtype with the primary efficacy endpoint AUC Wk0-12 was examined using a univariate regression model. A separate regression model was used to assess the interaction between treatment and the biomarker/leukocyte subtype clinical response. Results: Pharmacogenetic analysis of BD risk variants in HLA-B, IL-10, TLR2, ACE, TNF, GIMAP, PDGFRL, and UBAC2 + 55 genes associated with PDE4 biology yielded no candidate variants that were significantly associated with response to APR or PBO at a Bonferroni-corrected P value of 2 x 10 −6 . Clinical response to APR with respect to HLA-B51 yielded an odds ratio (OR) of 1.21 (95% CI, 0.53-2.75), indicating no significant relationship (Figure 1). Pharmacodynamic changes for IL-6, IL-3, IL-17A, IL-23, and TNF-α were not statistically significant. APR treatment was associated with a significant change in interferon-γ (mean: +107.4%; median: −19.2%) vs. PBO (mean: +78.8%; median: +7.9%) ( P =0.0077). Using a univariate regression model, TNF-α showed strong positive correlation with AUC Wk0-12 in the APR group ( r =0.90; P =0.0140); IL-8 had weak positive correlation with AUC Wk0-12 in the APR group ( r =0.04; P =0.0333). A significant negative correlation was observed between the percent change from baseline in the number of Th17 cells and AUC Wk0-12 in the APR group ( r =−0.79; P =0.0392) and a significant positive correlation was observed with the percent change from baseline in the number of Treg cells and AUC Wk0-12 in the PBO group ( r =0.94; P =0.0182). Of all the biomarkers and leukocyte subtypes examined in a regression model using treatment as a factor, only Treg had a statistically significant treatment interaction ( P =0.0069). Conclusion: Although there were no genetic predictors of clinical response to APR treatment, strong correlation was observed between the percent change from baseline in plasma TNF-α with AUC Wk0-12 in the APR group. A negative correlation was observed between percent change from baseline in Th17 cells and AUC Wk0-12 in the APR group and a positive association was observed between Treg cells and AUC W0-12 in the PBO group. References: [1]Schafer P. Biochem Pharmacol . 2012; 83:1583-1590. 2. Hatemi G, et al. Presented at: ACR/ARHP Annual Meeting; November 8–13, 2019; Atlanta, GA. Presentation 0946. Disclosure of Interests: : Joseph Maranville Employee of: Celgene Corporation – employment at the time of study conduct, Irina Medvedeva Employee of: Celgene Corporation – employment at the time of study conduct, Robert Yang Employee of: Celgene Corporation – employment at the time of study conduct, Mindy Chen Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of the conduct, Lorraine (Ruoying) Fang Employee of: Celgene Corporation – employment at the time of study conduct, Sandra Collazo Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of the conduct, Shannon McCue Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of the conduct, Peter Schafer Employee of: Bristol-Myers Squibb – employment; Celgene Corporation – employment at the time of study conduct