A quercetin derivative as a selective inhibitor of 12‐lipoxygenase activity in human platelets
Bibliographic record
Abstract
Introduction Eicosanoids are lipid mediators involved in several critical steps of the inflammatory response. While the response is necessary for the host's defense against pathogens, uncontrolled or unregulated production of these mediators has been associated with several types of chronic inflammatory diseases, including arthritis, asthma and cardiovascular diseases. Since chronic inflammation is the foundation and a key manifestation for various inflammatory diseases, it is not surprising that enzymes implicated in the production of eicosanoids have been strategically targeted for potential therapeutic approaches. The 12‐hydroxyeicosatetraenoic acid (12(S)‐HETE) lipid mediator is among those inflammatory molecules abundantly produce in various diseases and is primarily biosynthesized via the 12(S)‐lipoxygenase (12‐LO) pathway. The abundance of 12(S)‐HETE and its contribution to several chronic inflammatory diseases, such as arthritis or cancer, has been well established over the last few years. While most developed compounds primarily target the 5‐lipoxygenase (5‐LO) or the cyclooxygenase (COX) pathways, very few compounds have been designed to selectively inhibit the 12‐LO pathway. Given the physiological importance of 12(S)‐HETE in inflammatory diseases, it is somewhat surprising that the enzyme has not been the focus of more pharmacological targeting. Amongst the compounds that have generated interest in the field of eicosanoids are natural‐occurring flavonoids, a class of bioactive plant compounds. Flavonoids exhibit several beneficial properties including anti‐inflammatory and anti‐oxidant capacities and can impact on some cardiovascular risk factors. In this study, we examined whether the distribution of hydroxyl groups among flavones could influence their potency as 12‐LO inhibitors. Research approach Platelets isolated from healthy consenting volunteer were pre‐incubated in presence of the test compounds or vehicle and then stimulated with thrombin to initiate 12(S)‐HETE production. The 12(S)‐HETE was quantified by high‐performance liquid chromatography with UV detection. Platelet activation was accessed by CD62P externalization (P‐Selectin) using flow cytometry. Finally, the effects of the compounds on the cell's metabolism were evaluated by high‐resolution respiratory assays (Oroboros Instrument). This study was approved by Université de Moncton review committee for research involving human subjects. Results We demonstrated that the peracetylated quercetin (1 μM) inhibits 12(S)‐HETE production by 39.2% ± 7.42 (mean±SEM). For comparison, baicalein the most commonly used 12‐LO inhibitor, had very similar inhibitory potency as it reduced 12(S)‐HETE production by 39.4 ± 4.57% (mean±SEM). Peracetylated quercetin and baicalein had IC 50 values of 1.62 and 1.22 μM respectively. However, peracetylated quercetin demonstrated selectivity for the 12‐LO, which was not the case for baicalein when compounds where tested for COX‐1 inhibition. Finally, the quercetin derivative did not exhibit any off‐target effects on platelet activation (P‐Selectin expression) or metabolism (electron transfer chain). Conclusion This study characterizes the peracetylated quercetin as a more selective platelet‐type 12‐LO inhibitor than baicalein, with no measurable undesirable effects on other cellular pathways. Support or Funding Information Canadian Institutes of Health Research (LHB, JLJ, MES), New Brunswick Innovation Foundation (LHB, MES), New Brunswick Health Research Foundation (JLJ, JLL), Natural Sciences and Engineering Research Council of Canada (NP, MT) This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.003 | 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".