Hierarchically Engineered Self-Adaptive Nanoplatform Guided Intuitive and Precision Interventions for Deep-Seated Glioblastoma.

Abstract

Glioblastoma multiforme (GBM), particularly the deep-seated tumor where surgical removal is not feasible, poses great challenges for clinical treatments due to complicated biological barriers and the risk of damaging healthy brain tissue. Here, we hierarchically engineer a self-adaptive nanoplatform (SAN) that overcomes delivery barriers by dynamically adjusting its structure, surface charge, particle size, and targeting moieties to precisely distinguish between tumor and parenchyma cells. We further devise a <b><u>S</u></b>AN-<b><u>g</u></b>uided <b><u>i</u></b>ntuitive and <b><u>p</u></b>recision <b><u>i</u></b>ntervention (SGIPi) strategy which obviates the need for sophisticated facilities, skilled operations, and real-time magnetic resonance imaging (MRI) guidance required by current MRI-guided laser or ultrasound interventions. In a preclinical intracranial GBM mouse model, SGIPi-based photodynamic therapy effectively impedes GBM progression with high tumor specificity and significantly extends overall survival. Moreover, the SGIPi potentiates chemotherapy while minimizing adverse effects; it eradicates intracranial GBM lesions in 100% cases solely through Temozolomide chemotherapy. This SGIPi strategy holds potential to improve the clinical management of GBM, with the possibility of extending survival rates and even achieving complete remission, and may inspire research focus from expensive and complex hardware development to simpler, delivery-based GBM interventions.