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. 2021 Oct;112(5):573-581.
doi: 10.23736/S0026-4806.21.07623-0. Epub 2021 May 14.

Monoclonal antibodies in type 2 asthma: an updated network meta-analysis

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Monoclonal antibodies in type 2 asthma: an updated network meta-analysis

Ahmed Edris et al. Minerva Med. 2021 Oct.

Abstract

Introduction: Novel treatments target eosinophilic inflammation in type 2 asthma. We aimed to evaluate and meta-analyze the efficacy of monoclonal antibodies to reduce exacerbation rate.

Evidence acquisition: PubMed and Embase were searched for phase II and phase III randomized clinical trials with monoclonal antibodies targeting key mediators of type 2-associated asthma between 2019 and 2021 to update our previous meta-analysis covering studies published from 2005 to 2018. Five-hundred and sixty six publications have been identified, of which six recent trials (on top of 30 previously identified) involving mepolizumab, benralizumab, reslizumab and dupilumab met our inclusion criteria. As no head-to-head trials were retrieved from literature, we performed an arm-based network meta-analysis including a total of 19 RCTs to compare effects on exacerbation rate between the different treatments.

Evidence synthesis: Benralizumab significantly reduced the risk of exacerbations compared to the pooled placebo in our network meta-analysis (median effect difference: -0.520, 95% CI [-1.010- -0.048]). No biologic showed superiority over the others in indirect comparisons. Large reductions in exacerbation rates were observed compared to placebo, though only benralizumab was sufficiently powered (N.=2564) to demonstrate significantly decreased exacerbation rates both in the overall population and in the subgroup analysis of IL-5 acting agents compared to placebo.

Conclusions: Monoclonal antibodies have proven their benefit to reduce exacerbation rates in severe persistent eosinophilic asthma in the published trials. No biological showed superiority over the others emphasizing the need for clearly defined endotypes indicating those patients who will optimally benefit for each treatment.

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