Using the cooling agent icilin and synthetic analogs to attenuate autoreactive T cell function during experimental autoimmune encephalomyelitis.
Bibliographic record
Abstract
Abstract Targeting inflammatory function of autoreactive T lymphocytes defines a key facet of therapeutics for autoimmune diseases such as multiple sclerosis (MS). The super-cooling drug icilin has recently been shown to limit systemic inflammation through activation of the TRPM8 calcium channel in murine models of colitis. We thus aimed to define an anti-inflammatory mechanism for the drug candidate icilin and TRPM8 in T cell-mediated adaptive neuroinflammation. Using experimental autoimmune encephalomyelitis (EAE), a CD4+ T cell-driven model of MS, we assayed the influence of icilin on CD4+ T cell activation, proliferation, and effector function. Significant reductions in clinical disease were observed in WT EAE mice receiving icilin treatment, coinciding with fewer infiltrating leukocytes in the central nervous system. Unexpectedly, TRPM8−/− mice treated with icilin also displayed protection from EAE, showing similar reductions in disease score and leukocyte infiltration. Both WT and TRPM8−/− mice receiving icilin treatment showed significant delays in initial disease onset, attributed in vitro to reduced T cell proliferation via icilin-mediated G1 cell cycle arrest. A library of chemically and structurally related icilin analogs was screened to identify novel drug candidates targeting autoimmune T cell function. Amongst the screened drug analogs, a small fraction potently attenuated T cell proliferation in vitro. These results suggest that TRPM8-independent ablation of T cell function drives the anti-inflammatory properties of icilin during EAE. Further, novel drug candidates structurally related to icilin represent a promising class of molecules for limiting T cell responses in MS and other autoinflammatory 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".