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

Journal: Advanced drug delivery reviews
Published Date:

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.

Authors

  • Zeqing Bao
    Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
  • Jack Bufton
    Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada.
  • Riley J Hickman
    Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, Canada.
  • Alán Aspuru-Guzik
    Departments of Chemistry, Computer Science, University of Toronto St. George Campus Toronto ON Canada.
  • Pauric Bannigan
  • Christine Allen