Deep Genomics Secures Additional $40M via Series B Funding

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Deep Genomics, an artificial intelligence (AI)-based therapeutics developer spun out of the University of Toronto nearly five years ago, said today it has completed a $40 million Series B financing.

Proceeds from the financing will be used to fund development of new treatments for rare genetic diseases, as well as to expand the company’s proprietary AI discovery platform designed to support discovery and development of novel therapies for more common disorders, Deep Genomics said.

“This financing will enable us to expand our AI technology in the pursuit of new therapeutic opportunities, to advance our Wilson program into the clinic, and to strategically partner assets emerging from our overflowing preclinical pipeline,” Brendan Frey, founder and CEO of Deep Genomics, said in a statement.

Deep Genomics in September identified its first therapeutic candidate, the oligonucleotide therapy DG12P1, which is designed to treat Wilson disease in patients who possess Met645Arg, a mutation shown to lead to the loss of function of the ATP7B copper-binding protein.

The mutation is among an unknown number of mutations linked to Wilson disease, which causes copper to accumulate in the liver, brain and other vital organs. The rare and potentially life-threatening inherited disorder affects approximately one in every 30,000 people worldwide, a statistic referenced in a 2015 review article.

DG12P1 is one of two therapeutic programs that Deep Genomics plans to advance to an IND this year, with the Wilson disease candidate expected to generate Phase I/II data in 2021.

“Therapeutically re-engineering the human genome is the final frontier. Doing so requires systems that can predict information pertaining to the genome, and the best technology we have for prediction is AI,” Frey added. “We have found that the more we explore the universe of genetic therapies using AI, the more we discover dark regions that can be illuminated only with the development of new technology.”

Deep Genomics uses AI to power every stage of drug development, from identifying therapeutic targets previously viewed as undruggable, to designing novel therapeutic candidates, to designing animal models, as well as toxicity assessment and innovative trial design. Launched in 2015, Deep Genomics has used high throughput assays and advanced robotics systems to generate billions of data points and build dozens of carefully engineered and validated machine learning systems designed to support drug development.

According to the company, 70% of its research projects have led to therapeutic leads, with its programs progressing from target discovery to drug candidate in less than 12 months.

The $40 million financing was led by Future Ventures, a firm specializing in seed and early-stage investments in disruptive technologies that include AI, deep learning, and synthetic biology. The financing included participation by healthcare and technology funds Amplitude Ventures, Khosla Ventures, Magnetic Ventures, and True Ventures.

Deep Genomics completed a $13 million Series A financing round in 2017 led by Khosla Ventures, with participation by True Ventures. In 2018, the company launched a partnership with Wave Life Sciences to discover novel therapies for treating genetic neuromuscular disorders using AI Workbench. And in June 2019, Deep Genomics named as a strategic advisor Peter Barton Hutt, Senior Counsel at the law firm Covington & Burling, and a former Chief Counsel of the FDA, an appointment designed to bolster Deep Genomics’ clinical trial design and regulatory strategy expertise.

“Deep Genomics has pioneered a better way to systematically discover new therapies with a much higher success rate than traditional pharma methods,” stated Steve Jurvetson, co-founder of Future Ventures and board member of Tesla and SpaceX. “My partner Maryanna Saenko and I are excited to be joining them on a journey to modernize drug development by using AI to design and de-risk drug development programs up front, instead of relying on trial-and-error experiments that are fraught with time delays and high cost.”

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