If you work in the Pharmaceutical Industry you are familar with the estememly complex and tedious process of bringing a novel drug to market.  Not only is the financial risk a factor, but margins for failure can also place a company on the heels of bankruptcy.  Keep in mind clinical trials and FDA approcal are also hurdles that can shorten the reach of a company’s R&D.  Recently, scientists at the University of Toronto have successfully tested the use of machine learning models to guide the design of long-acting injectable drug formulations. The potential for machine learning algorithms to accelerate drug formulation could reduce the time and cost associated with drug development, making promising new medicines available faster.

The study was published today in Nature Communications and is one of the first to apply machine learning techniques to the design of polymeric long-acting injectable drug formulations.

The multidisciplinary research is led by Christine Allen from the University of Toronto’s department of pharmaceutical sciences and Alán Aspuru-Guzik, from the departments of chemistry and computer science. Both researchers are also members of the Acceleration Consortium, a global initiative that uses artificial intelligence and automation to accelerate the discovery of materials and molecules needed for a sustainable future.

“This study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables,” said Christine Allen, professor in pharmaceutical sciences at the Leslie Dan Faculty of Pharmacy, University of Toronto. “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.”


Once we had the data set, we split it into two subsets: one used for training the models and one for testing. We then asked the models to predict the results of the test set and directly compared with previous experimental data. We found that the tree-based models, and specifically lightGBM, delivered the most accurate predictions,” said Pauric Bannigan, research associate with the Allen research group at the Leslie Dan Faculty of Pharmacy, University of Toronto.


“AI is transforming the way we do science. It helps accelerate discovery and optimization. This is a perfect example of a ‘Before AI’ and an ‘After AI’ moment and shows how drug delivery can be impacted by this multidisciplinary research,” said Alán Aspuru-Guzik, professor in chemistry and computer science, University of Toronto who also holds the CIFAR Artificial Intelligence Research Chair at the Vector Institute in Toronto.

For more information: Scientists use machine learning to fast-track drug formulation development



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