CADD-49. IDENTIFICATION AND VALIDATION OF AZOLES AS HK2 INHIBITORS IN GLIOBLASTOMA <i>IN VITRO</i> AND <i>IN VIVO</i>
Bibliographic record
Abstract
Hexokinase I and 2 (HK1/HK2) catalyze the first committed step in glucose metabolism, ensuring a sustained glucose concentration gradient. Glioblastomas (GBMs) over-express HK2, and we have previously shown that its loss sensitizes GBM cells to treatment. In this study, we have conducted a systematic small-drug screen to identify potential HK2 inhibitors. Pathway analysis was conducted using Gene Set Enrichment Analysis on differentially expressed genes in control and HK2 siRNA-treated samples. The top 200 up- and down-regulated genes were used to query the Connectivity Map database for potential inhibitors. 15 candidate drugs were identified and their EC50 was determined in glioma cell lines, glioma stem cells (GSCs), and normal human astrocytes. Dynamic metabolic flux analysis with 13C-glucose labeling followed by liquid chromatography-mass spectrometry (LC-MS) was used to assess effect of candidate drugs on tumor cell glycolytic intermediates. Xenograft mice bearing glioma stem cells or U87 cells were treated with vehicle, ketoconazole, or posaconazle (25mg/kg). Mice were sacrificed when moribund and immunohistochemistry was used to assess proliferation (Ki67) and apoptosis (TUNEL assay). Mouse brain tissue drug concentration was determined using HPLC-MS/MS. HK2 knockdown affected glycolysis and angiogenesis. The EC50 of ketoconazole and posaconazole was within clinically achievable doses and below the concentration needed to affect normal human astrocytes or stem cells (<15µM). Compared to control, a significant decreased in several intermediate metabolites of glycolysis was also observed in vitro. Vehicle treated xenografts had a significantly shorter survival (44 ± 2 days) compared with mice treated with ketoconazole (54 ± 4 days, p<0.05) or posaconazole (56 ± 4 days, p<0.05). Both drugs led to significant reduction in proliferation and increase in apoptosis. Azoles can target genes and pathways regulated by HK2. These pre-clinical results support the value of investigating azoles as repurposed drugs in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".