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Hernández-Mitre MP, Morpeth SC, Venkatesh B, Hills TE, Davis J, Mahar RK, McPhee G, Jones M, Totterdell J, Tong SYC, Roberts JA. TMPRSS2 inhibitors for the treatment of COVID-19 in adults: a systematic review and meta-analysis of randomized clinical trials of nafamostat and camostat mesylate. Clin Microbiol Infect 2024; 30:743-754. [PMID: 38331253 DOI: 10.1016/j.cmi.2024.01.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/10/2024]
Abstract
BACKGROUND Synthetic serine protease inhibitors block the cellular enzyme transmembrane protease serine 2, thus preventing SARS-CoV-2 cell entry. There are two relevant drugs in this class, namely, nafamostat (intravenous formulation) and camostat (oral formulation). OBJECTIVE To determine whether transmembrane protease serine 2 inhibition with nafamostat or camostat is associated with a reduced risk of 30-day all-cause mortality in adults with COVID-19. DATA SOURCES Scientific databases and clinical trial registry platforms. STUDY ELIGIBILITY CRITERIA, INTERVENTIONS, AND PARTICIPANTS Preprints or published randomized clinical trials (RCTs) of nafamostat or camostat vs. usual care or placebo in adults requiring treatment for COVID-19. METHODS OF DATA SYNTHESIS AND RISK-OF-BIAS ASSESSMENT The primary outcome of the meta-analysis was 30-day all-cause mortality. Secondary outcomes included time to recovery, adverse events, and serious adverse events. Risk of bias (RoB) was assessed using the revised Cochrane RoB 2 tool for individually randomized trials. Meta-analysis was conducted in the R package meta (v7.0-0) using inverse variance and random effects. Protocol registration number was INPLASY202320120. RESULTS Twelve RCTs were included. Overall, the number of available patients was small (nafamostat = 387; camostat = 1061), the number of enrolled patients meeting the primary outcome was low (nafamostat = 12; camostat = 13), and heterogeneity was high. In hospitalized adults, we did not identify differences in 30-day all-cause mortality (risk ratio [95% CI]: 0.58 [0.19, 1.80], p 0.34; I2 = 0%; n = 6) and time to recovery (mean difference [95% CI]: 0.08 days [-0.74, 0.89], p 0.86; n = 2) between nafamostat vs. usual care; and for 30-day all-cause mortality (risk ratio [95% CI]: 0.99 [0.31, 3.18], p 0.99; n = 2) between camostat vs. placebo. CONCLUSION The RCT evidence is inconclusive to determine whether there is a mortality reduction and safety with either nafamostat or camostat for the treatment of adults with COVID-19. There were high RoB, small sample size, and high heterogeneity between RCTs.
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Affiliation(s)
| | - Susan C Morpeth
- Departments of Microbiology and Infectious Diseases, Middlemore Hospital, Te Whatu Ora Counties Manukau, New Zealand; Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Balasubramanian Venkatesh
- Intensive Care, Princess Alexandra Hospital, The University of Queensland, Brisbane, Queensland, Australia; Intensive Care, Wesley Hospital, Brisbane, Queensland, Australia; The George Institute for Global Health, UNSW Sydney, New South Wales, Australia
| | - Thomas E Hills
- Departments of Immunology and Infectious Diseases, Auckland District Health Board, Auckland, New Zealand; Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Joshua Davis
- Infection Research Program, Hunter Medical Research Institute, Univerity of Newcastle, Newcastle, New South Wales, Australia
| | - Robert K Mahar
- Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Grace McPhee
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Mark Jones
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - James Totterdell
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Steven Y C Tong
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; Victorian Infectious Diseases Service, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Jason A Roberts
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Herston, Queensland, Australia; Departments of Intensive Care Medicine and Pharmacy, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia; Division of Anaesthesiology Critical Care Emergency and Pain Medicine, Nîmes University Hospital, University of Montpellier, Nîmes, France; Herston Infectious Diseases Institute (HeIDI), Metro North Health, Herston, Queensland, Australia.
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Gunst JD, Søgaard OS. Host Receptor Targeting to Treat Covid-19. NEJM EVIDENCE 2023; 2:EVIDe2300222. [PMID: 38320534 DOI: 10.1056/evide2300222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Not long after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified as the cause of coronavirus disease 2019 (Covid-19), in vitro experiments revealed that SARS-CoV-2 infection of human cells depended on the binding of the viral spike protein to the human cell-surface receptor angiotensin-converting enzyme 2 (ACE-2).1 Additional experiments demonstrated that infection could be blocked by inhibiting transmembrane protease, serine 2 (TMPRSS2), which is a host enzyme that cleaves the viral spike protein after binding to ACE-2 and facilitates entry of the virus into the host cell.
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Affiliation(s)
- Jesper Damsgaard Gunst
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Infectious Diseases, Aarhus University Hospital, Aarhus, Denmark
| | - Ole Schmeltz Søgaard
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Infectious Diseases, Aarhus University Hospital, Aarhus, Denmark
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Siddique J, Aghabazaz Z. Prior Ground: Selection of Prior Distributions When Analyzing Clinical Trial Data Using Bayesian Methods. NEJM EVIDENCE 2023; 2:EVIDe2300250. [PMID: 38320533 PMCID: PMC11197078 DOI: 10.1056/evide2300250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Increasingly, investigators are choosing to use Bayesian methods for the analysis of clinical trial data. Unlike classical statistical methods that treat model parameter values (such as treatment effects) as fixed, Bayesian methods view parameters as following a probability distribution. As we have written previously,1 by analyzing clinical trial data using Bayesian methods one can obtain quantities that may be of interest to clinicians, providers, and patients, such as the probability that a treatment effect is more or less than 0, that is, the probability that a treatment is effective.
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Affiliation(s)
- Juned Siddique
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago
| | - Zeynab Aghabazaz
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago
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