Quercetin reduces COVID-19 risk: real-time meta analysis of 12 studies
Abstract
Significantly lower risk is seen for ICU admission, hospitalization, recovery, cases, and viral clearance. 11 studies from 9 independent teams in 7 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
46% [20‑64%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and after excluding studies using combined treatment and slightly worse for peer-reviewed studies.
Results are very robust — in exclusion sensitivity analysis 9 of
12 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Studies typically use advanced formulations for greatly improved bioavailability.
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
The quality of non-prescription supplements varies widely1,2.
All data and sources to reproduce this analysis are in the appendix.
Quercetin for COVID-19 — Highlights
Quercetin reduces
risk with very high confidence for ICU admission, recovery, cases, viral clearance, and in pooled analysis, high confidence for hospitalization, and very low confidence for mortality and ventilation.
Studies typically use advanced formulations for greatly improved bioavailability.
24th treatment shown effective in
July 2021, now with p = 0.002 from 12 studies.
Real-time updates
and corrections with a consistent protocol for 112 treatments. Outcome specific analysis and combined evidence from all
studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in quercetin studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 6.0 months, compared to using all studies. Efficacy based on specific outcomes was delayed by 6.0 months, compared to using pooled outcomes. Efficacy based on specific outcomes in RCTs was delayed by 17.5 months, compared to using pooled outcomes in RCTs.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury5-16 and
cognitive deficits8,13, cardiovascular
complications17-20, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factorsA,21-27, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk28, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
In Silico studies predict inhibition of SARS-CoV-2, or minimization of side effects, with quercetin or metabolites via binding to the spikeB,29-42, MproC,32-35,37,38,41-56, RNA-dependent RNA polymeraseD,38,41,42,57,58 , PLproE,41,45,51, ACE2F,31,37,39,45,46,59 , TMPRSS2G,31, nucleocapsidH,41, helicaseI,41,44,60, endoribonucleaseJ,29, NSP16/10K,61, cathepsin LL,62, Wnt-3M,31, FZDN,31, LRP6O,31, ezrinP,63, ADRPQ,35, NRP1R,39, EP300S,64, PTGS2T,46, HSP90AA1U,46,64, matrix metalloproteinase 9V,65, IL-6W,66,67, IL-10X,66, VEGFAY,67, and RELAZ,67 proteins.
In Vitro studies demonstrate inhibition of the MproC,53,68-70 protein, and inhibition of spike-ACE2 interactionAA,71.
In Vitro studies demonstrate efficacy in Calu-3AB,72, A549AC,66, HEK293-ACE2+AD,73, Huh-7AE,36, Caco-2AF,74, Vero E6AG,32,74,75, mTECAH,76, and RAW264.7AI,76 cells.
Animal studies demonstrate efficacy in K18-hACE2 miceAJ,77, db/db miceAK,76,78, BALB/c miceAL,79, and rats80.
Quercetin reduced proinflammatory cytokines and protected lung and kidney tissue against LPS-induced damage in mice79, inhibits LPS-induced cytokine storm by modulating key inflammatory and antioxidant pathways in macrophages81, and inhibits SARS-CoV-2 ORF3a ion channel activity, which contributes to viral pathogenicity and cytotoxicity82.
We analyze all significant
controlled studies of
quercetin
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
In Silico studies predict inhibition of SARS-CoV-2, or minimization of side effects, with quercetin or metabolites via binding to the spikeB,29-42, MproC,32-35,37,38,41-56, RNA-dependent RNA polymeraseD,38,41,42,57,58 , PLproE,41,45,51, ACE2F,31,37,39,45,46,59 , TMPRSS2G,31, nucleocapsidH,41, helicaseI,41,44,60, endoribonucleaseJ,29, NSP16/10K,61, cathepsin LL,62, Wnt-3M,31, FZDN,31, LRP6O,31, ezrinP,63, ADRPQ,35, NRP1R,39, EP300S,64, PTGS2T,46, HSP90AA1U,46,64, matrix metalloproteinase 9V,65, IL-6W,66,67, IL-10X,66, VEGFAY,67, and RELAZ,67 proteins.
In Vitro studies demonstrate inhibition of the MproC,53,68-70 protein, and inhibition of spike-ACE2 interactionAA,71.
In Vitro studies demonstrate efficacy in Calu-3AB,72, A549AC,66, HEK293-ACE2+AD,73, Huh-7AE,36, Caco-2AF,74, Vero E6AG,32,74,75, mTECAH,76, and RAW264.7AI,76 cells.
Animal studies demonstrate efficacy in K18-hACE2 miceAJ,77, db/db miceAK,76,78, BALB/c miceAL,79, and rats80.
Quercetin reduced proinflammatory cytokines and protected lung and kidney tissue against LPS-induced damage in mice79, inhibits LPS-induced cytokine storm by modulating key inflammatory and antioxidant pathways in macrophages81, and inhibits SARS-CoV-2 ORF3a ion channel activity, which contributes to viral pathogenicity and cytotoxicity82.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
SARS-CoV-2 infection and replication involves multiple steps as
shown in Table 1. Each step can be disrupted by therapeutics. The
timing of each step may vary significantly, and the cycle is continuous, with
released virions attaching to new host cells.
The efficacy of treatments depends on the delay from infection and the steps
targeted. Preclinical research suggests that quercetin is most likely to interfere with early steps in the viral lifecycle, suggesting greater benefit for prophylaxis and very early treatment.
| Step | Details | Approximate timing | Predicted benefit of quercetin |
|---|---|---|---|
| Viral attachment | Viral binding to specific receptors on host cell surface | Initial step | High: spike and ACE2 binding |
| Viral entry | Uptake of viral particle into host cell via mechanisms like endocytosis or membrane fusion | Within minutes to 1 hour | Moderate: spike binding |
| Viral uncoating and release | Disassembly of virion to release viral genome into host cell | 1-2 hours | - |
| Genome replication and transcription | Production of viral mRNAs from the genome template and genome copies | 2-4 hours | Moderate: RdRp binding |
| Translation and protein processing | Production of new viral proteins from the viral transcripts | 4-8 hours | Moderate: Mpro and PLpro binding |
| Viral assembly and budding | Self-assembly of viral components and encapsidation of viral genome to form new viral particles, often utilizing host cell membrane | 8-12 hours | - |
| Viral release | Escape of newly formed virions from the host cell to spread infection | 12-24 hours | - |
Table 2 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, with different exclusions, and for specific outcomes.
Table 3 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, 10, 11, 12, and 13
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, viral clearance, peer reviewed studies, and all studies excluding combined treatment studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 46% [20‑64%] ** | 12 | 1,496 | 116 |
| After exclusions | 38% [14‑56%] ** | 10 | 1,283 | 96 |
| Peer-reviewed studiesPeer-reviewed | 36% [16‑52%] ** | 11 | 1,383 | 109 |
| Excluding combined treatmentExc. combined | 55% [12‑77%] * | 6 | 692 | 73 |
| Randomized Controlled TrialsRCTs | 40% [17‑57%] ** | 11 | 1,383 | 111 |
| Mortality | 61% [-35‑89%] | 5 | 790 | 58 |
| ICU admissionICU | 74% [29‑90%] ** | 5 | 790 | 58 |
| HospitalizationHosp. | 36% [5‑56%] * | 4 | 361 | 47 |
| Recovery | 35% [21‑45%] **** | 8 | 998 | 73 |
| Cases | 93% [73‑98%] **** | 3 | 346 | 24 |
| Viral | 56% [38‑68%] **** | 3 | 200 | 26 |
| RCT mortality | 61% [-35‑89%] | 5 | 790 | 58 |
| RCT hospitalizationRCT hosp. | 36% [5‑56%] * | 4 | 361 | 47 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 32% [7‑50%] * | 35% [-21‑65%] | 93% [73‑98%] **** |
| After exclusions | 31% [7‑49%] * | 35% [-21‑65%] | 94% [64‑99%] ** |
| Peer-reviewed studiesPeer-reviewed | 32% [7‑50%] * | 35% [-21‑65%] | 94% [64‑99%] ** |
| Excluding combined treatmentExc. combined | 79% [-83‑98%] | 38% [-22‑68%] | 93% [9‑99%] * |
| Randomized Controlled TrialsRCTs | 32% [7‑50%] * | 35% [-21‑65%] | 92% [66‑98%] *** |
| Mortality | 79% [-83‑98%] | 47% [-138‑88%] | |
| ICU admissionICU | 87% [-5‑98%] | 75% [-10‑94%] | |
| HospitalizationHosp. | 68% [31‑85%] ** | 23% [-3‑43%] | |
| Recovery | 33% [16‑47%] *** | 37% [14‑54%] ** | |
| Cases | 93% [73‑98%] **** |
||
| Viral | 56% [38‑68%] **** | ||
| RCT mortality | 79% [-83‑98%] | 47% [-138‑88%] | |
| RCT hospitalizationRCT hosp. | 68% [31‑85%] ** | 23% [-3‑43%] |
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Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 5. Random effects meta-analysis for mortality results.
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Figure 6. Random effects meta-analysis for ventilation.
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Figure 7. Random effects meta-analysis for ICU admission.
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Figure 8. Random effects meta-analysis for hospitalization.
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Figure 9. Random effects meta-analysis for recovery.
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Figure 10. Random effects meta-analysis for cases.
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Figure 11. Random effects meta-analysis for viral clearance.
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Figure 12. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
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Figure 13. Random effects meta-analysis for all studies excluding combined treatment studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Figure 14 shows a comparison of results for RCTs and non-RCT studies.
Figure 15, 16, and 17
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCT results are included in Table 2 and Table 3.
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Figure 14. Results for RCTs and non-RCT studies.
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Figure 15. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 16. Random effects meta-analysis for RCT mortality results.
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Figure 17. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases98, and
analysis of double-blind RCTs has identified extreme levels of bias99.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 112 treatments we have analyzed,
66% of RCTs involve very late treatment 5+ days after
onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of
early treatments. They may more accurately represent results for treatments
that require visiting a medical facility, e.g., those requiring intravenous
administration.
