Discovery of a CCR2-targeting pepducin therapy for chronic pain
Bibliographic record
Abstract
Targeting the CCL2/CCR2 chemokine axis has been shown to be effective at relieving pain in rodent models of inflammatory and neuropathic pain, therefore representing a promising avenue for the development of non-opioid analgesics. However, clinical trials targeting this receptor for inflammatory conditions and painful neuropathies have failed to meet expectations and have all been discontinued due to lack of efficacy. To overcome the poor selectivity of CCR2 chemokine receptor antagonists, we generated and characterized the function of intracellular cell-penetrating allosteric modulators targeting CCR2, namely pepducins. In vivo, chronic intrathecal administration of the CCR2-selective pepducin PP101 was effective in alleviating neuropathic and bone cancer pain. In the setting of bone metastases, we found that T cells infiltrate dorsal root ganglia (DRG) and induce long-lasting pain hypersensitivity. By acting on CCR2-expressing DRG neurons, PP101 attenuated the altered phenotype of sensory neurons as well as the neuroinflammatory milieu of DRGs, and reduced bone cancer pain by blocking CD4+ and CD8+ T cell infiltration. Notably, PP101 demonstrated its efficacy in targeting the neuropathic component of bone cancer pain, as evidenced by its anti-nociceptive effects in a model of chronic constriction injury of the sciatic nerve. Importantly, PP101-induced reduction of CCR2 signaling in DRGs did not result in deleterious tumor progression or adverse behavioral effects. Thus, targeting neuroimmune crosstalk through allosteric inhibition of CCR2 could represent an effective and safe avenue for the management of chronic pain.
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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) | 0.001 | 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".