Niclosamide reduces COVID-19 risk: real-time meta-analysis of 7 studies

@CovidAnalysis, October 2026, Version 7V7 (V1 2024)

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (7)28%

Mortality (3)12%

Ventilation (1)80%

Hospitalization (1)13%

Recovery (5)30%

Cases (1)−2%

Viral clearance (5)2%

RCTs (7)28%

RCT mortality (3)12%

Prophylaxis (1)0%

Early (3)29%

Late (3)20%

after exclusions

00.511.5+

← Niclosamide
reduces riskIncreased
risk →

.hmva--anim-stagger .hmva__row{opacity:1}.hmva--anim .hmva__shade{opacity:var(--shade-opacity)}.hmva--anim-stagger .est .ci{transform:translateY(-50%)}.hmva--anim-stagger .est .pt{transform:translate(-50%,-50%)}.hmva--anim-stagger .hmva__bar{transform:none}.hmva--anim-settle .est .pt{left:calc(min(var(--pt), 1.5) / var(--max) * 100%)}.hmva--anim-settle .est .ci{left:calc(var(--lo) / var(--max) * 100%);width:calc((var(--hi) - var(--lo)) / var(--max) * 100%)}.hmva--anim-settle .hmva__bar--drug{width:calc(min(1, var(--rr)) * 100%)}.hmva--anim-settle .hmva__bar--control{width:calc(min(1, 1 / var(--rr)) * 100%)}

Abstract

Significantly lower risk is seen for recovery. 3 studies from 3 independent teams in 3 countries show significant benefit.

Meta-analysis using the most serious outcome reported shows 28% [9‑43%] lower risk. Results are similar for higher quality and peer-reviewed studies. Currently all studies are RCTs.

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (7)28%

Mortality (3)12%

Ventilation (1)80%

Hospitalization (1)13%

Recovery (5)30%

Cases (1)−2%

Viral clearance (5)2%

RCTs (7)28%

RCT mortality (3)12%

Prophylaxis (1)0%

Early (3)29%

Late (3)20%

after exclusions

00.511.5+

← Niclosamide
reduces riskIncreased
risk →

Currently there is limited data, with only 20 control events for the most serious outcome in trials to date.

1 RCT with 200 patients has not reported results (5 years late)1.

No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Other treatments are more effective. A lipid-optimized formulation may be required for therapeutic concentrations of niclosamide, and may aid distribution to target tissues. All data and sources to reproduce this analysis are in the appendix.

Download PDF

Niclosamide for COVID-19 — Highlights

Niclosamide reduces risk with very high confidence for recovery and in pooled analysis.

56th treatment shown effective in August 2025, now with p = 0.0069 from 7 studies.

Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.

.fp--anim .fp__study,.fp--anim .fp__sum,.fp--anim .fp__het,.fp--anim .fp__dia,.fp--anim .fp__verdict,.fp--anim .fp__pooled{opacity:1}.fp--anim .fp__ci{transform:none}.fp--anim .fp__pt{transform:translate(-50%,-50%)}.fp--anim .fp__plot::before{opacity:var(--shade-opacity)}.fp--anim-settle .fp__pt{left:calc(min(var(--pt),var(--max))/var(--max)*100%)}.fp--anim-settle .fp__ci,.fp--anim-settle .fp__dia{left:calc(var(--lo)/var(--max)*100%);width:calc((var(--hi) - var(--lo))/var(--max)*100%)}

c19early.org

October 2026

Niclosamide COVID-19 studies (1 unreported RCT)

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Cairns (DB RCT)66%0.34 · 0.01–7.98severe case0/331/34 Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30FINCOVCT¹ Kim (DB RCT)29%0.71 · 0.55–0.92recov. time99 (n)98 (n)

Early treatment29%0.71 · 0.55–0.911/1622/16229% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0077

Abdulamir (RCT)0%1.00 · 0.21–4.80death3/753/75 First Wa.. (DB RCT)23%0.77 · 0.33–1.82viral+8/6010/58RESERVOIR Rank (DB RCT)30%0.70 · 0.13–3.78death2/223/23 Erenme.. (DB RCT)n/a< NOT REPORTED >200 (est. total)NICLONEX>5 years late

Late treatment20%0.80 · 0.40–1.5913/15716/15620% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.53

Humphrey (DB RCT)0%1.00 · 0.14–7.07death2/8262/825PROTECT-V

Prophylaxis0%1.00 · 0.14–7.072/8262/8250% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

All studies28%0.72 · 0.57–0.9116/1,14520/1,14328% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.006900.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 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 niclosamide studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and pooled outcomes in RCTs.

Introduction

Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from2.

Immediate treatment recommended

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 injury3-19 and cognitive deficits6,11, cardiovascular complications20-26, DNA damage27-30, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits31—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.

Many treatments are expected to modulate infection

SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,32-39, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk40, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.

Extensive supporting research

In silico studies predict inhibition of SARS-CoV-2 with niclosamide or metabolites via binding to the spikeB,41, M^proC,41, RNA-dependent RNA polymeraseD,41, PLproE,41, nucleocapsidF,41, and helicaseG,41 proteins. Niclosamide inhibits endolysosomal acidification and suppresses TLR3-mediated pro-inflammatory signaling in human small airway epithelial cells stimulated with TLR3 agonists mimicking viral RNA42, modulates host lipid metabolism and reduces infectious SARS-CoV-2 virion production in Vero E6 cells43, reduces CD147 protein levels and inhibits SARS-CoV-2-induced upregulation of CD147 in A549-ACE2 cells, including the highly glycosylated form of CD147 which has been implicated in COVID-19 disease progression and post-COVID-19 cardiac complications44, blocked the formation of syncytia mediated by SARS-CoV-2 spike protein pseudovirus-producing cells45, may reduce inflammation, NLRP3 formation, and caspase-1 activity46, may inhibit viral uncoating, replication, and assembly via disruption of pH gradients and reduced ATP production in host cells47, may counter immune evasion by reversing E-, ORF7a-, and ORF8-mediated down-regulation of MHC-I, preserving CD8⁺ T-cell recognition48, and shows strong synergy when combined with ivermectin49.

Analysis

We analyze all significant controlled studies of niclosamide 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.

Treatment timing

Fig. 3 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.

Fig. 3. Treatment stages.

Evaluation Notes

Analyzing the data from first principles is always important, and may be especially important for COVID-19. Bias in the design, analysis, and reporting of clinical studies has long been reported, and may be increased for COVID-19 due to politicization. For example, Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."50 Social pressure to report politically acceptable results was very strong. There may therefore be unusually strong bias in design, analysis, and reporting. For example, Prof. Greenland reports that Hayward et al. applied an unjustified and undisclosed extremely tight, null-centered Bayesian prior that biases towards more politically acceptable results52. While the politicization focused on certain treatments, it was not limited to them. For example, media coverage was highly biased against positive results for low-cost treatments53 across the 227 treatments we cover. Studies should be evaluated from first principles, incorporating the treatment delay, treatment regimen, patient population, and other confounding factors. For COVID-19, there is no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]54—in both cases bias varies from minimal to extreme, and all studies must be evaluated individually.

US authorities claim only three high-profit drugs from companies with strong US lobbying are beneficial for early treatment (2 repurposed drugs - remdesivir and molnupiravir, and one novel drug - nirmatrelvir)H. COVID-19 involves the interplay of many viral and host proteins and factors, providing over 500 therapeutic targets55. Given the 12,000+ proposed treatments40, existing results for many treatments with overlapping secondary complications and therapeutic targets, and the combination of known safety and pharmocokinetics for certain treatments along with strong mechanistic evidence, the probability of only 3 high-profit drugs from top lobbying companies being beneficial is extremely low. This likely reflects a bias toward evaluation of drugs with strong financial resources and efforts devoted to passing the US approval process, and very limited or non-existing evaluation of low-cost treatments. Indeed, official recommendations varied widely between countries56. Some countries evaluated limited low-cost treatments early and 25 low-cost treatments were approved in one or more countries56.

Contrary to claims found online, we analyze both all studies and higher-quality studies (with evaluation focusing on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias), we analyze specific outcomes and pooled outcomes (with extensive analysis and validation of pooled outcomes), and we do not include preclinical studies or retracted studies in meta-analysis. Pooled outcomes can detect efficacy earlier but do not bias towards efficacy—the inclusion of less appropriate designs such as very late treatment may hide efficacy for appopriate use.

Preclinical Research

An in silico study supports the efficacy of niclosamide41.

8 in vitro studies support the efficacy of niclosamide42-47,49,57.

A study shows synergistic antiviral effects for niclosamide in combination with hydroxychloroquine49 and ivermectin49, which may allow increased efficacy and/or similar efficacy at lower doses, potentially reducing dose-dependent side effects.

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.

Results

Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 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, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.

