Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates
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 c...
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2025-06-01
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| author | Nazareno Gonzalez Melanie Pérez Küper Matías Garcia Fallit Jorge A. Peña Agudelo Alejandro Nicola Candia Maicol Suarez Velandia Ana Clara Romero Guillermo Videla Richardson Marianela Candolfi |
| author_facet | Nazareno Gonzalez Melanie Pérez Küper Matías Garcia Fallit Jorge A. Peña Agudelo Alejandro Nicola Candia Maicol Suarez Velandia Ana Clara Romero Guillermo Videla Richardson Marianela Candolfi |
| author_sort | Nazareno Gonzalez |
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| description | 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. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Brain Sciences |
| spelling | doaj-art-ae1e3e64e0ce41eea249ef91d1a5152f2025-08-20T03:26:21ZengMDPI AGBrain Sciences2076-34252025-06-0115663710.3390/brainsci15060637Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic CandidatesNazareno Gonzalez0Melanie Pérez Küper1Matías Garcia Fallit2Jorge A. Peña Agudelo3Alejandro Nicola Candia4Maicol Suarez Velandia5Ana Clara Romero6Guillermo Videla Richardson7Marianela Candolfi8Instituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaFundación Para la Lucha Contra las Enfermedades Neurológicas de la Infancia (FLENI), Buenos Aires C1121A6B, ArgentinaInstituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Buenos Aires C1121ABG, ArgentinaBackground: 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.https://www.mdpi.com/2076-3425/15/6/637glioblastomadrug repurposingblood-brain barrierDaporinadpredictive modelingNAMPT inhibitor |
| spellingShingle | Nazareno Gonzalez Melanie Pérez Küper Matías Garcia Fallit Jorge A. Peña Agudelo Alejandro Nicola Candia Maicol Suarez Velandia Ana Clara Romero Guillermo Videla Richardson Marianela Candolfi Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates Brain Sciences glioblastoma drug repurposing blood-brain barrier Daporinad predictive modeling NAMPT inhibitor |
| title | Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates |
| title_full | Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates |
| title_fullStr | Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates |
| title_full_unstemmed | Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates |
| title_short | Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates |
| title_sort | integrated workflow for drug repurposing in glioblastoma computational prediction and preclinical validation of therapeutic candidates |
| topic | glioblastoma drug repurposing blood-brain barrier Daporinad predictive modeling NAMPT inhibitor |
| url | https://www.mdpi.com/2076-3425/15/6/637 |
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