51
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Beahm DR, Deng Y, DeAngelo TM, Sarpeshkar R. Drug Cocktail Formulation via Circuit Design. IEEE TRANSACTIONS ON MOLECULAR, BIOLOGICAL, AND MULTI-SCALE COMMUNICATIONS 2023; 9:28-48. [PMID: 37397625 PMCID: PMC10312325 DOI: 10.1109/tmbmc.2023.3246928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Drug cocktail therapies are a powerful tool against diseases that exhibit such dynamics. We show that just six key states, which are represented in a feedback circuit, enable drug-cocktail formulation: 1) healthy cell number; 2) infected cell number; 3) extracellular pathogen number; 4) intracellular pathogenic molecule number; 5) innate immune system strength; and 6) adaptive immune system strength. To enable drug cocktail formulation, the model represents the effects of the drugs in the circuit. For example, a nonlinear feedback circuit model fits measured clinical data, represents cytokine storm and adaptive autoimmune behavior, and accounts for age, sex, and variant effects for SARS-CoV-2 with few free parameters. The latter circuit model provided three quantitative insights on the optimal timing and dosage of drug components in a cocktail: 1) antipathogenic drugs should be given early in the infection, but immunosuppressant timing involves a tradeoff between controlling pathogen load and mitigating inflammation; 2) both within and across-class combinations of drugs have synergistic effects; 3) if they are administered sufficiently early in the infection, anti-pathogenic drugs are more effective at mitigating autoimmune behavior than immunosuppressant drugs.
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Affiliation(s)
| | - Yijie Deng
- Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Thomas M DeAngelo
- Thayer School or Engineering, Dartmouth College, Hanover, NH 03755 USA
| | - Rahul Sarpeshkar
- Departments of Engineering, Physics, Microbiology & Immunobiology, and Molecular & Systems Biology, Dartmouth College, Hanover, NH 03755 USA
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52
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Gazeau S, Deng X, Ooi HK, Mostefai F, Hussin J, Heffernan J, Jenner AL, Craig M. The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2023; 9:100021. [PMID: 36643886 PMCID: PMC9826539 DOI: 10.1016/j.immuno.2023.100021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/16/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.
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Affiliation(s)
- Sonia Gazeau
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Xiaoyan Deng
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
| | - Hsu Kiang Ooi
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Fatima Mostefai
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Julie Hussin
- Montréal Heart Institute Research Centre, Montréal, Canada
- Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, Canada
| | - Jane Heffernan
- Modelling Infection and Immunity Lab, Mathematics Statistics, York University, Toronto, Canada
- Centre for Disease Modelling (CDM), Mathematics Statistics, York University, Toronto, Canada
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane Australia
| | - Morgan Craig
- Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
- Sainte-Justine University Hospital Research Centre, Montréal, Canada
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53
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Practical Understanding of Cancer Model Identifiability in Clinical Applications. Life (Basel) 2023; 13:life13020410. [PMID: 36836767 PMCID: PMC9961656 DOI: 10.3390/life13020410] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/28/2023] [Accepted: 01/29/2023] [Indexed: 02/05/2023] Open
Abstract
Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual's characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.
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54
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Chen A, Wessler T, Gregory Forest M. Antibody protection from SARS-CoV-2 respiratory tract exposure and infection. J Theor Biol 2023; 557:111334. [PMID: 36306828 PMCID: PMC9597531 DOI: 10.1016/j.jtbi.2022.111334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 10/12/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
The COVID-19 pandemic has underscored the need to understand the dynamics of SARS-CoV-2 respiratory infection and protection provided by the immune response. SARS-CoV-2 infections are characterized by a particularly high viral load, and further by the small number of inhaled virions sufficient to generate a high viral titer in the nasal passage a few days after exposure. SARS-CoV-2 specific antibodies (Ab), induced from vaccines, previous infection, or inhaled monoclonal Ab, have proven effective against SARS-CoV-2 infection. Our goal in this work is to model the protective mechanisms that Ab can provide and to assess the degree of protection from individual and combined mechanisms at different locations in the respiratory tract. Neutralization, in which Ab bind to virion spikes and inhibit them from binding to and infecting target cells, is one widely reported protective mechanism. A second mechanism of Ab protection is muco-trapping, in which Ab crosslink virions to domains on mucin polymers, effectively immobilizing them in the mucus layer. When muco-trapped, the continuous clearance of the mucus barrier by coordinated ciliary propulsion entrains the trapped viral load toward the esophagus to be swallowed. We model and simulate the protection provided by either and both mechanisms at different locations in the respiratory tract, parametrized by the Ab titer and binding-unbinding rates of Ab to viral spikes and mucin domains. Our results illustrate limits in the degree of protection by neutralizing Ab alone, the powerful protection afforded by muco-trapping Ab, and the potential for dual protection by muco-trapping and neutralizing Ab to arrest a SARS-CoV-2 infection. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Alex Chen
- Department of Mathematics, California State University-Dominguez Hills, Carson, CA 90747, USA.
| | - Timothy Wessler
- Department of Mathematics, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA
| | - M. Gregory Forest
- Department of Mathematics, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA,Department of Applied Physical Sciences, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599, USA,UNC/NCSU Joint Department of Biomedical Engineering, University of North Carolina—Chapel Hill, Chapel Hill, NC 27599 and North Carolina State University, Raleigh, NC 27606, USA
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55
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Phan T, Brozak S, Pell B, Gitter A, Xiao A, Mena KD, Kuang Y, Wu F. A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159326. [PMID: 36220466 PMCID: PMC9547654 DOI: 10.1016/j.scitotenv.2022.159326] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/15/2022] [Accepted: 10/05/2022] [Indexed: 06/12/2023]
Abstract
Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in a population-level SEIR model. We demonstrated that the effect of temperature on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020-Jan 25, 2021) in the Greater Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 6-16 days and 8.3-10.2 folds (R = 0.93). This work showcases a simple yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, NM, USA
| | - Samantha Brozak
- School of Mathematical and Statistical Sciences, Arizona State University, AZ, USA
| | - Bruce Pell
- Department of Mathematics and Computer Science, Lawrence Technological University, MI, USA
| | - Anna Gitter
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030
| | - Amy Xiao
- Center for Microbiome Informatics and Therapeutics; Department of Biological Engineering, Massachusetts Institute of Technology
| | - Kristina D Mena
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, AZ, USA.
| | - Fuqing Wu
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, TX, USA 77030.
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56
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Hattaf K, El Karimi MI, Mohsen AA, Hajhouji Z, El Younoussi M, Yousfi N. Mathematical Modeling and Analysis of the Dynamics of RNA Viruses in Presence of Immunity and Treatment: A Case Study of SARS-CoV-2. Vaccines (Basel) 2023; 11:vaccines11020201. [PMID: 36851079 PMCID: PMC9959189 DOI: 10.3390/vaccines11020201] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/08/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
The emergence of novel RNA viruses like SARS-CoV-2 poses a greater threat to human health. Thus, the main objective of this article is to develop a new mathematical model with a view to better understand the evolutionary behavior of such viruses inside the human body and to determine control strategies to deal with this type of threat. The developed model takes into account two modes of transmission and both classes of infected cells that are latently infected cells and actively infected cells that produce virus particles. The cure of infected cells in latent period as well as the lytic and non-lytic immune response are considered into the model. We first show that the developed model is well-posed from the biological point of view by proving the non-negativity and boundedness of model's solutions. Our analytical results show that the dynamical behavior of the model is fully determined by two threshold parameters one for viral infection and the other for humoral immunity. The effect of antiviral treatment is also investigated. Furthermore, numerical simulations are presented in order to illustrate our analytical results.
