Bebtelovimab for COVID-19: real-time meta analysis of 6 studies
ControlAbstract
Meta analysis using the most serious outcome reported shows
34% [-24‑65%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies and worse for Randomized Controlled Trials.
2 studies from 2 independent teams (both from the same country) show significant
benefit.
Currently there is limited data, with only 34 control events for the most serious outcome in trials to date.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BQ.1.11, BA.5, BA.2.75, XBB, XBB.1.5, XMM.1.9.12,3. mAb use may create new variants that spread globally4-6, and may be associated with prolonged viral loads, clinical deterioration, and immune escape5,7-9.
Prescription treatments have been preferentially used by patients at lower risk10. Retrospective studies may overestimate efficacy, for example patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
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.
Bebtelovimab for COVID-19 — Highlights
Bebtelovimab reduces risk with low confidence for mortality, ICU admission, and hospitalization, and very low confidence for pooled analysis, however increased risk is seen with low confidence for progression.
Efficacy is variant dependent.
Bebtelovimab may have reduced or no activity for recent variants.
Real-time updates and corrections with a consistent protocol for 172 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.
B
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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 bebtelovimab studies.
mAb treatments
AdintrevimabAmubarv../r..
Bamlaniv../e..
Bebtelovimab
Casirivimab/i..
Pemivibart
Ravulizumab
Regdanvimab
Siltuximab
Sotrovimab
Tixagev../c..
Vilobelimab
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 injury12-24 and
cognitive deficits15,20, cardiovascular
complications25-29, organ failure, and death.
Even mild untreated infections may result in persistent cognitive
deficits30—the spike protein binds to fibrin leading to
fibrinolysis-resistant blood clots, thromboinflammation, and
neuropathology.
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,31-38, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk39, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Bebtelovimab is a monoclonal antibody (mAb). mAbs are laboratory-engineered proteins designed to mimic the immune system’s ability to fight pathogens. In the context of COVID-19, mAbs typically target specific regions of the SARS-CoV-2 spike protein, inhibiting viral entry into human cells and neutralizing the virus. These antibodies are derived from the B cells of recovered patients or immunized animals and are produced in large quantities using recombinant DNA technology and cell culture methods.
We analyze all significant
controlled studies of
bebtelovimab
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, individual outcomes, peer-reviewed studies, and Randomized Controlled Trials (RCTs).
Figure 3 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Currently all bebtelovimab studies use early treatment.
Figure 3. Treatment stages.
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing
antibodies from monoclonal antibody treatments40,41, resulting in efficacy
that is highly variant dependent.
For example, in vitro research suggests
that bebtelovimab is not effective for omicron BQ.1.11.
Table 1 shows efficacy by variant for several monoclonal
antibodies. This table covers earlier SARS-CoV-2 variants and has not been
updated for more recent variants.
| Bamlanivimab/ etesevimab |
Casirivimab/ imdevimab |
Sotrovimab | Bebtelovimab | Tixagevimab/ cilgavimab |
|
|---|---|---|---|---|---|
| Alpha B.1.1.7 | |||||
| Beta/ Gamma BA1.351/ P.1 | |||||
| Delta B.1.617.2 | |||||
| Omicron BA.1/ BA.1.1 | |||||
| Omicron BA.2 | |||||
| Omicron BA.5 | |||||
| Omicron BA.4.6 | |||||
| Omicron BQ.1.1 |
Table 2 summarizes the results for all studies, for Randomized Controlled Trials, for peer-reviewed studies, and for specific outcomes.
Figure 4, 5, 6, 7, 8, 9, and 10
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ICU admission, hospitalization, progression, viral clearance, and peer reviewed studies.
| Relative Risk | Studies | Patients | |
|---|---|---|---|
| All studies | 0.66 [0.35‑1.24] | 6 | 10K |
| Peer-reviewedPeer-reviewed | 0.54 [0.27‑1.10] | 4 | 10K |
| RCTsRCTs | 1.46 [0.34‑6.22] | 2 | 760 |
| Mortality | 0.40 [0.14‑1.11] | 4 | 10K |
| HospitalizationHosp. | 0.67 [0.44‑1.03] | 5 | 10K |
| Viral | 0.85 [0.59‑1.22] | 2 | 633 |
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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.
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Figure 10. 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 11 shows a comparison of results for RCTs and observational studies.
Figure 12 and 13
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials and RCT mortality results.
RCT results are included in Table 2.
Figure 11. Results for RCTs and observational studies.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases45, and
analysis of double-blind RCTs has identified extreme levels of bias46.
