Targeting the Inflammatory Cascade: The Evolution of Monoclonal Antibodies for Severe Asthma Management
DOI:
https://doi.org/10.12775/QS.2026.49.67808Keywords
severe asthma, omalizumab, dupilumab, mepolizumab, benralizumab, reslizumab, tezepelumab, astegolimabAbstract
Background: Severe asthma affects approximately 5–10% of the asthma population and is characterized by significant heterogeneity in pathophysiology and clinical presentation. The condition is traditionally categorized into Type 2-high (T2-high) inflammation, driven by mechanisms such as eosinophilia and IgE mediation, and Type 2-low (T2-low) inflammation, which is less well-defined. Standard treatments involving broad immunosuppression with corticosteroids are often insufficient or associated with toxicity in severe cases. Consequently, the understanding of asthma has evolved from a "one-size-fits-all" approach to a precision medicine model based on specific phenotypes and endotypes.
Aim: The aim of this review is to examine the efficacy, safety, and selection of biological therapies for severe asthma.
Materials and Methods: The review included scientific papers sourced from the PubMed and Google Scholar databases.
Results: Monoclonal antibodies have transformed severe asthma management. Omalizumab addresses allergic asthma, while anti-IL-5 agents (mepolizumab, reslizumab, benralizumab) effectively reduce exacerbations in eosinophilic phenotypes. Dupilumab (targeting IL-4/IL-13) offers superior lung function improvement, particularly in mixed phenotypes. Notably, tezepelumab (anti-TSLP) and emerging anti-IL-33 agents demonstrate efficacy across a broader spectrum, offering new therapeutic options for previously refractory T2-low patients.
Conclusions: Biologics have shifted the therapeutic goal from disease control to clinical remission. While T2-high asthma is well-managed by existing targeted therapies, upstream inhibitors now provide vital solutions for the challenging T2-low population. Consequently, a precise, biomarker-driven approach is essential to select the optimal therapy and maximize patient outcomes.
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