Favipiravir for COVID-19: real-time meta analysis of 66 studies

An In Silico study supports the efficacy of favipiravir Unal.

2 In Vitro studies support the efficacy of favipiravir Unal, Yildiz Pekoz.

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, 7, 8, 9, 10, and 11 show forest plots for random effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, viral clearance, and peer reviewed studies.

1 favipiravir RCT has not reported results Kara. The trial reports total actual enrollment of 1,008 patients. The result is delayed over 2 years.

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 may underemphasize serious issues not captured in the checklists, overemphasize issues unlikely to alter outcomes in specific cases (for example, lack of blinding for an objective mortality outcome, or certain specifics of randomization with a very large effect size), or be easily influenced by potential bias. However, they can also be very high quality.

The studies excluded are as below. Figure 16 shows a forest plot for random effects meta-analysis of all studies after exclusions.

Abdulrahman, very late stage, ICU patients.

Acar Sevinc, very late stage, ICU patients.

Al Mutair, very late stage, ICU patients.

Arfijanto, unadjusted results with no group details.

Assiri, unadjusted results with no group details; very late stage, ICU patients.

Babayigit, substantial unadjusted confounding by indication possible.

Cilli, unadjusted results with no group details.

Damayanti, minimal details provided.

Khamis, study compares against another treatment showing significant efficacy.

Kurniyanto, unadjusted results with no group details.

Lokanuwatsatien, unadjusted results with no group details.

Shamsi, unadjusted results with no group details.

Sulaiman, very late stage, ICU patients.

Tawfik, unadjusted results with minimal group details.

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 hours 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.

Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 56 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

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 (as in López-Medina).

Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.

There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics 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.

Effectiveness may depend strongly on the dosage and treatment regimen.

The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.

The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. 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.

We present both pooled analyses and specific outcome analyses. Notably, pooled analysis often results in earlier detection of efficacy as shown in Figure 18. 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.

Currently, 38 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.3 months. When restricting to RCTs only, 57% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 3.9 months.

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. This may have a greater effect than pooling different outcomes such as mortality and hospitalization. For example a treatment may have 50% efficacy for mortality but only 40% for hospitalization when used within 48 hours. However efficacy could be 0% when used late.

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.

Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results Boulware, Meeus, Meneguesso.

One method to evaluate bias is to compare prospective vs. retrospective studies. Prospective studies are more likely to be published regardless of the result, while retrospective studies are more likely to exhibit bias. For example, researchers may perform preliminary analysis with minimal effort and the results may influence their decision to continue. Retrospective studies also provide more opportunities for the specifics of data extraction and adjustments to influence results.

Figure 19 shows a scatter plot of results for prospective and retrospective studies. 38% of retrospective studies report a statistically significant positive effect for one or more outcomes, compared to 53% of prospective studies, consistent with a bias toward publishing negative results. The median effect size for retrospective studies is 35% improvement, compared to 10% for prospective studies, suggesting a potential bias towards publishing results showing higher efficacy.

Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 20 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 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.

Pharmaceutical drug trials often have conflicts of interest whereby sponsors or trial staff have a financial interest in the outcome being positive. Favipiravir for COVID-19 lacks this because it is off-patent, has multiple manufacturers, and is very low cost. In contrast, most COVID-19 favipiravir trials have been run by physicians on the front lines with the primary goal of finding the best methods to save human lives and minimize the collateral damage caused by COVID-19. While pharmaceutical companies are careful to run trials under optimal conditions (for example, restricting patients to those most likely to benefit, only including patients that can be treated soon after onset when necessary, and ensuring accurate dosing), not all favipiravir trials represent the optimal conditions for efficacy.

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 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.

11 of the 66 studies compare against other treatments, which may reduce the effect seen. 4 of 66 studies combine treatments. The results of favipiravir alone may differ. 3 of 31 RCTs use combined treatment.

