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Phan T, Zitzmann C, Chew KW, Smith DM, Daar ES, Wohl DA, Eron JJ, Currier JS, Hughes MD, Choudhary MC, Deo R, Li JZ, Ribeiro RM, Ke R, Perelson AS. Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody. PLoS Pathog 2024; 20:e1011680. [PMID: 38635853 PMCID: PMC11060554 DOI: 10.1371/journal.ppat.1011680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 04/30/2024] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.
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Affiliation(s)
- Tin Phan
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Carolin Zitzmann
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Kara W. Chew
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Davey M. Smith
- Department of Medicine, University of California, San Diego, California, United States of America
| | - Eric S. Daar
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California, United States of America
| | - David A. Wohl
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Joseph J. Eron
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States of America
| | - Judith S. Currier
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| | - Michael D. Hughes
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Manish C. Choudhary
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rinki Deo
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Jonathan Z. Li
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ruy M. Ribeiro
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Ruian Ke
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alan S. Perelson
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
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2
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Phan T, Zitzmann C, Chew KW, Smith DM, Daar ES, Wohl DA, Eron JJ, Currier JS, Hughes MD, Choudhary MC, Deo R, Li JZ, Ribeiro RM, Ke R, Perelson AS. Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557679. [PMID: 37745410 PMCID: PMC10515893 DOI: 10.1101/2023.09.14.557679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The COVID-19 pandemic has led to over 760 million cases and 6.9 million deaths worldwide. To mitigate the loss of lives, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with susceptible variants. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3-4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response anti-viral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.
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Affiliation(s)
- Tin Phan
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Carolin Zitzmann
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Kara W. Chew
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Davey M. Smith
- Department of Medicine, University of California, San Diego, CA, USA
| | - Eric S. Daar
- Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - David A. Wohl
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Joseph J. Eron
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Judith S. Currier
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | | | - Manish C. Choudhary
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Rinki Deo
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan Z. Li
- Department of Medicine, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ruy M. Ribeiro
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ruian Ke
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Alan S. Perelson
- Theoretical Biology & Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
- Santa Fe Institute, Santa Fe, NM, USA
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3
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Li H, Kuga K, Ito K. Visual prediction and parameter optimization of viral dynamics in the mucus milieu of the upper airway based on CFPD-HCD analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107622. [PMID: 37257372 DOI: 10.1016/j.cmpb.2023.107622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/24/2023] [Accepted: 05/24/2023] [Indexed: 06/02/2023]
Abstract
BACKGROUND AND OBJECTIVE Respiratory diseases caused by viruses are a major human health problem. To better control the infection and understand the pathogenesis of these diseases, this paper studied SARS-CoV-2, a novel coronavirus outbreak, as an example. METHODS Based on coupled computational fluid and particle dynamics (CFPD) and host-cell dynamics (HCD) analyses, we studied the viral dynamics in the mucus layer of the human nasal cavity-nasopharynx. To reproduce the effect of mucociliary movement on the diffusive and convective transport of viruses in the mucus layer, a 3D-shell model was constructed using CT data of the upper respiratory tract (URT) of volunteers. Considering the mucus environment, the HCD model was established by coupling the target cell-limited model with the convection-diffusion term. Parameter optimization of the HCD model is the key problem in the simulation. Therefore, this study focused on the parameter optimization of the viral dynamics model, divided the geometric model into multiple compartments, and used Monolix to perform the nonlinear mixed effects (NLME) of pharmacometrics to discuss the influence of factors such as the number of mucus layers, number of compartments, diffusion rate, and mucus flow velocity on the prediction results. RESULTS The findings showed that sufficient experimental data can be used to estimate the corresponding parameters of the HCD model. The optimized convection-diffusion case with a two-layer multi-compartment low-velocity model could accurately predict the viral dynamics. CONCLUSIONS Its visualization process could explain the symptoms of the disease in the nose and contribute to the prevention and targeted treatment of respiratory diseases.
