Covid Analysis, February 2023
•Statistically significant improvement is seen for hospitalization. One study shows statistically significant improvement in isolation (not for the most serious outcome).
•Meta analysis using the most serious outcome reported shows 46% [-173‑89%] improvement, without reaching statistical significance. Results are worse for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
•Currently there is limited data, with only 885 patients and only 37 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
•Ensovibep requires IV infusion, but may be less variant dependent than monoclonal antibodies.
•No treatment, vaccine, or intervention is 100% effective and available. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. Only 50% of ensovibep studies show zero events with treatment.
•All data to reproduce this paper and sources are in the appendix.
|Early treatment||All studies||Late treatment||Studies||Patients||Authors|
|All studies||89% [-127‑99%]||46% [-173‑89%]||17% [-35‑49%]||2||885||81|
|Randomized Controlled TrialsRCTs||89% [-127‑99%]||46% [-173‑89%]||17% [-35‑49%]||2||885||81|
|Mortality||89% [-127‑99%]||46% [-173‑89%]||17% [-35‑49%]||2||885||81|
|RCT mortality||89% [-127‑99%]||46% [-173‑89%]||17% [-35‑49%]||2||885||81|
Ensovibep reduces risk for COVID-19 with low confidence for hospitalization.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
We analyze all significant studies concerning the use of ensovibep 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, 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.
An In Vitro study supports the efficacy of ensovibep [Rothenberger].
Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.
Table 1 summarizes the results for all stages combined, with different exclusions, and for specific outcomes. Table 2 shows results by treatment stage. Figure 3, 4, 5, 6, and 7 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, recovery, and peer reviewed studies.
|All studies||46% [-173‑89%]||2||885||81|
|Peer-reviewed studiesPeer-reviewed||17% [-31‑47%]||1||485||80|
|Randomized Controlled TrialsRCTs||46% [-173‑89%]||2||885||81|
|RCT mortality||46% [-173‑89%]||2||885||81|
|Early treatment||Late treatment|
|All studies||89% [-127‑99%]||17% [-35‑49%]|
|Peer-reviewed studiesPeer-reviewed||-||17% [-35‑49%]|
|Randomized Controlled TrialsRCTs||89% [-127‑99%]||17% [-35‑49%]|
|Mortality||89% [-127‑99%]||17% [-35‑49%]|
|RCT mortality||89% [-127‑99%]||17% [-35‑49%]|
Currently all studies are RCTs.
Heterogeneity in COVID-19 studies arises from many factors including:
[McLean, Treanor]. Baloxavir studies for influenza also show that treatment delay is critical — [Ikematsu] report an 86% reduction in cases for post-exposure prophylaxis, [Hayden] 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] report only 2.5 hours improvement for inpatient treatment.
|Post exposure prophylaxis||86% fewer cases [Ikematsu]|
|<24 hours||-33 hours symptoms [Hayden]|
|24-48 hours||-13 hours symptoms [Hayden]|
|Inpatients||-2.5 hours to improvement [Kumar]|
Figure 8 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 48 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
[Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
[Williams] analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. [Xu] analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.
Figure 9. For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, etc. An antiviral tested with a low-risk population may report zero mortality in both arms, however a reduction in severity and improved viral clearance may translate into lower mortality among a high-risk population, and including these results in pooled analysis allows faster detection of efficacy. Trials with high-risk patients may also be restricted due to ethical concerns for treatments that are known or expected to be effective.
Pooled analysis enables using more of the available information. While there is much more information available, for example dose-response relationships, the advantage of the method used here is simplicity and transparency. Note that pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral replication or early stage disease could show no efficacy in pooled analysis if most studies only examine viral clearance. While we present pooled results, we also present individual outcome analyses, which may be more informative for specific use cases.
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.
Figure 10 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 < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. 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.
heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses by 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 affiliated with special interests 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 alone [Alsaidi, Andreani, Biancatelli, De Forni, Gasmi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Thairu]. 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, vaccine, 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.
Statistically significant improvement is seen for hospitalization. One study shows statistically significant improvement in isolation (not for the most serious outcome). Meta analysis using the most serious outcome reported shows 46% [-173‑89%] improvement, without reaching statistical significance. Results are worse for peer-reviewed studies. Early treatment is more effective than late treatment. Currently all studies are RCTs.