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Affiliation(s)
- Khalid Hattaf
- Equipe de Recherche en Modélisation et Enseignement des Mathématiques (ERMEM), Centre Régional des Métiers de l’Education et de la Formation (CRMEF), Derb Ghalef, Casablanca 20340, Morocco
- Laboratory of Analysis, Modeling and Simulation (LAMS), Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Sidi Othman, Casablanca P.O. Box 7955, Morocco
- Correspondence:
| | - Mly Ismail El Karimi
- Laboratory of Analysis, Modeling and Simulation (LAMS), Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Sidi Othman, Casablanca P.O. Box 7955, Morocco
| | - Ahmed A. Mohsen
- Department of Mathematics, College of Education for Pure Science (Ibn Al-Haitham), University of Baghdad, Baghdad 10071, Iraq
- Ministry of Education, Baghdad 10071, Iraq
| | - Zakaria Hajhouji
- Laboratory of Analysis, Modeling and Simulation (LAMS), Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Sidi Othman, Casablanca P.O. Box 7955, Morocco
| | - Majda El Younoussi
- Laboratory of Analysis, Modeling and Simulation (LAMS), Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Sidi Othman, Casablanca P.O. Box 7955, Morocco
| | - Noura Yousfi
- Laboratory of Analysis, Modeling and Simulation (LAMS), Faculty of Sciences Ben M’Sick, Hassan II University of Casablanca, Sidi Othman, Casablanca P.O. Box 7955, Morocco
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57
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Elaiw AM, Alsulami RS, Hobiny AD. Global dynamics of IAV/SARS-CoV-2 coinfection model with eclipse phase and antibody immunity. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3873-3917. [PMID: 36899609 DOI: 10.3934/mbe.2023182] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Coronavirus disease 2019 (COVID-19) and influenza are two respiratory infectious diseases of high importance widely studied around the world. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while influenza is caused by one of the influenza viruses, A, B, C, and D. Influenza A virus (IAV) can infect a wide range of species. Studies have reported several cases of respiratory virus coinfection in hospitalized patients. IAV mimics the SARS-CoV-2 with respect to the seasonal occurrence, transmission routes, clinical manifestations and related immune responses. The present paper aimed to develop and investigate a mathematical model to study the within-host dynamics of IAV/SARS-CoV-2 coinfection with the eclipse (or latent) phase. The eclipse phase is the period of time that elapses between the viral entry into the target cell and the release of virions produced by that newly infected cell. The role of the immune system in controlling and clearing the coinfection is modeled. The model simulates the interaction between nine compartments, uninfected epithelial cells, latent/active SARS-CoV-2-infected cells, latent/active IAV-infected cells, free SARS-CoV-2 particles, free IAV particles, SARS-CoV-2-specific antibodies and IAV-specific antibodies. The regrowth and death of the uninfected epithelial cells are considered. We study the basic qualitative properties of the model, calculate all equilibria, and prove the global stability of all equilibria. The global stability of equilibria is established using the Lyapunov method. The theoretical findings are demonstrated via numerical simulations. The importance of considering the antibody immunity in the coinfection dynamics model is discussed. It is found that without modeling the antibody immunity, the case of IAV and SARS-CoV-2 coexistence will not occur. Further, we discuss the effect of IAV infection on the dynamics of SARS-CoV-2 single infection and vice versa.
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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
| | - Raghad S Alsulami
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia
| | - A D Hobiny
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia
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58
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Xue Y, Chen D, Smith SR, Ruan X, Tang S. Coupling the Within-Host Process and Between-Host Transmission of COVID-19 Suggests Vaccination and School Closures are Critical. Bull Math Biol 2023; 85:6. [PMID: 36536179 PMCID: PMC9762651 DOI: 10.1007/s11538-022-01104-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 11/02/2022] [Indexed: 12/23/2022]
Abstract
Most models of COVID-19 are implemented at a single micro or macro scale, ignoring the interplay between immune response, viral dynamics, individual infectiousness and epidemiological contact networks. Here we develop a data-driven model linking the within-host viral dynamics to the between-host transmission dynamics on a multilayer contact network to investigate the potential factors driving transmission dynamics and to inform how school closures and antiviral treatment can influence the epidemic. Using multi-source data, we initially determine the viral dynamics and estimate the relationship between viral load and infectiousness. Then, we embed the viral dynamics model into a four-layer contact network and formulate an agent-based model to simulate between-host transmission. The results illustrate that the heterogeneity of immune response between children and adults and between vaccinated and unvaccinated infections can produce different transmission patterns. We find that school closures play a significant effect on mitigating the pandemic as more adults get vaccinated and the virus mutates. If enough infected individuals are diagnosed by testing before symptom onset and then treated quickly, the transmission can be effectively curbed. Our multiscale model reveals the critical role played by younger individuals and antiviral treatment with testing in controlling the epidemic.
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Affiliation(s)
- Yuyi Xue
- grid.43169.390000 0001 0599 1243School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049 People’s Republic of China
| | - Daipeng Chen
- grid.43169.390000 0001 0599 1243School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049 People’s Republic of China ,grid.5132.50000 0001 2312 1970Mathematical Institute, Leiden University, Leiden, The Netherlands
| | - Stacey R. Smith
- grid.28046.380000 0001 2182 2255The Department of Mathematics and Faculty of Medicine, The University of Ottawa, Ottawa, Canada
| | - Xiaoe Ruan
- grid.43169.390000 0001 0599 1243School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049 People’s Republic of China
| | - Sanyi Tang
- School of Mathematics and Statistics, Shaanxi Normal university, Xi'an, 710062, People's Republic of China.
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59
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A simple in-host model for COVID-19 with treatments: model prediction and calibration. J Math Biol 2023; 86:20. [PMID: 36625956 PMCID: PMC9838461 DOI: 10.1007/s00285-022-01849-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/14/2022] [Accepted: 12/01/2022] [Indexed: 01/11/2023]
Abstract
In this paper, we provide a simple ODEs model with a generic nonlinear incidence rate function and incorporate two treatments, blocking the virus binding and inhibiting the virus replication to investigate the impact of calibration on model predictions for the SARS-CoV-2 infection dynamics. We derive conditions of the infection eradication for the long-term dynamics using the basic reproduction number, and complement the characterization of the dynamics at short-time using the resilience and reactivity of the virus-free equilibrium are considered to inform on the average time of recovery and sensitivity to perturbations in the initial virus free stage. Then, we calibrate the treatment model to clinical datasets for viral load in mild and severe cases and immune cells in severe cases. Based on the analysis, the model calibrated to these different datasets predicts distinct scenarios: eradication with a non reactive virus-free equilibrium, eradication with a reactive virus-free equilibrium, and failure of infection eradication. Moreover, severe cases generate richer dynamics and different outcomes with the same treatment. Calibration to different datasets can lead to diverse model predictions, but combining long- and short-term dynamics indicators allows the categorization of model predictions and determination of infection severity.
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60
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Aristotelous AC, Chen A, Forest MG. A hybrid discrete-continuum model of immune responses to SARS-CoV-2 infection in the lung alveolar region, with a focus on interferon induced innate response. J Theor Biol 2022; 555:111293. [PMID: 36208668 PMCID: PMC9533651 DOI: 10.1016/j.jtbi.2022.111293] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/16/2022] [Accepted: 09/21/2022] [Indexed: 01/14/2023]
Abstract
We develop a lattice-based, hybrid discrete-continuum modeling framework for SARS-CoV-2 exposure and infection in the human lung alveolar region, or parenchyma, the massive surface area for gas exchange. COVID-19 pneumonia is alveolar infection by the SARS-CoV-2 virus significant enough to compromise gas exchange. The modeling framework orchestrates the onset and progression of alveolar infection, spatially and temporally, beginning with a pre-immunity baseline, upon which we superimpose multiple mechanisms of immune protection conveyed by interferons and antibodies. The modeling framework is tunable to individual profiles, focusing here on degrees of innate immunity, and to the evolving infection-replication properties of SARS-CoV-2 variant strains. The model employs partial differential equations for virion, interferon, and antibody concentrations governed by diffusion in the thin fluid coating of alveolar cells, species and lattice interactions corresponding to sources and sinks for each species, and multiple immune protections signaled by interferons. The spatial domain is a two-dimensional, rectangular lattice of alveolar type I (non-infectable) and type II (infectable) cells with a stochastic, species-concentration-governed, switching dynamics of type II lattice sites from healthy to infected. Once infected, type II cells evolve through three phases: an eclipse phase during which RNA copies (virions) are assembled; a shedding phase during which virions and interferons are released; and then cell death. Model simulations yield the dynamic spread of, and immune protection against, alveolar infection and viral load from initial sites of exposure. We focus in this paper on model illustrations of the diversity of outcomes possible from alveolar infection, first absent of immune protection, and then with varying degrees of four known mechanisms of interferon-induced innate immune protection. We defer model illustrations of antibody protection to future studies. Results presented reinforce previous recognition that interferons produced solely by infected cells are insufficient to maintain a high efficacy level of immune protection, compelling additional mechanisms to clear alveolar infection, such as interferon production by immune cells and adaptive immunity (e.g., T cells). This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".
