Scientists from the University of Warwick and the University of Manchester have developed a cutting-edge computational framework that enhances the safe freezing of medicines and vaccines.
Treatments such as vaccines, fertility materials, blood donations, and cancer therapies often require rapid freezing to maintain their effectiveness. The molecules used in this process, known as “cryoprotectants,” are crucial to enabling these treatments. In fact, without cryopreservation, such therapies must be deployed immediately, thus limiting their availability for future use.
The breakthrough, published in Nature Communications, enables hundreds of new molecules to be tested virtually using a machine learning-based, data-driven model.
Prof. Gabriele Sosso, who led the research at Warwick, explained, “It’s important to understand that machine learning isn’t a magic solution for every scientific problem. In this work, we used it as one tool among many, and its success came from its synergy with molecular simulations and, most importantly, integration with experimental work.”
This innovative approach represents a significant shift in how cryoprotectants are discovered, replacing the costly and time-consuming trial-and-error methods currently in use.
Importantly, through this work, the research team identified a new molecule capable of preventing ice crystals from growing during freezing. This is key, as ice crystal growth during both freezing and thawing presents a major challenge in cryopreservation. Existing cryoprotectants are effective at protecting cells, but they do not stop ice crystals from