RCTs have a bias against finding an effect for interventions
that are widely available — patients that believe they need the
intervention are more likely to decline participation and take the
intervention. RCTs for quercetin are more likely to enroll low-risk
participants that do not need treatment to recover, making the results less
applicable to clinical practice. This bias is likely to be greater for widely
known treatments, and may be greater when the risk of a serious outcome is
overstated. This bias does not apply to the typical pharmaceutical trial of a
new drug that is otherwise unavailable.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.92‑1.08]
across 112 treatments101.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 112 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.92‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.92‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.82‑1.03].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see105,106.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.1 months (68% with 8.2 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
We need to evaluate each trial on its own merits. RCTs for a
given medication and disease may be more reliable, however they may also be
less reliable. For off-patent medications, very high conflict of interest
trials may be more likely to be RCTs, and more likely to be large trials that
dominate meta analyses.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 18 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Arslan, paper no longer available at the source, and the contact does not reply to queries.
Di Pierro, randomization resulted in significant baseline differences that were not adjusted for.
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Figure 18. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours109,110. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases111 |
| <24 hours | -33 hours symptoms112 |
| 24-48 hours | -13 hours symptoms112 |
| Inpatients | -2.5 hours to improvement113 |
Figure 19 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 112 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 112 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants115, for example the Gamma variant shows significantly
different characteristics116-119. Different
mechanisms of action may be more or less effective depending on variants, for
example the degree to which TMPRSS2 contributes to viral entry can differ
across variants120,121.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu (C) et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Non-prescription supplements may show very wide variations in quality1,2.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for quercetin as of January 2022. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes was delayed by 6.0 months compared to using pooled outcomes. Efficacy based on specific outcomes in RCTs was delayed by 17.5 months compared to using pooled outcomes in RCTs.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 112
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 20 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 21 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh (D) et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 22 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh (D) et al., with higher confidence due to the larger number of
studies. As with Singh (D) et al., the confidence increases
when excluding the outlier treatment, from p = 0.00000032 to p = 0.000000011.
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Figure 20. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 21. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 20. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months.
Figure 23 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 23. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results137-140.
For quercetin, there is currently not
enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
Figure 24 shows a scatter plot of
results for prospective and retrospective studies.
100% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
91% of prospective studies, consistent with a bias toward publishing positive results.
The median effect size for
retrospective studies is 94% improvement,
compared to 67% for prospective
studies, suggesting a potential bias towards publishing results showing higher efficacy.
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Figure 24. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 25 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.05141-148.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 25. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Quercetin for COVID-19
lacks this because it is an inexpensive and widely available supplement.
In contrast, most COVID-19 quercetin trials have been run by
physicians on the front lines with the primary goal of finding the best
methods to save human lives and minimize the collateral damage caused by
COVID-19. While pharmaceutical companies are careful to run trials under
optimal conditions (for example, restricting patients to those most likely to
benefit, only including patients that can be treated soon after onset when
necessary, and ensuring accurate dosing), not all quercetin trials
represent the optimal conditions for efficacy.
Summary statistics from
meta analysis necessarily lose information. As with all meta analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone124-135.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex
interplay of 50+ host and viral proteins and other
factors21-27, providing many therapeutic
targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk28, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 26 shows an overview of the results for quercetin
in the context of multiple COVID-19 treatments, and Figure 27 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 26.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.5% of 8,000+ proposed treatments show efficacy284.
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Figure 27. Efficacy vs. cost for COVID-19 treatments.
Studies to date show that quercetin is
an effective treatment for COVID-19.
Significantly lower risk is seen for ICU admission, hospitalization, recovery, cases, and viral clearance. 11 studies from 9 independent teams in 7 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
46% [20‑64%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and after excluding studies using combined treatment and slightly worse for peer-reviewed studies.
Results are very robust — in exclusion sensitivity analysis 9 of
12 studies must be excluded to avoid finding statistically significant
efficacy in pooled analysis.
Studies typically use advanced formulations for greatly improved bioavailability.
Small prophylaxis RCT with 113 patients showing fewer cases with quercetin + vitamin C + bromelain prophylaxis. Note that this paper disappeared from SSRN without explanation.
RCT 100 outpatients in Pakistan, 50 treated with quercetin phytosome, showing faster viral clearance and improved recovery with treatment. Patients in the treatment group were significantly younger (41 vs. 54).
RCT 152 outpatients in Pakistan, 76 treated with quercetin phytosome, showing lower mortality, ICU admission, and hospitalization with treatment.
Small RCT with 50 outpatients, 25 treated with curcumin, quercetin, and vitamin D, showing improved recovery and viral clearance with treatment. 168mg curcumin, 260mg, 360IU vitamin D3 daily for 14 days.
RCT 49 hospitalized COVID-19 patients, 25 treated with curcumin and quercetin, shower lower mortality/ICU admission and improved recovery with treatment. All patients received vitamin D.
336mg curcumin, 520mg quercetin, and 18μg vitamin D3 daily for 14 days. The control arm received 20μg vitamin D3 daily. Baseline fever favored treatment while vaccination favored control.
336mg curcumin, 520mg quercetin, and 18μg vitamin D3 daily for 14 days. The control arm received 20μg vitamin D3 daily. Baseline fever favored treatment while vaccination favored control.
RCT 50 COVID+ outpatients in Pakistan, 25 treated with curcumin, quercetin, and vitamin D, showing significantly faster viral clearance, significantly improved CRP, and faster resolution of acute symptoms (p=0.154). 168mg curcumin, 260mg quercetin and 360IU cholecalciferol.
Retrospective 113 outpatients, 53 (patient choice) treated with zinc, quercetin, vitamin C/D/E, l-lysine, and quina, showing lower cases with treatment. Results are subject to selection bias and limited information on the groups is provided. See285.
RCT 447 moderate-to-severe hospitalized patients in Turkey, 52 treated with quercetin, bromelain, and vitamin C, showing no statistically significant difference in clinical outcomes.
RCT 120 healthcare workers, 60 treated with quercetin phytosome, showing lower risk of cases with treatment. Quercetin phytosome 250mg twice a day.
Small RCT with 60 severe hospitalized patients in Iran, 30 treated with quercetin, showing shorter time until discharge. All patients received remdesivir or favipiravir, and vitamin C, vitamin D, famotidine, zinc, dexamethasone, and magnesium (depending on serum levels). Quercetin 1000mg daily for 7 days. IRCT20200419047128N2.
RCT 60 hospitalized COVID-19 patients with type 2 diabetes showing quercetin treatment decreased levels of inflammatory markers (interleukin-6, CRP, ferritin), reduced length of hospital stay, and improved capillaroscopy measures compared to standard care. Quercetin was administered at 0.5g intravenously once daily for 10 days. The authors hypothesize the benefits may be due to the anti-inflammatory, antioxidant and endothelium-protective effects of quercetin,
RCT 200 patients in Ukraine, 99 treated with IV quercetin/polyvinylirolidone followed by oral quercetin/pectin, showing improved recovery with treatment.
We perform ongoing searches of PubMed, medRxiv, Europe PMC,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms are quercetin and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of quercetin for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral test status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to286.
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted results have preference over unadjusted
results for a more serious outcome when the adjustments significantly alter
results. When needed, conversion between reported p-values and
confidence intervals followed Altman, Altman (B), and Fisher's exact
test was used to calculate p-values for event data. If continuity
correction for zero values is required, we use the reciprocal of the opposite
arm with the sum of the correction factors equal to 1289.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.13.1) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta290
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective109,110.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/qmeta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Di Pierro, 1/13/2023, Randomized Controlled Trial, Pakistan, peer-reviewed, mean age 47.6, 13 authors, study period December 2020 - September 2021, trial NCT04861298 (history), excluded in exclusion analyses: randomization resulted in significant baseline differences that were not adjusted for. | risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 50 (0.0%), control 1 of 50 (2.0%), NNT 50, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of ICU admission, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 50 (0.0%), control 1 of 50 (2.0%), NNT 50, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of hospitalization, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 50 (0.0%), control 1 of 50 (2.0%), NNT 50, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of no recovery, 36.8% lower, RR 0.63, p = 0.007, treatment 24 of 50 (48.0%), control 38 of 50 (76.0%), NNT 3.6, day 7. | |
| risk of no viral clearance, 57.9% lower, RR 0.42, p < 0.001, treatment 16 of 50 (32.0%), control 38 of 50 (76.0%), NNT 2.3, mid-recovery, day 7. | |
