Nafamostat reduces COVID-19 risk: real-time meta analysis of 7 studies
Control
Abstract
Significantly lower risk is seen for viral clearance. One study shows significant benefit.
Meta analysis using the most serious outcome reported shows
30% [10‑46%] lower risk. Results are similar for Randomized Controlled Trials.
Results are consistent with early treatment being more effective than late treatment.
Currently there is limited data, with only 18 control events for the most serious outcome in trials to date.
4 RCTs
with 742 patients have not reported results (up to 4 years late).
No treatment is 100%
effective. Protocols combine safe and effective options with individual
risk/benefit analysis and monitoring.
Other treatments are more effective.
All data and sources to reproduce this analysis are in the appendix.
Nafamostat for COVID-19 — Highlights
Nafamostat reduces risk with very high confidence for pooled analysis, low confidence for viral clearance, and very low confidence for ventilation, progression, and recovery, however increased risk is seen with very low confidence for hospitalization.
Real-time updates and corrections with a consistent protocol for 141 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in nafamostat 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 injury1-13 and
cognitive deficits4,9, cardiovascular
complications14-18, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 100+
host and viral proteins and other factorsA,19-26, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk27, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
nafamostat
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.
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.
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, and for specific outcomes.
Table 2 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, 6, 7, 8, 9, and 10
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, hospitalization, progression, recovery, and viral clearance.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 30% [10‑46%] ** | 7 | 16,265 | 177 |
| Randomized Controlled TrialsRCTs | 35% [15‑51%] ** | 5 | 342 | 157 |
| Mortality | 4% [-73‑47%] | 5 | 16,081 | 91 |
| Recovery | 29% [-12‑55%] | 3 | 271 | 86 |
| RCT mortality | 42% [-80‑81%] | 3 | 158 | 71 |
| Early treatment | Late treatment | |
|---|---|---|
| All studies | 33% [11‑50%] ** | 18% [-38‑52%] |
| Randomized Controlled TrialsRCTs | 33% [11‑50%] ** | 49% [-14‑77%] |
| Mortality | 4% [-73‑47%] | |
| Recovery | 29% [-12‑55%] | |
| RCT mortality | 42% [-80‑81%] |
Figure 3. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
Figure 4. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 5. Random effects meta-analysis for mortality results.
Figure 6. Random effects meta-analysis for ventilation.
Figure 7. Random effects meta-analysis for hospitalization.
Figure 8. Random effects meta-analysis for progression.
Figure 9. Random effects meta-analysis for recovery.
Figure 10. Random effects meta-analysis for viral clearance.
Figure 11 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
35% improvement,
compared to 7% for other studies.
Figure 12 and 13
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 1 and Table 2.
Figure 11. Results for RCTs and non-RCT studies.
Figure 12. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 13. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases33, and
analysis of double-blind RCTs has identified extreme levels of bias34.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 141 treatments we have analyzed,
66% of RCTs involve very late treatment 5+ days after
onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of
early treatments. They may more accurately represent results for treatments
that require visiting a medical facility, e.g., those requiring intravenous
administration.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 1.00 [0.93‑1.08]
across 141 treatments36.
Evidence shows that observational studies
can also provide reliable results. Concato et al. found that well-designed
observational studies do not systematically overestimate the magnitude of the
effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews
comparing RCTs to observational studies and found little evidence for
significant differences in effect estimates.
We performed a similar analysis across the 141 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.93‑1.08]. Similar results are found for all low-cost treatments, RR
1.02 [0.93‑1.12]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.93 [0.83‑1.04].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see40,41.
Currently, 52 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 60% have been confirmed in RCTs, with a mean delay of 7.7 months (66% with 8.8 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.
We need to evaluate each trial on its own merits. RCTs for a
given medication and disease may be more reliable, however they may also be
less reliable. For off-patent medications, very high conflict of interest
trials may be more likely to be RCTs, and more likely to be large trials that
dominate meta analyses.
