HH-120 for COVID-19: real-time meta analysis of 2 studies
Abstract
Significantly lower risk is seen for cases and viral clearance. 2 studies (both from the same team) show significant
benefit.
Meta analysis using the most serious outcome reported shows
49% [-60‑84%] lower risk, without reaching statistical significance.
Currently there is limited data, with only 345 patients in trials to date. All studies to date are from the same group.
HH-120 for COVID-19 — Highlights
HH-120 reduces
risk with low confidence for cases and viral clearance.
Real-time updates
and corrections with a consistent protocol for 112 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 HH-120 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 injury1-12 and
cognitive deficits4,9, cardiovascular
complications13-15, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factorsA,16-22, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk23, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
HH-120
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, and individual outcomes.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Table 1 summarizes the results for all stages combined.
Table 2 shows results by treatment stage.
Figure 3 plots individual results by treatment stage.
Figure 4, 5, and 6
show forest plots for random effects meta-analysis of
all studies with pooled effects, cases, and viral clearance.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 49% [-60‑84%] | 2 | 345 | 31 |
| Late treatment | Prophylaxis | |
|---|---|---|
| All studies | 20% [9‑30%] ** | 76% [19‑93%] * |
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.
Loading..
Loading..
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.
Loading..
Figure 5. Random effects meta-analysis for cases.
Loading..
Figure 6. Random effects meta-analysis for viral clearance.
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 hours24,25. 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 cases26 |
| <24 hours | -33 hours symptoms27 |
| 24-48 hours | -13 hours symptoms27 |
| Inpatients | -2.5 hours to improvement28 |
Figure 7 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 112 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Loading..
Figure 7. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 112 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
variants30, for example the Gamma variant shows significantly
different characteristics31-34. 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 variants35,36.
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 112
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 8 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 9 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 10 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 = 0.00000032 to p = 0.000000011.
Loading..
Figure 8. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 9. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Loading..
Figure 8. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 48 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 89% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.0 months. When restricting to RCTs only, 56% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months.
Figure 11 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Loading..
Loading..
Figure 11. 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. Trials with patented drugs may have a financial conflict of interest that
results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to
date (CTRI/2021/05/033864 and CTRI/2021/08/0354242).
For HH-120, there is currently not
enough data to evaluate publication bias with high confidence.
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 alone39-50.
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 50+ host and viral proteins and other
factors16-22, providing many therapeutic
targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk23, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 12 shows an overview of the results for HH-120
in the context of multiple COVID-19 treatments, and Figure 13 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Loading..
Figure 12.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.5% of 8,000+ proposed treatments show efficacy52.
Loading..
Loading..
Figure 13. Efficacy vs. cost for COVID-19 treatments.
Significantly lower risk is seen for cases and viral clearance. 2 studies (both from the same team) show significant
benefit.
Meta analysis using the most serious outcome reported shows
49% [-60‑84%] lower risk, without reaching statistical significance.
Currently there is limited data, with only 345 patients in trials to date. All studies to date are from the same group.
RCT 269 participants showing significantly reduced risk of infection and symptomatic infection with IgM-like ACE2 fusion protein HH-120 nasal spray used as post-exposure prophylaxis. Participants self-administered HH-120 or placebo 5-10 times daily for up to 10 days. HH-120 reduced risk of infection by 64.6% in general contacts and 43.8% in close contacts, and reduced risk of symptomatic infection by 77.1% and 72.5%, respectively.
PSM analysis of 65 HH-120 patients and 103 controls contemporaneously hospitalized in the same hospital, showing faster viral clearance with HH-120 treatment, with improved results for patients with higher baseline viral load.
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 HH-120 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 HH-120 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 to53.
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 156.
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
(3.13.1) with
scipy (1.14.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta57
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 effective24,25.
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/hhmeta.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.
| Song, 5/25/2023, retrospective, China, peer-reviewed, 13 authors, study period 3 August, 2022 - 7 October, 2022. | time to viral-, 20.0% lower, relative time 0.80, p = 0.001, treatment 65, control 103, propensity score matching. |
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.
| Song (B), 12/6/2023, retrospective, placebo-controlled, China, peer-reviewed, mean age 36.0, 18 authors, study period June 2022 - December 2022, trial NCT05747677 (history). | risk of symptomatic case, 75.9% lower, HR 0.24, p = 0.02, treatment 120, control 57, both parts combined. |
| risk of symptomatic case, 76.3% lower, HR 0.24, p = 0.20, treatment 1 of 120 (0.8%), control 2 of 57 (3.5%), NNT 37, Cox proportional hazards, Part 1. | |
| risk of symptomatic case, 75.8% lower, HR 0.24, p = 0.03, treatment 3 of 40 (7.5%), control 6 of 22 (27.3%), NNT 5.1, Cox proportional hazards, Part 2. | |
| risk of case, 58.2% lower, HR 0.42, p = 0.03, treatment 124, control 62, both parts combined. | |
| risk of case, 65.8% lower, HR 0.34, p = 0.06, treatment 5 of 124 (4.0%), control 7 of 62 (11.3%), NNT 14, Cox proportional hazards, Part 1. | |
| risk of case, 50.4% lower, HR 0.50, p = 0.18, treatment 7 of 41 (17.1%), control 7 of 23 (30.4%), NNT 7.5, Cox proportional hazards, Part 2. |
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.
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.
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.
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.
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.
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.
Sweeting et al., What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data, Statistics in Medicine, doi:10.1002/sim.1761.
Please send us corrections, updates, or comments.
c19early involves the extraction of 100,000+ datapoints from
thousands of papers. Community updates
help ensure high accuracy.
Treatments and other interventions are complementary.
All practical, effective, and safe
means should be used based on risk/benefit analysis.
No treatment or intervention is 100% available and effective for all current
and future variants.
We do not provide medical advice. Before taking any medication,
consult a qualified physician who can provide personalized advice and details
of risks and benefits based on your medical history and situation. FLCCC and WCH
provide treatment protocols.