On 27th June, QurAlis, a biotech company focused on developing precision medicines for amyotrophic lateral sclerosis (ALS), announced that it had partnered with Unlearn.AI, a machine learning startup, to accelerate and optimise its clinical programme in ALS.
In what represents a significant shift in clinical trial methodology, QurAlis will use Unlearn.AI’s advanced ML algorithms to create digital twins or synthetic versions of patients enrolled in clinical trials. These digital twins, generated from patients’ baseline data, will produce regulatory-suitable evidence by predicting individual health outcomes under the control treatment over time.
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“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,” said Kasper Roet, Ph.D., founder and chief executive officer (CEO) of QurAlis.
“We are excited to partner with Unlearn to help advance our clinical program with AI and other innovative technologies to generate evidence suitable for supporting regulatory decisions and help speed new, lifesaving precision medicines to patients with ALS and other neurodegenerative diseases.”
This “intelligent control arm” approach, as well as controlling for Type-1 error, can reduce the number of patients required to participate in a control group in clinical trials, thereby shortening timescales while minimising patients’ exposure to potentially ineffective treatments. This is especially significant in clinical trials for conditions like ALS, where time is a critical factor for patients.
Research analyst, GlobalData, says that these twin RCTs could transform the clinical trial landscape: “Unlearn.AI’s genAI approach might be a game-changer for the healthcare industry, offering a more efficient and ethical means of testing new treatments, particularly for diseases like ALS where the stakes are high.”
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By GlobalData“As the technology matures and achieves regulatory approvals, more clinical trials could leverage this method to accelerate drug discovery and improve patient outcomes. The collaboration will commence with an ALS clinical trial, which will serve as a proving ground for the technology. If successful, this could lead to broader adoption of the technology in clinical trials across a range of conditions.”
