Immune checkpoint blockade targeting the PD-1/PD-L1 (Programmed cell death protein 1/Programmed death-ligand 1) axis has transformed cancer therapy. However, antibodies non-specifically bind to PD-1 or PD-L1 on both malignant and normal cells, resulting in immune-related adverse events and limited therapeutic selectivity. Additionally, antibodies only target cell-surface PD-1/PD-L1, whereas intracellular proteins can translocate to the membrane, enabling immune evasion. In contrast, small interfering RNA (siRNA) can specifically silence PD-1 or PD-L1 on the cell surface and within the cytoplasm, mitigating immune suppression, reducing drug resistance, and limiting systemic off-target effects. Despite the clinical success of immune checkpoint inhibitors, monotherapy benefits only a fraction of patients. Combination therapies incorporating chemotherapy, radiotherapy, or photo-mediated therapy have shown improved efficacy.Nanoparticles offer a promising approach for combination therapy by overcoming RNA delivery challenges, enabling efficient tumor-targeting capacity, providing tumor-responsive behavior, and versatility for combination therapy.This review presents an overview of different nanoparticles, including polymer and lipid nanoparticles, developed for the codelivery of PD-L1 siRNA and other therapeutic modalities with different properties. Furthermore, discusses mechanisms underlying PD-L1-mediated tumor therapy, and finally, highlights current challenges and perspectives for translating nanoparticle-based combinatorial immunotherapy into clinical applications.
Nanotechnology-based PD-L1 siRNA codelivery systems for improving cancer immunotherapy.
Naghmeh Jabarimani,Mohammad Saleh Sadeghi,Maryam Edalat,F. Atyabi,F. Jadidi‐Niaragh,Farid Dorkoosh
Published 2025 in Journal of drug targeting (Print)
ABSTRACT
PUBLICATION RECORD
- Publication year
2025
- Venue
Journal of drug targeting (Print)
- Publication date
2025-12-16
- Fields of study
Medicine, Materials Science, Engineering
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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