Dose-dependent suppression of cytokine production from T cells by a novel phosphoinositide 3-kinase delta inhibitor (P6275)
Bibliographic record
Abstract
Abstract The use of specific cytokine inhibitors to treat inflammatory conditions is common but typically utilizes monoclonal antibodies, fusion proteins, and other molecules whose large size necessitates parenteral administration. There remains a significant need for development of effective small molecule cytokine inhibitors for inflammatory conditions. Phosphoinositide 3-kinase (PI3K) plays a critical role in multiple cell signaling pathways, cell cycle progression, and cell growth, and is thus a potential target for drug development. We examined the effect of a novel PI3Kδ inhibitor on CD3/CD28 stimulated T-cell cytokine production. When added to culture after allowing naïve T-cells to polarize into effector T-cells, we observed dose-dependent suppression of multiple cytokines by Luminex and ELISA, including IL-17, IFNγ, and IL-4 from Th17, Th1, and Th2 cells, respectively. Real time PCR confirmed the suppression of cytokine gene expression suggesting the inhibition is mediated at the transcriptional level. However, we do not observe suppressed gene expression of Tbet, GATA3, or RORc, the critical transcription factors for production of those cytokines. A potential mechanism is that the PI3Kδ inhibitor alters nuclear localization of these transcription factors post-translationally. These experiments show effectiveness of this small molecule inhibitor of PI3Kδ activity to modulate inflammation in vitro and exhibit promise as a treatment for in vivo models of 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".