Table 1. Random-effects meta-analysis for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. ** p<0.01.
	Relative Risk	Studies	Patients

All studies	0.72 [0.57‑0.91]**	7	2,288
After exclusions	0.72 [0.57‑0.91]**	6	2,228
Peer-reviewedPeer-reviewed	0.72 [0.56‑0.92]**	5	2,125
RCTsRCTs	0.72 [0.57‑0.91]**	7	2,288

Mortality	0.88 [0.33‑2.38]	3	1,846
Recovery	0.70 [0.59‑0.84]****	5	520
Viral	0.98 [0.88‑1.09]	5	488

RCT mortality	0.88 [0.33‑2.38]	3	1,846

Table 2. Random-effects meta-analysis results by treatment stage. Results show the relative risk with treatment and the 95% confidence interval.treatment and the 95% confidence interval. ** p<0.01.
	Early treatment	Late treatment	Prophylaxis

All studies	0.71 [0.55‑0.91]0.71 [0.55‑0.91]	0.80 [0.40‑1.59]0.80 [0.40‑1.59]	1.00 [0.14‑7.07]1.00 [0.14‑7.07]
After exclusions	0.71 [0.55‑0.91]0.71 [0.55‑0.91]	0.80 [0.40‑1.59]0.80 [0.40‑1.59]	1.00 [0.14‑7.07]1.00 [0.14‑7.07]
Peer-reviewedPeer-reviewed	0.71 [0.55‑0.91]0.71 [0.55‑0.91]	1.00 [0.21‑4.80]1.00 [0.21‑4.80]	1.00 [0.14‑7.07]1.00 [0.14‑7.07]
RCTsRCTs	0.71 [0.55‑0.91]0.71 [0.55‑0.91]	0.80 [0.40‑1.59]0.80 [0.40‑1.59]	1.00 [0.14‑7.07]1.00 [0.14‑7.07]

Mortality		0.85 [0.27‑2.67]0.85 [0.27‑2.67]	1.00 [0.14‑7.07]1.00 [0.14‑7.07]
Recovery	0.74 [0.60‑0.90]0.74 [0.60‑0.90]	0.61 [0.43‑0.87]0.61 [0.43‑0.87]
Viral	0.76 [0.49‑1.17]0.76 [0.49‑1.17]	1.00 [0.89‑1.11]1.00 [0.89‑1.11]

RCT mortality		0.85 [0.27‑2.67]0.85 [0.27‑2.67]	1.00 [0.14‑7.07]1.00 [0.14‑7.07]

Fig. 4. 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.

c19early.org

October 2026

Niclosamide COVID-19 studies (1 unreported RCT)

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Cairns (DB RCT)66%0.34 · 0.01–7.98severe case0/331/34 Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30FINCOVCT¹ Kim (DB RCT)29%0.71 · 0.55–0.92recov. time99 (n)98 (n)

Early treatment29%0.71 · 0.55–0.911/1622/16229% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0077

Late treatment20%0.80 · 0.40–1.5913/15716/15620% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.53

Humphrey (DB RCT)0%1.00 · 0.14–7.07death2/8262/825PROTECT-V

Prophylaxis0%1.00 · 0.14–7.072/8262/8250% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

All studies28%0.72 · 0.57–0.9116/1,14520/1,14328% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.006900.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 5. 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.

c19early.org

October 2026

Niclosamide COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Abdulamir (RCT)0%1.00 · 0.21–4.803/753/75 Rank (DB RCT)30%0.70 · 0.13–3.782/223/23

Late treatment15%0.85 · 0.27–2.675/976/9815% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.79

Humphrey (DB RCT)0%1.00 · 0.14–7.072/8262/825PROTECT-V

Prophylaxis0%1.00 · 0.14–7.072/8262/8250% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

All studies12%0.88 · 0.33–2.387/9238/92312% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.8200.511.52+

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 6. Random-effects meta-analysis for mortality results.

c19early.org

October 2026

Niclosamide COVID-19 mechanical ventilation results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Humphrey (DB RCT)80%0.20 · 0.01–4.160/8262/825PROTECT-V

Prophylaxis80%0.20 · 0.01–4.160/8262/82580% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.3

All studies80%0.20 · 0.01–4.160/8262/82580% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.300.511.52+

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 7. Random-effects meta-analysis for ventilation.

c19early.org

October 2026

Niclosamide COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Humphrey (DB RCT)13%0.87 · 0.41–1.81hosp.13/82615/825PROTECT-V

Prophylaxis13%0.87 · 0.41–1.8113/82615/82513% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.71

All studies13%0.87 · 0.41–1.8113/82615/82513% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.7100.511.52+

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 8. Random-effects meta-analysis for hospitalization.

c19early.org

October 2026

Niclosamide COVID-19 progression results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Siripongbo.. (RCT)0%1.00 · 0.07–15.261/301/30FINCOVCT¹

Early treatment0%1.00 · 0.07–15.261/301/300% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

All studies0%1.00 · 0.07–15.261/301/300% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.00.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 9. Random-effects meta-analysis for progression.

c19early.org

October 2026

Niclosamide COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Cairns (DB RCT)18%0.82 · 0.58–1.17recov. time33 (n)34 (n) Siripongbo.. (RCT)39%0.61 · 0.28–1.29symp. score30 (n)30 (n)FINCOVCT¹ Kim (DB RCT)29%0.71 · 0.55–0.92recov. time99 (n)98 (n)

Early treatment26%0.74 · 0.60–0.90162 (n)162 (n)26% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0026

Abdulamir (RCT)39%0.61 · 0.43–0.87no recov.75 (n)75 (n) Rank (DB RCT)70%0.30 · 0.00–918793479862327328112640recov. time23 (n)23 (n)

Late treatment39%0.61 · 0.43–0.8798 (n)98 (n)39% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.007

All studies30%0.70 · 0.59–0.84260 (n)260 (n)30% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 10. Random-effects meta-analysis for recovery.

c19early.org

October 2026

Niclosamide COVID-19 case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Humphrey (DB RCT)−2%1.02 · 0.79–1.32symp. case103/826133/825PROTECT-V

Prophylaxis−2%1.02 · 0.80–1.29103/826133/8252% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.88

All studies−2%1.02 · 0.80–1.29103/826133/8252% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.8800.511.52+

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 11. Random-effects meta-analysis for cases.

c19early.org

October 2026

Niclosamide COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Cairns (DB RCT)24%0.76 · 0.41–1.39viral+11/3315/34 Siripongbo.. (RCT)6%0.94 · 0.65–1.35viral+30 (n)30 (n)FINCOVCT¹ Kim (DB RCT)56%0.44 · 0.14–1.39viral load99 (n)98 (n)

Early treatment24%0.76 · 0.49–1.1711/16215/16224% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.21

First Wa.. (DB RCT)23%0.77 · 0.33–1.82viral+8/6010/58RESERVOIR Rank (DB RCT)0%1.00 · 0.90–1.11viral time23 (n)23 (n)

Late treatment0%1.00 · 0.89–1.118/8310/810% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.94

All studies2%0.98 · 0.88–1.0919/24525/2432% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.7100.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 12. Random-effects meta-analysis for viral clearance.

c19early.org

October 2026

Niclosamide COVID-19 peer reviewed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Cairns (DB RCT)66%0.34 · 0.01–7.98severe case0/331/34 Siripongbo.. (RCT)0%1.00 · 0.07–15.26progression1/301/30FINCOVCT¹ Kim (DB RCT)29%0.71 · 0.55–0.92recov. time99 (n)98 (n)

Early treatment29%0.71 · 0.55–0.911/1622/16229% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0077

Abdulamir (RCT)0%1.00 · 0.21–4.80death3/753/75

Late treatment0%1.00 · 0.21–4.803/753/750% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

Humphrey (DB RCT)0%1.00 · 0.14–7.07death2/8262/825PROTECT-V

Prophylaxis0%1.00 · 0.14–7.072/8262/8250% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

All studies28%0.72 · 0.56–0.926/1,0637/1,06228% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.00900.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 13. Random-effects meta-analysis for peer reviewed studies. 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. 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.

Randomized Controlled Trials (RCTs)

Currently all studies are RCTs.

Unreported RCTs

1 niclosamide RCT has not reported results1. The trial reports report an estimated total of 200 patients. The result is delayed over 5 years.

Exclusions

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. Fig. 14 shows a forest plot for random-effects meta-analysis of all studies after exclusions.

Siripongboonsitti, data consistency issues, very low risk patients/variants with almost no progression, all patients received known effective antiviral, baseline differences.

c19early.org

October 2026

Niclosamide COVID-19 studies after exclusions

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Cairns (DB RCT)66%0.34 · 0.01–7.98severe case0/331/34 Kim (DB RCT)29%0.71 · 0.55–0.92recov. time99 (n)98 (n)

Early treatment29%0.71 · 0.55–0.910/1321/13229% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0074

Abdulamir (RCT)0%1.00 · 0.21–4.80death3/753/75 First Wa.. (DB RCT)23%0.77 · 0.33–1.82viral+8/6010/58RESERVOIR Rank (DB RCT)30%0.70 · 0.13–3.78death2/223/23

Late treatment20%0.80 · 0.40–1.5913/15716/15620% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.53

Humphrey (DB RCT)0%1.00 · 0.14–7.07death2/8262/825PROTECT-V

Prophylaxis0%1.00 · 0.14–7.072/8262/8250% lower risk

Tau² = 0.00, I² = 0.0%, p = 1.

All studies28%0.72 · 0.57–0.9115/1,11519/1,11328% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.006700.511.52+

Rotate screen for more detailsIncrease width for more details

← Niclosamide
reduces riskNiclosamide
increases risk →

Fig. 14. 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.

Media Censorship

Low-cost treatments were subject to bias and censorship during the pandemic. Scientific bias is seen in the design, analysis, presentation, and selective reporting of studies, which often favored negative results. A similar bias is seen in the media coverage for low-cost treatments. While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."50.

