A machine learning-based analysis method for small molecule high content screening of three-dimensional cancer
Vishakha Goyal1, Dvir Blivis1, Steven A Titus1
1Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland.
Molecular Pharmacology
|September 6, 2025
Summary
This study validates a 3D spheroid morphological profiling method for drug discovery, identifying potential glioblastoma therapies by analyzing compound-induced cellular changes and mechanisms of action.
Area of Science:
- Cellular Morphology
- Drug Discovery
- 3D Cell Culture
Background:
- Multiparameter cellular morphological profiling and 3D biological models offer insights for pharmaceutical discovery.
- Combining these approaches is relatively underexplored.
Purpose of the Study:
- Validate a multiparameter cellular morphological profiling screening method using 3D U87 glioblastoma spheroids.
- Characterize reference compounds and screen a library of kinase inhibitors.
Main Methods:
- 3D spheroid culture, automated imaging, image segmentation, and machine learning for morphological profiling.
- Comparison with CellTiter-Glo viability assay.
- Concentration-response experiments and mechanism of action analysis.
Main Results:
- Validated the method by characterizing 7 reference anticancer compounds.
- Screened 925 kinase pathway compounds, identifying active molecules via morphological profiling.
- Confirmed phenotypes and correlated morphology with cell viability, revealing distinct mechanisms of action.
Conclusions:
- The developed method effectively distinguishes spheroid structural changes induced by anticancer compounds.
- Identified potential new mechanisms of action for glioblastoma drug candidates.
- Advances the search for more effective glioblastoma therapies through phenotypic screening.
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