Direct inhibition of hexokinase activity by metformin at least partially impairs glucose metabolism and tumor growth in experimental breast cancer
Bibliographic record
Abstract
Emerging evidence suggests that metformin, a widely used anti-diabetic drug, may be useful in the prevention and treatment of different cancers. In the present study, we demonstrate that metformin directly inhibits the enzymatic function of hexokinase (HK) I and II in a cell line of triple-negative breast cancer (MDA-MB-231). The inhibition is selective for these isoforms, as documented by experiments with purified HK I and II as well as with cell lysates. Measurements of (18)F-fluoro-deoxyglycose uptake document that it is dose- and time-dependent and powerful enough to virtually abolish glucose consumption despite unchanged availability of membrane glucose transporters. The profound energetic imbalance activates phosphorylation and is subsequently followed by cell death. More importantly, the "in vivo" relevance of this effect is confirmed by studies of orthotopic xenografts of MDA-MB-231 cells in athymic (nu/nu) mice. Administration of high drug doses after tumor development caused an evident tumor necrosis in a time as short as 48 h. On the other hand, 1 mo metformin treatment markedly reduced cancer glucose consumption and growth. Taken together, our results strongly suggest that HK inhibition contributes to metformin therapeutic and preventive potential in breast cancer.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".