We analyze media coverage for the 227 treatments we cover using Altmetric61, which reports the number of ~12,000 tracked news outlets that covered each study62. Studies are considered to have received significant media coverage if they were covered by at least 0.5% of the tracked news outlets. Fig. 15, 16, and 17 show the bias toward negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments. This may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit treatments and treatment equity, and high-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

The mainstream media did not cover any of the positive studies for niclosamide.

Fig. 15. Media was more likely to cover negative or inconclusive results for low-cost treatments, and positive results for high-profit treatments.

Fig. 16. Mainstream media was biased against positive results for low-cost treatments.

Fig. 17. In contrast to the results for low-cost treatments, mainstream media was biased towards positive results for high-cost treatments.

A combination of factors may have led to the media's suppression of low-cost treatments:

•

Politicization

led to a media environment where coverage was often framed to support a political narrative rather than to provide objective scientific information. As Scott Alexander said:

"if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."

There was strong social pressure to discredit low-cost treatments.

•

Censorship

of information conflicting with selected authorities. For example, individuals and organizations presenting conflicting science were often banned on Twitter and YouTube.

•

FDA requires "no adequate, approved, and available alternatives"

in order to grant an EUA for novel high-profit interventions, creating a strong incentive for authorities to ignore or downplay existing low-cost treatments.

•

Regulatory capture

biases authorities towards high-profit interventions.

•

Authorities ignored most evidence for low-cost treatments

, for example the NIH references only 2% of studies in delayed, rarely-updated, biased commentaries with no quantitive analysis.

•

Media coverage of science is often not very accurate

, e.g., misunderstanding confounding issues. For example the media widely considered the RECOVERY HCQ RCT to be conclusive on efficacy, but very late treatment of late stage patients (mostly on oxygen already) with an excessive toxic dose (shown dangerous in a dose comparison RCT) provides no information on the recommended early/prophylactic treatment. With difficulting in understanding basic confounders like treatment delay and dose, the media may favor deferring to authorities. Many studies for low-cost treatments require greater expertise to analyze. Relatively few journalists have a strong ability to analyze clinical trials and are outnumbered by the rest.

•

Substantial funding from pharmaceutical advertising

biases editorial decisions towards high-profit interventions.

•

PR power

- companies/teams with strong PR presence are favored in the media, which correlates with high-profit and high conflict of interest studies.

•

The media was very negative in general

, inflating risk, fear, and anxieties. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage. A combination of low-cost treatments greatly reducing risk conflicts with the negative narrative.

Authority Delay

25 low-cost treatments were approved in one or more countries, yet many countries approved no low-cost treatments. The countries that did adopt low-cost treatments analyzed the evidence early and made timely approvals. With few exceptions, authorities did not change their initial views, regardless of how much evidence accumulated showing either efficacy or harm. Why?

The harms of smoking here hidden for 25 yearsI. Authorities did not analyze the data in real-time, failing to act when harm was known. Widespread acknowledgement of harm came only after attempts by two new surgeon generals, along with pressure from health advocates and a new president, and a review of 7,000 studies.

Similarly for COVID-19, most authorities and experts did not proactively analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by which time moral, legal, career, and reputational liabilities strongly disincentivize any admission of error. Claims of no efficacy (for effective treatments) or safety (for harmful treatments) were often made prior to strong data being available. Correction would require admitting to errors that increased mortality, which is unlikely with the same generation of officials.

Analysis of potential treatments was rarely done, and when done these were typically minimal efforts. For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments, reference only 2% of studies for the treatments covered, and include no quantitative analysis. They appear as rarely updated side projects from external panels implicitly tasked with justifying prior failures. As with smoking, the thousands of studies could (and should) have been analyzed and acted on in real-time.

A key structural improvement, applicable to all current and future diseases, is for authorities to implement real-time proactive analysis of clinical evidence. This does not remove all bias, but does make it possible to act on evidence, whereas delayed action may be unlikely due to moral, legal, career, and reputational liabilities.

c19early.org

Delayed public health acknowledgments

Official acknowledgment of efficacy or harm is often delayed—legal, career, and status risks disincentivize admission of error.

	Evidence	Official Acknowledgment	Approx. Delay
Citrus Fruit (vitamin C) for Scurvy (effectiveness)	1747: James Lind conducted one of the first-ever controlled clinical trials, proving that oranges and lemons cured scurvy in sailors.	1795: The British Royal Navy finally made a daily ration of lemon juice a standard issue for all its sailors, effectively eliminating the disease.	48 years
Handwashing (lower mortality)	1847: Dr. Ignaz Semmelweis provided conclusive proof that having doctors wash their hands with a chlorine solution before delivering babies reduced maternal mortality rates from over 18% to around 1%.	~1870s: Semmelweis's findings were rejected and he was ridiculed. His work was only validated decades later (after his death).	~20+ years
Helicobacter pylori (bacteria causes ulcers)	1982-1984: Marshall and Warren discovered that Helicobacter pylori bacteria causes ulcers, confirmed via direct exposure. Officials maintained that ulcers were caused by stress and spicy food.	1994: The US NIH released a consensus statement officially recommending antibiotics as the standard treatment for peptic ulcers, overturning decades of acid-suppression therapy.	~12 years
Asbestos (causes asbestosis & cancer)	1924: The British Medical Journal published the first case study of a death from "asbestosis." By 1918, U.S. insurance companies had stopped selling life insurance to asbestos workers.	1971 (US): The Occupational Safety and Health Administration (OSHA) was formed and began regulating asbestos as a carcinogen, setting the first federal workplace safety standards for it.	~47 years
Leaded Gasoline (neurotoxicity)	~1924: Dangers of low-level lead exposure were known. Experts like Alice Hamilton warned the U.S. Surgeon General that adding lead to gasoline would cause widespread public poisoning.	1973 (US): The Environmental Protection Agency (EPA) ordered the first phasedown of lead in gasoline, following the Clean Air Act of 1970. A full ban for on-road vehicles took effect in 1996.	~49 years
Harms of Smoking (causes lung cancer)	1939: Franz Müller (Germany) published the first case-control epidemiological study strongly linking tobacco smoking to lung cancer. This was followed by major U.S. & U.K. studies in the 1950s.	1964 (US): The U.S. Surgeon General's report, "Smoking and Health," was released. It was the first U.S. government report to definitively link smoking to lung cancer and heart disease.	25 years

Heterogeneity

Heterogeneity in COVID-19 studies arises from many factors including:

Treatment delay

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 hours63,64. 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.

Table 3. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delay	Result
Post-exposure prophylaxis	86% fewer cases65
<24 hours	-33 hours symptoms66
24-48 hours	-13 hours symptoms66
Inpatients	-2.5 hours to improvement67

Fig. 18 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

Fig. 18. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.

Patient demographics

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.

SARS-CoV-2 variants

Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants69, for example the Gamma variant shows significantly different characteristics70-73. 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 variants74,75.

Treatment regimen

Effectiveness may depend strongly on the dosage and treatment regimen.

Medication quality

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 et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.

Other treatments

The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic49,78-101, therefore efficacy may depend strongly on combined treatments.

Effect measured

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.

Meta-analysis

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.

Pooled Effects

Combining studies is required

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.

Specific outcome and pooled analyses

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.

Ethical and practical issues limit high-risk trials

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.

Validating pooled outcome analysis for COVID-19

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 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 19 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 20 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). Considering the extremes, Singh 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. Fig. 21 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.

Fig. 19. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.

Fig. 20. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.

Fig. 19. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.

Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)

Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 22 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Fig. 22. 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.

Limitations

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 differences in treatment delay are more likely to hide efficacy.

Summary

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.

Discussion

Publication bias

Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For niclosamide, there is currently not enough data to evaluate publication bias with high confidence.

Funnel plot 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. Fig. 23 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 < 0.05103-110. 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.

Fig. 23. Example funnel plot analysis for simulated perfect trials.

Limitations

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 alone49,78-101. 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.

Notes

1 of 7 studies combine treatments. The results of niclosamide alone may differ. 1 of 7 RCTs use combined treatment.

Reviews

Many reviews cover niclosamide for COVID-19, presenting additional background on mechanisms and related results, including48,111-117.

Other studies

Additional preclinical or review papers suggesting potential benefits of niclosamide for COVID-19 include124-163. We have not reviewed these studies in detail.

Perspective

Results compared with other treatments

SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors32-39, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk40, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 24 shows an overview of the results for niclosamide in the context of multiple COVID-19 treatments, and Fig. 25 shows a plot of efficacy vs. cost for COVID-19 treatments.

Fig. 24. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy164.

Fig. 25. Efficacy vs. cost for COVID-19 treatments.

Conclusion

Studies to date show that niclosamide is an effective treatment for COVID-19. Significantly lower risk is seen for recovery. 3 studies from 3 independent teams in 3 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 28% [9‑43%] lower risk. Results are similar for higher quality and peer-reviewed studies. Currently all studies are RCTs.

Currently there is limited data, with only 20 control events for the most serious outcome in trials to date.

Contact.

Funding.

We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.

Conflicts of interest.

We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

AI.

We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.

Updates.

Our COVID-19 meta-analyses involve the extraction of over 228,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/ncmeta.html.

Dedication.

This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.

Public domain.

This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.

Study Notes

Abdulamir

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Improved recovery with niclosamide

p = 0.007

RCT with 75 COVID-19 patients showing significantly faster recovery but no change in mortality with niclosamide.

The treatment group had more patients aged 60+ and more patients treated over a week after symptom onset. Submit Corrections or Updates.

