As drug hunting gets more complex, where and how data is generated in partnerships between Big Pharmas and AI biotechs is becoming a headache for dealmakers.
Data has become a new currency in biopharma transactions, particularly the ones and zeros that can be used to train AI models to better select drug targets.
When signing major deals, AI drug hunters are having to undergo complex negotiations to ensure their platforms can continue advancing—and pharmas are having to adjust to the new norm if they want in on the action.
“I just turn on my ‘out of office’ when I hear a data deal is coming,” quipped Chad Diehl, legal team lead for licensing and acquisitions at Astellas Pharma, during a panel discussion at the BIO International Convention last month.
Pharmas have long wanted to have strict control over the data developed in a partnership, explained Deepa Talpade, head of business development and licensing for oncology at Bayer. But now, details such as whose chemistry lab is used or where the data is physically generated are becoming decision points during deal signing.
Previously, biotech platform companies would hand off most of the data generated during a licensing deal, keeping partnership programs siloed. They used the learnings to build a better platform, but typically the data itself wasn’t fed back into a computer to be used on another project. That’s changed as AI companies need that information.
“Those types of deals are not going to fly these days when we’re thinking about AI-focused deals,” said Rachel Lane, senior vice president of business development and operations at Xaira Therapeutics.
Xaira, which is running complex labs, sees a deal as a way to gain access to new datasets it may not have on hand, Lane explained.
Pharmas are of course not shying away from the challenge. Merck signed a pact with Protillion Biosciences in June worth $510 million upfront. Eli Lilly, Bristol Myers Squibb and Incyte did the same with other AI companies in May.
Besides dealing with biotechs working on AI drug discovery, Lilly has itself built massive capabilities with the technology. Last year, Lilly opened up TuneLab, which provides access to the pharma giant’s AI suite to select biotechs in exchange for data sharing—proving that the thirst for data points goes both ways.
Biotechs using AI to find new targets or drugs need to understand the power of what they hold and can offer to pharma. But they also need the partnership to prove their concept and advance their work, according to Lane.
“One plus one equals five in the context of the deal,” Lane said.
For Maha Radhakrishnan, an executive partner at Sofinnova Investments who has been involved in myriad deals, when AI data issues come up, it’s time to call in the pros. “That’s where we get the patent attorneys, legal teams involved to make sure that there’s clarity from the get-go,” she told BioSpace in an interview at BIO.
