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

Bannigan, Pauric; Bao, Zeqing; Hickman, Riley J.; Aldeghi, Matteo; Häse, Florian; Aspuru‐Guzik, Alán; Allen, Christine

doi:10.1038/s41467-022-35343-w

articleNature CommunicationsJan 10, 2023GOLD OA

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

PBPauric Bannigan ZBZeqing Bao RJRiley J. Hickman MAMatteo Aldeghi FHFlorian Häse

University of Toronto · Vector Institute · +1 more institution

PubMed

Indexed incrossrefdoajpubmed

Abstract

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…

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Topics

Keywords

Computer science
Drug delivery
Biochemical engineering
Machine learning
Artificial intelligence
Nanotechnology
Risk analysis (engineering)
Medicine

UN Sustainable Development Goals

Good health and well-being

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