Innovation in trial design doesn’t have to involve increased risk or resources. Through the use of powerful computational models and rigorous statistical approaches, Digital Twins can increase the confidence of the results and reduce sample size. Join Unlearn Founders Charles Fisher, CEO, and Jonathan Walsh, Head of Data Science, as they walk through novel trial designs incorporating Digital Twins. They will demonstrate how and why adding Digital Twins to trial protocols can increase the likelihood of technical success and accelerate the clinical research process.
Clinical trials are critical for developing new treatments, but face chronic challenges, such as optimal trial design, enrollment, costs, timeline delays, and high failure rates. The gold standard of trial design is randomization, but it is not always ethical or feasible to set up a trial where the control group receives a standard of care or a placebo. Leveraging historical data sets and novel machine learning methods, Digital Twins, or statistically indistinguishable virtual placebo patients, can be generated to match actual patients in clinical studies. Adding external control arms populated with Digital Twins, called Intelligent Control Arms, can streamline clinical trials, increase power, and decrease enrollment, without adding regulatory risk. The FDA has expressed support for innovation in trial design and alternative forms of evidence - Digital Twins are the next frontier for modern trials.
In this 3 part webinar series, you will learn about:
Who should attend?
Charles is a scientist with interests at the intersection of physics, machine learning, and computational biology. Previously, Charles worked as a machine learning engineer at Leap Motion and a computational biologist at Pfizer. He was a Philippe Meyer Fellow in theoretical physics at École Normale Supérieure in Paris, France, and a postdoctoral scientist in biophysics at Boston University. Charles holds a Ph.D. in biophysics from Harvard University and a B.S. in biophysics from the University of Michigan.
Jon is a physicist whose research interests focus on data science and machine learning. Jon received his Ph.D. from the University of Washington and was a postdoctoral fellow in theoretical particle physics at UC Berkeley. His academic work was focused on building computational tools to analyze, simulate, and calculate properties of data from the Large Hadron Collider (LHC). He was an LHC Theory Initiative Fellow during graduate school and as a postdoc. After academia, Jon worked as a data scientist at Leap Motion.
Arsalan Arif is a news media entrepreneur who set out in 2015 to build his vision of an independent biotech news company at Endpoints News.