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Viagra Inventor Raises $10M to Accelerate Rare Disease Drug Discovery with AI

July 26, 2018

Rare Daily Staff

Healx, a U.K.-based technology company using artificial intelligence to accelerate the discovery of treatments for rare diseases, said it completed a $10 million series A funding round.

Balderton Capital led the funding round with existing investors Jonathan Milner and Amadeus Capital Partners also participated in the financing. The investment will be used to more than double Healx diverse and multi-disciplinary team of software engineers, data scientists, pharmacologists and drug development experts to expand its world class artificial intelligence and machine learning technologies.

There are 7,000 known rare diseases that affect 350 million people worldwide. 1 in 10 people on the planet suffer from a rare disease, yet 95 percent of rare diseases still do not have an approved treatment.

“The traditional drug discovery process takes 10 to 15 years at a cost of $2 billion per new drug and with a failure rate of 95 percent. It’s broken, it’s slow, it’s high failure, and it’s not economic for rare diseases,” said David Brown, co-founder, chief scientific officer, and a co-inventor of Viagra. “However, today’s technology can change that and help 350 million under-served rare disease patients. Healx is showing that we can massively transform the rate of discovery of new medicines, reducing timelines and costs.”

Healx’s uses artificial intelligence, deep pharmacological expertise, and extensive patient engagement to accelerate drug discovery. The company starts with existing drugs and applies artificial intelligence to niche disease populations, working with patient groups to accelerate treatment development. The company says its multi-disciplinary team of experts, by leveraging artificial intelligence techniques, can cut the discovery-to-treatment time from years to months.

Healx’s technology has already been used successfully, including most recently in collaboration with FRAXA, a Fragile-X syndrome patient group. A drug for Fragile-X that was discovered by Healx’s platform was ready for the clinic in less than 15 months, cutting typical drug development timelines by 80 percent and doing so at a substantially reduced cost.

The company’s platform, dubbed HealNet, is a comprehensive knowledgebase for rare disease, mapping more than 1 billion unique disease, patient, and drug interactions. HealNet was built and is maintained using a variety of machine learning methods applied to a wide range of data types from both publicly available and exclusive sources including scientific literature, patents, clinical trials, disease symptoms, drug targets, multi-omic data, and underlying chemical structures.

Along with Brown, Tim Guilliams, a biochemical engineer and founder of the Cambridge Rare Disease Network, and Andreas Bender, a lecturer and researcher at Cambridge University’s Centre for Molecular Sciences Informatics, founded the company in 2014.

Though the company said its platform could be used to identify new molecular entities for treating diseases, it has so far focused on repurposing existing drugs to improve its speed to market.

Balderton’s Suranga Chandratillake will be joining the Healx board along with Shaun Grady, global vice president of business development operations for AstraZeneca; Hermann Hauser, co-founder of Amadeus Capital Partners; and Jonathan Milner, founder and ex-CEO of Abcam.

“Historically, the pharmaceutical industry has focused on what is the same about everyone, building blockbuster drugs that affect large groups of people,” said Suranga Chandratillake, partner at Balderton Capital. “The future of medicine will focus on what is different about everyone, personalizing treatment and taking the individual into context. The first frontier of this exciting new direction for medicine is rare disease and the only way it can be solved is by using automation to deliver a step-change in the volume of discovery that is possible.”

July 26, 2018
Photo: Healx co-founders Tim Guilliams and David Brown

 

 

 

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