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The evolving algorithm of biological selection in severe asthma

Authors: Nikolaos G. Papadopoulos; Peter Barnes; Giorgio Walter Canonica; Mina Gaga; Liam Heaney; Andrew Menzies‐Gow; Vicky Kritikos; +1 Authors

The evolving algorithm of biological selection in severe asthma

Abstract

AbstractNew therapeutic options for severe asthma have recently emerged, mostly in the form of monoclonal antibodies (“biologicals”) targeting relevant inflammatory pathways. Currently available agents target different aspects of “Type 2” immunity, and their indications often include overlapping patient groups. We present a round‐table discussion that took place during the Annual Meeting of the Respiratory Effectiveness Group (REG), on the reasoning behind the use of different add‐on medications for severe asthma, and crucially, on selection strategies. The proposed rational is based on current evidence, including real‐life studies, as well as on the appreciation of the relevant complexities. Direct head‐to‐head comparisons of biologicals are lacking; therefore, algorithms for initial choice and potential switch between agents should be based on understanding the key characteristics of different options and the development of a clear plan with predefined targets and shared decision‐making, in a structured way.

Keywords

610, Antibodies, Monoclonal, Humans, Anti-Asthmatic Agents, Algorithms, Asthma

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 10%
Green
bronze