Photo source: Tim Guilliams’ archive
Could you please introduce yourself and tell us about your main activities and roles? (Both at Healx and CRDN)
My name is Tim Guilliams, I am the founder and CEO of Healx. I am a scientist by background, I got a MSc degree in bioengineering and chemical engineering and moved to Cambridge for my PhD in biophysics. After getting my PhD, I decided to quit academia and started a social venture named Healx three and a half years ago. Our focus is on repurposing existing drugs for rare and genetic diseases by using artificial intelligence and genomics.
I am also the founding director of Cambridge Rare Disease Network which is a charity aiming to bring together parties interested in rare diseases around Cambridge and to bridge gaps between patients, researchers, start ups, clinicians and bring them all around the table.
CDRN grew out of Healx and GeneAdviser: we were interested in rare diseases and realised we didn’t have anyone to go to in Cambridge because there was nothing for rare diseases. Together with Jelena Aleksic we started this charity, so it seems we did the opposite of DNAdigest and Repositive!
In terms of CDRN activities, there is a number of events including the annual summit. We are also currently launching a round table of companies interested in rare diseases with policy makers and patient advocates. There is also a pilot project on Rare diseases nurses. In addition, we have other activities with the aim to support families with children with rare diseases, including dancing, yoga etc, so it is more focused on supporting families and having fun than on treatments.
To summarise, I’m a scientist and entrepreneur and the thing I am really passionate about is to use machine learning and artificial intelligence to help rare disease patients who have no other treatments available.
What are you going to talk about at the BioData World congress in November?
The title of my talk is “Leveraging artificial intelligence to advance rare disease treatments” and I will talk about new approaches with machine learning that you can apply to rare disease drug discovery and how you can use data-driven principles to accelerate translation of therapies for rare disease.
As you know, about 95% of rare diseases don’t have approved therapies, we have developed algorithms that could identify which of the already approved drugs could potentially help them. We are also now looking at nutrients, or nutraceuticals because we want to help patients with tools that are available today so that they don’t have to wait for gene therapies that can be there in ten years but can start those treatments that are available now.
What role does data access and reuse play in the work of Healx? (Do you rely on any external data that is not produced within Healx?)
Very good question! We rely almost entirely on external data – i.e. data that other people have produced; it can be both public data and private data. In some cases, we generate our own data but this is more an exception than a rule. We want to make the most of scientific knowledge that is already there, knowledge about drugs and therapies that is already available and reuse it in the most effective possible way.
The easiest information to get is about drugs, clinical trials, academic publications; we can reuse algorithms to mine those automatically. However, raw omic data (genomic, transcriptomic, proteomic etc) is much more difficult to access. There are many data gaps in rare diseases, but the world is not perfect so we have to work with whatever data we can find and/or access. I think what Repositive is doing would be fantastic to help people access data.
What are the biggest achievements of Healx so far? What are the biggest challenges?
Our biggest achievements so far: we are revenue generating, we have raised $2,5 million from star investors, we have successfully identified drugs for several rare diseases and validated our technology platform. The team has about 15 people and is growing further. Healx also won a number of awards including Cambridge Graduate Business of the Year 2016 and Life Science Business of the Year 2015, was selected in the global “Disrupt 100” list 2017 and covered in the Harvard Business Review.
Our biggest challenges are hiring computer scientists with biological understanding and accessing good quality data.
What would you advise to young scientists who are at the beginning of their career these days?
- Start learning to code and you will never be out of job!
- If you are still in academia – you might want to go and see the outside world…
- Find something you are passionate about it and focus on that.