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Machine learning models to accelerate the design of polymeric long-acting injectables

Medicine and Health

Machine learning models to accelerate the design of polymeric long-acting injectables

P. Bannigan, Z. Bao, et al.

Explore groundbreaking research conducted by Pauric Bannigan, Zeqing Bao, and other experts from the University of Toronto, revealing how machine learning algorithms can efficiently predict drug release from long-acting injectables, paving the way for faster, cost-effective formulation development.

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Playback language: English
Abstract
This study explores the use of machine learning (ML) algorithms to predict drug release from long-acting injectables (LAIs) and guide the design of new formulations. The researchers demonstrate that ML models can accurately predict in vitro drug release and that these models can be used to inform the design of new LAIs, potentially reducing the time and cost of drug formulation development.
Publisher
Nature Communications
Published On
Jan 10, 2023
Authors
Pauric Bannigan, Zeqing Bao, Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik, Christine Allen
Tags
machine learning
drug release
long-acting injectables
formulation development
predictive modeling

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