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Affiliation(s)
- Andreas C. Aristotelous
- Department of Mathematics, The University of Akron, Akron, OH 44325-4002, United States of America,Corresponding author
| | - Alex Chen
- Department of Mathematics, California State University, Dominguez Hills, CA 90747, United States of America
| | - M. Gregory Forest
- Departments of Mathematics, Applied Physical Sciences, and Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3250, United States of America
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61
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Hadjichrysanthou C, Beukenhorst AL, Koch CM, Alter G, Goudsmit J, Anderson RM, de Wolf F. Exploring the Role of Antiviral Nasal Sprays in the Control of Emerging Respiratory Infections in the Community. Infect Dis Ther 2022; 11:2287-2296. [PMID: 36309921 PMCID: PMC9618272 DOI: 10.1007/s40121-022-00710-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 09/30/2022] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION The COVID-19 pandemic has demonstrated that there is an unmet need for the development of novel prophylactic antiviral treatments to control the outbreak of emerging respiratory virus infections. Passive antibody-based immunisation approaches such as intranasal antibody prophylaxis have the potential to provide immediately accessible universal protection as they act directly at the most common route of viral entry, the upper respiratory tract. The need for such products is very apparent for SARS-CoV-2 at present, given the relatively low effectiveness of vaccines to prevent infection and block virus onward transmission. We explore the benefits and challenges of the use of antibody-based nasal sprays prior and post exposure to the virus. METHODS The classic susceptible-exposed-infectious-removed (SEIR) mathematical model was extended to describe the potential population-level impact of intranasal antibody prophylaxis on controlling the spread of an emerging respiratory infection in the community. RESULTS Intranasal administration of monoclonal antibodies provides only a short-term protection to the mucosal surface. Consequently, sustained intranasal antibody prophylaxis of a substantial proportion of the population would be needed to contain infections. Post-exposure prophylaxis against the development of severe disease would be essential for the overall reduction in hospital admissions. CONCLUSION Antibody-based nasal sprays could provide protection against infection to individuals that are likely to be exposed to the virus. Large-scale administration for a long period of time would be challenging. Intranasal antibody prophylaxis alone cannot prevent community-wide transmission of the virus. It could be used along with other protective measures, such as non-pharmaceutical interventions, to bridge the time required to develop and produce effective vaccines, and complement active immunisation strategies.
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Affiliation(s)
| | - Anna L. Beukenhorst
- Leyden Laboratories B.V., Leiden, The Netherlands ,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | | | - Galit Alter
- Leyden Laboratories B.V., Leiden, The Netherlands ,Ragon Institute of MGH, MIT and Harvard, Cambridge, MA USA
| | - Jaap Goudsmit
- Leyden Laboratories B.V., Leiden, The Netherlands ,Departments of Epidemiology, Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA USA
| | - Roy M. Anderson
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
| | - Frank de Wolf
- Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK
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62
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Hay JA, Kissler SM, Fauver JR, Mack C, Tai CG, Samant RM, Connolly S, Anderson DJ, Khullar G, MacKay M, Patel M, Kelly S, Manhertz A, Eiter I, Salgado D, Baker T, Howard B, Dudley JT, Mason CE, Nair M, Huang Y, DiFiori J, Ho DD, Grubaugh ND, Grad YH. Quantifying the impact of immune history and variant on SARS-CoV-2 viral kinetics and infection rebound: A retrospective cohort study. eLife 2022; 11:81849. [PMID: 36383192 PMCID: PMC9711520 DOI: 10.7554/elife.81849] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/15/2022] [Indexed: 11/17/2022] Open
Abstract
Background The combined impact of immunity and SARS-CoV-2 variants on viral kinetics during infections has been unclear. Methods We characterized 1,280 infections from the National Basketball Association occupational health cohort identified between June 2020 and January 2022 using serial RT-qPCR testing. Logistic regression and semi-mechanistic viral RNA kinetics models were used to quantify the effect of age, variant, symptom status, infection history, vaccination status and antibody titer to the founder SARS-CoV-2 strain on the duration of potential infectiousness and overall viral kinetics. The frequency of viral rebounds was quantified under multiple cycle threshold (Ct) value-based definitions. Results Among individuals detected partway through their infection, 51.0% (95% credible interval [CrI]: 48.3-53.6%) remained potentially infectious (Ct <30) 5 days post detection, with small differences across variants and vaccination status. Only seven viral rebounds (0.7%; N=999) were observed, with rebound defined as 3+days with Ct <30 following an initial clearance of 3+days with Ct ≥30. High antibody titers against the founder SARS-CoV-2 strain predicted lower peak viral loads and shorter durations of infection. Among Omicron BA.1 infections, boosted individuals had lower pre-booster antibody titers and longer clearance times than non-boosted individuals. Conclusions SARS-CoV-2 viral kinetics are partly determined by immunity and variant but dominated by individual-level variation. Since booster vaccination protects against infection, longer clearance times for BA.1-infected, boosted individuals may reflect a less effective immune response, more common in older individuals, that increases infection risk and reduces viral RNA clearance rate. The shifting landscape of viral kinetics underscores the need for continued monitoring to optimize isolation policies and to contextualize the health impacts of therapeutics and vaccines. Funding Supported in part by CDC contract #200-2016-91779, a sponsored research agreement to Yale University from the National Basketball Association contract #21-003529, and the National Basketball Players Association.
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Affiliation(s)
- James A Hay
- Harvard TH Chan School of Public HealthBostonUnited States
| | | | - Joseph R Fauver
- Yale School of Public HealthNew HavenUnited States
- University of Nebraska Medical CenterOmahaUnited States
| | | | | | | | | | - Deverick J Anderson
- Duke Center for Antimicrobial Stewardship and Infection PreventionDurhamUnited States
| | | | | | | | | | | | | | | | | | | | | | | | - Manoj Nair
- Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | - Yaoxing Huang
- Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | - John DiFiori
- Hospital for Special SurgeryNew YorkUnited States
- National Basketball AssociationNew YorkUnited States
| | - David D Ho
- Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | | | - Yonatan H Grad
- Harvard TH Chan School of Public HealthBostonUnited States
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63
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Mohammadi M, Antoine D, Vitt M, Dickie JM, Sultana Jyoti S, Wall JG, Johnson PA, Wawrousek KE. A fast, ultrasensitive SERS immunoassay to detect SARS-CoV-2 in saliva. Anal Chim Acta 2022; 1229:340290. [PMID: 36156215 PMCID: PMC9395977 DOI: 10.1016/j.aca.2022.340290] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 01/13/2023]
Abstract
The COVID-19 pandemic has emphasized the need for accurate, rapid, point-of-care diagnostics to control disease transmission. We have developed a simple, ultrasensitive single-particle surface-enhanced Raman spectroscopy (SERS) immunoassay to detect the SARS-CoV-2 spike protein in saliva. This assay relies on the use of single chain Fv (scFv) recombinant antibody expressed in E. coli to bind the SARS-CoV-2 spike protein. Recombinant scFv labeled with a SERS-active dye in solution is mixed with unlabeled scFv conjugated to gold-coated magnetic nanoparticles and a sample to be tested. In the presence of the SARS-CoV-2 spike protein, immunocomplexes form and concentrate the labeled scFv close to the gold surface of the nanoparticles, causing an increased SERS signal. The assay detects inactivated SARS-CoV-2 virus and spike protein in saliva at concentrations of 1.94 × 103 genomes mL-1 and 4.7 fg mL-1, respectively, making this direct detection antigen test only 2-3 times less sensitive than some qRT-PCR tests. All tested SARS-CoV-2 spike proteins, including those from alpha, beta, gamma, delta, and omicron variants, were detected without recognition of the closely related SARS and MERS spike proteins. This 30 min, no-wash assay requires only mixing, a magnetic separation step, and signal measurements using a hand-held, battery-powered Raman spectrometer, making this assay ideal for ultrasensitive detection of the SARS-CoV-2 virus at the point-of-care.
