The fact that asthma management has changed little since the 1960s should be a wake up call that we need a new approach to research in order to advance patient outcomes, says an international expert who delivered the Ann Woolcock lecture last week.
Professor Adnan Custovic, chair in paediatric allergy at the Imperial College London, told the limbic that we had hit a brick wall in terms of reducing the morbidity associated with asthma.
“It is increasingly clear and widely accepted that asthma is a heterogeneous group of conditions with both shared and unique underlying mechanisms. Yet we haven’t made any inroads into reducing exacerbations or reducing hospital admissions since the year 2000.”
He said the term asthma will eventually be confined to history as unique endotypes of the disease emerge – but we weren’t there yet.
“For example, together with a Danish group, we identified the CDHR3 gene which is associated with severe early onset asthma requiring hospital admissions. This is a small but high cost group of patients.”
“A US team has also looked at this gene and identified it as a receptor for rhinovirus C so we have a disease mechanism and a potential target for treatment,” he said.
Professor Custovic, in Australia to present the Ann Woolcock Lecture at the Woolcock Institute of Medical Research, shared his vision for improving ‘team science’.
“I’ve been very interested over 10-15 years in longitudinal data to inform our understanding of temporal patterns of disease. In the UK, we’ve pulled together five birth cohorts and the US is doing the same with 12 birth cohorts.”
“But we also need the patient case studies, the randomised controlled trials and the basic science. Each type of study has a different truth and the challenge is how to reconcile and integrate information from each,” he said.
He said more data driven methods of analysis such as pattern recognition, mathematical modeling and machine learning would generate new hypotheses.
“However creating these repositories of data is not sufficient and will not in itself translate to biomarkers we can use in the clinic.”
“We also need better communication and teamwork between basic scientists, clinicians, mathematicians, statisticians, computer scientists, epidemiologists and more.”
“The clinical community will not be able to solve these problems alone. We need novel methods and clinicians will have to actively engage with people with different skills sets and from different backgrounds to find new solutions.”