Dichotomic effects of clinically used drugs on tumor growth, bone remodeling and pain management
Bibliographic record
Abstract
Abstract Improvements in the survival of breast cancer patients have led to the emergence of bone health and pain management as key aspects of patient’s quality of life. Here, we used a female rat MRMT-1 model of breast cancer-induced bone pain to compare the effects of three drugs used clinically morphine, nabilone and zoledronate on tumor progression, bone remodeling and pain relief. We found that chronic morphine reduced the mechanical hypersensitivity induced by the proliferation of the luminal B aggressive breast cancer cells in the tumor-bearing femur and prevented spinal neuronal and astrocyte activation. Using MTT cell viability assay and MRI coupled to 18 FDG PET imaging followed by ex vivo 3D µCT, we further demonstrated that morphine did not directly exert tumor growth promoting or inhibiting effects on MRMT-1 cancer cells but induced detrimental effects on bone healing by disturbing the balance between bone formation and breakdown. In sharp contrast, both the FDA-approved bisphosphonate zoledronate and the synthetic cannabinoid nabilone prescribed as antiemetics to patients receiving chemotherapy were effective in limiting the osteolytic bone destruction, thus preserving the bone architecture. The protective effect of nabilone on bone metabolism was further accompanied by a direct inhibition of tumor growth. As opposed to zoledronate, nabilone was however not able to manage bone tumor-induced pain and reactive gliosis. Altogether, our results revealed that morphine, nabilone and zoledronate exert disparate effects on tumor growth, bone metabolism and pain control. These findings also support the use of nabilone as an adjuvant therapy for bone metastases.
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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 | 0.000 |
| 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".