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Affiliation(s)
- Moein Mohammadi
- Chemical Engineering, University of Wyoming, 1000 E. University Ave. Dept. 3295, Laramie, WY, 82071, USA
| | - Delphine Antoine
- Microbiology, School of Biological and Chemical Sciences, and SFI Centre for Medical Devices (CÚRAM), University of Galway, Galway H91 TK33, Ireland
| | - Madison Vitt
- Chemical Engineering, University of Wyoming, 1000 E. University Ave. Dept. 3295, Laramie, WY, 82071, USA
| | - Julia Marie Dickie
- Chemical Engineering, University of Wyoming, 1000 E. University Ave. Dept. 3295, Laramie, WY, 82071, USA
| | - Sharmin Sultana Jyoti
- Chemical Engineering, University of Wyoming, 1000 E. University Ave. Dept. 3295, Laramie, WY, 82071, USA
| | - J Gerard Wall
- Microbiology, School of Biological and Chemical Sciences, and SFI Centre for Medical Devices (CÚRAM), University of Galway, Galway H91 TK33, Ireland
| | - Patrick A Johnson
- Chemical Engineering, University of Wyoming, 1000 E. University Ave. Dept. 3295, Laramie, WY, 82071, USA
| | - Karen E Wawrousek
- Chemical Engineering, University of Wyoming, 1000 E. University Ave. Dept. 3295, Laramie, WY, 82071, USA.
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64
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Song H, Yuan Z, Liu S, Jin Z, Sun G. Mathematical modeling the dynamics of SARS-CoV-2 infection with antibody-dependent enhancement. NONLINEAR DYNAMICS 2022; 111:2943-2958. [PMID: 36246668 PMCID: PMC9540275 DOI: 10.1007/s11071-022-07939-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
The advent and swift global spread of the novel coronavirus (COVID-19) transmitted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have caused massive deaths and economic devastation worldwide. Antibody-dependent enhancement (ADE) is a common phenomenon in virology that directly affects the effectiveness of the vaccine, and there is no fully effective vaccine for diseases. In order to study the potential role of ADE on SARS-CoV-2 infection, we establish the SARS-CoV-2 infection dynamics model with ADE. The basic reproduction number is computed. We prove that when R 0 < 1 , the infection-free equilibrium is globally asymptotically stable, and the system is uniformly persistent when R 0 > 1 . We carry out the sensitivity analysis by the partial rank correlation coefficients and the extended version of the Fourier amplitude sensitivity test. Numerical simulations are implemented to illustrate the theoretical results. The potential impact of ADE on SARS-CoV-2 infection is also assessed. Our results show that ADE may accelerate SARS-CoV-2 infection. Furthermore, our findings suggest that increasing antibody titers can have the ability to control SARS-CoV-2 infection with ADE, but enhancing the neutralizing power of antibodies may be ineffective to control SARS-CoV-2 infection with ADE. Our study presumably contributes to a better understanding of the dynamics of SARS-CoV-2 infection with ADE.
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Affiliation(s)
- Haitao Song
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006 China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, 030006 China
| | - Zepeng Yuan
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006 China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, 030006 China
- School of Mathematical Sciences, Shanxi University, Taiyuan, 030006 China
| | - Shengqiang Liu
- School of Mathematical Sciences, Tiangong University, Tianjin, 300387 China
| | - Zhen Jin
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006 China
- Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, 030006 China
| | - Guiquan Sun
- Complex Systems Research Center, Shanxi University, Taiyuan, 030006 China
- Department of Mathematics, North University of China, Taiyuan, 030051 China
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65
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Desikan R, Linderman SL, Davis C, Zarnitsyna VI, Ahmed H, Antia R. Vaccine models predict rules for updating vaccines against evolving pathogens such as SARS-CoV-2 and influenza in the context of pre-existing immunity. Front Immunol 2022; 13:985478. [PMID: 36263031 PMCID: PMC9574365 DOI: 10.3389/fimmu.2022.985478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
Currently, vaccines for SARS-CoV-2 and influenza viruses are updated if the new vaccine induces higher antibody-titers to circulating variants than current vaccines. This approach does not account for complex dynamics of how prior immunity skews recall responses to the updated vaccine. We: (i) use computational models to mechanistically dissect how prior immunity influences recall responses; (ii) explore how this affects the rules for evaluating and deploying updated vaccines; and (iii) apply this to SARS-CoV-2. Our analysis of existing data suggests that there is a strong benefit to updating the current SARS-CoV-2 vaccines to match the currently circulating variants. We propose a general two-dose strategy for determining if vaccines need updating as well as for vaccinating high-risk individuals. Finally, we directly validate our model by reanalysis of earlier human H5N1 influenza vaccine studies.
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Affiliation(s)
- Rajat Desikan
- Clinical Pharmacology Modeling & Simulation, GlaxoSmithKline (GSK), Stevenage, Hertfordshire, United Kingdom
- *Correspondence: Rajat Desikan, ; Rustom Antia,
| | - Susanne L. Linderman
- Department of Microbiology and Immunology, Emory University, Atlanta, GA, United States
| | - Carl Davis
- Department of Microbiology and Immunology, Emory University, Atlanta, GA, United States
| | | | - Hasan Ahmed
- Department of Biology, Emory University, Atlanta, GA, United States
| | - Rustom Antia
- Department of Biology, Emory University, Atlanta, GA, United States
- *Correspondence: Rajat Desikan, ; Rustom Antia,
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66
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A single-administration therapeutic interfering particle reduces SARS-CoV-2 viral shedding and pathogenesis in hamsters. Proc Natl Acad Sci U S A 2022; 119:e2204624119. [PMID: 36074824 PMCID: PMC9522362 DOI: 10.1073/pnas.2204624119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The high transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a primary driver of the COVID-19 pandemic. While existing interventions prevent severe disease, they exhibit mixed efficacy in preventing transmission, presumably due to their limited antiviral effects in the respiratory mucosa, whereas interventions targeting the sites of viral replication might more effectively limit respiratory virus transmission. Recently, intranasally administered RNA-based therapeutic interfering particles (TIPs) were reported to suppress SARS-CoV-2 replication, exhibit a high barrier to resistance, and prevent serious disease in hamsters. Since TIPs intrinsically target the tissues with the highest viral replication burden (i.e., respiratory tissues for SARS-CoV-2), we tested the potential of TIP intervention to reduce SARS-CoV-2 shedding. Here, we report that a single, postexposure TIP dose lowers SARS-CoV-2 nasal shedding, and at 5 days postinfection, infectious virus shed is below detection limits in 4 out of 5 infected animals. Furthermore, TIPs reduce shedding of Delta variant or WA-1 from infected to uninfected hamsters. Cohoused "contact" animals exposed to infected, TIP-treated animals exhibited significantly lower viral loads, reduced inflammatory cytokines, no severe lung pathology, and shortened shedding duration compared to animals cohoused with untreated infected animals. TIPs may represent an effective countermeasure to limit SARS-CoV-2 transmission.
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67
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Allender MC, Adkesson MJ, Langan JN, Delk KW, Meehan T, Aitken‐Palmer C, McEntire MM, Killian ML, Torchetti M, Morales SA, Austin C, Fredrickson R, Olmstead C, Ke R, Smith R, Hostnik ET, Terio K, Wang L. Multi-species outbreak of SARS-CoV-2 Delta variant in a zoological institution, with the detection in two new families of carnivores. Transbound Emerg Dis 2022; 69:e3060-e3075. [PMID: 35839756 PMCID: PMC9349917 DOI: 10.1111/tbed.14662] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 02/05/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a worldwide distribution in humans and many other mammalian species. In late September 2021, 12 animals maintained by the Chicago Zoological Society's Brookfield Zoo were observed with variable clinical signs. The Delta variant of SARS-CoV-2 was detected in faeces and nasal swabs by qRT-PCR, including the first detection in animals from the families Procyonidae and Viverridae. Test positivity rate was 12.5% for 35 animals tested. All animals had been vaccinated with at least one dose of a recombinant vaccine designed for animals and all recovered with variable supportive treatment. Sequence analysis showed that six zoo animal strains were closely correlated with 18 human SARS-CoV-2 strains, suggestive of potential human-to-animal transmission events. This report documents the expanding host range of COVID-19 during the ongoing pandemic.
