Famotidine reduces COVID-19 risk: real-time meta analysis of 30 studies
Abstract
Significantly lower risk is seen for mortality, hospitalization, recovery, and viral clearance. 15 studies from 15 independent teams in 7 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
17% [8‑24%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Early treatment is more effective than late treatment.
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.
Famotidine for COVID-19 — Highlights
Famotidine reduces risk with very high confidence for mortality, hospitalization, recovery, and in pooled analysis, and low confidence for viral clearance, however increased risk is seen with low confidence for progression.
Early treatment is more effective than late treatment.
28th treatment shown effective in October 2021, now with p = 0.00028 from 30 studies, recognized in 2 countries.
Real-time updates and corrections with a consistent protocol for 131 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 famotidine studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes, one or more specific outcome, pooled outcomes in RCTs, and one or more specific outcome in RCTs. Efficacy based on RCTs only was delayed by 4.7 months, compared to using all studies. Efficacy based on specific outcomes in RCTs was delayed by 5.8 months, compared to using pooled outcomes in RCTs.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury2-14 and
cognitive deficits5,10, cardiovascular
complications15-19, 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,20-27, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk28, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
famotidine
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
An In Vitro study supports the efficacy of famotidine29.
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, for peer-reviewed studies, after exclusions, 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, 10, 11, 12, and 13
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 17% [8‑24%] *** | 30 | 114,119 | 242 |
| After exclusions | 18% [7‑27%] ** | 26 | 113,292 | 204 |
| Peer-reviewed studiesPeer-reviewed | 16% [7‑24%] *** | 29 | 114,099 | 236 |
| Randomized Controlled TrialsRCTs | 27% [5‑44%] * | 4 | 461 | 56 |
| Mortality | 18% [9‑27%] *** | 21 | 86,617 | 150 |
| VentilationVent. | 4% [-18‑21%] | 3 | 1,694 | 15 |
| ICU admissionICU | -2% [-75‑41%] | 5 | 1,056 | 37 |
| HospitalizationHosp. | 15% [7‑22%] *** | 5 | 528 | 38 |
| Recovery | 10% [5‑14%] **** | 6 | 890 | 68 |
| Cases | 12% [-10‑30%] | 4 | 21,333 | 28 |
| RCT mortality | 15% [-26‑43%] | 2 | 386 | 19 |
| RCT hospitalizationRCT hosp. | 17% [12‑22%] **** | 3 | 349 | 25 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 48% [-32‑80%] | 15% [0‑28%] * | 16% [4‑27%] * |
| After exclusions | 48% [-32‑80%] | 14% [-2‑28%] | 21% [4‑34%] * |
| Peer-reviewed studiesPeer-reviewed | 48% [-32‑80%] | 14% [-2‑27%] | 16% [4‑27%] * |
| Randomized Controlled TrialsRCTs | 48% [-32‑80%] | 25% [1‑43%] * | |
| Mortality | 20% [6‑31%] ** | 15% [-4‑29%] | |
| VentilationVent. | 4% [-18‑21%] | ||
| ICU admissionICU | -2% [-75‑41%] | ||
| HospitalizationHosp. | 17% [13‑21%] **** | 6% [3‑9%] *** |
|
| Recovery | 48% [-32‑80%] | 10% [5‑14%] **** | 37% [-54‑74%] |
| Cases | 12% [-10‑30%] | ||
| RCT mortality | 15% [-26‑43%] | ||
| RCT hospitalizationRCT hosp. | 17% [12‑22%] **** |
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 ICU admission.
Figure 8. Random effects meta-analysis for hospitalization.
Figure 9. Random effects meta-analysis for progression.
Figure 10. Random effects meta-analysis for recovery.
Figure 11. Random effects meta-analysis for cases.
Figure 12. Random effects meta-analysis for viral clearance.
Figure 13. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
Figure 14 shows a comparison of results for RCTs and non-RCT studies.
Random effects meta analysis of RCTs shows
27% improvement,
compared to 16% for other studies.
Figure 15, 16, and 17
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCT results are included in Table 1 and Table 2.
Figure 14. Results for RCTs and non-RCT studies.
Figure 15. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Figure 16. Random effects meta-analysis for RCT mortality results.
Figure 17. Random effects meta-analysis for RCT hospitalization results.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases32, and
analysis of double-blind RCTs has identified extreme levels of bias33.
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 131 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 [0.93‑1.07]
across 131 treatments35.
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 131 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 1.00 [0.93‑1.07]. 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 0.91 [0.81‑1.03].
Details can be found in the
supplementary data.
Lee et al. showed that only 14% of the guidelines of the Infectious
Diseases Society of America were based on RCTs. Evaluation of studies relies
on an understanding of the study and potential biases. Limitations in an RCT
can outweigh the benefits, for example excessive dosages, excessive treatment
delays, or remote survey bias may have a greater effect on results. Ethical
issues may also prevent running RCTs for known effective treatments. For more
on issues with RCTs see39,40.
