Revolutionizing drug formulation development: The increasing impact of machine learning.

Autor: Bao Z; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada., Bufton J; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada., Hickman RJ; Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada., Aspuru-Guzik A; Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada; Vector Institute for Artificial Intelligence, Toronto, ON M5S 1M1, Canada; Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON M5S 1M1, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Department of Materials Science & Engineering, University of Toronto, Toronto, ON M5S 3E4, Canada; CIFAR Artificial Intelligence Research Chair, Vector Institute, Toronto, ON M5S 1M1, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada., Bannigan P; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada. Electronic address: pauric.bannigan@utoronto.ca., Allen C; Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada; Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada; Acceleration Consortium, Toronto, ON M5S 3H6, Canada. Electronic address: cj.allen@utoronto.ca.
Jazyk: angličtina
Zdroj: Advanced drug delivery reviews [Adv Drug Deliv Rev] 2023 Nov; Vol. 202, pp. 115108. Date of Electronic Publication: 2023 Sep 27.
DOI: 10.1016/j.addr.2023.115108
Abstrakt: Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.
Competing Interests: Declaration of Competing Interest Z.B., R.J.H., A.A.-G., P.B., and C.A. are founding members of a new company, 15073383 Canada Inc. A.A.-G is a founding member of Kebotix, Inc.
(Copyright © 2023. Published by Elsevier B.V.)
Databáze: MEDLINE
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