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Affiliation(s)
- Matthew C. Allender
- Brookfield ZooChicago Zoological SocietyBrookfieldIllinoisUSA
- Veterinary Diagnostic LabUniversity of Illinois Wildlife Epidemiology LaboratoryUrbanaIllinoisUSA
| | | | - Jennifer N. Langan
- Brookfield ZooChicago Zoological SocietyBrookfieldIllinoisUSA
- Department of Veterinary Clinical Medicine, College of Veterinary MedicineUniversity of IllinoisUrbanaIllinoisUSA
| | - Katie W. Delk
- Brookfield ZooChicago Zoological SocietyBrookfieldIllinoisUSA
| | - Thomas Meehan
- Brookfield ZooChicago Zoological SocietyBrookfieldIllinoisUSA
| | | | - Michael M. McEntire
- Illinois Zoological and Aquatic Animal ResidencyUniversity of IllinoisUrbanaIllinoisUSA
| | - Mary L. Killian
- National Veterinary Services Laboratories, Animal and Plant Health Inspection ServiceUnited States Department of AgricultureAmesIowaUSA
| | - Mia Torchetti
- National Veterinary Services Laboratories, Animal and Plant Health Inspection ServiceUnited States Department of AgricultureAmesIowaUSA
| | | | - Connie Austin
- Illinois Department of Public HealthSpringfieldIllinoisUSA
| | - Richard Fredrickson
- Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, College of Veterinary MedicineUniversity of IllinoisUrbanaIllinoisUSA
| | - Colleen Olmstead
- Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, College of Veterinary MedicineUniversity of IllinoisUrbanaIllinoisUSA
| | - Ruian Ke
- T‐6, Theoretical Biology and Biophysics, T DivisionLos Alamos National LaboratoryLos AlamosNew MexicoUSA
| | - Rebecca Smith
- Department of PathobiologyUniversity of Illinois at Urbana–ChampaignUrbanaIllinoisUSA
| | - Eric T. Hostnik
- Brookfield ZooChicago Zoological SocietyBrookfieldIllinoisUSA
- Department of Veterinary Clinical SciencesOhio State UniversityColumbusOhioUSA
| | - Karen Terio
- Zoological Pathology Program, College of Veterinary MedicineUniversity of IllinoisBrookfieldIllinoisUSA
| | - Leyi Wang
- Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, College of Veterinary MedicineUniversity of IllinoisUrbanaIllinoisUSA
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68
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Identifiability of parameters in mathematical models of SARS-CoV-2 infections in humans. Sci Rep 2022; 12:14637. [PMID: 36030320 PMCID: PMC9418662 DOI: 10.1038/s41598-022-18683-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 08/16/2022] [Indexed: 12/12/2022] Open
Abstract
Determining accurate estimates for the characteristics of the severe acute respiratory syndrome coronavirus 2 in the upper and lower respiratory tracts, by fitting mathematical models to data, is made difficult by the lack of measurements early in the infection. To determine the sensitivity of the parameter estimates to the noise in the data, we developed a novel two-patch within-host mathematical model that considered the infection of both respiratory tracts and assumed that the viral load in the lower respiratory tract decays in a density dependent manner and investigated its ability to match population level data. We proposed several approaches that can improve practical identifiability of parameters, including an optimal experimental approach, and found that availability of viral data early in the infection is of essence for improving the accuracy of the estimates. Our findings can be useful for designing interventions.
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69
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Sanche S, Cassidy T, Chu P, Perelson AS, Ribeiro RM, Ke R. A simple model of COVID-19 explains disease severity and the effect of treatments. Sci Rep 2022; 12:14210. [PMID: 35988008 PMCID: PMC9392071 DOI: 10.1038/s41598-022-18244-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/08/2022] [Indexed: 12/23/2022] Open
Abstract
Considerable effort has been made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue by focusing on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation, identifying the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and reveal conditions that can increase the likelihood of desired clinical outcome from treatment administration. In particular, the analysis suggest that antivirals need to be administered early during infection to have an impact on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.
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70
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A single-administration therapeutic interfering particle reduces SARS-CoV-2 viral shedding and pathogenesis in hamsters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022. [PMID: 35982679 DOI: 10.1101/2022.08.10.503534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The high transmissibility of SARS-CoV-2 is a primary driver of the COVID-19 pandemic. While existing interventions prevent severe disease, they exhibit mixed efficacy in preventing transmission, presumably due to their limited antiviral effects in the respiratory mucosa, whereas interventions targeting the sites of viral replication might more effectively limit respiratory virus transmission. Recently, intranasally administered RNA-based therapeutic interfering particles (TIPs) were reported to suppress SARS-CoV-2 replication, exhibit a high barrier to resistance, and prevent serious disease in hamsters. Since TIPs intrinsically target the tissues with the highest viral replication burden (i.e., respiratory tissues for SARS-CoV-2), we tested the potential of TIP intervention to reduce SARS-CoV-2 shedding. Here, we report that a single, post-exposure TIP dose lowers SARS-CoV-2 nasal shedding and at 5 days post-infection infectious virus shed is below detection limits in 4 out of 5 infected animals. Furthermore, TIPs reduce shedding of Delta variant or WA-1 from infected to uninfected hamsters. Co-housed 'contact' animals exposed to infected, TIP-treated, animals exhibited significantly lower viral loads, reduced inflammatory cytokines, no severe lung pathology, and shortened shedding duration compared to animals co-housed with untreated infected animals. TIPs may represent an effective countermeasure to limit SARS-CoV-2 transmission. Significance COVID-19 vaccines are exceptionally effective in preventing severe disease and death, but they have mixed efficacy in preventing virus transmission, consistent with established literature that parenteral vaccines for other viruses fail to prevent mucosal virus shedding or transmission. Likewise, small-molecule antivirals, while effective in reducing viral-disease pathogenesis, also appear to have inconsistent efficacy in preventing respiratory virus transmission including for SARS-CoV-2. Recently, we reported the discovery of a single-administration antiviral Therapeutic Interfering Particle (TIP) against SARS-CoV-2 that prevents severe disease in hamsters and exhibits a high genetic barrier to the evolution of resistance. Here, we report that TIP intervention also reduces SARS-CoV-2 transmission between hamsters.
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71
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Phan T, Brozak S, Pell B, Gitter A, Mena KD, Kuang Y, Wu F. A simple SEIR-V model to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission using wastewater-based surveillance data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.07.17.22277721. [PMID: 35898336 PMCID: PMC9327624 DOI: 10.1101/2022.07.17.22277721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Wastewater-based surveillance (WBS) has been widely used as a public health tool to monitor SARS-CoV-2 transmission. However, epidemiological inference from WBS data remains understudied and limits its application. In this study, we have established a quantitative framework to estimate COVID-19 prevalence and predict SARS-CoV-2 transmission through integrating WBS data into an SEIR-V model. We conceptually divide the individual-level viral shedding course into exposed, infectious, and recovery phases as an analogy to the compartments in population-level SEIR model. We demonstrated that the temperature effect on viral losses in the sewer can be straightforwardly incorporated in our framework. Using WBS data from the second wave of the pandemic (Oct 02, 2020 â€" Jan 25, 2021) in the Great Boston area, we showed that the SEIR-V model successfully recapitulates the temporal dynamics of viral load in wastewater and predicts the true number of cases peaked earlier and higher than the number of reported cases by 16 days and 8.6 folds ( R = 0.93), respectively. This work showcases a simple, yet effective method to bridge WBS and quantitative epidemiological modeling to estimate the prevalence and transmission of SARS-CoV-2 in the sewershed, which could facilitate the application of wastewater surveillance of infectious diseases for epidemiological inference and inform public health actions.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, New Mexico, USA
| | - Samantha Brozak
- School of Mathematical and Statistical Sciences, Arizona State University, Arizona, USA
| | - Bruce Pell
- Department of Mathematics and Computer Science, Lawrence Technological University, MI, USA
| | - Anna Gitter
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, USA 77030
| | - Kristina D. Mena
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, USA 77030
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, Arizona, USA
| | - Fuqing Wu
- The University of Texas Health Science Center at Houston, School of Public Health, Houston, Texas, USA 77030
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Alexandre M, Marlin R, Prague M, Coleon S, Kahlaoui N, Cardinaud S, Naninck T, Delache B, Surenaud M, Galhaut M, Dereuddre-Bosquet N, Cavarelli M, Maisonnasse P, Centlivre M, Lacabaratz C, Wiedemann A, Zurawski S, Zurawski G, Schwartz O, Sanders RW, Le Grand R, Levy Y, Thiébaut R. Modelling the response to vaccine in non-human primates to define SARS-CoV-2 mechanistic correlates of protection. eLife 2022; 11:75427. [PMID: 35801637 PMCID: PMC9282856 DOI: 10.7554/elife.75427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 06/22/2022] [Indexed: 11/29/2022] Open
Abstract
The definition of correlates of protection is critical for the development of next-generation SARS-CoV-2 vaccine platforms. Here, we propose a model-based approach for identifying mechanistic correlates of protection based on mathematical modelling of viral dynamics and data mining of immunological markers. The application to three different studies in non-human primates evaluating SARS-CoV-2 vaccines based on CD40-targeting, two-component spike nanoparticle and mRNA 1273 identifies and quantifies two main mechanisms that are a decrease of rate of cell infection and an increase in clearance of infected cells. Inhibition of RBD binding to ACE2 appears to be a robust mechanistic correlate of protection across the three vaccine platforms although not capturing the whole biological vaccine effect. The model shows that RBD/ACE2 binding inhibition represents a strong mechanism of protection which required significant reduction in blocking potency to effectively compromise the control of viral replication.
