Amaryllidaceae Alkaloids Screen Unveils Potent Anticoronaviral Compounds and Associated Structural Determinants
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Betacoronaviruses encompass a spectrum of respiratory diseases, from common cold caused by the human coronavirus (HCoV)-OC43 to life-threatening severe acute respiratory syndrome (SARS)-CoV-2. Addressing the constant need for novel antiviral compounds, we turned to the exploration of 40 plant-specialized metabolites produced by the medicinal plant family Amaryllidaceae, known to produce lycorine, a strong antiviral alkaloid. The present screen included 35 alkaloids with representatives of 8 ring-type structures. Pancracine, crinamine, hemanthamine, and hemanthidine exhibited potency comparable to lycorine in blocking HCoV–OC43 replication, while amarbellisine demonstrated superior efficacy (SI = 60, EC 50 = 0.2 μM). Their anticoronaviral activity was confirmed using a SARS-CoV-2 replicon system. Time-of-drug-addition experiments established that a postentry step consistent with ribonucleic acid (RNA) replication or translation was targeted. Most antiviral Amaryllidaceae alkaloids selectively induced the expression of transcripts associated with the integrated stress response. Structure–activity relationship analyses elucidated key functional groups contributing to antiviral properties in the crinine- and lycorine-type. This study reveals that Amaryllidaceae produce a diverse repertoire of promising antiviral compounds in addition to lycorine, offering insights for developing new 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 | 0.001 | 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 | 0.001 |
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".