AI has been touted as a tool to streamline drug R&D, which can cost $2.6 billion and take up to 15 years, according to the Journal of Medicinal Chemistry.
But whether AI will actually develop better drugs remains an open and complicated question. To date, few AI drugs have reached clinical trials. Those that have tend to see more success in phase 1 than traditional drugs. However, that advantage might not last in phase 2.
The space has also been marked by high-profile setbacks, including Verge Genomics’s AI-identified ALS candidate, VRG50635, which failed to move beyond phase 1.
While the overall picture is still emerging, a few closely-watched candidates offer some real-world insights into how AI-developed drugs are performing.
Insilico’s leading candidates
Insilico Medicine has become one of the highest-profile players in AI drug development and has struck a number of deals with Big Pharma companies looking to leverage its capabilities. Insilico also has its own candidates in the works as part of its expansive pipeline that includes over 40 programs in a wide swath of indications.
Often considered pharma’s leading AI-designed drug candidate, the company’s rentosertib was discovered with an AI-powered biology tool that pointed to a novel mechanism called TNIK inhibition. The approach has the potential to slow or reverse the progression of idiopathic pulmonary fibrosis, a deadly disease that stiffens tissue in the lungs that has few effective treatment options. Even with existing drugs, IPF patients typically only live for two to four years after diagnosis.
Boehringer Ingelheim notched an FDA nod for an IPF drug called Jascayd last year, marking the first new approval for the condition in more than a decade. But Jascayd’s potential impact on the disease was dubbed by one analyst as “modest” because of its moderate effect on lung function and potential adverse interactions with other medications.
In a small phase 2 study, rentosertib improved lung function and is now moving into a 52-week phase 3 in China, which the company said marks a major milestone for the treatment and AI-driven drug discovery.
“Rentosertib was not discovered by starting from a conventional target and simply screening more compounds. It came from a biology-first, aging-informed AI workflow that connected TNIK to fibrotic and inflammatory disease mechanisms, and then used generative chemistry to create a drug candidate with the properties required for clinical development,” the company said in a press release.
Insilico is also advancing another closely-watched drug called garutadustat, an oral PHD inhibitor for inflammatory bowel disease. The treatment, which is now in phase 2 in China and will be tested in patients with ulcerative colitis, has been touted as a potential best-in-class option by the company. The dual-mechanism drug, flagged by AI during a 12-month analysis, aims to both reduce inflammation and repair the intestinal damage that marks the condition.
Recursion overcomes setbacks and charges ahead
Recursion Pharma has also become a leading company in AI drug development, but has grappled with a few stumbles in the clinic.
Disappointing results for its lead drug asset for cerebral cavernous malformation and a setback with another drug targeting neurofibromatosis type II led the company to prune its pipeline last spring. But Recursion is still advancing a number of candidates, including REC-4881, a MEK inhibitor it in-licensed from Takeda Pharmaceuticals after AI identified the mechanism as a promising treatment for familial adenomatous polyposis, a condition marked by the development of hundreds or thousands of precancerous colorectal polyps.
Recursion announced results from the ongoing phase 1b/2 trial for the drug REC-4881, showing a 43% median reduction in polyp burden in treated patients over three months. Most patients in the trial continued to show reductions even after they were off the medication for 12 weeks — a promising sign for a condition with no existing treatment options. The trial is slated for completion in 2027.