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 172 treatments we have analyzed,
67% 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.
Figure 14.
For COVID-19, observational study results do not systematically
differ from RCTs, RR 0.98 [0.92‑1.05]
across 172 treatments48.
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 172 treatments
we cover, showing no significant difference in the results of RCTs compared to
observational studies, RR 0.98 [0.92‑1.05]51. Similar results are found for all low-cost treatments, RR
1.00 [0.91‑1.09]. High-cost treatments
show a non-significant trend towards RCTs showing greater efficacy,
RR 0.92 [0.84‑1.02].
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 see53,54.
Currently, 55 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, 58% have been confirmed in RCTs, with a mean delay of 7.7 months (64% with 8.9 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.
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Figure 12. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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 hours55,56. 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 cases57 |
| <24 hours | -33 hours symptoms58 |
| 24-48 hours | -13 hours symptoms58 |
| Inpatients | -2.5 hours to improvement59 |
Figure 15 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 172 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 15. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 172 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
variants61, for example the Gamma variant shows significantly
different characteristics62-65. 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 variants66,67.
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 172
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 16 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 17 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 18 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 = 0.000000082 to p = 0.0000000033.
Figure 16. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 17. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 16. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 55 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 4.9 months. When restricting to RCTs only, 57% 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 19 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 19. 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.
Wilcock et al. show that COVID-19 prescription treatments have been preferentially used by patients at lower risk. Retrospective studies may overestimate efficacy, and data for accurate adjustment may not be available. For example, patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
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 bebtelovimab, there is currently not
enough data to evaluate publication bias with high confidence.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 20 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.0588-95.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 20. Example funnel plot analysis for simulated perfect trials.
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 alone70-86.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex
interplay of 100+ host and viral proteins and other
factors31-38, providing many therapeutic
targets.
Over 9,000 compounds have been predicted to reduce COVID-19
risk39, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 21 shows an overview of the results for bebtelovimab
in the context of multiple COVID-19 treatments, and Figure 22 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 21.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 9,000+ proposed treatments show efficacy107.
Figure 22. Efficacy vs. cost for COVID-19 treatments.
Meta analysis using the most serious outcome reported shows
34% [-24‑65%] lower risk, without reaching statistical significance. Results are similar for peer-reviewed studies and worse for Randomized Controlled Trials.
2 studies from 2 independent teams (both from the same country) show significant
benefit.
Currently there is limited data, with only 34 control events for the most serious outcome in trials to date.
Efficacy is variant dependent. In Vitro research suggests a lack of efficacy for omicron BQ.1.11, BA.5, BA.2.75, XBB, XBB.1.5, XMM.1.9.12,3. mAb use may create new variants that spread globally4-6, and may be associated with prolonged viral loads, clinical deterioration, and immune escape5,7-9.
Prescription treatments have been preferentially used by patients at lower risk10. Retrospective studies may overestimate efficacy, for example patients with greater knowledge of effective treatments may be more likely to access prescription treatments but result in confounding because they are also more likely to use known beneficial non-prescription treatments.
RCT showing improved viral clearance with bebtelovimab. Results refer to the placebo controlled portion of the trial.
Retrospective 377 outpatients in the USA and matched controls, showing lower hospitalization/mortality with bebtelovimab treatment, without statistical significance. Notably, none of the patients that died in the control group were hospitalized within 14 days (later hospitalization is not reported). There may be a difference in the populations in terms of propensity to receive SOC.
Retrospective 2,571 patients treated with mAbs in the USA, and 5,135 control patients, showing lower combined mortality/hospitalization for bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, sotrovimab, and bebtelovimab, with statistical significance only for casirivimab/imdevimab.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene108,109, vitamin D110, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene108,109, vitamin D110, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
RCT with 127 bamlanivimab, etesevimab, and bebtelovimab patients, 125 bebtelovimab patients, and 128 control patients, showing no significant differences in hospitalization and mortality. Viral clearance was improved although not statistically significant. NCT04634409.
Retrospective 3,739 patients treated with bebteloviman in the USA and matched controls, showing lower mortality and hospitalization with treatment, but higher emergency department visits.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene108,109, vitamin D110, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene108,109, vitamin D110, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
PSM retrospective 19,778 high-risk outpatients in the USA, showing no significant difference in outcomes with bebtelovimab treatment.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene108,109, vitamin D110, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of treatments.
