Machine Learning Advances Cryoprotectant Research
A new computational framework using machine learning enhances cryoprotectant discovery.
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Summary
Researchers from the University of Warwick and University of Manchester developed a machine learning-based model to enhance cryoprotectant discovery. The new approach identifies molecules that prevent ice crystal growth during freezing, potentially improving the storage of vaccines, blood, and other medical treatments, while reducing reliance on traditional methods.
Key Takeaways
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 enable these treatments. In fact, without cryopreservation, such therapies must be deployed immediately, thus limiting their availability for future use.
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Subscribe for FREEProf. 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 forming.
The team developed a computer models that was used to analyse large libraries of chemical compounds, identifying which ones would be most effective as cryoprotectants.
Dr. Matt Warren, the PhD student who spearheaded the project, remarked: “After years of labour-intensive data collection in the lab, it’s incredibly exciting to now have a machine learning model that enables a data-driven approach to predicting cryoprotective activity. This is a prime example of how machine learning can accelerate scientific research, reducing the time researchers spend on routine experiments and allowing them to focus on more complex challenges that still require human ingenuity and expertise.”
The team also conducted experiments using blood, demonstrating that the amount of conventional cryoprotectant required for blood storage could be reduced by adding the newly discovered molecules. This development could speed up the post-freezing blood washing process, allowing blood to be transfused more quickly.
Reference: Warren MT, Biggs CI, Bissoyi A, Gibson MI, Sosso GC. Data-driven discovery of potent small molecule ice recrystallisation inhibitors. Nat Commun. 2024;15(1):8082. doi: 10.1038/s41467-024-52266-w
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