SAN FRANCISCO--(BUSINESS WIRE)--Unlearn.AI, developer of the first machine-learning platform that creates Digital Twins used to populate Intelligent Control Arms in clinical studies, today announced that it has closed a $12 million Series A financing. The financing round was led by 8VC with participation from all the company’s existing investors including DCVC, DCVC Bio and Mubadala Capital Ventures. Through its investment, 8VC Principal Francisco Gimenez, Ph.D., has joined the Unlearn Board of Directors.
“This new financing marks an important milestone in our growth and will contribute to the significant progress we are making with regulators and with our commercial partners, who are already running studies with Digital Twins and demonstrating their value in generating robust evidence and increasing the potential for trial success,” said Charles K. Fisher, Ph.D., founder and CEO of Unlearn.AI. “Clinical trials are facing a number of persistent challenges that have only been exacerbated in recent weeks. With support from our forward-thinking investors and industry partners, we are excited to continue growing our exceptional team and advancing the science behind our first-of-its-kind Digital Twin approach.”
The company’s proprietary DiGenesis™ platform processes historical clinical trial datasets from thousands of patients to build the disease-specific machine learning models used to create Digital Twins and their corresponding virtual medical records. Digital Twin records are longitudinal and include demographic information, common lab tests, and endpoints and/or biomarkers that look identical to actual patient records in a clinical trial. Through this novel approach of generating control patient data, Unlearn aims to reduce the number of patients required to run a trial while maintaining rigorous standards of evidence and study randomization and blinding.
“Unlearn's pioneering use of Digital Twins will limit the number of patients that need to go on placebo while also reducing overall trial enrollment time,” said Dr. Gimenez. “As investors at the intersection of healthcare and technology, we're passionate about companies that pair cutting-edge computational techniques and innovative business models to meaningfully improve patient care. 8VC is excited to partner with Unlearn to bring about the biggest change in the drug approval process since the RCT.”
Unlearn was founded in 2017 by a team of world-class machine learning scientists who shared a vision of building novel technologies that drive better clinical trial decision making through more comprehensive clinical evidence. The company’s initial focus is on neurological diseases, starting with Alzheimer’s Disease and Multiple Sclerosis, because of the significant need for new treatment options and the inherent challenges of enrolling patients in clinical trials.
“Clinical trial recruitment in Alzheimer’s Disease is daunting and study success continues to be challenged by a limited pool of patients. To improve success rates, we need a new paradigm in trial design,” said Craig Lipset, founder of Clinical Innovation Partners and Unlearn Advisory Board member. “Unlearn’s unique model has shown that it can provide realistic digital patient records to supplement actual control patients in Alzheimer’s Disease trials. This is a potential game-changer, not only in trial recruitment strategies, but also in the delivery of high-quality data that supports study confidence and success.”
Unlearn has developed the first machine learning (ML) platform for creating Intelligent Control Arms with Digital Twins through its proprietary DiGenesis™ process, allowing drug developers to dramatically improve clinical evidence and trial power, while lowering the risk of trial failure, thereby increasing confidence in clinical trial results. Unlearn is working closely with biopharmaceutical and medical device companies as well as regulators to ensure its methods meet the highest scientific and regulatory standards. Visit https://www.unlearn.ai or follow @UnlearnAI on Twitter, @unlearn-ai on LinkedIn.