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.
1 famotidine
RCT has not reported results1.
The trial reports
total actual enrollment of 528 patients.
The result is delayed over 1.5 years.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 18 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Elhadi, unadjusted results with no group details.
Fung, not fully adjusting for the different baseline risk of systemic autoimmune patients.
Shamsi, unadjusted results with no group details.
Taşdemir, excessive unadjusted differences between groups.
Figure 18. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours45,46. 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 cases47 |
| <24 hours | -33 hours symptoms48 |
| 24-48 hours | -13 hours symptoms48 |
| Inpatients | -2.5 hours to improvement49 |
Figure 19 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 131 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 131 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
variants51, for example the Gamma variant shows significantly
different characteristics52-55. 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 variants56,57.
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.
This section validates the use of pooled effects for COVID-19, which enables
earlier detection of efficacy, however pooled effects are no longer required
for famotidine as of October 2021. Efficacy is now known based on specific outcomes for all studies and when restricted to RCTs. Efficacy based on specific outcomes in RCTs was delayed by 5.8 months compared to using pooled outcomes in RCTs.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes" is not a good reason for
disregarding results.
Pooling the results of studies reporting different outcomes allows us to use
more of the available information. Logically we should, and do, use additional
information when evaluating treatments—for example dose-response and
treatment delay-response relationships provide additional evidence of efficacy
that is considered when reviewing the evidence for a treatment.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster and safer collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 131
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 20 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 21 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 22 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh 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 = 0.00000019 to p = 0.0000000094.
Figure 20. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 21. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 20. 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 23 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 23. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results78-81.
For famotidine, there is currently not
enough data to evaluate publication bias with high confidence.
One method to evaluate bias is to compare prospective vs.
retrospective studies. Prospective studies are more likely to be published
regardless of the result, while retrospective studies are more likely to
exhibit bias. For example, researchers may perform preliminary analysis with
minimal effort and the results may influence their decision to continue.
Retrospective studies also provide more opportunities for the specifics of
data extraction and adjustments to influence results.
Figure 24 shows a scatter plot of
results for prospective and retrospective studies.
44% of retrospective studies
report a statistically significant positive effect for
one or more outcomes, compared to
80% of prospective studies, consistent with a bias toward publishing negative results.
The median effect size for
retrospective studies is 21% improvement,
compared to 16% for prospective
studies, showing similar results.
Figure 24. Prospective vs. retrospective studies.
The diamonds show the results of random effects meta-analysis.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 25 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.0582-89.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 25. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. Famotidine for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 famotidine 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 famotidine 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 alone60-76.
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.
1 of the 30
studies compare against other treatments, which may reduce the effect
seen.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors20-27, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk28, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 26 shows an overview of the results for famotidine
in the context of multiple COVID-19 treatments, and Figure 27 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 26.
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 27. Efficacy vs. cost for COVID-19 treatments.
Famotidine is
an effective treatment for COVID-19.
Significantly lower risk is seen for mortality, hospitalization, recovery, and viral clearance. 15 studies from 15 independent teams in 7 countries show significant
benefit.
Meta analysis using the most serious outcome reported shows
17% [8‑24%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies.
Early treatment is more effective than late treatment.
Survey of 307 patients in the USA, showing no significant difference in COVID-19 cases with famotidine use.
Small RCT with 27 famotidine and 28 placebo patients, showing improved recovery with treatment. Recovery was faster with treatment for 14 of 16 symptoms. There was no mortality or hospitalization. NCT04724720.
Retrospective 952 COVID-19 patients in Hong Kong, showing no significant difference in severe disease with famotidine use or PPI use.
RCT 208 ICU patients in Bangladesh, showing improved recovery with famotidine. Famotidine 40mg (<60kg) or 60mg every 8 hours.
Prospective study of 465 COVID-19 ICU patients in Libya showing no significant differences with treatment.
PSM retrospective 1,620 hospitalized patients in the USA, 84 with existing famotidine use, showing lower risk of combined death/intubation with treatment.
Retrospective database analysis of 374,229 patients in the USA, showing higher cases, lower hospitalizations, and no change in mortality with famotidine use.
PSM retrospective in South Korea, showing lower risk of COVID-19 cases with H2RA (including famotidine) and PPI use, but no significant difference in severe outcomes (results provided for the combined groups only).
PSM retrospective 9,565 COVID-19 hospitalized patients in the USA, 1,593 receiving famotidine, showing no significant difference in mortality.
PSM retrospective 6,556 COVID-19 patients in South Korea, showing higher risk of poor outcomes with famotidine vs. other H2-blocker use.
Retrospective 15,968 COVID-19 hospitalized patients in Spain, showing lower mortality with existing use of several medications including metformin, HCQ, azithromycin, aspirin, vitamin D, vitamin C, and budesonide. Since only hospitalized patients are included, results do not reflect different probabilities of hospitalization across treatments.