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 hours46,47. Baloxavir marboxil studies for influenza
also show that treatment delay is critical — Ikematsu et al. report
an 86% reduction in cases for post-exposure prophylaxis, Hayden et al.
show a 33 hour reduction in the time to alleviation of symptoms for treatment
within 24 hours and a reduction of 13 hours for treatment within 24-48 hours,
and Kumar et al. report only 2.5 hours improvement for inpatient
treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases48 |
| <24 hours | -33 hours symptoms49 |
| 24-48 hours | -13 hours symptoms49 |
| Inpatients | -2.5 hours to improvement50 |
Figure 14 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 141 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 14. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 141 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may depend critically on the distribution of
SARS-CoV-2 variants encountered by patients. Risk varies significantly across
variants52, for example the Gamma variant shows significantly
different characteristics53-56. 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 variants57,58.
Effectiveness may depend strongly on the dosage and treatment regimen.
The quality of medications may vary significantly between
manufacturers and production batches, which may significantly affect efficacy
and safety. Williams et al. analyze ivermectin from 11 different sources,
showing highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 141
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 15 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 16 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
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.
Figure 17 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 = 0.00000011 to p = 0.0000000048.
Figure 15. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 16. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 15. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 52 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.3 months.
Figure 18 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 18. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results77-80.
For nafamostat, there is currently not
enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
Figure 19 shows a scatter plot of
results for prospective and retrospective studies.
The median effect size for
retrospective studies is 26% improvement,
compared to 55% for prospective
studies, suggesting a potential bias towards publishing results showing lower efficacy.
Figure 19. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 20 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 < 0.0581-88.
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 20. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Nafamostat for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 nafamostat 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 nafamostat trials
represent the optimal conditions for efficacy.
Summary statistics from
meta analysis necessarily lose information. As with all meta analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone31,32,61-75.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors19-26, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk27, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 21 shows an overview of the results for nafamostat
in the context of multiple COVID-19 treatments, and Figure 22 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 21.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy135.
Figure 22. Efficacy vs. cost for COVID-19 treatments.
Studies to date show that nafamostat is
an effective treatment for COVID-19.
Significantly lower risk is seen for viral clearance. One study shows significant benefit.
Meta analysis using the most serious outcome reported shows
30% [10‑46%] lower risk. Results are similar for Randomized Controlled Trials.
Results are consistent with early treatment being more effective than late treatment.
Currently there is limited data, with only 18 control events for the most serious outcome in trials to date.
Estimated 84 patient nafamostat late treatment RCT with results not reported over 4 years after estimated completion.
Retrospective multicenter observational study of 15,859 hospitalized COVID-19 patients in Japan showing no significant difference in in-hospital mortality with nafamostat mesylate. Very few patients received treatment and they had more severe disease on average. There may be significant residual confounding by indication.
Estimated 586 patient nafamostat late treatment RCT with results not reported over 2 years after estimated completion.
13 patient nafamostat late treatment RCT with results not reported over 4 years after completion.
RCT 160 hospitalized non-critically ill COVID-19 patients showing a 93% posterior probability that nafamostat reduced the odds of death or receipt of ventilatory or vasopressor support by day 28 compared to usual care. Nafamostat, a TMPRSS2 inhibitor with potent in vitro antiviral activity against SARS-CoV-2, was administered as a continuous intravenous infusion for up to 7 days. The trial was conducted across 21 hospitals in Australia, New Zealand, and Nepal. Despite promising results, the trial was stopped early due to slowing recruitment, low event rates, and funding constraints, limiting definitive conclusions. Authors note that the posterior probability of effectiveness was higher among those with earlier disease onset, but lower during the Omicron era when variants were less dependent on the TMPRSS2 pathway.
RCT 30 early-onset COVID-19 patients showing significantly improved viral load reduction with nafamostat.
RCT 42 hospitalized patients with COVID-19 pneumonitis showing no benefit with intravenous nafamostat mesylate.
RCT 15 hospitalized COVID-19 patients showing a positive safety profile with nafamostat mesylate treatment. While the study was underpowered to detect differences in efficacy, Bayesian analysis suggested a signal for potential benefit (69-88% probability that nafamostat is effective depending on prior assumptions). Authors note that nafamostat, as a potent TMPRSS2 inhibitor with anticoagulant properties, could theoretically prevent virus entry into cells and complications like disseminated intravascular coagulation in COVID-19 patients. The study was significantly limited by small sample size due to recruitment challenges.
