Bromhexine for COVID-19: real-time meta-analysis of 7 studies

@CovidAnalysis, October 2026, Version 22V22 (V1 2021)

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (7)43%

Mortality (3)77%

Ventilation (1)89%

ICU admission (1)82%

Hospitalization (4)10%

Viral clearance (3)−24%

RCTs (7)43%

RCT mortality (3)77%

Prophylaxis (2)65%

Early (2)84%

Late (3)44%

00.511.5+

← Bromhexine
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 ventilation and ICU admission. 3 studies from 3 independent teams in 2 countries show significant benefit.

Meta-analysis using the most serious outcome reported shows 43% [-5‑69%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (7)43%

Mortality (3)77%

Ventilation (1)89%

ICU admission (1)82%

Hospitalization (4)10%

Viral clearance (3)−24%

RCTs (7)43%

RCT mortality (3)77%

Prophylaxis (2)65%

Early (2)84%

Late (3)44%

00.511.5+

← Bromhexine
reduces riskIncreased
risk →

2 RCTs with 304 patients have not reported results (up to 6 years late)1,2.

Bromhexine efficacy may vary depending on the degree of TMPRSS-dependent fusion for different variants3,4.

No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Administration with a meal may increase the bioavailability of bromhexine. All data and sources to reproduce this analysis are in the appendix.

Download PDF

Bromhexine for COVID-19

Highlights

Bromhexine reduces risk with low confidence for mortality, ventilation, ICU admission, cases, and in pooled analysis, and very low confidence for recovery.
However increased risk is seen with very low confidence for viral clearance.
Efficacy may vary depending on the degree of TMPRSS-dependent fusion across variants.
Early treatment is more effective than late treatment.
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

Bromhexine COVID-19 studies (2 unreported RCTs)

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Ansarin (RCT)91%0.09 · 0.01–1.59death0/395/3924mg Vila Méndez (RCT)67%0.33 · 0.01–7.94oxygen0/981/9348mg

Early treatment84%0.16 · 0.02–1.350/1376/13284% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.093

Li (RCT)75%0.25 · 0.06–1.00no disch.2/124/696mg Mareev (RCT)11%0.89 · 0.65–1.22no recov.33 (n)33 (n)32mgCT¹ Tolouian (RCT)76%0.24 · 0.01–8.03death48 (n)52 (n)32mg Mežnar (RCT)n/a< NOT REPORTED >90 (est. total)CT¹ · >6 years late

Late treatment44%0.56 · 0.22–1.482/934/9144% lower risk

Tau² = 0.35, I² = 43.4%, p = 0.25

Mikhaylov (RCT)80%0.20 · 0.01–3.97hosp.0/252/2524mg Tolouian (DB RCT)33%0.67 · 0.04–10.46death0/1871/18524mg Granados.. (DB RCT)n/a< NOT REPORTED >214 (est. total)ELEVATECT¹ · >5 years late

Prophylaxis65%0.35 · 0.04–3.120/2123/21065% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.35

All studies43%0.57 · 0.31–1.052/44213/43343% lower risk

Tau² = 0.12, I² = 13.3%, p = 0.07200.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
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 bromhexine studies.

Introduction

Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from5.

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 injury6-22 and cognitive deficits9,14, cardiovascular complications23-29, DNA damage30-33, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits34—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,35-42, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk43, 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 bromhexine or metabolites via binding to the spikeB,44, M^proC,44, RNA-dependent RNA polymeraseD,44, and TMPRSS2E,45 proteins. In vitro studies demonstrate inhibition of the TMPRSS2E,46 and acid sphingomyelinaseF,47 proteins. Bromhexine is a mucolytic agent that helps thin mucus secretions in the respiratory tract and has been shown to have antiviral properties against respiratory viruses. Bromhexine inhibits the expression of TMPRSS2 which plays an important role in SARS-CoV-2 cell entry and replication45,46,48, may prevent SARS-CoV-2 infection by disrupting spike-ACE2 binding through direct interaction with the ACE2 receptor49, and bromhexine metabolite ambroxol inhibits SARS-CoV-2 via inhibition of acid sphingomyelinase in epithelial cells47.

Analysis

We analyze all significant controlled studies of bromhexine 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, and Randomized Controlled Trials (RCTs).

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)G. COVID-19 involves the interplay of many viral and host proteins and factors, providing over 500 therapeutic targets55. Given the 12,000+ proposed treatments43, 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 26 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

In silico studies predict inhibition of SARS-CoV-2 with bromhexine or metabolites via binding to the spikeB,44, M^proC,44, RNA-dependent RNA polymeraseD,44, and TMPRSS2E,45 proteins. In vitro studies demonstrate inhibition of the TMPRSS2E,46 and acid sphingomyelinaseF,47 proteins. Bromhexine inhibits the expression of TMPRSS2 which plays an important role in SARS-CoV-2 cell entry and replication45,46,48, may prevent SARS-CoV-2 infection by disrupting spike-ACE2 binding through direct interaction with the ACE2 receptor49, and bromhexine metabolite ambroxol inhibits SARS-CoV-2 via inhibition of acid sphingomyelinase in epithelial cells47.

2 in silico studies support the efficacy of bromhexine44,45.

4 in vitro studies support the efficacy of bromhexine46-49.