Currently there is limited data, with only 885 patients and only 37 control events for the most serious outcome in trials to date. Studies to date are from only 2 different groups.
Ensovibep requires IV infusion, but may be less variant dependent than monoclonal antibodies.
[Barkauskas] RCT 485 hospitalized patients showing no significant differences with ensovibep treatment. Intravenous ensovibep, 600mg.
[Novartis] EMPATHY Part A RCT with 407 patients, 301 treated with ensovibep, showing statistically significant viral load reduction (details not provided), and lower mortality and hospitalization. For discussion see [twitter.com].
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org. Search terms were ensovibep, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of ensovibep 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing 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 no room for an effective treatment to do better). 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 computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome 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 1 [Sweeting]. 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 (3.10.9) with scipy (1.9.3), pythonmeta (1.26), numpy (1.23.5), statsmodels (0.13.5), and plotly (5.11.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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 effective [McLean, Treanor].
A summary of study results is below. Please submit updates and corrections at https://c19early.org/evmeta.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.
|[Novartis], 1/10/2022, Randomized Controlled Trial, multiple countries, preprint, 1 author.||risk of death, 89.0% lower, RR 0.11, p = 0.06, treatment 0 of 301 (0.0%), control 2 of 99 (2.0%), NNT 49, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).|
|risk of hospitalization, 86.8% lower, RR 0.13, p = 0.01, treatment 2 of 301 (0.7%), control 5 of 99 (5.1%), NNT 23.|
|risk of hospitalization/ER, 78.1% lower, RR 0.22, p = 0.02, treatment 4 of 301 (1.3%), control 6 of 99 (6.1%), NNT 21.|
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.
|[Barkauskas], 8/9/2022, Double Blind Randomized Controlled Trial, placebo-controlled, multiple countries, peer-reviewed, 80 authors, average treatment delay 8.0 days, trial NCT04501978 (history) (ACTIV-3/TICO).||risk of death, 17.0% lower, HR 0.83, p = 0.46, treatment 30 of 247 (12.1%), control 35 of 238 (14.7%), NNT 39, Kaplan–Meier, day 90.|
|risk of no recovery, 5.7% lower, HR 0.94, p = 0.55, treatment 44 of 247 (17.8%), control 48 of 238 (20.2%), NNT 42, adjusted per study, inverted to make HR<1 favor treatment.|
|risk of no recovery, 7.5% higher, HR 1.08, p = 0.68, treatment 247, control 238, adjusted per study, inverted to make HR<1 favor treatment, pulmonary ordinal outcome, day 5.|
|risk of no hospital discharge, 6.5% lower, HR 0.93, p = 0.46, treatment 28 of 247 (11.3%), control 34 of 238 (14.3%), adjusted per study, inverted to make HR<1 favor treatment.|
Alsaidi et al., Marine Drugs, doi:10.3390/md19080418, Griffithsin and Carrageenan Combination Results in Antiviral Synergy against SARS-CoV-1 and 2 in a Pseudoviral Model, https://www.mdpi.com/1660-3397/19/8/418.
Altman, D., BMJ, doi:10.1136/bmj.d2304, How to obtain the P value from a confidence interval, https://www.bmj.com/content/343/bmj.d2304.
Altman (B) et al., BMJ, doi:10.1136/bmj.d2090, How to obtain the confidence interval from a P value, https://www.bmj.com/content/343/bmj.d2090.
Andreani et al., Microbial Pathogenesis, doi:/10.1016/j.micpath.2020.104228, In vitro testing of combined hydroxychloroquine and azithromycin on SARS-CoV-2 shows synergistic effect, https://www.sciencedirect.com/science/article/pii/S0882401020305155.
Barkauskas et al., Annals of Internal Medicine, doi:10.7326/M22-1503, Efficacy and Safety of Ensovibep for Adults Hospitalized With COVID-19, https://www.acpjournals.org/doi/10.7326/M22-1503.
