Ketoconazole and Posaconazole Selectively Target HK2-expressing Glioblastoma Cells
Bibliographic record
Abstract
Abstract Purpose: Hexokinase II (HK2) protein expression is elevated in glioblastoma (GBM), and we have shown that HK2 could serve as an effective therapeutic target for GBM. Here, we interrogated compounds that target HK2 effectively and restrict tumor growth in cell lines, patient-derived glioma stem cells (GSCs), and mouse models of GBM. Experimental Design: We performed a screen using a set of 15 drugs that were predicted to inhibit the HK2-associated gene signature. We next determined the EC50 of the compounds by treating glioma cell lines and GSCs. Selected compounds showing significant impact in vitro were used to treat mice and examine their effect on survival and tumor characteristics. The effect of compounds on the metabolic activity in glioma cells was also assessed in vitro. Results: This screen identified the azole class of antifungals as inhibitors of tumor metabolism. Among the compounds tested, ketoconazole and posaconazole displayed the greatest inhibitory effect on GBM both in vitro and in vivo. Treatment of mice bearing GBM with ketoconazole and posaconazole increased their survival, reduced tumor cell proliferation, and decreased tumor metabolism. In addition, treatment with azoles resulted in increased proportion of apoptotic cells. Conclusions: Overall, we provide evidence that azoles exert their effect by targeting genes and pathways regulated by HK2. These findings shed light on the action of azoles in GBM. Combined with existing literature and preclinical results, these data support the value of repurposing azoles in GBM clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".