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, 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, ICU admission, hospitalization, 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, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval.
	Relative Risk	Studies	Patients

All studies	0.57 [0.31‑1.05]	7	875
Peer-reviewedPeer-reviewed	0.46 [0.21‑1.04]	6	503
RCTsRCTs	0.57 [0.31‑1.05]	7	875

Mortality	0.23 [0.04‑1.39]	3	550
HospitalizationHosp.	0.90 [0.76‑1.08]	4	679
Recovery	0.54 [0.21‑1.39]	3	181
Cases	0.38 [0.13‑1.11]	2	422
Viral	1.24 [0.66‑2.31]	3	321

RCT mortality	0.23 [0.04‑1.39]	3	550
RCT hospitalizationRCT hosp.	0.90 [0.76‑1.08]	4	679

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.
	Early treatment	Late treatment	Prophylaxis

All studies	0.16 [0.02‑1.35]0.16 [0.02‑1.35]	0.56 [0.22‑1.48]0.56 [0.22‑1.48]	0.35 [0.04‑3.12]0.35 [0.04‑3.12]
Peer-reviewedPeer-reviewed	0.16 [0.02‑1.35]0.16 [0.02‑1.35]	0.56 [0.22‑1.48]0.56 [0.22‑1.48]	0.20 [0.01‑3.97]0.20 [0.01‑3.97]
RCTsRCTs	0.16 [0.02‑1.35]0.16 [0.02‑1.35]	0.56 [0.22‑1.48]0.56 [0.22‑1.48]	0.35 [0.04‑3.12]0.35 [0.04‑3.12]

Mortality	0.09 [0.01‑1.59]0.09 [0.01‑1.59]	0.24 [0.01‑8.03]0.24 [0.01‑8.03]	0.67 [0.04‑10.46]0.67 [0.04‑10.46]
HospitalizationHosp.	0.33 [0.01‑7.94]0.33 [0.01‑7.94]	0.92 [0.77‑1.09]0.92 [0.77‑1.09]	0.26 [0.05‑1.46]0.26 [0.05‑1.46]
Recovery	0.29 [0.03‑2.68]0.29 [0.03‑2.68]	0.57 [0.17‑1.86]0.57 [0.17‑1.86]
Cases			0.38 [0.13‑1.11]0.38 [0.13‑1.11]
Viral	1.07 [0.64‑1.77]1.07 [0.64‑1.77]	0.70 [0.06‑8.13]0.70 [0.06‑8.13]

RCT mortality	0.09 [0.01‑1.59]0.09 [0.01‑1.59]	0.24 [0.01‑8.03]0.24 [0.01‑8.03]	0.67 [0.04‑10.46]0.67 [0.04‑10.46]
RCT hospitalizationRCT hosp.	0.33 [0.01‑7.94]0.33 [0.01‑7.94]	0.92 [0.77‑1.09]0.92 [0.77‑1.09]	0.26 [0.05‑1.46]0.26 [0.05‑1.46]

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

Bromhexine COVID-19 studies (2 unreported RCTs)

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Ansarin (RCT)91%0.09 · 0.01–1.59death0/395/3924mg Vila Méndez (RCT)67%0.33 · 0.01–7.94oxygen0/981/9348mg

Early treatment84%0.16 · 0.02–1.350/1376/13284% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.093

Late treatment44%0.56 · 0.22–1.482/934/9144% lower risk

Tau² = 0.35, I² = 43.4%, p = 0.25

Mikhaylov (RCT)80%0.20 · 0.01–3.97hosp.0/252/2524mg Tolouian (DB RCT)33%0.67 · 0.04–10.46death0/1871/18524mg Granados.. (DB RCT)n/a< NOT REPORTED >214 (est. total)ELEVATECT¹ · >5 years late

Prophylaxis65%0.35 · 0.04–3.120/2123/21065% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.35

All studies43%0.57 · 0.31–1.052/44213/43343% lower risk

Tau² = 0.12, I² = 13.3%, p = 0.07200.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
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

Bromhexine COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlDose (1d)Relative Risk

Ansarin (RCT)91%0.09 · 0.01–1.590/395/3924mg

Early treatment91%0.09 · 0.01–1.590/395/3991% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.1

Tolouian (RCT)76%0.24 · 0.01–8.0348 (n)52 (n)32mg

Late treatment76%0.24 · 0.01–7.8248 (n)52 (n)76% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.43

Tolouian (DB RCT)33%0.67 · 0.04–10.460/1871/18524mg

Prophylaxis33%0.67 · 0.03–16.370/1871/18533% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.82

All studies77%0.23 · 0.04–1.390/2746/27677% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.1100.511.52+

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

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

c19early.org

October 2026

Bromhexine COVID-19 mechanical ventilation results

StudyImprovementRR · 95% CITreatmentControlDose (1d)Relative Risk

Ansarin (RCT)89%0.11 · 0.01–0.841/399/3924mg

Early treatment89%0.11 · 0.01–0.841/399/3989% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.033

All studies89%0.11 · 0.01–0.841/399/3989% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.03300.511.52+

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

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

c19early.org

October 2026

Bromhexine COVID-19 ICU results

StudyImprovementRR · 95% CITreatmentControlDose (1d)Relative Risk

Ansarin (RCT)82%0.18 · 0.04–0.772/3911/3924mg

Early treatment82%0.18 · 0.04–0.772/3911/3982% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.02

