Integrated Workflow for Drug Repurposing in Glioblastoma: Computational Prediction and Preclinical Validation of Therapeutic Candidates

Abstract

Background: Glioblastoma (GBM) remains a significant challenge in oncologydue to its resistance to standard treatments including temozolomide. This study aimed todevelop and validate an integrated model for predicting GBM sensitivity to alternativechemotherapeutics and identifying new drugs and combinations with therapeutic potential.Research Design and Methods: We analyzed drug sensitivity data for 272 compoundsfrom CancerRxTissue and employed in silico algorithms to assess blood-brain barrier permeability.The model was used to predict GBM sensitivity to various drugs, which wasthen validated using GBM cellular models. Alternative drugs targeting overexpressedand negative prognostic biomarkers in GBM were experimentally validated. Results: Themodel predicted that GBM is more sensitive to Etoposide and Cisplatin compared toTemozolomide, which was confirmed by experimental validation in GBM cells. We alsoidentified novel drugs with high predicted sensitivity in GBM. Daporinad, a NAMPTinhibitor that permeates the blood-brain barrier was selected for further preclinical evaluation.This evaluation supported the in silico predictions of high potential efficacy andsafety in GBM. Conclusions: Our findings using different cellular models suggest that thiscomputational prediction model could constitute a valuable tool for drug repurposing inGBM and potentially in other tumors, which could accelerate the development of moreeffective cancer treatments.