Abstract P1-03-08: Targeting Neurotensinergic Receptors for Bone Cancer Pain Relief
Bibliographic record
Abstract
Abstract Metastatic breast cancer spreads principally to long bones like femur. The presence of breast cancer metastasis in bone microenvironment results in severe pain increasing with disease progression. Opioids, including morphine, are a standard for bone cancer pain management despite their adverse effects at high doses. Our aim here was to examine the effects of opioidergic-independent neurotensin (NT) receptor (NTS1, NTS2) activation in alleviating bone cancer pain in a new clinically relevant mouse model. To this end, neurotensinergic agonists (PD149163, JMV-2012 or JMV-431) were intrathecally administrated to C57BL/6 mice implanted with syngenic E0771 metastatic breast cancer cells in the femoral intramedullary canal. Compounds were analysed for touch-evoked (von Frey), forced movement-evoked (Rotarod) and ambulatory (dynamic weight bearing) pain 18 days following E0771 implantation. The intrathecal injection of PD149163, a NTS1-selective agonist, induced a marked decrease of touch-evoked pain reaching 64 ± 9%. On the Rotarod, PD149163 treatment failed to improve either time spent on the rotating cylinder or the visual pain score. The injection of a non-selective agonist, JMV-2012, reduced touch-evoked pain of 16 ± 5%, but was not efficient in reversing pain under a physical effort on the Rotarod. JMV-431, a NTS2-selective agonist, produced a significant reduction of touch-evoked pain of 70 ± 9% in cancer mice, which also spent more time (10 ± 13%) on the Rotarod and expressed less movement-evoked pain (97 ± 20%) than saline-treated cancer mice. However, acute spinal JMV-431 failed to restore ipsilateral weight bearing toward basal values. The analysis of spinal neuronal activity of mice injected with JMV-431 showed a significant reduction of 55 ± 6% of c-Fos expression in laminae V-VII of the ipsilateral horn 18 days post implantation. In conclusion, these results indicate that the NTS2 receptor represents a promising target for treating bone cancer pain. Further investigations of continuous delivery of NTS2-selective agonists at the central and peripheral levels are under way to evaluate the real potential of these compounds for prophylactic and curative treatments against cancer pain. Supported by CIHR/Canadian Pain Society/AstraZeneca and Cancer Research Society grants. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P1-03-08.
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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) | 0.005 | 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".