BACKGROUND:Belatacept-treated kidney transplant recipients (KT) experience a lower incidence of de novo donor-specific anti-HLA antibody (DSA) formation despite their higher risk of acute cellular rejection. In vitro studies show that concentrations associated with half-maximal suppression (EC50) of target cells correlate with therapeutic trough concentrations (C0). PURPOSE:To determine whether belatacept inhibits B-cell alloantigen presentation and B-cell alloresponse at known therapeutic C0. METHODS:Peripheral blood leukocytes (PBL) from healthy adults were cultured with HLA-mismatched PBL or fluorochrome-labeled alloantigenic lysate and increasing belatacept concentrations. EC50 was calculated with best-fit four-parameter log-logistic function under a Poisson assumption, as described. RESULTS:After overnight allostimulation, frequencies of alloreactive CD154 + B-cells and their subsets decreased with increasing belatacept concentrations (n = 10). Median (range) EC50s were 1.3 (0.01-37) µg/ml for unfractionated B-cells, 2 (0.03-50) µg/ml for naïve, 3.6 (0.2-92) µg/ml for unswitched memory, 6.2 (0.05-32) µg.ml for transitional, and 7.8 (0.01-101) µg/ml for plasmablasts. Median EC50s were highest at 19 and 31 µg/ml, respectively for CD27- and CD27 + isotype-switched memory B-cells. No effect was seen on B-cell presentation of alloantigen. CONCLUSIONS:At lower C0 levels of < 10 µg/ml, belatacept suppresses alloresponses of B-cells and most B-cell subsets, thereby explaining lower levels of DSA in kidney transplant patients.

Objective To use cell‐based gene signatures to identify patients with systemic lupus erythematous (SLE) in the phase II/III APRIL–SLE and phase IIb ADDRESS II trials most likely to respond to atacicept. Methods A published immune cell deconvolution algorithm based on Affymetrix gene array data was applied to whole blood gene expression from patients entering APRIL‐SLE. Five distinct patient clusters were identified. Patient characteristics, biomarkers, and clinical response to atacicept were assessed per cluster. A modified immune cell deconvolution algorithm was developed based on RNA sequencing data and applied to ADDRESS II data to identify similar patient clusters and their responses. Results Patients in APRIL‐SLE (N = 105) were segregated into the following five clusters (P1‐5) characterized by dominant cell subset signatures: high neutrophils, T helper cells and natural killer (NK) cells (P1), high plasma cells and activated NK cells (P2), high B cells and neutrophils (P3), high B cells and low neutrophils (P4), or high activated dendritic cells, activated NK cells, and neutrophils (P5). Placebo‐ and atacicept‐treated patients in clusters P2,4,5 had markedly higher British Isles Lupus Assessment Group (BILAG) A/B flare rates than those in clusters P1,3, with a greater treatment effect of atacicept on lowering flares in clusters P2,4,5. In ADDRESS II, placebo‐treated patients from P2,4,5 were less likely to be SLE Responder Index (SRI)‐4, SRI‐6, and BILAG‐Based Combined Lupus Assessment responders than those in P1,3; the response proportions again suggested lower placebo effect and a greater treatment differential for atacicept in P2,4,5. Conclusion This exploratory analysis indicates larger differences between placebo‐ and atacicept‐treated patients with SLE in a molecularly defined patient subset.

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple organ systems. Many investigational agents have failed or shown only modest effects when added to standard of care (SoC) therapy in placebo-controlled trials, and only two therapies have been approved for SLE in the last 60 years. Clinical trial outcomes have shown discordance in drug effects between clinical endpoints. Herein, we characterized longitudinal disease activity in the SLE population and the sources of variability by developing a latent disease trajectory model for SLE component endpoints (Systemic Lupus Erythematosus Disease Activity Index [SLEDAI], Physician's Global Assessment [PGA], British Isles Lupus Assessment Group Index [BILAG]) and composite endpoints (Systemic Lupus Erythematosus Responder Index [SRI], BILAG-based Composite Lupus Assessment [BICLA], and Lupus Low Disease Activity State [LLDAS]) using patient-level historical SoC data from nine phase II and III studies. Across all endpoints, in predictions up to 52 weeks from the final disease trajectory model, the following baseline covariates were associated with a greater decrease in SLE disease activity and higher response to placebo + SoC: Hispanic ethnicity from Central/South America, absence of hypocomplementemia, recent SLE diagnosis, and high baseline disease activity score using SLEDAI and BILAG separately. No discernible differences were observed in the trajectory of response to placebo + SoC across different SoC medications (antimalarial and immunosuppressant such as mycophenolate, methotrexate, and azathioprine). Across all endpoints, disease trajectory showed no difference in Asian versus non-Asian patients, supporting Asia-inclusive global SLE drug development. These results describe the first population approach to support a model-informed drug development framework in SLE.

