Esculatin‐4‐carboxylic acid ethyl ester: A novel class of marine‐derived anti‐SARS‐coronavirus 3CL protease inhibitors
Bibliographic record
Abstract
Marine sponges, which produce a remarkable array of secondary metabolites with pharmaceutical potential, are currently under investigation for cancer/pain treatments and anti‐viral therapy. This study describes our discovery of the first small‐molecule protease inhibitor from a marine organism directed at the Severe Acute Respiratory Syndrome (SARS)‐associated coronovirus 3CL protease. By analogy with other coronaviruses, SARS‐CoV encodes a main protease, 3CL, that plays an essential role in the viral life cycle and is currently the prime target for development of new anti‐coronavirus agents. The inhibitory compound was identified as esculatin‐4‐carboxylic acid ethyl ester (MC8: ID 50 = 46 □ M), a novel coumarin derivative extracted from the tropical marine sponge Axinella cf. corrugata , an important model of marine organism. Using a cell‐culture‐based assay of SARS‐CoV infection, we demonstrated that MC8 is an anti‐SARS agent with an EC 50 of 112□ M and a median toxic concentration higher than 800 □M. We are currently synthesizing new analogs of the MC8 coumarin compound and are evaluating their mechanism of inhibition and anti‐SARS‐CoV effects. The results of our work underscore the importance of screening chemically diverse metabolite libraries, obtained from marine sponges, for the discovery of novel protease inhibitors with potent antiviral activities and a good therapeutic index. Supported by CIHR (F. Jean).
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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) | 0.002 | 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".