Top
Overview
Introduction
Results
RCTs
Heterogeneity
Discussion
Conclusion
 
Study Notes
Methods and Data
Supplementary
References
 
All studies
Mortality
Hospitalization
Progression
Recovery
Viral clearance
All RCTs
RCT mortality
RCT hospitalization
 
Feedback
Home
c19early.org COVID-19 treatment researchBemnifosbuvirBemnifosbuvir (more..)
Melatonin Meta
Metformin Meta
Azvudine Meta
Bromhexine Meta Molnupiravir Meta
Budesonide Meta
Colchicine Meta
Conv. Plasma Meta Nigella Sativa Meta
Curcumin Meta Nitazoxanide Meta
Famotidine Meta Paxlovid Meta
Favipiravir Meta Quercetin Meta
Fluvoxamine Meta Remdesivir Meta
Hydroxychlor.. Meta Thermotherapy Meta
Ivermectin Meta

Loading...
More

Bemnifosbuvir for COVID-19: real-time meta analysis of 3 studies

@CovidAnalysis, February 2024, Version 3V3
 
0 0.5 1 1.5+ All studies 47% 3 359 Improvement, Studies, Patients Relative Risk Mortality 86% 1 83 Hospitalization 71% 1 207 Progression 28% 2 288 Recovery 12% 2 288 Viral clearance 3% 3 322 RCTs 47% 3 359 Early 38% 2 276 Late 86% 1 83 Bemnifosbuvir for COVID-19 c19early.org February 2024 Favorsbemnifosbuvir Favorscontrol
Abstract
Statistically significant lower risk is seen for hospitalization. 2 studies from 2 independent teams in 2 countries show statistically significant improvements.
Meta analysis using the most serious outcome reported shows 47% [-57‑82%] lower risk, without reaching statistical significance. Currently all studies are RCTs.
Currently there is limited data, with only 359 patients and only 10 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
No treatment or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Bemnifosbuvir p=0.25 Acetaminophen p=0.00000029 2020 2021 2022 2023 Effective Harmful c19early.org February 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
Highlights
Bemnifosbuvir reduces risk for COVID-19 with low confidence for hospitalization and viral clearance, and very low confidence for mortality.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 66 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ MOONSONG Boffito (DB RCT) 3% 0.97 [0.93-1.01] viral load 29 (n) 40 (n) Improvement, RR [CI] Treatment Control MORNINGSKY Horga (DB RCT) 71% 0.29 [0.09-0.96] hosp. 4/137 7/70 Tau​2 = 0.54, I​2 = 74.3%, p = 0.42 Early treatment 38% 0.62 [0.20-1.94] 4/166 7/110 38% lower risk Horga (DB RCT) 86% 0.14 [0.01-2.71] death 0/41 3/42 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Late treatment 86% 0.14 [0.01-2.71] 0/41 3/42 86% lower risk All studies 47% 0.53 [0.18-1.57] 4/207 10/152 47% lower risk 3 bemnifosbuvir COVID-19 studies c19early.org February 2024 Tau​2 = 0.56, I​2 = 63.7%, p = 0.25 Effect extraction pre-specified(most serious outcome, see appendix) Favors bemnifosbuvir Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ MOONSONG Boffito (DB RCT) 3% viral- Improvement Relative Risk [CI] MORNINGSKY Horga (DB RCT) 71% hospitalization Tau​2 = 0.54, I​2 = 74.3%, p = 0.42 Early treatment 38% 38% lower risk Horga (DB RCT) 86% death Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Late treatment 86% 86% lower risk All studies 47% 47% lower risk 3 bemnifosbuvir C19 studies c19early.org February 2024 Tau​2 = 0.56, I​2 = 63.7%, p = 0.25 Effect extraction pre-specifiedRotate device for details Favors bemnifosbuvir Favors control
B
0 0.25 0.5 0.75 1 1.25 1.5+ All studies Late treatment Early treatment Efficacy in COVID-19 bemnifosbuvir studies (pooled effects) Favors bemnifosbuvir Favors control c19early.org February 2024
C
Loading..
D
-100% -50% 0% 50% 100% Timeline of COVID-19 bemnifosbuvir studies (pooled effects) 2020 2021 2022 2023 Favorsbemnifosbuvir Favorscontrol c19early.org February 2024
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. 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. C. Results within the context of multiple COVID-19 treatments. 0.6% of 6,632 proposed treatments show efficacy c19early.org. D. Timeline of results in bemnifosbuvir studies.
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 issues Scardua-Silva, Yang, cardiovascular complications Eberhardt, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factors Note A, Malone, Murigneux, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 6,000 compounds may reduce COVID-19 risk c19early.org (B), either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
We analyze all significant controlled studies of bemnifosbuvir 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, and Randomized Controlled Trials (RCTs).
Figure 2 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early Treatment refers to treatment immediately or soon after symptoms appear, while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Table 1 summarizes the results for all stages combined, after exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, 7, and 8 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, progression, recovery, and viral clearance.
Table 1. Random effects meta-analysis for all stages combined, after exclusions, and for specific outcomes. Results show the percentage improvement with treatment and the 95% confidence interval.
