Doxycycline and other tetracyclines in the treatment of bone metastasis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 it