Favipiravir is an effective treatment for COVID-19. Statistically significant lower risk is seen for recovery and viral clearance. 30 studies from 30 independent teams in 16 countries show statistically significant improvements. Meta analysis using the most serious outcome reported shows 18% [8‑27%] lower risk. Results are similar for Randomized Controlled Trials, higher quality studies, and peer-reviewed studies. Studies to date show no significant difference for mortality. A small mortality improvement is seen, without statistical significance, however meta regression with followup duration shows decreasing efficacy with longer followup. There is also no benefit seen for mechanical ventilation or ICU admission. This may reflect antiviral efficacy being offset by side effects of treatment.

Potential risks of the mechanism of action include the creation of dangerous variants, and mutagenicity, carcinogenicity, teratogenicity, and embryotoxicity Hadj Hassine, Waters, Zhirnov.

Abdulrahman: Retrospective 1,699 ICU patients in Saudi Arabia, 193 treated with favipiravir, showing no significant difference in mortality.

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Acar Sevinc: Retrospective 100 ICU patients in Turkey, showing improved survival with favipiravir vs. lopinavir/ritonavir.

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Adhikari: Preliminary report for an RCT in Nepal with 38 favipiravir patients and 32 control patients, showing no significant differences. There were no serious side effects.

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Aghajani: Retrospective 991 hospitalized patients in Iran focusing on aspirin use but also showing results for HCQ, remdesivir, and favipiravir.

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Al Mutair: Retrospective 269 favipiravir ICU patients in Saudi Arabia and 269 matched controls receiving different treatments, showing no significant difference.

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Al-Muhsen: Prospective observational study of 598 hospitalized patients in Saudi Arabia, showing higher risk of mortality and longer hospitalization time with favipiravir.

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Alamer: Retrospective 234 favipiravir and 223 control patients in Saudi Arabia, showing shorter time to discharge and lower progression to ventilation, but no significant difference in mortality.

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Alattar: PSM retrospective with 1,493 patients, showing significantly improved viral clearance with favipiravir. There were no significant differences in clinical improvement or mortality. Mortality was lower (2.1% vs 3.1%), without statistical significance with the small number of events.

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Almoosa: Retrospective 226 COVID-19 pneumonia patients, 110 treated with favipiravir, showing higher mortality (p=0.1) and ICU admission (p=0.02) with treatment in multivariate analysis.

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Alosaimi: Retrospective 200 hospitalized COVID-19 patients in Saudi Arabia, showing no significant difference in outcomes between HCQ and favipiravir.

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Alotaibi: Retrospective hospitalized patients in Saudi Arabia, showing lower mortality with favipiravir compared to HCQ, not quite reaching statistical significance. Authors do not indicate the factors behind which therapy was chosen. May be subject to significant confounding by indication and confounding by time.

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AlQahtani: RCT with 54 favipiravir, 51 HCQ, and 52 SOC hospitalized patients in Bahrain, showing no significant differences. Viral clearance improved with both treatments, but did not reach statistical significance with the small sample size.

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Alshamrani: PSM retrospective 29 hospitals in Saudi Arabia, showing higher mortality with favipiravir treatment, without statistical significance.

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Arfijanto: Retrospective 162 hospitalized COVID-19 patients in Indonesia, showing lower incidence of delayed viral clearance with favipiravir treatment in unadjusted results.

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Assiri: Retrospective 118 ICU patients in Saudi Arabia showing no significant differences in unadjusted results with zinc, vitamin D, and favipiravir treatment.

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Atipornwanich: RCT 200 moderate/severe patients in Thailand, showing significantly lower progression with favipiravir vs. oseltamivir. NCT04303299.

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Babayigit: Retrospective 1,472 hospitalized patients in Turkey, showing a higher ICU admission and ventilation with favipiravir. Results may be subject to confounding by indication.

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Behboodikhah: Retrospective 2,174 hospitalized patients showing significantly shorter length of stay with favipiravir treatment.

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Bosaeed: RCT with 112 favipiravir and 119 control patients showing no significant differences in outcomes. Viral clearance and clinical recovery for patients treated within 48 hours was better than those treated later. NCT04464408.

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Bruminhent: Retrospective 514 patients in Thailand, showing higher risk of progression with favipiravir treatment.