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Affiliation(s)
- Marie Alexandre
- Department of Public Health, Inserm Bordeaux Population Health Research Centre, University of Bordeaux, Inria SISTM, UMR 1219, Bordeaux, France
| | - Romain Marlin
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Mélanie Prague
- Department of Public Health, Inserm Bordeaux Population Health Research Centre, University of Bordeaux, Inria SISTM, UMR 1219, Bordeaux, France
| | - Severin Coleon
- Vaccine Research Institute, Inserm U955, Créteil, France
| | - Nidhal Kahlaoui
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | | | - Thibaut Naninck
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Benoit Delache
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | | | - Mathilde Galhaut
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Nathalie Dereuddre-Bosquet
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Mariangela Cavarelli
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Pauline Maisonnasse
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | | | | | | | - Sandra Zurawski
- Baylor Scott and White Research Institute, Dallas, United States
| | - Gerard Zurawski
- Baylor Scott and White Research Institute, Dallas, United States
| | | | - Rogier W Sanders
- Department of Medical Microbiology, University of Amsterdam, Amsterdam, Netherlands
| | - Roger Le Grand
- Center for Immunology of Viral, Auto-immune, Hematological and Bacterial Diseases (IMVA-HB/IDMIT), Université Paris-Saclay, Inserm, CEA, Fontenay-aux-Roses, France
| | - Yves Levy
- Vaccine Research Institute, Inserm U955, Créteil, France
| | - Rodolphe Thiébaut
- Department of Public Health, Inserm Bordeaux Population Health Research Centre, University of Bordeaux, Inria SISTM, UMR 1219, Bordeaux, France
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Desikan R, Padmanabhan P, Kierzek AM, van der Graaf PH. Mechanistic Models of COVID-19: Insights into Disease Progression, Vaccines, and Therapeutics. Int J Antimicrob Agents 2022; 60:106606. [PMID: 35588969 PMCID: PMC9110059 DOI: 10.1016/j.ijantimicag.2022.106606] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/27/2022] [Accepted: 05/08/2022] [Indexed: 12/02/2022]
Abstract
The COVID-19 pandemic has severely impacted health systems and economies worldwide. Significant global efforts are therefore ongoing to improve vaccine efficacies, optimize vaccine deployment, and develop new antiviral therapies to combat the pandemic. Mechanistic viral dynamics and quantitative systems pharmacology models of SARS-CoV-2 infection, vaccines, immunomodulatory agents, and antiviral therapeutics have played a key role in advancing our understanding of SARS-CoV-2 pathogenesis and transmission, the interplay between innate and adaptive immunity to influence the outcomes of infection, effectiveness of treatments, mechanisms and performance of COVID-19 vaccines, and the impact of emerging SARS-CoV-2 variants. Here, we review some of the critical insights provided by these models and discuss the challenges ahead.
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Affiliation(s)
- Rajat Desikan
- Quantitative Systems Pharmacology (QSP) group, Certara, Sheffield and Canterbury, United Kingdom.
| | - Pranesh Padmanabhan
- Clem Jones Centre for Ageing Dementia Research, Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Andrzej M Kierzek
- Quantitative Systems Pharmacology (QSP) group, Certara, Sheffield and Canterbury, United Kingdom; School of Biosciences and Medicine, University of Surrey, Guildford, United Kingdom
| | - Piet H van der Graaf
- Quantitative Systems Pharmacology (QSP) group, Certara, Sheffield and Canterbury, United Kingdom; Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands.
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74
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Characterizing SARS-CoV-2 Viral Clearance Kinetics to Improve the Design of Antiviral Pharmacometric Studies. Antimicrob Agents Chemother 2022; 66:e0019222. [PMID: 35736134 PMCID: PMC9295592 DOI: 10.1128/aac.00192-22] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
A consensus methodology for the pharmacometric assessment of candidate SARS-CoV-2 antiviral drugs would be useful for comparing trial results and improving trial design. The time to viral clearance, assessed by serial qPCR of nasopharyngeal swab samples, has been the most widely reported measure of virological response in clinical trials, but it has not been compared formally with other metrics, notably model-based estimates of the rate of viral clearance. We analyzed prospectively gathered viral clearance profiles from 280 infection episodes in vaccinated and unvaccinated individuals. We fitted different phenomenological pharmacodynamic models (single exponential decay, bi-exponential, penalized splines) and found that the clearance rate, estimated from a mixed effects single exponential decay model, is a robust pharmacodynamic summary of viral clearance. The rate of viral clearance, estimated from viral densities during the first week following peak viral load, provides increased statistical power (reduced type 2 error) compared with time to clearance. Antiviral effects approximately equivalent to those with currently used and recommended SARS-CoV-2 antiviral treatments, notably nirmatrelvir and molnupiravir, can be detected from randomized trials with sample sizes of only 35 to 65 patients per arm. We recommend that pharmacometric antiviral assessments should be conducted in early COVID-19 illness with serial qPCR samples taken over 1 week.
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75
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Prentiss M, Chu A, Berggren KK. Finding the infectious dose for COVID-19 by applying an airborne-transmission model to superspreader events. PLoS One 2022; 17:e0265816. [PMID: 35679278 PMCID: PMC9182663 DOI: 10.1371/journal.pone.0265816] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 03/08/2022] [Indexed: 12/19/2022] Open
Abstract
We probed the transmission of COVID-19 by applying an airborne transmission model to five well-documented case studies—a Washington state church choir, a Korean call center, a Korean exercise class, and two different Chinese bus trips. For all events the likely index patients were pre-symptomatic or mildly symptomatic, which is when infective patients are most likely to interact with large groups of people. Applying the model to those events yields results that suggest the following: (1) transmission was airborne; (2) superspreading events do not require an index patient with an unusually high viral load; (3) the viral loads for all of the index patients were of the same order of magnitude and consistent with experimentally measured values for patients at the onset of symptoms, even though viral loads across the population vary by a factor of >108. In particular we used a Wells-Riley exposure model to calculate q, the total average number of infectious quanta inhaled by a person at the event. Given the q value for each event, the simple airborne transmission model was used to determined Sq, the rate at which the index patient exhaled infectious quanta and N0, the characteristic number of COVID-19 virions needed to induce infection. Despite the uncertainties in the values of some parameters of the superspreading events, all five events yielded (N0∼300–2,000 virions), which is similar to published values for influenza. Finally, this work describes the conditions under which similar methods can provide actionable information on the transmission of other viruses.
