Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Published Date: Jun 19, 2025

Abstract

In recent years, nanomedicine has emerged as a promising approach to deliver therapeutic agents directly to tumors. However, despite its potential, cancer nanomedicine encounters significant challenges. The synthesis of nanomedicines involves numerous parameters, and the complexity of nano-bio interactions in vivo presents further difficulties. Therefore, innovative approaches are needed to optimize nanoparticle (NP) design and functionality, enhancing their delivery efficiency and therapeutic outcomes. Recent advancements in Machine Learning (ML) and computational methods have shown great promise for precision cancer drug delivery. This review summarizes the potential use of ML across all stages of NP drug delivery systems, along with a discussion of ongoing challenges and future directions. The authors first examine the synthesis and formulation of NPs, highlighting how ML can accelerate the process by searching for optimal synthesis parameters. Next, they delve into nano-bio interactions in drug delivery, including NP-protein interactions, blood circulation, NP extravasation into the tumor microenvironment (TME), tumor penetration and distribution, as well as cellular internalization. Through this comprehensive overview, the authors aim to highlight the transformative potential of ML in overcoming current challenges, assisting nanoscientists in the rational design of NPs, and advancing precision cancer nanomedicine.

Authors

  • Qingquan Wang
    School of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.
  • Yujian Liu
  • Chenchen Li
  • Bin Xu
    Department of Orthopaedics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Shidang Xu
    School of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.
  • Bin Liu
    Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Endocrinology, Neijiang First People's Hospital, Chongqing, China.

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