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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., (© 2023 EMD Serono, Inc and The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology.)

Because of its role in mediating both B cell and Fc receptor signaling, Bruton's tyrosine kinase (BTK) is a promising target for the treatment of autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). Evobrutinib is a novel, highly selective, irreversible BTK inhibitor that potently inhibits BCR- and Fc receptor - mediated signaling and, thus, subsequent activation and function of human B cells and innate immune cells such as monocytes and basophils. We evaluated evobrutinib in preclinical models of RA and SLE and characterized the relationship between BTK occupancy and inhibition of disease activity. In mouse models of RA and SLE, orally administered evobrutinib displayed robust efficacy, as demonstrated by reduction of disease severity and histological damage. In the SLE model, evobrutinib inhibited B cell activation, reduced autoantibody production and plasma cell numbers, and normalized B and T cell subsets. In the RA model, efficacy was achieved despite failure to reduce autoantibodies. Pharmacokinetic/pharmacodynamic modeling showed that mean BTK occupancy in blood cells of 80% was linked to near-complete disease inhibition in both RA and SLE mouse models. In addition, evobrutinib inhibited mast cell activation in a passive cutaneous anaphylaxis model. Thus, evobrutinib achieves efficacy by acting both on B cells and innate immune cells. Taken together, our data show that evobrutinib is a promising molecule for the chronic treatment of B cell - driven autoimmune disorders., (Copyright © 2019 by The American Association of Immunologists, Inc.)

Background: Pharmacodynamic biomarkers are becoming increasingly valuable for assessing drug activity and target modulation in clinical trials. However, identifying quality biomarkers is challenging due to the increasing volume and heterogeneity of relevant data describing the biological networks that underlie disease mechanisms. A biological pathway network typically includes entities (e.g. genes, proteins and chemicals/drugs) as well as the relationships between these and is typically curated or mined from structured databases and textual co-occurrence data. We propose a hybrid Natural Language Processing and directed relationships-based network analysis approach using IBM Watson for Drug Discovery to rank all human genes and identify potential candidate biomarkers, requiring only an initial determination of a specific target-disease relationship., Methods: Through natural language processing of scientific literature, Watson for Drug Discovery creates a network of semantic relationships between biological concepts such as genes, drugs, and diseases. Using Bruton's tyrosine kinase as a case study, Watson for Drug Discovery's automatically extracted relationship network was compared with a prominent manually curated physical interaction network. Additionally, potential biomarkers for Bruton's tyrosine kinase inhibition were predicted using a matrix factorization approach and subsequently compared with expert-generated biomarkers., Results: Watson's natural language processing generated a relationship network matching 55 (86%) genes upstream of BTK and 98 (95%) genes downstream of Bruton's tyrosine kinase in a prominent manually curated physical interaction network. Matrix factorization analysis predicted 11 of 13 genes identified by Merck subject matter experts in the top 20% of Watson for Drug Discovery's 13,595 ranked genes, with 7 in the top 5%., Conclusion: Taken together, these results suggest that Watson for Drug Discovery's automatic relationship network identifies the majority of upstream and downstream genes in biological pathway networks and can be used to help with the identification and prioritization of pharmacodynamic biomarker evaluation, accelerating the early phases of disease hypothesis generation., Competing Interests: Funding for this study was provided by Merck KGaA, Darmstadt Germany (https://www.emdgroup.com). Sonja Hatz, Philipp Haselmayer, Harsha Gurulingappa, and Ulrich Betz are employed by Merck KGaA. Andrew Bender and Matthew Studham are employed by EMD Serono. Scott Spangler, Alix Lacoste, Van C Willis, and Richard L Martin were all employed by IBM Watson Health during the time this research was conducted. None of these interests affected the choice of what and where to publish but the topic (pharmacodynamic biomarkers for BTK therapy) was of interest to Merck and learning what IBM's techniques showed in this case was also of interest to them. Watson for Drug Discovery is a product of IBM Watson Health. There are no patents, products in development or other marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on data and materials.

The TNF family cytokines B-cell activating factor (BAFF) and a proliferation-inducing ligand (APRIL) support plasma cell survival. It is known that inhibitors of BAFF only (BAFFR-Fc) or BAFF and APRIL (TACI-Fc) administered early enough in an NZB/NZW F1 mouse model of systemic lupus erythematosus (SLE) ameliorate clinical outcomes, pointing to a pathogenic role of BAFF. In the present study, TACI-Fc administrated at a later stage of disease, after onset of autoimmunity, decreased the number of bone marrow plasma cells and slowed down further formation of autoantibodies. TACI-Fc prevented renal damage during a 12-week treatment period regardless of autoantibody levels, while BAFFR-Fc did not despite a similar BAFF-blocking activity in vivo. TACI-Fc also decreased established plasma cells in a T-dependent hapten/carrier immunization system better than single inhibitors of BAFF or APRIL, and sometimes better than combined single inhibitors with at least equivalent BAFF and APRIL inhibitory activities. These results indicate that TACI-Fc can prevent symptoms of renal damage in a mouse model of SLE when BAFFR-Fc cannot, and point to a plasticity of plasma cells for survival factors. Targeting plasma cells with TACI-Fc might be beneficial to prevent autoantibody-mediated damages in SLE., (© 2017 The Authors. European Journal of Immunology published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)