At 82 years old, with an aggressive form of blood cancer that six courses of chemotherapy had failed to eliminate, “Paul” appeared to be out of options. With each long and unpleasant round of treatment, his doctors had been working their way down a list of common cancer drugs, hoping to hit on something that would prove effective—and crossing them off one by one. The usual cancer killers were not doing their job.
With nothing to lose, Paul’s doctors enrolled him in a trial set up by the Medical University of Vienna in Austria, where he lives. The university was testing a new matchmaking technology developed by a UK-based company called Exscientia that pairs individual patients with the precise drugs they need, taking into account the subtle biological differences between people.
The researchers took a small sample of tissue from Paul (his real name is not known because his identity was obscured in the trial). They divided the sample, which included both normal cells and cancer cells, into more than a hundred pieces and exposed them to various cocktails of drugs. Then, using robotic automation and computer vision (machine-learning models trained to identify small changes in cells), they watched to see what would happen.
In effect, the researchers were doing what the doctors had done: trying different drugs to see what worked. But instead of putting a patient through multiple months-long courses of chemotherapy, they were testing dozens of treatments all at the same time.
The approach allowed the team to carry out an exhaustive search for the right drug. Some of the medicines didn’t kill Paul’s cancer cells. Others harmed his healthy cells. Paul was too frail to take the drug that came out on top. So he was given the runner-up in the matchmaking process: a cancer drug marketed by the pharma giant Johnson & Johnson that Paul’s doctors had not tried because previous trials had suggested it was not effective at treating his type of cancer.
It worked. Two years on, Paul was in complete remission—his cancer was gone.
On average, it takes more than 10 years and billions of dollars to develop a new drug.
Incorporating AI into the drug development pipeline could help create cheaper pharmaceuticals faster. https://t.co/xYVZFyA5fY
— MIT Technology Review (@techreview) February 15, 2023