AI to fast-track drug formulation development

Researchers Christine Allen and Alán Aspuru-Guzik used machine learning to predict experimental drug release from long-acting injectables - In a bid to reduce the time and cost associated with developing promising new medicines, University of Toronto scientists have successfully tested the use of artificial intelligence to guide the design of long-acting injectable drug formulations. The study, published this week in  Nature Communication , was led by Professor  Christine Allen  in the Leslie Dan Faculty of Pharmacy and  Alán Aspuru-Guzik  in the departments of chemistry and computer science in the Faculty of Arts & Science. Their multidisciplinary research shows that machine-learning algorithms can be used to predict experimental drug release from long-acting injectables (LAI) and can also help guide the design of new LAIs. "This study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables," said Allen, who is a member of University of Toronto's  Acceleration Consortium , a global initiative that uses artificial intelligence and automation to accelerate the discovery of materials and molecules needed for a sustainable future. "We've seen how machine learning has enabled incredible leap-step advances in the discovery of new molecules that have the potential to become medicines. We are now working to apply the same techniques to help us design better drug formulations and, ultimately, better medicines." Considered one of the most promising therapeutic strategies for the treatment of chronic diseases, long-acting injectables are a class of advanced drug delivery systems that are designed to release their cargo over extended periods of time to achieve a prolonged therapeutic effect.