Retrospective 26,121 cases and 2,369,020 controls ≥65yo in Canada, showing no significant difference in cases with chronic use of famotidine.
PSM retrospective 878 hospitalized patients in the USA, 83 with existing famotidine use, showing significantly lower mortality with treatment.
Retrospective study of 917,198 hospitalized COVID-19 cases covered by the Iran Health Insurance Organization over 26 months showing that antithrombotics, corticosteroids, and antivirals reduced mortality while diuretics, antibiotics, and antidiabetics increased it. Confounding makes some results very unreliable. For example, diuretics like furosemide are often used to treat fluid overload, which is more likely in ICU or advanced disease requiring aggressive fluid resuscitation. Hospitalization length has increased risk of significant confounding, for example longer hospitalization increases the chance of receiving a medication, and death may result in shorter hospitalization. Mortality results may be more reliable.
Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.
Confounding by indication is likely to be significant for many medications. Authors adjustments have very limited severity information (admission type refers to ward vs. ER department on initial arrival). We can estimate the impact of confounding from typical usage patterns, the prescription frequency, and attenuation or increase of risk for ICU vs. all patients.
PSM retrospective TriNetX database analysis of 1,379 severe COVID-19 patients requiring respiratory support, showing lower mortality with aspirin (not reaching statistical significance) and famotidine, and improved results from the combination of both.
RCT with 89 famotidine and 89 control patients in Pakistan, showing faster recovery but no significant difference in mortality. 40mg oral famotidine daily.
Retrospective 10,074 hospitalized veterans with COVID-19 in the USA, showing lower mortality with existing famotidine use.
Very small RCT with 20 patients in Iran, showing shorter hospitalization time with famotidine treatment. There was no mortality or ICU admission. Famotidine 160mg four times a day. IRCT20200509047364N2.
Retrospective 183 hospitalized pediatric COVID-19 patients in Iran, showing no significant difference in mortality with famotidine in unadjusted results.
Retrospective 1,816 famotidine users and 26,820 non-users hospitalized for COVID-19 in the USA, showing no significant differences with treatment.
Retrospective 1,000 COVID+ hospitalized patients in India, showing lower mortality with famotidine and remdesivir in multivariable logistic regression.
Retrospective 489 COVID+ hospitalized patients in the USA, showing higher mortality with famotidine treatment.
Retrospective 179 hospitalized patients in Turkey, 85 treated with famotidine and 94 treated with pantoprazole, showing faster recovery with famotidine in unadjusted results.
528 patient famotidine early treatment RCT with results not reported over 1.5 years after completion.
Retrospective 2,184 hospitalized patients in the USA, 638 treated with famotidine, showing lower mortality with treatment.
Retrospective 9,532 hospitalized COVID+ veterans in the USA, showing no significant difference in mortality with famotidine use. The study provides results for use before, after, and before+after. Before+after should more accurately represent prophylaxis up to COVID-19 infection (and continued use). Before included use up to 2 years before, and after included use up to 60 days later.
Retrospective 7,158 hospitalized COVID-19 patients in the USA, showing higher risk or mortality with in-hospital famotidine use, but lower risk when there was pre-existing at-home use, without statistical significance in both cases.
Retrospective 193 ICU patients in Iran, showing lower mortality with famotidine treatment.
Retrospective 4,445 COVID+ patients in China, showing higher risk of combined death/intubation/ICU with famotidine and with PPIs.
Retrospective 59 ICU patients in Turkey, showing no significant difference in 30-day mortality or invasive mechanical ventilation with 160mg/day famotidine treatment. However, the famotidine group had lower fibrinogen and procalcitonin, suggesting possible benefits for coagulation, inflammation, and secondary infections. Limitations include the small sample size, lack of randomization, and other confounding treatments.
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 famotidine 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 famotidine for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral test status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according 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 (1.15.2), 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 0.8.0 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 effective45,46.
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/fmmeta.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.
| Brennan, 2/10/2022, Double Blind Randomized Controlled Trial, USA, peer-reviewed, 31 authors, study period January 2021 - April 2021, average treatment delay 4.0 days, trial NCT04724720 (history). | risk of no recovery, 48.1% lower, RR 0.52, p = 0.23, treatment 5 of 27 (18.5%), control 10 of 28 (35.7%), NNT 5.8, day 28, ITT. |
| risk of no recovery, 43.2% lower, RR 0.57, p = 0.34, treatment 4 of 19 (21.1%), control 10 of 27 (37.0%), NNT 6.3, day 28, PP. | |
| estimated time to 50% resolution, 28.1% lower, relative time 0.72, p < 0.01, treatment 27, control 28. | Together Trial, 11/1/2023, Double Blind Randomized Controlled Trial, placebo-controlled, trial NCT04727424 (history) (TOGETHER). | 528 patient RCT with results unknown and over 1.5 years late. |
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.