All studies82%0.18 · 0.04–0.772/3911/3982% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.0200.511.52+

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

Fig. 8. Random-effects meta-analysis for ICU admission.

c19early.org

October 2026

Bromhexine COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Vila Méndez (RCT)67%0.33 · 0.01–7.94hosp.0/981/9348mg

Early treatment67%0.33 · 0.01–7.940/981/9367% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.5

Mareev (RCT)8%0.92 · 0.77–1.09hosp. time33 (n)33 (n)32mgCT¹

Late treatment8%0.92 · 0.77–1.0933 (n)33 (n)8% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.35

Mikhaylov (RCT)80%0.20 · 0.01–3.97hosp.0/252/2524mg Tolouian (DB RCT)70%0.30 · 0.05–1.78hosp.1/1876/18524mg

Prophylaxis74%0.26 · 0.05–1.461/2128/21074% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.13

All studies10%0.90 · 0.76–1.081/3439/33610% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2600.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

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

c19early.org

October 2026

Bromhexine COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Vila Méndez (RCT)71%0.29 · 0.03–2.68no recov.1/523/4548mg

Early treatment71%0.29 · 0.03–2.681/523/4571% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.28

Li (RCT)75%0.25 · 0.06–1.00no disch.2/124/696mg Mareev (RCT)11%0.89 · 0.65–1.22no recov.33 (n)33 (n)32mgCT¹

Late treatment43%0.57 · 0.17–1.862/454/3943% lower risk

Tau² = 0.54, I² = 67.2%, p = 0.36

All studies46%0.54 · 0.21–1.393/977/8446% lower risk

Tau² = 0.36, I² = 48.8%, p = 0.200.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

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

c19early.org

October 2026

Bromhexine COVID-19 case results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Mikhaylov (RCT)91%0.09 · 0.01–1.56symp. case0/255/2524mg Tolouian (DB RCT)53%0.47 · 0.25–0.87symp. case16/18734/18524mg Granados.. (DB RCT)n/a< NOT REPORTED >214 (est. total)ELEVATECT¹ · >5 years late

Prophylaxis62%0.38 · 0.13–1.1116/21239/21062% lower risk

Tau² = 0.26, I² = 19.0%, p = 0.077

All studies62%0.38 · 0.13–1.1116/21239/21062% lower risk

Tau² = 0.26, I² = 19.0%, p = 0.07700.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

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

c19early.org

October 2026

Bromhexine COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Vila Méndez (RCT)−7%1.07 · 0.64–1.77viral load98 (n)93 (n)48mg

Early treatment−7%1.07 · 0.64–1.7798 (n)93 (n)7% higher risk

Tau² = 0.00, I² = 0.0%, p = 0.82

Mareev (RCT)87%0.13 · 0.01–2.25viral+0/173/1332mgCT¹ Tolouian (RCT)−75%1.75 · 1.13–2.71viral+29/4818/5232mg

Late treatment30%0.70 · 0.06–8.1329/6521/6530% lower risk

Tau² = 2.35, I² = 68.0%, p = 0.79

All studies−24%1.24 · 0.66–2.3129/16321/15824% higher risk

Tau² = 0.15, I² = 58.0%, p = 0.5100.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
increases risk →

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

c19early.org

October 2026

Bromhexine COVID-19 peer reviewed studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlDose (1d)Relative Risk

Ansarin (RCT)91%0.09 · 0.01–1.59death0/395/3924mg Vila Méndez (RCT)67%0.33 · 0.01–7.94oxygen0/981/9348mg

Early treatment84%0.16 · 0.02–1.350/1376/13284% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.093

Li (RCT)75%0.25 · 0.06–1.00no disch.2/124/696mg Mareev (RCT)11%0.89 · 0.65–1.22no recov.33 (n)33 (n)32mgCT¹ Tolouian (RCT)76%0.24 · 0.01–8.03death48 (n)52 (n)32mg

Late treatment44%0.56 · 0.22–1.482/934/9144% lower risk

Tau² = 0.35, I² = 43.4%, p = 0.25

Mikhaylov (RCT)80%0.20 · 0.01–3.97hosp.0/252/2524mg

Prophylaxis80%0.20 · 0.01–3.970/252/2580% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.29

All studies54%0.46 · 0.21–1.042/25512/24854% lower risk

Tau² = 0.28, I² = 27.7%, p = 0.06100.511.52+

¹ CT: study uses combined treatment

Rotate screen for more detailsIncrease width for more details

← Bromhexine
reduces riskBromhexine
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

2 bromhexine RCTs have not reported results1,2. The trials report report an estimated total of 304 patients. The results are delayed from 5 years to over 6 years.

Media Bias

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 Altmetric59 and PlumX Metrics60. Altmetric covers ~14,000 news outlets61 and PlumX covers ~55,000 news sources62. The two services are complementary with coverage of different news sources and different methods for identifying articles not containing a DOI. We consider studies to have received significant media coverage if they were covered by at least 100 articles in either Altmetric of PlumX62,63. The most widespread study was covered by 1,047 outlets. Studies covered by major syndicated sources shows hundreds of news articles, while studies covered by few outlets typically appear in niche medical news sources or press releases. Fig. 14 and 15 show bias towards negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments.