Cairns

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Faster recovery and improved viral clearance

Not statistically significant · p = 0.28 (recovery) and p = 0.45 (viral clearance)

RCT with 73 mild to moderate outpatients, showing faster recovery and improved viral clearance with niclosamide, without statistical significance. Greater improvements in recovery were seen for high-risk patients, again without statistical significance. The study was underpowered due to decreased enrollment related to falling COVID-19 cases. Submit Corrections or Updates.

Erenmemisoglu

Estimated 200 patient niclosamide late treatment RCT with results not reported over 5 years after estimated completion. Submit Corrections or Updates.

First Wave BioPharma

Outcome

Improvement

Relative Risk · 95% CI

Viral clearance, day 43

23%0.77 · 0.33–1.82

Viral clearance, day 36

33%0.67 · 0.31–1.44

Viral clearance, day 29

42%0.58 · 0.28–1.22

Viral clearance, day 22

27%0.73 · 0.38–1.40

Viral clearance, day 15

23%0.77 · 0.49–1.23

Viral clearance, day 8

1%0.99 · 0.76–1.29

Viral clearance, day 4

-3%1.03 · 0.88–1.19

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Trial underpowered to detect differences

RCT 118 hospitalized patients with gastrointestinal infection showing no significant difference in viral clearance with niclosamide. Viral clearance results are available on clinicaltrials.gov but clinical results are missing. Submit Corrections or Updates.

Humphrey

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Lower ventilation with niclosamide

Not statistically significant · p = 0.25

RCT 1,651 patients with kidney disease showing no significant difference in symptomatic COVID-19, hospitalization, or mortality with intranasal niclosamide compared to placebo. The UNI911 nasal spray had very poor adherence and a higher withdrawal rate (40% vs. 23.8% for placebo), partially due to local nasal and upper airway irritation. Submit Corrections or Updates.

Kim

Outcome

Improvement

Relative Risk · 95% CI

Recovery time, both groups

29%0.71 · 0.55–0.92

Recovery time, low...

33%

Recovery time, high...

25%

Recovery time, both groups (b)

4%0.96 · 0.71–1.30

Recovery time, low...

22%

Recovery time, high...

-7%

Viral load, both groups

56%0.44 · 0.14–1.39

Viral load, low dose...

26%

Viral load, high dose...

66%

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Faster recovery with niclosamide

p = 0.0085

RCT 300 patients with mild to moderate COVID-19 showing significant symptom improvement with niclosamide nanohybrid (CP-COV03). The high-dose group showed no significant benefit in time to symptom improvement in the primary analysis, which authors attribute to gastrointestinal side effects from excess magnesium oxide content confounding symptom assessment. A post-hoc analysis with three COVID-19 representative symptoms showed significant improvement in both groups. Submit Corrections or Updates.

Rank

Outcome

Improvement

Relative Risk · 95% CI

Time to viral clearance

0%1.00 · 0.90–1.11

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Trial underpowered for serious outcomes

RCT 46 moderate to severe hospitalized COVID-19 patients showing shorter time to discharge and WHO clinical scale improvement with niclosamide, but no significant difference for resolution of all symptoms and viral clearance. Submit Corrections or Updates.

Siripongboonsitti

Outcome

Improvement

Relative Risk · 95% CI

Progression, xray, day 6

0%1.00 · 0.07 to >9

Progression, xray, day 3

67%0.33 · 0.01–7.87

Recovery, combined symptoms

39%0.61 · 0.28–1.29

Recovery, day 6, cough

no CI

primary

00.511.52+

← Niclosamide
reduces riskNiclosamide
increases risk →

Improved recovery with treatment

Not statistically significant · p = 0.19

RCT 60 low-risk outpatients, median age 31, with mild to moderate COVID-19 showing no significant differences with combined favipiravir/ivermectin/niclosamide treatment compared to favipiravir alone. There was limited room for improvement with almost no progression and no hospitalization, ICU admission, supplemental oxygen, or mortality.

The combined group showed significantly improved visual analog scale (VAS) scores for cough, runny nose, and diarrhea from day 3.

Authors note that "the WHO-CPS were significantly decreased among FPV/IVM/NCL vs FPV alone on day 10", however the degree of improvement cannot be determined based on the values reported.

Authors state that "All data generated or analyzed during this study are included in this published article", which is incorrect - only summary statistics are published. The trial registration states that data will not be made available. This raises concerns, especially given many inconsistencies in the published data:

- E gene and ORF1 a/b gene day 1 Ct values are different between Table S1 and Table S3.
- Figure S1 shows 25% ≥ 38.5, however no number of 30 patients is 25%, and this does not match Table 2 (at most 23.3% ≥ 38.5).
- The first three calculations in section 3.3 all show p = 0.515, an unusual match for different calculations.
- "the FPV/IVM/NCL group had 0.62 cycles per day fewer than did the FPV group" does not match the data.
- Table 1 shows 30% loss of taste in the control group, however Table S2 shows a Q3 VAS score of 0, which is inconsistent.
- The abstract reference to significant differences for sore throat does not match the results in Table S6.

Not releasing data is a change from earlier COVID-19 trials by the same main author: TCTR20210615002 and TCTR20210609001 both indicate that individual patient level data would be available.

The trial was registered retrospectively. Discussion of prior research is very biased, however this may be required for publication. There were more patients with fever, anosmia, and loss of taste at baseline in the combined group. The adverse events reported show none of the expected side effects of ivermectin at the dose used. Submit Corrections or Updates.

.hsva .va--anim-stagger :is(.va__row,.plot__row,.va__verdict){opacity:1}.hsva .va--anim .plot__shade{opacity:var(--shade-opacity)}.hsva .va--anim-stagger .ci{transform:translateY(-50%)}.hsva .va--anim-stagger .pt{transform:none}.hsva .va--anim-settle .pt{left:calc(min(var(--pt), 2) / 2 * 100%)}.hsva .va--anim-settle .ci{left:calc(var(--lo) / 2 * 100%);width:calc((var(--hi) - var(--lo)) / 2 * 100%)}

Appendix 1. Methods and Data

Search methods

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, which regularly receives notification of studies upon publication. Search terms are niclosamide 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 niclosamide 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. Studies with major unexplained data issues, for example major outcome data that is impossible to be correct with no response from the authors, are excluded.

Fig. 26. Mid-recovery results can more accurately reflect efficacy when almost all patients recover. Mateja et al. confirm that intermediate viral load results more accurately reflect hospitalization/death.

Effect extraction

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 reported then they are both used in specific outcome analyses, while mortality is used for pooled analysis. If symptomatic results are reported at multiple times, we use 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 outcomes. 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. An IPD meta-analysis confirms that intermediate viral load reduction is more closely associated with hospitalization/death than later viral load reduction165. 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.

Statistical methods

Forest plots are computed using PythonMeta166 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. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang et al. Reported confidence intervals and p-values are used when available, and adjusted values are used 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 1170. 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 (3.14.8) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.3), statsmodels (0.15.0), and plotly (6.9.0). 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 0.8.2 is used to parse PDF documents.

When evaluating potential effect modification across groups, we use an interaction test as described by Altman (C) et al. We compared the log-transformed relative risks using a z-test, deriving the standard error of the difference from the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was considered a statistically significant difference in treatment effect between the groups.

Quality evaluation

Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of providing standardized rules that can be applied with minimal understanding of the domain and study. However, the rules do not account for many real-world issues, often overemphasize or underemphasize others, and studies show low inter-rater reliability178. Certain domains are more applicable for these tools, however the time-sensitive nature of a pandemic, with significant mortality for every day of delay in evidence assessment, and the characteristics of COVID-19 make them inappropriate for this domain. This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low risk of bias, however this is the opposite of reality—the trial not only has very high risk of bias, but has very high actual known bias, refusing to release data despite pledging to, reporting multiple impossible numbers, having blinding and randomization failure, and many other issues180. Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however this is the opposite of reality—the very late treatment and excessive dosage used produces results with no relevance to recommended usage. HCQ shows poor results with late treatment and excessive dosage, and the combination shows harmJ. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias176,177 are far from the most informative. Both involve very late treatment, providing no information on recommended usage, and ROBINS-I does a very poor job of accounting for the impact of confounding factorsK.

Our quality evaluation focuses on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias. The estimated potential impact of each confounding factor, and the direction of the impact is considered. For example, consider a study that shows significantly lower risk, the value of the study varies significantly if confounding points to an underestimate or an overestimate of efficacy. In one case, the real effect may be null, while the other case provides stronger evidence of efficacy (which may be greater than the study shows). Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical or technical issues that have no or minimal impact on outcomes. Analysis also depends on the outcome, for example certain issues are less relevant for objective outcomes such as mortality. Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major issues affecting outcomes, increases in significance during a pandemic when immediate recognition of new evidence is critical, and when considering all global studies, as required during a pandemic. Investigators in other countries may have different customs for design, analysis, and reporting, and different English language skills, however they may not be less diligent or have greater bias. Investigators in lower-pharmaceutical-profit countries may have lower bias towards profitable interventions.

Treatment time

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 effective63,64.

Living analysis

This is a living analysis and is updated regularly. Submit updates or corrections with the form below. We received no funding, this research is done in our spare time. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

A summary of study results is below. Please submit updates and corrections at the bottom of this page.

A summary of study results is below. Please submit updates and corrections at https://c19early.org/ncmeta.html.

Early treatment

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.

