PNA6, a Lactosyl Analogue of Angiotensin-(1-7), Reverses Pain Induced in Murine Models of Inflammation, Chemotherapy-Induced Peripheral Neuropathy, and Metastatic Bone Disease
Bibliographic record
Abstract
Pain is the most significant impairment and debilitating challenge for patients with bone metastasis. Therefore, the primary objective of current therapy is to mitigate and prevent the persistence of pain. Thus, cancer-induced bone pain is described as a multifaceted form of discomfort encompassing both inflammatory and neuropathic elements. We have developed a novel non-addictive pain therapeutic, PNA6, that is a derivative of the peptide Angiotensin-(1-7) and binds the Mas receptor to decrease inflammation-related cancer pain. In the present study, we provide evidence that PNA6 attenuates inflammatory, chemotherapy-induced peripheral neuropathy (CIPN) and cancer pain confined to the long bones, exhibiting longer-lasting efficacious therapeutic effects. PNA6, Asp-Arg-Val-Tyr-Ile-His-Ser-(O-β-Lact)-amide, was successfully synthesized using solid phase peptide synthesis (SPPS). PNA6 significantly reversed inflammatory pain induced by 2% carrageenan in mice. A second murine model of platinum drug-induced painful peripheral neuropathy was established using oxaliplatin. Mice in the oxaliplatin-vehicle treatment groups demonstrated significant mechanical allodynia compared to the oxaliplatin-PNA6 treatment group mice. In a third study modeling a complex pain state, E0771 breast adenocarcinoma cells were implanted into the femur of female C57BL/6J wild-type mice to induce cancer-induced bone pain (CIBP). Both acute and chronic dosing of PNA6 significantly reduced the spontaneous pain behaviors associated with CIBP. These data suggest that PNA6 is a viable lead candidate for treating chronic inflammatory and complex neuropathic 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 | 0.002 |
| 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".