Exploiting metabolic vulnerability in glioblastoma using a brain-penetrant drug with a safe profile
Bibliographic record
Abstract
Abstract Glioblastoma is one of the most treatment-resistant and lethal cancers, with a subset of self-renewing brain tumour stem cells (BTSCs), driving therapy resistance and relapse. Here, we report that mubritinib effectively impairs BTSC stemness and growth. Mechanistically, bioenergetic assays and rescue experiments showed that mubritinib targets complex I of the electron transport chain, thereby impairing BTSC self-renewal and proliferation. Gene expression profiling and Western blot analysis revealed that mubritinib disrupts the AMPK/p27 Kip1 pathway, leading to cell-cycle impairment. By employing in vivo pharmacokinetic assays, we established that mubritinib crosses the blood-brain barrier. Using preclinical patient-derived and syngeneic models, we demonstrated that mubritinib delays glioblastoma progression and extends animal survival. Moreover, combining mubritinib with radiotherapy or chemotherapy offers survival advantage to animals. Notably, we showed that mubritinib alleviates hypoxia, thereby enhancing ROS generation, DNA damage, and apoptosis in tumours when combined with radiotherapy. Encouragingly, toxicological and behavioural studies revealed that mubritinib is well tolerated and spares normal cells. Our findings underscore the promising therapeutic potential of mubritinib, warranting its further exploration in clinic for glioblastoma therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".