Cairns, 2/9/2022, Double Blind Randomized Controlled Trial, placebo-controlled, China, peer-reviewed, 9 authors, study period July 2021 - September 2021, trial NCT04399356 (history). Submit Corrections or Updates.	risk of severe case, 66.3% lower, RR 0.34, p = 1.00, treatment 0 of 33 (0.0%), control 1 of 34 (2.9%), NNT 34, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	recovery time, 17.8% lower, relative time 0.82, p = 0.28, treatment 12.01 [8.82-15.2] n=33, control 14.61 [11.25-17.96] n=34.
	risk of no viral clearance, 24.4% lower, RR 0.76, p = 0.45, treatment 11 of 33 (33.3%), control 15 of 34 (44.1%), NNT 9.3.
Kim, 8/1/2025, Double Blind Randomized Controlled Trial, placebo-controlled, South Korea, peer-reviewed, mean age 42.6, 12 authors, study period 11 May, 2022 - 28 November, 2022, trial KCT0007307. Submit Corrections or Updates.	recovery time, 29.1% lower, relative time 0.71, p = 0.008, treatment 99, control 98, both groups.
	recovery time, 33.3% lower, relative time 0.67, p = 0.03, treatment 4.0 [3.5-4.5] n=99, control 6.0 [4.0-7.5] n=98, low dose, post-hoc COVID-19 symptoms.
	recovery time, 25.0% lower, relative time 0.75, p = 0.11, treatment 4.5 [4.0-5.0] n=96, control 6.0 [4.0-7.5] n=98, high dose, post-hoc COVID-19 symptoms.
	recovery time, 4.1% lower, relative time 0.96, p = 0.80, treatment 99, control 98, both groups.
	recovery time, 22.2% lower, relative time 0.78, p = 0.28, treatment 10.5 [9.0-18.0] n=99, control 13.5 [11.0-17.0] n=98, low dose, all symptoms, Table S51.
	recovery time, 7.4% higher, relative time 1.07, p = 0.65, treatment 14.5 [12.5-18.5] n=96, control 13.5 [11.0-17.0] n=98, high dose, all symptoms, Table S51.
	viral load, 55.8% lower, relative load 0.44, p = 0.16, treatment 99, control 98, both groups.
	viral load, 26.4% lower, relative load 0.74, p = 0.77, treatment mean 62918 (±399629) n=99, control mean 46282 (±413674) n=98, low dose, relative reduction in viral load, mid-recovery, day 2.
	viral load, 65.7% lower, relative load 0.34, p = 0.13, treatment mean 135111 (±407691) n=96, control mean 46282 (±413674) n=98, high dose, relative reduction in viral load, mid-recovery, day 2.
Siripongboonsitti, 3/29/2024, Randomized Controlled Trial, Thailand, peer-reviewed, 5 authors, study period 7 December, 2022 - 3 February, 2023, this trial uses multiple treatments in the treatment arm (combined with niclosamide) - results of individual treatments may vary, trial TCTR20230403007 (FINCOV), excluded in exclusion analyses: data consistency issues, very low risk patients/variants with almost no progression, all patients received known effective antiviral, baseline differences. Submit Corrections or Updates.	risk of progression, no change, RR 1.00, p = 1.00, treatment 1 of 30 (3.3%), control 1 of 30 (3.3%), chest xray progression, day 6.
	risk of progression, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 30 (0.0%), control 1 of 30 (3.3%), NNT 30, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), chest xray progression, day 3.
	risk of no recovery, 39.4% lower, RR 0.61, p = 0.19, treatment 30, control 30, combined symptoms.
	risk of no recovery, 70.0% lower, RR 0.30, p = 0.048, treatment 30, control 30, mid-recovery, day 3, cough.
	risk of no recovery, 14.3% lower, RR 0.86, p = 0.38, treatment 30, control 30, mid-recovery, day 3, sore throat.
	risk of no recovery, 66.7% lower, RR 0.33, p = 0.41, treatment 30, control 30, inverted to make RR<1 favor treatment, mid-recovery, day 3, runny nose.
	risk of no recovery, 50.0% lower, RR 0.50, p = 0.32, treatment 30, control 30, inverted to make RR<1 favor treatment, day 6, cough.
	relative Ct improvement, 6.1% better, RR 0.94, p = 0.75, treatment median 9.15 IQR 9.7 n=30, control median 8.59 IQR 8.24 n=30, E gene, mid-recovery, day 5, primary outcome.

Late treatment

Abdulamir, 9/30/2021, Randomized Controlled Trial, Iraq, peer-reviewed, 7 authors, trial NCT04753619 (history). Submit Corrections or Updates.	risk of death, no change, RR 1.00, p = 1.00, treatment 3 of 75 (4.0%), control 3 of 75 (4.0%).
	risk of no recovery, 38.7% lower, HR 0.61, p = 0.007, treatment 75, control 75, adjusted per study, inverted to make HR<1 favor treatment, multivariable, Cox proportional hazards.
Erenmemisoglu, 2/14/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Turkey, trial NCT04558021 (history) (NICLONEX). Submit Corrections or Updates.	Estimated 200 patient RCT with results unknown and over 5 years late.
First Wave BioPharma, 5/1/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, preprint, 1 author, trial NCT04858425 (history) (RESERVOIR). Submit Corrections or Updates.	risk of no viral clearance, 22.7% lower, RR 0.77, p = 0.61, treatment 8 of 60 (13.3%), control 10 of 58 (17.2%), NNT 26, day 43.
	risk of no viral clearance, 33.1% lower, RR 0.67, p = 0.35, treatment 9 of 60 (15.0%), control 13 of 58 (22.4%), NNT 13, day 36.
	risk of no viral clearance, 42.0% lower, RR 0.58, p = 0.17, treatment 9 of 60 (15.0%), control 15 of 58 (25.9%), NNT 9.2, day 29.
	risk of no viral clearance, 27.5% lower, RR 0.73, p = 0.39, treatment 12 of 60 (20.0%), control 16 of 58 (27.6%), NNT 13, day 22.
	risk of no viral clearance, 22.7% lower, RR 0.77, p = 0.34, treatment 20 of 60 (33.3%), control 25 of 58 (43.1%), NNT 10, day 15.
	risk of no viral clearance, 0.8% lower, RR 0.99, p = 1.00, treatment 39 of 60 (65.0%), control 38 of 58 (65.5%), NNT 193, day 8.
	risk of no viral clearance, 2.6% higher, RR 1.03, p = 0.80, treatment 52 of 60 (86.7%), control 49 of 58 (84.5%), day 4.
Rank, 8/6/2024, Double Blind Randomized Controlled Trial, placebo-controlled, USA, preprint, 1 author, trial NCT04603924 (history). Submit Corrections or Updates.	risk of death, 30.3% lower, RR 0.70, p = 1.00, treatment 2 of 22 (9.1%), control 3 of 23 (13.0%), NNT 25.
	recovery time, 70.2% lower, relative time 0.30, p = 0.97, treatment 4.15 [2.98-6.96] n=23, control 13.91 [5.85-999] n=23, time to 2 point improvement.
	recovery time, 6.2% higher, relative time 1.06, p = 0.90, treatment 17.0 [9.0-36.0] n=23, control 16.0 [11.0-24.0] n=23, time to resolution of symptoms.
	time to discharge, 56.7% lower, relative time 0.43, p = 0.98, treatment 6.03 [3.07-14.89] n=23, control 13.92 [5.85-999] n=23.
	time to viral-, 0.1% lower, relative time 1.00, p = 1.00, treatment 13.09 [2.87-27.04] n=23, control 13.1 [2.99-15.1] n=23.

Prophylaxis

Humphrey, 7/26/2023, Double Blind Randomized Controlled Trial, placebo-controlled, United Kingdom, peer-reviewed, median age 55.9, 21 authors, study period 19 February, 2021 - 28 November, 2022, trial NCT04870333 (history) (PROTECT-V). Submit Corrections or Updates.	risk of death, 0.1% lower, RR 1.00, p = 1.00, treatment 2 of 826 (0.2%), control 2 of 825 (0.2%), NNT 340725.
	risk of mechanical ventilation, 80.0% lower, RR 0.20, p = 0.25, treatment 0 of 826 (0.0%), control 2 of 825 (0.2%), NNT 412, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of hospitalization, 13.4% lower, RR 0.87, p = 0.71, treatment 13 of 826 (1.6%), control 15 of 825 (1.8%), NNT 409.
	risk of symptomatic case, 2.0% higher, HR 1.02, p = 0.89, treatment 103 of 826 (12.5%), control 133 of 825 (16.1%), adjusted per study, multivariable, Cox proportional hazards.

Supplementary Data

Footnotes

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 M^pro, also known as 3CL^pro or nsp5, is a cysteine protease that cleaves viral polyproteins into functional units needed for replication. Inhibiting M^pro 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 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.

Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.

Smoking was known to cause lung cancer since at least 1939, but this was not widely recognized in the US until 1964, 25 years later. Surgeon general Leroy Burney tried publicizing the danger starting in 1957, with limited success. Surgeon general Luther Terry, appointed in 1961, prompted by President Kennedy in 1962 amid pressure from health advocates, finally got recognition in 1964. The 1964 report reviewed 7,000+ studies, but these could (and should) have been reviewed and acted upon in real-time as they were published. Historians attribute the 25 year delay to an industry campaign to manufacture doubt and controversy, through tactics like funding biased and fraudulent research from "independent" organizations, attacking scientists, and political lobbying. The success of the industry campaign is only possible because officials did not analyze the research in detail. Fraudulent industry research supported prior failures, but would have been called out as fraudulent by officials that analyzed and understood the research in real-time.

