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Towards precision medicine in severe asthma : Treatment algorithms based on treatable traits

Papaioannou, Andriana I.; Diamant, Zuzana LU ; Bakakos, Petros and Loukides, Stelios (2018) In Respiratory Medicine 142. p.15-22
Abstract

Asthma is a common disease, and although its clinical manifestations may be similar among patients, recent research discoveries have shown that it consists of several distinct clinical clusters or phenotypes, each with different underlying molecular pathways yielding different treatment responses. Based on these observations, an alternative approach - known as ‘precision medicine’ - has been proposed for the management of patients with severe asthma. Precision medicine advocates identification of treatable traits, linking them to therapeutic approaches targeting genetic, immunological, environmental, and/or lifestyle factors in individual patients. The main “goal” of this personalised approach is to enable choosing a treatment which... (More)

Asthma is a common disease, and although its clinical manifestations may be similar among patients, recent research discoveries have shown that it consists of several distinct clinical clusters or phenotypes, each with different underlying molecular pathways yielding different treatment responses. Based on these observations, an alternative approach - known as ‘precision medicine’ - has been proposed for the management of patients with severe asthma. Precision medicine advocates identification of treatable traits, linking them to therapeutic approaches targeting genetic, immunological, environmental, and/or lifestyle factors in individual patients. The main “goal” of this personalised approach is to enable choosing a treatment which will be more likely to produce a beneficial response in the individual patient rather than a ‘one size fits all’ approach. The aim of the present review is to discuss different ways of phenotyping asthma and to provide a rationale for treatment algorithms based on principles of precision medicine.

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author
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Asthma, Biologics, Biomarkers, Endotype, Phenotype, Precision medicine
in
Respiratory Medicine
volume
142
pages
8 pages
publisher
Elsevier
external identifiers
  • scopus:85050166062
ISSN
0954-6111
DOI
10.1016/j.rmed.2018.07.006
language
English
LU publication?
no
id
58e649f7-1c42-46ab-9d0c-6d9a893de0ad
date added to LUP
2018-07-31 14:02:06
date last changed
2019-04-10 04:11:37
@article{58e649f7-1c42-46ab-9d0c-6d9a893de0ad,
  abstract     = {<p>Asthma is a common disease, and although its clinical manifestations may be similar among patients, recent research discoveries have shown that it consists of several distinct clinical clusters or phenotypes, each with different underlying molecular pathways yielding different treatment responses. Based on these observations, an alternative approach - known as ‘precision medicine’ - has been proposed for the management of patients with severe asthma. Precision medicine advocates identification of treatable traits, linking them to therapeutic approaches targeting genetic, immunological, environmental, and/or lifestyle factors in individual patients. The main “goal” of this personalised approach is to enable choosing a treatment which will be more likely to produce a beneficial response in the individual patient rather than a ‘one size fits all’ approach. The aim of the present review is to discuss different ways of phenotyping asthma and to provide a rationale for treatment algorithms based on principles of precision medicine.</p>},
  author       = {Papaioannou, Andriana I. and Diamant, Zuzana and Bakakos, Petros and Loukides, Stelios},
  issn         = {0954-6111},
  keyword      = {Asthma,Biologics,Biomarkers,Endotype,Phenotype,Precision medicine},
  language     = {eng},
  month        = {09},
  pages        = {15--22},
  publisher    = {Elsevier},
  series       = {Respiratory Medicine},
  title        = {Towards precision medicine in severe asthma : Treatment algorithms based on treatable traits},
  url          = {http://dx.doi.org/10.1016/j.rmed.2018.07.006},
  volume       = {142},
  year         = {2018},
}