The Bisphosphonate Olpadronate Inhibits Skeletal Prostate Cancer Progression in a Green Fluorescent Protein Nude Mouse Model
Bibliographic record
Abstract
PURPOSE: Metastatic bone disease is one of the major causes of morbidity and mortality in prostate cancer patients. Bisphosphonates are currently used to inhibit bone resorption and reduce tumor-induced skeletal complications. More effective bisphosphonates would enhance their clinical value. EXPERIMENTAL DESIGN: We tested several bisphosphonates in a green fluorescent protein (GFP)-expressing human prostate cancer nude mouse model. The in vivo effects of four bisphosphonates, including pamidronate, etidronic acid, and olpadronate, on bone tumor burden in mice intratibially inoculated with PC-3-GFP human prostate cancer cells were visualized by whole-body fluorescence imaging and X-ray. RESULTS: The PC-3-GFP cells produced extensive bone lesions when injected into the tibia of immunocompromised mice. The skeletal progression of the PC-3-GFP cell growth was monitored by GFP fluorescence and the bone destruction was evaluated by X-ray. We showed that 3,3-dimethylaminopropane-1-hydroxy-1,1-diphosphonic acid (olpadronate) was the most effective bisphosphonate treatment in reducing tumor burden as assessed by GFP imaging and radiography. The GFP tumor area and X-ray score significantly correlated. Reduced tumor growth in the bone was accompanied by reduced serum calcium, parathyroid hormone-related protein, and osteoprotegerin. CONCLUSIONS: The serum calcium, parathyroid hormone-related protein, and osteoprotegerin levels were significantly correlated with GFP area and X-ray scores. Treatment with olpadronate reduced tumor growth in the bone measured by GFP and X-ray imaging procedures. Imaging of GFP expression enables monitoring of tumor growth in the bone and the GFP results complement the X-ray assessment of bone disease. The data in this report suggest that olpadronate has potential as an effective inhibitor of the skeletal progression of clinical prostate 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".