Publication date: Available online 27 May 2019
Source: Journal of Allergy and Clinical Immunology
Author(s): Adnan Custovic, John Henderson, Angela Simpson
Summary
Despite the development of novel treatments, improvement in the design of delivery devices, and new technologies for monitoring and improving adherence, the burden of asthma is not decreasing. Predicting individual patient’s response to asthma drugs remains challenging, and the provision of personalized treatment elusive. Although biomarkers such as allergic sensitization and blood eosinophilia may be important predictors of response to inhaled corticosteroids in pre-school children, these relatively cheap and available investigations are seldom used in clinical practice to select patients for corticosteroid prescription. However, for the majority of patients, response to different treatments cannot be accurately predicted. One of the key factors which is preventing further advances is the reductionist view of asthma as a single disease, which is forcing patients with different asthma subtypes into a single group for empirical treatment. This inevitably results in treatment failures, and for some, unacceptable risk/benefit ratio. Approach to asthma today is an example of the traditional symptom (diagnosis)-based, one-size-fits-all, rather than stratified approach, and our guidelines-driven management based on a unitary diagnosis may not be the optimal way to deliver care. The only way to deliver stratified medicine and find a cure is through the understanding of asthma endotypes. We propose that the way to discover endotypes, biomarkers and personalized treatments is through the iterative process based on the interpretation of big data analytics from birth and patient cohorts, responses to treatments in RCTs, in vitro mechanistic studies using human samples and experimental animal models, with technological and methodological advances at its core.
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