Computational Investigation of Darapladib and Rilapladib Binding to Platelet Activating Factor Receptor. A Possible Mechanism of Their Involvement in Atherosclerosis
Bibliographic record
Abstract
Platelet Activating Factor (PAF), the most potent inflammatory mediator, is involved in a wide range of pathophysiological actions. PAF signal transduction is mediated through PAF receptors (PAFR) that are coupled with several isoforms of G-proteins. PAF hydrolysis is mediated through specific enzymes clustered as PAF acetylhydrolases (PAF-AH). The plasma isoform is known as lipoprotein-associated PLA2 (Lp-PLA2), and is considered a marker, or a mediator in the mechanism of atherosclerosis. Darapladib and rilapladib are selective Lp-PLA2 inhibitors. They are, thus, proposed as a novel therapeutic approach for cardiovascular disease (CVD). The data derived from the computational methods used in this paper suggest that darapladib and rilapladib are potential PAFR antagonists, predicted to bind inside the PAF-binding site with a comparable binding affinity to the endogenous agonist (?G = –11.1 Kcal mol–1). Darapladib (?G = –10.6 Kcal mol–1) exhibited a higher affinity than rilapladib (?G = –8.2 Kcal mol–1). The fact that darapladib down-regulates PAFR expression, while PAFR inhibitors down-regulate the expression of CD36, could be the biochemical explanation in the observed necrotic core reduction, both in animals and humans. The reported results in conjunction with bibliographical data lead to the hypothesis that the involvement of darapladib and rilapladib in atherosclerosis could be through direct inhibition of PAF activity as well as modification of PAF metabolism.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".