| risk of no viral clearance, 50.0% higher, RR 1.50, p = 1.00, treatment 3 of 50 (6.0%), control 2 of 50 (4.0%), day 14. | |
| risk of no viral clearance, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 50 (0.0%), control 1 of 50 (2.0%), NNT 50, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 21. | |
| Di Pierro (B), 6/8/2021, Randomized Controlled Trial, Pakistan, peer-reviewed, 19 authors, study period September 2020 - March 2021, trial NCT04578158 (history). | risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 76 (0.0%), control 3 of 76 (3.9%), NNT 25, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of ICU admission, 94.1% lower, RR 0.06, p = 0.006, treatment 0 of 76 (0.0%), control 8 of 76 (10.5%), NNT 9.5, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of hospitalization, 68.2% lower, RR 0.32, p = 0.003, treatment 7 of 76 (9.2%), control 22 of 76 (28.9%), NNT 5.1. | |
| Din Ujjan, 1/18/2023, Randomized Controlled Trial, Pakistan, peer-reviewed, 6 authors, study period 21 September, 2021 - 21 January, 2022, this trial uses multiple treatments in the treatment arm (combined with curcumin and vitamin D) - results of individual treatments may vary, trial NCT04603690 (history). | risk of no recovery, 28.6% lower, RR 0.71, p = 0.11, treatment 15 of 25 (60.0%), control 21 of 25 (84.0%), NNT 4.2, no symptoms, day 7. |
| risk of no recovery, 71.4% lower, RR 0.29, p < 0.001, treatment 6 of 25 (24.0%), control 21 of 25 (84.0%), NNT 1.7, <= 1 symptom, day 7. | |
| risk of no recovery, 76.9% lower, RR 0.23, p = 0.005, treatment 3 of 25 (12.0%), control 13 of 25 (52.0%), NNT 2.5, <= 2 symptoms, day 7. | |
| risk of no recovery, 85.7% lower, RR 0.14, p = 0.23, treatment 0 of 25 (0.0%), control 3 of 25 (12.0%), NNT 8.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), <= 3 symptoms, day 7. | |
| risk of no viral clearance, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 25 (0.0%), control 5 of 25 (20.0%), NNT 5.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 14. | |
| risk of no viral clearance, 73.7% lower, RR 0.26, p < 0.001, treatment 5 of 25 (20.0%), control 19 of 25 (76.0%), NNT 1.8, day 7. | |
| Khan, 5/1/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, 7 authors, study period 2 September, 2021 - 28 November, 2021, this trial uses multiple treatments in the treatment arm (combined with curcumin and vitamin D) - results of individual treatments may vary, trial NCT05130671 (history). | risk of no recovery, 33.3% lower, RR 0.67, p = 0.15, treatment 10 of 25 (40.0%), control 15 of 25 (60.0%), NNT 5.0. |
| relative CRP reduction, 39.1% better, RR 0.61, p = 0.006, treatment 25, control 25. | |
| risk of no viral clearance, 50.0% lower, RR 0.50, p = 0.009, treatment 10 of 25 (40.0%), control 20 of 25 (80.0%), NNT 2.5. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Gérain, 6/22/2023, Randomized Controlled Trial, Belgium, peer-reviewed, 8 authors, study period 1 April, 2021 - 29 October, 2021, this trial uses multiple treatments in the treatment arm (combined with curcumin) - results of individual treatments may vary, trial NCT04844658 (history). | risk of death, 67.1% lower, RR 0.33, p = 0.49, treatment 0 of 25 (0.0%), control 1 of 24 (4.2%), NNT 24, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 7. |
| risk of death/ICU, 91.1% lower, RR 0.09, p = 0.02, treatment 0 of 25 (0.0%), control 5 of 24 (20.8%), NNT 4.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 7. | |
| risk of mechanical ventilation, 89.1% lower, RR 0.11, p = 0.05, treatment 0 of 25 (0.0%), control 4 of 24 (16.7%), NNT 6.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 7. | |
| risk of ICU admission, 89.1% lower, RR 0.11, p = 0.05, treatment 0 of 25 (0.0%), control 4 of 24 (16.7%), NNT 6.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 7. | |
| risk of no hospital discharge, 72.6% lower, RR 0.27, p = 0.07, treatment 2 of 25 (8.0%), control 7 of 24 (29.2%), NNT 4.7, day 14. | |
| risk of no hospital discharge, 58.9% lower, RR 0.41, p = 0.02, treatment 6 of 25 (24.0%), control 14 of 24 (58.3%), NNT 2.9, day 7. | |
| hospitalization time, 37.5% lower, relative time 0.62, p = 0.008, treatment median 5.0 IQR 4.0 n=25, control median 8.0 IQR 6.0 n=24. | |
| relative WHO score, 50.0% better, RR 0.50, p = 0.04, treatment 22, control 24, day 7. | |
| Onal, 1/19/2021, Randomized Controlled Trial, Turkey, peer-reviewed, 10 authors, study period 7 May, 2020 - 8 July, 2020, this trial uses multiple treatments in the treatment arm (combined with bromelain and vitamin C) - results of individual treatments may vary. | risk of death, 29.3% higher, RR 1.29, p = 0.57, treatment 1 of 49 (2.0%), control 6 of 380 (1.6%). |
| risk of ICU admission, 94.0% lower, RR 0.06, p = 0.39, treatment 0 of 49 (0.0%), control 14 of 380 (3.7%), NNT 27, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). | |
| risk of no hospital discharge, 77.8% lower, RR 0.22, p = 0.10, treatment 1 of 49 (2.0%), control 35 of 380 (9.2%), NNT 14. | |
| Shohan, 12/2/2021, Randomized Controlled Trial, Iran, peer-reviewed, mean age 50.9 (treatment) 52.7 (control), 8 authors, study period December 2020 - January 2021, average treatment delay 7.8 days. | risk of death, 85.7% lower, RR 0.14, p = 0.24, treatment 0 of 30 (0.0%), control 3 of 30 (10.0%), NNT 10.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of ICU admission, 40.0% lower, RR 0.60, p = 0.71, treatment 3 of 30 (10.0%), control 5 of 30 (16.7%), NNT 15. | |
| time to discharge from end of intervention, 32.4% lower, relative time 0.68, p = 0.04, treatment 30, control 30. | |
| Tylishchak, 12/6/2024, Randomized Controlled Trial, Ukraine, peer-reviewed, 7 authors. | risk of no recovery, 40.0% lower, RR 0.60, p = 0.71, treatment 3 of 30 (10.0%), control 5 of 30 (16.7%), NNT 15, SpO2<90. |
| hospitalization time, 14.6% lower, relative time 0.85, p < 0.001, treatment mean 13.77 (±0.75) n=30, control mean 16.13 (±0.79) n=30. | |
| Zupanets, 9/1/2021, Randomized Controlled Trial, Ukraine, peer-reviewed, 14 authors. | risk of no recovery, 29.4% lower, RR 0.71, p = 0.50, treatment 9 of 99 (9.1%), control 13 of 101 (12.9%), NNT 26. |
| recovery time, 18.2% lower, relative time 0.82, p = 0.03, treatment 99, control 101. |
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Arslan, 11/16/2020, Randomized Controlled Trial, Turkey, preprint, 7 authors, study period 20 March, 2020 - 31 August, 2020, this trial uses multiple treatments in the treatment arm (combined with vitamin C and bromelain) - results of individual treatments may vary, trial NCT04377789 (history), excluded in exclusion analyses: paper no longer available at the source, and the contact does not reply to queries. | risk of case, 91.7% lower, RR 0.08, p = 0.03, treatment 1 of 71 (1.4%), control 9 of 42 (21.4%), NNT 5.0, adjusted per study, inverted to make RR<1 favor treatment. |
| Margolin, 7/6/2021, retrospective, USA, peer-reviewed, 5 authors, this trial uses multiple treatments in the treatment arm (combined with zinc, vitamin C/D/E, l-lysine, and quina) - results of individual treatments may vary. | risk of case, 94.4% lower, RR 0.06, p = 0.003, treatment 0 of 53 (0.0%), control 9 of 60 (15.0%), NNT 6.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of COVID-19 or flu-like illness, 81.1% lower, RR 0.19, p = 0.01, treatment 2 of 53 (3.8%), control 12 of 60 (20.0%), NNT 6.2. | |
| Rondanelli, 1/4/2022, Double Blind Randomized Controlled Trial, placebo-controlled, Italy, peer-reviewed, 12 authors, study period 12 January, 2021 - 25 May, 2021, trial NCT05037240 (history). | risk of symptomatic case, 92.9% lower, HR 0.07, p = 0.04, treatment 1 of 60 (1.7%), control 4 of 60 (6.7%), adjusted per study, inverted to make HR<1 favor treatment, Cox proportional risk. |
Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.
The trimeric spike (S) protein is a glycoprotein that mediates viral entry by binding to the host ACE2 receptor, is critical for SARS-CoV-2's ability to infect host cells, and is a target of neutralizing antibodies. Inhibition of the spike protein prevents viral attachment, halting infection at the earliest stage.
The main protease or Mpro, also known as 3CLpro or nsp5, is a cysteine protease that cleaves viral polyproteins into functional units needed for replication. Inhibiting Mpro disrupts the SARS-CoV-2 lifecycle within the host cell, preventing the creation of new copies.
RNA-dependent RNA polymerase (RdRp), also called nsp12, is the core enzyme of the viral replicase-transcriptase complex that copies the positive-sense viral RNA genome into negative-sense templates for progeny RNA synthesis. Inhibiting RdRp blocks viral genome replication and transcription.
The papain-like protease (PLpro) has multiple functions including cleaving viral polyproteins and suppressing the host immune response by deubiquitination and deISGylation of host proteins. Inhibiting PLpro may block viral replication and help restore normal immune responses.
The angiotensin converting enzyme 2 (ACE2) protein is a host cell transmembrane protein that serves as the cellular receptor for the SARS-CoV-2 spike protein. ACE2 is expressed on many cell types, including epithelial cells in the lungs, and allows the virus to enter and infect host cells. Inhibition may affect ACE2's physiological function in blood pressure control.
Transmembrane protease serine 2 (TMPRSS2) is a host cell protease that primes the spike protein, facilitating cellular entry. TMPRSS2 activity helps enable cleavage of the spike protein required for membrane fusion and virus entry. Inhibition may especially protect respiratory epithelial cells, buy may have physiological effects.
The nucleocapsid (N) protein binds and encapsulates the viral genome by coating the viral RNA. N enables formation and release of infectious virions and plays additional roles in viral replication and pathogenesis. N is also an immunodominant antigen used in diagnostic assays.
The helicase, or nsp13, protein unwinds the double-stranded viral RNA, a crucial step in replication and transcription. Inhibition may prevent viral genome replication and the creation of new virus components.
The endoribonuclease, also known as NendoU or nsp15, cleaves specific sequences in viral RNA which may help the virus evade detection by the host immune system. Inhibition may hinder the virus's ability to mask itself from the immune system, facilitating a stronger immune response.
The NSP16/10 complex consists of non-structural proteins 16 and 10, forming a 2'-O-methyltransferase that modifies the viral RNA cap structure. This modification helps the virus evade host immune detection by mimicking host mRNA, making NSP16/10 a promising antiviral target.
Cathepsin L is a host lysosomal cysteine protease that can prime the spike protein through an alternative pathway when TMPRSS2 is unavailable. Dual targeting of cathepsin L and TMPRSS2 may maximize disruption of alternative pathways for virus entry.
Wingless-related integration site (Wnt) ligand 3 is a host signaling molecule that activates the Wnt signaling pathway, which is important in development, cell growth, and tissue repair. Some studies suggest that SARS-CoV-2 infection may interfere with the Wnt signaling pathway, and that Wnt3a is involved in SARS-CoV-2 entry.
The frizzled (FZD) receptor is a host transmembrane receptor that binds Wnt ligands, initiating the Wnt signaling cascade. FZD serves as a co-receptor, along with ACE2, in some proposed mechanisms of SARS-CoV-2 infection. The virus may take advantage of this pathway as an alternative entry route.
Low-density lipoprotein receptor-related protein 6 is a cell surface co-receptor essential for Wnt signaling. LRP6 acts in tandem with FZD for signal transduction and has been discussed as a potential co-receptor for SARS-CoV-2 entry.
The ezrin protein links the cell membrane to the cytoskeleton (the cell's internal support structure) and plays a role in cell shape, movement, adhesion, and signaling. Drugs that occupy the same spot on ezrin where the viral spike protein would bind may hindering viral attachment, and drug binding could further stabilize ezrin, strengthening its potential natural capacity to impede viral fusion and entry.