Confounding by treatment propensity. This study analyzes a population where only a fraction of eligible patients received the treatment. Patients receiving treatment may be more likely to follow other recommendations, more likely to receive additional care, and more likely to use additional treatments that are not tracked in the data (e.g., nasal/oral hygiene108,109, vitamin D110, etc.) — either because the physician recommending bebtelovimab also recommended them, or because the patient seeking out bebtelovimab is more likely to be familiar with the efficacy of additional treatments and more likely to take the time to use them. Therefore, these kind of studies may overestimate the efficacy of 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 bebtelovimab 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 bebtelovimab for COVID-19 that report
a comparison with a control group are included in the main analysis.
Studies with major unexplained data issues, for example major outcome data that
is impossible to be correct with no response from the authors, are excluded.
This is a living analysis and is updated regularly.
Figure 23.
Mid-recovery results can more accurately reflect efficacy when almost all patients
recover. Mateja et al. confirm that intermediate viral load results more accurately
reflect hospitalization/death.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
reported then they are both used in specific outcome analyses, while mortality
is used for pooled analysis.
If symptomatic
results are reported at multiple times, we use the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral outcomes.
When basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
An IPD meta-analysis confirms that intermediate viral load reduction
is more closely associated with hospitalization/death than later
viral load reduction111.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to Zhang et al.
Reported confidence intervals and p-values are used when available,
and adjusted values are used when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted results have preference over unadjusted
results for a more serious outcome when the adjustments significantly alter
results. When needed, conversion between reported p-values and
confidence intervals followed Altman, Altman (B), and Fisher's exact
test was used to calculate p-values for event data. If continuity
correction for zero values is required, we use the reciprocal of the opposite
arm with the sum of the correction factors equal to 1115.
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
(3.13.5) with
scipy (1.16.0), pythonmeta (1.26), numpy (2.3.1), statsmodels (0.14.4), and plotly (6.2.0).
Forest plots are computed using PythonMeta116
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor
(4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.2 is used to parse PDF documents.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective55,56.
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/btmeta.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.
| Dougan, 3/12/2022, Randomized Controlled Trial, USA, preprint, 22 authors, study period 19 April, 2021 - 19 July, 2021, average treatment delay 3.0 days, trial NCT04634409 (history) (BLAZE-4). | risk of hospitalization, 27.0% higher, RR 1.27, p = 1.00, treatment 5 of 252 (2.0%), control 2 of 128 (1.6%), combined bebtelovimab arms. |
| risk of hospitalization, 2.4% higher, RR 1.02, p = 1.00, treatment 2 of 125 (1.6%), control 2 of 128 (1.6%), bebtelovimab only. | |
| relative viral load reduction, 4.0% better, RR 0.96, p < 0.001, treatment mean 3.77 (±0.21) n=125, control mean 3.62 (±0.2) n=128, day 7. | |
| relative viral load reduction, 29.7% better, RR 0.70, p < 0.001, treatment mean 3.03 (±0.19) n=125, control mean 2.13 (±0.19) n=128, day 5. | |
| relative viral load reduction, 15.4% better, RR 0.85, p < 0.001, treatment mean 1.43 (±0.2) n=125, control mean 1.21 (±0.2) n=128, day 3. | |
| risk of no viral clearance, 38.6% lower, RR 0.61, p = 0.12, treatment 15 of 125 (12.0%), control 25 of 128 (19.5%), NNT 13, persistently high viral load, day 7, primary outcome. | |
| Dryden-Peterson, 10/27/2022, retrospective, USA, peer-reviewed, 7 authors, study period 16 March, 2022 - 31 May, 2022, average treatment delay 3.0 days. | risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 377 (0.0%), control 3 of 377 (0.8%), NNT 126, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm). |
