AI GETS CLINICAL
Companies run virtual drug trials, aim to improve success of human studies
Artificial intelligence companies say they are working with some Drugmakers on virtual trials to boost the odds an experimental medicine will prove safe and effective when used by actual patients in an traditional clinical trial.
TECHNOLOGY
Novartis' experimental drug del-desiran, intended to treat a rare type of muscular dystrophy, was expected to be a winner, with the Swiss company's CEO predicting annual peak sales of $5 billion. But when a late-stage trial missed its goal last month, shares fell 11%, erasing $30 billion in market value.
AI startup BioinvestGPT was not surprised. In July, it ran a simulated trial to measure how virtual patients would respond to del-desiran and predicted an insignificant clinical benefit.
BioinvestGPT and other artificial intelligence companies say they are working with some drugmakers on virtual trials to boost the odds an experimental medicine will prove safe and effective when used by actual patients in an expensive, traditional clinical trial.
'Dipping their toes'
The global biopharmaceutical industry spends about $140 billion a year on human clinical testing, but only about 12% of drug candidates are approved by regulators — a rate that has changed little in decades.
Human clinical trials typically start with small Phase 1 studies primarily to gauge safety, followed by mid-stage Phase 2 and the large Phase 3 trials required by regulators that also assess efficacy. The process takes years, compared with a month or less for some AI simulations.
"We shouldn't only ask how to run trials faster. We should ask how to run fewer trials that are going to fail," said Francisco Beca, chief medical officer at QuantHealth, an AI clinical trial simulation platform headquartered in Tel Aviv.
Investment in AI drug discovery more than doubled to $8.4 billion in 2025 compared with 2023, according to a recent report from McKinsey. It said spending is focused where the technology currently works best, particularly molecule design, rather than the industry's major bottlenecks, such as proving a drug will work as intended through lengthy trials.
Pharma companies are "dipping their toes" into AI trial modeling, said McKinsey partner Alex Devereson, such as using it to assess drug candidates before committing capital to a program.
U.S. health regulators recently announced a set of initiatives aimed at speeding drug trials. If successful, the program could help create a path for predictive AI to be used in clinical development, a federal health official said.
Prediction accuracy varies
Copenhagen-based BioinvestGPT in July shared with Reuters its detailed analyzes of the likely outcome of several high-profile drug trials before the results were known and has so far been correct in five of six.
They include the negative result for del-desiran, success for Moderna and Merck's melanoma vaccine, a weak clinical benefit for AstraZeneca and Ionis' heart drug Wainua, the first failed trial for Novo Nordisk's heart drug ziltivekimab and success for Vaxcyte's pneumococcal vaccine.
The fundamental goal of the pharma industry is proof of a superior clinical benefit compared with the standard of care, said Bragi Lovetrue, who co-founded BioinvestGPT with his wife Idonae Lovetrue in 2024.
The AI platform uses DNA sequencing to simulate a human body matching the eligibility criteria for a specific clinical trial. A virtual trial is then done using a model of the test drug.
"We can pinpoint the reason why a drug is effective and safe, and in many cases, why not," Lovetrue said.
The simulations are not always right. The predictive AI forecast positive results for Novartis' pelacarsen, which lowers blood levels of a cholesterol-carrier called lipoprotein (a).
Novartis in September said the drug did not cut the risk of a major heart attack or stroke in patients with a genetic risk factor in a late-stage trial.
A subsequent analysis showed "we got the mechanism wrong," by not accounting for genetically set variations in the size of the lipoprotein, Lovetrue said.
Assessing a drug's potential
BioinvestGPT runs predictions for a wide range of trials, including those whose results are expected before the end of the year.
The AI company predicts failure for two Phase 3 trials of Biogen's litifilimab in the most common type of lupus, as well as for Phase 2 studies of Japanese drugmaker Takeda's zasocitinib in Crohn's disease and ulcerative colitis.
The AI model shows that both drugs are "suboptimal" for those specific trial populations.
Diana Gallagher, Biogen's head of clinical development for multiple sclerosis, immunology and Alzheimer's, said the company uses "every tool available to us," including artificial intelligence.
She said only two biologic drugs have been approved for lupus, and AI algorithms that rely on historical data could be prone to predicting a negative result.
Takeda filed recently for U.S. approval of zasocitinib, which blocks an enzyme called tyrosine kinase 2, for the treatment of plaque psoriasis and has a Phase 3 trial in psoriatic arthritis.
Takeda research chief Andy Plump said TYK2 was identified as a target by analyzing human genetics, while machine learning was used to optimize and "polish" the structure of the daily pill.
"I have immense confidence in this mechanism," Plump said. "I don't think we are near being able to use tools like AI to make definitive predictions."
QuantHealth's Beca and other experts said human trials will always be necessary, but AI simulations can be valuable when assessing the potential of an experimental drug.
"Is it still ethical in 2026 to expose patients to a trial that most likely will fail?" he said. "With the advancement of this (AI) technology, come 2027, 2028, or 2029, probably the answer is going to be that it no longer is."