59 patient nafamostat late treatment RCT with results not reported over 2 years after completion.
Retrospective 64 hospitalized patients with moderate COVID-19 showing no significant difference in clinical outcomes with nafamostat mesylate.
RCT 104 hospitalized patients with moderate to severe COVID-19 pneumonia showing no significant difference in the primary endpoint of time to clinical improvement with nafamostat. However, in patients with baseline National Early Warning Score (NEWS) ≥7, nafamostat treatment significantly shortened time to clinical improvement and recovery. Patients in the nafamostat group with NEWS ≥7 also had higher recovery rates and significantly reduced NEWS scores by day 11.
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 nafamostat 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 nafamostat 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 to136.
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 1139.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.13.3) with
scipy (1.15.3), pythonmeta (1.26), numpy (2.2.5), statsmodels (0.14.4), and plotly (6.0.1).
Forest plots are computed using PythonMeta140
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.2 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 effective46,47.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/nfmeta.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.
| Okugawa, 9/30/2023, Randomized Controlled Trial, Japan, peer-reviewed, mean age 39.3, 22 authors, study period July 2021 - July 2022. | viral load, 33.3% lower, relative load 0.67, p = 0.007, treatment mean 3.0 (±0.91) n=19, control mean 2.0 (±0.8) n=10, relative reduction in viral load, day 6. |
| viral load, 60.0% lower, relative load 0.40, p = 0.01, treatment mean 1.5 (±0.91) n=19, control mean 0.6 (±0.79) n=10, relative reduction in viral load, mid-recovery, day 3. |
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.
| Bae, 4/30/2021, Randomized Controlled Trial, South Korea, trial NCT04418128 (history). | Estimated 84 patient RCT with results unknown and over 4 years late. |
| Inokuchi, 12/26/2021, retrospective, Japan, peer-reviewed, 11 authors, study period 1 January, 2020 - 31 December, 2020. | risk of death, 27.0% higher, OR 1.27, p = 0.52, treatment 121, control 15,738, RR approximated with OR. | Kim, 8/1/2022, Double Blind Randomized Controlled Trial, placebo-controlled, South Korea, trial NCT04871646 (history). | Estimated 586 patient RCT with results unknown and over 2 years late. | Kim (B), 4/5/2021, Randomized Controlled Trial, South Korea, trial NCT04628143 (history). | 13 patient RCT with results unknown and over 4 years late. |
| Morpeth, 10/24/2023, Randomized Controlled Trial, multiple countries, peer-reviewed, 64 authors, study period 17 May, 2021 - 12 August, 2022, death$c$ ventilation$c$ or vasopressor support,ADJ, trial NCT04483960 (history) (ASCOT). | risk of mechanical ventilation, 55.5% lower, RR 0.45, p = 0.23, treatment 4 of 82 (4.9%), control 8 of 73 (11.0%), NNT 16, day 28. |
| risk of progression, 57.2% lower, RR 0.43, p = 0.14, treatment 4 of 82 (4.9%), control 8 of 73 (11.0%), NNT 16, odds ratio converted to relative risk, day 28. | |
| risk of no recovery, 28.0% lower, OR 0.72, p = 0.36, treatment 82, control 73, WHO scale, day 28, RR approximated with OR. | |
| Quinn, 2/28/2022, Randomized Controlled Trial, United Kingdom, peer-reviewed, mean age 63.6, 49 authors, study period September 2020 - February 2021, average treatment delay 8.5 days, trial NCT04473053 (history) (DEFINE). | risk of death, no change, RR 1.00, p = 1.00, treatment 3 of 21 (14.3%), control 3 of 21 (14.3%). |
| hospitalization time, 47.5% higher, relative time 1.48, p = 0.21, treatment mean 9.0 (±8.4) n=21, control mean 6.1 (±6.0) n=21. | |