Biancatelli et al., Frontiers in Immunology, doi:10.3389/fimmu.2020.01451, Quercetin and Vitamin C: An Experimental, Synergistic Therapy for the Prevention and Treatment of SARS-CoV-2 Related Disease (COVID-19), https://www.frontiersin.org/articles/10.3389/fimmu.2020.01451/full.
De Forni et al., PLoS ONE, doi:10.1371/journal.pone.0276751, Synergistic drug combinations designed to fully suppress SARS-CoV-2 in the lung of COVID-19 patients, https://journals.plos.org/plosone/..le?id=10.1371/journal.pone.0276751.
Egger et al., BMJ, doi:10.1136/bmj.315.7109.629, Bias in meta-analysis detected by a simple, graphical test, https://www.bmj.com/content/315/7109/629.
Faria et al., Science, doi:10.1126/science.abh2644, Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil, https://www.science.org/lookup/doi/10.1126/science.abh2644.
Gasmi et al., Pharmaceuticals, doi:10.3390/ph15091049, Quercetin in the Prevention and Treatment of Coronavirus Infections: A Focus on SARS-CoV-2, https://www.mdpi.com/1424-8247/15/9/1049.
Harbord et al., Statistics in Medicine, doi:10.1002/sim.2380, A modified test for small-study effects in meta-analyses of controlled trials with binary endpoints, https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fsim.2380.
Hayden et al., New England Journal of Medicine, doi:10.1056/NEJMoa1716197, Baloxavir Marboxil for Uncomplicated Influenza in Adults and Adolescents, http://www.nejm.org/doi/10.1056/NEJMoa1716197.
Ikematsu et al., New England Journal of Medicine, doi:10.1056/NEJMoa1915341, Baloxavir Marboxil for Prophylaxis against Influenza in Household Contacts, http://www.nejm.org/doi/10.1056/NEJMoa1915341.
Jeffreys et al., International Journal of Antimicrobial Agents, doi:10.1016/j.ijantimicag.2022.106542 (preprint 12/24/2020), Remdesivir-ivermectin combination displays synergistic interaction with improved in vitro activity against SARS-CoV-2, https://www.sciencedirect.com/science/article/pii/S0924857922000309.
Jitobaom et al., Research Square, doi:10.21203/rs.3.rs-941811/v1, Favipiravir and Ivermectin Showed in Vitro Synergistic Antiviral Activity against SARS-CoV-2, https://www.researchsquare.com/article/rs-941811/v1.
Jitobaom (B) et al., BMC Pharmacology and Toxicology, doi:10.1186/s40360-022-00580-8 (preprint 11/30/2021), Synergistic anti-SARS-CoV-2 activity of repurposed anti-parasitic drug combinations, https://bmcpharmacoltoxicol.biomed..rticles/10.1186/s40360-022-00580-8.
Karita et al., medRxiv, doi:10.1101/2021.08.27.21262754, Trajectory of viral load in a prospective population-based cohort with incident SARS-CoV-2 G614 infection, https://www.medrxiv.org/content/10.1101/2021.08.27.21262754v1.
Kumar et al., The Lancet Infectious Diseases, doi:10.1016/S1473-3099(21)00469-2, 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, https://www.sciencedirect.com/science/article/pii/S1473309921004692.
López-Medina et al., JAMA, doi:10.1001/jama.2021.3071, Effect of Ivermectin on Time to Resolution of Symptoms Among Adults With Mild COVID-19: A Randomized Clinical Trial, https://jamanetwork.com/journals/jama/fullarticle/2777389.
Macaskill et al., Statistics in Medicine, doi:10.1002/sim.698, A comparison of methods to detect publication bias in meta-analysis, https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fsim.698.
McLean et al., Open Forum Infect. Dis. September 2015, 2:3, doi:10.1093/ofid/ofv100, Impact of Late Oseltamivir Treatment on Influenza Symptoms in the Outpatient Setting: Results of a Randomized Trial, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4525010/.
Moreno et al., BMC Medical Research Methodology, doi:10.1186/1471-2288-9-2, Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study, http://link.springer.com/article/10.1186/1471-2288-9-2/fulltext.html.
Nonaka et al., International Journal of Infectious Diseases, doi:10.1016/j.ijid.2021.08.003, 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, https://www.sciencedirect.com/science/article/pii/S1201971221006354.
