gem-Difluorobisarylic derivatives: design, synthesis and anti-inflammatory effect
Bibliographic record
Abstract
Abstract Introduction New fluorinated diaryl ethers and bisarylic ketones were designed and evaluated for their anti-inflammatory effects in primary macrophages. Methods The synthesis of the designed molecules started from easily accessible and versatilegem-difluoro propargylic derivatives. The desired aromatic systems were obtained using Diels–Alder/aromatization sequences and this was followed by Pd-catalyzed coupling reactions and, when required, final functionalization steps. Both direct inhibitory effects on cyclooxygenase-1 or -2 activities, protein expression of cyclooxygenase-2 and nitric oxide synthase-II and the production of prostaglandin E2, the pro-inflammatory nitric oxide and interleukin-6 were evaluated in primary murine bone marrow-derived macrophages in response to lipopolysaccharide. Docking of the designed molecules in cyclooxygenase-1 or -2 was performed. Results Only fluorinated compounds exerted anti-inflammatory activities by lowering the secretion of interleukin-6, nitric oxide, and prostaglandin E2, and decreasing the protein expression of inducible nitric oxide synthase and cyclooxygenase-2 in mouse primary macrophages exposed to lipopolysaccharide, as well as cyclooxygenase activity for some inhibitors with different efficiencies depending on the R-groups. Docking observation suggested an inhibitory role of cyclooxygenase-1 or -2 for compoundsA3,A4andA5in addition to their capacity to inhibit nitrite, interleukin-6, and nitric oxide synthase-II and cyclooxygenase-2 expression. Conclusion The new fluorinated diaryl ethers and bisarylic ketones have anti-inflammatory effects in macrophages. These fluorinated compounds have improved potential anti-inflammatory properties due to the fluorine residues in the bioactive molecules.
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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".