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Affiliation(s)
- Mara Prentiss
- Department of Physics, Harvard University, Cambridge, MA, United States of America
- * E-mail:
| | - Arthur Chu
- QVT Family Office, New York, NY, United States of America
| | - Karl K. Berggren
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
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76
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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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77
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Goyal A, Duke ER, Cardozo-Ojeda EF, Schiffer JT. Modeling explains prolonged SARS-CoV-2 nasal shedding relative to lung shedding in remdesivir treated rhesus macaques. iScience 2022; 25:104448. [PMID: 35634576 PMCID: PMC9130309 DOI: 10.1016/j.isci.2022.104448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/19/2022] [Accepted: 05/16/2022] [Indexed: 12/12/2022] Open
Abstract
In clinical trials, remdesivir decreased recovery time in hospitalized patients with SARS- CoV-2 and prevented hospitalization when given early during infection, despite not reducing nasal viral loads. In rhesus macaques, early remdesivir prevented pneumonia and lowered lung viral loads, but viral loads increased in nasal passages after five days. We developed mathematical models to explain these results. Our model raises the hypotheses that: 1) in contrast to nasal passages viral load monotonically decreases in lungs during therapy because of infection-dependent generation of refractory cells, 2) slight reduction in lung viral loads with an imperfect agent may result in a substantial decrease in lung damage, and 3) increases in nasal viral load may occur due to a blunting of peak viral load which decreases the intensity of the innate immune response. We demonstrate that a higher potency drug could lower viral loads in nasal passages and lung.
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Affiliation(s)
- Ashish Goyal
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center
| | - Elizabeth R Duke
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center.,Department of Medicine, University of Washington, Seattle
| | | | - Joshua T Schiffer
- Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center.,Department of Medicine, University of Washington, Seattle.,Clinical Research Division, Fred Hutchinson Cancer Research Center
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78
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Desikan R, Linderman SL, Davis C, Zarnitsyna V, Ahmed H, Antia R. Modeling suggests that multiple immunizations or infections will reveal the benefits of updating SARS-CoV-2 vaccines. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.05.21.492928. [PMID: 35665010 PMCID: PMC9164442 DOI: 10.1101/2022.05.21.492928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
When should vaccines to evolving pathogens such as SARS-CoV-2 be updated? Our computational models address this focusing on updating SARS-CoV-2 vaccines to the currently circulating Omicron variant. Current studies typically compare the antibody titers to the new variant following a single dose of the original-vaccine versus the updated-vaccine in previously immunized individuals. These studies find that the updated-vaccine does not induce higher titers to the vaccine-variant compared with the original-vaccine, suggesting that updating may not be needed. Our models recapitulate this observation but suggest that vaccination with the updated-vaccine generates qualitatively different humoral immunity, a small fraction of which is specific for unique epitopes to the new variant. Our simulations suggest that these new variant-specific responses could dominate following subsequent vaccination or infection with either the currently circulating or future variants. We suggest a two-dose strategy for determining if the vaccine needs updating and for vaccinating high-risk individuals.
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Affiliation(s)
- Rajat Desikan
- Clinical Pharmacology Modeling & Simulation, GlaxoSmithKline (GSK), Gunnels Wood Rd, Stevenage, Hertfordshire, SG1 2NY, United Kingdom
- These authors contributed equally
| | - Susanne L. Linderman
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30322, USA
| | - Carl Davis
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30322, USA
| | - Veronika Zarnitsyna
- Department of Microbiology and Immunology, Emory University, Atlanta, GA 30322, USA
| | - Hasan Ahmed
- Department of Biology, Emory University, Atlanta, GA 30322, USA
| | - Rustom Antia
- Department of Biology, Emory University, Atlanta, GA 30322, USA
- These authors contributed equally
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79
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Chen A, Wessler T, Daftari K, Hinton K, Boucher RC, Pickles R, Freeman R, Lai SK, Forest MG. Modeling insights into SARS-CoV-2 respiratory tract infections prior to immune protection. Biophys J 2022; 121:1619-1631. [PMID: 35378080 PMCID: PMC8975607 DOI: 10.1016/j.bpj.2022.04.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/27/2021] [Accepted: 03/31/2022] [Indexed: 11/19/2022] Open
Abstract
Mechanistic insights into human respiratory tract (RT) infections from SARS-CoV-2 can inform public awareness as well as guide medical prevention and treatment for COVID-19 disease. Yet the complexity of the RT and the inability to access diverse regions pose fundamental roadblocks to evaluation of potential mechanisms for the onset and progression of infection (and transmission). We present a model that incorporates detailed RT anatomy and physiology, including airway geometry, physical dimensions, thicknesses of airway surface liquids (ASLs), and mucus layer transport by cilia. The model further incorporates SARS-CoV-2 diffusivity in ASLs and best-known data for epithelial cell infection probabilities, and, once infected, duration of eclipse and replication phases, and replication rate of infectious virions. We apply this baseline model in the absence of immune protection to explore immediate, short-term outcomes from novel SARS-CoV-2 depositions onto the air-ASL interface. For each RT location, we compute probability to clear versus infect; per infected cell, we compute dynamics of viral load and cell infection. Results reveal that nasal infections are highly likely within 1-2 days from minimal exposure, and alveolar pneumonia occurs only if infectious virions are deposited directly into alveolar ducts and sacs, not via retrograde propagation to the deep lung. Furthermore, to infect just 1% of the 140 m2 of alveolar surface area within 1 week, either 103 boluses each with 106 infectious virions or 106 aerosols with one infectious virion, all physically separated, must be directly deposited. These results strongly suggest that COVID-19 disease occurs in stages: a nasal/upper RT infection, followed by self-transmission of infection to the deep lung. Two mechanisms of self-transmission are persistent aspiration of infected nasal boluses that drain to the deep lung and repeated rupture of nasal aerosols from infected mucosal membranes by speaking, singing, or cheering that are partially inhaled, exhaled, and re-inhaled, to the deep lung.
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Affiliation(s)
- Alexander Chen
- Department of Mathematics, CSU Dominguez Hills, Carson, California
| | - Timothy Wessler
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina.
| | - Katherine Daftari
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Kameryn Hinton
- Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Richard C Boucher
- Marsico Lung Institute, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Raymond Pickles
- Marsico Lung Institute, UNC Chapel Hill, Chapel Hill, North Carolina; Department of Microbiology and Immunology, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Ronit Freeman
- Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina
| | - Samuel K Lai
- Department of Microbiology and Immunology, UNC Chapel Hill, Chapel Hill, North Carolina; Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Chapel Hill and Raleigh, North Carolina; Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, UNC Chapel Hill, Chapel Hill, North Carolina
| | - M Gregory Forest
- Department of Mathematics, UNC Chapel Hill, Chapel Hill, North Carolina; Department of Applied Physical Sciences, UNC Chapel Hill, Chapel Hill, North Carolina; Joint Department of Biomedical Engineering, UNC Chapel Hill and NC State University, Chapel Hill and Raleigh, North Carolina.
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80
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Killingley B, Mann AJ, Kalinova M, Boyers A, Goonawardane N, Zhou J, Lindsell K, Hare SS, Brown J, Frise R, Smith E, Hopkins C, Noulin N, Löndt B, Wilkinson T, Harden S, McShane H, Baillet M, Gilbert A, Jacobs M, Charman C, Mande P, Nguyen-Van-Tam JS, Semple MG, Read RC, Ferguson NM, Openshaw PJ, Rapeport G, Barclay WS, Catchpole AP, Chiu C. Safety, tolerability and viral kinetics during SARS-CoV-2 human challenge in young adults. Nat Med 2022; 28:1031-1041. [PMID: 35361992 DOI: 10.1038/s41591-022-01780-9] [Citation(s) in RCA: 221] [Impact Index Per Article: 110.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 03/09/2022] [Indexed: 12/16/2022]
Abstract
Since its emergence in 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused hundreds of millions of cases and continues to circulate globally. To establish a novel SARS-CoV-2 human challenge model that enables controlled investigation of pathogenesis, correlates of protection and efficacy testing of forthcoming interventions, 36 volunteers aged 18-29 years without evidence of previous infection or vaccination were inoculated with 10 TCID50 of a wild-type virus (SARS-CoV-2/human/GBR/484861/2020) intranasally in an open-label, non-randomized study (ClinicalTrials.gov identifier NCT04865237 ; funder, UK Vaccine Taskforce). After inoculation, participants were housed in a high-containment quarantine unit, with 24-hour close medical monitoring and full access to higher-level clinical care. The study's primary objective was to identify an inoculum dose that induced well-tolerated infection in more than 50% of participants, with secondary objectives to assess virus and symptom kinetics during infection. All pre-specified primary and secondary objectives were met. Two participants were excluded from the per-protocol analysis owing to seroconversion between screening and inoculation, identified post hoc. Eighteen (~53%) participants became infected, with viral load (VL) rising steeply and peaking at ~5 days after inoculation. Virus was first detected in the throat but rose to significantly higher levels in the nose, peaking at ~8.87 log10 copies per milliliter (median, 95% confidence interval (8.41, 9.53)). Viable virus was recoverable from the nose up to ~10 days after inoculation, on average. There were no serious adverse events. Mild-to-moderate symptoms were reported by 16 (89%) infected participants, beginning 2-4 days after inoculation, whereas two (11%) participants remained asymptomatic (no reportable symptoms). Anosmia or dysosmia developed more slowly in 15 (83%) participants. No quantitative correlation was noted between VL and symptoms, with high VLs present even in asymptomatic infection. All infected individuals developed serum spike-specific IgG and neutralizing antibodies. Results from lateral flow tests were strongly associated with viable virus, and modeling showed that twice-weekly rapid antigen tests could diagnose infection before 70-80% of viable virus had been generated. Thus, with detailed characterization and safety analysis of this first SARS-CoV-2 human challenge study in young adults, viral kinetics over the course of primary infection with SARS-CoV-2 were established, with implications for public health recommendations and strategies to affect SARS-CoV-2 transmission. Future studies will identify the immune factors associated with protection in those participants who did not develop infection or symptoms and define the effect of prior immunity and viral variation on clinical outcome.
