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颠覆药物制剂研发:机器学习的影响日益增大。

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

机构信息

Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.

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.

出版信息

Adv Drug Deliv Rev. 2023 Nov;202:115108. doi: 10.1016/j.addr.2023.115108. Epub 2023 Sep 27.

Abstract

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.

摘要

在过去的几年中,机器学习 (ML) 技术在包括制剂科学在内的许多研究领域得到了迅速的应用。与此同时,脂质纳米粒在最近的 COVID-19 大流行中成功传递 mRNA 疫苗的应用,展示了制剂科学的影响力。然而,先进的药物制剂的设计并非易事,主要依赖于昂贵且耗时的湿实验证。2021 年,我们小组发表了一篇综述文章,重点介绍了使用机器学习作为加速药物制剂开发的一种手段。自那以后,该领域取得了显著的发展和进步,这反映在该领域发表的研究数量不断增加。这篇更新的综述总结了当前机器学习指导药物制剂开发的现状,介绍了已在制剂设计中实施的先进的机器学习技术,并分享了使自动驾驶实验室成为现实的进展。此外,该综述还强调了机器学习的几个未来应用,这些应用尚未被充分利用,以推进药物制剂的研究和开发。

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