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SARS-CoV-2 VOC type and biological sex affect molnupiravir efficacy in severe COVID-19 dwarf hamster model. Nat Commun 2022; 13:4416. [PMID: 35906230 PMCID: PMC9338273 DOI: 10.1038/s41467-022-32045-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/14/2022] [Indexed: 11/23/2022] Open
Abstract
SARS-CoV-2 variants of concern (VOC) have triggered infection waves. Oral antivirals such as molnupiravir promise to improve disease management, but efficacy against VOC delta was questioned and potency against omicron is unknown. This study evaluates molnupiravir against VOC in human airway epithelium organoids, ferrets, and a lethal Roborovski dwarf hamster model of severe COVID-19-like lung injury. VOC were equally inhibited by molnupiravir in cells and organoids. Treatment reduced shedding in ferrets and prevented transmission. Pathogenicity in dwarf hamsters was VOC-dependent and highest for delta, gamma, and omicron. All molnupiravir-treated dwarf hamsters survived, showing reduction in lung virus load from one (delta) to four (gamma) orders of magnitude. Treatment effect size varied in individual dwarf hamsters infected with omicron and was significant in males, but not females. The dwarf hamster model recapitulates mixed efficacy of molnupiravir in human trials and alerts that benefit must be reassessed in vivo as VOC evolve. Molnupiravir was the first orally available SARS-CoV-2 antiviral approved for outpatient use against SARS-CoV-2, but its efficacy against variants of concern, especially delta, was questioned. Here the authors evaluate molnupiravir against variant of concern in numerous models, including human airway epithelium organoids, ferrets and Roborovski dwarf hamsters.
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White JM, Schiffer JT, Bender Ignacio RA, Xu S, Kainov D, Ianevski A, Aittokallio T, Frieman M, Olinger GG, Polyak SJ. Drug Combinations as a First Line of Defense against Coronaviruses and Other Emerging Viruses. mBio 2021; 12:e0334721. [PMID: 34933447 PMCID: PMC8689562 DOI: 10.1128/mbio.03347-21] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The world was unprepared for coronavirus disease 2019 (COVID-19) and remains ill-equipped for future pandemics. While unprecedented strides have been made developing vaccines and treatments for COVID-19, there remains a need for highly effective and widely available regimens for ambulatory use for novel coronaviruses and other viral pathogens. We posit that a priority is to develop pan-family drug cocktails to enhance potency, limit toxicity, and avoid drug resistance. We urge cocktail development for all viruses with pandemic potential both in the short term (<1 to 2 years) and longer term with pairs of drugs in advanced clinical testing or repurposed agents approved for other indications. While significant efforts were launched against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in vitro and in the clinic, many studies employed solo drugs and had disappointing results. Here, we review drug combination studies against SARS-CoV-2 and other viruses and introduce a model-driven approach to assess drug pairs with the highest likelihood of clinical efficacy. Where component agents lack sufficient potency, we advocate for synergistic combinations to achieve therapeutic levels. We also discuss issues that stymied therapeutic progress against COVID-19, including testing of agents with low likelihood of efficacy late in clinical disease and lack of focus on developing virologic surrogate endpoints. There is a need to expedite efficient clinical trials testing drug combinations that could be taken at home by recently infected individuals and exposed contacts as early as possible during the next pandemic, whether caused by a coronavirus or another viral pathogen. The approach herein represents a proactive plan for global viral pandemic preparedness.
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Affiliation(s)
- Judith M. White
- University of Virginia, Department of Cell Biology, Charlottesville, Virginia, USA
- University of Virginia, Department of Microbiology, Charlottesville, Virginia, USA
| | - Joshua T. Schiffer
- University of Washington, Division of Allergy and Infectious Diseases, Seattle, Washington, USA
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Diseases Division, Seattle, Washington, USA
| | - Rachel A. Bender Ignacio
- University of Washington, Division of Allergy and Infectious Diseases, Seattle, Washington, USA
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Diseases Division, Seattle, Washington, USA
| | - Shuang Xu
- Fred Hutchinson Cancer Research Center, Vaccine and Infectious Diseases Division, Seattle, Washington, USA
| | - Denis Kainov
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Institute of Technology, University of Tartu, Tartu, Estonia
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Aleksandr Ianevski
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Oslo, Norway
- Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Matthew Frieman
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | | | - Stephen J. Polyak
- Virology Division, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Microbiology, University of Washington, Seattle, Washington, USA
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Vaidya NK, Bloomquist A, Perelson AS. Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets. Viruses 2021; 13:1635. [PMID: 34452499 PMCID: PMC8402735 DOI: 10.3390/v13081635] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/27/2021] [Accepted: 08/09/2021] [Indexed: 12/26/2022] Open
Abstract
The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies.
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Affiliation(s)
- Naveen K. Vaidya
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA;
- Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Angelica Bloomquist
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA;
- Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Alan S. Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, NM 87545, USA;
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