Structure–activity relationship of caffeic acid phenethyl ester analogs as new 5‐lipoxygenase inhibitors
Bibliographic record
Abstract
Leukotrienes (LTs) are a class of lipid mediators implicated in numerous inflammatory disorders. Caffeic acid phenethyl ester (CAPE) possesses potent anti‐LTs activity through the inhibition of 5‐lipoxygenase (5‐LO), the key enzyme in the biosynthesis of LTs. In this study, we describe the design and synthesis of CAPE analogs as radical scavengers and 5‐LO inhibitors. Caffeic esters bearing propargyl and allyl linkers between the caffeoyl and aryl moieties (4a–i and 5a–i, respectively) were synthesized by Sonogashira and Heck cross‐coupling reactions to probe the effects of flexibility and aryl substitution on 5‐LO inhibition. Caffeoyl alcohol and ethers (6, 7a–b) as well as caffeoyl aldehyde and ketones (8a–e) were synthesized to elucidate the importance of the ester linkage for inhibitory activity. All tested compounds proved to be good radical scavengers (IC50 of 10–30 μm). After preliminary anti‐LTs activity screening in HEK293 cell models, 5‐LO inhibition potential of selected compounds was determined in human polymorphonuclear leukocytes (PMNL). Most screened compounds outperformed CAPE 3 in concentration‐dependent assays on PMNL, with ester dimers 4i and 5i along with caffeoyl ethers 7a–b being roughly eight‐, seven‐, and 16‐fold more potent than Zileuton, with IC50 values of 0.36, 0.43, and 0.18 μm, respectively.
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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".