Moneyball Medicine Podcast #49: Charles Fisher on Using Digital Twins to Speed Clinical Trials

EPISODE SUMMARY

Charles Fisher is the founder and CEO at Unlearn, a San Francisco company using purpose-built machine learning algorithms that use historical clinical trial data to create “digital twins” of actual participants in randomized controlled drug trials to help predict how each participant would have fared if they’d been given a placebo. By comparing a patient’s actual record to their digital twin, Fisher says, the company can estimate the treatment effect at the patient level and conduct trials with fewer placebo patients.

EPISODE NOTES

Charles Fisher is the founder and CEO at Unlearn, a San Francisco company using purpose-built machine learning algorithms that use historical clinical trial data to create “digital twins” of actual participants in controlled drug trials to help predict how each participant would have fared if they’d been given a placebo. By comparing a patient’s actual record to their digital twin, Fisher says, the company can pinpoint the treatment effect at the patient level and conduct trials with fewer placebo patients. Fisher tells Harry that Unlearn’s software can help drug companies run clinical trials “twice as fast, using half as many people.”

Fisher’s own history is somewhat unconventional for someone in the pharmaceutical business. He holds a  B.S. in biophysics from the University of Michigan and a Ph.D. in biophysics from Harvard University. He was a postdoctoral scientist in biophysics at Boston University and a Philippe Meyer Fellow in theoretical physics at École Normale Supérieure in Paris, France, then went on to work as a computational biologist at Pfizer and a machine learning engineer at Leap Motion, a startup building virtual reality interfaces.

Unlearn built a custom machine-learning software stack because it wasn’t convinced existing ML packages from other companies to help in the simulation of clinical data. Fisher says the company focuses on the quality rather than the quantity of its training data, with a preference for the rich, detailed, longitudinal kind of data that comes from past clinical trials. The outcome is a simulated medical record for each treated patient in a trial,  in the same data format used for the trial itself, that predicts how that patient would have responded if they had received a placebo instead of the treatment. These simulated records can be used to augment existing randomized controlled trials or provide an AI-based “control arm” for trials that don’t have a placebo group.

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Webinars

Part 3: Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape‍

Webinars

Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins‍

Webinars

Part 1: AI, Digital Twins, and the Future of Clinical Research‍

Learn about how Digital Twins are created and how they are incorporated into clinical trials to increase power, accelerate timelines, and enable patient level insights.
Watch an overview of specific use cases for Digital Twins and learn how novel trial designs with Digital Twins enable smaller trials that maintain their power.
Watch a panel discussion on the regulatory landscape where experts share perspectives on the future of AI-based drug development tools like Digital Twins.