When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment172,173. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage174. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.

Peters (B) et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.

References

Erenmemisoglu et al., A Phase III, Randomized, Placebo-controlled, Clinical Trial to Evaluate the Efficacy and Safety of Co-administered Niclosamide in Patients Treated With an Established Regimen for Novel Coronavirus Infectious Disease (COVID-19), NCT04558021, clinicaltrials.gov/study/NCT04558021clinicaltrials.gov.

Ryu et al., Fibrin drives thromboinflammation and neuropathology in COVID-19, Nature, doi:10.1038/s41586-024-07873-4.nature.com, doi.org.

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.sciencedirect.com, doi.org.

Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.sciencedirect.com, doi.org.

Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.nature.com, doi.org.

Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.nejm.org, doi.org.

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.mdpi.com, doi.org.

Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.mdpi.com, doi.org.

Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.oup.com, doi.org.

10.

Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.biorxiv.org, doi.org.

11.

Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.oup.com, doi.org.

12.

Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.mdpi.com, doi.org.

13.

Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.mdpi.com, doi.org.

14.

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.sagepub.com, doi.org.

15.

Wang et al., SARS-CoV-2 membrane protein induces neurodegeneration via affecting Golgi-mitochondria interaction, Translational Neurodegeneration, doi:10.1186/s40035-024-00458-1.biomedcentral.com, doi.org.

16.

Freitas et al., Central nervous system and systemic inflammatory networks associated with acute neurological outcomes in COVID-19, Scientific Reports, doi:10.1038/s41598-025-08632-9.nature.com, doi.org.

17.

Lu et al., Risk of neuropsychiatric and related conditions associated with SARS-CoV-2 infection: a difference-in-differences analysis, Nature Communications, doi:10.1038/s41467-025-61961-1.nature.com, doi.org.

18.

Jachman-Kapułka et al., Cross-Section of Neurological Manifestations Among SARS-CoV-2 Omicron Subvariants—Single-Center Study, Brain Sciences, doi:10.3390/brainsci14111161.mdpi.com, doi.org.

19.

Beckman et al., SARS-CoV-2 infects neurons and induces neuroinflammation in a non-human primate model of COVID-19, Cell Reports, doi:10.1016/j.celrep.2022.111573.sciencedirect.com, doi.org.

20.

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.nature.com, doi.org.

21.

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.mdpi.com, doi.org.

22.

Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.imrpress.com, doi.org.

23.

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.fortunejournals.com, doi.org.

24.

Saha et al., COVID-19 beyond the lungs: Unraveling its vascular impact and cardiovascular complications—mechanisms and therapeutic implications, Science Progress, doi:10.1177/00368504251322069.sagepub.com, doi.org.

25.

Yin et al., COVID-19: a vascular nightmare unfolding, Frontiers in Immunology, doi:10.3389/fimmu.2025.1593885.frontiersin.org, doi.org.

26.

Bruno et al., Accelerated vascular ageing after COVID-19 infection: the CARTESIAN study, European Heart Journal, doi:10.1093/eurheartj/ehaf430.oup.com, doi.org.

27.

Abiri et al., The silent legacy of COVID-19: exploring genomic instability in long-term COVID-19 survivors, BMC Infectious Diseases, doi:10.1186/s12879-025-11419-y.biomedcentral.com, doi.org.

28.

Gioia et al., SARS-CoV-2 infection induces DNA damage, through CHK1 degradation and impaired 53BP1 recruitment, and cellular senescence, Nature Cell Biology, doi:10.1038/s41556-023-01096-x.nature.com, doi.org.

29.

Doğan et al., Clinical Investigation of Leukocyte DNA Damage in COVID-19 Patients, Current Issues in Molecular Biology, doi:10.3390/cimb45020062.mdpi.com, doi.org.

30.

Rueda-Torres et al., Longitudinal Assessment of DNA Damage in PBMCs From Hospitalized COVID‐19 Patients via Alkaline Comet Assay, Environmental and Molecular Mutagenesis, doi:10.1002/em.70055.wiley.com, doi.org.

31.

Trender et al., Changes in memory and cognition during the SARS-CoV-2 human challenge study, eClinicalMedicine, doi:10.1016/j.eclinm.2024.102842.sciencedirect.com, doi.org.

32.

Dugied et al., Multimodal SARS-CoV-2 interactome sketches the virus-host spatial organization, Communications Biology, doi:10.1038/s42003-025-07933-z.nature.com, doi.org.

33.

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.nature.com, doi.org.

34.

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.nature.com, doi.org.

35.

Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.springer.com, doi.org.

36.

Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.asm.org, doi.org.

37.

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.frontiersin.org, doi.org.

38.

Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.mdpi.com, doi.org.

39.

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.tandfonline.com, doi.org.

40.

c19early.org, c19early.org/treatments.html.

41.

Haque et al., Exploring potential therapeutic candidates against COVID-19: a molecular docking study, Discover Molecules, doi:10.1007/s44345-024-00005-5.springer.com, doi.org.

42.

Pejler et al., Blockade of endolysosomal acidification suppresses TLR3-mediated pro-inflammatory signaling in airway epithelial cells, Journal of Allergy and Clinical Immunology, doi:10.1016/j.jaci.2024.05.031.sciencedirect.com, doi.org.

43.

Garrett et al., Niclosamide as a chemical probe for analyzing SARS-CoV-2 modulation of host cell lipid metabolism, Frontiers in Microbiology, doi:10.3389/fmicb.2023.1251065.frontiersin.org, doi.org.

44.

Yang (B) et al., Repurposing Niclosamide as a Novel Anti-SARS-CoV-2 Drug by Restricting Entry Protein CD147, Biomedicines, doi:10.3390/biomedicines11072019.mdpi.com, doi.org.

45.

Sheng et al., A pseudovirus-based method to dynamically mimic SARS-CoV-2-associated cell-to-cell fusion and transmission, Acta Biochimica et Biophysica Sinica, doi:10.3724/abbs.2023129.scichina.com, doi.org.

46.

Munson et al., Niclosamide and ivermectin modulate caspase-1 activity and proinflammatory cytokine secretion in a monocytic cell line, British Society For Nanomedicine Early Career Researcher Summer Meeting, 2021, web.archive.org/web/20230401070026/https://michealmunson.github.io/COVID.pdfarchive.org.

47.

Needham, D., The pH Dependence of Niclosamide Solubility, Dissolution, and Morphology: Motivation for Potentially Universal Mucin-Penetrating Nasal and Throat Sprays for COVID19, its Variants and other Viral Infections, Pharmaceutical Research, doi:10.1007/s11095-021-03112-x.springer.com, doi.org.

48.

Mothae et al., SARS-CoV-2 host-pathogen interactome: insights into more players during pathogenesis, Virology, doi:10.1016/j.virol.2025.110607.sciencedirect.com, doi.org.

49.

Jitobaom 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.biomedcentral.com, doi.org.

50.

web.archive.org, web.archive.org/web/20211117052139/https://astralcodexten.substack.com/p/ivermectin-much-more-than-you-wanted.

51.

Hayward et al., Ivermectin for COVID-19 in adults in the community (PRINCIPLE): an open, randomised, controlled, adaptive platform trial of short- and longer-term outcomes, Journal of Infection, doi:10.1016/j.jinf.2024.106130.sciencedirect.com, doi.org.

52.

ibc2024.org, www.ibc2024.org/viewdocument/sander-greenland-ibc-2024-president.

53.

c19early.org (B), c19early.org/ncmeta.html#media.

54.

c19early.org (C), c19early.org/rctobs.html.

55.

c19early.org (D), c19early.org/mechanisms.html.

56.

c19early.org (E), c19early.org/adoption.html.

57.

Walia et al., SARS-CoV-2 virulence factor ORF3a blocks lysosome function by modulating TBC1D5-dependent Rab7 GTPase cycle, Nature Communications, doi:10.1038/s41467-024-46417-2.nature.com, doi.org.

58.

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.bmj.com, doi.org.

59.

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.sciencedirect.com, doi.org.

60.

Siripongboonsitti et al., A Randomized Trial to Assess the Acceleration of Viral Clearance by the Combination Favipiravir/Ivermectin/Niclosamide in Mild-to-Moderate COVID-19 Adult Patients (FINCOV), Journal of Infection and Public Health, doi:10.1016/j.jiph.2024.03.030.sciencedirect.com, doi.org.

61.

altmetric.com, www.altmetric.com/.

62.

help.altmetric.com, help.altmetric.com/support/solutions/articles/6000235983-attention-sources-tracked-by-altmetric.

63.

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.jamanetwork.com, doi.org.

64.

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.nih.gov, doi.org.

65.

Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.nejm.org, doi.org.

66.

Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.nejm.org, doi.org.

67.

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.sciencedirect.com, doi.org.

68.

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.jamanetwork.com, doi.org.

69.

Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.medrxiv.org, doi.org.

70.

Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.science.org, doi.org.

71.

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.sciencedirect.com, doi.org.

72.

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.medrxiv.org, doi.org.

73.

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.researchsquare.com, doi.org.

74.

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.medrxiv.org, doi.org.

75.

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.biorxiv.org, doi.org.

76.

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-equalsubstack.com.

77.

Xu 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.wiley.com, doi.org.

78.

Jitobaom (B) 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.researchsquare.com, doi.org.

79.

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.sciencedirect.com, doi.org.

80.

Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.mdpi.com, doi.org.

81.

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.mdpi.com, doi.org.

