Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

ABSTRACT

Introduction

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

Areas covered

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

Expert opinion

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

KEYWORDS:

• Artificial intelligence
• automation
• drug delivery
• machine learning
• nanomedicine
• pharmaceutical formulation development

Article highlights

• Nanomedicines are attracting increasing attention, particularly due to their role in addressing the COVID-19 pandemic.

• Nanomedicines include a variety of organic and inorganic systems, with lipids and polymers being the dominant materials for organic-based systems.

• Current nanomedicine design and development rely on a trial-and-error approach.

• A scan of the drug delivery literature reveals selective reporting of details on experimental methods and properties of materials used to prepare nanomedicines.

• The integration of automation and AI/ML has the potential to improve how pharmaceutical scientists approach the development of nanomedicines.

This box summarizes key points contained in the article.

Acknowledgments

The authors are thankful to Zeqing Bao, Max Regenold, and Riley Hickman for helpful discussions.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17425247.2023.2167978

Additional information

Funding

This work was supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) CGS M awarded to J Zaslavsky and NSERC Discovery Grant (RGPIN-2022-04910) awarded to C Allen.