QurAlis links with Unlearn to leverage machine learning in ALS trials

QurAlis, a biotech focused on developing treatments for amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases, inked a collaboration deal with Unlearn to leverage its AI digital technology for clinical trials.

San Francisco-based Unlearn has developed technology that uses digital twins of clinical trial patients to help predict an individual’s health outcome under the control treatment over time. The digital twins are used in randomized controlled trials (RCTs)—dubbed TwinRCTs—to operate more efficient trials that produce regulatory-suitable evidence, according to a June 27 press release.

Financial terms of the collaboration deal weren’t disclosed.

With the agreement, QurAlis and Unlearn will be able to zero in on reducing variabilities and give a boost to QurAlis' clinical trials for QRL-201 and QRL-101, the company’s lead candidates to treat ALS.

“Advances in machine learning and AI make it possible to enhance trial power to detect a positive result when one truly exists while controlling for Type-1 error and significantly shorten timelines without introducing bias into the study,” QurAlis CEO Kasper Roet, Ph.D., said in the release.

After training on existing clinical data, Unlearn’s programs are used to develop digital twins from baseline data for each patient enrolled in a TwinRCT, regardless of their randomization assignment. Predictive scores generated from digital twins are then used in the trial's primary analysis to help determine treatment effects and control for Type-1 error—a term for when a trial investigator rejects a hypothesis that is actually true in the population

Founded in 2016, QurAlis announced in March that it had raised $88 million in series B financing, bringing its total fundraising haul to $143.5 million.