Broad Antiviral Effects of Echinacea purpurea against SARS-CoV-2 Variants of Concern and Potential Mechanism of Action
Why this work is in the frame
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Bibliographic record
Abstract
SARS-CoV-2 variants of concern (VOCs) represent an alarming threat as they show altered biological behavior and may escape vaccination effectiveness. Broad-spectrum antivirals could play an important role to control infections. The activity of Echinacea purpurea (Echinaforce® extract, EF) against (i) VOCs B1.1.7 (alpha), B.1.351.1 (beta), P.1 (gamma), B1.617.2 (delta), AV.1 (Scottish), B1.525 (eta), and B.1.1.529.BA1 (omicron); (ii) SARS-CoV-2 spike (S) protein-pseudotyped viral particles and reference strain OC43 as well as (iii) wild type SARS-CoV-2 (Hu-1) was analyzed. Molecular dynamics (MD) were applied to study the interaction of Echinacea’s phytochemical markers with known pharmacological viral and host cell targets. EF extract broadly inhibited the propagation of all investigated SARS-CoV-2 VOCs as well as the entry of SARS-CoV-2 pseudoparticles at EC50′s ranging from 3.62 to 12.03 µg/mL. The preventive addition of 25 µg/mL EF to epithelial cells significantly reduced sequential infection with SARS-CoV-2 (Hu-1) and OC43. MD analyses showed constant binding affinities to VOC-typical S protein variants for alkylamides, caftaric acid, and feruloyl-tartaric acid in EF extract and interactions with serine protease TMPRSS-2. EF extract demonstrated stable virucidal activity across seven tested VOCs, likely due to the constant affinity of the contained phytochemical substances to all spike variants. A possible interaction of EF with TMPRSS-2 partially would explain the cell protective benefits of the extract by the inhibition of membrane fusion and cell entry. EF may therefore offer a supportive addition to vaccination endeavors in the control of existing and future SARS-CoV-2 virus mutations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it