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Cai: Comparison of 35 FPV patients and 35 LPV/RTV patients, showing significant improvements in chest CT and faster viral clearance with FPV.

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Chandiwana: Very high COI low-risk patient RCT in South Africa, showing no significant differences with favipiravir plus nitazoxanide. There were no deaths and no COVID-19 hospitalizations for favipiravir plus nitazoxanide. More patients were seropositive at baseline in the treatment arm (28% vs 22%). Favipiravir 1600mg 12-hourly for 1 day, then 600mg 12-hourly for 6 days. Nitazoxanide 1000mg 12-hourly for 7 days.

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Chen: Very late stage (9 days from symptom onset) RCT with 116 favipiravir patients and 120 arbidol patients in China, showing no significant difference in clinical recovery (relief of fever and cough, respiratory frequency ≤24 times/min, and oxygen saturation ≥98%), however the time to resolution of fever and cough was significantly lower with favipiravir. ChiCTR2000030254.

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Chuah: RCT 500 hospitalized patients in Malaysia, showing no significant differences with favipiravir treatment.

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Cilli: Retrospective 46 idiopathic pulmonary fibrosis patients with COVID-19 in Turkey, showing lower mortality with favipiravir in unadjusted results, without statistical significance.

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Damayanti: Retrospective 192 hospitalized patients in Indonesia, 96 patients treated with favipiravir, showing improved recovery with treatment. Only the abstract is currently available.

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Delen: Retrospective 69 COVID-19 patients in Turkey, showing improved fever recovery with the addition of favipiravir to HCQ, but no significant difference in discharge, ICU admission, or hospitalization time.

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Finberg: Small very late treatment RCT in the USA, with 25 favipiravir and 25 control patients, showing faster viral clearance with treatment. The benefit was only seen in patients <8 days from symptom onset. There were no significant differences in clinical outcomes. The death in the favipiravir group occurred after discharge and was believed to be unrelated to COVID-19 or favipiravir.

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Golan: RCT low-risk (1 death in the control arm) patients in the USA, showing no significant differences with favipiravir. A majority of trial outcomes were modified after completion: clinicaltrials.gov. 44% of patients had no detectable viral load at baseline in the viral shedding sub-study. The primary outcome required 4 days of sustained clinical recovery and occurred after a median of 7 days, suggesting there was limited room for improvement in the population studied. The percentages for viral clearance at day 10 do not match any number of the reported group sizes. Authors write "of the six RCTs conducted", however there has been at least 24 other RCTs at the time of publication c19favipiravir.com. 1800mg bid day 1, 800mg bid days 2-10.

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Hafez: Retrospective hospitalized patients in the United Arab Emirates, showing no significant difference in viral clearance with different combinations of HCQ, AZ, favipiravir, and lopinavir/ritonavir.

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Hartantri: Retrospective 689 hospitalized patients in Indonesia, showing lower mortality with favipiravir treatment.

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Hassaniazad: RCT comparing favipiravir and lopinavir/ritonavir, showing no significant differences. All patients received interferon-beta. Favipiravir 1600mg bid for the first day and 600mg bid for the following 4 days.

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Holubar: Small RCT 116 mITT patients in the USA, 59 treated with favipiravir, showing no significant differences with treatment.

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Horcajada: Underpowered RCT with 44 hospitalized patients in Spain, showing no significant difference with favipiravir treatment in the primary outcome of time to clinical improvement, or in the secondary efficacy outcomes. Adverse events were more frequent in the favipiravir group (68%) compared to placebo (32%), but most were mild.

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Ivashchenko: Intermin results for a small RCT with 40 favipiravir and 20 control patients showing faster viral clearance with favipiravir. There is limited data in this report to evaluate the results. The report indicates that 75% of the control group received HCQ/CQ.

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Kara: 1,008 patient favipiravir early treatment RCT with results not reported over 2 years after completion.

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Khamis: Small 89 patient RCT comparing favipiravir and inhaled interferon with HCQ for moderate to severe COVID-19 pneumonia, not finding significant differences. There was no control group.

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