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Rethinking how we classify diseases

Posted by
Duncan McHale, Global Exploratory Development
The way we currently classify diseases is largely based on patients’ symptoms. This approach dates back centuries to a time when the only information available to doctors was what they could see and what their patient could tell them.

Today, there is a much deeper understanding of the underlying causes of disease. For example in oncology, advances in genetics and molecular biology help to explain the root of the tumour and can even predict which therapies patients will respond to.

Some of the information emerging from this revolution in biomedicine suggests that the old way of classifying diseases needs to be revised.

Not every patient with dementia or joint pain is the same. Despite having the same label attached to their condition, they do not necessarily respond to the same treatments.

At the same time there may be overlapping clusters of patients whose conditions are similar at a molecular level but whose diseases have different names.

Public-private partnership
Addressing this mismatch requires us to tap into the huge wealth of information contained in clinical data, imaging, biomarker and genetic data, to scan for patterns.

Harnessing the power of ‘big data’ is a big undertaking. It requires collaboration between companies and academia, along with investment from industry and public sources.

This is where the Innovative Medicines Initiative (IMI) comes into its own. As a public-private partnership working on projects that will accelerate drug discovery, it has the means to tackle the grand challenge of rethinking disease classification.

UCB is jointly leading the IMI’s AETIONOMY project which looks at classifying neurodegenerative conditions, particularly Alzheimer’s disease and Parkinson’s disease.

In collaboration with academics and other companies, we are working to collect and organise the large volumes of molecular data and information on symptoms. Based on this, the project aims to classify patient groups based on the underlying cause of their disease.

The result will be new tools which can be used by the biomedical community to develop new treatments and diagnostic tests.

UCB is also leading the sister IMI project which aims to reclassify lupus, connective tissue diseases and rheumatoid arthritis. The PRECISESADS research consortium will draw on the power of bioinformatics and OMICs to identify new classifications for diseases known to share common pathophysiological mechanisms.

The project will look at 2,000 patients with systemic autoimmune diseases and 600 healthy controls to identify molecular clusters and work out how these manifest at a cellular and clinical level.

This work could contribute to the most radical overhaul of disease classification in modern medicine, helping to use existing treatments better and to develop new therapies.

The revolution begins here.

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