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Ishwar K Puri

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Open access Aug 2026

Repurposing Alzheimer’s and ovarian cancer drugs as sonosensitizers for glioblastoma via a positive-unlabeled learning and 3D bioprinting-based new approach methodology (NAM)

Glioblastoma (GBM) remains a lethal primary brain tumor, in part because therapeutic efficacy is limited by the blood–brain barrier (BBB) and the complex tumor microenvironment (TME). Sonodynamic therapy (SDT), i.e., use of ultrasound to activate chemical sensitizers and generate cytotoxic stress, offers a non-invasive strategy for treating deep-seated intracranial disease, but progress is constrained by the scarcity of validated sonosensitizers and the inefficiency of conventional in vitro screening methods. Here, we introduce a New Approach Methodology (NAM) that couples a neural network-based positive-unlabeled (PU) learning framework with a high-throughput, magnetic field–guided 3D bioprinting platform to accelerate identification and experimental validation of SDT-sensitizing agents. Using curated drug and small-molecule data and RDKit-derived molecular descriptors, the PU classifier identifies candidate ultrasound-responsive compounds without requiring reliable negative labels. We then validate the AI-based predictions in physiologically relevant U-87 MG glioblastoma spheroids that reproduce key TME features, including spatial heterogeneity and a hypoxic core. The NAM identifies two FDA-approved drugs, carboplatin (advanced ovarian cancer) and memantine hydrochloride (Alzheimer’s disease), as effective ultrasound-responsive agents. In 3D spheroids, combining low-intensity pulsed ultrasound with either drug significantly reduces viability compared with drug-only controls, and both combinations outperform temozolomide (TMZ), the current standard chemotherapeutic. Time-resolved responses reveal distinct kinetics: memantine produces strong early cytotoxicity (24 h) enhanced by ultrasound, whereas carboplatin shows delayed but pronounced cytotoxicity (72 h), also improved by ultrasound. Together, these results establish an integrated computational–experimental NAM that enables rapid repurposing of approved drugs as SDT sensitizers and provides a scalable framework for advancing GBM therapeutic discovery while reducing reliance on animal studies.

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