| Chowdhury, 8/16/2022, Randomized Controlled Trial, Bangladesh, peer-reviewed, mean age 57.1, 11 authors, study period 1 August, 2020 - 15 April, 2021, trial NCT04504240 (history). | risk of death, 16.1% lower, RR 0.84, p = 0.53, treatment 26 of 104 (25.0%), control 31 of 104 (29.8%), NNT 21. |
| ICU time, 9.3% lower, relative time 0.91, p = 0.33, treatment 78, control 73. | |
| time to improvement, 32.9% lower, relative time 0.67, p < 0.001, treatment mean 9.53 (±5.0) n=78, control mean 14.21 (±5.6) n=73, time to clinical improvement. | |
| recovery time, 7.3% lower, relative time 0.93, p = 0.14, treatment mean 17.9 (±5.4) n=78, control mean 19.3 (±6.3) n=73, time to symptomatic recovery. | |
| hospitalization time, 17.0% lower, relative time 0.83, p = 0.01, treatment 78, control 73. | |
| time to viral-, 13.0% lower, relative time 0.87, p = 0.002, treatment 78, control 73. | |
| Elhadi, 4/30/2021, prospective, Libya, peer-reviewed, 21 authors, study period 29 May, 2020 - 30 December, 2020, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 7.1% lower, RR 0.93, p = 0.57, treatment 34 of 60 (56.7%), control 247 of 405 (61.0%), NNT 23. |
| Kuno, 10/11/2021, retrospective, propensity score matching, USA, peer-reviewed, 4 authors, study period 1 March, 2020 - 30 March, 2021. | risk of death, no change, OR 1.00, p = 0.97, treatment 1,593, control 7,972, RR approximated with OR. |
| Mehrizi, 12/18/2023, retrospective, Iran, peer-reviewed, 10 authors, study period 1 February, 2020 - 20 March, 2022. | risk of death, 19.0% lower, OR 0.81, p < 0.001, RR approximated with OR. |
| Mura, 3/31/2021, retrospective, database analysis, multiple countries, peer-reviewed, 6 authors. | risk of death, 20.9% lower, RR 0.79, p = 0.02, treatment 563, control 563, odds ratio converted to relative risk, famotidine only, control prevalence approximated with treatment prevalence, propensity score matching. |
| risk of death, 37.3% lower, RR 0.63, p = 0.001, treatment 305, control 305, odds ratio converted to relative risk, famotidine and aspirin, control prevalence approximated with treatment prevalence, propensity score matching. | |
| Pahwani, 2/20/2022, Randomized Controlled Trial, Pakistan, peer-reviewed, mean age 52.0, 8 authors, study period December 2020 - September 2021. | risk of death, 11.1% lower, RR 0.89, p = 1.00, treatment 8 of 89 (9.0%), control 9 of 89 (10.1%), NNT 89. |
| risk of mechanical ventilation, 12.5% lower, RR 0.88, p = 0.73, treatment 21 of 89 (23.6%), control 24 of 89 (27.0%), NNT 30. | |
| risk of ICU admission, 10.0% lower, RR 0.90, p = 0.86, treatment 18 of 89 (20.2%), control 20 of 89 (22.5%), NNT 44. | |
| hospitalization time, 16.5% lower, relative time 0.83, p < 0.001, treatment mean 8.6 (±1.6) n=89, control mean 10.3 (±2.2) n=89. | |
| recovery time, 9.6% lower, relative time 0.90, p = 0.001, treatment mean 8.5 (±1.7) n=89, control mean 9.4 (±1.9) n=89. | |
| Samimagham, 4/27/2021, Single Blind Randomized Controlled Trial, placebo-controlled, Iran, preprint, 6 authors. | hospitalization time, 33.3% lower, relative time 0.67, p = 0.04, treatment 10, control 10. |
| risk of no recovery, no change, RR 1.00, p = 1.00, treatment 5 of 10 (50.0%), control 5 of 10 (50.0%), >50% CT lung involvment. | |
| risk of no recovery, 50.0% lower, RR 0.50, p = 0.37, treatment 3 of 10 (30.0%), control 6 of 10 (60.0%), NNT 3.3, no improvement in cough. | |
| Shamsi, 7/17/2023, retrospective, Iran, peer-reviewed, 4 authors, study period 1 March, 2020 - 1 August, 2021, excluded in exclusion analyses: unadjusted results with no group details. | risk of death, 74.9% lower, RR 0.25, p = 0.21, treatment 1 of 27 (3.7%), control 23 of 156 (14.7%), NNT 9.1. |
