Sialyl Lewis<sup>x</sup> Glycomimetics as E- and P-Selectin Antagonists Targeting Hyperinflammation
Bibliographic record
Abstract
Inflammatory disorders, such as sepsis, pancreatitis, and severe COVID-19, often cause immune dysfunction and high mortality. These conditions trigger excessive immune cell influx, leading to cytokine storms, organ damage, and compensatory immune suppression that results in immunoparalysis, organ dysfunction, and reinfection. Controlled and reversible immunosuppression limiting immune cell recruitment to inflammation sites could reduce hyperinflammation and prevent immune exhaustion. PSGL-1 on leukocytes binds to vascular P- and E-selectins via its sialyl Lewis x pharmacophore, triggering key features of systemic inflammatory response syndrome and sepsis. We report the discovery of two immunomodulators, sialyl Lewis x glycomimetics ( 12 and 13 ), with a tetrazole carboxyl bioisostere of 3a, which binds P- and E-selectin and blocks their interaction with PSGL-1. In an in vivo hyperinflammation model, they reduced immune cell recruitment, evidenced by decreased neutrophils, CD11b+, monocytes/macrophages, and PSGL-1-positive cells at various time points. These glycomimetics may be promising leads for managing the systemic inflammatory response syndrome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".