Lopinavir/ritonavir for COVID-19: real-time meta analysis of 17 studies
ControlAbstract
Meta analysis using the most serious outcome reported shows
10% [-5‑28%] higher risk, without reaching statistical significance.
All data and sources to reproduce this analysis are in the appendix.
Lopinavir/ritonavir for COVID-19 — Highlights
Meta analysis of studies to date shows no significant improvements with lopinavir/ritonavir.
Real-time updates and corrections with a consistent protocol for 175 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 lopinavir/ritonavir studies.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological injury2-15 and
cognitive deficits5,10, cardiovascular
complications16-20, organ failure, and death.
Even mild untreated infections may result in persistent cognitive
deficits21—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,22-29, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 9,000 compounds may
reduce COVID-19 risk30, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
lopinavir/ritonavir
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, studies within each treatment stage, individual outcomes, peer-reviewed studies, Randomized Controlled Trials (RCTs), and higher quality studies.
Figure 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.
Figure 3. Treatment stages.
Table 1 summarizes the results for all stages combined, for Randomized Controlled Trials, for peer-reviewed studies, after exclusions, and for specific outcomes.
Table 2 shows results by treatment stage.
Figure 4 plots individual results by treatment stage.
Figure 5, 6, 7, 8, 9, 10, 11, 12, and 13
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, hospitalization, progression, recovery, cases, viral clearance, and peer reviewed studies.
| Relative Risk | Studies | Patients | |
|---|---|---|---|
| All studies | 1.10 [0.95‑1.28] | 17 | 10K |
| After exclusions | 1.08 [0.93‑1.25] | 14 | 10K |
| Peer-reviewedPeer-reviewed | 1.09 [0.94‑1.28] | 16 | 10K |
| RCTsRCTs | 1.03 [0.94‑1.13] | 10 | 9,777 |
| Mortality | 1.01 [0.92‑1.11] | 8 | 10K |
| VentilationVent. | 1.10 [0.95‑1.27] | 2 | 7,381 |
| HospitalizationHosp. | 1.09 [0.86‑1.38] | 5 | 1,227 |
| Recovery | 1.18 [0.82‑1.71] | 5 | 6,491 |
| Viral | 1.02 [0.90‑1.16] | 8 | 893 |
| RCT mortality | 1.02 [0.93‑1.12] | 6 | 9,223 |
| RCT hospitalizationRCT hosp. | 1.08 [0.85‑1.37] | 4 | 1,102 |
| Early treatment | Late treatment | Prophylaxis | |
|---|---|---|---|
| All studies | 1.97 [1.46‑2.66]****1.97**** [1.46‑2.66] | 1.01 [0.91‑1.13]1.01 [0.91‑1.13] | 7.30 [0.97‑54.80]7.30 [0.97‑54.80] |
| After exclusions | 1.97 [1.46‑2.66]****1.97**** [1.46‑2.66] | 1.00 [0.89‑1.12]1.00 [0.89‑1.12] | |
| Peer-reviewedPeer-reviewed | 1.97 [1.46‑2.66]****1.97**** [1.46‑2.66] | 1.00 [0.90‑1.12]1.00 [0.90‑1.12] | 7.30 [0.97‑54.80]7.30 [0.97‑54.80] |
| RCTsRCTs | 2.35 [0.36‑15.59]2.35 [0.36‑15.59] | 1.02 [0.93‑1.13]1.02 [0.93‑1.13] | 7.30 [0.97‑54.80]7.30 [0.97‑54.80] |
| Mortality | 1.01 [0.92‑1.11]1.01 [0.92‑1.11] | ||
| VentilationVent. | 1.10 [0.95‑1.27]1.10 [0.95‑1.27] | ||
| HospitalizationHosp. | 1.07 [0.82‑1.38]1.07 [0.82‑1.38] | 1.24 [0.68‑2.26]1.24 [0.68‑2.26] | |
| Recovery | 1.22 [0.43‑3.48]1.22 [0.43‑3.48] | 1.02 [0.96‑1.09]1.02 [0.96‑1.09] | |
| Viral | 1.00 [0.82‑1.22]1.00 [0.82‑1.22] | 1.01 [0.86‑1.19]1.01 [0.86‑1.19] | |
| RCT mortality | 1.02 [0.93‑1.12]1.02 [0.93‑1.12] | ||
| RCT hospitalizationRCT hosp. | 1.07 [0.82‑1.38]1.07 [0.82‑1.38] | 1.17 [0.63‑2.19]1.17 [0.63‑2.19] | |
Figure 4. Scatter plot showing the most serious outcome in all studies, and for studies within each stage. Diamonds shows the results of random effects meta-analysis.