| Seccia, 10/19/2023, Double Blind Randomized Controlled Trial, placebo-controlled, Italy, peer-reviewed, 15 authors, study period June 2021 - August 2022, trial NCT04352400 (history) (RACONA). | risk of death, 66.7% lower, RR 0.33, p = 1.00, treatment 0 of 7 (0.0%), control 1 of 7 (14.3%), NNT 7.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of no recovery, 20.0% lower, RR 0.80, p = 1.00, treatment 4 of 7 (57.1%), control 5 of 7 (71.4%), NNT 7.0, no improvement in WHO score of 2+ points. | Seydi, 2/8/2023, Randomized Controlled Trial, Senegal, trial NCT04390594 (history) (SEN-CoV-Fadj). | 59 patient RCT with results unknown and over 2 years late. |
| Soma, 9/30/2022, retrospective, Japan, peer-reviewed, 9 authors, study period 29 March, 2020 - 21 January, 2021. | risk of death, 79.5% lower, RR 0.20, p = 0.49, treatment 0 of 31 (0.0%), control 2 of 33 (6.1%), NNT 16, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of severe case, 6.5% higher, RR 1.06, p = 1.00, treatment 10 of 31 (32.3%), control 10 of 33 (30.3%). | |
| Zhuravel, 11/30/2021, Randomized Controlled Trial, placebo-controlled, Russia, peer-reviewed, mean age 58.6, 7 authors, study period 25 September, 2020 - 14 November, 2020, trial NCT04623021 (history). | risk of death, 76.0% lower, RR 0.24, p = 0.20, treatment 1 of 52 (1.9%), control 4 of 50 (8.0%), NNT 16. |
| risk of no improvement, 42.3% lower, RR 0.58, p = 0.28, treatment 6 of 52 (11.5%), control 10 of 50 (20.0%), NNT 12. | |
| risk of no recovery, 42.3% lower, RR 0.58, p = 0.28, treatment 6 of 52 (11.5%), control 10 of 50 (20.0%), NNT 12. | |
| risk of no recovery, 40.9% lower, RR 0.59, p = 0.09, treatment mean 1.3 (±2.3) n=52, control mean 2.2 (±3.0) n=50, relative NEWS score at day 11, day 11. |
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.
Rong et al., Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19, Cell Host & Microbe, doi:10.1016/j.chom.2024.11.007.
Yang et al., SARS-CoV-2 infection causes dopaminergic neuron senescence, Cell Stem Cell, doi:10.1016/j.stem.2023.12.012.
Scardua-Silva et al., Microstructural brain abnormalities, fatigue, and cognitive dysfunction after mild COVID-19, Scientific Reports, doi:10.1038/s41598-024-52005-7.
Hampshire et al., Cognition and Memory after Covid-19 in a Large Community Sample, New England Journal of Medicine, doi:10.1056/NEJMoa2311330.
Duloquin et al., Is COVID-19 Infection a Multiorganic Disease? Focus on Extrapulmonary Involvement of SARS-CoV-2, Journal of Clinical Medicine, doi:10.3390/jcm13051397.
Sodagar et al., Pathological Features and Neuroinflammatory Mechanisms of SARS-CoV-2 in the Brain and Potential Therapeutic Approaches, Biomolecules, doi:10.3390/biom12070971.
Sagar et al., COVID-19-associated cerebral microbleeds in the general population, Brain Communications, doi:10.1093/braincomms/fcae127.
Verma et al., Persistent Neurological Deficits in Mouse PASC Reveal Antiviral Drug Limitations, bioRxiv, doi:10.1101/2024.06.02.596989.
Panagea et al., Neurocognitive Impairment in Long COVID: A Systematic Review, Archives of Clinical Neuropsychology, doi:10.1093/arclin/acae042.
Ariza et al., COVID-19: Unveiling the Neuropsychiatric Maze—From Acute to Long-Term Manifestations, Biomedicines, doi:10.3390/biomedicines12061147.
Vashisht et al., Neurological Complications of COVID-19: Unraveling the Pathophysiological Underpinnings and Therapeutic Implications, Viruses, doi:10.3390/v16081183.
Ahmad et al., Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium, The Neurohospitalist, doi:10.1177/19418744241292487.
Wang et al., SARS-CoV-2 membrane protein induces neurodegeneration via affecting Golgi-mitochondria interaction, Translational Neurodegeneration, doi:10.1186/s40035-024-00458-1.
Eberhardt et al., SARS-CoV-2 infection triggers pro-atherogenic inflammatory responses in human coronary vessels, Nature Cardiovascular Research, doi:10.1038/s44161-023-00336-5.
