As the dream of the autonomous clinical trial dawns, agent supervision is today’s human job

Brian Buntz 

In 2020, the average phase 3 protocol collected approximately 3.56 million data points. By 2025, that figure had reached about 5.96 million, a five-year increase of 67% and 6.4 times the 2012 average of 929,203, according to collaborative research from the nonprofit industry group TransCelerate BioPharma and the Tufts Center for the Study of Drug Development. The study found potential room for greater discipline as nearly one-third of phase 3 procedures and associated data were classified as non-core or non-essential. The peer-reviewed paper also noted that AI and ML processing power “may be serving as a disincentive to reduce data volume.”

Nearly eight months later, that observation about AI looks prescient. “I don’t think [sponsors] are as worried about collecting too much data,” said Venu Mallarapu, chief transformation and AI officer at eClinical Solutions. He argued that AI has made expanding datasets easier to manage and analyze, while sensors and wearables could drive volumes higher still. “Of course, you still want to make sure that you’re collecting the right data,” Mallarapu said. “But crunching that data and generating the insights that would be required, AI has certainly made that a lot easier with the powerful capability of some of the models.”

Where agents are landing first

The data processing layer is one layer; the other lies in the complexity of collecting the data, whether it is staff, say, collecting a patient’s blood, running the imaging or sitting with the patient through the questionnaire. As clinical trial complexity has grown, so has burnout among clinical research staff. “The sites are understaffed and overwhelmed,” said Janice Chang, CEO of TransCelerate BioPharma, in a January interview. She said site staff report being overburdened by administrative work including training, paperwork and contracts.

At eClinical Solutions, the initial agent deployments are concentrated in the data-processing side of that workload. Mallarapu said an average customer may have three to five defined AI use cases within data review and reporting. Most remain assistive, with an agent drafting or triaging material and a person taking the action. “The most advanced ones are running scoped agents inside a workflow which has been reengineered to take advantage of the agents,” he said.