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Big data leads to insights into Amyotrophic Lateral Sclerosis

Treeway, a Biotechnology company founded in 2012 with a mission to develop therapies to cure Amyotrophic Lateral Sclerosis (ALS), has announced results of its Treeway Summer Challenge 2015 – which aims to accelerate the discovery of new ALS therapies.  

The challenge saw nine Masters and PhD students travel to New York, Boston, Portugal, Spain and The Netherlands with the goal of developing new insights into ALS to speed up the discovery of potential treatments.

Inez de Greef, CEO of Treeway, said: ‘The creativity and motivation of the team was very inspiring and the results of the three groups provide new angles towards finding new therapy for ALS.’

Amyotrophic Lateral Sclerosis, also known as Lou Gehrig’s Disease, is a progressive neurodegenerative disease that causes muscle weakness, disability and eventually death, with a median survival of three years. To date, there is no cure for ALS. 

The students' analysed sponsored data to develop an ALS model, which can help improve scientists' understanding of the disease. Ultimately Treeway hopes that the joint results of the challenge can help contribute to finding a new drug to effectively treat ALS.

The students were split up into three sub groups to work on different assignments. The first group developed a mind map to describe and link the different biological processes involved in ALS. The mind map is a useful tool to gather information about this debilitating condition, to identify new connections between biological processes involved in ALS, and to identify possible new drug targets. In the future, this could possibly lead to better therapeutics.

The second group worked on developing a population model, using the PRO-ACT database, to describe a patient's disease progression. The PRO-ACT database consists of more than eight million measurements from ALS patients, recorded during several clinical trials. The population model can help predict a patient's disease progression based on different influencing factors such as age and gender. In the future this model will be used to provide patients with a more accurate prognosis and it can help to improve the design of clinical trials.

Group three tried to understand how a drug might work in an ALS patient. The group developed physiology-based models, which describe different processes in ALS patients. These models can support the drug development process by improving our understanding of how a drug will behave in the body of an ALS patient.

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