Adintrevimab for COVID-19: real-time meta analysis of 2 studies
Abstract
Significantly lower risk is seen for hospitalization and cases. 2 studies (both from the same team) show significant
benefit.
Meta analysis using the most serious outcome reported shows
43% [-169‑88%] lower risk, without reaching statistical significance. Currently all studies are RCTs.
Currently there is limited data, with only 4 control events for the most serious outcome in trials to date. All studies to date are from the same group.
mAb use may create new variants that spread globally1,2, and may be associated with prolonged viral loads, clinical deterioration, and immune escape2-5.
Adintrevimab for COVID-19 — Highlights
Adintrevimab reduces
risk with low confidence for hospitalization and cases.
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 adintrevimab 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 injury6-18 and
cognitive deficits9,14, cardiovascular
complications19-22, 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,23-29, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 8,000 compounds may
reduce COVID-19 risk30, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
Adintrevimab 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
adintrevimab
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, and Randomized Controlled Trials (RCTs).
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
Extensive mutations in SARS-CoV-2 have resulted in variants that evade neutralizing
antibodies from monoclonal antibody treatments31,32, resulting in efficacy
that is highly variant dependent.
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 and more recent monoclonal antibodies.
| 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, and for specific outcomes.
Figure 3, 4, 5, and 6
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, hospitalization, and cases.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 43% [-169‑88%] | 2 | 2,483 | 30 |
| Randomized Controlled TrialsRCTs | 43% [-169‑88%] | 2 | 2,483 | 30 |
| Mortality | 43% [-169‑88%] | 2 | 2,483 | 30 |
| HospitalizationHosp. | 83% [2‑97%] * | 2 | 1,831 | 30 |
| Cases | 71% [49‑84%] **** | 2 | 1,831 | 30 |
| RCT mortality | 43% [-169‑88%] | 2 | 2,483 | 30 |
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Figure 3. 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 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for hospitalization.
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Figure 6. Random effects meta-analysis for cases.
Currently all studies are RCTs.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours34,35. 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 cases36 |
| <24 hours | -33 hours symptoms37 |
| 24-48 hours | -13 hours symptoms37 |
| Inpatients | -2.5 hours to improvement38 |
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.
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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
variants40, for example the Gamma variant shows significantly
different characteristics41-44. 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 variants45,46.
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.
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Figure 8. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 9. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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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 4.8 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 6.5 months.
Figure 11 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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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 adintrevimab, 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 alone49-60.
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.
Focosi (B) et al. present a review covering adintrevimab for COVID-19.
SARS-CoV-2 infection and replication involves a complex
interplay of 50+ host and viral proteins and other
factors23-29, providing many therapeutic
targets.
Over 8,000 compounds have been predicted to reduce COVID-19
risk30, 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 adintrevimab
in the context of multiple COVID-19 treatments, and Figure 13 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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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 efficacy66.
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Figure 13. Efficacy vs. cost for COVID-19 treatments.
Significantly lower risk is seen for hospitalization and cases. 2 studies (both from the same team) show significant
benefit.
Meta analysis using the most serious outcome reported shows
43% [-169‑88%] lower risk, without reaching statistical significance. Currently all studies are RCTs.
Currently there is limited data, with only 4 control events for the most serious outcome in trials to date. All studies to date are from the same group.
PEP RCT 351 patients, showing lower COVID-19 cases with adintrevimab. The PrEP trial is listed separately63.
PrEP RCT 1480 patients, showing lower COVID-19 cases with adintrevimab. The PEP trial is listed separately62.
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 adintrevimab 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 adintrevimab 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 to67.
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 170.
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 (1.15.1), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.4), and plotly (5.24.1).
Forest plots are computed using PythonMeta71
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 effective34,35.
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/admeta.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.
| Ison, 6/13/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 15 authors, study period 27 April, 2021 - 11 January, 2022, trial NCT04859517 (history) (EVADE PEP). | risk of death, 66.6% lower, RR 0.33, p = 1.00, treatment 0 of 240 (0.0%), control 1 of 241 (0.4%), NNT 241, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), COVID-19, 6 months, supplementary table 6. |
| risk of death, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 240 (0.0%), control 2 of 241 (0.8%), NNT 120, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), all-cause, 6 months, supplementary table 6. | |
| risk of hospitalization, 80.0% lower, RR 0.20, p = 0.50, treatment 0 of 175 (0.0%), control 2 of 176 (1.1%), NNT 88, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28. | |
| risk of symptomatic case, 74.9% lower, RR 0.25, p = 0.03, treatment 3 of 175 (1.7%), control 12 of 176 (6.8%), NNT 20, day 28. | |
| Ison (B), 6/13/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, 15 authors, study period 27 April, 2021 - 11 January, 2022, trial NCT04859517 (history) (EVADE PrEP). | risk of death, 33.3% lower, RR 0.67, p = 1.00, treatment 2 of 1,001 (0.2%), control 3 of 1,001 (0.3%), NNT 1001, COVID-19, 6 months, supplementary table 6. |
| risk of death, 50.0% lower, RR 0.50, p = 0.51, treatment 3 of 1,001 (0.3%), control 6 of 1,001 (0.6%), NNT 334, all-cause, 6 months, supplementary table 6. | |
| risk of death, 83.9% lower, RR 0.16, p = 0.07, treatment 1 of 752 (0.1%), control 6 of 728 (0.8%), NNT 145, 3 months. | |
| risk of hospitalization, 83.9% lower, RR 0.16, p = 0.07, treatment 1 of 752 (0.1%), control 6 of 728 (0.8%), NNT 145, 3 months. | |
| risk of symptomatic case, 70.2% lower, RR 0.30, p < 0.001, treatment 12 of 752 (1.6%), control 39 of 728 (5.4%), NNT 27, 3 months. |
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