Two years ago, molecular biologist Zemer Gitai and his research group announced that they had discovered an antibiotic that simultaneously pierced through a disease's defenses while poisoning it from within, like a poison-tipped arrow. And better yet, it was not susceptible to antibiotic resistance.
Zemer Gitai
Discovering the “poisoned arrow" required a novel combination of biochemical expertise and machine learning, which turned out to be greater than the sum of their parts, and can lead to the discovery of many more powerful medicines, said Gitai, Princeton's Edwin Grant Conklin Professor of Biology and a professor of molecular biology.
To that end, Gitai founded ArrePath, a drug discovery spinout that recently announced a seed round of $20 million to advance its machine learning-based platform for discovering new classes of antibiotics and other drugs.
“Antimicrobial resistance is one of the greatest threats of our times,” said Gitai. “I am thrilled that ArrePath will help address this impending crisis by innovating the process of antibiotic drug discovery with novel technologies, insightful company leadership (President and CEO Lloyd Payne and Vice President for Technology and Data Science Kurt Thorn of the Class of 1996), and forward-looking investors.”
ArrePath is located at Princeton Innovation Center BioLabs, the University’s co-working lab and office space for high-tech startup companies founded by Princeton faculty, students, alumni and members of the wider New Jersey community. One of the company's first employees there is James Martin, a 2019 Ph.D. alumnus of Princeton who devoted his graduate school years to discovering and testing Irresistin-16, the first antibiotic identified through the ArrePath method.
Gitai, Martin and a team of colleagues published findings in Cell in 2020 identifying the "poisoned arrow" Irresistin-16, which can kill deadly bacteria using two independent mechanisms simultaneously.
Gitai poses with James Martin, who led the research team and is first author on the new article about the ‘poisoned arrow’ antibiotic, at Martin’s 2019 Ph.D. thesis defense.
“We used a computational machine learning approach with Irresistin, and our idea here is that we can use similar types of approaches to very quickly identify other novel drugs,” said Gitai. “A key thing at the heart of our thinking is: What are the strengths of working with bacteria in the antibiotic space? The big strength, the big advantage comes from bacteria’s short lifespan. The studies are very fast and cheap and scalable and quantitative, so we can generate a ton of data super quickly — and that's vitally important, because data is the fodder for machine learning.”
In many ways, said Gitai, Irresistin-16 is a proof of concept for ArrePath's new method of developing future antibiotics, antivirals, antifungals and more, collectively known as anti-infectives.
“Princeton is tremendously excited at the launch of ArrePath,” said Tony Williams, new ventures associate in Princeton’s Office of Technology Licensing. "The substantial seed funding commitment, in combination with the experienced and capable management team, provides the perfect platform for the company to further develop Professor Gitai’s important technology and discover next-generation anti-infective therapeutics. ArrePath is the latest in a growing portfolio of high potential, well-resourced companies that have emerged out of Princeton research in recent years."
Gitai serves as the chair of the company’s science advisory board. Barbara E. Englehardt, professor of computational biology in the Department of Computer Science at Princeton also serves on ArrePath’s scientific advisory board, and Gary Laevsky, director of Princeton’s Confocal Imaging Facility, also acts as an advisor to the company.
The research was supported primarily by the National Institutes of Health (DP1AI124669 to ZG, JPS, BPB, JKM) with additional funding from the National Science Foundation (NSF PHY-1734030). Flow cytometry was performed at the Princeton University Flow Cytometry Resource Facility, supported by the National Cancer Institute (NCI-CCSG P30CA072720-5921).