Van Tin et al., Spike Protein of SARS-CoV-2 Activates Cardiac Fibrogenesis through NLRP3 Inflammasomes and NF-κB Signaling, Cells, doi:10.3390/cells13161331.
Borka Balas et al., COVID-19 and Cardiac Implications—Still a Mystery in Clinical Practice, Reviews in Cardiovascular Medicine, doi:10.31083/j.rcm2405125.
AlTaweel et al., An In-Depth Insight into Clinical, Cellular and Molecular Factors in COVID19-Associated Cardiovascular Ailments for Identifying Novel Disease Biomarkers, Drug Targets and Clinical Management Strategies, Archives of Microbiology & Immunology, doi:10.26502/ami.936500177.
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.
Dugied et al., Multimodal SARS-CoV-2 interactome sketches the virus-host spatial organization, Communications Biology, doi:10.1038/s42003-025-07933-z.
Malone et al., Structures and functions of coronavirus replication–transcription complexes and their relevance for SARS-CoV-2 drug design, Nature Reviews Molecular Cell Biology, doi:10.1038/s41580-021-00432-z.
Murigneux et al., Proteomic analysis of SARS-CoV-2 particles unveils a key role of G3BP proteins in viral assembly, Nature Communications, doi:10.1038/s41467-024-44958-0.
Lv et al., Host proviral and antiviral factors for SARS-CoV-2, Virus Genes, doi:10.1007/s11262-021-01869-2.
Lui et al., Nsp1 facilitates SARS-CoV-2 replication through calcineurin-NFAT signaling, Virology, doi:10.1128/mbio.00392-24.
Niarakis et al., Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches, Frontiers in Immunology, doi:10.3389/fimmu.2023.1282859.
Katiyar et al., SARS-CoV-2 Assembly: Gaining Infectivity and Beyond, Viruses, doi:10.3390/v16111648.
Wu et al., Decoding the genome of SARS-CoV-2: a pathway to drug development through translation inhibition, RNA Biology, doi:10.1080/15476286.2024.2433830.
González-Paz et al., Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data, ACS Omega, doi:10.1021/acsomega.3c06968.
Umar et al., Inhibitory potentials of ivermectin, nafamostat, and camostat on spike protein and some nonstructural proteins of SARS-CoV-2: Virtual screening approach, Jurnal Teknologi Laboratorium, doi:10.29238/teknolabjournal.v11i1.344.
Sgrignani et al., Computational Identification of a Putative Allosteric Binding Pocket in TMPRSS2, Frontiers in Molecular Biosciences, doi:10.3389/fmolb.2021.666626.
Schultz et al., Pyrimidine inhibitors synergize with nucleoside analogues to block SARS-CoV-2, Nature, doi:10.1038/s41586-022-04482-x.
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.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Seydi et al., Multicentre, Open Label, Randomised, Adaptative Clinical Trial of Efficacy and Safety of Treatment Regimens in Adult COVID-19 Patients in Senegal, NCT04390594, clinicaltrials.gov/study/NCT04390594.
Kim et al., A Double-blind, Multi-center, Multi-regional, Randomized Controlled, Phase 3 Clinical Trial to Evaluate the Efficacy and Safety of CKD-314 in Hospitalized Adult Patients Diagnosed With COVID-19, NCT04871646, clinicaltrials.gov/study/NCT04871646.
Bae et al., Treatment Effect of Nafamostat Mesylate in Patients With COVID-19 Pneumonia: Open Labelled Randomized Controlled Clinical Trial, NCT04418128, clinicaltrials.gov/study/NCT04418128.
Kim (B) et al., Open-label, Multi-center, Randomized Controlled, Phase 2 Clinical Trial to Evaluate the Efficacy and Safety of CKD-314 in Hospitalized Adult Patients Diagnosed With COVID-19, NCT04628143, clinicaltrials.gov/study/NCT04628143.
Treanor et al., Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016.
McLean et al., Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100.
Ikematsu et al., Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, New England Journal of Medicine, doi:10.1056/NEJMoa1915341.
Hayden et al., Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, New England Journal of Medicine, doi:10.1056/NEJMoa1716197.
