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

Autor: Bannigan P; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada., Bao Z; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada., Hickman RJ; Department of Computer Science, University of Toronto, Toronto, ON, M5S 3H6, Canada.; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada., Aldeghi M; Department of Computer Science, University of Toronto, Toronto, ON, M5S 3H6, Canada.; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada., Häse F; Department of Computer Science, University of Toronto, Toronto, ON, M5S 3H6, Canada.; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada.; Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada., Aspuru-Guzik A; Department of Computer Science, University of Toronto, Toronto, ON, M5S 3H6, Canada. alan@aspuru.com.; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON, M5S 3E5, Canada. alan@aspuru.com.; Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada. alan@aspuru.com.; Department of Materials Science & Engineering, University of Toronto, Toronto, ON, M5S 3E4, Canada. alan@aspuru.com.; Lebovic Fellow, Canadian Institute for Advanced Research, Toronto, ON, M5S 1M1, Canada. alan@aspuru.com.; CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, ON, M5S 1M1, Canada. alan@aspuru.com., Allen C; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, M5S 3M2, Canada. cj.allen@utoronto.ca.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2023 Jan 10; Vol. 14 (1), pp. 35. Date of Electronic Publication: 2023 Jan 10.
DOI: 10.1038/s41467-022-35343-w
Abstrakt: Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. Here we show that machine learning algorithms can be used to predict experimental drug release from these advanced drug delivery systems. We also demonstrate that these trained models can be used to guide the design of new long acting injectables. The implementation of the described data-driven approach has the potential to reduce the time and cost associated with drug formulation development.
(© 2023. The Author(s).)
Databáze: MEDLINE
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