The Adipocyte Differentiation-Related Protein (ADRP, also known as Perilipin 2 or PLIN2) is a lipid droplet protein regulating the storage and breakdown of fats in cells. SARS-CoV-2 may hijack the lipid handling machinery of host cells and ADRP may play a role in this process. Disrupting ADRP's interaction with the virus may hinder the virus's ability to use lipids for replication and assembly.
Neuropilin-1 (NRP1) is a cell surface receptor with roles in blood vessel development, nerve cell guidance, and immune responses. NRP1 may function as a co-receptor for SARS-CoV-2, facilitating viral entry into cells. Blocking NRP1 may disrupt an alternative route of viral entry.
EP300 (E1A Binding Protein P300) is a transcriptional coactivator involved in several cellular processes, including growth, differentiation, and apoptosis, through its acetyltransferase activity that modifies histones and non-histone proteins. EP300 facilitates viral entry into cells and upregulates inflammatory cytokine production.
Prostaglandin G/H synthase 2 (PTGS2, also known as COX-2) is an enzyme crucial for the production of inflammatory molecules called prostaglandins. PTGS2 plays a role in the inflammatory response that can become severe in COVID-19 and inhibitors (like some NSAIDs) may have benefits in dampening harmful inflammation, but note that prostaglandins have diverse physiological functions.
Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1) is a chaperone protein that helps other proteins fold correctly and maintains their stability. HSP90AA1 plays roles in cell signaling, survival, and immune responses. HSP90AA1 may interact with numerous viral proteins, but note that it has diverse physiological functions.
Matrix metalloproteinase 9 (MMP9), also called gelatinase B, is a zinc-dependent enzyme that breaks down collagen and other components of the extracellular matrix. MMP9 levels increase in severe COVID-19. Overactive MMP9 can damage lung tissue and worsen inflammation. Inhibition of MMP9 may prevent excessive tissue damage and help regulate the inflammatory response.
The interleukin-6 (IL-6) pro-inflammatory cytokine (signaling molecule) has a complex role in the immune response and may trigger and perpetuate inflammation. Elevated IL-6 levels are associated with severe COVID-19 cases and cytokine storm. Anti-IL-6 therapies may be beneficial in reducing excessive inflammation in severe COVID-19 cases.
The interleukin-10 (IL-10) anti-inflammatory cytokine helps regulate and dampen immune responses, preventing excessive inflammation. IL-10 levels can also be elevated in severe COVID-19. IL-10 could either help control harmful inflammation or potentially contribute to immune suppression.
Vascular Endothelial Growth Factor A (VEGFA) promotes the growth of new blood vessels (angiogenesis) and has roles in inflammation and immune responses. VEGFA may contribute to blood vessel leakiness and excessive inflammation associated with severe COVID-19.
RELA is a transcription factor subunit of NF-kB and is a key regulator of inflammation, driving pro-inflammatory gene expression. SARS-CoV-2 may hijack and modulate NF-kB pathways.
The interaction between the SARS-CoV-2 spike protein and the human ACE2 receptor is a primary method of viral entry, inhibiting this interaction can prevent the virus from attaching to and entering host cells, halting infection at an early stage.
Calu-3 is a human lung adenocarcinoma cell line with moderate ACE2 and TMPRSS2 expression and SARS-CoV-2 susceptibility. It provides a model of the human respiratory epithelium, but many not be ideal for modeling early stages of infection due to the moderate expression levels of ACE2 and TMPRSS2.
A549 is a human lung carcinoma cell line with low ACE2 expression and SARS-CoV-2 susceptibility. Viral entry/replication can be studied but the cells may not replicate all aspects of lung infection.
HEK293-ACE2+ is a human embryonic kidney cell line engineered for high ACE2 expression and SARS-CoV-2 susceptibility.
Huh-7 cells were derived from a liver tumor (hepatoma).
Caco-2 cells come from a colorectal adenocarcinoma (cancer). They are valued for their ability to form a polarized cell layer with properties similar to the intestinal lining.
Vero E6 is an African green monkey kidney cell line with low/no ACE2 expression and high SARS-CoV-2 susceptibility. The cell line is easy to maintain and supports robust viral replication, however the monkey origin may not accurately represent human responses.
mTEC is a mouse tubular epithelial cell line.
RAW264.7 is a mouse macrophage cell line.
A mouse model expressing the human ACE2 receptor under the control of the K18 promoter.
A mouse model of obesity and severe insulin resistance leading to type 2 diabetes due to a mutation in the leptin receptor gene that impairs satiety signaling.
A mouse model commonly used in infectious disease and cancer research due to higher immune response and susceptibility to infection.
Crawford et al., Analysis of Select Dietary Supplement Products Marketed to Support or Boost the Immune System, JAMA Network Open, doi:10.1001/jamanetworkopen.2022.26040.
Crighton et al., Toxicological screening and DNA sequencing detects contamination and adulteration in regulated herbal medicines and supplements for diet, weight loss and cardiovascular health, Journal of Pharmaceutical and Biomedical Analysis, doi:10.1016/j.jpba.2019.112834.
Ziaei et al., The effect of quercetin supplementation on clinical outcomes in COVID‐19 patients: A systematic review and meta‐analysis, Food Science & Nutrition, doi:10.1002/fsn3.3715.
Cheema et al., Quercetin for the treatment of COVID-19 patients: A systematic review and meta-analysis, Reviews in Medical Virology, doi:10.1002/rmv.2427.
Rong et al., Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19, Cell Host & Microbe, doi:10.1016/j.chom.2024.11.007.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.
Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.
Ahmad et al., Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium, The Neurohospitalist, doi:10.1177/19418744241292487.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Van Tin et al., Spike Protein of SARS-CoV-2 Activates Cardiac Fibrogenesis through NLRP3 Inflammasomes and NF-κB Signaling, Cells, doi:10.3390/cells13161331.
Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.
AlTaweel et al., An In-Depth Insight into Clinical, Cellular and Molecular Factors in COVID19-Associated Cardiovascular Ailments for Identifying Novel Disease Biomarkers, Drug Targets and Clinical Management Strategies, Archives of Microbiology & Immunology, doi:10.26502/ami.936500177.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.
Wu et al., Decoding the genome of SARS-CoV-2: a pathway to drug development through translation inhibition, RNA Biology, doi:10.1080/15476286.2024.2433830.
Alavi et al., Interaction of Epigallocatechin Gallate and Quercetin with Spike Glycoprotein (S-Glycoprotein) of SARS-CoV-2: In Silico Study, Biomedicines, doi:10.3390/biomedicines10123074.
Azmi et al., The role of vitamin D receptor and IL‐6 in COVID‐19, Molecular Genetics & Genomic Medicine, doi:10.1002/mgg3.2172.
Chandran et al., Molecular docking analysis of quercetin with known CoVid-19 targets, Bioinformation, doi:10.6026/973206300191081.
Kandeil et al., Bioactive Polyphenolic Compounds Showing Strong Antiviral Activities against Severe Acute Respiratory Syndrome Coronavirus 2, Pathogens, doi:10.3390/pathogens10060758.
Mandal et al., In silico anti-viral assessment of phytoconstituents in a traditional (Siddha Medicine) polyherbal formulation – Targeting Mpro and pan-coronavirus post-fusion Spike protein, Journal of Traditional and Complementary Medicine, doi:10.1016/j.jtcme.2023.07.004.
Moschovou et al., Exploring the Binding Effects of Natural Products and Antihypertensive Drugs on SARS-CoV-2: An In Silico Investigation of Main Protease and Spike Protein, International Journal of Molecular Sciences, doi:10.3390/ijms242115894.
Nguyen et al., The Potential of Ameliorating COVID-19 and Sequelae From Andrographis paniculata via Bioinformatics, Bioinformatics and Biology Insights, doi:10.1177/11779322221149622.
Pan et al., Quercetin: A promising drug candidate against the potential SARS-CoV-2-Spike mutants with high viral infectivity, Computational and Structural Biotechnology Journal, doi:10.1016/j.csbj.2023.10.029.
Thapa et al., In-silico Approach for Predicting the Inhibitory Effect of Home Remedies on Severe Acute Respiratory Syndrome Coronavirus-2, Makara Journal of Science, doi:10.7454/mss.v27i3.1609.
Raman et al., Phytoconstituents of Citrus limon (Lemon) as Potential Inhibitors Against Multi Targets of SARS‐CoV‐2 by Use of Molecular Modelling and In Vitro Determination Approaches, ChemistryOpen, doi:10.1002/open.202300198.
Şimşek et al., In silico identification of SARS-CoV-2 cell entry inhibitors from selected natural antivirals, Journal of Molecular Graphics and Modelling, doi:10.1016/j.jmgm.2021.108038.
Asad et al., Exploring the antiviral activity of Adhatoda beddomei bioactive compounds in interaction with coronavirus spike protein, Archives of Medical Reports, 1:1, archmedrep.com/index.php/amr/article/view/3.
Haque et al., Exploring potential therapeutic candidates against COVID-19: a molecular docking study, Discover Molecules, doi:10.1007/s44345-024-00005-5.
Al balawi et al., Assessing multi-target antiviral and antioxidant activities of natural compounds against SARS-CoV-2: an integrated in vitro and in silico study, Bioresources and Bioprocessing, doi:10.1186/s40643-024-00822-z.
Akinwumi et al., Evaluation of therapeutic potentials of some bioactive compounds in selected African plants targeting main protease (Mpro) in SARS-CoV-2: a molecular docking study, Egyptian Journal of Medical Human Genetics, doi:10.1186/s43042-023-00456-4.
Alanzi et al., Structure-based virtual identification of natural inhibitors of SARS-CoV-2 and its Delta and Omicron variant proteins, Future Virology, doi:10.2217/fvl-2022-0184.
Ibeh et al., Computational studies of potential antiviral compounds from some selected Nigerian medicinal plants against SARS-CoV-2 proteins, Informatics in Medicine Unlocked, doi:10.1016/j.imu.2023.101230.
Qin et al., Exploring the bioactive compounds of Feiduqing formula for the prevention and management of COVID-19 through network pharmacology and molecular docking, Medical Data Mining, doi:10.53388/MDM202407003.
Rehman et al., Natural Compounds as Inhibitors of SARS-CoV-2 Main Protease (3CLpro): A Molecular Docking and Simulation Approach to Combat COVID-19, Current Pharmaceutical Design, doi:10.2174/1381612826999201116195851.