82.

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.sciencedirect.com, doi.org.

83.

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.plos.org, doi.org.

84.

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.nature.com, doi.org.

85.

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.frontiersin.org, doi.org.

86.

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.mdpi.com, doi.org.

87.

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.oup.com, doi.org.

88.

Chen et al., Synergistic Inhibition of SARS-CoV-2 Replication Using Disulfiram/Ebselen and Remdesivir, ACS Pharmacology & Translational Science, doi:10.1021/acsptsci.1c00022.acs.org, doi.org.

89.

Hempel et al., Synergistic inhibition of SARS-CoV-2 cell entry by otamixaban and covalent protease inhibitors: pre-clinical assessment of pharmacological and molecular properties, Chemical Science, doi:10.1039/D1SC01494C.rsc.org, doi.org.

90.

Schultz et al., Pyrimidine inhibitors synergize with nucleoside analogues to block SARS-CoV-2, Nature, doi:10.1038/s41586-022-04482-x.nature.com, doi.org.

91.

Ohashi et al., Potential anti-COVID-19 agents, cepharanthine and nelfinavir, and their usage for combination treatment, iScience, doi:10.1016/j.isci.2021.102367.sciencedirect.com, doi.org.

92.

Al Krad et al., The protease inhibitor Nirmatrelvir synergizes with inhibitors of GRP78 to suppress SARS-CoV-2 replication, bioRxiv, doi:10.1101/2025.03.09.642200.biorxiv.org, doi.org.

93.

Abd Elsalam et al., Anti-COVID-19 activity and simple HPLC method for concurrent detection of repurposed drugs in novel binary mixtures, AMB Express, doi:10.1186/s13568-026-02030-8.springer.com, doi.org.

94.

Rana et al., Ivermectin and Doxycycline combination as a promising anti-viral drug candidate: an in-silico and DFT study, In Silico Pharmacology, doi:10.1007/s40203-026-00576-w.springer.com, doi.org.

95.

Setz et al., Synergistic Virus Neutralizing Activities of European Black Elderberry Fruit Extract and Iota-Carrageenan Against SARS-CoV-2, Influenza A Virus and Respiratory Syncytial Virus, Nutrients, doi:10.3390/nu18081205.mdpi.com, doi.org.

96.

Ali et al., Preclinical Screening Platform Identifies Azatadine‐Dimaleate as a Potent Repurposed Therapeutic Against SARS‐CoV‐2 Infection, Journal of Medical Virology, doi:10.1002/jmv.70713.wiley.com, doi.org.

97.

Lew et al., Combination of Remdesivir and Ivermectin Exerts Highly Potent and Synergistic Antiviral Activity Against Murine Coronavirus and SARS-CoV-2 Infections, Cells, doi:10.3390/cells15131146.mdpi.com, doi.org.

98.

Khatun et al., An All-Oral Antiviral Combination Strategy against COVID-19 Enabled by the Synergistic Mechanism of Action of Auranofin and Remdesivir, Elsevier BV, doi:10.2139/ssrn.6942990.ssrn.com, doi.org.

99.

How et al., The Combination of Nirmatrelvir and Ivermectin Exerts Strongly Synergistic Antiviral and Anti-Inflammatory Effects Against Murine Coronavirus Infection of Macrophages, International Journal of Molecular Sciences, doi:10.3390/ijms27167316.mdpi.com, doi.org.

100.

Tan et al., Combination Treatment With Remdesivir and Ivermectin Exerts Highly Synergistic and Potent Antiviral Activity Against Murine Coronavirus Infection, Frontiers in Cellular and Infection Microbiology, doi:10.3389/fcimb.2021.700502.frontiersin.org, doi.org.

101.

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.journaljpri.com, doi.org.

102.

Singh 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.oup.com, doi.org.

103.

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-9780470870143wiley.com.

104.

Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.wiley.com, doi.org.

105.

Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.wiley.com, doi.org.

106.

Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.jamanetwork.com, doi.org.

107.

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.springer.com, doi.org.

108.

Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.wiley.com, doi.org.

109.

Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.bmj.com, doi.org.

110.

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.wiley.com, doi.org.

111.

Saha (B) et al., Inhaled Dry Powder of Antiviral Agents: A Promising Approach to Treating Respiratory Viral Pathogens, Viruses, doi:10.3390/v17020252.mdpi.com, doi.org.

112.

Ali (B) et al., SARS-CoV-2 Syncytium under the Radar: Molecular Insights of the Spike-Induced Syncytia and Potential Strategies to Limit SARS-CoV-2 Replication, Journal of Clinical Medicine, doi:10.3390/jcm12186079.mdpi.com, doi.org.

113.

Vibhute et al., Niclosamide: a potential treatment option for COVID-19, International Journal of Applied Pharmaceutics, doi:10.22159/ijap.2023v15i1.45850.innovareacademics.in, doi.org.

114.

Abed et al., Evaluation the plausibility of repurpose of levamisole and niclosamide in treatment of covid-19, Journal of Pharmaceutical Technolgy, doi:10.37662/jpt.2022.999481.org.tr, doi.org.

115.

Cesar-Silva et al., The Endolysosomal System: The Acid Test for SARS-CoV-2, International Journal of Molecular Sciences, doi:10.3390/ijms23094576.mdpi.com, doi.org.

116.

Singh (B) et al., Niclosamide—A promising treatment for COVID‐19, British Journal of Pharmacology, doi:10.1111/bph.15843.wiley.com, doi.org.

117.

Al-kuraishy et al., Niclosamide for Covid-19: bridging the gap, Molecular Biology Reports, doi:10.1007/s11033-021-06770-7.springer.com, doi.org.

118.

Kim et al., A randomized, double-blind, placebo-controlled trial of niclosamide nanohybrid for the treatment of patients with mild to moderate COVID-19, Nature Communications, doi:10.1038/s41467-025-62423-4.nature.com, doi.org.

119.

Cairns et al., Efficacy of Niclosamide vs Placebo in SARS-CoV-2 Respiratory Viral Clearance, Viral Shedding, and Duration of Symptoms Among Patients With Mild to Moderate COVID-19, JAMA Network Open, doi:10.1001/jamanetworkopen.2021.44942.jamanetwork.com, doi.org.

120.

Rank et al., A Phase 2/3 Randomized and Placebo-Controlled Study of ANA001 in Moderate and Severe COVID-19 Patients, NCT04603924, clinicaltrials.gov/study/NCT04603924clinicaltrials.gov.

121.

First Wave BioPharma, A 2-Part, 2-Arm, Phase 2, Randomized, Double-Blind, Placebo-Controlled Study on the Safety and Efficacy of Niclosamide in Patients With COVID-19 With Gastrointestinal Infection, NCT04858425, clinicaltrials.gov/study/NCT04858425clinicaltrials.gov.

122.

Abdulamir et al., A randomised controlled trial of effectiveness and safety of Niclosamide as add on therapy to the standard of care measures in COVID-19 management, Annals of Medicine & Surgery, doi:10.1016/j.amsu.2021.102779.sciencedirect.com, doi.org.

123.

Humphrey et al., Prophylaxis for renal patients at risk of COVID-19 infection: results from the intranasal niclosamide randomised, double blinded, placebo controlled arm of the PROTECT-V platform trial, BMC Infectious Diseases, doi:10.1186/s12879-025-10584-4.biomedcentral.com, doi.org.

124.

Jagtap et al., Exploring Niclosamide as a Multi-target Drug Against SARS-CoV-2: Molecular Dynamics Simulation Studies on Host and Viral Proteins, Molecular Biotechnology, doi:10.1007/s12033-024-01296-2.springer.com, doi.org.

125.

Chen (B) et al., Repositioning niclosamide and alectinib to inhibit the hyperphosphorylation of SARS‐CoV‐2's nucleocapsid protein, British Journal of Pharmacology, doi:10.1111/bph.70679.wiley.com, doi.org.

126.

Nodola et al., Recent Advances in the Design of Inhibitors Targeting the Viral Entry and Replication of the SARS-CoV-2 Virus, Driven by In Silico Approaches, Molecules, doi:10.3390/molecules31162877.mdpi.com, doi.org.

127.

Du et al., Drug Repurposing as a Broad-Spectrum Strategy Against Coronaviruses: Frontiers in Mechanisms and Clinical Translation, Viruses, doi:10.3390/v18070804.mdpi.com, doi.org.

128.

Jeong et al., Development of niclosamide-based COVID-19 therapeutic agent using porous silicon nanoparticles, Nanotechnology, doi:10.1088/1361-6528/ae5301.iop.org, doi.org.

129.

Alizadeh Saghati et al., Unraveling the relevance of SARS-Cov-2 infection and ferroptosis within the heart of COVID-19 patients, Heliyon, doi:10.1016/j.heliyon.2024.e36567.sciencedirect.com, doi.org.

130.

Bolinger et al., Lessons learnt from broad-spectrum coronavirus antiviral drug discovery, Expert Opinion on Drug Discovery, doi:10.1080/17460441.2024.2385598.tandfonline.com, doi.org.

131.

Zeng et al., Novel receptor, mutation, vaccine, and establishment of coping mode for SARS-CoV-2: current status and future, Frontiers in Microbiology, doi:10.3389/fmicb.2023.1232453.frontiersin.org, doi.org.

132.

Liu et al., Discovery and structural optimization of 3-O-β-Chacotriosyl betulonic acid saponins as potent fusion inhibitors of Omicron virus infections, Bioorganic Chemistry, doi:10.1016/j.bioorg.2022.106316.sciencedirect.com, doi.org.