The media bias 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. High-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

c19early.org

October 2026

Media coverage of low-cost COVID‑19 treatments

Media covered only 0.8% of studies showing significantly lower COVID-19 risk

Only 5 positive studies were covered

2020 2021 2022 2023

0.8%

one each: budesonide, cannabidiol, fluvoxamine, HCQ, vitamin D

34 negative studies were covered

2020 2021 2022 2023

HCQ 12, ivermectin 6, lopinavir/ritonavir 4, azithromycin 3, vitamin D 3, fluvoxamine 2, one each: ibuprofen, metformin, vitamin C, zinc

5.4×

Negative bias for low-cost treatments

negative studies were 5.4 times more likely to be covered

Fig. 14. Mainstream media coverage was biased against positive results for low-cost treatments. When restricting to RCTs, the bias was even larger, with negative RCTs 6.9× more likely to be covered.

c19early.org

October 2026

Media coverage of high-profit COVID‑19 treatments

Media was 3.8× more likely to cover positive studies

19 positive studies were covered

2020 2021 2022 2023

11%

paxlovid 5, remdesivir 3, casirivimab/imdevimab 2, convalescent plasma 2, molnupiravir 2, one each: antiandrogens, peginterferon lambda, sargramostim, tixagevimab/cilgavimab, tocilizumab

Only 6 negative studies were covered

2020 2021 2022 2023

remdesivir 3, one each: bamlanivimab/etesevimab, bebtelovimab, sotrovimab

3.8×

Positive bias for high-profit treatments

positive studies were 3.8 times more likely to be covered

Fig. 15. For high-profit treatments, mainstream media coverage was biased towards positive results. When restricting to RCTs, the bias was even larger, with positive RCTs 12.0× more likely to be covered.

A combination of factors may have led to the very low media coverage 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 shadowbanned or completely banned on Twitter and YouTube if the science they were reporting conflicted with the opinions of specific authorities selected by those companies, including when the authorities had not even reviewed the scientific results in question.

•

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

in order to grant an EUA for novel high-profit interventions, creating an 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.

•

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, and elevating fear and anxiety. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage.

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 hours64,65. 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 cases66
<24 hours	-33 hours symptoms67
24-48 hours	-13 hours symptoms67
Inpatients	-2.5 hours to improvement68

Fig. 16 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. 16. 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 variants70, for example the Gamma variant shows significantly different characteristics71-74. 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 variants3,4.

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 synergistic77-101, 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. 17 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 18 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. 19 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. 17. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.

Fig. 18. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.

Fig. 17. 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. 20 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Fig. 20. 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, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results103-106. For bromhexine, there is currently not enough data to evaluate publication bias with high confidence.

Genetic variants

Genetic variants have been shown to affect COVID-19 infection, severity, and mortality risk107. Patients with certain TMPRSS2 variants may potentially benefit more from TMPRSS2 inhibitors like bromhexine107.

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. 21 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 < 0.05108-115. 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. 21. Example funnel plot analysis for simulated perfect trials.

Conflicts of interest

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Bromhexine for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 bromhexine trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all bromhexine trials represent the optimal conditions for efficacy.

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 alone77-101. 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

3 of 7 studies combine treatments. The results of bromhexine alone may differ. 3 of 7 RCTs use combined treatment.

Reviews

Multiple reviews cover bromhexine for COVID-19, presenting additional background on mechanisms and related results, including116-119.

Other studies

Additional preclinical or review papers suggesting potential benefits of bromhexine for COVID-19 include127-138. 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 factors35-42, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk43, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Fig. 22 shows an overview of the results for bromhexine in the context of multiple COVID-19 treatments, and Fig. 23 shows a plot of efficacy vs. cost for COVID-19 treatments.

Fig. 22. 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 efficacy139.

Fig. 23. Efficacy vs. cost for COVID-19 treatments.

Conclusion

Significantly lower risk is seen for ventilation and ICU admission. 3 studies from 3 independent teams in 2 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 43% [-5‑69%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.

Bromhexine efficacy may vary depending on the degree of TMPRSS-dependent fusion for different variants3,4.

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 229,000 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/bmeta.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

Ansarin et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Lower ventilation and ICU admission

p = 0.014 (ventilation) and p = 0.013 (ICU admission)

RCT with 39 bromhexine and 39 control patients showing lower mortality, intubation, and ICU admission with treatment. The treatment group received bromhexine hydrochloride 8 mg three times a day for two weeks. All patients received SOC including HCQ. Submit Corrections or Updates.

Granados-Montiel et al.

Estimated 214 participant bromhexine + HCQ prophylaxis RCT with results not reported over 5 years after estimated completion. Submit Corrections or Updates.

Li et al.

Outcome

Improvement

Relative Risk · 95% CI

no CI

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Higher discharge and lower oxygen therapy

Not statistically significant · p = 0.11 (discharge) and p = 0.57 (oxygen)

Tiny RCT with 12 bromhexine and 6 control patients showing non-statistically significant improvements in chest CT, need for oxygen therapy, and discharge rate within 20 days. Authors recommend a larger scale trial. Submit Corrections or Updates.

Mareev et al.

Outcome

Improvement

Relative Risk · 95% CI

SHOKS-COVID score

11%0.89 · 0.65–1.22

PCR+ on day 10 or hospitalization >10 days

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Improved recovery and viral clearance

Not statistically significant · p = 0.47 (recovery) and p = 0.077 (viral clearance)

Prospective 103 PCR+ patients in Russia, 33 treated with bromexhine+spironolactone, showing lower PCR+ at day 10 or hospitalization >10 days with treatment. Bromhexine 8mg 4 times daily, spironolactone 25-50 mg/day for 10 days. Submit Corrections or Updates.

