Doxycycline and other tetracyclines in the treatment of bone metastasis
Bibliographic record
Abstract
The tetracycline family includes tetracycline, doxycycline and minocycline, all of which have been used as antibiotics effectively for decades. New uses emerged for these compounds after their effect on mitochondrial function was discovered. Cytostatic and cytotoxic activity of these compounds was shown against cell lines of various tumor origins. In addition, tetracyclines and chemically modified tetracyclines inhibit the activity of several matrix metalloproteinases (MMPs). Given the importance of these enzymes in tumor cell invasion and metastatic ability, the potential use of tetracyclines in cancer therapy needed to be investigated. Col-3, a chemically modified tetracycline, is now the subject of clinical trials in cancer patients. However, the potential of tetracyclines in cancer therapy takes on an added dimension in the bone. MMPs have been shown to be important mediators of metastasis formation in the bone, contributing largely to the morbidity of breast cancer and prostate cancer patients. The natural osteotropism of tetracyclines would allow them to be highly effective in the inhibition of MMPs produced by osteoclasts or tumor cells in the bone. This hypothesis has now been confirmed by experimental evidence showing that doxycycline reduces tumor burden in a mouse model of breast cancer-derived osteolytic bone metastasis. This effect is likely due to a combination of multiple roles of doxycycline, including MMP inhibition and a negative effect on osteoclast differentiation and survival. These encouraging results have now paved the way for an ongoing trial of doxycycline in early combination therapy for breast cancer and prostate cancer patients.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".