| Shoaibi, 9/24/2020, retrospective, database analysis, USA, peer-reviewed, 5 authors. | risk of death, 3.0% higher, RR 1.03, p = 0.67, treatment 1,816, control 26,820. |
| risk of death/ICU, 3.0% higher, RR 1.03, p = 0.62, treatment 1,816, control 26,820. | |
| Siraj, 2/28/2022, retrospective, India, peer-reviewed, median age 56.0, 13 authors, study period March 2020 - December 2020. | risk of death, 36.2% lower, RR 0.64, p = 0.002, treatment 183 of 711 (25.7%), control 122 of 289 (42.2%), NNT 6.1, adjusted per study, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, multivariable. |
| Stolow, 10/31/2021, retrospective, USA, peer-reviewed, 9 authors. | risk of death, 518.9% higher, OR 6.19, p < 0.001, treatment 137, control 352, RR approximated with OR. |
| risk of ICU admission, 2389.6% higher, OR 24.90, p < 0.001, treatment 137, control 352, RR approximated with OR. | |
| Taşdemir, 7/12/2021, retrospective, Turkey, peer-reviewed, 7 authors, this trial compares with another treatment - results may be better when compared to placebo, excluded in exclusion analyses: excessive unadjusted differences between groups. | risk of death, 44.7% lower, RR 0.55, p = 0.29, treatment 5 of 85 (5.9%), control 10 of 94 (10.6%), NNT 21. |
| risk of ICU admission, 36.8% lower, RR 0.63, p = 0.36, treatment 8 of 85 (9.4%), control 14 of 94 (14.9%), NNT 18. | |
| hospitalization time, 18.1% lower, relative time 0.82, p = 0.003, treatment 85, control 94. | |
| recovery time, 20.0% lower, relative time 0.80, p = 0.04, treatment 85, control 94, duration of fever. | |
| Wagner, 10/31/2021, retrospective, USA, peer-reviewed, 5 authors, study period 1 March, 2020 - 1 March, 2021. | risk of death, 64.5% lower, RR 0.36, p < 0.001, treatment 82 of 638 (12.9%), control 182 of 819 (22.2%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| risk of mechanical ventilation, 6.4% lower, RR 0.94, p = 0.77, treatment 48 of 638 (7.5%), control 75 of 819 (9.2%), adjusted per study, odds ratio converted to relative risk, multivariable. | |
| Yeramaneni, 2/28/2021, retrospective, USA, peer-reviewed, 6 authors, study period 11 February, 2020 - 8 May, 2020. | risk of death, 59.0% higher, OR 1.59, p = 0.09, treatment 410, control 746, adjusted per study, hospital use only, multivariable, RR approximated with OR, late treatment result. |
| Zangeneh, 5/13/2022, retrospective, Iran, peer-reviewed, 3 authors. | risk of death, 39.0% lower, HR 0.61, p = 0.01, Cox proportional hazards. |
| Zhou, 12/4/2020, retrospective, propensity score matching, China, peer-reviewed, 7 authors, study period 1 January, 2020 - 22 August, 2020. | risk of severe case, 84.0% higher, HR 1.84, p < 0.001, treatment 72 of 519 (13.9%), control 198 of 2,595 (7.6%), adjusted per study, death/intubation/ICU, propensity score matching, multivariable, Cox proportional hazards. |
| Özden, 2/28/2023, retrospective, Turkey, peer-reviewed, mean age 65.3, 2 authors, study period September 2020 - February 2021, trial NCT05122208 (history). | risk of death, 28.8% lower, RR 0.71, p = 0.19, treatment 14 of 30 (46.7%), control 19 of 29 (65.5%), NNT 5.3. |
| risk of mechanical ventilation, 1.1% higher, RR 1.01, p = 1.00, treatment 23 of 30 (76.7%), control 22 of 29 (75.9%). | |
| ventilation time, 33.3% higher, relative time 1.33, p = 0.28, treatment 30, control 29. | |
| ICU time, 25.5% lower, relative time 0.74, p = 0.60, treatment 30, control 29. |
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.
