AI-driven drug discovery has gotten much of the glory in pharma.
Isomorphic Labs — which is leveraging an AI drug discovery platform based on Nobel Prize-winning AlphaFold software — pulled in the largest biotech investment haul this year to date with a $600 million fundraising round in March.
And drug discovery is a prime focus for many of the highest valued AI companies working in pharma. Tempus AI, which has a market cap of about $9 billion, got its start leveraging molecular data for precision cancer care and has since expanded its capabilities to include drug discovery. Its services have been used by almost all of pharma’s largest public companies.
So far, none of this momentum has resulted in a new drug approval.
Insilico Medicine is one of the frontrunners in that race with a candidate it calls the “first end-to-end generative AI-assisted drug,” that could soon move into phase 3 after garnering positive topline results late last year.
But before an AI-discovered drug proves its worth in the clinic, the technology is shining in other areas such as diagnostics and data management, according to Jessica Owens, co-founder of the healthcare-focused venture capital firm Initiate Ventures.
“I think there are going to be winners on the periphery far before we’re high-fiving about AI-discovered drugs,” she said.
“What they’re doing is not glamorous, but it’s super important."
Jessica Owens
Co-founder, Initiate Ventures
These AI-driven advances may fly under the radar despite their impact because the space is currently dominated by small companies with “baby point solutions,” Owens said.
“They are all picking off one piece of the preclinical value chain,” she said.
Startups offering single service-based solutions also don’t attract as much buzz on the investment scene.
“Historically, VCs don’t like service companies because the margins don’t work as well and they’re too hard to scale,” Owens said. “With those business models, it’s hard to get that hockey stick kind of growth.”
But that isn’t to say these startups aren’t offering strong ROI. Here’s where the technology is delivering breakthroughs throughout pharma.
Clinical trial gains
While shaving time off of drug discovery for a single asset can be a boon for one company, Owens sees wider value in AI technologies that offer to streamline development for the entire industry — especially in clinical trials.
In particular, Owens pointed to Axiom Bio, a startup taking aim at predicting liver toxicities, which it says are linked to 20% to 25% of clinical drug failures. Using what it describes as the “world’s largest primary human liver dataset,” Axiom tested its AI model against assays used by Pfizer and AstraZeneca that demonstrated it was just as effective at a much lower cost.
Ultimately, Axiom is positioning the technology to help phase out animal modeling and plans to extend its use to other organ systems including the heart and brain.
AI solutions are also making an impact for ICON, a large research, development and commercialization firm based in Ireland. The company has implemented several AI tools, some of the proprietary, that tackle onerous aspects of clinical trial work including document storage, endpoint strategies and resource forecasting.
In one case, ICON’s site selection tool, One Search, helped a sponsor bounce back from enrollment challenges by quickly identifying “high-performing sites” and reversing “enrollment shortfalls,” said Gerard Quinn, ICON’s vice president of IT innovation and informatics, in an email.
Across the clinical trials industry, AI companies are working to develop solutions that automate patient enrollment and recruitment, and “match patients to trials in minutes rather than months,” Owens said.
Diagnostics and the data deluge
Initiate’s portfolio is also giving rise to several biotechs. The VC is currently building a startup called Tensor Bio that’s developed an AI-powered blood test for MASH that will create personalized treatment plans.
Pinpointing effectiveness will be critical as new and high-profile MASH therapies come into play.
“This is big,” Owens said. “The first drug for MASH was recently approved, but there was a graveyard of drugs that failed before that. Some GLP-1s [could get] label expansions but none are silver bullets, so we are moving towards a combo treatment approach.”
Another Initiative-backed company, Cornerstone AI, has developed a solution for a major behind-the-scenes problem in healthcare — cleaning large amounts of “messy” real-world data.
“What they’re doing is not glamorous, but it’s super important,” Owens said.
Cornerstone has worked with a wide swath of companies, including biopharmas and large healthcare systems, to leverage an AI assistant that Owens said works like a “spellcheck for data” by identifying and correcting errors.
“What would take four data scientists four months to do, can now be [done] in a week with one data scientist,” Owens said.
Areas of potential failure — and opportunity
AI capabilities aren’t quite ready to steal the spotlight in every area of R&D.
Like many industry leaders, Owens argued that pharma still has a long way to go before animal modeling can be replaced by alternative testing methods based on AI, partially because the technology can’t yet replicate the intricacies of biology.
“There’s a lot [that] AI can’t predict with respect to biology. It’s just too complex,” Owens said. “But we’ll get there.”
And although pharma’s AI space has already been flooded with scores of companies, there are still lingering gaps in the market.
For example, there could be an opportunity for a company pulling multiple solutions under one roof and providing more end-to-end drug development services based on AI.
“We are interested in companies linking these solutions together,” Owens said.
And as the AI market continues to develop, the companies that ultimately succeed will also likely have a platform based on proprietary — rather than public — data.
“The way I would predict who the winners are going to be is by … looking at companies that were built on proprietary data,” Owens said. “Those are the companies that will create value over time.”