133.

Kumar (B) et al., Advancements in the development of antivirals against SARS-Coronavirus, Frontiers in Cellular and Infection Microbiology, doi:10.3389/fcimb.2025.1520811.frontiersin.org, doi.org.

134.

Rath et al., A Glimpse for the subsistence from pandemic SARS-CoV-2 infection, Bioorganic Chemistry, doi:10.1016/j.bioorg.2024.107977.sciencedirect.com, doi.org.

135.

Saini et al., The Potential of Drug Repurposing as a Rapid Response Strategy in COVID-19 Therapeutics, Journal of Advances in Medical and Pharmaceutical Sciences, doi:10.9734/jamps/2024/v26i12728.journaljamps.com, doi.org.

136.

Barghash et al., Navigating the COVID-19 Therapeutic Landscape: Unveiling Novel Perspectives on FDA-Approved Medications, Vaccination Targets, and Emerging Novel Strategies, MDPI AG, doi:10.20944/preprints202409.2409.v1.preprints.org, doi.org.

137.

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.nature.com, doi.org.

138.

Zhong et al., Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and sarcopenia, Frontiers in Medicine, doi:10.3389/fmed.2024.1378846.frontiersin.org, doi.org.

139.

Choi et al., Review of COVID-19 Therapeutics by Mechanism: From Discovery to Approval, Journal of Korean Medical Science, doi:10.3346/jkms.2024.39.e134.jkms.org, doi.org.

140.

Sharun et al., A comprehensive review on pharmacologic agents, immunotherapies and supportive therapeutics for COVID-19, Narra J, doi:10.52225/narra.v2i3.92.narraj.org, doi.org.

141.

Jeon et al., Identification of antiviral drug candidates against SARS-CoV-2 from FDA-approved drugs, bioRxiv, doi:10.1101/2020.03.20.999730.biorxiv.org, doi.org.

142.

Gassen et al., Analysis of SARS-CoV-2-controlled autophagy reveals spermidine, MK-2206, and niclosamide as putative antiviral therapeutics, bioRxiv, doi:10.1101/2020.04.15.997254.biorxiv.org, doi.org.

143.

Mirabelli et al., Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19, bioRxiv, doi:10.1101/2020.05.27.117184.biorxiv.org, doi.org.

144.

Patten et al., Identification of druggable host targets needed for SARS-CoV-2 infection by combined pharmacological evaluation and cellular network directed prioritization both in vitro and in vivo, bioRxiv, doi:10.1101/2021.04.20.440626.biorxiv.org, doi.org.

145.

Braga et al., Drugs that inhibit TMEM16 proteins block SARS-CoV-2 spike-induced syncytia, Nature, doi:10.1038/s41586-021-03491-6.nature.com, doi.org.

146.

Marwick et al., Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients, SLAS Discovery, doi:10.1177/24725552211019405.sciencedirect.com, doi.org.

147.

Bess et al., Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2, Frontiers in Pharmacology, doi:10.3389/fphar.2023.1297924.frontiersin.org, doi.org.

148.

Cesar-Silva (B) et al., Lipid compartments and lipid metabolism as therapeutic targets against coronavirus, Frontiers in Immunology, doi:10.3389/fimmu.2023.1268854.frontiersin.org, doi.org.

149.

Mushebenge et al., Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors, International Journal of Molecular Sciences, doi:10.3390/ijms242115518.mdpi.com, doi.org.

150.

Jeon (B) et al., Identification of Antiviral Drug Candidates against SARS-CoV-2 from FDA-Approved Drugs, Antimicrobial Agents and Chemotherapy, doi:10.1128/AAC.00819-20.asm.org, doi.org.

151.

Mirabelli (B) et al., Morphological cell profiling of SARS-CoV-2 infection identifies drug repurposing candidates for COVID-19, Proceedings of the National Academy of Sciences, doi:10.1073/pnas.2105815118.pnas.org, doi.org.

152.

Mushebenge (B) 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.preprints.org, doi.org.

153.

Low et al., COVID-19 Therapeutic Potential of Natural Products, International Journal of Molecular Sciences, doi:10.3390/ijms24119589.mdpi.com, doi.org.

154.

Wang (B) et al., Inflammasomes: a rising star on the horizon of COVID-19 pathophysiology, Frontiers in Immunology, doi:10.3389/fimmu.2023.1185233.frontiersin.org, doi.org.

155.

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.sagepub.com, doi.org.

156.

Wang (C) et al., Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations, Circulation Research, doi:10.1161/circresaha.122.321879.ahajournals.org, doi.org.

157.

Mostafa et al., FDA-Approved Drugs with Potent In Vitro Antiviral Activity against Severe Acute Respiratory Syndrome Coronavirus 2, Pharmaceuticals, doi:10.3390/ph13120443.mdpi.com, doi.org.

158.

Zeng (B) et al., Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning, Journal of Proteome Research, doi:10.1021/acs.jproteome.0c00316.acs.org, doi.org.

159.

Wang (D) et al., Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization, Frontiers in Genetics, doi:10.3389/fgene.2021.749256.frontiersin.org, doi.org.

160.

Arshad et al., Prioritization of Anti‐SARS‐Cov‐2 Drug Repurposing Opportunities Based on Plasma and Target Site Concentrations Derived from their Established Human Pharmacokinetics, Clinical Pharmacology & Therapeutics, doi:10.1002/cpt.1909.wiley.com, doi.org.

161.

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.plos.org, doi.org.

162.

Gassen (B) et al., SARS-CoV-2-mediated dysregulation of metabolism and autophagy uncovers host-targeting antivirals, Nature Communications, doi:10.1038/s41467-021-24007-w.nature.com, doi.org.

163.

Basu et al., Therapeutics for COVID-19 and post COVID-19 complications: An update, Current Research in Pharmacology and Drug Discovery, doi:10.1016/j.crphar.2022.100086.sciencedirect.com, doi.org.

164.

c19early.org (F), c19early.org/timeline.html.

165.

Mateja et al., The choice of viral load endpoint in early phase trials of COVID-19 treatments aiming to reduce 28-day hospitalization and/or death, The Journal of Infectious Diseases, doi:10.1093/infdis/jiaf282.oup.com, doi.org.

166.

Deng, H., PyMeta, Python module for meta-analysis, www.pymeta.com/pymeta.com.

167.

Zhang 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.jamanetwork.com, doi.org.

168.

Altman, D., How to obtain the P value from a confidence interval, BMJ, doi:10.1136/bmj.d2304.bmj.com, doi.org.

169.

Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.bmj.com, doi.org.

170.

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.wiley.com, doi.org.

171.

Altman (C) et al., Interaction revisited: the difference between two estimates, BMJ, doi:10.1136/bmj.326.7382.219.bmj.com, doi.org.

172.

Michelucci et al., SARS-CoV-2 ORF3a accessory protein is a water-permeable channel that induces lysosome swelling, Communications Biology, doi:10.1038/s42003-024-07442-5.nature.com, doi.org.

173.

Ghosh et al., β-Coronaviruses Use Lysosomes for Egress Instead of the Biosynthetic Secretory Pathway, Cell, doi:10.1016/j.cell.2020.10.039.sciencedirect.com, doi.org.

174.

Kamga Kapchoup et al., In vitro effect of hydroxychloroquine on pluripotent stem cells and their cardiomyocytes derivatives, Frontiers in Pharmacology, doi:10.3389/fphar.2023.1128382.frontiersin.org, doi.org.

175.

Bobrowski et al., Synergistic and Antagonistic Drug Combinations against SARS-CoV-2, Molecular Therapy, doi:10.1016/j.ymthe.2020.12.016.sciencedirect.com, doi.org.

176.

Peters (B) et al., Outcomes of Persons With COVID-19 in Hospitals With and Without Standard Treatment With (Hydroxy)chloroquine, Clinical Microbiology and Infection, doi:10.1016/j.cmi.2020.10.004.clinicalmicrobiologyandinfection.com, doi.org.

177.

Mahévas et al., Clinical efficacy of hydroxychloroquine in patients with covid-19 pneumonia who require oxygen: observational comparative study using routine care data, BMJ 2020, doi:10.1136/bmj.m1844.bmj.com, doi.org.

178.

Minozzi et al., The revised Cochrane risk of bias tool for randomized trials (RoB 2) showed low interrater reliability and challenges in its application, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2020.06.015.sciencedirect.com, doi.org.

179.

Popp et al., Ivermectin for preventing and treating COVID-19, Cochrane Database of Systematic Reviews, doi:10.1002/14651858.CD015017.pub3.wiley.com, doi.org.

180.

Reis et al., Effect of Early Treatment with Ivermectin among Patients with Covid-19, New England Journal of Medicine, doi:10.1056/NEJMoa2115869.nejm.org, doi.org.

181.

Axfors et al., Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials, Nature, doi:10.1038/s41467-021-22446-z.nature.com, doi.org.

182.

Horby et al., Effect of Hydroxychloroquine in Hospitalized Patients with COVID-19: Preliminary results from a multi-centre, randomized, controlled trial, NEJM, doi:10.1056/NEJMoa2022926.nejm.org, doi.org.

183.

Hempenius et al., Bias in observational studies on the effectiveness of in hospital use of hydroxychloroquine in COVID-19, Pharmacoepidemiology and Drug Safety, doi:10.1002/pds.5632.wiley.com, doi.org.