AstraZeneca (AZN) has teamed up with artificial intelligence biotech insitro to discover and develop treatments for liver and heart conditions that have proved hard to treat, the British drugmaker said.
Under the agreement, AstraZeneca will pay $40mn up front. California-based insitro is eligible for up to about $1.46bn in development and commercial milestones, as well as single-digit percentage royalties on future sales.
The multi-year collaboration will focus on metabolic dysfunction-associated steatohepatitis (MASH), a severe liver disease, and cardiomyopathies, a group of diseases affecting the heart muscle. Both have historically lacked effective therapies because researchers have struggled to pinpoint their root biological drivers, AstraZeneca said.
Insitro will use its “Virtual Human” platform, which combines lab-grown cell models with machine learning, to identify causal disease biology and advance AstraZeneca-nominated targets from discovery to development-candidate stage. AstraZeneca will then run and fund future clinical trials.
The deal builds on insitro’s expansion across the pharmaceuticals industry. The biotech recently widened its partnership with Bristol Myers Squibb (BMY) to advance a broader portfolio of targets for amyotrophic lateral sclerosis (ALS), and has teamed up with Eli Lilly (LLY) to build AI models that predict the pharmacological properties of small molecules in living organisms.
“Choosing the new targets that we want to put into our discovery pipeline is one of the hardest things we do across research and development,” said Sharon Barr, AstraZeneca’s executive vice-president of biopharmaceuticals R&D. “By the time we read about a new pathway or target in major scientific journals, it is no longer novel. It’s in the public domain and we’re already a few steps too late. But when we identify these unique connections early and test them in cellular systems, we’re ahead of the game.”
Barr added that pairing AI tools with experienced scientists allows the company to test fewer molecules in the lab, reduce guesswork and speed the search for effective medicines.