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Figure 5. 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 13. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
Figure 14 shows a comparison of results for RCTs and observational studies.
Figure 15, 16, and 17
show forest plots for random effects meta-analysis of
all Randomized Controlled Trials, RCT mortality results, and RCT hospitalization results.
RCT results are included in Table 1 and Table 2.
Figure 14. Results for RCTs and observational studies.
RCTs help to make study groups more similar and can provide a higher level of
evidence, however they are subject to many biases33, and
analysis of double-blind RCTs has identified extreme levels of bias34.
For COVID-19, the overhead may delay treatment, dramatically compromising
efficacy; they may encourage monotherapy for simplicity at the cost of
efficacy which may rely on combined or synergistic effects; the participants
that sign up may not reflect real world usage or the population that benefits
most in terms of age, comorbidities, severity of illness, or other factors;
standard of care may be compromised and unable to evolve quickly based on
emerging research for new diseases; errors may be made in randomization and
medication delivery; and investigators may have hidden agendas or vested
interests influencing design, operation, analysis, reporting, and the
potential for fraud. All of these biases have been observed with COVID-19
RCTs. There is no guarantee that a specific RCT provides a higher level of
evidence.
RCTs are expensive and many RCTs are funded
by pharmaceutical companies or interests closely aligned with pharmaceutical
companies. For COVID-19, this creates an incentive to show efficacy for
patented commercial products, and an incentive to show a lack of efficacy for
inexpensive treatments. The bias is expected to be significant, for example
Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that
trials funded by for-profit organizations were 5 times more likely to
recommend the experimental drug compared with those funded by nonprofit
organizations. For COVID-19, some major philanthropic organizations are
largely funded by investments with extreme conflicts of interest for and
against specific COVID-19 interventions.
High quality RCTs for novel acute diseases are more challenging, with
increased ethical issues due to the urgency of treatment, increased risk due
to enrollment delays, and more difficult design with a rapidly evolving
evidence base. For COVID-19, the most common site of initial infection is the
upper respiratory tract. Immediate treatment is likely to be most successful
and may prevent or slow progression to other parts of the body. For a
non-prophylaxis RCT, it makes sense to provide treatment in advance and
instruct patients to use it immediately on symptoms, just as some governments
have done by providing medication kits in advance. Unfortunately, no RCTs have
been done in this way. Every treatment RCT to date involves delayed treatment.
Among the 175 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.
Currently, 56 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, 59% have been confirmed in RCTs, with a mean delay of 7.5 months (65% with 8.6 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 15. Random effects meta-analysis for all Randomized Controlled Trials.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
To avoid bias in the selection of studies, we analyze all
non-retracted studies. Here we show the results after excluding
studies with major issues likely to alter results, non-standard studies, and
studies where very minimal detail is currently available. Our bias evaluation
is based on analysis of each study and identifying when there is a significant
chance that limitations will substantially change the outcome of the study. We
believe this can be more valuable than checklist-based approaches such as
Cochrane GRADE, which can be easily influenced by potential bias, may ignore
or underemphasize serious issues not captured in the checklists, and may
overemphasize issues unlikely to alter outcomes in specific cases (for example
certain specifics of randomization with a very large effect size and
well-matched baseline characteristics).
The studies excluded are as below.
Figure 18 shows a forest plot for random
effects meta-analysis of all studies after exclusions.
Ader, very late stage, >50% on oxygen/ventilation at baseline.
Değirmenci, unadjusted results with no group details.
Labhardt, significant confounding by time possible.
