ArticlePloS one2026
Repurposing Alzheimer's and ovarian cancer drugs as sonosensitizers for glioblastoma via a positive-unlabeled learning and 3D bioprinting-based new approach methodology (NAM).
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.