Mežnar

Estimated 90 patient bromhexine + HCQ late treatment RCT with results not reported over 6 years after estimated completion. Submit Corrections or Updates.

Mikhaylov et al.

Outcome

Improvement

Relative Risk · 95% CI

primary

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Lower hospitalization and fewer symptomatic cases

Not statistically significant · p = 0.49 (hospitalization) and p = 0.05 (fewer symptomatic cases)

Small prophylaxis RCT with 25 treatment and 25 control health care workers, showing lower PCR+, symptomatic cases, and hospitalization with treatment, although not statistically significant with the small sample size. Submit Corrections or Updates.

Tolouian et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Fewer symptomatic cases and cases

p = 0.007 (Fewer symptomatic cases) and p = 0.028 (cases)

PEP RCT with 372 close contacts of COVID+ patients, 187 treated with bromhexine, showing significantly lower cases with treatment. IRCT20120703010178N22. Submit Corrections or Updates.

Tolouian et al.

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Worse viral clearance with bromhexine

p = 0.016

Small RCT with 100 patients, 48 with bromhexine added to SOC, showing slower viral- conversion but lower mortality and greater clinical improvement with bromhexine (not statistically significant with few deaths and very high recovery). The very large difference between unadjusted and adjusted results is due to much higher risk for patients with renal disease and the much higher prevalence of renal disease in the bromhexine group.

The study also shows 90% of patients in the control group had BMI ≥30 compared to 0% in the treatment group, suggesting a possible problem with randomization. Due to the imbalance between groups, results were adjusted for BMI>30, smoking, and renal disease.

11 patients were lost to followup in the treatment group compared to zero in the control group, perhaps in part due to faster recovery in the treatment group. 9 patients were excluded from the treatment group because they did not want to take bromhexine after discharge. Therefore up to 29% of treatment patients may have been excluded because they recovered quickly. Submit Corrections or Updates.

Vila Méndez et al.

Outcome

Improvement

Relative Risk · 95% CI

-187%2.87 · 0.12 to >9

Viral clearance, day 14

13%0.87 · 0.68–1.11

Viral clearance, day 7

-14%1.14 · 0.94–1.37

primary

00.511.52+

← Bromhexine
reduces riskBromhexine
increases risk →

Lower need for oxygen therapy and lower hospitalization

Not statistically significant · p = 0.49 (need for oxygen) and p = 0.49 (hospitalization)

RCT 191 low risk (no mortality) outpatients in Spain, showing no significant differences with bromhexine. Authors note that "statistical differences between the study groups were observed in the percentage of patients treated with bronchodilators (p = 0.033) and receiving symptomatic treatment (p = 0.034), which were higher in the SOC alone group", but do not provide details or perform adjustments. There were more moderate/severe cases in the treatment group (9 vs. 5).

Many results appear to be missing including: reduction in the severity of each symptom (0-10 NRS score) at days 4, 7, 14, and 28 as compared with baseline; proportion of patients with clinical improvement and time to clinical improvement; proportion of patients with disappearance of each symptom at days 4, 7, 14, and 28, and time to disappearance; proportion of asymptomatic patients at days 4, 7, 14, and 28.

Bromhexine 48 mg/day for seven days. SOC included acetaminophen. 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 bromhexine 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 bromhexine for COVID-19 that report a comparison with a control group are included in the main analysis. 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. 24. 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 reduction140. 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 PythonMeta141 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 1145. 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 (3.15.0) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.4), 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 reliability153. 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 issues155. 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 harmH. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias151,152 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 factorsI.

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 effective64,65.

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/bmeta.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.

Ansarin, 7/19/2020, Randomized Controlled Trial, Iran, peer-reviewed, 11 authors, study period 18 April, 2020 - 19 May, 2020. Submit Corrections or Updates.	risk of death, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 39 (0.0%), control 5 of 39 (12.8%), NNT 7.8, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of mechanical ventilation, 88.9% lower, RR 0.11, p = 0.01, treatment 1 of 39 (2.6%), control 9 of 39 (23.1%), NNT 4.9.
	risk of ICU admission, 81.8% lower, RR 0.18, p = 0.01, treatment 2 of 39 (5.1%), control 11 of 39 (28.2%), NNT 4.3.
Vila Méndez, 12/24/2022, Randomized Controlled Trial, Spain, peer-reviewed, 38 authors, study period 24 February, 2022 - 28 July, 2022, trial EudraCT2021-001227-41. Submit Corrections or Updates.	risk of oxygen therapy, 67.3% lower, RR 0.33, p = 0.49, treatment 0 of 98 (0.0%), control 1 of 93 (1.1%), NNT 93, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of hospitalization, 67.3% lower, RR 0.33, p = 0.49, treatment 0 of 98 (0.0%), control 1 of 93 (1.1%), NNT 93, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of no recovery, 71.2% lower, RR 0.29, p = 0.33, treatment 1 of 52 (1.9%), control 3 of 45 (6.7%), NNT 21, dyspnea.
	risk of no recovery, 186.5% higher, RR 2.87, p = 1.00, treatment 1 of 52 (1.9%), control 0 of 45 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), fever.
	viral load, 6.6% higher, relative load 1.07, p = 0.82, treatment mean 13.54 (±26.02) n=98, control mean 14.43 (±26.94) n=93, relative change in ORF1ab Ct value, day 4, primary outcome.
	viral load, 17.4% lower, relative load 0.83, p = 0.60, treatment mean 6.36 (±17.05) n=98, control mean 7.7 (±18.47) n=93, relative change in N Ct value, day 4, primary outcome.
	viral load, 41.5% higher, relative load 1.41, p = 0.32, treatment mean 9.74 (±29.54) n=98, control mean 13.78 (±26.81) n=93, relative change in S Ct value, day 4, primary outcome.
	risk of no viral clearance, 13.4% lower, RR 0.87, p = 0.31, treatment 52 of 98 (53.1%), control 57 of 93 (61.3%), NNT 12, day 14.
	risk of no viral clearance, 13.6% higher, RR 1.14, p = 0.21, treatment 73 of 98 (74.5%), control 61 of 93 (65.6%), day 7.

