Dr Bruce Bloom

Chief Collaboration Officer, Healx

Dr Tim Guilliams

Co-Founder and CEO, Healx

True medical breakthroughs happen when researchers see challenges through the eyes of those living with a particular condition and when industry, patient groups and academic institutions work hand-in-hand.

In drug discovery, novel technologies such as artificial intelligence (AI) and machine learning  (ML) offer new and exciting  ways to dramatically speed up the process, but collaboration between patients and researchers remains as crucial as ever.

But what makes us so certain of this? At Healx, we use a combination of AI, drug discovery expertise and patient insight to accelerate the development of repurposed treatments for rare diseases.

This approach has been applied across multiple disease areas and with partners from around the globe, including Mission: Cure, the Foundation for Angelman Therapeutics and the Children’s Tumor Foundation.

We have found the best results come from adopting a collaborative method. By working together, there has been a dramatic reduction in the time needed to take treatments from the drug-prediction stage towards clinical trials.

The role of AI in rare treatment discovery

Within the drug discovery field, AI and ML are powerful tools with the potential to make the process of finding and developing treatments much faster, safer and more cost-effective. This is particularly necessary in the rare disease space – where 95% of conditions lack any approved treatments.

To make the most of these technological advances, we created Healnet, an AI-powered drug discovery platform. It integrates data from multiple sources (everything from disease and clinical trial data to scientific literature) to form the world’s most detailed knowledge graph of rare disease information. This graph is then analysed by state-of-the-art AI and natural language processing (NLP) models, to uncover previously unknown relationships between existing drugs and rare diseases.

The benefit of looking at known drugs (i.e. those that have already been approved for use in another condition) is that we’re often able to move directly to Phase IIa clinical trials, since the safety profile of the drug has already been established. This, in turn, further accelerates and de-risks the treatment discovery process.

The support we’ve received from patients and their families has been invaluable – they are the real rare disease experts after all.

Collaboration is key

Whilst cutting-edge AI technology is integral to our work, we recognise that without the involvement of such dedicated patient groups, academics and industry partners we couldn’t have achieved as much as we have. In particular, the support we’ve received from patients and their families has been invaluable – they are the real rare disease experts after all.

Working together with groups such as Muscular Dystrophy UK, FRAXA Research Foundation and more to share data, expertise and patient insight is where real advances in the realm of rare disease research can be made.

This type of collaborative work takes different forms depending on the project, partner and disease area. Whilst we often partner with patient groups from the outset of a project, we’re also able to work with partners who already have a drug repurposing asset but lack the financial resources or clinical expertise to take these to the clinic.

In these cases, we’re also often able to use our technology either to validate or enhance their current repurposing assets. This type of partnership is a particular focus of the Rare Treatment Accelerator – our partnering programme designed to keep collaboration firmly at the heart of our work.

Using technology to enhance existing work

The Rare Treatment Accelerator connects academic groups, patient groups and early-stage biotechs with Healx to unlock the power of repurposed drugs for rare diseases. Working together, we can quickly turn research, insights and promising repurposing assets into novel treatments.

Together with rare disease partners we’re taking a new approach to developing treatments – one that is AI-powered but, even more importantly, one that is patient-inspired.