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Google Pours “Incredible” Computing Power into Antibody Drug Discovery With Adimab

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many well-characterized models for the structure of certain protein targets on cells, and the number of those available structures is growing, Gerngross says. What they don’t have is a computing model that can take the 3-D information on the target structure, and build on it, to say precisely where a certain Y-shaped antibody might bind to its target, for example. Once a biologist knows that, the next step is to ask whether the antibody can elicit a certain desired biological response, like cell death, or an immune system reaction. It’s an immense mathematical problem, and requires “formidable” computing infrastructure, Gerngross says.

When Gerngross first met with Google representatives on the Dartmouth campus, he wasn’t sure this was anything more than science fiction.

“In the past, that was a problem that was beyond computational means. But we think it’s actually not that far away,” Gerngross says.

Based on the commitment Google has shown since it invested in Adimab in October, Gerngross says it may now be possible to identify the optimal antibody for clinical trials entirely “in silico.” That would subtract a huge amount of time and effort from the notoriously lengthy, and risky, wet lab drug development process.

If Google’s computing power can actually achieve this lofty goal, a customer will still have to come to Adimab with a specific target in mind, and Adimab will still perform its usual 8-week process to synthesize a batch of antibodies that binds with a particular target. The key difference will be in speeding up what comes later, by giving the pharmaceutical customer a precise idea of exactly which of those 100 antibodies has the best shot as a drug.

“If you now can say, ‘OK, we have the structure, now we can design specific antibodies to hit a particular domain of that protein,’ that’s a capability no one has,” Gerngross says. “When we saw the opportunity to do this, we realized, if this works, we are out of business. If you can do what we just described, then it will be very serious competition. We want to be ahead of it.”

Gerngross said he hasn’t personally met with Google founders Larry Page or Sergey Brin to get a sense of their interest in this computing problem. But Bill Maris of Google Ventures has joined the Adimab board in connection with the latest financing, and “he has the ear” of Google board members, Gerngross says. Adimab also has an important man on the ground close to the Googleplex. Its senior director of computational biology, Max Vasquez, formerly of PDL Biopharma, lives in the San Francisco Bay Area and meets regularly with counterparts at Google, Gerngross says.

None of this is to say that “in-silico” derived antibodies will arrive anytime soon. Adimab still has other tough R&D projects on its plate, like making antibodies that can reliably hit hard targets like certain membrane-associated proteins, ion channels, and G-protein coupled receptors that weave in and out of cell surfaces.

But I took careful notes on what Gerngross said about this, because of his track record with GlycoFi and his outspoken criticism toward hype in the biotech industry. He told me that many of his pharma customers have been burned by other companies that overpromise and underdeliver. “The pharma companies think they’re buying a VW Bug, and then find out they got a tricycle.”

Despite those strong views, Gerngross sure sounds like he’s promising big things from in-silico antibody discovery. He just doesn’t have a specific timetable on when it will arrive.

“We think there’s a way of getting there, and it will be very powerful,” Gerngross says.

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