Late treatment

Li, 9/3/2020, Randomized Controlled Trial, China, peer-reviewed, 10 authors, study period 16 February, 2020 - 10 May, 2020, trial NCT04273763 (history). Submit Corrections or Updates.	risk of no hospital discharge, 75.0% lower, RR 0.25, p = 0.11, treatment 2 of 12 (16.7%), control 4 of 6 (66.7%), NNT 2.0.
	risk of oxygen therapy, 50.0% lower, RR 0.50, p = 0.57, treatment 2 of 12 (16.7%), control 2 of 6 (33.3%), NNT 6.0.
Mareev, 12/3/2020, Randomized Controlled Trial, Russia, peer-reviewed, 20 authors, this trial uses multiple treatments in the treatment arm (combined with spironolactone) - results of individual treatments may vary, trial NCT04424134 (history). Submit Corrections or Updates.	relative SHOKS-COVID score, 11.3% better, RR 0.89, p = 0.47, treatment mean 2.12 (±1.39) n=33, control mean 2.39 (±1.59) n=33.
	risk of PCR+ on day 10 or hospitalization >10 days, 38.8% lower, RR 0.61, p = 0.02, treatment 14 of 24 (58.3%), control 20 of 21 (95.2%), NNT 2.7, odds ratio converted to relative risk.
	hospitalization time, 8.2% lower, relative time 0.92, p = 0.35, treatment 33, control 33.
	risk of no viral clearance, 87.4% lower, RR 0.13, p = 0.08, treatment 0 of 17 (0.0%), control 3 of 13 (23.1%), NNT 4.3, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 10.
Mežnar, 7/31/2020, Randomized Controlled Trial, this trial uses multiple treatments in the treatment arm (combined with HCQ) - results of individual treatments may vary, trial NCT04355026 (history). Submit Corrections or Updates.	Estimated 90 patient RCT with results unknown and over 6 years late.
Tolouian, 3/15/2021, Randomized Controlled Trial, Iran, peer-reviewed, 7 authors. Submit Corrections or Updates.	risk of death, 76.0% lower, OR 0.24, p = 0.43, treatment 48, control 52, adjusted per study, Table 3, RR approximated with OR.
	risk of no improvement, 75.9% better, OR 0.24, p = 0.43, treatment 48, control 52, adjusted per study, inverted to make OR<1 favor treatment, Table 2, RR approximated with OR.
	risk of no viral clearance, 74.5% higher, RR 1.75, p = 0.02, treatment 29 of 48 (60.4%), control 18 of 52 (34.6%), mid-recovery day 7.

Prophylaxis

Granados-Montiel, 6/30/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, this trial uses multiple treatments in the treatment arm (combined with HCQ) - results of individual treatments may vary, trial NCT04340349 (history) (ELEVATE). Submit Corrections or Updates.	Estimated 214 patient RCT with results unknown and over 5 years late.
Mikhaylov, 3/8/2021, Randomized Controlled Trial, Russia, peer-reviewed, 8 authors, study period 13 May, 2020 - 25 July, 2020, trial NCT04405999 (history). Submit Corrections or Updates.	risk of hospitalization, 80.0% lower, RR 0.20, p = 0.49, treatment 0 of 25 (0.0%), control 2 of 25 (8.0%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of symptomatic case, 90.9% lower, RR 0.09, p = 0.05, treatment 0 of 25 (0.0%), control 5 of 25 (20.0%), NNT 5.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of case, 71.4% lower, RR 0.29, p = 0.14, treatment 2 of 25 (8.0%), control 7 of 25 (28.0%), NNT 5.0, primary outcome.
Tolouian (B), 12/20/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Iran, preprint, 16 authors, study period 21 December, 2020 - 25 July, 2021. Submit Corrections or Updates.	risk of death, 32.9% lower, RR 0.67, p = 0.76, treatment 0 of 187 (0.0%), control 1 of 185 (0.5%), odds ratio converted to relative risk, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	risk of hospitalization, 70.3% lower, RR 0.30, p = 0.14, treatment 1 of 187 (0.5%), control 6 of 185 (3.2%), adjusted per study, odds ratio converted to relative risk.
	risk of symptomatic case, 53.0% lower, RR 0.47, p = 0.007, treatment 16 of 187 (8.6%), control 34 of 185 (18.4%), NNT 10, odds ratio converted to relative risk.
	risk of case, 50.2% lower, RR 0.50, p = 0.03, treatment 13 of 187 (7.0%), control 26 of 185 (14.1%), NNT 14, odds ratio converted to relative risk.