| risk of death/hospitalization, 43.0% lower, RR 0.57, p = 0.14, treatment 10 of 377 (2.7%), control 17 of 377 (4.5%), NNT 54. | |
| risk of hospitalization, 28.6% lower, RR 0.71, p = 0.53, treatment 10 of 377 (2.7%), control 14 of 377 (3.7%), NNT 94. | |
| Kip, 4/4/2023, retrospective, USA, peer-reviewed, 16 authors, study period 8 December, 2020 - 31 August, 2022. | risk of death/hospitalization, 20.0% lower, RR 0.80, p = 0.65, treatment 6 of 157 (3.8%), control 15 of 314 (4.8%), NNT 105, omicron variant, day 28. |
| Lilly, 2/12/2022, Randomized Controlled Trial, USA, preprint, 1 author, study period 29 October, 2020 - 18 October, 2021, average treatment delay 3.6 days, trial NCT04634409 (history). | risk of death, 150.8% higher, RR 2.51, p = 1.00, treatment 1 of 252 (0.4%), control 0 of 128 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), combined bebtelovimab arms. |
| risk of death, 200.8% higher, RR 3.01, p = 0.50, treatment 1 of 127 (0.8%), control 0 of 128 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), bamlanivimab, etesevimab, and bebtelovimab. | |
| risk of hospitalization, 27.0% higher, RR 1.27, p = 1.00, treatment 5 of 252 (2.0%), control 2 of 128 (1.6%), combined bebtelovimab arms. | |
| risk of hospitalization, 51.2% higher, RR 1.51, p = 0.68, treatment 3 of 127 (2.4%), control 2 of 128 (1.6%), bamlanivimab, etesevimab, and bebtelovimab. | |
| risk of hospitalization, 2.4% higher, RR 1.02, p = 1.00, treatment 2 of 125 (1.6%), control 2 of 128 (1.6%), bebtelovimab. | |
| risk of no viral clearance, 35.5% lower, RR 0.64, p = 0.07, treatment 33 of 252 (13.1%), control 26 of 128 (20.3%), NNT 14, day 7 persistently high viral load, combined bebtelovimab arms, primary outcome. | |
| risk of no viral clearance, 38.0% lower, RR 0.62, p = 0.13, treatment 16 of 127 (12.6%), control 26 of 128 (20.3%), NNT 13, day 7 persistently high viral load, bamlanivimab, etesevimab, and bebtelovimab, primary outcome. | |
| risk of no viral clearance, 33.0% lower, RR 0.67, p = 0.18, treatment 17 of 125 (13.6%), control 26 of 128 (20.3%), NNT 15, day 7 persistently high viral load, bebtelovimab, primary outcome. | |
| Molina, 4/16/2023, retrospective, USA, peer-reviewed, 11 authors, study period 6 April, 2022 - 11 October, 2022. | risk of death, 57.0% lower, RR 0.43, p = 0.14, treatment 3 of 3,739 (0.1%), control 11 of 5,423 (0.2%), NNT 816, adjusted per study, odds ratio converted to relative risk, propensity score matching, multivariable. |
| risk of ICU admission, 58.6% lower, RR 0.41, p = 0.05, treatment 6 of 3,739 (0.2%), control 21 of 5,423 (0.4%), NNT 441. | |
| risk of hospitalization, 55.5% lower, RR 0.44, p < 0.001, treatment 38 of 3,739 (1.0%), control 107 of 5,423 (2.0%), NNT 105, adjusted per study, odds ratio converted to relative risk, COVID-19, propensity score matching, multivariable. | |
| risk of hospitalization, 46.5% lower, RR 0.54, p < 0.001, treatment 48 of 3,739 (1.3%), control 116 of 5,423 (2.1%), NNT 117, adjusted per study, odds ratio converted to relative risk, all cause, propensity score matching, multivariable. | |
| risk of progression, 32.6% higher, RR 1.33, p = 0.001, treatment 260 of 3,739 (7.0%), control 275 of 5,423 (5.1%), adjusted per study, odds ratio converted to relative risk, ED visit, propensity score matching, multivariable. | |
| Sridhara, 4/28/2023, retrospective, USA, peer-reviewed, 13 authors, study period 5 April, 2022 - 1 August, 2022. | risk of death, 85.7% lower, RR 0.14, p = 0.25, treatment 0 of 1,091 (0.0%), control 3 of 1,091 (0.3%), NNT 364, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), propensity score matching, day 30. |
| risk of death/hospitalization, 25.0% lower, HR 0.75, p = 0.31, treatment 24 of 1,091 (2.2%), control 28 of 1,091 (2.6%), adjusted per study, propensity score matching, multivariable, Cox proportional hazards. | |
| risk of hospitalization, 11.1% lower, RR 0.89, p = 0.78, treatment 24 of 1,091 (2.2%), control 27 of 1,091 (2.5%), NNT 364, propensity score matching, day 30RETRO. |
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.
Planas et al., Resistance of Omicron subvariants BA.2.75.2, BA.4.6 and BQ.1.1 to neutralizing antibodies, bioRxiv, doi:10.1101/2022.11.17.516888.
Haars et al., Prevalence of SARS-CoV-2 Omicron Sublineages and Spike Protein Mutations Conferring Resistance against Monoclonal Antibodies in a Swedish Cohort during 2022–2023, Microorganisms, doi:10.3390/microorganisms11102417.
