Journal Article

High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity

Kirschenbaum, Daniel, Bihl, Michel, Wollscheid, Bernd, Lee, Sohyon, Bühler, Marcel, Rushing, Elisabeth J., Sun, Miaomiao, Hediger, Matthias A., Buck, Alicia, Snijder, Berend, Lottenbach, Zuzanna, Augustynek, Bartłomiej, Goetze, Sandra, Weiss, Tobias, Baumann, Sven P., Weller, Michael, Wegmann, Rebekka, Neidert, Marian C., Zeitlberger, Anna M., Festl, Yasmin, Pedrioli, Patrick G. A., Mena, Julien, van Drogen, Audrey, Penton, David, Vasella, Flavio

2024

Overview

Glioblastoma, the most aggressive primary brain cancer, has a dismal prognosis, yet systemic treatment is limited to DNA-alkylating chemotherapies. New therapeutic strategies may emerge from exploring neurodevelopmental and neurophysiological vulnerabilities of glioblastoma. To this end, we systematically screened repurposable neuroactive drugs in glioblastoma patient surgery material using a clinically concordant and single-cell resolved platform. Profiling more than 2,500 ex vivo drug responses across 27 patients and 132 drugs identified class-diverse neuroactive drugs with potent anti-glioblastoma efficacy that were validated across model systems. Interpretable molecular machine learning of drug–target networks revealed neuroactive convergence on AP-1/BTG-driven glioblastoma suppression, enabling expanded in silico screening of more than 1 million compounds with high patient validation accuracy. Deep multimodal profiling confirmed Ca 2+ -driven AP-1/BTG-pathway induction as a neuro-oncological glioblastoma vulnerability, epitomized by the anti-depressant vortioxetine synergizing with current standard-of-care chemotherapies in vivo. These findings establish an actionable framework for glioblastoma treatment rooted in its neural etiology. A single-cell ex vivo screening of repurposable drugs in glioblastoma and machine learning of drug–target networks show that anti-tumor neuroactive drugs converge on the AP-1/BTG pathway, based on which prediction models and experimental in vivo and in silico validation identify the anti-depressant vortioxetine as a potential therapeutic agent.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 631/114/1305 / 631/154/1435/2417 / 631/553/2706 / 692/308/575 / 692/699/67/1922 / Alkylation / Animals / Antidepressants / Antineoplastic Agents - pharmacology / Biomedical and Life Sciences / Biomedicine / Brain cancer / Brain Neoplasms - drug therapy / Brain Neoplasms - pathology / Brain tumors / Calcium ions / Cancer Research / Cell Line, Tumor / Chemotherapy / Depth profiling / Drug Repositioning / Drug screening / Drugs / Glioblastoma / Glioblastoma - drug therapy / Glioblastoma - pathology / Glioma / High-Throughput Screening Assays / Humans / Infectious Diseases / Learning algorithms / Machine learning / Metabolic Diseases / Mice / Molecular machines / Molecular Medicine / Neurosciences / Patients / Pharmacology / Prediction models / Transcription factors