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.

Transmembrane protease serine 2 (TMPRSS2) is a host cell protease that primes the spike protein, facilitating cellular entry. TMPRSS2 activity helps enable cleavage of the spike protein required for membrane fusion and virus entry. Inhibition may especially protect respiratory epithelial cells, buy may have physiological effects.

Acid sphingomyelinase (ASM) is a lysosomal enzyme that hydrolyzes sphingomyelin into ceramide and phosphorylcholine. ASM activity is upregulated by SARS-CoV-2 infection, leading to ceramide-enriched membrane domains that facilitate viral entry and replication. Inhibiting ASM may disrupt viral entry and reduce infection severity while potentially restoring membrane stability and immune homeostasis.

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

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 environment147,148. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage149. 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

Granados-Montiel et al., New prophylaxis regimen for SARS-CoV-2 infection in health professionals with low doses of hydroxychloroquine and bromhexine: a randomised, double-blind placebo clinical trial (ELEVATE Trial), BMJ Open, doi:10.1136/bmjopen-2020-045190.bmj.com, doi.org.

Mežnar et al., Use of Bromhexine and Hydroxychloroquine for Treatment of COVID-19 Pneumonia, NCT04355026, clinicaltrials.gov/study/NCT04355026clinicaltrials.gov.

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.

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.

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.

10.

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.

11.

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.

12.

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

13.

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.

14.

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

15.

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

16.

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

17.

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.

18.

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.

19.

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.

20.

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.

21.

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.

22.

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.

23.

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.

24.

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.

25.

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.

26.

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.

27.

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.

28.

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

29.

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.

30.

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.

31.

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.

32.

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.

33.

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.

34.

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.

35.

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.

36.

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.

37.

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.

38.

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.

39.

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

40.

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.

41.

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

42.

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.

43.

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

44.

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.

45.

Sgrignani et al., Computational Identification of a Putative Allosteric Binding Pocket in TMPRSS2, Frontiers in Molecular Biosciences, doi:10.3389/fmolb.2021.666626.frontiersin.org, doi.org.

46.

Martins et al., In Vitro Inhibition of SARS-CoV-2 Infection by Bromhexine hydrochloride, bioRxiv, doi:10.1101/2022.12.23.521817.biorxiv.org, doi.org.

47.

Carpinteiro et al., Inhibition of acid sphingomyelinase by ambroxol prevents SARS-CoV-2 entry into epithelial cells, Journal of Biological Chemistry, doi:10.1016/j.jbc.2021.100701.jbc.org, doi.org.

48.

Hoffman et al., SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor, Cell, doi:10.1016/j.cell.2020.02.052.cell.com, doi.org.

49.

Zúñiga et al., Bromhexine inhibits SARS-CoV-2 Omicron and variant pseudovirus infection via ACE2-targeted mechanisms, Frontiers in Pharmacology, doi:10.3389/fphar.2025.1745277.frontiersin.org, 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/bmeta.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.

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.

58.

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.

59.

altmetric.com, www.altmetric.com/.

60.

elsevier.com, www.elsevier.com/insights/metrics/plumx.

61.

help.altmetric.com, help.altmetric.com/en/articles/9802369.

62.

Meschede et al., Cross-metric compatability and inconsistencies of altmetrics, Scientometrics, doi:10.1007/s11192-018-2674-1.springer.com, doi.org.

63.

Ortega, J., Reliability and accuracy of altmetric providers: a comparison among Altmetric.com, PlumX and Crossref Event Data, Scientometrics, doi:10.1007/s11192-018-2838-z.springer.com, doi.org.

64.

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.

65.

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.

66.

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.

67.

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.

68.

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.

69.

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.

70.

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.

71.

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.

72.

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.

73.

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.

74.

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.

75.

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.

76.

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.

77.

Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.researchsquare.com, doi.org.

78.

Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.biomedcentral.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.

Meneguesso, A., Médica defende tratamento precoce da Covid-19, www.youtube.com/watch?v=X5FCrIm_19Uyoutube.com.

104.

Boulware, D., Comments regarding paper rejection, twitter.com/boulware_dr/status/1311331372884205570twitter.com.

105.

Meeus, G., Online Comment, twitter.com/gertmeeus_MD/status/1386636373889781761twitter.com.

106.

twitter.com, twitter.com/KashPrime/status/1768487878454124914.

107.

Ren et al., Association of genetic polymorphisms with COVID-19 infection and outcomes: An updated meta-analysis based on 62 studies, Heliyon, doi:10.1016/j.heliyon.2023.e23662.sciencedirect.com, doi.org.

108.

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.

109.

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

110.

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.

111.

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.

112.

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.

113.

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.

114.

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.

115.

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.

116.

Mitev, V., Prevention and Treatment of COVID-19 and Influenza with Bromhexine and High Doses of Colchicine, MDPI AG, doi:10.20944/preprints202504.1220.v1.preprints.org, doi.org.

117.

Mitev (B), V., Comparison of treatment of COVID-19 with inhaled bromhexine, higher doses of colchicine and hymecromone with WHO-recommended paxlovid, molnupiravir, remdesivir, anti-IL-6 receptor antibodies and baricitinib, Pharmacia, doi:10.3897/pharmacia.70.e112550.pensoft.net, doi.org.