Osteoarthritis (OA) is a common disease worldwide with large unmet medical needs. To bring innovative treatments to OA patients, we at Merck have implemented a comprehensive strategy for drug candidate evaluation. We have a clear framework for decision-making in our preclinical pipeline, to design our clinical proof-of-concept trials for OA patients. We have qualified our strategy to define and refine dose and dosing regimen, for treatments administered either systemically or intra-articularly (IA). We do this through preclinical in vitro and in vivo studies, and by back-translating results from clinical studies in OA patients.

Background: The Phase 2/3 APRIL-SLE study evaluated the safety and efficacy of atacicept, a dual inhibitor of the B lymphocyte stimulator (BLyS) and a proliferation-inducing ligand (APRIL), in systemic lupus erythematosus (SLE). Objectives: The goal of this post-hoc analysis was to use cell-based gene signatures on gene expression data from the APRIL-SLE study to identify clusters of patients with potential to flare and to assess clusters for differences in treatment effects of atacicept vs placebo. Methods: A published immune cell deconvolution algorithm (Abbas et al, 2009) was applied to whole-blood gene expression data from APRIL-SLE patients to identify relative proportions of 17 immune cell types. Patients were then grouped into clusters based on these immune cell profiles using a k-medoid clustering algorithm and were compared to each other based on patient characteristics, biomarkers and clinical efficacy. In addition, baseline expression and change in expression of putative APRIL-responder genes were compared among clusters. APRIL-responder genes were identified by combining differential expression results from the APRIL-SLE study (Week 52 vs Day 1 randomization) and tabalumab (targets BLyS only) Phase 3 studies (Week 52 vs baseline; GSE88887). Results: Patient gene expression data (N=105; placebo, n=30; atacicept 75 mg, n=40; atacicept 150 mg, n=35) were used to group patients into five main clusters (P1-P5) by predominant characteristic cells: P1, T helper cells; P2, plasma cells; P3, neutrophils and B cells; P4, B cells; P5, activated dendritic cells. Patients in P2 and P5 were more likely to have positive anti-dsDNA antibodies (≥30 IU/ml), elevated BLyS; ≥1.6 ng/ml, and high interferon gene signature in the blood than other those in other clusters. Patients in P2 were most likely to have low complement C3 and C4 levels. Placebo-group flare rates in P2 (100%), P4 (100%) and P5 (83%) were markedly higher than in P1 (33%) and P3 (29%). In P2, P4, and P5 the median time-to-flare was much lower with placebo (85, 98.5, and 115.5 days, respectively) than with atacicept 150 mg (over 364 days for all three clusters). A comparison of differentially-expressed genes from clinical studies of SLE patients treated with atacicept and tabalumab revealed possible APRIL-responder genes: SDC1, PARM1 and MZB1. These genes had a higher baseline expression in P2 and P4 compared with other clusters. SDC1 was reduced from baseline more in P2, P4, and P5 after atacicept treatment, while PARM1 and MZB1 decreased after atacicept treatment in P2 and P4. Conclusion: These post-hoc analyses revealed different subsets of SLE patients based on their molecular profiles. Atacicept may have different treatment effects in the identified patient subsets vs placebo, providing insights into potential mechanisms of flare in SLE. References: [1] Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF. Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS ONE. 2009;4(7):e6098. Disclosure of Interests: Eileen Samy Employee of: Current employees of EMD Serono, Matthew Studham Employee of: Current employees of EMD Serono, Amy Kao Employee of: Current employees of EMD Serono, Philipp Haselmayer Employee of: Current employee of Merck KGaA, Peter Chang Employee of: Current employees of EMD Serono, P. Alexander Rolfe Employee of: Current employees of EMD Serono, David Wofsy Consultant for: GlaxoSmithKline – Member, data safety monitoring board Novartis – Member, data safety monitoring board Celgene – member, scientific advisory board, Julie DeMartino Shareholder of: Shares in Merck & Co, Employee of: Current employees of EMD Serono; Past employee of Merck & Co. & Roche, Robert Townsend Employee of: Current employees of EMD Serono

Preventing conversion of donor-specific anti-HLA antibodies (DSAs) from an IgM-to-IgG could a way to prevent chronic rejection. We evaluated whether belatacept-treated patients (belatacept less-intensive [LI] or more-intensive [MI] regimens) have a lower rate of conversion than do cyclosporine A (CsA)-treated patients. We included 330 HLA-mismatched patients from 2 phase 3 trials with either (a) complete donor/recipient HLA-A, -B, -DR, and -DQ loci typing or (b) incomplete HLA typing with IgG DSAs detected pretransplant or posttransplant. IgM and IgG DSAs were tested with single antigen beads at 0, 6, 12, 24, and 36 months posttransplant. The overall (preexisting or de novo) rates of IgM- and IgG-positive DSAs were 29% and 34%, respectively. The pretransplant IgM and IgG DSA-positive frequencies were similar between treatment groups. The IgG-positive dnDSA rate was significantly higher in the CsA-treated group (34%) compared with the belatacept-LI (8%) and belatacept-MI (11%) (P < .001) groups. In IgM-positive dnDSA patients, the IgG-positive dnDSA rate of conversion was 2.8 times higher in the CsA group than in the combined belatacept groups (P = .006). However, the observed association between belatacept treatment and more limited conversion of IgM-to-IgG dnDSAs was based on a limited number of patients and requires further validation.