| Balouch, 1/20/2021, retrospective, USA, peer-reviewed, 5 authors. | risk of symptomatic case, 22.0% lower, RR 0.78, p = 0.49, treatment 18 of 80 (22.5%), control 49 of 227 (21.6%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| recovery time, 36.9% lower, relative time 0.63, p = 0.32, treatment 80, control 227. | |
| Cheung, 4/30/2021, retrospective, China, peer-reviewed, 3 authors. | risk of severe case, 34.0% higher, OR 1.34, p = 0.72, treatment 23, control 929, adjusted per study, multivariable, RR approximated with OR. |
| Freedberg, 5/21/2020, retrospective, propensity score matching, USA, peer-reviewed, 15 authors. | risk of death/intubation, 57.0% lower, HR 0.43, p = 0.02, treatment 8 of 84 (9.5%), control 332 of 1,536 (21.6%), NNT 8.3, adjusted per study, propensity score matching, multivariable, Cox proportional hazards. |
| Fung, 10/1/2021, retrospective, population-based cohort, USA, peer-reviewed, 6 authors, excluded in exclusion analyses: not fully adjusting for the different baseline risk of systemic autoimmune patients. | risk of death, no change, HR 1.00, p = 1.00, vs. never used. |
| risk of hospitalization, 6.0% lower, HR 0.94, p < 0.001, vs. never used. | |
| risk of case, 12.0% higher, HR 1.12, p < 0.001, vs. never used. | |
| Kim, 3/21/2023, retrospective, South Korea, peer-reviewed, 8 authors, study period 1 January, 2020 - 4 June, 2020. | risk of case, 36.3% lower, RR 0.64, p < 0.001, treatment 105 of 5,594 (1.9%), control 480 of 15,432 (3.1%), NNT 81, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable, model 3. |
| Kwon, 5/31/2023, retrospective, South Korea, peer-reviewed, 8 authors, study period 1 July, 2020 - 31 December, 2020. | risk of progression, 107.0% higher, OR 2.07, p = 0.06, treatment 204, control 204, adjusted per study, ICU, mechanical ventilation, or death, famotidine vs. other H2-blocker use, multivariable, RR approximated with OR. |
| risk of progression, 256.0% higher, OR 3.56, p = 0.04, treatment 204, control 204, adjusted per study, high oxygen, ICU, mechanical ventilation, or death, famotidine vs. other H2-blocker use, multivariable, RR approximated with OR. | |
| risk of oxygen therapy, 109.0% higher, OR 2.09, p = 0.07, treatment 204, control 204, adjusted per study, famotidine vs. other H2-blocker use, multivariable, RR approximated with OR. | |
| Loucera, 8/16/2022, retrospective, Spain, peer-reviewed, 8 authors, study period January 2020 - November 2020. | risk of death, 17.5% lower, HR 0.82, p = 0.25, treatment 207, control 15,761, Cox proportional hazards, day 30. |
| MacFadden, 3/29/2022, retrospective, Canada, peer-reviewed, 9 authors, study period 15 January, 2020 - 31 December, 2020. | risk of case, 7.0% lower, OR 0.93, p = 0.16, RR approximated with OR. |
| Mather, 8/26/2020, retrospective, USA, peer-reviewed, 3 authors. | risk of death, 61.4% lower, HR 0.39, p = 0.004, treatment 83, control 689, propensity score matching, Cox proportional hazards. |
| risk of death/intubation, 50.5% lower, HR 0.49, p = 0.003, treatment 83, control 689, propensity score matching, Cox proportional hazards. | |
| Razjouyan, 10/25/2021, retrospective, USA, peer-reviewed, 7 authors. | risk of death, 27.0% lower, OR 0.73, p = 0.006, treatment 93, control 9,981, adjusted per study, RR approximated with OR. |
| Wallace, 12/31/2021, retrospective, database analysis, USA, peer-reviewed, 6 authors. | risk of death, 11.0% higher, RR 1.11, p = 0.33, treatment 98 of 423 (23.2%), control 1,436 of 7,521 (19.1%), adjusted per study, odds ratio converted to relative risk, multivariable. |
| Yeramaneni, 2/28/2021, retrospective, USA, peer-reviewed, 6 authors, study period 11 February, 2020 - 8 May, 2020. | risk of death, 51.0% lower, OR 0.49, p = 0.22, treatment 351, control 6,807, adjusted per study, with home use, multivariable, day 30, RR approximated with OR. |
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.
Together Trial, Repurposed Approved and Under Development Therapies for Patients With Early-Onset COVID-19 and Mild Symptoms, NCT04727424, clinicaltrials.gov/study/NCT04727424.
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.
Loffredo et al., The in-vitro effect of famotidine on SARS-CoV-2 proteases and virus replication, Scientific Reports, doi:10.1038/s41598-021-84782-w.
Zeraatkar et al., Consistency of covid-19 trial preprints with published reports and impact for decision making: retrospective review, BMJ Medicine, doi:10.1136/bmjmed-2022-0003091.
Davidson et al., No evidence of important difference in summary treatment effects between COVID-19 preprints and peer-reviewed publications: a meta-epidemiological study, Journal of Clinical Epidemiology, doi:10.1016/j.jclinepi.2023.08.011.
Jadad et al., Randomized Controlled Trials: Questions, Answers, and Musings, Second Edition, doi:10.1002/9780470691922.
Gøtzsche, P., Bias in double-blind trials, Doctoral Thesis, University of Copenhagen, www.scientificfreedom.dk/2023/05/16/bias-in-double-blind-trials-doctoral-thesis/.
Als-Nielsen et al., Association of Funding and Conclusions in Randomized Drug Trials, JAMA, doi:10.1001/jama.290.7.921.
Anglemyer et al., Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials, Cochrane Database of Systematic Reviews 2014, Issue 4, doi:10.1002/14651858.MR000034.pub2.
Lee et al., Analysis of Overall Level of Evidence Behind Infectious Diseases Society of America Practice Guidelines, Arch Intern Med., 2011, 171:1, 18-22, doi:10.1001/archinternmed.2010.482.
