Novel therapies for cancer-induced bone pain
Bibliographic record
Abstract
Cancer pain is a growing problem, especially with the substantial increase in cancer survival. Reports indicate that bone metastasis, whose primary symptom is bone pain, occurs in 65-75% of patients with advanced breast or prostate cancer. We optimized a preclinical in vivo model of cancer-induced bone pain (CIBP) involving the injection of Lewis Lung Carcinoma cells into the intramedullary space of the femur of C57BL/6 mice or transgenic mice on a C57BL/6 background. Mice gradually reduce the use of the affected limb, leading to altered weight bearing. Symptoms of secondary cutaneous heat sensitivity also manifest themselves. Following optimization, three potential analgesic treatments were assessed; 1) single ion channel targets (targeting the voltage-gated sodium channels NaV1.7, NaV1.8, or acid-sensing ion channels), 2) silencing Mu-opioid receptor-expressing neurons by modified botulinum compounds, and 3) targeting two inflammatory mediators simultaneously (nerve growth factor (NGF) and tumor necrosis factor (TNF)). Unlike global NaV1.8 knockout mice which do not show any reduction in CIBP-related behavior, embryonic conditional NaV1.7 knockout mice in sensory neurons exhibit a mild reduction in CIBP-linked behavior. Modified botulinum compounds also failed to cause a detectable analgesic effect. In contrast, inhibition of NGF and/or TNF resulted in a significant reduction in CIBP-driven weight-bearing alterations and prevented the development of secondary cutaneous heat hyperalgesia. Our results support the inhibition of these inflammatory mediators; and more strongly their dual inhibition to treat CIBP, given the superiority of combination therapies in extending the time needed to reach limb use score zero in our CIBP model.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".