Sekiou et al., In-Silico Identification of Potent Inhibitors of COVID-19 Main Protease (Mpro) and Angiotensin Converting Enzyme 2 (ACE2) from Natural Products: Quercetin, Hispidulin, and Cirsimaritin Exhibited Better Potential Inhibition than Hydroxy-Chloroquine Against COVID-19 Main Protease Active Site and ACE2, ChemRxiv, doi:10.26434/chemrxiv.12181404.v1.
Singh et al., Unlocking the potential of phytochemicals in inhibiting SARS-CoV-2 M Pro protein - An in-silico and cell-based approach, Research Square, doi:10.21203/rs.3.rs-3888947/v1.
Wang et al., Computational Analysis of Lianhua Qingwen as an Adjuvant Treatment in Patients with COVID-19, Society of Toxicology Conference, 2023, www.researchgate.net/publication/370491709_Y_Wang_A_E_Tan_O_Chew_A_Hsueh_and_D_E_Johnson_2023_Computational_Analysis_of_Lianhua_Qingwen_as_an_Adjuvant_Treatment_in_Patients_with_COVID-19_Toxicologist_1921_507.
Zhang et al., In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus, Journal of Integrative Medicine, doi:10.1016/j.joim.2020.02.005.
Shaik et al., Computational identification of selected bioactive compounds from Cedrus deodara as inhibitors against SARS-CoV-2 main protease: a pharmacoinformatics study, Indian Drugs, doi:10.53879/id.61.02.13859.
Waqas et al., Discovery of Novel Natural Inhibitors Against SARS-CoV-2 Main Protease: A Rational Approach to Antiviral Therapeutics, Current Medicinal Chemistry, doi:10.2174/0109298673292839240329081008.
Nalban et al., Targeting COVID-19 (SARS-CoV-2) main protease through phytochemicals of Albizia lebbeck: molecular docking, molecular dynamics simulation, MM–PBSA free energy calculations, and DFT analysis, Journal of Proteins and Proteomics, doi:10.1007/s42485-024-00136-w.
Irfan et al., Phytoconstituents of Artemisia Annua as potential inhibitors of SARS CoV2 main protease: an in silico study, BMC Infectious Diseases, doi:10.1186/s12879-024-09387-w.
Sunita et al., Characterization of Phytochemical Inhibitors of the COVID-19 Primary Protease Using Molecular Modelling Approach, Asian Journal of Microbiology and Biotechnology, doi:10.56557/ajmab/2024/v9i28800.
Corbo et al., Inhibitory potential of phytochemicals on five SARS-CoV-2 proteins: in silico evaluation of endemic plants of Bosnia and Herzegovina, Biotechnology & Biotechnological Equipment, doi:10.1080/13102818.2023.2222196.
Metwaly et al., Integrated study of Quercetin as a potent SARS-CoV-2 RdRp inhibitor: Binding interactions, MD simulations, and In vitro assays, PLOS ONE, doi:10.1371/journal.pone.0312866.
Alkafaas et al., A study on the effect of natural products against the transmission of B.1.1.529 Omicron, Virology Journal, doi:10.1186/s12985-023-02160-6.
Singh (B) et al., Flavonoids as Potent Inhibitor of SARS-CoV-2 Nsp13 Helicase: Grid Based Docking Approach, Middle East Research Journal of Pharmaceutical Sciences, doi:10.36348/merjps.2023.v03i04.001.
Tamil Selvan et al., Computational Investigations to Identify Potent Natural Flavonoid Inhibitors of the Nonstructural Protein (NSP) 16/10 Complex Against Coronavirus, Cureus, doi:10.7759/cureus.68098.
Ahmed et al., Evaluation of the Effect of Zinc, Quercetin, Bromelain and Vitamin C on COVID-19 Patients, International Journal of Diabetes Management, doi:10.61797/ijdm.v2i2.259.
Chellasamy et al., Docking and molecular dynamics studies of human ezrin protein with a modelled SARS-CoV-2 endodomain and their interaction with potential invasion inhibitors, Journal of King Saud University - Science, doi:10.1016/j.jksus.2022.102277.
Hasanah et al., Decoding the therapeutic potential of empon-empon: a bioinformatics expedition unraveling mechanisms against COVID-19 and atherosclerosis, International Journal of Applied Pharmaceutics, doi:10.22159/ijap.2024v16i2.50128.
Sai Ramesh et al., Computational analysis of the phytocompounds of Mimusops elengi against spike protein of SARS CoV2 – An Insilico model, International Journal of Biological Macromolecules, doi:10.1016/j.ijbiomac.2023.125553.
Yang (B) et al., Active ingredient and mechanistic analysis of traditional Chinese medicine formulas for the prevention and treatment of COVID-19: Insights from bioinformatics and in vitro experiments, Medicine, doi:10.1097/MD.0000000000036238.
Yang (C) et al., In silico evidence implicating novel mechanisms of Prunella vulgaris L. as a potential botanical drug against COVID-19-associated acute kidney injury, Frontiers in Pharmacology, doi:10.3389/fphar.2023.1188086.
Bahun et al., Inhibition of the SARS-CoV-2 3CLpro main protease by plant polyphenols, Food Chemistry, doi:10.1016/j.foodchem.2021.131594.
Zhang (B) et al., Discovery of the covalent SARS‐CoV‐2 Mpro inhibitors from antiviral herbs via integrating target‐based high‐throughput screening and chemoproteomic approaches, Journal of Medical Virology, doi:10.1002/jmv.29208.
Aguilera-Rodriguez et al., Inhibition of SARS-CoV-2 3CLpro by chemically modified tyrosinase from Agaricus bisporus, RSC Medicinal Chemistry, doi:10.1039/D4MD00289J.
Emam et al., Establishment of in-house assay for screening of anti-SARS-CoV-2 protein inhibitors, AMB Express, doi:10.1186/s13568-024-01739-8.
DiGuilio et al., Quercetin improves and protects Calu-3 airway epithelial barrier function, Frontiers in Cell and Developmental Biology, doi:10.3389/fcell.2023.1271201.
Singh (C) et al., The spike protein of SARS-CoV-2 virus induces heme oxygenase-1: Pathophysiologic implications, Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, doi:10.1016/j.bbadis.2021.166322.
Roy et al., Quercetin inhibits SARS-CoV-2 infection and prevents syncytium formation by cells co-expressing the viral spike protein and human ACE2, Virology Journal, doi:10.1186/s12985-024-02299-w.
El-Megharbel et al., Chemical and spectroscopic characterization of (Artemisinin/Quercetin/ Zinc) novel mixed ligand complex with assessment of its potent high antiviral activity against SARS-CoV-2 and antioxidant capacity against toxicity induced by acrylamide in male rats, PeerJ, doi:10.7717/peerj.15638.
Wu (B) et al., SARS-CoV-2 N protein induced acute kidney injury in diabetic db/db mice is associated with a Mincle-dependent M1 macrophage activation, Frontiers in Immunology, doi:10.3389/fimmu.2023.1264447.
Aguado et al., Senolytic therapy alleviates physiological human brain aging and COVID-19 neuropathology, bioRxiv, doi:10.1101/2023.01.17.524329.
Wu (C) et al., Treatment with Quercetin inhibits SARS-CoV-2 N protein-induced acute kidney injury by blocking Smad3-dependent G1 cell cycle arrest, Molecular Therapy, doi:10.1016/j.ymthe.2022.12.002.
Shaker et al., Anti-cytokine Storm Activity of Fraxin, Quercetin, and their Combination on Lipopolysaccharide-Induced Cytokine Storm in Mice: Implications in COVID-19, Iranian Journal of Medical Sciences, doi:10.30476/ijms.2023.98947.3102.
El-Megharbel (B) et al., Chemical and spectroscopic characterization of (Artemisinin/Quercetin/ Zinc) novel mixed ligand complex with assessment of its potent high antiviral activity against SARS-CoV-2 and antioxidant capacity against toxicity induced by acrylamide in male rats, PeerJ, doi:10.7717/peerj.15638.
Xu et al., Quercetin inhibited LPS-induced cytokine storm by interacting with the AKT1-FoxO1 and Keap1-Nrf2 signaling pathway in macrophages, Scientific Reports, doi:10.1038/s41598-024-71569-y.
Fam et al., Channel activity of SARS-CoV-2 viroporin ORF3a inhibited by adamantanes and phenolic plant metabolites, Scientific Reports, doi:10.1038/s41598-023-31764-9.
Al balawi (B) et al., Assessing multi-target antiviral and antioxidant activities of natural compounds against SARS-CoV-2: an integrated in vitro and in silico study, Bioresources and Bioprocessing, doi:10.1186/s40643-024-00822-z.
Pan (B) et al., Decoding the mechanism of Qingjie formula in the prevention of COVID-19 based on network pharmacology and molecular docking, Heliyon, doi:10.1016/j.heliyon.2024.e39167.
Wu (D) et al., Biomarkers Prediction and Immune Landscape in Covid-19 and “Brain Fog”, Elsevier BV, doi:10.2139/ssrn.4897774.
Yuan et al., Network pharmacology and molecular docking reveal the mechanisms of action of Panax notoginseng against post-COVID-19 thromboembolism, Review of Clinical Pharmacology and Pharmacokinetics - International Edition, doi:10.61873/DTFA3974.
Zhou et al., Bioinformatics and system biology approaches to determine the connection of SARS-CoV-2 infection and intrahepatic cholangiocarcinoma, PLOS ONE, doi:10.1371/journal.pone.0300441.
Thapa (B) et al., In-silico Approach for Predicting the Inhibitory Effect of Home Remedies on Severe Acute Respiratory Syndrome Coronavirus-2, Makara Journal of Science, doi:10.7454/mss.v27i3.1609.
Azmi (B) et al., Utilization of quercetin flavonoid compounds in onion (Allium cepa L.) as an inhibitor of SARS-CoV-2 spike protein against ACE2 receptors, 11th International Seminar on New Paradigm and Innovation on Natural Sciences and its Application, doi:10.1063/5.0140285.
Sekiou (B) et al., In-Silico Identification of Potent Inhibitors of COVID-19 Main Protease (Mpro) and Angiotensin Converting Enzyme 2 (ACE2) from Natural Products: Quercetin, Hispidulin, and Cirsimaritin Exhibited Better Potential Inhibition than Hydroxy-Chloroquine Against COVID-19 Main Protease Active Site and ACE2, ChemRxiv, doi:10.26434/chemrxiv.12181404.v1.