Deaton et al., Understanding and misunderstanding randomized controlled trials, Social Science & Medicine, 210, doi:10.1016/j.socscimed.2017.12.005.
Nichol et al., Challenging issues in randomised controlled trials, Injury, 2010, doi: 10.1016/j.injury.2010.03.033, www.injuryjournal.com/article/S0020-1383(10)00233-0/fulltext.
Elhadi et al., Epidemiology, outcomes, and utilization of intensive care unit resources for critically ill COVID-19 patients in Libya: A prospective multi-center cohort study, PLOS ONE, doi:10.1371/journal.pone.0251085.
Fung et al., Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients, PLoS ONE, doi:10.1371/journal.pone.0266922.
Shamsi et al., Survival and Mortality in Hospitalized Children with COVID-19: A Referral Center Experience in Yazd, Iran, Canadian Journal of Infectious Diseases and Medical Microbiology, doi:10.1155/2023/5205188.
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.
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.
Schultz et al., Pyrimidine inhibitors synergize with nucleoside analogues to block SARS-CoV-2, Nature, doi:10.1038/s41586-022-04482-x.
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.
Enyeji et al., Effective Treatment of COVID-19 Infection with Repurposed Drugs: Case Reports, Viral Immunology, doi:10.1089/vim.2024.0034.
Malone (B) et al., COVID-19: Famotidine, Histamine, Mast Cells, and Mechanisms, Frontiers in Pharmacology, doi:10.3389/fphar.2021.633680.
Brennan et al., Oral famotidine versus placebo in non-hospitalised patients with COVID-19: a randomised, double-blind, data-intense, phase 2 clinical trial, Gut, doi:10.1136/gutjnl-2022-326952.
Mehrizi et al., Drug prescription patterns and their association with mortality and hospitalization duration in COVID-19 patients: insights from big data, Frontiers in Public Health, doi:10.3389/fpubh.2023.1280434.
Files et al., Report of the first seven agents in the I-SPY COVID trial: a phase 2, open label, adaptive platform randomised controlled trial, eClinicalMedicine, doi:10.1016/j.eclinm.2023.101889.
Özden et al., Effects of Famotidine on COVID-19 Patients in Intensive Care Unit: A Retrospective Clinical Trial, Boğazi̇çi̇ Tip Dergi̇si̇, doi:10.14744/bmj.2023.77044.
Chowdhury et al., Role of H2 receptor blocker famotidine over the clinical recovery of COVID-19 patients: A randomized controlled trial, World Journal of Clinical Cases, doi:10.12998/wjcc.v10.i23.8170.
Zangeneh et al., Survival analysis based on body mass index in patients with Covid-19 admitted to the intensive care unit of Amir Al-Momenin Hospital in Arak – 2021, Obesity Medicine, doi:10.1016/j.obmed.2022.100420.
Siraj et al., Efficacy of Various Treatment Modalities on Patient-related Outcome in Hospitalized COVID-19 Patients – A Retrospective Study, Indian Journal of Clinical Practice, 32:9, ijcp.in/Admin/CMS/PDF/6.%20OriginalResearch_IJCP_Feb2022.pdf.
Pahwani et al., Efficacy of Oral Famotidine in Patients Hospitalized With Severe Acute Respiratory Syndrome Coronavirus 2, Cureus, doi:10.7759/cureus.22404.
Wagner et al., A retrospective analysis of clinical outcomes between hospitalized patients with COVID‐19 who received famotidine or pantoprazole, JGH Open, doi:10.1002/jgh3.12905.
Stolow et al., A Retrospective Review: Famotidine Use Is Not Associated With Improved Outcomes in Hospitalized Patients With COVID-19, American Journal of Gastroenterology, doi:10.14309/01.ajg.0000778736.01714.cd.
Kuno et al., The association between famotidine and in-hospital mortality of patients with COVID-19, Journal of Medical Virology, doi:10.1002/jmv.27375.
Samimagham et al., The Efficacy of Famotidine in improvement of outcomes in Hospitalized COVID-19 Patients: A phase III randomised clinical trial, Research Square, doi:10.21203/rs.3.rs-462937/v1.
Mura et al., Real-world evidence for improved outcomes with histamine antagonists and aspirin in 22,560 COVID-19 patients, Signal Transduction and Targeted Therapy, doi:10.1038/s41392-021-00689-y.
Yeramaneni et al., Famotidine Use Is Not Associated With 30-day Mortality: A Coarsened Exact Match Study in 7158 Hospitalized Patients With Coronavirus Disease 2019 From a Large Healthcare System, Gastroenterology, doi:10.1053/j.gastro.2020.10.011.
Zhou et al., Proton pump inhibitor or famotidine use and severe COVID-19 disease: a propensity score-matched territory-wide study, Gut, doi:10.1136/gutjnl-2020-323668.
Shoaibi et al., Comparative Effectiveness of Famotidine in Hospitalized COVID-19 Patients, American Journal of Gastroenterology, doi:10.14309/ajg.0000000000001153.
