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Updated: Sep 18, 2025
A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells
Published on: March 28, 2021
Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of
Nazareno Gonzalez1, Melanie Pérez Küper1, Matías Garcia Fallit1,2
1Instituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, Argentina.
Background:
Glioblastoma (GBM) remains a significant challenge in oncology due to its resistance to standard treatments including temozolomide. This study aimed to develop and validate an integrated model for predicting GBM sensitivity to alternative chemotherapeutics and identifying new drugs and combinations with therapeutic potential.
Research Design And Methods:
We analyzed drug sensitivity data for 272 compounds from CancerRxTissue and employed in silico algorithms to assess blood-brain barrier permeability. The model was used to predict GBM sensitivity to various drugs, which was then validated using GBM cellular models. Alternative drugs targeting overexpressed and negative prognostic biomarkers in GBM were experimentally validated.
Results:
The model predicted that GBM is more sensitive to Etoposide and Cisplatin compared to Temozolomide, which was confirmed by experimental validation in GBM cells. We also identified novel drugs with high predicted sensitivity in GBM. Daporinad, a NAMPT inhibitor that permeates the blood-brain barrier was selected for further preclinical evaluation. This evaluation supported the in silico predictions of high potential efficacy and safety in GBM.
Conclusions:
Our findings using different cellular models suggest that this computational prediction model could constitute a valuable tool for drug repurposing in GBM and potentially in other tumors, which could accelerate the development of more effective cancer treatments.
Insights
This study developed a computational model to predict glioblastoma (GBM) drug sensitivity, identifying Etoposide and Cisplatin as more effective than Temozolomide. The model aids in discovering new GBM treatments and drug repurposing.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Glioblastoma (GBM) presents a significant therapeutic challenge due to its inherent resistance to standard treatments like temozolomide.
- Developing novel therapeutic strategies is crucial for improving patient outcomes in GBM.
Purpose of the Study:
- To create and validate an integrated computational model for predicting glioblastoma (GBM) sensitivity to chemotherapeutics.
- To identify novel drugs and drug combinations with therapeutic potential for GBM treatment.
Main Methods:
- Analysis of drug sensitivity data for 272 compounds.
- Utilized in silico algorithms to assess blood-brain barrier permeability.
- Validated model predictions using GBM cellular models and experimental testing of novel drug candidates.
Main Results:
- The computational model predicted higher GBM sensitivity to Etoposide and Cisplatin compared to Temozolomide, which was experimentally confirmed.
- Identified novel drugs with high predicted sensitivity, including Daporinad, a NAMPT inhibitor with blood-brain barrier penetration.
- Preclinical evaluation of Daporinad supported its predicted high efficacy and safety in GBM.
Conclusions:
- The developed computational prediction model is a valuable tool for drug repurposing in glioblastoma (GBM).
- This approach can accelerate the discovery of more effective cancer treatments for GBM and potentially other tumor types.