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Affiliation(s)
- Hanyu Li
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Japan.
| | - Kazuki Kuga
- Faculty of Engineering Sciences, Kyushu University, Japan
| | - Kazuhide Ito
- Faculty of Engineering Sciences, Kyushu University, Japan
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4
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Elaiw AM, Alsaedi AJ, Hobiny AD, Aly S. Stability of a delayed SARS-CoV-2 reactivation model with logistic growth and adaptive immune response. PHYSICA A 2023; 616:128604. [PMID: 36909816 PMCID: PMC9957504 DOI: 10.1016/j.physa.2023.128604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/05/2022] [Indexed: 06/18/2023]
Abstract
This paper develops and analyzes a SARS-CoV-2 dynamics model with logistic growth of healthy epithelial cells, CTL immune and humoral (antibody) immune responses. The model is incorporated with four mixed (distributed/discrete) time delays, delay in the formation of latent infected epithelial cells, delay in the formation of active infected epithelial cells, delay in the activation of latent infected epithelial cells, and maturation delay of new SARS-CoV-2 particles. We establish that the model's solutions are non-negative and ultimately bounded. We deduce that the model has five steady states and their existence and stability are perfectly determined by four threshold parameters. We study the global stability of the model's steady states using Lyapunov method. The analytical results are enhanced by numerical simulations. The impact of intracellular time delays on the dynamical behavior of the SARS-CoV-2 is addressed. We noted that increasing the time delay period can suppress the viral replication and control the infection. This could be helpful to create new drugs that extend the delay time period.
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Affiliation(s)
- A M Elaiw
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - A J Alsaedi
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
- Department of Mathematics, University College in Al-Jamoum, Umm Al-Qura University, P.O. Box 715, Makkah 21955, Saudi Arabia
| | - A D Hobiny
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia
| | - S Aly
- Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut Branch, Assiut, Egypt
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5
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Zhang W, Liu S, Osgood N, Zhu H, Qian Y, Jia P. Using simulation modelling and systems science to help contain COVID-19: A systematic review. SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE 2022; 40:SRES2897. [PMID: 36245570 PMCID: PMC9538520 DOI: 10.1002/sres.2897] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 05/23/2022] [Accepted: 08/03/2022] [Indexed: 06/16/2023]
Abstract
This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent-based model (ABM) and discrete event simulation (DES), and their hybrids in COVID-19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID-19 transmission dynamics, 204 evaluated both pharmaceutical and non-pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID-19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID-19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio-economic systems involved.
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Affiliation(s)
- Weiwei Zhang
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Shiyong Liu
- Institute of Advanced Studies in Humanities and Social SciencesBeijing Normal University at ZhuhaiZhuhaiChina
| | - Nathaniel Osgood
- Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
- Department of Community Health and EpidemiologyUniversity of SaskatchewanSaskatoonCanada
| | - Hongli Zhu
- Research Institute of Economics and ManagementSouthwestern University of Finance and EconomicsChengduChina
| | - Ying Qian
- Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina
| | - Peng Jia
- School of Resource and Environmental SciencesWuhan UniversityWuhanHubeiChina
- International Institute of Spatial Lifecourse HealthWuhan UniversityWuhanHubeiChina
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6
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Du Z, Wang S, Bai Y, Gao C, Lau EHY, Cowling BJ. Within-host dynamics of SARS-CoV-2 infection: A systematic review and meta-analysis. Transbound Emerg Dis 2022; 69:3964-3971. [PMID: 35907777 PMCID: PMC9353427 DOI: 10.1111/tbed.14673] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/23/2022] [Accepted: 07/28/2022] [Indexed: 02/04/2023]
Abstract
Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/ml]-1 day-1 ) and 0.92 (95% CI:-0.09, 1.93) day-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.
