Design of a peripherally biased NPSR1 antagonist for neuropeptide S induced inflammation
Bibliographic record
Abstract
Neuropeptide S (NPS) is a potent agonist for the GPCR receptor NPSR1, implicated in various physiological and pathological processes, including inflammation. NPSR1 gene polymorphisms have been linked to asthma, inflammatory bowel disease, and endometriosis. Activation of NPSR1 triggers signaling through Gαq and Gαs leading to activation of calcium and cAMP respectively, which induces the expression of pro-inflammatory cytokines. Given NPSR1 is widely expressed in the brain and modulates behavioral responses, the development of a non-brain-penetrant NPSR1 antagonist with favorable pharmacokinetics would represent a significant advancement. While promising NPSR1 antagonists like SHA-68R and NPSR-QA1 exist, suboptimal ADME profiles and/or brain penetrance limit pharmacological evaluation of NPSR1 peripheral inhibition. In the present study, a structure-activity relationship analysis of NPSR-QA1 led to the identification of two potent, peripherally restricted NPSR1 antagonists with favorable pharmacokinetic properties. NPSR-QA1 derivatives were screened for NPSR1 antagonism in cell-based calcium and cAMP signaling assays. Two lead compounds were identified that demonstrated sub-nanomolar potency, high solubility, decent unbound clearance, and low brain penetration in mice. In vitro assays using human fibroblasts with enforced expression of NPSR1 established that NPS triggered expression of pro-inflammatory markers IL-6, PTGS2, IL-20, and CXCL8, all of which were effectively inhibited by the lead compounds. Further, murine studies with zymosan-induced inflammation showed that NPSR1 antagonism significantly increased resident macrophages in the peritoneum and reduced TNF-α cytokine levels. These findings highlight the potential of NPSR1 antagonism to block inflammation without CNS side effects, advancing the development of NPSR1 antagonists as therapeutic agents for peripheral inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".