Asthma is a chronic, variable condition, characterized by the constriction of airways, wheezing, coughing, shortness of breath, and chest tightness. Affecting an estimated 358 million individuals globally and causing 400,000 deaths each year, asthma’s severity of symptoms ranges from mild to life threatening, with around 5-10% of patients experiencing severe disease.
In recent years the classification of asthma has evolved, recognising several disease subtypes based on distinct underlying mechanism. Some asthma phenotypes have been shown to have a high degree of heritability though there are still questions surrounding the genetics of asthma. Genetic studies have shown that many genes are associated with asthma, helping to explain some of the variability in asthmatic populations. Though there are many genes now known to be associated with asthma, there is still poor understanding of heritability in asthma genetics. Current research is beginning to look at non-genetic factors such as DNA modifications and the gene activity to understand the processes by which asthma is inherited and how it manifests.
Given that severe asthma patients tend to be non-responders to standard therapeutic approaches, there is an unmet need for treatment in these patients. Biologic treatment options do exist for people who do not respond to inhaled corticosteroids, though they are still novel in the asthma treatment landscape. They are only recommended however in severe patients that show little or no response to standard therapies, and their use is limited to recommendations by asthma specialists.
Real world data in the form of electronic healthcare records (EHRs) have been shown to increase understanding of diseases and help identify new treatment options for them. Observed patterns in EHR data can help identify specific phenotypes more cheaply, quickly, and broadly than clinical trials or longitudinal research projects. Genetic and omic data can identify new genes and proteins that may represent new drug targets for which new treatments can be developed to correct defective pathways causing disease. In addition to providing new therapeutic targets, it is possible to identify new tests that can be used to diagnose particular disease endpoints such as disease severity and decreasing lung function. The project is estimated to last 3 years, during which time genetics and omics data of asthma patients will be collected and compared to their anonymised EHR. This will be used to better understand and characterise different types of asthma by severity and response to treatment, and identify new molecules for drug development.