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Figure 18. Random effects meta-analysis for all studies after exclusions.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
Heterogeneity in COVID-19 studies arises from many factors including:
The time between infection or the onset of symptoms and
treatment may critically affect how well a treatment works. For example an
antiviral may be very effective when used early but may not be effective in
late stage disease, and may even be harmful. Oseltamivir, for example, is
generally only considered effective for influenza when used within 0-36 or
0-48 hours39,40. 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 cases41 |
| <24 hours | -33 hours symptoms42 |
| 24-48 hours | -13 hours symptoms42 |
| Inpatients | -2.5 hours to improvement43 |
Figure 19 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 175 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
Figure 19. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 175 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
variants45, for example the Gamma variant shows significantly
different characteristics46-49. 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 variants50,51.
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 175
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 20 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 21 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 22 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.000000082 to p = 0.0000000033.
Figure 20. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
Figure 21. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
Figure 20. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 56 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 5.0 months. When restricting to RCTs only, 55% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.3 months.
Figure 23 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
Figure 23. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results. 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 lopinavir/ritonavir, 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 24 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 < 0.0572-79.
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 24. 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 alone54-70.
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
factors22-29, providing many therapeutic
targets.
Over 9,000 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 25 shows an overview of the results for lopinavir/ritonavir
in the context of multiple COVID-19 treatments, and Figure 26 shows a plot
of efficacy vs. cost for COVID-19 treatments.
Figure 25.
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 efficacy80.
Figure 26. Efficacy vs. cost for COVID-19 treatments.
Meta analysis using the most serious outcome reported shows
10% [-5‑28%] higher risk, without reaching statistical significance.
Early terminated very late stage (95% on oxygen at baseline) DISCOVERY trial showing no significant differences with lopinavir/ritonavir.
RCT 199 hospitalized COVID-19 patients showing no significant difference with lopinavir-ritonavir treatment. 28-day mortality was lower in the treatment group, without statistical significance. 3 treatment patients died within 24 hours after randomization and did not receive lopinavir-ritonavir. No significant difference was found in viral RNA load over time between the groups.
Retrospective 125 pregnant women hospitalized with COVID-19 in Turkey, showing no significant difference in hospitalization length with HCQ, and longer hospitalization with lopinavir/ritonavir use.
Retrospective 3,451 hospitalized COVID-19 patients in Italy showing higher mortality with darunavir/cobicistat.
RCT with 1,616 hospitalized COVID-19 patients showing no significant differences with lopinavir-ritonavir treatment compared to usual care.
RCT 101 mild to moderate COVID-19 patients showing no significant difference in antiviral effectiveness among three treatment regimens: ribavirin plus interferon-alpha, lopinavir/ritonavir plus interferon-alpha, and ribavirin plus lopinavir/ritonavir plus interferon-alpha.
RCT 437 non-hospitalized COVID-19 patients showing no significant differences with lopinavir/ritonavir (LPV/r) treatment.
Retrospective 1,500 hospitalized late stage (median SaO2 87.7) patients in Turkey, showing no significant difference in mortality with lopinavir/ritonavir treatment.
Open-label, cluster-randomized RCT 318 asymptomatic close contacts in Switzerland and Brazil showing no statistically significant difference in symptomatic COVID-19 at 21 days with LPV/r prophylaxis. The mid-trial changes in allocation and 10-month recruitment window introduces potential calendar-time confounding - e.g., if community incidence fell over time, this may over-represent lower-risk weeks in the LPV/r arm, thereby overestimating efficacy. Authors reports 2 hospitalizations but do not specify which group the patients were in.
RCT 86 mild/moderate COVID-19 patients showing no significant difference in outcomes with lopinavir/ritonavir or arbidol compared to control.
240 patient RCT comparing favipiravir, favipiravir + LPV/r, LPV/r, and placebo, showing improved viral clearance with favipiravir, but no significant difference for LPV/r. Efficacy was lower in the combined favipiravir + LPV/r arm, where plasma levels of favipiravir were lower. Favipiravir 1800mg twice daily on day 1 followed by 400mg four times daily on days 2-7.
Early terminated RCT in Brazil showing no significant differences with lopinavir/ritonavir treatment.
WHO SOLIDARITY open-label RCT showing no significant difference in outcomes with lopinavir/ritonavir treatment.
Retrospective 178 hospitalized COVID-19 patients in China
Retrospective 933 pediatric COVID-19 patients in Hong Kong showing worse outcomes with early lopinavir/ritonavir (LPV/r) use.