Kwon et al., Effectiveness of famotidine on the risk of poor prognosis in patients with COVID-19: A nationwide cohort study in Korea, Heliyon, doi:10.1016/j.heliyon.2023.e16171.
Kim et al., Histamine-2 Receptor Antagonists and Proton Pump Inhibitors Are Associated With Reduced Risk of SARS-CoV-2 Infection Without Comorbidities Including Diabetes, Hypertension, and Dyslipidemia: A Propensity Score-Matched Nationwide Cohort Study, Journal of Korean Medical Science, doi:10.3346/jkms.2023.38.e99.
Loucera et al., Real-world evidence with a retrospective cohort of 15,968 COVID-19 hospitalized patients suggests 21 new effective treatments, Virology Journal, doi:10.1186/s12985-023-02195-9.
MacFadden et al., Screening Large Population Health Databases for Potential COVID-19 Therapeutics: A Pharmacopeia-Wide Association Study (PWAS) of Commonly Prescribed Medications, Open Forum Infectious Diseases, doi:10.1093/ofid/ofac156.
Wallace et al., Association of the patterns of use of medications with mortality of COVID-19 infection: a hospital-based observational study, BMJ Open, doi:10.1136/bmjopen-2021-050051.
Razjouyan et al., Smoking Status and Factors associated with COVID-19 In-Hospital Mortality among US Veterans, Nicotine & Tobacco Research, doi:10.1093/ntr/ntab223.
Cheung et al., Association Between Famotidine Use and COVID-19 Severity in Hong Kong: A Territory-wide Study, Gastroenterology, doi:10.1053/j.gastro.2020.05.098.
Balouch et al., Role of Famotidine and Other Acid Reflux Medications for SARS-CoV-2: A Pilot Study, Journal of Voice, doi:10.1016/j.jvoice.2021.01.007.
Mather et al., Impact of Famotidine Use on Clinical Outcomes of Hospitalized Patients With COVID-19, American Journal of Gastroenterology, doi:10.14309/ajg.0000000000000832.
Freedberg et al., Famotidine Use Is Associated With Improved Clinical Outcomes in Hospitalized COVID-19 Patients: A Propensity Score Matched Retrospective Cohort Study, Gastroenterology, doi:10.1053/j.gastro.2020.05.053.
Malone (C), R., More Than Just Heartburn: Does Famotidine Effectively Treat Patients with COVID-19?, Digestive Diseases and Sciences, doi:10.1007/s10620-021-06875-w.
Mura (B) et al., Real-world Evidence for Improved Outcomes with Histamine Antagonists and Aspirin in 22,560 COVID-19 Patients, Research Square, doi:10.21203/rs.3.rs-369927/v1.
Tomera et al., Famotidine with Celecoxib Adjuvant Therapy on Hospitalized COVID-19 Patients: A Case Series, SSRN Electronic Journal, doi:10.2139/ssrn.3646583.
Malone (D) et al., Famotidine and Celecoxib COVID-19 Treatment Without and With Dexamethasone; Retrospective Comparison of Sequential Continuous Cohorts, Research Square, doi:10.21203/rs.3.rs-526394/v1.
Chenchula et al., Famotidine Repurposing for Novel Corona Virus Disease of 2019: A
Systematic Review, Drug Research, doi:10.1055/a-1397-6763.
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.
Tachoua et al., Highlights in TMPRSS2 inhibition mechanism with guanidine derivatives approved drugs for COVID-19 treatment, Journal of Biomolecular Structure and Dynamics, doi:10.1080/07391102.2023.2169762.
Masoudi-Sobhanzadeh et al., Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries, Briefings in Bioinformatics, doi:10.1093/bib/bbab113.
Calleja et al., Inhibitors of SARS-CoV-2 PLpro, Frontiers in Chemistry, doi:10.3389/fchem.2022.876212.
Wu (B) et al., Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods, Acta Pharmaceutica Sinica B, doi:10.1016/j.apsb.2020.02.008.
Ponnampalli et al., COVID-19: Vaccines and therapeutics, Bioorganic & Medicinal Chemistry Letters, doi:10.1016/j.bmcl.2022.128987.
Pandit et al., e-Pharmacophore modeling and in silico study of CD147 receptor against SARS-CoV-2 drugs, Genomics & Informatics, doi:10.5808/gi.23005.
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.
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.
Islam et al., Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding, Scientific Reports, doi:10.1038/s41598-023-30095-z.
Esam et al., In silico investigation of the therapeutic and prophylactic potential of medicinal substances bearing guanidine moieties against COVID-19, Chemical Papers, doi:10.1007/s11696-022-02528-y.
Sperry et al., Target-agnostic drug prediction integrated with medical record analysis uncovers differential associations of statins with increased survival in COVID-19 patients, PLOS Computational Biology, doi:10.1371/journal.pcbi.1011050.
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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