In silico studies predict inhibition of SARS-CoV-2 with montelukast or metabolites via binding to the spikeB,43 (and specifically the receptor binding domainC,44), M^proD,43, RNA-dependent RNA polymeraseE,43,45, PLproF,43, nucleocapsidG,43, and helicaseH,43 proteins. Montelukast inhibits SARS-CoV-2 omicron infection in Vero cells at 1μM44 and inhibits platelet activation induced by plasma from COVID-19 patients46.

3 in silico studies support the efficacy of montelukast43-45.

2 in vitro studies support the efficacy of montelukast44,46.

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 studies, for Randomized Controlled Trials, after exclusions, for specific outcomes, and by treatment stage. Fig. 4 plots individual results by treatment stage. Fig. 5, 6, 7, 8, 9, 10, and 11 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, ICU admission, hospitalization, progression, recovery, and cases.

Fig. 12 shows a comparison of results for RCTs and observational studies. Fig. 13, 14, and 15 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.

2 montelukast RCTs have not reported results1,2. The trials report report an estimated total of 664 patients. The results are delayed from 3 years to over 4 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 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. Fig. 17 shows a forest plot for random-effects meta-analysis of all studies after exclusions.

Low-cost treatments were subject to bias and censorship during the pandemic. Scientific bias is seen in the design, analysis, presentation, and selective reporting of studies, which often favored negative results. A similar bias is seen in the media coverage for low-cost treatments. While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."47.

We analyze media coverage for the 227 treatments we cover using Altmetric66 and PlumX Metrics67. Altmetric covers ~14,000 news outlets68 and PlumX covers ~55,000 news sources69. The two services are complementary with coverage of different news sources and different methods for identifying articles not containing a DOI. We consider studies to have received significant media coverage if they were covered by at least 100 articles in either Altmetric of PlumX69,70. The most widespread study was covered by 1,047 outlets. Studies covered by major syndicated sources shows hundreds of news articles, while studies covered by few outlets typically appear in niche medical news sources or press releases.

Fig. 18 and 19 analyze coverage for all low-cost treatments and all high-profit treatments, showing significant bias. For low-cost treatments media coverage favored negative results 5.4× more than positive results, whereas for high-profit treatments coverage favored positive results 3.8× more than negative results.

The media bias may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit treatments and treatment equity. High-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

A combination of factors may have led to the very low media coverage of low-cost treatments:

26 low-cost treatments were approved in one or more countries, yet many countries approved no low-cost treatments. The countries that did adopt low-cost treatments analyzed the evidence early and made timely approvals. With few exceptions, authorities did not change their initial views, regardless of how much evidence accumulated showing either efficacy or harm. Why?

The harms of smoking here hidden for 25 yearsJ. Authorities did not analyze the data in real-time, failing to act when harm was known. Widespread acknowledgement of harm came only after attempts by two new surgeon generals, along with pressure from health advocates and a new president, and a review of 7,000 studies.

Similarly for COVID-19, most authorities and experts did not proactively analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by which time moral, legal, career, and reputational liabilities strongly disincentivize any admission of error. Claims of no efficacy (for effective treatments) or safety (for harmful treatments) were often made prior to strong data being available. Correction would require admitting to errors that increased mortality, which is unlikely with the same generation of officials.

Analysis of potential treatments was rarely done, and when done these were typically minimal efforts. For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments, reference only 2% of studies for the treatments covered, and include no quantitative analysis. They appear as rarely updated side projects from external panels implicitly tasked with justifying prior failures. As with smoking, the thousands of studies could (and should) have been analyzed and acted on in real-time.

A key structural improvement, applicable to all current and future diseases, is for authorities to implement real-time proactive analysis of clinical evidence. This does not remove all bias, but does make it possible to act on evidence, whereas delayed action may be unlikely due to moral, legal, career, and reputational liabilities.

Heterogeneity in COVID-19 studies arises from many factors including:

Studies to date show that montelukast is an effective treatment for COVID-19. Significantly lower risk is seen for hospitalization and cases. 4 studies from 4 independent teams in 4 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 39% [14‑56%] lower risk. Results are similar for Randomized Controlled Trials and higher quality studies.