Kumar et al., Combining baloxavir marboxil with standard-of-care neuraminidase inhibitor in patients hospitalised with severe influenza (FLAGSTONE): a randomised, parallel-group, double-blind, placebo-controlled, superiority trial, The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2.
López-Medina et al., Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, JAMA, doi:10.1001/jama.2021.3071.
Korves et al., SARS-CoV-2 Genetic Variants and Patient Factors Associated with Hospitalization Risk, medRxiv, doi:10.1101/2024.03.08.24303818.
Faria et al., Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, Science, doi:10.1126/science.abh2644.
Nonaka et al., SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003.
Karita et al., Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, medRxiv, doi:10.1101/2021.08.27.21262754.
Zavascki et al., Advanced ventilatory support and mortality in hospitalized patients with COVID-19 caused by Gamma (P.1) variant of concern compared to other lineages: cohort study at a reference center in Brazil, Research Square, doi:10.21203/rs.3.rs-910467/v1.
Willett et al., The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, medRxiv, doi:10.1101/2022.01.03.21268111.
Peacock et al., The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, bioRxiv, doi:10.1101/2021.12.31.474653.
Williams, T., Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, Do Your Own Research, doyourownresearch.substack.com/p/not-all-ivermectin-is-created-equal.
Xu et al., A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358.
Jitobaom et al., Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, Research Square, doi:10.21203/rs.3.rs-941811/v1.
Jitobaom (B) et al., Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8.
Jeffreys et al., Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542.
Ostrov et al., Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, Pathogens, doi:10.3390/pathogens10111514.
Alsaidi et al., Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, Marine Drugs, doi:10.3390/md19080418.
Andreani et al., In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, Microbial Pathogenesis, doi:10.1016/j.micpath.2020.104228.
De Forni et al., Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, PLoS ONE, doi:10.1371/journal.pone.0276751.
Wan et al., Synergistic inhibition effects of andrographolide and baicalin on coronavirus mechanisms by downregulation of ACE2 protein level, Scientific Reports, doi:10.1038/s41598-024-54722-5.
Said et al., The effect of Nigella sativa and vitamin D3 supplementation on the clinical outcome in COVID-19 patients: A randomized controlled clinical trial, Frontiers in Pharmacology, doi:10.3389/fphar.2022.1011522.
Fiaschi et al., In Vitro Combinatorial Activity of Direct Acting Antivirals and Monoclonal Antibodies against the Ancestral B.1 and BQ.1.1 SARS-CoV-2 Viral Variants, Viruses, doi:10.3390/v16020168.
Xing et al., Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals, Briefings in Bioinformatics, doi:10.1093/bib/bbab249.
Chen et al., Synergistic Inhibition of SARS-CoV-2 Replication Using Disulfiram/Ebselen and Remdesivir, ACS Pharmacology & Translational Science, doi:10.1021/acsptsci.1c00022.
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.
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.
Thairu et al., A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328.
Singh et al., The relationship between viral clearance rates and disease progression in early symptomatic COVID-19: a systematic review and meta-regression analysis, Journal of Antimicrobial Chemotherapy, doi:10.1093/jac/dkae045.
Boulware, D., Comments regarding paper rejection, twitter.com/boulware_dr/status/1311331372884205570.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, www.wiley.com/en-ae/Publication+Bias+in+Meta+Analysis:+Prevention,+Assessment+and+Adjustments-p-9780470870143.
Stanley et al., Meta-regression approximations to reduce publication selection bias, Research Synthesis Methods, doi:10.1002/jrsm.1095.
Rücker et al., Arcsine test for publication bias in meta-analyses with binary outcomes, Statistics in Medicine, doi:10.1002/sim.2971.
Peters, J., Comparison of Two Methods to Detect Publication Bias in Meta-analysis, JAMA, doi:10.1001/jama.295.6.676.
Moreno et al., Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2.
Macaskill et al., A comparison of methods to detect publication bias in meta-analysis, Statistics in Medicine, doi:10.1002/sim.698.
Egger et al., Bias in meta-analysis detected by a simple, graphical test, BMJ, doi:10.1136/bmj.315.7109.629.
Harbord et al., A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, Statistics in Medicine, doi:10.1002/sim.2380.