Retrospective 120 hospitalized non-critically ill COVID-19 patients showing that early administration of lopinavir/ritonavir was associated with shorter duration of SARS-CoV-2 RNA shedding.
Retrospective 191 hospitalized COVID-19 patients in China showing no significant difference in viral clearance with lopinavir/ritonavir.
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 lopinavir/ritonavir 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 lopinavir/ritonavir for COVID-19 that report
a comparison with a control group are included in the main analysis.
Sensitivity analysis is performed, excluding studies with major issues,
epidemiological studies, and studies with minimal available information.
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 27.
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 reduction81.
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 185.
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 (1.16.1), pythonmeta (1.26), numpy (2.2.6), statsmodels (0.14.5), and plotly (6.2.0).
Forest plots are computed using PythonMeta86
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 effective39,40.
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/lpvmeta.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.
| Huang, 7/14/2020, Randomized Controlled Trial, China, peer-reviewed, mean age 42.5, 15 authors, study period 29 January, 2020 - 25 February, 2020, trial ChiCTR2000029387. | risk of severe case, 106.2% higher, RR 2.06, p = 0.61, treatment 2 of 32 (6.2%), control 1 of 33 (3.0%), RBV plus LPV/r plus IFN-a vs. RBV plus IFN-a. |
| hospitalization time, 5.9% higher, relative time 1.06, p = 0.68, treatment median 18.0 IQR 9.0 n=32, control median 17.0 IQR 16.0 n=33, RBV plus LPV/r plus IFN-a vs. RBV plus IFN-a. | |
| recovery time, 33.3% lower, relative time 0.67, p = 0.32, treatment median 3.0 IQR 6.5 n=16, control median 4.5 IQR 5.5 n=20, RBV plus LPV/r plus IFN-a vs. RBV plus IFN-a, fever. | |
| time to improvement, 18.2% lower, relative time 0.82, p = 0.07, treatment median 9.0 IQR 5.0 n=29, control median 11.0 IQR 6.0 n=29, RBV plus LPV/r plus IFN-a vs. RBV plus IFN-a. | |
| time to viral-, 15.4% higher, relative time 1.15, p = 0.41, treatment median 15.0 IQR 8.5 n=32, control median 13.0 IQR 16.5 n=33, RBV plus LPV/r plus IFN-a vs. RBV plus IFN-a. | |
| Lowe, 2/15/2022, Double Blind Randomized Controlled Trial, placebo-controlled, United Kingdom, peer-reviewed, 18 authors, study period 6 October, 2020 - 4 November, 2021, trial NCT04499677 (history) (FLARE). | risk of hospitalization, 200.0% higher, RR 3.00, p = 1.00, treatment 1 of 60 (1.7%), control 0 of 60 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of no viral clearance, 7.2% lower, RR 0.93, p = 0.56, treatment 39 of 54 (72.2%), control 38 of 52 (73.1%), NNT 117, inverted to make RR<1 favor treatment, odds ratio converted to relative risk, day 5, primary outcome. | |
| Wong, 4/16/2022, retrospective, China, peer-reviewed, 11 authors, study period 21 January, 2020 - 31 January, 2021. | risk of no hospital discharge, 96.1% higher, HR 1.96, p < 0.001, treatment 49, control 884, inverted to make HR<1 favor treatment, propensity score weighting. |
| risk of no recovery, 96.1% higher, HR 1.96, p < 0.001, treatment 49, control 884, inverted to make HR<1 favor treatment, propensity score weighting. |
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.
