CCR2-targeting pepducins reduce T cell-nociceptor interaction driving bone cancer pain
Bibliographic record
Abstract
Abstract Inhibition of the CCL2/CCR2 chemokine signaling represents a promising avenue for the development of non-opioid pain treatment, particularly for painful bone metastases. To investigate the involvement of CCR2 in cancer-induced bone pain, we generated and characterized the functional activities of a novel cell-penetrating pepducin, namely PP101, acting as an intracellular negative allosteric modulator of CCR2. In vivo , PP101 was effective in relieving neuropathic and bone cancer pain. By targeting CCR2, PP101 reduced bone cancer pain by preventing infiltration of CD4 + and CD8 + T cells and by decreasing the neuroimmune communication network within the dorsal root ganglia. Importantly, reduced neuroinflammatory milieu in the dorsal root ganglia induced by PP101 did not result in deleterious tumor progression or behavioral adverse effects. Thus, targeting the neuroimmune crosstalk through allosteric inhibition of CCR2 may represent an effective and safe avenue for the management of bone cancer pain. Graphical Abstract Highlights Breast cancer bone metastases induce pain by activating CCR2 on sensory neurons. DRG-infiltrating CD4 + and CD8 + T cells promote the development of bone cancer pain. CCR2 inhibition by PP101 suppresses DRG neuroinflammation and neuronal excitability. PP101 alleviates bone cancer pain without behavioral or physiological side effects.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".