Montelukast has a boxed warning for neuropsychiatric side effects3.

Currently there is limited data, with only 37 control events for the most serious outcome in trials to date.

Retrospective 616 COVID-19 patients with asthma in Qatar showing no significant difference in hospitalization risk with montelukast use. Submit Corrections or Updates.

Retrospective 445 elderly patients with severe asthma showing reduced risk of COVID-19 infection with montelukast treatment. Submit Corrections or Updates.

Estimated 284 patient montelukast late treatment RCT with results not reported over 3 years after estimated completion. Submit Corrections or Updates.

Estimated 380 patient montelukast early treatment RCT with results not reported over 4 years after estimated completion. Submit Corrections or Updates.

RCT 180 hospitalized COVID-19 patients in Turkey showing faster reduction in inflammatory markers, improved pulmonary function, and lower rates of macrophage activation syndrome, respiratory failure and mortality with montelukast treatment (10mg or 20mg daily) in addition to standard care. The higher dose of 20mg daily showed greater improvement in pulmonary function compared to 10mg daily. There was no mortality in the montelukast groups compared to 6.7% mortality with standard care alone. Submit Corrections or Updates.

Retrospective 92 hospitalized patients showing lower clinical deterioration with montelukast treatment, without statistical significance in multivariable analysis. The treatment group was older. Submit Corrections or Updates.

RCT 90 mild to moderate COVID-19 patients showing no significant differences with montelukast treatment. Submit Corrections or Updates.

RCT 68 post-COVID-19 outpatients showing improvement in cough severity measures with montelukast treatment. The montelukast group had a greater reduction in number of cough paroxysms per day, cough severity visual analog scale, cough severity index, and improved cough quality of life scores compared to the control group. The montelukast group also had a shorter duration of cough. Submit Corrections or Updates.

RCT 1,250 outpatients with mild to moderate COVID-19 showing no significant difference in time to sustained recovery with montelukast treatment. There were no deaths and only 2 hospitalizations in each group.

Notably, results were better with patients that had mild COVID-19 at baseline compared to moderate/severe cases, and overall efficacy is reduced by poor results with extremely late treatment 9 days after onset, and with patients that had no symptoms at baseline.

Authors note the treatment drug was voluntarily recalled and replaced from another source but do not report why the drug was recalled. Authors describe previous research testing 10mg and 20mg doses, noting that only 20mg showed improved pulmonary function testing, however authors do not indicate why they chose to test the lower dose for COVID-19.

It is unclear why authors only report all-cause hospitalization and urgent care and do not report COVID-19 specific outcomes. Given the low rate of urgent care visits and hospitalization, and the expected baseline frequency of these events independent of COVID-19, most or all of these events may be unrelated to COVID-19.

Authors note that previous research showed improvements specifically for cough, and authors collected cough data, however no results for cough are reported.

ACTIV trial authors have reported a number of issues that may affect the reliability of the results in ACTIV trials including participant fraud158, biased participant demographics159, resource issues that may have led to protocol deviations159, differences in trial design including inconsistent inclusion/exclusion criteria159, participant self-selection bias158,159, underrepresentation of older patients due to web-based recruitment159, changes in treatment and public health policies during trials159, treatment delay determination from shipping logs and delivery that may not be directly to the patient158, variable placebo responses (e.g., oral vs. inhaled)160, logistical challenges maintaining blinding160, errors from complex data collection systems160, unplanned design changes including endpoint changes160, and inconsistent SoC across trial sites and time periods160. Submit Corrections or Updates.

RCT 180 hospitalized COVID-19 patients showing improved cough symptoms and shorter hospitalization with montelukast/gabapentin compared to gabapentin. For gabapentin vs. dextromethorphan there was no significant difference in hospitalization and reduced improvement in cough symptoms. Submit Corrections or Updates.

Retrospective 75 hospitalized COVID-19 patients over 60 in Turkey showing no significant differences with montelukast treatment. Submit Corrections or Updates.