A Conjugate of Gemcitabine With Bisphosphonate (Gem/BP) Shows Potential as a Targeted Bone-Specific Therapeutic Agent in an Animal Model of Human Breast Cancer Bone Metastases
Bibliographic record
Abstract
Bone metastases in advanced breast cancer patients remains a significant treatment challenge. Bisphosphonates are now a routine first line treatment for prevention and treatment of skeletal damage caused by malignancies and, moreover, have shown an ability to transport therapeutic drugs to the bone. Here, we describe the effect of a conjugate between the potent anticancer drug gemcitabine and a bisphosphonate molecule (Gem/BP) in an animal model of breast cancer metastases. We have previously demonstrated the targeting of this compound to bone in normal mice using an analog labeled with the radionuclide 99mTc. Using a bone metastasis model in nude mice produced by intracardiac injection of the human breast cancer cell line MDA-MB-231BO, we examined the effect of Gem/BP and gemcitabine in reducing the frequency and severity of osteolytic bone lesions. High-resolution radiographs and microPET images showed that Gem/ BP reduced the number and size of bone metastases relative to the gemcitabine-treated and the untreated control groups. Histological examination of the humeri and femurs of the control and gemcitabine groups revealed large metastatic cancer lesions in the outer and inner cortices and the medullary cavities. In contrast, Gem/BP-treated mice showed occasional small wedge-shaped metastases under the periosteum of the outer cortex and very occasionally in the medulla. These findings suggest that Gem/BP should be further evaluated for use in the treatment of bone metastases in breast cancer.
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.
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".