A clinical trial can cost hundreds of millions of dollars, and one of the major contributors to costs is recruiting and keeping human subjects. According to Dr. Raviv Pryluk, PhD, CEO and co-founder at PhaseV, machine learning can cut the number of subjects by 30 to 50 percent. In this video, he describes other ways that AI can make drug development more efficient.

At the design stage, a drug company can run millions of simulations to determine where risks lie and what parameters are most promising. AI can help to choose not just the number of subjects, but their demographics, and what your goal (clinical endpoint) should be.

During a trial, researchers can vary doses or other parameters based on information collected on the subjects. Pryluk compares the process to using a GPS navigator that adjusts your route as you drive. The company can also do adaptive enrichment, which determines which subsets of patients are likely to benefit the most from the drug.

AI can cut the time to market, flag trials that are failing and should be stopped early, and help financial backers predict the cost of a drug.

Pryluk also discusses equity and explainability. Watch the video for more insights.

Learn more about PhaseV: https://phasevtrials.com/

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