Pharma is betting big on AI, pouring billions into the technology. But how it will ultimately influence the trajectory of the drug industry remains a mystery. Can pharma leaders’ priorities provide a glimpse into that future?
A recent Bloomberg Intelligence survey of pharma’s C-suite execs shows that while leaders are still leery of the technology, they see its potential benefits, predicting it will cut drug development costs by an average of 16% over time, with limited job losses.
“What was a little bit surprising for me, at least, was that they did say there would be a high impact on near-term sales potential, but they also said they see low headcount reduction potential from AI integration,” said Andrew Galler, a biopharmaceuticals analyst at Bloomberg Intelligence.
Pharma execs seem to be scrambling to position themselves for the inevitable changes AI will bring. Almost nine out of 10 pharma industry leaders surveyed anticipate a high or very high degree of disruption in the industry from the technology.
“The 50 biopharma C-suite executives in our survey don't regard AI as fully validated in drug development and have concerns about the patentability of AI developed drugs,” the survey report found. But Galler said these patent fears may be overblown. While the current framework requires a human inventor, the patent would likely be granted to the scientist directing the AI development process.
There’s also uncertainty regarding other aspects of the regulatory process.
“The current administration is supportive of AI,” Galler said, noting, however, that there’s limited formal regulatory guidance indicating how the FDA will view AI-generated data.
And although the media has recently trumpeted fears of an AI bubble, survey respondents believed there might not be enough AI infrastructure to support their plans, and were less concerned that AI companies were overbuilding.
Putting AI to use
While companies are taking an individualized approach to AI, many pharma leaders plan to task AI with preclinical work. Respondents anticipated 10% to 30% of those functions will go to the machines.
“This coincides well with the FDA's intentions to move more preclinical work away from animal models, with the agency specifically calling out AI models as a potential alternative,” the surveyors reported. “As many as 94% of respondents indicated they planned to decrease their reliance on animal models for preclinical work, and we expect that a significant portion of that should flow through to AI models.”
The technology could also shorten drug development timelines by six to 18 months, Galler said.
“That's important, because when you get to market quicker, it increases the [net present value] of your drug [with] a longer time on the market for patent exclusivity,” he said.
Compressing the development timeline could help close, but likely not eliminate, the widening speed gap between U.S. and Chinese companies.
“That’s still a little bit longer than what we typically see in China,” Galler said.
Shorter timeframes also mean cost reductions, which could theoretically translate into job cuts. But Galler said it’s likely that large pharma companies and biotechs, which always need to replenish their pipelines with new programs, will instead choose to use those savings to expand their development programs.
“You might see leaner teams, but more individual product teams,” he said.
Certain drugs will likely benefit more than others from AI, particularly small molecules, because AI could help reduce off-target effects they’re prone to causing. Biologics and gene therapies could also benefit, but to a lesser degree because they’re newer and have generated fewer datasets to train AI programs, Galler said.
A new paradigm
As AI takes on new tasks, it will likely disrupt more operations on pharma’s back end, such as automating office functions, Galler said. GSK, for example, created a custom AI tool to assist employees with HR-related questions, he said.
“We saw a pretty broad mix of what companies are planning to do between internal and external use of AI,” Galler said. “Obviously, they're going to try to build their own models for R&D.”
This could put to work troves of internal data from decades of running preclinical experiments that may not have been published.
Many companies are employing AI for generative molecule design with varying success. But uncertainty is causing some to move cautiously.
“I think people are waiting for a little bit more validation, where they start investing super heavily, because pharma is a very conservative industry, typically,” Galler said.
Some companies are more aggressive in their AI adoption than others, though.
“You have companies like Insilico [Medicine] that are kind of pure-play, AI-enabled drug developers, and they do everything from target discovery all the way to candidate nomination in an AI model,” Galler said.
Others are taking a measured approach, but more could come on board as the technology gains validation.
“Once you start getting some actual breakthroughs from companies developing first-in-class or best-in-class molecules with [AI], [we’re] going to start more rapidly seeing acceptance,” Galler said.
