Epigallocatechin Gallate and Isoquercetin Synergize With Remdesivir to Reduce SARS-CoV-2 Replication In Vitro
Bibliographic record
Abstract
The ongoing pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) urgently needs effective antivirals. After over 2 years since the beginning of the pandemic, only a few FDA approved therapeutic options are available to treat the population. Combination therapies have become a standard for the treatment of other infectious diseases such as HIV and hepatitis C due to their improved efficacy compared to monotherapy, reduced toxicity, the ability to prevent the development of resistant viral strains and their potential to treat co-infection. The interest in identifying molecules displaying bioactivity against SARS-CoV-2 has led to extensive search for promising molecules from the natural pharmacopoeia and polyphenols have been shown to display antiviral activity against a number of viruses including SARS-CoV-2. Here we evaluated the in vitro efficacy of two polyphenols, Epigallocatechin gallate (EGCG) and Isoquercetin, in combination with Remdesivir, the first-approved drug for the treatment of severe COVID-19. We confirmed the inhibitory effects of EGCG and isoquercetin against SARS-CoV-2 and demonstrated their strong antiviral synergistic effects with Remdesivir in vitro. These combinational therapies represent an interesting avenue for the treatment of COVID-19 and grant further studies.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".