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

Pauric Bannigan, Zeqing Bao, Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik () and Christine Allen ()
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Pauric Bannigan: University of Toronto
Zeqing Bao: University of Toronto
Riley J. Hickman: University of Toronto
Matteo Aldeghi: University of Toronto
Florian Häse: University of Toronto
Alán Aspuru-Guzik: University of Toronto
Christine Allen: University of Toronto

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: 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 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.

Date: 2023
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DOI: 10.1038/s41467-022-35343-w

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