Improvement Studies Patients Authors
All studies47% [-57‑82%]3 359 35
Randomized Controlled TrialsRCTs47% [-57‑82%]3 359 35
Recovery12% [-69‑55%]2 288 24
Viral3% [-1‑7%]3 322 35
Table 2. Random effects meta-analysis results by treatment stage. Results show the percentage improvement with treatment, the 95% confidence interval, and the number of studies for the stage.treatment and the 95% confidence interval.
Early treatment Late treatment
All studies38% [-94‑80%]86% [-171‑99%]
Randomized Controlled TrialsRCTs38% [-94‑80%]86% [-171‑99%]
Recovery23% [-107‑72%]2% [-135‑59%]
Viral6% [-9‑19%]9% [-51‑46%]
Loading..
Loading..
Figure 3. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, see the specific outcome analyses for individual outcomes, and the heterogeneity section for discussion. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Loading..
Loading..
Figure 4. Random effects meta-analysis for mortality results.
Loading..
Figure 5. Random effects meta-analysis for hospitalization.
Loading..
Figure 6. Random effects meta-analysis for progression.
Loading..
Figure 7. Random effects meta-analysis for recovery.
Loading..
Figure 8. Random effects meta-analysis for viral clearance.
Currently all studies are RCTs.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours McLean, Treanor. Baloxavir studies for influenza also show that treatment delay is critical — Ikematsu report an 86% reduction in cases for post-exposure prophylaxis, Hayden 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 report only 2.5 hours improvement for inpatient treatment.
Table 3. Studies of baloxavir for influenza show that early treatment is more effective.
Treatment delayResult
Post exposure prophylaxis86% fewer cases Ikematsu
<24 hours-33 hours symptoms Hayden
24-48 hours-13 hours symptoms Hayden
Inpatients-2.5 hours to improvement Kumar
Figure 9 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 66 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Loading..
Figure 9. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 66 treatments.
Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results (as in López-Medina).
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics Faria, Karita, Nonaka, Zavascki. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 10. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
Currently, 44 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 treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.5 months. When restricting to RCTs only, 46% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.1 months.
Loading..
Loading..
Figure 10. 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.
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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.
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.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For bemnifosbuvir, there is currently not enough data to evaluate publication bias with high confidence.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 11 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 < 0.05 Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 11. Example funnel plot analysis for simulated perfect trials.
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 by 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 affiliated with special interests 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 alone Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu. 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, vaccine, 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.
Statistically significant lower risk is seen for hospitalization. 2 studies from 2 independent teams in 2 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 47% [-57‑82%] lower risk, without reaching statistical significance. Currently all studies are RCTs.
Currently there is limited data, with only 359 patients and only 10 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
0 0.5 1 1.5 2+ Viral load, 1,100mg 3% Improvement Relative Risk Viral load, 550mg 7% Bemnifosbuvir  MOONSONG  EARLY TREATMENT  DB RCT Is early treatment with bemnifosbuvir beneficial for COVID-19? Double-blind RCT 69 patients in the United Kingdom (Feb - Oct 2021) No significant difference in viral clearance c19early.org Boffito et al., Microbiology Spectrum, Aug 2023 Favors bemnifosbuvir Favors control
Boffito: RCT 100 mild/moderate COVID-19 patients showing no significant difference in nasopharyngeal viral load reduction between bemnifosbuvir (550mg or 1100mg twice daily for 5 days) and placebo groups.
0 0.5 1 1.5 2+ Hospitalization 71% Improvement Relative Risk Medical visit 28% Recovery time -28% no CI Recovery 23% Viral clearance 22% Bemnifosbuvir  MORNINGSKY  EARLY TREATMENT  DB RCT Is early treatment with bemnifosbuvir beneficial for COVID-19? Double-blind RCT 207 patients in Mexico Lower hospitalization with bemnifosbuvir (p=0.047) c19early.org Horga et al., Future Virology, November 2023 Favors bemnifosbuvir Favors control
