Etifoxine inhibits NLRP3 inflammasome activity in human and murine myeloid cells
Bibliographic record
Abstract
ABSTRACT Background Multiple sclerosis (MS) is a chronic neuroinflammatory disease that is characterized by immune-mediated demyelination within the central nervous system. NLRP3 inflammasome activation has been previously reported as a possible pathophysiological contributor to microglial activation and oligodendroglial loss in MS, particularly in progressive forms of the disease. Methods Using both in vivo and in vitro approaches, this report investigated the use of a previously described ligand of the 18kDa translocator protein (TSPO), etifoxine, as an immunomodulator that inhibits inflammasome activation in primary human and murine macrophages and microglia. To further elucidate pathologic relevance in the MS context, investigations were also performed ex vivo using peripheral blood mononuclear cells and purified CD14 + monocytes derived from secondary progressive MS patients. Results Herein, it is demonstrated that etifoxine attenuated clinical symptoms in a mouse model of MS and significantly inhibited NLRP3 inflammasome activation in human and murine myeloid-derived cells in vitro by decreasing inflammasome-associated genes and inflammatory cytokine production. These anti-inflammatory effects of etifoxine were mediated independently of its previously described mechanisms related to engagement with TSPO and the GABA A receptor. Furthermore, we observed a similar anti-inflammatory effect of etifoxine on MS patient-derived monocytes, which provides clinical relevance for the investigation of etifoxine as a potential therapeutic in progressive MS. Lastly, through the use of a gene array, we identified multiple signalling pathways in order to elucidate a novel mechanism whereby etifoxine may be inhibiting NLRP3 inflammasome activation. Conclusions Our results suggest that the anti-inflammatory effects of etifoxine were mediated independently of its previously described mechanisms related to engagement with TSPO and the GABA A receptor. Furthermore, we observed an anti-inflammatory effect in murine- and human-derived myeloid cells, as well as in MS patient-derived monocytes, which provides clinically relevant evidence to support the exploration of etifoxine as a possible form of therapy for secondary progressive MS.
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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".