Polaris Picks Immuneering, Developer of Personalized Cancer Test, as First Life Sciences Startup in Dog Patch Incubator

10/8/09Follow @xconomy

Immuneering, the Cambridge, MA-based developer of computer models that aim to predict which patients are likely to respond to certain cancer drugs, has found a new home as the first life sciences startup to join Polaris Venture Partners‘ new Dog Patch Labs startup incubator in Cambridge.

Immuneering is the brainchild of CEO Ben Zeskind, a 28-year-old entrepreneur with a PhD in bioengineering from the Whitehead Institute and an MBA from Harvard Business School. He’s been getting help lately from chairman Bob Carpenter, a biotech entrepreneur who has served on the board of Cambridge, MA-based Genzyme (NASDAQ: GENZ) for 15 years.

Setting up shop at Dog Patch Labs Cambridge, which Xconomy featured last month, means that Zeskind won’t have to keep working in his apartment in Boston’s Back Bay, Carpenter’s house in Brookline, or relying on conference rooms at law offices for meetings, he says. More importantly, it will give the fledgling company some free space for two to three months, and the chance to network with other bright entrepreneurs from a variety of disciplines, largely with computing backgrounds, Zeskind says.

The original Dog Patch Labs, as Bob described, set up at Pier 38 along San Francisco’s Embarcadero to house promising consumer and Internet companies, while the new office near Kendall Square is looking to bring together a wide range of ideas from computing, life sciences, and energy.

“It’s a really great environment. It’s also an opportunity to get to know the folks at Polaris, and it’s an indication of their interest in getting to know us better,” Zeskind says.

Polaris partner Mike Hirshland didn’t immediately respond to an e-mail request for comment. But Zeskind said he figures Polaris may be interested because Immuneering isn’t a classic biotech looking to develop drugs, but rather is more about combining computer models with knowledge of biology. The company uses mathematical models to see if they can predict which patients are likely to respond to Interleukin-2 therapy. The drug can be highly effective in a small percentage of patients, but also is expensive and causes significant side effects that doctors could prevent if they knew the drug won’t work.

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