Towards precision medicine in severe asthma : Treatment algorithms based on treatable traits
(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
- Papaioannou, Andriana I. ; Diamant, Zuzana LU ; Bakakos, Petros and Loukides, Stelios
- publishing date
- 2018-09-01
- 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
- pmid:30170796
- 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
- 2024-04-01 08:46:23
@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}},
keywords = {{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}},
doi = {{10.1016/j.rmed.2018.07.006}},
volume = {{142}},
year = {{2018}},
}