Okugawa et al., Antiviral effect and safety of nafamostat mesilate in patients with mild early-onset COVID-19: An exploratory multicentre randomized controlled clinical trial, International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2023.106922.
Morpeth et al., A Randomized Trial of Nafamostat for Covid-19, NEJM Evidence, doi:10.1056/EVIDoa2300132.
Seccia et al., RAndomized Clinical Trial Of NAfamostat Mesylate, A Potent Transmembrane Protease Serine 2 (TMPRSS2) Inhibitor, in Patients with COVID-19 Pneumonia, Journal of Clinical Medicine, doi:10.3390/jcm12206618.
Soma et al., Nafamostat Mesylate Monotherapy in Patients with Moderate COVID-19: a Single-Center, Retrospective Study, Japanese Journal of Infectious Diseases, doi:10.7883/yoken.JJID.2021.699.
Quinn et al., Randomised controlled trial of intravenous nafamostat mesylate in COVID pneumonitis: Phase 1b/2a experimental study to investigate safety, Pharmacokinetics and Pharmacodynamics, eBioMedicine, doi:10.1016/j.ebiom.2022.103856.
Inokuchi et al., Association between Nafamostat Mesylate and In-Hospital Mortality in Patients with Coronavirus Disease 2019: A Multicenter Observational Study, Journal of Clinical Medicine, doi:10.3390/jcm11010116.
Zhuravel et al., Nafamostat in hospitalized patients with moderate to severe COVID-19 pneumonia: a randomised Phase II clinical trial, eClinicalMedicine, doi:10.1016/j.eclinm.2021.101169.
Baby et al., Exploring TMPRSS2 Drug Target to Combat Influenza and Coronavirus Infection, Scientifica, doi:10.1155/sci5/3687892.
Kumar (B) et al., Advancements in the development of antivirals against SARS-Coronavirus, Frontiers in Cellular and Infection Microbiology, doi:10.3389/fcimb.2025.1520811.
Bogoyavlenskiy et al., The Ability of Combined Flavonol and Trihydroxyorganic Acid to Suppress SARS-CoV-2 Reproduction, Viruses, doi:10.3390/v17010037.
Haque et al., Exploring potential therapeutic candidates against COVID-19: a molecular docking study, Discover Molecules, doi:10.1007/s44345-024-00005-5.
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.
Barghash et al., Navigating the COVID-19 Therapeutic Landscape: Unveiling Novel Perspectives on FDA-Approved Medications, Vaccination Targets, and Emerging Novel Strategies, Molecules, doi:10.3390/molecules29235564.
Ali et al., SARS-CoV-2 Syncytium under the Radar: Molecular Insights of the Spike-Induced Syncytia and Potential Strategies to Limit SARS-CoV-2 Replication, Journal of Clinical Medicine, doi:10.3390/jcm12186079.
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.
Lei et al., Small molecules in the treatment of COVID-19, Signal Transduction and Targeted Therapy, doi:10.1038/s41392-022-01249-8.
Sharun et al., A comprehensive review on pharmacologic agents, immunotherapies and supportive therapeutics for COVID-19, Narra J, doi:10.52225/narra.v2i3.92.
Ma et al., Integration of human organoids single‐cell transcriptomic profiles and human genetics repurposes critical cell type‐specific drug targets for severe COVID ‐19, Cell Proliferation, doi:10.1111/cpr.13558.
Behboudi et al., SARS-CoV-2 mechanisms of cell tropism in various organs considering host factors, Heliyon, doi:10.1016/j.heliyon.2024.e26577.
Gordon et al., A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing, bioRxiv, doi:10.1101/2020.03.22.002386.
Rensi et al., Homology Modeling of TMPRSS2 Yields Candidate Drugs That May Inhibit Entry of SARS-CoV-2 into Human Cells, American Chemical Society (ACS), doi:10.26434/chemrxiv.12009582.v1.
Yamamoto et al., Identification of Nafamostat as a Potent Inhibitor of Middle East Respiratory Syndrome Coronavirus S Protein-Mediated Membrane Fusion Using the Split-Protein-Based Cell-Cell Fusion Assay, Antimicrobial Agents and Chemotherapy, doi:10.1128/AAC.01043-16.
