Personalised medicine in asthma: from curative to preventive medicine

Abstract

The concept of asthma has changed substantially in recent years. Asthma is now recognised as a heterogeneous entity that is complex to treat. The subdivision of asthma, provided by "cluster" analyses, has revealed various groups of asthma patients who share phenotypic features. These phenotypes underlie the need for personalised asthma therapy because, in contrast to the previous approach, treatment must be tailored to the individual patient. Determination of the patient's asthma phenotype is therefore essential but sometimes challenging, particularly in elderly patients with a multitude of comorbidities and a complex exposure history. This review first describes the various asthma phenotypes, some of which were defined empirically and others through cluster analysis, and then discusses personalisation of the patient's diagnosis and therapy, addressing in particular biological therapies and patient education. This personalised approach to curative medicine should make way in the coming years for personalised preventive and predictive medicine, focused on subjects at risk who are not yet ill, with the aim of preventing asthma before it occurs. The concept of personalised preventive medicine may seem a long way off, but is it really?

FIGURE 1

Comparison between the a) traditional and b) new holistic approach to biomedical research. The traditional approach starts with observation of symptoms. The symptoms are grouped into syndromes or diseases based on current knowledge of the pathophysiology of the disease. Phenotype B is detected through cluster analysis. Due to the limited number of variables used, phenotypes A and C are not detected. Pathophysiological knowledge leads to a focus on the most common, distinctive disease pattern. Cell and animal models are developed in order to identify targeted therapies for phenotype B. This process excludes rarer and less well understood phenotypes. The use of targeted therapy helps refine our understanding of the pathophysiology of phenotype B by identifying responders and nonresponders. Responder and nonresponder biomarkers may be discovered and screened for. In this traditional process, several phenotypes, such as A, C and nonresponders of B, will not benefit from innovative therapies. In summary, this reductionist process starts with symptoms and culminates in laboratory tests. In the holistic process, the subject may be symptomatic or healthy and the analysis of each individual takes into account conventional variables as well as a large body of data including, for instance, their microbiome, genetic data, sputum data, occupation, diet, etc. These data are fed into mathematical algorithms to model the outcome of each subject, generating “n” models, “n” predicted syndromes and “n” “diseases”. The pathophysiology will be studied in biological models and in silico models to identify therapies (or management) targeting defined groups (strata) of subjects and to propose preventive, predictive, personalised strategies. In the holistic process, the subject may be healthy, and the “syndrome” is based on biological data.