Evidence map›Paper›PMID 40536233›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

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

Qingquan Wang, Yujian Liu, Chenchen Li, Bin Xu, Shidang Xu, Bin Liu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Review
  7. Review
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Review
  16. Article
  17. Review
  18. Review
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Qingquan WangSchool of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.ORCID https://orcid.org/0009-0000-2350-9158
Yujian LiuSchool of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.ORCID https://orcid.org/0009-0006-2150-0962
Chenchen LiSchool of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.ORCID https://orcid.org/0009-0002-5256-2040
Bin XuSchool of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.ORCID https://orcid.org/0000-0002-7013-3672
Shidang XuSchool of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology, Guangzhou, 511442, P. R. China.
Bin LiuDepartment of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore.ORCID https://orcid.org/0000-0002-0956-2777

Funding

Guangdong Basic and Applied Basic Research Foundation 2024B1515020048National Key R&D Program of China 2022YFB3804700National Natural Science Foundation of China 52373136Science and Technology Program of Guangzhou, China 2024A04J3809Singapore Ministry of Education Research Centre of Excellence through the Institute for Functional Intelligent Materials A-0001423-06-00
6 · The paper itself

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.

Indexed as

Antineoplastic AgentsDrug Delivery SystemsMachine LearningNanomedicineNanoparticlesNeoplasmsPrecision MedicineAnimalsHumansTumor MicroenvironmentAntineoplastic Agentsbiomaterialdrug deliverymachine learningnanomedicines

Identifiers

PMID40536233
PMCPMC12376635

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.