Uraki et al., Antiviral efficacy against and replicative fitness of an XBB.1.9.1 clinical isolate, iScience, doi:10.1016/j.isci.2023.108147.
Focosi et al., Analysis of SARS-CoV-2 mutations associated with resistance to therapeutic monoclonal antibodies that emerge after treatment, Drug Resistance Updates, doi:10.1016/j.drup.2023.100991.
Leducq et al., Spike protein genetic evolution in patients at high-risk of severe COVID-19 treated by monoclonal antibodies, The Journal of Infectious Diseases, doi:10.1093/infdis/jiad523.
Bruhn et al., Somatic hypermutation shapes the viral escape profile of SARS-CoV-2 neutralising antibodies, eBioMedicine, doi:10.1016/j.ebiom.2025.105770.
Choudhary et al., Emergence of SARS-CoV-2 Resistance with Monoclonal Antibody Therapy, medRxiv, doi:10.1101/2021.09.03.21263105.
Günther et al., Variant-specific humoral immune response to SARS-CoV-2 escape mutants arising in clinically severe, prolonged infection, medRxiv, doi:10.1101/2024.01.06.24300890.
Casadevall et al., Single monoclonal antibodies should not be used for COVID-19 therapy: a call for antiviral stewardship, Clinical Infectious Diseases, doi:10.1093/cid/ciae408.
Wilcock et al., Clinical Risk and Outpatient Therapy Utilization for COVID-19 in the Medicare Population, JAMA Health Forum, doi:10.1001/jamahealthforum.2023.5044.
Ryu et al., Fibrin drives thromboinflammation and neuropathology in COVID-19, Nature, doi:10.1038/s41586-024-07873-4.
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.
Trender et al., Changes in memory and cognition during the SARS-CoV-2 human challenge study, eClinicalMedicine, doi:10.1016/j.eclinm.2024.102842.
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.
Hattab et al., SARS-CoV-2 journey: from alpha variant to omicron and its sub-variants, Infection, doi:10.1007/s15010-024-02223-y.
Focosi (B), D., Monoclonal Antibody Therapies Against SARS-CoV-2: Promises and Realities, Current Topics in Microbiology and Immunology, doi:10.1007/82_2024_268.
Davis et al., The Promise and Peril of Anti-SARS-CoV-2 Monoclonal Antibodies, Clinical Infectious Diseases, doi:10.1093/cid/ciac902.
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.
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.
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.
Sridhara et al., Lack of effectiveness of Bebtelovimab monoclonal antibody among high-risk patients with SARS-Cov-2 Omicron during BA.2, BA.2.12.1 and BA.5 subvariants dominated era, PLOS ONE, doi:10.1371/journal.pone.0279326.
Molina et al., Real-World Evaluation of Bebtelovimab Effectiveness During the Period of COVID-19 Omicron Variants including BA.4/BA.5, International Journal of Infectious Diseases, doi:10.1016/j.ijid.2023.04.396.
Kip et al., Evolving Real-World Effectiveness of Monoclonal Antibodies for Treatment of COVID-19, Annals of Internal Medicine, doi:10.7326/M22-1286.
Dryden-Peterson et al., Bebtelovimab for high-risk outpatients with early COVID-19 in a large US health system, Open Forum Infectious Diseases, doi:10.1093/ofid/ofac565.
Dougan et al., Bebtelovimab, alone or together with bamlanivimab and etesevimab, as a broadly neutralizing monoclonal antibody treatment for mild to moderate, ambulatory COVID-19, medRxiv, doi:10.1101/2022.03.10.22272100.
Lilly, A Study of Immune System Proteins in Participants With Mild to Moderate COVID-19 Illness, clinicaltrials.gov/ct2/show/NCT04634409.
Parua et al., Therapeutic Potential of Neutralizing Monoclonal Antibodies (nMAbs) against SARS-CoV-2 Omicron Variant, Current Pharmaceutical Design, doi:10.2174/0113816128334441241108050528.
Huang et al., Advancements in the Development of Anti-SARS-CoV-2 Therapeutics, International Journal of Molecular Sciences, doi:10.3390/ijms251910820.
Dong et al., Development of SARS-CoV-2 entry antivirals, Cell Insight, doi:10.1016/j.cellin.2023.100144.
Liu et al., DRAVP: A Comprehensive Database of Antiviral Peptides and Proteins, Viruses, doi:10.3390/v15040820.
Mateja et al., The choice of viral load endpoint in early phase trials of COVID-19 treatments aiming to reduce 28-day hospitalization and/or death, The Journal of Infectious Diseases, doi:10.1093/infdis/jiaf282.
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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