| Ader, 10/6/2020, Randomized Controlled Trial, multiple countries, preprint, baseline oxygen required 95.4%, 59 authors, study period 22 March, 2020 - 29 June, 2020, average treatment delay 9.0 days, excluded in exclusion analyses: very late stage, >50% on oxygen/ventilation at baseline. | risk of death, 36.1% higher, RR 1.36, p = 0.39, treatment 17 of 147 (11.6%), control 13 of 149 (8.7%), adjusted per study, odds ratio converted to relative risk, day 90. |
| risk of no viral clearance, 25.8% lower, RR 0.74, p = 0.65, treatment 4 of 86 (4.7%), control 5 of 81 (6.2%), NNT 66, odds ratio converted to relative risk, Table S2, day 29. | |
| Cao, 5/7/2020, Randomized Controlled Trial, China, peer-reviewed, median age 58.0, 65 authors, study period 18 January, 2020 - 3 February, 2020, trial NCT02845843 (history) (LOTUS China). | risk of death, 23.2% lower, RR 0.77, p = 0.39, treatment 19 of 99 (19.2%), control 25 of 100 (25.0%), NNT 17. |
| risk of no improvement, 29.3% lower, RR 0.71, p = 0.19, treatment 21 of 99 (21.2%), control 30 of 100 (30.0%), NNT 11, day 28. | |
| risk of no improvement, 22.1% lower, RR 0.78, p = 0.03, treatment 54 of 99 (54.5%), control 70 of 100 (70.0%), NNT 6.5, day 14. | |
| risk of no improvement, 4.1% lower, RR 0.96, p = 0.17, treatment 93 of 99 (93.9%), control 98 of 100 (98.0%), NNT 25, day 7. | |
| risk of no viral clearance, 3.7% lower, RR 0.96, p = 1.00, treatment 24 of 59 (40.7%), control 30 of 71 (42.3%), NNT 63, day 28. | |
| risk of no viral clearance, 0.3% higher, RR 1.00, p = 1.00, treatment 25 of 59 (42.4%), control 30 of 71 (42.3%), day 21. | |
| risk of no viral clearance, 4.8% higher, RR 1.05, p = 0.86, treatment 27 of 59 (45.8%), control 31 of 71 (43.7%), day 14. | |
| risk of no viral clearance, 2.4% lower, RR 0.98, p = 1.00, treatment 30 of 59 (50.8%), control 37 of 71 (52.1%), NNT 79, day 10. | |
| risk of no viral clearance, 2.2% lower, RR 0.98, p = 1.00, treatment 39 of 59 (66.1%), control 48 of 71 (67.6%), NNT 66, day 5. | |
| Değirmenci, 7/30/2024, retrospective, Turkey, peer-reviewed, mean age 29.3, 7 authors, study period March 2020 - January 2021, excluded in exclusion analyses: unadjusted results with no group details. | risk of hospitalization, 136.0% higher, OR 2.36, p = 0.43, treatment 70, control 55, adjusted per study, multivariable, RR approximated with OR. |
| Di Castelnuovo, 6/9/2021, retrospective, Italy, peer-reviewed, 110 authors, study period 19 February, 2020 - 23 May, 2020, trial NCT04318418 (history) (CORIST). | risk of death, 6.0% lower, HR 0.94, p = 0.52, treatment 1,148, control 1,824. |
| Horby, 10/31/2020, Randomized Controlled Trial, United Kingdom, peer-reviewed, mean age 66.2, 26 authors, study period 19 March, 2020 - 29 June, 2020, trial NCT04381936 (history) (RECOVERY). | risk of death, 3.0% higher, RR 1.03, p = 0.60, treatment 374 of 1,616 (23.1%), control 767 of 3,424 (22.4%). |
| risk of mechanical ventilation, 15.0% higher, RR 1.15, p = 0.15, treatment 152 of 1,556 (9.8%), control 279 of 3,280 (8.5%). | |
| risk of no hospital discharge, 2.0% higher, RR 1.02, p = 0.53, treatment 1,616, control 3,424, inverted to make RR<1 favor treatment. | |
| Kaizer, 3/31/2023, Double Blind Randomized Controlled Trial, placebo-controlled, USA, peer-reviewed, mean age 41.0, 16 authors, study period June 2020 - December 2021, trial NCT04372628 (history) (TREATNOW). | risk of death, 202.7% higher, RR 3.03, p = 0.49, treatment 1 of 220 (0.5%), control 0 of 226 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm). |
| risk of hospitalization, 19.8% higher, RR 1.20, p = 0.78, treatment 7 of 220 (3.2%), control 6 of 226 (2.7%). | |
