Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery
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–...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-08-01
|
| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202503138 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233289709092864 |
|---|---|
| author | Qingquan Wang Yujian Liu Chenchen Li Bin Xu Shidang Xu Bin Liu |
| author_facet | Qingquan Wang Yujian Liu Chenchen Li Bin Xu Shidang Xu Bin Liu |
| author_sort | Qingquan Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e6e6cceea9774131b5eeb3cad99b0576 |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-e6e6cceea9774131b5eeb3cad99b05762025-08-20T11:56:10ZengWileyAdvanced Science2198-38442025-08-011230n/an/a10.1002/advs.202503138Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug DeliveryQingquan Wang0Yujian Liu1Chenchen Li2Bin Xu3Shidang Xu4Bin Liu5School of Biomedical Sciences and Engineering Guangzhou International Campus South China University of Technology Guangzhou 511442 P. R. ChinaSchool of Biomedical Sciences and Engineering Guangzhou International Campus South China University of Technology Guangzhou 511442 P. R. ChinaSchool of Biomedical Sciences and Engineering Guangzhou International Campus South China University of Technology Guangzhou 511442 P. R. ChinaSchool of Biomedical Sciences and Engineering Guangzhou International Campus South China University of Technology Guangzhou 511442 P. R. ChinaSchool of Biomedical Sciences and Engineering Guangzhou International Campus South China University of Technology Guangzhou 511442 P. R. ChinaDepartment of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 Singapore 117585 SingaporeAbstract 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.https://doi.org/10.1002/advs.202503138biomaterialdrug deliverymachine learningnanomedicines |
| spellingShingle | Qingquan Wang Yujian Liu Chenchen Li Bin Xu Shidang Xu Bin Liu Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery Advanced Science biomaterial drug delivery machine learning nanomedicines |
| title | Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery |
| title_full | Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery |
| title_fullStr | Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery |
| title_full_unstemmed | Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery |
| title_short | Machine Learning‐Enhanced Nanoparticle Design for Precision Cancer Drug Delivery |
| title_sort | machine learning enhanced nanoparticle design for precision cancer drug delivery |
| topic | biomaterial drug delivery machine learning nanomedicines |
| url | https://doi.org/10.1002/advs.202503138 |
| work_keys_str_mv | AT qingquanwang machinelearningenhancednanoparticledesignforprecisioncancerdrugdelivery AT yujianliu machinelearningenhancednanoparticledesignforprecisioncancerdrugdelivery AT chenchenli machinelearningenhancednanoparticledesignforprecisioncancerdrugdelivery AT binxu machinelearningenhancednanoparticledesignforprecisioncancerdrugdelivery AT shidangxu machinelearningenhancednanoparticledesignforprecisioncancerdrugdelivery AT binliu machinelearningenhancednanoparticledesignforprecisioncancerdrugdelivery |