The Significance of Natural Product Derivatives and Traditional Medicine for COVID-19
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To date, there have been more than 10 million reported cases, more than 517,000 deaths in 215 countries, areas or territories. There is no effective antiviral medicine to prevent or treat COVID-19. Natural products and traditional medicine products with known safety profiles are a promising source for the discovery of new drug leads. There is increasing number of publications reporting the effect of natural products and traditional medicine products on COVID-19. In our review, we provide an overview of natural products and their derivatives or mimics, as well as traditional medicine products, which were reported to exhibit potential to inhibit SARS-CoV-2 infection in vitro, and to manage COVID-19 in vivo, or in clinical reports or trials. These natural products and traditional medicine products are categorized in several classes: (1) anti-malaria drugs including chloroquine and hydroxychloroquine, (2) antivirals including nucleoside analogs (remdesivir, favipiravir, β-D-N4-hydroxycytidine, ribavirin and among others), lopinavir/ritonavir and arbidol, (3) antibiotics including azithromycin, ivermectin and teicoplanin, (4) anti-protozoal drug, emetine, anti-cancer drug, homoharringtonine, and others, as well as (5) traditional medicine (Lian Hua Qing Wen Capsule, Shuang Huang Lian Oral Liquid, Qingfei Paidu Decoction and Scutellariae Radix). Randomized, double-blind and placebo-controlled large clinical trials are needed to provide solid evidence for the potential effective treatment. Currently, drug repurposing is a promising strategy to quickly find an effective treatment for COVID-19. In addition, carefully combined cocktails need to be examined for preventing a COVID-19 pandemic and the resulting global health concerns.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".