Fang et al., Development of nanoparticles incorporated with quercetin and ACE2-membrane as a novel therapy for COVID-19, Journal of Nanobiotechnology, doi:10.1186/s12951-024-02435-2.
Xu (B) et al., Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19, Proceedings of the National Academy of Sciences, doi:10.1073/pnas.2301775120.
Goc et al., Inhibitory effects of specific combination of natural compounds against SARS-CoV-2 and its Alpha, Beta, Gamma, Delta, Kappa, and Mu variants, European Journal of Microbiology and Immunology, doi:10.1556/1886.2021.00022.
Munafò et al., Quercetin and Luteolin Are Single-digit Micromolar Inhibitors of the SARS-CoV-2 RNA-dependent RNA Polymerase, Research Square, doi:10.21203/rs.3.rs-1149846/v1.
Vaiss et al., Curcumin and quercetin co-encapsulated in nanoemulsions for nasal administration: a promising therapeutic and prophylactic treatment for viral respiratory infections, European Journal of Pharmaceutical Sciences, doi:10.1016/j.ejps.2024.106766.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Davidson et al., No evidence of important difference in summary treatment effects between COVID-19 preprints and peer-reviewed publications: a meta-epidemiological study, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2023.08.011.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Arslan et al., Synergistic Effect of Quercetin and Vitamin C Against COVID-19: Is a Possible Guard for Front Liners, SSRN, doi:10.2139/ssrn.3682517.
Di Pierro et al., Quercetin as a possible complementary agent for early-stage COVID-19: Concluding results of a randomized clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1096853.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu (C) et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:10.1016/j.micpath.2020.104228.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Xing et al., Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals, Briefings in Bioinformatics, doi:10.1093/bib/bbab249.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Singh (D) et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Boulware, D., Comments regarding paper rejection, twitter.com/boulware_dr/status/1311331372884205570.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Beşler et al., Investigation of potential effects of quercetin on COVID-19 treatment: a systematic review of randomized controlled trials, Clinical Science of Nutrition, doi:10.62210/ClinSciNutr.2024.86.
Ho et al., Therapeutic Implications of Quercetin and its Derived-products in COVID-19 Protection and Prophylactic, Heliyon, doi:10.1016/j.heliyon.2024.e30080.
Vajdi et al., Effect of polyphenols against complications of COVID-19: current evidence and potential efficacy, Pharmacological Reports, doi:10.1007/s43440-024-00585-6.
Chen et al., Effect and mechanism of quercetin or quercetin‐containing formulas against COVID‐19: From bench to bedside, Phytotherapy Research, doi:10.1002/ptr.8175.
Yong et al., Natural Products-Based Inhaled Formulations for Treating Pulmonary Diseases, International Journal of Nanomedicine, doi:10.2147/ijn.s451206.
Agrawal et al., Antiviral Significance of Isoquercetin (Quercetin-3-O-Glucoside) With Special Reference to its Anti-Coronaviral Potential, Natural Product Communications, doi:10.1177/1934578X231219560.
Matías-Pérez et al., Relationship of quercetin intake and oxidative stress in persistent Covid, Frontiers in Nutrition, doi:10.3389/fnut.2023.1278039.
Donzelli, A., Neglected Effective Early Therapies against COVID-19: Focus on Functional Foods and Related Active Substances. A Review, MDPI AG, doi:10.20944/preprints202312.1178.v1.
Dinda et al., Anti-SARS-CoV-2, antioxidant and immunomodulatory potential of dietary flavonol quercetin: Focus on molecular targets and clinical efficacy, European Journal of Medicinal Chemistry Reports, doi:10.1016/j.ejmcr.2023.100125.
Mirza et al., Quercetin as a Therapeutic Product: Evaluation of Its Pharmacological Action and Clinical Applications—A Review, Pharmaceuticals, doi:10.3390/ph16111631.
Massimo Magro et al., Use of Quercetin for Therapeutic Purposes in COVID-19 Infections: The Opinion of the Geriatrician Doctor, Journal of Modern Biology and Drug Discovery, doi:10.53964/jmbdd.2023004.
Shorobi et al., Quercetin: A Functional Food-Flavonoid Incredibly Attenuates Emerging and Re-Emerging Viral Infections through Immunomodulatory Actions, Molecules, doi:10.3390/molecules28030938.
Gasmi et al., Quercetin in the Prevention and Treatment of Coronavirus Infections: A Focus on SARS-CoV-2, Pharmaceuticals, doi:10.3390/ph15091049.
Rizky et al., The pharmacological mechanism of quercetin as adjuvant therapy of COVID-19, Life Research, doi:10.53388/life2022-0205-302.
Imran et al., The Therapeutic and Prophylactic Potential of Quercetin against COVID-19: An Outlook on the Clinical Studies, Inventive Compositions, and Patent Literature, Antioxidants, doi:10.3390/antiox11050876.
Derosa et al., A role for quercetin in coronavirus disease 2019 (COVID-19), Phytotherapy Research, doi:10.1002/ptr.6887.
Biancatelli et al., Quercetin and Vitamin C: An Experimental, Synergistic Therapy for the Prevention and Treatment of SARS-CoV-2 Related Disease (COVID-19), Frontiers in Immunology, doi:10.3389/fimmu.2020.01451.
Świątek et al., Flavonoids as potential inhibitors targeting SARS-CoV-2, Traditional Medicines and Natural Products as Preventive and Therapeutic Agents Against COVID-19, doi:10.1016/B978-0-443-18757-5.00008-6.
Lu, L., Chinese herbal medicines for the treatment of COVID-19, Traditional Medicines and Natural Products as Preventive and Therapeutic Agents Against COVID-19, doi:10.1016/B978-0-443-18757-5.00001-3.
Motyl et al., SARS-CoV-2 Infection and Alpha-Synucleinopathies: Potential Links and Underlying Mechanisms, International Journal of Molecular Sciences, doi:10.3390/ijms252212079.
Navacchia et al., Insights into SARS-CoV-2: Small-Molecule Hybrids for COVID-19 Treatment, Molecules, doi:10.3390/molecules29225403.
TİLKAT et al., Anatolian medicinal plants as potential antiviral agents: bridging traditional knowledge and modern science in the fight against COVID-19 and related viral infections, Turkish Journal of Biology, doi:10.55730/1300-0152.2699.
Smail et al., Decoding the intricacies: a comprehensive analysis of microRNAs in the pathogenesis, diagnosis, prognosis and therapeutic strategies for COVID-19, Frontiers in Medicine, doi:10.3389/fmed.2024.1430974.
Chatatikun et al., Potential of traditional medicines in alleviating COVID-19 symptoms, Frontiers in Pharmacology, doi:10.3389/fphar.2024.1452616.
Guan et al., Exploring new therapeutic potential of five commonly used Pteris medicinal plants through pharmaphylogenomics and network pharmacology, Chinese Herbal Medicines, doi:10.1016/j.chmed.2024.09.003.
Jabeen et al., Insights for Future Pharmacology: Exploring Phytochemicals as Potential Inhibitors Targeting SARS-CoV-2 Papain-like Protease, Future Pharmacology, doi:10.3390/futurepharmacol4030029.
Al Adem et al., 3-chymotrypsin-like protease in SARS-CoV-2, Bioscience Reports, doi:10.1042/BSR20231395.
unknown, u., A network pharmacological study to investigate the combination of LHQW-XYS in the treatment of COVID-19 olfactory impairment-associated, Journal of Chinese Pharmaceutical Sciences, doi:10.5246/jcps.2024.07.047.
Shanmugam et al., Targeting SARS-CoV-2 Spike and Main protease with phytochemicals from Herbs and spices: Molecular Docking and dynamics simulation studies., Journal of Physics: Conference Series, doi:10.1088/1742-6596/2801/1/012013.
Zagórska et al., Inhibitors of SARS-CoV-2 Main Protease (Mpro) as Anti-Coronavirus Agents, Biomolecules, doi:10.3390/biom14070797.
Ebrahimi et al., Systems biology approaches to identify driver genes and drug combinations for treating COVID-19, Scientific Reports, doi:10.1038/s41598-024-52484-8.
Sathishkumar, K., From nature to medicine: bioactive compounds in the battle against SARS-CoV-2, Natural Product Research, doi:10.1080/14786419.2024.2375754.
Nur Hannani Ahmad Rozani et al., Molecular Docking of Phytochemicals from M. Charantia Targeting SARS-CoV-2 Main Protease, Journal of Advanced Research in Applied Sciences and Engineering Technology, doi:10.37934/araset.47.1.152165.
Trischitta et al., Pseudovirus-Based Systems for Screening Natural Antiviral Agents: A Comprehensive Review, International Journal of Molecular Sciences, doi:10.3390/ijms25105188.
Gupta et al., Exploring Binding Pockets in the Conformational States of the SARS-CoV-2 Spike Trimers for the Screening of Allosteric Inhibitors Using Molecular Simulations and Ensemble-Based Ligand Docking, International Journal of Molecular Sciences, doi:10.3390/ijms25094955.
Chavda et al., SARS-CoV-2 Variants and Global Population Vulnerability, Apple Academic Press, doi:10.1201/9781003467939.
Lei et al., Small molecules in the treatment of COVID-19, Signal Transduction and Targeted Therapy, doi:10.1038/s41392-022-01249-8.
Xu (D) et al., Discovery of common molecular signatures and drug repurposing for COVID-19/Asthma comorbidity: ACE2 and multi-partite networks, Cell Cycle, doi:10.1080/15384101.2024.2340859.
Al-Jamal et al., Treating COVID-19 with Medicinal Plants: Is It Even Conceivable? A Comprehensive Review, Viruses, doi:10.3390/v16030320.
Yousefi Rad et al., Moringa oleifera and Its Biochemical Compounds: Potential Multi-targeted Therapeutic Agents Against COVID-19 and Associated Cancer Progression, Biochemical Genetics, doi:10.1007/s10528-024-10758-w.
Masoudi-Sobhanzadeh et al., Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries, Briefings in Bioinformatics, doi:10.1093/bib/bbab113.
Jamal, Q., Antiviral Potential of Plants against COVID-19 during Outbreaks—An Update, International Journal of Molecular Sciences, doi:10.3390/ijms232113564.
Szabó et al., Natural products as a source of Coronavirus entry inhibitors, Frontiers in Cellular and Infection Microbiology, doi:10.3389/fcimb.2024.1353971.
