Artificial intelligence is attracting a lot of attention for its role in drug discovery, where it is intended to speed up the process of identifying targets and the molecules that can anesthetize them. But this is just one of the places where AI is gaining ground in the life sciences. A panel at the MedCity News INVEST Digital Health conference discussed how AI can solve other pain points for biopharma companies.
Steve Prewitt, senior vice president and global head of digital innovation at Sumitomo Pharma Americas, said most of the new technologies for clinical trials are for project management. He doesn’t see many good tools that help with clinical trial strategy—how to design the study to make a trade-off to improve recruitment and improve outcomes. As an example, he pointed to a Sumitomo study that tested a schizophrenia drug in young people. The trial required an overnight stay. But Prewitt said it was difficult to get parents of a teenager with newly diagnosed schizophrenia to commit to an overnight stay. Study recruitment was therefore difficult.
Prewitt said that in a Phase 3 study for a common indication, most of the cost is not per patient recruitment. The main cost is elapsed time. Every day a trial runs, it’s spending money. Sumitomo is working hard to shorten the trial period. For example, the company is looking for doctors who can access certain patient populations. The firm also analyzes patient recruitment to find ways to recruit patients faster, which in turn reduces the cost of a study.
Massive Bio’s technology uses AI to match cancer patients to clinical trials. CEO and co-founder Selin Kurnaz said that for a cancer clinical trial testing a drug that doesn’t require a specific biomarker, it costs about $65,000 to find a patient. But for a biomarker-based study, it costs about $150,000 to find each patient. Kurnaz said she has seen pharmaceutical companies pay $2 million per patient in studies that require a particularly rare biomarker.
“It’s the scale of the cost structure that we’re talking about the burden on drugs to find the right patient in oncology,” she said.
Kurnaz said it takes about 25 minutes to manually pre-screen a single patient for a clinical trial. With its technology, Massive Bio tries to reduce that time to just over a minute. But Kurnaz noted that even before treating participants in clinical trials, the first step is finding them. The company’s technology can mine de-identified patient data to find potential participants in clinical trials.
Sorcero’s artificial intelligence platform provides life science companies with analytics and insights to inform decision-making in a range of areas, such as regulatory affairs and market access. CEO Dipanwita Das compared the approach to the way the retail industry analyzes data to gain insights about customers and customer behavior. An important difference between the retail industry and the life science sector is that life science data is not stored in one place. Data can be found in many places, such as electronic health records, payer information, peer-reviewed articles, and regulatory bodies.
Despite the data differences, Das said the life sciences industry can still learn from the retail sector. Retailers have reached a level of understanding of customer preferences, down to the colors they like for shoes and the channels they choose to make their purchases. It is a level of granularity that providers of life science services and products must achieve.
“When you look at it, you see a lot of possibilities, not only [for] AI, but the technology itself,” Das said.
Photo by MedCity News