ArticleiScience2026
A comprehensive pharmacological survey across heterogeneous patient-derived glioblastoma stem cell models.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lack of advancement in the treatment of glioblastoma (GBM) over the past two decades calls for more innovation to address the inter- and intra-patient heterogeneity confounding modern target-directed drug discovery strategies. In this study, we incorporate a panel of patient-derived GBM stem cell lines into an automated and unbiased "cell painting" assay to quantify multiple GBM stem cell phenotypes. By screening several compound libraries, followed by dose-response validation of hit compounds, we present a comprehensive survey of distinct pharmacological classes and druggable targets upon multiple GBM stem cell phenotypes. We further characterize two validated target classes, histone deacetylase and cyclin dependent kinase inhibitors. We demonstrate that unbiased Cell Painting phenotypic screening is a productive approach to identifying new targets, drug classes and future drug combinations that address the heterogeneity of GBM. We provide all GBM cell painting data for each compound perturbation for the research community to explore further.
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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 OpenQuestion graph.