Ellinger et al., Identification of inhibitors of SARS-CoV-2 in-vitro cellular toxicity in human (Caco-2) cells using a large scale drug repurposing collection, Research Square, doi:10.21203/rs.3.rs-23951/v1.
Kuo et al., Kinetic Characterization and Inhibitor Screening for the Proteases Leading to Identification of Drugs against SARS-CoV-2, Antimicrobial Agents and Chemotherapy, doi:10.1128/AAC.02577-20.
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.
Gupta et al., Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics, Molecular Aspects of Medicine, doi:10.1016/j.mam.2022.101151.
Ellinger (B) et al., A SARS-CoV-2 cytopathicity dataset generated by high-content screening of a large drug repurposing collection, Scientific Data, doi:10.1038/s41597-021-00848-4.
Kushwaha et al., A comprehensive review on the global efforts on vaccines and repurposed drugs for combating COVID-19, European Journal of Medicinal Chemistry, doi:10.1016/j.ejmech.2023.115719.
Jeong et al., Evaluation of the Antiviral Efficacy of Subcutaneous Nafamostat Formulated with Glycyrrhizic Acid against SARS-CoV-2 in a Murine Model, International Journal of Molecular Sciences, doi:10.3390/ijms24119579.
Farkaš et al., A Tale of Two Proteases: MPro and TMPRSS2 as Targets for COVID-19 Therapies, Pharmaceuticals, doi:10.3390/ph16060834.
Guo et al., Multi-omics in COVID-19: Driving development of therapeutics and vaccines, National Science Review, doi:10.1093/nsr/nwad161.
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.
Wang (B) et al., Repurposing Drugs for the Treatment of COVID-19 and Its Cardiovascular Manifestations, Circulation Research, doi:10.1161/circresaha.122.321879.
Xue et al., Repurposing clinically available drugs and therapies for pathogenic targets to combat SARS‐CoV‐2, MedComm, doi:10.1002/mco2.254.
Pati et al., Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network, Multimedia Tools and Applications, doi:10.1007/s11042-023-15270-8.
Kumar (C) 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.
Astasio-Picado et al., Therapeutic Targets in the Virological Mechanism and in the Hyperinflammatory Response of Severe Acute Respiratory Syndrome Coronavirus Type 2 (SARS-CoV-2), Applied Sciences, doi:10.3390/app13074471.
Săndulescu et al., Therapeutic developments for SARS-CoV-2 infection—Molecular mechanisms of action of antivirals and strategies for mitigating resistance in emerging variants in clinical practice, Frontiers in Microbiology, doi:10.3389/fmicb.2023.1132501.
Gautam et al., Promising Repurposed Antiviral Molecules to Combat SARS-CoV-2: A Review, Current Pharmaceutical Biotechnology, doi:10.2174/1389201024666230302113110.
Raghav et al., Potential treatments of COVID-19: Drug repurposing and therapeutic interventions, Journal of Pharmacological Sciences, doi:10.1016/j.jphs.2023.02.004.
Supianto et al., Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature, Journal of Taibah University Medical Sciences, doi:10.1016/j.jtumed.2022.12.015.
Ravindran et al., Discovery of host-directed modulators of virus infection by probing the SARS-CoV-2–host protein–protein interaction network, Briefings in Bioinformatics, doi:10.1093/bib/bbac456.
Wagoner et al., Combinations of Host- and Virus-Targeting Antiviral Drugs Confer Synergistic Suppression of SARS-CoV-2, Microbiology Spectrum, doi:10.1128/spectrum.03331-22.
Zhong et al., Recent advances in small-molecular therapeutics for COVID-19, Precision Clinical Medicine, doi:10.1093/pcmedi/pbac024.
Jamalipour Soufi et al., Potential inhibitors of SARS-CoV-2: recent advances, Journal of Drug Targeting, doi:10.1080/1061186X.2020.1853736.
Sarkar et al., Potential Therapeutic Options for COVID-19: Current Status, Challenges, and Future Perspectives, Frontiers in Pharmacology, doi:10.3389/fphar.2020.572870.
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.
Altman (B) et al., How to obtain the confidence interval from a P value, BMJ, doi:10.1136/bmj.d2090.
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