Canakinumab 150mg powder for solution for injection vials

BackgroundInflammation is causally implicated in the development and progression of atherosclerotic cardiovascular disease, but clinical trials of anti-inflammatory therapies have yielded inconsistent effects on cardiovascular outcomes.MethodsWe conducted a systematic review and meta-analysis to evaluate the cardiovascular efficacy and safety of a broad range of anti-inflammatory agents. Medline, Embase and Cochrane databases were searched from inception to 08 October 2024 for randomised controlled trials evaluating the effect of anti-inflammatory therapies on a primary cardiovascular outcome with at least 100 patient-years follow-up per treatment arm. Trial level meta-analysis was performed using a random effects model. The primary outcome was major adverse cardiovascular events (MACE); other outcomes included myocardial infarction, stroke, heart failure, serious adverse events, infection, and malignancy.ResultsThirteen trials enrolling 82,208 participants were included. The effect of anti-inflammatory agents on MACE varied by drug class (P-heterogeneity=0.049), driven primarily by benefits observed with colchicine (RR 0.76; 95 % CI 0.65-0.90; moderate certainty) and canakinumab (RR 0.88; 95 % CI 0.79-0.97; moderate certainty), with no benefit observed for other agents. Among colchicine trials, heterogeneity was identified (P-heterogeneity=0.003), and subgroup analyses suggested greater benefit in coronary artery disease and/or recent myocardial infarction trials (P-heterogeneity=0.068). Safety outcomes also varied by drug class, with significant heterogeneity in serious adverse events (P-heterogeneity=0.005), largely attributable to methotrexate, and some evidence of heterogeneity for infection (P-heterogeneity=0.076) and malignancy (P-heterogeneity=0.077).ConclusionSome anti-inflammatory agents may reduce the risk of cardiovascular outcomes, but their effects appear to vary substantially across drug classes, with important differences in both efficacy and safety. These findings underscore the need for rigorous evaluation of new anti-inflammatory therapeutics to ascertain benefits and harms across different populations and therapeutic indications.

Abstract licence: CC BY-NC-ND

Model-based meta-analysis (MBMA) informs key drug development decisions by integrating data, published or unpublished, from multiple studies. Due to these various sources of information and the use of summary-level data (e.g., mean responses over treatment arms or percent responders), MBMA models require careful implementation. This tutorial provides a comprehensive guide for conducting an MBMA with MonolixSuite, focusing on longitudinal continuous and categorical data. Two case studies are presented: the first examining naproxen in osteoarthritis and the second evaluating canakinumab compared to existing treatments in rheumatoid arthritis. The tutorial explains the process of model building and handling study heterogeneity in Monolix, including how to include between-study variability and between-treatment-arm variability. It also shows how to apply appropriate weighting due to the use of summary-level data. For model evaluation, the tutorial demonstrates the use of automatically generated diagnostic plots, statistical tests, and convergence assessment tools. Furthermore, it illustrates how to use the model in Simulx to support the decision-making process, such as by simulating clinical trials. This step-by-step guidance offers practical insights for leveraging MBMA in model-informed drug development.

Abstract licence: CC BY-NC-ND