Sinapic acid phenethyl ester as a potent selective 5‐lipoxygenase inhibitor: Synthesis and structure–activity relationship
Bibliographic record
Abstract
Given the hepatotoxicity and an unfavorable pharmacokinetic profile of zileuton (Zyflo®), currently the only approved and clinically used 5‐Lipoxygenase (5‐LO) inhibitor, the search for potent and safe 5‐LO inhibitors is highly demanded. The action of several phenolic acid phenethyl esters as potential 5‐Lipoxygenase (5‐LO) inhibitors has been investigated. For this purpose, a series of 14 phenethyl esters was synthesized and their impact on 5‐LO inhibition was evaluated. The effects of position and number of hydroxyl and methoxy groups on the phenolic acid were investigated. The shortening of the linker between the carbonyl and the catechol moiety as well as the presence of the α,β‐unsaturated carbonyl group was also explored. The sinapic acid phenethyl ester (10), which can be named SAPE (10) by analogy to caffeic acid phenethyl ester (CAPE), inhibited 5‐LO in a concentration‐dependent manner and outperformed both zileuton (1) and CAPE (2). With an IC50 of 0.3 μm, SAPE (10) was threefold more potent than CAPE (2) and 10‐fold more potent than zileuton (1), the only 5‐LO inhibitor approved for clinical use. Unlike CAPE (2), SAPE (10) had no effect on 12‐lipoxygenase (12‐LO) and less effect on cyclooxygenase 1 (COX‐1) which makes it a more selective 5‐LO inhibitor.
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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) | 0.001 | 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 | 0.001 |
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".