ObjectiveTo develop an objective, readily measurable pharmacodynamic biomarker of glucocorticoid (GC) activity.MethodsGenes modulated by prednisolone were identified from in vitro studies using peripheral blood mononuclear cells from normal healthy volunteers. Using the criteria of a >2‐fold change relative to vehicle controls and an adjusted P value cutoff of less than 0.05, 64 up‐regulated and 18 down‐regulated genes were identified. A composite score of the up‐regulated genes was generated using a single‐sample gene set enrichment analysis algorithm.ResultsGC gene signature expression was significantly elevated in peripheral blood leukocytes from normal healthy volunteers following oral administration of prednisolone. Expression of the signature increased in a dose‐dependent manner, peaked at 4 hours postadministration, and returned to baseline levels by 48 hours after dosing. Lower expression was detected in normal healthy volunteers who received a partial GC receptor agonist, which is consistent with the reduced transactivation potential of this compound. In cohorts of patients with systemic lupus erythematosus and patients with rheumatoid arthritis, expression of the GC signature was negatively correlated with the percentages of peripheral blood lymphocytes and positively correlated with peripheral blood neutrophil counts, which is consistent with the known biology of the GC receptor. Expression of the signature largely agreed with reported GC use in these populations, although there was significant interpatient variability within the dose cohorts.ConclusionThe GC gene signature identified in this study represents a pharmacodynamic marker of GC exposure.

The proteomic analysis of bronchoalveolar lavage fluid (BALF) can give insight into pulmonary disease pathology and response to therapy. Here, we describe the first gel-free quantitative analysis of BALF in idiopathic pulmonary fibrosis (IPF), a chronic and fatal scarring lung disease. We utilized two-dimensional reversed-phase liquid chromatography and ion-mobility-assisted data-independent acquisition (HDMSE) for quantitation of >1000 proteins in immunodepleted BALF from the right middle and lower lobes of normal controls and patients with IPF. Among the analytes that were increased in IPF were well-described mediators of pulmonary fibrosis (osteopontin, MMP7, CXCL7, CCL18), eosinophil- and neutrophil-derived proteins, and proteins associated with fibroblast foci. For additional discovery and targeted validation, BALF was also screened by multiple reaction monitoring (MRM), using the JPT Cytokine SpikeMix library of >400 stable isotope-labeled peptides. A refined MRM assay confirmed the robust expression of osteopontin, and demonstrated, for the first time, upregulation of the pro-fibrotic cytokine, CCL24, in BALF in IPF. These results show the utility of BALF proteomics for the molecular profiling of fibrotic lung diseases and the targeted quantitation of soluble markers of IPF. More generally, this study addresses critical quality control measures that should be widely applicable to BALF profiling in pulmonary disease.

Belatacept blocks CD28-mediated T-cell costimulation and prevents renal transplant rejection. Understanding T-cell subset sensitivity to belatacept may identify cellular markers for immunosuppression failure to better guide treatment selection. Here, we evaluate the belatacept sensitivity of allo-antigen-specific CD154-expressing-T-cells, whose T-cytotoxic memory (TcM) subset predicts rejection with high sensitivity after non-renal transplantation. The belatacept concentration associated with half-maximal reduction (EC50) of CD154 expression was calculated for 36 T-cell subsets defined by combinations of T-helper (Th), Tc, T-memory and CD28 receptors, following allostimulation of peripheral blood leukocytes from 20 normal healthy subjects. Subsets were ranked by median EC50, and by whether subset EC50 was correlated with and therefore could be represented by the frequency of other subsets. No single subset frequency emerged as the significant correlate of EC50 for a given subset. Most (n = 25) T-cell subsets were sensitive to belatacept. Less sensitive subsets demonstrated a memory phenotype and absence of CD28 receptor. Potential drug-resistance markers for future validation include the low frequency highly differentiated, Th-memory-CD28-negative T-cells with the highest median EC50, and the least differentiated, high-frequency Tc subset, with the most CD28-negative T-cells, the third highest median EC50, and significant correlations with frequencies of the highest number of CD28-negative and memory subsets.

Belatacept is a first-in-class, selective co-stimulation blocker recently approved for the prophylaxis of organ rejection in adult kidney transplant recipients. The objective of this study was to report the pharmacokinetics, pharmacodynamics, and immunogenicity of belatacept.

Shen, J.; Block, A.; Townsend, R.; You, Xiaoli X.; Shen, Y.; Zhou, S.; Geng, D.; McGirr, N.; Soucek, K.; Pursley, J.; Di Russo, G.; Kaul, S. Author Information