More money continues to flow into startups seeking to harness the massive amount of data available in healthcare using artificial intelligence and machine learning. This time, GV, the venture capital arm of Google parent Alphabet, is adding about $7 million to the Series A round for Owkin, bringing its total funding to $18 million.
Owkin, which has offices in Paris and New York, aims to use its machine learning tool to help medical researchers discover biomarkers and disease targets by analyzing a wide range of information, from biomedical images to genomics and clinical data, according to a news release. The company says it already has a range of partnerships with organizations including large drug developers, such as Roche and Amgen, and academic institutions, such as Mount Sinai.
Owkin announced its initial $11 million Series A round in January, led by Otium Venture with help from Cathay Innovation, Plug and Play, and NJF Capital.
The company is part of a massive wave of startups that are trying to organize and interpret the massive and growing pool of biomedical data—and it’s no surprise that big businesses are also looking for their share of the pie. IBM (NYSE: [[ticker:IBM]]) and GE (NYSE: [[ticker:GE]]) have been leaders in the healthcare data business, spending billions of dollars on partnerships and acquisitions, such as IBM’s buyout of Ann Arbor, MI-based Truven Health Analytics and GE’s collaborations with UC San Francisco’s Center for Digital Health Innovation, Boston Children’s Hospital, and others. And drug companies are investing in data analytics businesses; see, for example, Celgene’s work with GNS Healthcare on its software for predicting whether treatments will work for specific patients.
Meanwhile, dozens of healthcare organizations around the U.S. use analytics software developed by Verona, WI-based Epic, one of the country’s leading vendors of electronic health records software, to help them intervene when a patient’s health is deteriorating, as Xconomy reported in March. Microsoft’s (NASDAQ: [[ticker:MSFT]]) Azure cloud computing service has powered trials of the software for some of the health systems that use it.
As Xconomy reported last year, tech giants like Google (NASDAQ: [[ticker:GOOGL]]) and Amazon are potential game-changers for this field; see, for instance, Amazon’s (NASDAQ: [[ticker:AMZN]]) healthcare applications for Alexa and Google’s ongoing work with its DeepMind subsidiary. GV’s investment in Owkin may show broader interest by Google parent Alphabet in pushing forward data analytics for things like electronic health records.
“Utilizing EHR-derived clinical data, pathology imaging and sequencing data, Owkin has created a differentiated approach to rich datasets that are highly valuable to research institutions and the pharmaceutical industry at-large,” Adam Ghobarah, a general partner at GV, said in a news release.
Dozens of smaller businesses around the country have been working on the problem. Seed-stage company Bioz, based in Palo Alto, CA, has machine learning software that sifts through millions of pages of scientific papers to select products, help plan experiments, and perform other research-related functions. Meanwhile, after it raised a $14 million Series B round in 2016, San Francisco-based 3Scan decided to add machine learning capabilities to its hardware, which is used to deconstruct and analyze tissue samples for biological research.
Earlier this month, Paige.AI announced a $25 million Series A round of funding to use machine learning in pathology to increase accuracy, save pathologists’ time, and deliver better patient outcomes, Xconomy reported. Similarly in San Antonio, radiology contractor Intrinsic Imaging has seen an uptick in the number of clients that want help assessing the accuracy of AI software used to diagnose conditions such as cancer in X-rays and CT scans.
Applying the technology to drug development has also been a prime target. In January, Engine Biosciences raised $10 million to develop its artificial intelligence-based drug discovery tool. And in March, Atomwise added a $45 million Series A round to bolster its ability to mine data about biochemical interactions, in order to search for molecules with the properties needed to block faulty processes in the body that lead to disease, Xconomy reported. The San Francisco company received the money from Monsanto Growth Ventures, the venture capital arm of Monsanto, DCVC (Data Collective), and B Capital Group, as well as previous investors.
One of the largest funding deals came just last month: BenevolentAI raised $115 million for its algorithmic-based analysis of disease mechanisms, which the company says can help it draw insights, propose drug candidates, and identify patients who might benefit from specific experimental therapies, as Xconomy reported. BenevolentAI would only reveal one investor—Woodford Investment Management, which has contributed $200 million to the company—noting the rest include family offices and strategic investors.
Other examples include Grail, a startup spun out by Illumina (NASDAQ: [[ticker:ILMN]]) that is developing a diagnostic to detect fragments of cancer DNA in a routine blood sample.
For as much hype as A.I. technology has generated in many fields—not just healthcare—there is no question that in some form it will change the sector.
“Machine learning will be a core of biotech within not very many years,” Arch Venture Partners co-founder and managing director Bob Nelsen told Xconomy in a Q&A last week. “The question has always been having enough data, and enough high-quality data, to be able to do machine learning.”
