Nanomedicines targeting protease-activated receptor 2 in endosomes provide sustained analgesia
Bibliographic record
Abstract
Although many internalized G protein-coupled receptors (GPCRs) continue to signal, the mechanisms and outcomes of intracellular GPCR signaling are uncertain due to the challenges of measuring organelle-specific signals and of selectively antagonizing receptors in intracellular compartments. Herein, genetically encoded biosensors targeted to the plasma membrane and early endosomes were used to analyze compartmentalized signaling of protease-activated receptor 2 (PAR 2 ); the propensity of nanoparticles (NPs) to accumulate in endosomes was leveraged to preferentially antagonize intracellular PAR 2 signaling of pain. PAR 2 agonists evoked sustained activation of PAR 2 , Gαq, and β-arrestin-1 in early endosomes and activated extracellular signal regulated kinase (ERK) in the cytosol and nucleus, measured with targeted biosensors. Fluorescent dendrimer and core-shell polymeric NPs accumulated in endosomes of HEK293T cells, colonic epithelial cells, and nociceptors, detected by confocal microscopy. NPs efficiently encapsulated and slowly released AZ3451, a negative allosteric PAR 2 modulator. NP-encapsulated AZ3451, but not unencapsulated AZ3451, rapidly and completely reversed PAR 2 , Gαq, and β-arrestin-1 activation in early endosomes and ERK activation in the cytosol and nucleus. When administered into the mouse colon lumen, fluorescent dendrimer NPs accumulated in endosomes of colonocytes and polymeric NPs accumulated in neurons, sites of PAR 2 expression. Both NP formulations of AZ3451, but not unencapsulated AZ3451, caused long-lasting analgesia and normalized aberrant behavior in preclinical models of inflammatory bowel disease. These results provide evidence that PAR 2 endosomal signaling mediates pain and that nanomedicines that antagonize PAR 2 in endosomes effectively relieve pain. NP-mediated delivery may improve the efficacy of other GPCR antagonists for treatment of diverse diseases.
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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 | 0.000 |
| 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".