Synthesis and pharmacological evaluation of nature-inspired phenacyl glycosides
Bibliographic record
Abstract
Phenylethanoid glycosides are a well-studied class of bioactive compounds found throughout the plant kingdom. In contrast, research on the synthesis and pharmacological activity of phenacyl glycosides, a specific subgroup of phenylethanoid glycosides with a ketone functionality at the alpha position of the phenol ring, has been limited. In this study, we report the synthesis, cytotoxic, antiviral, and anti-inflammatory evaluation of a series of 18 4'-hydroxyphenacyl glycosides. These compounds consist of six different sugar residues (β-d-glucose, β-d-galactose, α-l-arabinose, β-d-xylose, α-l-rhamnose, and β-d-glucuronic acid) and display three distinct methoxylation patterns at the phenacyl ring, similar to the substitution motifs of anthocyanins. We obtained the target phenacyl glycosides in high yield and stereoselectivity through the coupling of benzoyl-protected trichloroacetimidate glycosyl donors and corresponding acetophenones. Our work represents the first total synthesis of the natural products 4'-hydroxyphenacyl-β-d-glucopyranoside (1) and 4'-hydroxy-3'-methoxyphenacyl-β-d-glucopyranoside (2). None of the phenacyl glycosides showed cytotoxicity against the tested cell lines. Notably, several of the synthesized compounds exhibited antiviral activity, with natural product 2 being the most active against herpes simplex virus type 1, while phenacyl arabinoside 9 and natural product 2 were the most active against human coronavirus OC43. Natural product 2 significantly inhibited the production of interleukin-6 in lipopolysaccharide-stimulated microglia cells. Overall, our findings highlight the importance of the sugar residue and phenacyl ring substitution pattern in modulating the antiviral activity of phenacyl glycosides. Natural product 2 and phenacyl arabinoside 9 emerge as promising leads for the development of antiviral agents.
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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".