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Affiliation(s)
- Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Shuqi Wang
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Chao Gao
- School of Artificial Intelligence, Optics, and Electronics (iOPEN)Northwestern Polytechnical UniversityXianChina
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
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7
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Global Stability of a Humoral Immunity COVID-19 Model with Logistic Growth and Delays. MATHEMATICS 2022. [DOI: 10.3390/math10111857] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The mathematical modeling and analysis of within-host or between-host coronavirus disease 2019 (COVID-19) dynamics are considered robust tools to support scientific research. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of COVID-19. This paper proposes and investigates a within-host COVID-19 dynamics model with latent infection, the logistic growth of healthy epithelial cells and the humoral (antibody) immune response. Time delays can affect the dynamics of SARS-CoV-2 infection predicted by mathematical models. Therefore, we incorporate four time delays into the model: (i) delay in the formation of latent infected epithelial cells, (ii) delay in the formation of active infected epithelial cells, (iii) delay in the activation of latent infected epithelial cells, and (iv) maturation delay of new SARS-CoV-2 particles. We establish that the model’s solutions are non-negative and ultimately bounded. This confirms that the concentrations of the virus and cells should not become negative or unbounded. We deduce that the model has three steady states and their existence and stability are perfectly determined by two threshold parameters. We use Lyapunov functionals to confirm the global stability of the model’s steady states. The analytical results are enhanced by numerical simulations. The effect of time delays on the SARS-CoV-2 dynamics is investigated. We observe that increasing time delay values can have the same impact as drug therapies in suppressing viral progression. This offers some insight useful to develop a new class of treatment that causes an increase in the delay periods and then may control SARS-CoV-2 replication.
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8
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Bai Y, Shen M, Zhang L. Antiviral Efficacy of Molnupiravir for COVID-19 Treatment. Viruses 2022; 14:v14040763. [PMID: 35458493 PMCID: PMC9031952 DOI: 10.3390/v14040763] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 03/29/2022] [Accepted: 04/03/2022] [Indexed: 02/01/2023] Open
Abstract
The ongoing global pandemic of COVID-19 poses unprecedented public health risks for governments and societies around the world, which have been exacerbated by the emergence of SARS-CoV-2 variants. Pharmaceutical interventions with high antiviral efficacy are expected to delay and contain the COVID-19 pandemic. Molnupiravir, as an oral antiviral prodrug, is active against SARS-CoV-2 and is now (23 February 2022) one of the seven widely-used coronavirus treatments. To estimate its antiviral efficacy of Molnupiravir, we built a granular mathematical within-host model. We find that the antiviral efficacy of Molnupiravir to stop the growth of the virus is 0.56 (95% CI: 0.49, 0.64), which could inhibit 56% of the replication of infected cells per day. There has been good progress in developing high-efficacy antiviral drugs that rapidly reduce viral load and may also reduce the infectiousness of treated cases if administered as early as possible.
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Affiliation(s)
- Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Mingwang Shen
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China;
- Correspondence:
| | - Lei Zhang
- China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi’an Jiaotong University Health Science Center, Xi’an 710061, China;
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC 3004, Australia
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC 3800, Australia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
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9
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SARS-CoV-2 Dynamics in the Mucus Layer of the Human Upper Respiratory Tract Based on Host–Cell Dynamics. SUSTAINABILITY 2022. [DOI: 10.3390/su14073896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A thorough understanding of the inhalation dynamics of infectious aerosols indoors and infection dynamics within the host by inhaled viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in the assessment and control of infection risks indoors. Here, by combining computational fluid–particle dynamics (CFPD) and host–cell dynamics (HCD), SARS-CoV-2 infection dynamics in the mucus layer of the human upper airway were studied. To reproduce the diffusive and convective transport of the virus in the nasal cavity–nasopharynx by mucociliary motion, a three-dimensional (3D)-shell model with a mucus layer was developed. The initial virus concentrations for HCD calculation were estimated based on the deposition distribution of droplets with representative sizes analyzed by CFPD. To develop a new HCD model, the target-cell-limited model was integrated with the convection–diffusion equation. Additionally, the sensitivity of the infection rate β to the infection dynamics was systematically investigated. The results showed that the time series of SARS-CoV-2 concentration in the mucus layer strongly depended on diffusion, convection, and β. Although the SARS-CoV-2 dynamics obtained here have not been verified by corresponding clinical data, they can preliminarily reveal its transmission mode in the upper airway, which will contribute to the prevention and treatment of coronavirus disease 2019.
