Clinical trials and drug discovery
figure generation were conducted in Microsoft Excel, GraphPad Prism V.9, and R. Flow cytometry panels were generated using FlowJo 10. IFN gene signature expression was evaluated using Nanostring’s nCounter Autoimmune Profiling Panel vali- dated and run at Q2 Genomics (Durham, North Caro- lina). Whole blood samples collected in PAXgene tube were used for RNA isolation and analysis. Analysis was conducted on nSolver Advanced Analysis Visualizations (NanoString).
cells, monocytes, granulocytes, plasmablasts and plasma cells in whole blood. Additionally, CD38 expression and mezagitamab receptor occupancy were evaluated on the respective cell types by comparing CD38 fluores- cence signal for two independent flow cytometry samples containing either labelled mezagitamab (for quantifica- tion of ‘free’ CD38 receptor) or labelled TSF-19 (non- competitive CD38 antibody for quantification of ‘total’ CD38 receptor). PK, IgG and autoantibody quantification Mezagitamab serum concentrations were quantified through a clinically validated electrochemilumines- cence immunoassay with a lower limit of quantification (LLOQ) of 5ng/mL. IgG measurements were obtained from Roche Cobas 8000 analysers at a central labora- tory. SLE-associated autoantibodies were quantified via Thermo Scientific Phadia 250 Analysers at a central labo- ratory using clinically validated enzyme-linked immuno- assay methodology. Autoantibodies assessed included the following (with positivity defined based on cut-off values): anti-dsDNA (>15IU/mL); anti-SmD p , ribonucleopro- tein-70, Sjogrens SS-A, Sjogrens SS-B, beta-2 glycoprotein IgG, beta-2 glycoprotein IgM (>10 IU/mL); cardiolipin antibody IgG and cardiolipin antibody IgM (>40 IU/mL); and cardiolipin antibody IgA (>20 IU/mL). Immune profiling Cytometry by time of flight (CyTOF) analysis was conducted at CellCarta (Fremont, California). Samples were run in batches of approximately 10. Each sample was thawed and washed, stained with a dye indicating cell viability, then hybridised with the defined antibody panel. Following wash, samples were fixed and incubated with a DNA intercalating agent for 3–7 days at 4°C. Fixative was then removed, and samples were suspended in water for CyTOF analysis. 100 000–250 000 events were analysed per sample. In addition to viability assessments, the expression of 39 cellular markers was analysed via metal ion-coupled antibodies. This antibody panel included TSF-19 as a non-competitive antibody against CD38. Samples were obtained at baseline (during screening, up to 28 days prior to mezagitamab initiation) and at days 15, 36, 57 and 85. Data were collected as flow cytometry standard files and assessed as randomly selected analysis of 100 000 cells per patient sample. Cluster identification and analysis was done using the R package CyTofWorkflow. 14 FlowSOM 15 and Consen- susClusterPlus 16 were used to identify distinct cell popu- lations based on their expression profiles, and results were visualised using t-distributed stochastic neighbour embedding (TSNE) plots. 17 FlowSOM employed a self- organising map algorithm to cluster cells into metaclus- ters, and ConsensusClusterPlus employed a consensus clustering approach to identify stable clusters. Additional gated analysis was conducted and expressed as median CD38 expression and percentage of parent population (±SE of mean, where applicable). Statistical analyses and
RESULTS Patient demographics
Twenty-three out of the planned 24 patients were enrolled and randomised across 13 study sites in the USA. The study was conducted between 26 November 2018 and 4 November 2021; however, enrolment was closed early due to recruitment challenges. One randomised patient with- drew from the study before receiving study drug, and the remaining 22 patients received at least one dose of study drug. Key demographic characteristics are summarised in table 1. Demographic variables were generally similar across treatment groups, with the exception of baseline weight and age. Placebo-treated patients were on average approx- imately 13 years younger (36.4±6.6 years) compared with mezagitamab-treated patients (pooled mezagitamab group: 49.1±14.6 years). The average weight of patients in the mezagitamab 135 mg group was less than in the other treatment arms (placebo, mezagitamab 45 mg or 90mg). Most patients were women, and there was a similar distri- bution of African American and Caucasian study partic- ipants. All patients were taking at least one background SLE medication, with the most common being hydroxy- chloroquine (n=13), prednisone (n=9) and mycopheno- late mofetil (n=4). There were no changes to background medications during the treatment period. Among those patients receiving corticosteroids, slightly higher mean daily doses were observed in the placebo (8.3 mg) and mezagitamab 45mg (10.0mg) groups compared with the mezagitamab 90 mg (7.0 mg) and 135 mg (4.7 mg) groups during the treatment period of the study. Overall, use of background medications was balanced across treatment groups and consistent with the standard of care practices for patients with SLE. Safety In total, 17 patients were exposed to mezagitamab, and 5 patients were exposed to placebo. Mean (range) compli- ance was 80.0% (50.0%–100.0%) in the pooled placebo group and 79.2% (50.0%–100.0%), 62.5% (25.0%– 100.0%) and 70.0% (50.0%–100.0%) in the 45 mg, 90 mg and 135 mg mezagitamab arms, respectively (table 2). Many patients did not receive all four doses of study drug (60% in the pooled placebo group and 70.6% in the pooled mezagitamab group) due to a number of factors,
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McDonnell SRP, et al . Lupus Science & Medicine 2024; 11 :e001112. doi:10.1136/lupus-2023-001112
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