Broadening Role for External Control Arms in Clinical Trials

Unlearn.AI simulates patient trajectories in neurodegenerative and inflammatory diseases based on control group data from dozens of trials. The method learns the probability distributions underlying the historical training data, and uses those distributions to generate new data. CEO Charles Fisher said Unlearn.AI chose to focus on Alzheimer’s and inflammatory conditions like rheumatoid arthritis and multiple sclerosis because the complexity of the indications is a good match for its machine learning tools, and because it was able to access sufficient historical data through non-profits like Vivli and Transcelerate Biopharma Inc. Unlearn.AI’s simulations are being used to guide trial design by simulating hypothetic outcomes under different study conditions. In collaboration with an undisclosed pharma, the company has used its model to “ask questions about trial design, size and inclusion criteria” for Alzheimer’s, said Fisher. The company aims to use its simulations to supplement control arms, and is preparing to seek guidance from regulatory agencies on using its platform to run synthetic controls for Alzheimer’s trials. Unlearn.AI also hopes to generate “digital twins” of individual patients in experimental arms. “We can ask, what would have happened to this person if they had received the placebo,” said Fisher.

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Using AI Digital Twins for Drug Testing

Dr. Charles Fisher, CEO of Unlearn AI, discusses creating digital clones by using artificial intelligence for use in clinical drug trials.
A fascinating approach to the problem of how to make clinical trials more efficient, and understand more about what may be possible with more and better patient data.
At Unlearn, our goal is to use the data available from historical trials, to generate new evidence to inform and advance research.