Boosting immunity: synergistic antiviral effects of luteolin, vitamin C, magnesium and zinc against SARS-CoV-2 3CLpro
Bibliographic record
Abstract
SARS-CoV-2 was first discovered in 2019 and has disseminated throughout the globe to pandemic levels, imposing significant health and economic burdens. Although vaccines against SARS-CoV-2 have been developed, their long-term efficacy and specificity have not been determined, and antiviral drugs remain necessary. Flavonoids, which are commonly found in plants, fruits, and vegetables and are part of the human diet, have attracted considerable attention as potential therapeutic agents due to their antiviral and antimicrobial activities and effects on other biological activities, such as inflammation. The present study uses a combination of biochemical, cellular, molecular dynamics, and molecular docking experiments to provide compelling evidence that the flavonoid luteolin (2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one) has antiviral activity against SARS-CoV-2 3-chymotrypsin-like protease (3CLpro) that is synergistically enhanced by magnesium, zinc, and vitamin C. The IC50 of luteolin against 2 µM 3CLpro is 78 µM and decreases 10-fold to 7.6 µM in the presence of zinc, magnesium, and vitamin C. Thermodynamic stability analyses revealed that luteolin has minimal effects on the structure of 3CLpro, whereas metal ions and vitamin C significantly alter the thermodynamic stability of the protease. Interactome analysis uncovered potential host-virus interactions and functional clusters associated with luteolin activity, supporting the relevance of this flavone for combating SARS-CoV-2 infection. This comprehensive investigation sheds light on luteolin's therapeutic potential and provides insights into its mechanisms of action against SARS-CoV-2. The novel formulation of luteolin, magnesium, zinc, and vitamin C may be an effective avenue for treating COVID-19 patients.
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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".