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 an individual patient's response to asthma drugs remains challenging, and the provision of personalized treatment remains elusive. Although biomarkers, such as allergic sensitization and blood eosinophilia, might be important predictors of response to inhaled corticosteroids in preschool 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 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 empiric treatment. This inevitably results in treatment failures and, for some, an unacceptable risk/benefit ratio. The approach to asthma today is an example of the traditional symptom (diagnosis)–based, one-size-fits-all approach rather than a stratified approach, and our guidelines-driven management based on a unitary diagnosis might 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 interpretation of big data analytics from birth and patient cohorts, responses to treatments in randomized controlled trials, and in vitro mechanistic studies using human samples and experimental animal models, with technological and methodological advances at its core.
- data driven
- team science