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Updated: Mar 17, 2026

Anticancer Efficacy of Photodynamic Therapy with Lung Cancer-Targeted Nanoparticles
Published on: December 1, 2016
Self-Immolative Nanotheranostics with Albumin-Guided Tumor Targeting for Oxidative Stress-Amplified Photodynamic
Yuben Zhang1, Jie Cen1, Runjie Li1
1Department of Pharmacy, and State Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, Anhui Province, China.
None:
Conventional photosensitizers suffer from intrinsic limitations in photodynamic therapy (PDT), including poor tumor selectivity, rapid systemic clearance, and inefficient reactive oxygen species (ROS) generation in the hypoxic tumor microenvironment. Herein, we report a self-immolative nanotheranostic (SINT) that integrates albumin-guided transport with tumor microenvironment-activated cascade degradation, achieving covalent tumor anchoring, sustained retention, and simultaneous activation of fluorescence imaging and PDT. SINT is composed of a self-immolative polymer backbone bearing New Indocyanine Green (NIG) and maleimide (MI) functional groups. After intravenous injection, the MI groups covalently bind to the Cys34 residues of circulating albumin, forming an albumin-enriched corona that enhances tumor targeting. In acidic tumor conditions, SINT undergoes self-immolation to generate an azaquinone methide-NIG (AQM-NIG) intermediate. This electrophilic species reacts with thiol-containing biomolecules, leading to covalent anchoring within tumor tissues, disruption of intracellular redox homeostasis, and amplified PDT efficiency. Pharmacokinetic analyses revealed that tumor accumulation of SINT was 7.75-fold higher than that of NIG at 24 h post-injection, and over 48% of the maximal fluorescence persisted after 96 h. The prolonged retention and amplified oxidative stress enabled complete tumor suppression without recurrence in murine models.
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