Sharun et al., A comprehensive review on pharmacologic agents, immunotherapies and supportive therapeutics for COVID-19, Narra J, doi:10.52225/narra.v2i3.92.
Wang (B) et al., Investigating the Mechanism of Qu Du Qiang Fei 1 Hao Fang Formula against Coronavirus Disease 2019 Based on Network Pharmacology Method, World Journal of Traditional Chinese Medicine, doi:10.4103/2311-8571.395061.
Bizzoca et al., Natural compounds may contribute in preventing SARS-CoV-2 infection: a narrative review, Food Science and Human Wellness, doi:10.1016/j.fshw.2022.04.005.
Dong et al., Development of SARS-CoV-2 entry antivirals, Cell Insight, doi:10.1016/j.cellin.2023.100144.
Abubakar et al., Potential benefits and challenges on the use of phytochemicals for obese COVID-19 patients: A review, Phytomedicine Plus, doi:10.1016/j.phyplu.2024.100526.
Banerjee et al., Potential SARS-CoV-2 main protease inhibitors, Drug Discovery Today, doi:10.1016/j.drudis.2020.12.005.
Liu et al., Plant‐derived compounds as potential leads for new drug development targeting COVID‐19, Phytotherapy Research, doi:10.1002/ptr.8105.
Taguchi et al., Novel Method for Detection of Genes With Altered Expression Caused by Coronavirus Infection and Screening of Candidate Drugs for SARS-CoV-2, MDPI AG, doi:10.20944/preprints202004.0431.v1.
Taguchi (B) et al., A New Advanced In Silico Drug Discovery Method for Novel Coronavirus (SARS-CoV-2) with Tensor Decomposition-Based Unsupervised Feature Extraction, MDPI AG, doi:10.20944/preprints202004.0524.v1.
Gysi et al., Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19, arXiv, doi:10.48550/arXiv.2004.07229.
Glinsky, G., Harnessing powers of genomics to build molecular maps of coronavirus targets in human cells: a guide for existing drug repurposing and experimental studies identifying candidate therapeutics to mitigate the pandemic COVID-19., American Chemical Society (ACS), doi:10.26434/chemrxiv.12052512.v2.
Smith et al., Repurposing Therapeutics for COVID-19: Supercomputer-Based Docking to the SARS-CoV-2 Viral Spike Protein and Viral Spike Protein-Human ACE2 Interface, American Chemical Society (ACS), doi:10.26434/chemrxiv.11871402.v4.
Abarova et al., Emerging Therapeutic Potential of Polyphenols from Geranium sanguineum L. in Viral Infections, Including SARS-CoV-2, Biomolecules, doi:10.3390/biom14010130.
Huang et al., Signaling pathways and potential therapeutic targets in acute respiratory distress syndrome (ARDS), Respiratory Research, doi:10.1186/s12931-024-02678-5.
unknown (B), u., The Potential of Herbal Combinations as Adjunctive Therapy for Standard Care of COVID-19 Patients: A Lesson Learned from Indonesia, Pharmaceutical Sciences and Research, doi:10.7454/psr.v10i3.1349.
Shukla et al., Virtual Screening of Orally Active Lead Compounds of Pearl Millet and Their Structural Activity Against Target Protein of COVID-19, Russian Journal of Bioorganic Chemistry, doi:10.1134/S1068162023080058.
Khalifa et al., After the Hurricane: Anti-COVID-19 Drugs Development, Molecular Mechanisms of Action and Future Perspectives, International Journal of Molecular Sciences, doi:10.3390/ijms25020739.
Velásquez et al., Effectiveness of Drug Repurposing and Natural Products Against SARS-CoV-2: A Comprehensive Review, Clinical Pharmacology: Advances and Applications, doi:10.2147/CPAA.S429064.
Dofuor et al., The Global Impact of COVID-19: Historical Development, Molecular Characterization, Drug Discovery and Future Directions, Clinical Pathology, doi:10.1177/2632010x231218075.
Olanike et al., Phytochemical compounds present in COVI-MXG herbal preparation inhibits RNA-Dependent RNA polymerase from SARS-CoV-2: A molecular docking study, Journal of Medicinal Plants Research, doi:10.5897/JMPR2020.7065.
Zaa et al., Neuroprotective Agents with Therapeutic Potential for COVID-19, Biomolecules, doi:10.3390/biom13111585.
Comunale et al., The Functional Implications of Broad Spectrum Bioactive Compounds Targeting RNA-Dependent RNA Polymerase (RdRp) in the Context of the COVID-19 Pandemic, Viruses, doi:10.3390/v15122316.
Syamsi et al., Efek Potensial Moringa Oleifera pada Infeksi COVID-19, Malahayati Nursing Journal, doi:10.33024/mnj.v5i3.8144.
Yang (D) et al., Network Pharmacology and Experimental Validation of Qingwen Baidu
Decoction Therapeutic Potential in COVID-19-related Lung Injury, Combinatorial Chemistry & High Throughput Screening, doi:10.2174/0113862073236899230919062725.
Beg et al., Are herbal drugs effective in COVID management? A review to demystify the current facts and claims, ScienceOpen, doi:10.14293/s2199-1006.1.sor-.ppxfif7.v2.
TAOFEEK, O., Molecular Docking and Admet Analyses of Photochemicals from Nigella sativa (blackseed), Trigonella foenum-graecum (Fenugreek) and Anona muricata (Soursop) on SARS-CoV-2 Target, ScienceOpen, doi:10.14293/s2199-1006.1.sor-.ppknvfy.v1.
Zeng et al., Jinhua Qinggan Granule UHPLC-Q-extractive-Orbitrap-MS assay: Putative identification of 45 potential anti-Covid-19 constituents, confidential addition, and pharmacopoeia quality-markers recommendation, Journal of Food and Drug Analysis, doi:10.38212/2224-6614.3466.
Li et al., Molecular mechanism of the effect of Gegen Qinlian decoction on COVID-19 comorbid with diabetes mellitus based on network pharmacology and molecular docking: A review, Medicine, doi:10.1097/MD.0000000000034683.
Souza et al., Molecular Docking and ADME-TOX Profiling of Moringa oleifera Constituents against SARS-CoV-2, Advances in Respiratory Medicine, doi:10.3390/arm91060035.
Singh (E) et al., Medicinal plants, phytoconstituents and traditional formulation as potential therapies for SARS-CoV-2: a review update, Vegetos, doi:10.1007/s42535-023-00706-1.
Biswas et al., Antiviral Phytocompounds Against Animal-to-Human Transmittable SARS-CoV-2, Ethnopharmacology and Drug Discovery for COVID-19: Anti-SARS-CoV-2 Agents from Herbal Medicines and Natural Products, doi:10.1007/978-981-99-3664-9_7.
He et al., Recent advances towards natural plants as potential inhibitors of SARS-Cov-2 targets, Pharmaceutical Biology, doi:10.1080/13880209.2023.2241518.
Jaber, S., The effect of a small polyphenolic and terpenoids phytochemical constituent on curing and preventing of Covid-19 infections, Pharmacia, doi:10.3897/pharmacia.70.e106534.
Mushebenge et al., Assessing the Potential Contribution of in Silico Studies in Discovering Drug Candidates that Interact with Various SARS-CoV-2 Receptors, MDPI AG, doi:10.20944/preprints202308.0434.v1.
Onyango, O., In Silico Models for Anti-COVID-19 Drug Discovery: A Systematic Review, Advances in Pharmacological and Pharmaceutical Sciences, doi:10.1155/2023/4562974.
Akter et al., Plausibility of natural immunomodulators in the treatment of COVID-19–A comprehensive analysis and future recommendations, Heliyon, doi:10.1016/j.heliyon.2023.e17478.
Rafiq et al., A Comprehensive Update of Various Attempts by Medicinal Chemists to Combat COVID-19 through Natural Products, Molecules, doi:10.3390/molecules28124860.
Badrunanto et al., In-silico approaches potential compounds in ginger (Zingiber officinale) as inhibitors of membrane, envelope, nucleocapsid, Plpro, and Helicase proteins of the SARS-CoV-2, INTERNATIONAL CONFERENCE ON APPLIED COMPUTATIONAL INTELLIGENCE AND ANALYTICS (ACIA-2022), doi:10.1063/5.0127236.
Jaka Fajar Fatriansyah et al., Molecular Docking and Molecular Dynamics of Herbal Plants Phylantus Niruri Linn (Green Meniran) towards of SARS-CoV-2 Main Protease, Evergreen, doi:10.5109/6792822.
Schake et al., An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds, Scientific Reports, doi:10.1038/s41598-023-35671-x.
CHANDRA et al., Indian medicinal plants are effective in the treatment and management of COVID-19, BIOCELL, doi:10.32604/biocell.2023.026081.
Xie et al., Exploring the mechanism of Ge-Gen-Qin-Lian-Wan against Novel coronavirus pneumonia based on network pharmacology and molecular docking techniques, Medical Data Mining, doi:10.53388/MDM202306016.
Tiwari et al., Virtual Screening of Phytochemicals in Search of a Potential Drug Candidate for COVID-19: DFT Study and Molecular Docking, COVID-19: Origin, Impact and Management (Part 2), doi:10.2174/9789815165944123010012.
Fan et al., Pharmaceutical approaches for COVID-19: An update on current therapeutic opportunities, Acta Pharmaceutica, doi:10.2478/acph-2023-0014.
Lundstrom et al., COVID-19 signalome: Potential therapeutic interventions, Cellular Signalling, doi:10.1016/j.cellsig.2022.110559.
Mehyar, N., Coronaviruses SARS-CoV, MERS-CoV, and SARS-CoV-2 helicase inhibitors: A systematic review of in vitro studies, Journal of Virus Eradication, doi:10.1016/j.jve.2023.100327.
CHEN et al., Deciphering suppressive effects of Lianhua Qingwen Capsule on COVID-19 and synergistic effects of its major botanical drug pairs, Chinese Journal of Natural Medicines, doi:10.1016/S1875-5364(23)60455-8.
Wang (C) et al., Inflammasomes: a rising star on the horizon of COVID-19 pathophysiology, Frontiers in Immunology, doi:10.3389/fimmu.2023.1185233.
Oliver et al., Different drug approaches to COVID-19 treatment worldwide: an update of new drugs and drugs repositioning to fight against the novel coronavirus, Therapeutic Advances in Vaccines and Immunotherapy, doi:10.1177/25151355221144845.
