Longitude Prize on ALS

ALS (amyotrophic lateral sclerosis) is a fatal neurodegenerative disease with an average life expectancy post-diagnosis of just two to five years. It is the most common form of motor neurone disease (MND), in which messages from the motor neurones gradually stop reaching the muscles. This leads the muscles to weaken, stiffen and waste, which can affect how individuals walk, talk, eat, drink and breathe. Some people also get changes to their thinking and behaviour, but the disease affects everyone differently. Not all symptoms will affect everyone, or in the same order. Symptoms also progress at varying speeds, which makes the course of the disease difficult to predict. There is no cure.

Although often described as a rare disease, incidence is not uncommon – according to the MND Association, a person’s lifetime risk of developing MND is up to 1 in 300. For most patients, treatment is currently limited to one approved drug, Riluzole, which extends life by a matter of months.

In recent years, there have been significant advances in understanding the biology of ALS, including the discovery of new biomarkers and treatment pathways. Yet for the vast majority of those diagnosed, ALS remains an extremely life-limiting disease. Progress towards a treatment is slow – the push for new treatments must continue at pace.

Recent breakthroughs have shown promise, but the drug development process takes a long time (12-15 years), is expensive (on average costing 1-2 billion US dollars), and pharmaceutical companies are hesitant to invest as there are still very few high-potential validated therapeutic targets.  

ALS is a hugely complex disease, but it is this complexity that lends the disease to AI-based target and therapeutic discovery, which could be much more impactful than traditional research methods in identifying and validating possible therapeutic targets for complex diseases.

AI has the potential to materially alter the economics of innovation for ALS, by finding and validating high-potential therapeutic targets at speed, reducing programme risk and attracting investment from industry.  In other diseases, AI has successfully been used to de-risk drug programmes and attract investment from industry, but use of AI within ALS is currently very limited. Investment in AI for pharmaceutical development in oncology (27%) is more than twice that of neurological conditions (11%), highlighting a significant disparity in focus across therapeutic areas. A major reason for this disparity is the relative difference in data availability between disease areas. 

As the global ALS research portfolio has grown, more and more datasets have been created with a plethora of different data types. Many of these datasets remain out of reach to commercial entities, particularly in Europe, but analysis of such data may hold the key to truly understanding the disease and identifying promising new treatments.

To maximise the potential for AI-driven target discovery in ALS, the Prize will offer participants access to a unique harmonised dataset on an easy-to-use platform offering a  powerful opportunity to discover and validate new therapeutic targets in this particularly challenging disease.

If AI-driven identification work funded through this programme uncovers strong new evidence for a previously known therapeutic target, it may be eligible for applications into the next phase. We will consider such targets if they have not been previously validated and are not part of any known therapeutic development efforts. What’s important is whether your findings highlight the target as a promising and underexplored opportunity for therapy by clearly strengthening the case for its role in the biological mechanisms that drive ALS.