Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Advances in Nano Research Volume 20, Number 1, January 2026 , pages 63-78 DOI: https://doi.org/10.12989/anr.2026.20.1.063 |
|
|
|
ML-driven design of targeted nanotherapeutics for rheumatoid arthritis synovial macrophage subtypes |
||
Guo Hongling, Zhou Weili, Wu Yue
|
||
| Abstract | ||
| Rheumatoid arthritis generally manifests evidence of chronic inflammation of the synovium; one major cell type responsible for this condition is heterogeneously classified macrophage populations. Nanotherapeutics would selectively undertake this task, but adapting the agents will require considerable carefulness, rendering the design optimization rather tricky. The development of a machine-learning (ML)-guided framework for a rational design of nanoparticles with discrete synovial macrophage subtype targeting will hence be the goal of this study. A dataset of 400 nanoparticle formulations was used to investigate the effects of size, surface charge, drug loading, and ligand density on cellular uptake, cytokine suppression (TNF-α and IL-6), cytotoxicity, and synovial targeting efficiency. Ensemble ML approaches, such as random forest and neural nets, produce solid predictions of therapeutic outcome and the feature-related design parameters governing that outcome. Cellular uptake, ligand density, and drug loading efficiency are identified as independent determinants of anti-inflammatory response, while size and charge play secondary yet significant roles. Multi-dimensional analyses illustrate a trade-off between efficacy and safety and reveal subtype-specific responses, with M2 macrophages showing high cytokine suppression at low cytotoxicity, whereas M1 macrophages demonstrated increased uptake with moderate levels of inflammatory modulation. This integrated ML approach has allowed us to gain mechanistic insight into nanoparticle-macrophage interactions and to rapidly in silico optimize RA nanotherapeutics, thus paving the way for personalized and efficient design strategies against inflammatory diseases. | ||
| Key Words | ||
| consumer perception; corporate visual identity; immersive marketing; nano-scale vr modeling; purchase intention | ||
| Address | ||
| Guo Hongling: Pharmacy Department, Changsha Hospital of Traditional Chinese Medicine (Changsha No. 8 Hospital), Changsha City, Hunan Province, PR China Zhou Weili: Department of Orthopedics, Changsha Third Hospital, 176 Laodong West Road, Changsha City Wu Yue: Department of Orthopedics, Beijing Chaoyang Hospital, affiliated with Capital Medical University, No. 8, Gongti South Road, Chaoyang District, Beijing | ||