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10
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Fatehi F, Bingham RJ, Dechant PP, Stockley PG, Twarock R. Therapeutic interfering particles exploiting viral replication and assembly mechanisms show promising performance: a modelling study. Sci Rep 2021; 11:23847. [PMID: 34903795 PMCID: PMC8668974 DOI: 10.1038/s41598-021-03168-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/22/2021] [Indexed: 11/09/2022] Open
Abstract
Defective interfering particles arise spontaneously during a viral infection as mutants lacking essential parts of the viral genome. Their ability to replicate in the presence of the wild-type (WT) virus (at the expense of viable viral particles) is mimicked and exploited by therapeutic interfering particles. We propose a strategy for the design of therapeutic interfering RNAs (tiRNAs) against positive-sense single-stranded RNA viruses that assemble via packaging signal-mediated assembly. These tiRNAs contain both an optimised version of the virus assembly manual that is encoded by multiple dispersed RNA packaging signals and a replication signal for viral polymerase, but lack any protein coding information. We use an intracellular model for hepatitis C viral (HCV) infection that captures key aspects of the competition dynamics between tiRNAs and viral genomes for virally produced capsid protein and polymerase. We show that only a small increase in the assembly and replication efficiency of the tiRNAs compared with WT virus is required in order to achieve a treatment efficacy greater than 99%. This demonstrates that the proposed tiRNA design could be a promising treatment option for RNA viral infections.
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Affiliation(s)
- Farzad Fatehi
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
| | - Richard J Bingham
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
- Department of Biology, University of York, York, YO10 5DD, UK
| | - Pierre-Philippe Dechant
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
- School of Science, Technology and Health, York St John University, York, YO31 7EX, UK
| | - Peter G Stockley
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK.
| | - Reidun Twarock
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK.
- Department of Mathematics, University of York, York, YO10 5DD, UK.
- Department of Biology, University of York, York, YO10 5DD, UK.
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11
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Rodriguez T, Dobrovolny HM. Estimation of viral kinetics model parameters in young and aged SARS-CoV-2 infected macaques. ROYAL SOCIETY OPEN SCIENCE 2021; 8:202345. [PMID: 34804559 PMCID: PMC8595996 DOI: 10.1098/rsos.202345] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 10/25/2021] [Indexed: 06/13/2023]
Abstract
The SARS-CoV-2 virus disproportionately causes serious illness and death in older individuals. In order to have the greatest impact in decreasing the human toll caused by the virus, antiviral treatment should be targeted to older patients. For this, we need a better understanding of the differences in viral dynamics between SARS-CoV-2 infection in younger and older adults. In this study, we use previously published averaged viral titre measurements from the nose and throat of SARS-CoV-2 infection in young and aged cynomolgus macaques to parametrize a viral kinetics model. We find that all viral kinetics parameters differ between young and aged macaques in the nasal passages, but that there are fewer differences in parameter estimates from the throat. We further use our parametrized model to study the antiviral treatment of young and aged animals, finding that early antiviral treatment is more likely to lead to a lengthening of the infection in aged animals, but not in young animals.
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Affiliation(s)
- Thalia Rodriguez
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, USA
| | - Hana M. Dobrovolny
- Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX, USA
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12
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Contreras C, Newby JM, Hillen T. Personalized Virus Load Curves for Acute Viral Infections. Viruses 2021; 13:1815. [PMID: 34578396 PMCID: PMC8472998 DOI: 10.3390/v13091815] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 07/09/2021] [Accepted: 09/03/2021] [Indexed: 12/11/2022] Open
Abstract
We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.
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Affiliation(s)
- Carlos Contreras
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Jay M. Newby
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Thomas Hillen
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada; (C.C.); (J.M.N.)
- Collaborative Mathematical Biology Group, University of Alberta, Edmonton, AB T6G 2R3, Canada
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