Priyandoko et al., Inflammation Inhibitory Activity of Green Tea, Soybean, and Guava Extracts During Sars-Cov-2 Infection Through TNF Protein in Cytokine Storm, Computational Biology and Chemistry, doi:10.1016/j.compbiolchem.2023.107898.
Fu et al., The mechanism of TCM prevention and treatment of heavy COVID-19 based on data mining and network pharmacology, Drug Combination Therapy, doi:10.53388/DCT20230010.
Abou Baker et al., An overview on medicinal plants used for combating coronavirus: Current potentials and challenges, Journal of Agriculture and Food Research, doi:10.1016/j.jafr.2023.100632.
Xue et al., Repurposing clinically available drugs and therapies for pathogenic targets to combat SARS‐CoV‐2, MedComm, doi:10.1002/mco2.254.
Akanchise et al., Ginkgo Biloba and Long COVID: In Vivo and In Vitro Models for the Evaluation of Nanotherapeutic Efficacy, Pharmaceutics, doi:10.3390/pharmaceutics15051562.
Toigo et al., Flavonoid as possible therapeutic targets against COVID-19: a scoping review of in silico studies, DARU Journal of Pharmaceutical Sciences, doi:10.1007/s40199-023-00461-3.
unknown (C), u., Trainee Poster Honorable Mention Poster Abstracts Presented at the 123rd Annual Meeting of the American Association of Colleges of Pharmacy, July 23-27, 2022, American Journal of Pharmaceutical Education, doi:10.5688/ajpe9169.
Srivastava et al., A Brief Review on Medicinal Plants-At-Arms against COVID-19, Interdisciplinary Perspectives on Infectious Diseases, doi:10.1155/2023/7598307.
Giordano et al., Food Plant Secondary Metabolites Antiviral Activity and Their Possible Roles in SARS-CoV-2 Treatment: An Overview, Molecules, doi:10.3390/molecules28062470.
England et al., Plants as Biofactories for Therapeutic Proteins and Antiviral Compounds to Combat COVID-19, Life, doi:10.3390/life13030617.
Yang (E) et al., A Comprehensive Review of Natural Flavonoids with Anti-SARS-CoV-2 Activity, Molecules, doi:10.3390/molecules28062735.
Trivedi et al., Antiviral and Anti-Inflammatory Plant-Derived Bioactive Compounds and Their Potential Use in the Treatment of COVID-19-Related Pathologies, Journal of Xenobiotics, doi:10.3390/jox12040020.
Taguchi (C) et al., A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction, PLOS ONE, doi:10.1371/journal.pone.0238907.
Khaerunnisa et al., Potential Inhibitor of COVID-19 Main Protease (M<sup>pro</sup>) From Several Medicinal Plant Compounds by Molecular Docking Study, MDPI AG, doi:10.20944/preprints202003.0226.v1.
Mardaneh et al., Inhibiting NF-κB During Cytokine Storm in COVID-19: Potential Role of Natural Products as a Promising Therapeutic Approach, MDPI AG, doi:10.20944/preprints202106.0130.v1.
Bekheit et al., Potential RNA-dependent RNA polymerase inhibitors as prospective drug candidates for SARS-CoV-2, European Journal of Medicinal Chemistry, doi:10.1016/j.ejmech.2023.115292.
Dutta et al., A systemic review on medicinal plants and their bioactive constituents against avian influenza and further confirmation through in-silico analysis, Heliyon, doi:10.1016/j.heliyon.2023.e14386.
Heleno et al., Plant Extracts and SARS-CoV-2: Research and Applications, Life, doi:10.3390/life13020386.
Zeyad Bazbouz et al., Plant phytochemicals as potential candidates for treating post‐COVID‐19 lung infections, Phytotherapy Research, doi:10.1002/ptr.7650.
Jiang et al., Exploration of Fuzheng Yugan Mixture on COVID-19 based on network pharmacology and molecular docking, Medicine, doi:10.1097/MD.0000000000032693.
Guo et al., Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy, Briefings in Bioinformatics, doi:10.1093/bib/bbac628.
Zhang (C) et al., Herbal medicines exhibit a high affinity for ACE2 in treating COVID-19, BioScience Trends, doi:10.5582/bst.2022.01534.
Supianto et al., Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature, Journal of Taibah University Medical Sciences, doi:10.1016/j.jtumed.2022.12.015.
Guerra et al., Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules, ACS Omega, doi:10.1021/acsomega.2c05766.
Fatriansyah et al., Molecular Dynamics Simulation of Ligands from Anredera cordifolia (Binahong) to the Main Protease (Mpro) of SARS-CoV-2, Journal of Tropical Medicine, doi:10.1155/2022/1178228.
Chen (B) et al., The exploration of phytocompounds theoretically combats SARS-CoV-2 pandemic against virus entry, viral replication and immune evasion, Journal of Infection and Public Health, doi:10.1016/j.jiph.2022.11.022.
Azeem et al., Virtual screening of phytochemicals by targeting multiple proteins of severe acute respiratory syndrome coronavirus 2: Molecular docking and molecular dynamics simulation studies, International Journal of Immunopathology and Pharmacology, doi:10.1177/03946320221142793.
Bijelić et al., Phytochemicals in the Prevention and Treatment of SARS-CoV-2—Clinical Evidence, Antibiotics, doi:10.3390/antibiotics11111614.
Milton-Laskibar et al., Potential usefulness of Mediterranean diet polyphenols against COVID-19-induced inflammation: a review of the current knowledge, Journal of Physiology and Biochemistry, doi:10.1007/s13105-022-00926-0.
Ur Rehman et al., Flavonoids and other polyphenols against SARS-CoV-2, Application of Natural Products in SARS-CoV-2, doi:10.1016/B978-0-323-95047-3.00014-9.
de Matos et al., Bioactive compounds as potential angiotensin-converting enzyme II inhibitors against COVID-19: a scoping review, Inflammation Research, doi:10.1007/s00011-022-01642-7.
Youn et al., Robust therapeutic effects on COVID-19 of novel small molecules: Alleviation of SARS-CoV-2 S protein induction of ACE2/TMPRSS2, NOX2/ROS, and MCP-1, Frontiers in Cardiovascular Medicine, doi:10.3389/fcvm.2022.957340.
Amalia et al., The Potential of Phyllanthus Niruri Plant Secondary Metabolites in Providing Antiviral Protection Against Sars-Cov-2: A Literature Review, KnE Medicine, doi:10.18502/kme.v2i3.11915.
Tiwari (B) et al., Molecular docking studies on the phytoconstituents as therapeutic leads against SARS-CoV-2, Polimery, doi:10.14314/polimery.2022.7.8.
Liu (B) et al., Investigating the Potential Bioactive Components of Qing-Fei-Pai-Du Decoction Against COVID-19 in Diabetes/Diabetic Patients Based on Network Pharmacology and Molecular Docking, Natural Product Communications, doi:10.1177/1934578X221124769.
BİLGİNER et al., Molecular Docking Study of Several Seconder Metabolites from Medicinal Plants as Potential Inhibitors of COVID-19 Main Protease, Turkish Journal of Pharmaceutical Sciences, doi:10.4274/tjps.galenos.2021.83548.
Dinda (B) et al., Some natural compounds and their analogues having potent anti- SARS-CoV-2 and anti-proteases activities as lead molecules in drug discovery for COVID-19, European Journal of Medicinal Chemistry Reports, doi:10.1016/j.ejmcr.2022.100079.
Babu et al., Identification of differentially expressed genes and their major pathways among the patient with COVID-19, cystic fibrosis, and chronic kidney disease, Informatics in Medicine Unlocked, doi:10.1016/j.imu.2022.101038.
Rieder et al., A Review of In Silico Research, SARS-CoV-2, and Neurodegeneration: Focus on Papain-Like Protease, Neurotoxicity Research, doi:10.1007/s12640-022-00542-2.
Liu (C) et al., Network Pharmacology and Molecular Docking Elucidate the Underlying Pharmacological Mechanisms of the Herb Houttuynia cordata in Treating Pneumonia Caused by SARS-CoV-2, Viruses, doi:10.3390/v14071588.
Sperry et al., Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients, PLOS Computational Biology, doi:10.1371/journal.pcbi.1011050.
Negru et al., Virtual Screening of Substances Used in the Treatment of SARS-CoV-2 Infection and Analysis of Compounds With Known Action on Structurally Similar Proteins From Other Viruses, Biomedicine & Pharmacotherapy, doi:10.1016/j.biopha.2022.113432.
Zhang (D) et al., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes, JAMA, 80:19, 1690, doi:10.1001/jama.280.19.1690.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
Sweeting et al., What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data, Statistics in Medicine, doi:10.1002/sim.1761.
Di Pierro (B) et al., Possible Therapeutic Effects of Adjuvant Quercetin Supplementation Against Early-Stage COVID-19 Infection: A Prospective, Randomized, Controlled, and Open-Label Study, International Journal of General Medicine, doi:10.2147/IJGM.S318720.
Din Ujjan et al., The possible therapeutic role of curcumin and quercetin in the early-stage of COVID-19—Results from a pragmatic randomized clinical trial, Frontiers in Nutrition, doi:10.3389/fnut.2022.1023997.
Khan et al., Oral Co-Supplementation of Curcumin, Quercetin, and Vitamin D3 as an Adjuvant Therapy for Mild to Moderate Symptoms of COVID-19—Results From a Pilot Open-Label, Randomized Controlled Trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.898062.
Gérain et al., NASAFYTOL® supplementation in adults hospitalized with COVID-19 infection: results from an exploratory open-label randomized controlled trial, Frontiers in Nutrition, doi:10.3389/fnut.2023.1137407.
Onal et al., Treatment of COVID-19 patients with quercetin: a prospective, single center, randomized, controlled trial, Turk. J. Biol., 45:518-529 (preprint 1/19/2021), journals.tubitak.gov.tr/biology/vol45/iss7/13/.
Shohan et al., The therapeutic efficacy of quercetin in combination with antiviral drugs in hospitalized COVID-19 patients: A randomized controlled trial, European Journal of Pharmacology, doi:10.1016/j.ejphar.2021.1746158.
Tylishchak et al., Effectiveness of the quercetin use in patients with COVID-19 with concomitant type 2 diabetes mellitus, Wiadomości Lekarskie, doi:10.36740/WLek/191875.
Zupanets et al., Quercetin effectiveness in patients with COVID-19 associated pneumonia, Zaporozhye Med. J., doi:10.14739/2310-1210.2021.5.231714.
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