| risk of no recovery, 3.1% higher, HR 1.03, p = 0.88, treatment 220, control 226, inverted to make HR<1 favor treatment, ordinal category, day 15. | |
| Kokturk, 4/28/2021, retrospective, database analysis, Turkey, peer-reviewed, 68 authors. | risk of death, 118.2% higher, RR 2.18, p = 0.38, treatment 7 of 55 (12.7%), control 60 of 1,445 (4.2%), adjusted per study, odds ratio converted to relative risk. |
| Li, 12/31/2020, Randomized Controlled Trial, China, peer-reviewed, mean age 49.4, 20 authors, study period 1 February, 2020 - 28 March, 2020, trial NCT04252885 (history) (ELACOI). | risk of progression, 100% higher, RR 2.00, p = 0.46, treatment 8 of 34 (23.5%), control 2 of 17 (11.8%), progression to severe/critical. |
| risk of no recovery, 400.0% higher, RR 5.00, p = 0.56, treatment 3 of 27 (11.1%), control 0 of 9 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm), day 14, fever. | |
| risk of no recovery, 57.1% lower, RR 0.43, p = 0.12, treatment 5 of 21 (23.8%), control 5 of 9 (55.6%), NNT 3.1, day 14, cough. | |
| chest CT improvement, 250.0% higher, RR 3.50, p = 0.23, treatment 7 of 28 (25.0%), control 1 of 14 (7.1%), day 14. | |
| time to viral-, 3.2% lower, relative time 0.97, p = 0.84, treatment mean 9.0 (±5.0) n=34, control mean 9.3 (±5.2) n=17. | |
| Reis, 4/22/2021, Double Blind Randomized Controlled Trial, Brazil, peer-reviewed, 18 authors, study period 2 June, 2020 - 30 September, 2020, trial NCT04403100 (history) (TOGETHER). | risk of death, 86.1% higher, RR 1.86, p = 1.00, treatment 2 of 244 (0.8%), control 1 of 227 (0.4%). |
| risk of hospitalization, 16.0% higher, HR 1.16, p = 0.72, treatment 14 of 244 (5.7%), control 11 of 227 (4.8%), ITT, Cox proportional hazards. | |
| SOLIDARITY Trial Consortium, 10/15/2020, Randomized Controlled Trial, multiple countries, peer-reviewed, 15 authors, trial NCT04315948 (history) (SOLIDARITY). | risk of death, no change, RR 1.00, p = 1.00, treatment 148 of 1,399 (10.6%), control 146 of 1,372 (10.6%), NNT 1602, Kaplan-Meier, day 28. |
| risk of mechanical ventilation, 1.8% higher, RR 1.02, p = 0.89, treatment 126 of 1,287 (9.8%), control 121 of 1,258 (9.6%). | |
| Wen, 8/1/2020, retrospective, China, peer-reviewed, 11 authors, study period 20 January, 2020 - 10 February, 2020. | risk of severe case, 3.6% higher, RR 1.04, p = 1.00, treatment 3 of 56 (5.4%), control 3 of 58 (5.2%). |
| time to viral-, 20.9% higher, relative time 1.21, p = 0.008, treatment mean 10.2 (±3.49) n=59, control mean 8.44 (±3.51) n=58. | |
| Yan, 5/19/2020, retrospective, China, peer-reviewed, median age 52.0, 7 authors, study period 31 January, 2020 - 9 March, 2020. | prolonged viral shedding, 40.0% lower, HR 0.60, p = 0.010, treatment 78, control 42, adjusted per study, multivariable, Cox proportional hazards. |
| Zhou, 3/31/2020, retrospective, China, peer-reviewed, 19 authors. | time to viral-, 10.0% higher, relative time 1.10, p = 0.06, treatment median 22.0 IQR 6.0 n=29, control median 20.0 IQR 7.0 n=108. |
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.
| Labhardt, 12/31/2021, Randomized Controlled Trial, multiple countries, peer-reviewed, mean age 39.7, 26 authors, study period March 2020 - March 2021, trial NCT04364022 (history) (COPEP), excluded in exclusion analyses: significant confounding by time possible. | risk of progression, 630.1% higher, RR 7.30, p = 0.02, treatment 14 of 209 (6.7%), control 1 of 109 (0.9%), level 2-4. |
| risk of symptomatic case, 40.0% lower, HR 0.60, p = 0.17, treatment 35 of 209 (16.7%), control 13 of 109 (11.9%), adjusted per study. |
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.
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