Pharma’s plenty enthusiastic when it comes to artificial intelligence. The thing is, companies aren’t exactly sure yet how they should be using it.
That was the prevailing opinion at a recent roundtable in the AI hotbed of Montreal, Canada, where the Government of Quebec—which recently rolled out an ambitious plan to become a top-5 sector hub in North America by 2027—assembled pros from across the field to discuss the state of AI today.
“We get requests from pharma companies who say, ‘OK, AI is so hot in Montreal. We should be coming to Montreal to do what?’ They don’t really know. They don’t really have a strategy trying to figure out what they could do with it,” said Frank Béraud, CEO of Montréal InVivo, an economic organization that fosters growth of life sciences companies.
“They don’t want to miss the train, basically,” but “they don’t know where the train goes,” he added.
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Sarah Jenna, Ph.D., co-founder and CEO of bioinformatics AI company MIMs, agreed that “there is currently huge excitement” around AI, but there’s “a lot of education to do” around what’s feasible to do with it.
One reason some pharma companies may be foggy on what they can do with AI? Information about what data a company has available for analyzing isn’t always shared across divisions within that company.
“When we want to do integration, the idea is that we pull (data) together,” said Sébastien Lemieux, Ph.D., principal investigator at the functional and structural bioinformatics research unit of the University of Montreal’s Institute for Research in Immunology and Cancer. The mechanics for doing that within a large drugmaker “are rather rudimentary,” he said. “Everything is walled off.”
Still, there’s been a shift in attitude, at least in the genomics space, MIMs’ Jenna said. For a long time, pharma companies “were thinking they were all set up, there was no problem,” with the bioinformatics teams they had in place. And now, they’ve “realized they are overwhelmed by the amount of data they are producing”—a factor that’s led them to open up to AI.
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And there’s reason for that “huge excitement” Jenna mentioned. Where pharma has dabbled in AI, drugmakers and their partners have come up with a couple of key applications that could save companies money and generate new revenue streams, Nathalie Le Prohon, healthcare VP at IBM Canada, said.
“Globally, there’s lots of interest” around how to reduce product development time, and studies have shown that IBM’s Watson AI supercomputer, along with other algorithms, can make that happen, she said. Companies have also used AI for drug discovery, she noted, pointing to a Watson project that helped a movement disorders clinic identify six existing drug candidates that could potentially treat dyskinesia.
Currently, for pharma, AI is all about “how do they leverage the data that they have and … get to market faster,” Le Prohon said.