118.

Al-Kuraishy et al., The potential role of Bromhexine in the management of COVID-19: Decipher and a real game-changer, Current Medical and Drug Research, www.researchgate.net/publication/347446399_The_potential_role_of_Bromhexine_in_the_management_of_COVID-19_Decipher_and_a_real_game-changerresearchgate.net.

119.

Maggio et al., Repurposing the mucolytic cough suppressant and TMPRSS2 protease inhibitor bromhexine for the prevention and management of SARS-CoV-2 infection, Pharmacol Res., doi:10.1016/j.phrs.2020.104837.nih.gov, doi.org.

120.

Vila Méndez et al., Efficacy of Bromhexine versus Standard of Care in Reducing Viral Load in Patients with Mild-to-Moderate COVID-19 Disease Attended in Primary Care: A Randomized Open-Label Trial, Journal of Clinical Medicine, doi:10.3390/jcm12010142.mdpi.com, doi.org.

121.

Ansarin et al., Effect of bromhexine on clinical outcomes and mortality in COVID-19 patients: A randomized clinical trial, Bioimpacts, doi:10.34172/bi.2020.27.ac.ir, doi.org.

122.

Tolouian et al., Effect of bromhexine in hospitalized patients with COVID-19, J. Investig. Med., doi:10.1136/jim-2020-001747.nih.gov, doi.org.

123.

Mareev et al., Results of Open-Label non-Randomized Comparative Clinical Trial: “BromhexIne and Spironolactone for CoronаvirUs Infection requiring hospiTalization (BISCUIT), Кардиология, doi:10.18087/cardio.2020.11.n1440.ossn.ru, doi.org.

124.

Li et al., Bromhexine Hydrochloride Tablets for the Treatment of Moderate COVID-19: An Open-Label Randomized Controlled Pilot Study, Clinical and Translational Science, doi:10.1111/cts.12881.wiley.com, doi.org.

125.

Tolouian (B) et al., Bromhexine, for Post Exposure COVID-19 Prophylaxis: A Randomized, Double-Blind, Placebo Control Trial, SSRN, doi:10.2139/ssrn.3989.ssrn.com, doi.org.

126.

Mikhaylov et al., Bromhexine Hydrochloride Prophylaxis of COVID-19 for Medical Personnel: A Randomized Open-Label Study, Interdisciplinary Perspectives on Infectious Diseases, doi:10.1155/2022/4693121.hindawi.com, doi.org.

127.

Song et al., SARS-CoV-2 pseudoparticles preferentially infect ectoderm in human embryonic tissues, Frontiers in Cell and Developmental Biology, doi:10.3389/fcell.2026.1733662.frontiersin.org, doi.org.

128.

Baby et al., Exploring TMPRSS2 Drug Target to Combat Influenza and Coronavirus Infection, Scientifica, doi:10.1155/sci5/3687892.wiley.com, doi.org.

129.

Chowdhury et al., Exploring Natural Plant Compounds Against the SARS-CoV-2 Spike Protein: An Analytical and Computational Approach for Drug Discovery, Pharmacological Research - Natural Products, doi:10.1016/j.prenap.2025.100191.sciencedirect.com, doi.org.

130.

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.

131.

Sharma et al., Reviewing the insights of SARS-CoV-2: Its Epidemiology, Pathophysiology, and Potential Preventive Measures in Traditional Medicinal System, Clinical Traditional Medicine and Pharmacology, doi:10.1016/j.ctmp.2024.200147.sciencedirect.com, doi.org.

132.

Ku et al., Repurposing Screens of FDA-Approved Drugs Identify 29 Inhibitors of SARS-CoV-2, Journal of Microbiology and Biotechnology, doi:10.4014/jmb.2009.09009.or.kr, doi.org.

133.

Lan et al., Clinical development of antivirals against SARS-CoV-2 and its variants, Current Research in Microbial Sciences, doi:10.1016/j.crmicr.2023.100208.sciencedirect.com, doi.org.

134.

Yadav et al., Role of Structural and Non-Structural Proteins and Therapeutic Targets of SARS-CoV-2 for COVID-19, Cells, doi:10.3390/cells10040821.mdpi.com, doi.org.

135.

Lundstrom et al., COVID-19 signalome: Potential therapeutic interventions, Cellular Signalling, doi:10.1016/j.cellsig.2022.110559.sciencedirect.com, doi.org.

136.

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.

137.

Mitev (C) et al., Colchicine, Bromhexine, and Hymecromone as Part of COVID-19 Treatment-Cold, Warm, Hot, Current Overview on Disease and Health Research Vol. 10, doi:10.9734/bpi/codhr/v10/5310A.bookpi.org, doi.org.

138.

Kumar (B) et al., COVID-19 therapeutics: Clinical application of repurposed drugs and futuristic strategies for target-based drug discovery, Genes & Diseases, doi:10.1016/j.gendis.2022.12.019.sciencedirect.com, doi.org.

139.

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

140.

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.

141.

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

142.

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.

143.

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

144.

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

145.

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.

146.

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

147.

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.

148.

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.

149.

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.

150.

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.

151.

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.

152.

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.

153.

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.

154.

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.

155.

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.

156.

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.

157.

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.

158.

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.