Horga: MORNINGSKY RCT which was terminated early after enrolling only 216 of 1,386 planned participants. The trial did not meet its primary endpoint, with the bemnifosbuvir group having longer time to symptom improvement than placebo. However, compared to placebo, bemnifosbuvir was associated with a 71% relative risk reduction in COVID-19 hospitalizations and fewer COVID-19 complications and medically attended visits, despite no significant improvement in viral load.
0 0.5 1 1.5 2+ Mortality 86% Improvement Relative Risk Progression 27% Recovery 2% Viral clearance 9% Bemnifosbuvir  Horga et al.  LATE TREATMENT  DB RCT Is late treatment with bemnifosbuvir beneficial for COVID-19? Double-blind RCT 83 patients in the USA Lower mortality with bemnifosbuvir (not stat. sig., p=0.24) c19early.org Horga et al., Future Virology, June 2023 Favors bemnifosbuvir Favors control
Horga (B): Phase 2 RCT investigating bemnifosbuvir for the treatment of 81 high-risk COVID-19 patients hospitalized with moderate disease. The trial was terminated early due to difficulties with enrollment. There was no significant difference between bemnifosbuvir and placebo for the primary outcome of disease progression or most secondary outcomes. However, viral load declined faster in the bemnifosbuvir group between days 2 and 8. All 3 deaths occurred in the placebo group. The safety profile was similar between groups. While results are very limited by small sample size and early termination, they suggest bemnifosbuvir may accelerate viral clearance and could play a role in preventing progression.
We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms are bemnifosbuvir 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 bemnifosbuvir for COVID-19 that report a comparison with a control group are included in the main analysis. This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome specific analyses. For example, if effects for mortality and cases are both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcomes are considered more important than viral test status. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available. After most or all patients have recovered there is little or no room for an effective treatment to do better, however faster recovery is valuable. If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO2 is more important than cough. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1 Sweeting. 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 (3.11.7) with scipy (1.12.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.1), and plotly (5.19.0).
Forest plots are computed using PythonMeta Deng with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I2 statistic. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective McLean, Treanor.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
A summary of study results is below. Please submit updates and corrections at the bottom of this page.
A summary of study results is below. Please submit updates and corrections at https://c19early.org/bfmeta.html.
Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.
Boffito, 8/17/2023, Double Blind Randomized Controlled Trial, placebo-controlled, United Kingdom, peer-reviewed, 11 authors, study period February 2021 - October 2021, trial NCT04709835 (history) (MOONSONG). viral load, 2.9% lower, relative load 0.97, p = 0.14, treatment mean 2.78 (±0.236) n=29, control mean 2.7 (±0.207) n=40, 1,100mg, day 7.
viral load, 7.4% lower, relative load 0.93, p < 0.001, treatment mean 3.38 (±0.22) n=29, control mean 3.13 (±0.22) n=29, 550mg, day 7.
Horga, 11/1/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Mexico, peer-reviewed, 16 authors, trial NCT04889040 (history) (MORNINGSKY). risk of hospitalization, 70.8% lower, RR 0.29, p = 0.047, treatment 4 of 137 (2.9%), control 7 of 70 (10.0%), NNT 14.
medical visit, 28.5% lower, RR 0.72, p = 0.49, treatment 14 of 137 (10.2%), control 10 of 70 (14.3%), NNT 25.
risk of no recovery, 23.4% lower, RR 0.77, p = 0.58, treatment 9 of 137 (6.6%), control 6 of 70 (8.6%), NNT 50.
risk of no viral clearance, 21.8% lower, RR 0.78, p = 0.21, treatment 44 of 128 (34.4%), control 29 of 66 (43.9%), NNT 10, day 14.
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.
Horga (B), 6/23/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 8 authors, trial NCT04396106 (history). risk of death, 85.6% lower, RR 0.14, p = 0.24, treatment 0 of 41 (0.0%), control 3 of 42 (7.1%), NNT 14, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
risk of progression, 26.8% lower, RR 0.73, p = 0.71, treatment 3 of 41 (7.3%), control 4 of 40 (10.0%), NNT 37, PRI.
risk of no recovery, 2.4% lower, RR 0.98, p = 1.00, treatment 8 of 41 (19.5%), control 8 of 40 (20.0%), NNT 205, clinical recovery.
risk of no viral clearance, 9.4% lower, RR 0.91, p = 0.80, treatment 13 of 27 (48.1%), control 17 of 32 (53.1%), NNT 20, day 14.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
  or use drag and drop   
Submit