Novartis Press Release, Novartis and Molecular Partners report positive topline data from Phase 2 study for ensovibep (MP0420), a DARPin antiviral therapeutic for COVID-19, https://www.novartis.com/news/medi..pin-antiviral-therapeutic-covid-19.
Ostrov et al., Pathogens, doi:10.3390/pathogens10111514, Highly Specific Sigma Receptor Ligands Exhibit Anti-Viral Properties in SARS-CoV-2 Infected Cells, https://www.mdpi.com/2076-0817/10/11/1514/htm.
Peacock et al., bioRxiv, doi:10.1101/2021.12.31.474653, The SARS-CoV-2 variant, Omicron, shows rapid replication in human primary nasal epithelial cultures and efficiently uses the endosomal route of entry, https://www.biorxiv.org/content/10.1101/2021.12.31.474653.
Peters, J., JAMA, doi:10.1001/jama.295.6.676, Comparison of Two Methods to Detect Publication Bias in Meta-analysis, http://jamanetwork.com/journals/jama/fullarticle/202337.
Rothenberger et al., bioRxiv, doi:10.1101/2021.02.03.429164, Ensovibep, a novel trispecific DARPin candidate that protects against SARS-CoV-2 variants, https://www.biorxiv.org/content/10.1101/2021.02.03.429164.
Rothstein, H., Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, https://www.wiley.com/en-ae/Public..nt+and+Adjustments-p-9780470870143.
Rücker et al., Statistics in Medicine, doi:10.1002/sim.2971, Arcsine test for publication bias in meta-analyses with binary outcomes, https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fsim.2971.
Stanley et al., Research Synthesis Methods, doi:10.1002/jrsm.1095, Meta-regression approximations to reduce publication selection bias, https://api.wiley.com/onlinelibrar..dm/v1/articles/10.1002%2Fjrsm.1095.
Sweeting et al., Statistics in Medicine, doi:10.1002/sim.1761, What to add to nothing? Use and avoidance of continuity corrections in meta‐analysis of sparse data, https://onlinelibrary.wiley.com/doi/10.1002/sim.1761.
Thairu et al., Journal of Pharmaceutical Research International, doi:10.9734/jpri/2022/v34i44A36328 (preprint 2/25/2022), A Comparison of Ivermectin and Non Ivermectin Based Regimen for COVID-19 in Abuja: Effects on Virus Clearance, Days-to-discharge and Mortality, https://journaljpri.com/index.php/JPRI/article/view/36328.
Treanor et al., JAMA, 2000, 283:8, 1016-1024, doi:10.1001/jama.283.8.1016, Efficacy and Safety of the Oral Neuraminidase Inhibitor Oseltamivir in Treating Acute Influenza: A Randomized Controlled Trial, https://jamanetwork.com/journals/jama/fullarticle/192425.
Willett et al., medRxiv, doi:10.1101/2022.01.03.21268111, The hyper-transmissible SARS-CoV-2 Omicron variant exhibits significant antigenic change, vaccine escape and a switch in cell entry mechanism, https://www.medrxiv.org/content/10.1101/2022.01.03.21268111.
Williams, T., Do Your Own Research, Not All Ivermectin Is Created Equal: Comparing The Quality of 11 Different Ivermectin Sources, https://doyourownresearch.substack..ot-all-ivermectin-is-created-equal.
Xu et al., Rapid Communications in Mass Spectrometry, doi:10.1002/rcm.9358, A study of impurities in the repurposed COVID-19 drug hydroxychloroquine sulfate by UHPLC-Q/TOF-MS and LC-SPE-NMR, https://onlinelibrary.wiley.com/doi/10.1002/rcm.9358.
Zavascki et al., Research Square, doi:10.21203/rs.3.rs-910467/v1, 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, https://www.researchsquare.com/article/rs-910467/v1.
Zeraatkar et al., medRxiv, doi:10.1101/2022.04.04.22273372, The trustworthiness and impact of trial preprints for COVID-19 decision-making: A methodological study, https://www.medrxiv.org/content/10.1101/2022.04.04.22273372.
Please send us corrections, updates, or comments. Vaccines and treatments are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.