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Affiliation(s)
- Ben Killingley
- Department of Infectious Diseases, University College London Hospital, London, UK
| | | | | | | | | | - Jie Zhou
- Department of Infectious Disease, Imperial College London, London, UK
| | - Kate Lindsell
- UK Vaccine Taskforce, Department for Business, Energy and Industrial Strategy, London, UK
| | - Samanjit S Hare
- Department of Radiology, Royal Free London NHS Foundation Trust, London, UK
| | - Jonathan Brown
- Department of Infectious Disease, Imperial College London, London, UK
| | - Rebecca Frise
- Department of Infectious Disease, Imperial College London, London, UK
| | - Emma Smith
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Claire Hopkins
- ENT Department, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | | | | | - Tom Wilkinson
- Faculty of Medicine and Institute for Life Sciences, University of Southampton, and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Stephen Harden
- Department of Radiology, Southampton General Hospital, Southampton, UK
| | - Helen McShane
- Department of Paediatrics, University of Oxford, Oxford, UK
| | | | - Anthony Gilbert
- UK Vaccine Taskforce, Department for Business, Energy and Industrial Strategy, London, UK
| | - Michael Jacobs
- Department of Infectious Diseases, Royal Free London NHS Foundation Trust, London, UK
| | - Christine Charman
- UK Vaccine Taskforce, Department for Business, Energy and Industrial Strategy, London, UK
| | - Priya Mande
- UK Vaccine Taskforce, Department for Business, Energy and Industrial Strategy, London, UK
| | - Jonathan S Nguyen-Van-Tam
- Division of Epidemiology and Public Health, University of Nottingham School of Medicine, Nottingham, UK
| | - Malcolm G Semple
- Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool; Respiratory Department, Alder Hey Children's Hospital, Liverpool, UK
| | - Robert C Read
- Faculty of Medicine and Institute for Life Sciences, University of Southampton, and NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Neil M Ferguson
- MRC Centre for Global Infectious Disease Analysis, School of Public Health, Imperial College London, London, UK
| | - Peter J Openshaw
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Garth Rapeport
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Wendy S Barclay
- Department of Infectious Disease, Imperial College London, London, UK
| | | | - Christopher Chiu
- Department of Infectious Disease, Imperial College London, London, UK.
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81
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Ke R, Martinez PP, Smith RL, Gibson LL, Mirza A, Conte M, Gallagher N, Luo CH, Jarrett J, Zhou R, Conte A, Liu T, Farjo M, Walden KKO, Rendon G, Fields CJ, Wang L, Fredrickson R, Edmonson DC, Baughman ME, Chiu KK, Choi H, Scardina KR, Bradley S, Gloss SL, Reinhart C, Yedetore J, Quicksall J, Owens AN, Broach J, Barton B, Lazar P, Heetderks WJ, Robinson ML, Mostafa HH, Manabe YC, Pekosz A, McManus DD, Brooke CB. Daily longitudinal sampling of SARS-CoV-2 infection reveals substantial heterogeneity in infectiousness. Nat Microbiol 2022; 7:640-652. [PMID: 35484231 PMCID: PMC9084242 DOI: 10.1038/s41564-022-01105-z] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 03/15/2022] [Indexed: 02/07/2023]
Abstract
The dynamics of SARS-CoV-2 replication and shedding in humans remain poorly understood. We captured the dynamics of infectious virus and viral RNA shedding during acute infection through daily longitudinal sampling of 60 individuals for up to 14 days. By fitting mechanistic models, we directly estimated viral expansion and clearance rates and overall infectiousness for each individual. Significant person-to-person variation in infectious virus shedding suggests that individual-level heterogeneity in viral dynamics contributes to 'superspreading'. Viral genome loads often peaked days earlier in saliva than in nasal swabs, indicating strong tissue compartmentalization and suggesting that saliva may serve as a superior sampling site for early detection of infection. Viral loads and clearance kinetics of Alpha (B.1.1.7) and previously circulating non-variant-of-concern viruses were mostly indistinguishable, indicating that the enhanced transmissibility of this variant cannot be explained simply by higher viral loads or delayed clearance. These results provide a high-resolution portrait of SARS-CoV-2 infection dynamics and implicate individual-level heterogeneity in infectiousness in superspreading.
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Affiliation(s)
- Ruian Ke
- T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Pamela P Martinez
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Statistics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Rebecca L Smith
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Pathobiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Laura L Gibson
- Division of Infectious Diseases and Immunology, Departments of Medicine and Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA
| | - Agha Mirza
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Madison Conte
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nicholas Gallagher
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Chun Huai Luo
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Junko Jarrett
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ruifeng Zhou
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Abigail Conte
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Tongyu Liu
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mireille Farjo
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Kimberly K O Walden
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Gloria Rendon
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher J Fields
- High-Performance Biological Computing at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Leyi Wang
- Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Richard Fredrickson
- Veterinary Diagnostic Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Darci C Edmonson
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Melinda E Baughman
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Karen K Chiu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hannah Choi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Kevin R Scardina
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Shannon Bradley
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Stacy L Gloss
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Crystal Reinhart
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jagadeesh Yedetore
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jessica Quicksall
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Alyssa N Owens
- Center for Clinical and Translational Research, University of Massachusetts Medical School, Worcester, MA, USA
| | - John Broach
- UMass Memorial Medical Center, Worcester, MA, USA
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Bruce Barton
- Division of Biostatistics and Health Services Research, University of Massachusetts Medical School, Worcester, MA, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
| | - Peter Lazar
- Division of Biostatistics and Health Services Research, University of Massachusetts Medical School, Worcester, MA, USA
| | - William J Heetderks
- National Institute for Biomedical Imaging and Bioengineering, Bethesda, MD, USA
| | - Matthew L Robinson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Heba H Mostafa
- Division of Medical Microbiology, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David D McManus
- Division of Cardiology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Christopher B Brooke
- Department of Microbiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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82
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Dutta A. Optimizing antiviral therapy for COVID-19 with learned pathogenic model. Sci Rep 2022; 12:6873. [PMID: 35477965 PMCID: PMC9044392 DOI: 10.1038/s41598-022-10929-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 04/15/2022] [Indexed: 01/08/2023] Open
Abstract
COVID-19 together with variants have caused an unprecedented amount of mental and economic turmoil with ever increasing fatality and no proven therapies in sight. The healthcare industry is racing to find a cure with multitude of clinical trials underway to access the efficacy of repurposed antivirals, however the much needed insights into the dynamics of pathogenesis of SARS-CoV-2 and corresponding pharmacology of antivirals are lacking. This paper introduces systematic pathological model learning of COVID-19 dynamics followed by derivative free optimization based multi objective drug rescheduling. The pathological model learnt from clinical data of severe COVID-19 patients treated with remdesivir could additionally predict immune T cells response and resulted in a dramatic reduction in remdesivir dose and schedule leading to lower toxicities, however maintaining a high virological efficacy.
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Affiliation(s)
- Abhishek Dutta
- Department of Electrical & Computer Engineering, Storrs, 06269, USA.
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Within-host models of SARS-CoV-2: What can it teach us on the biological factors driving virus pathogenesis and transmission? Anaesth Crit Care Pain Med 2022; 41:101055. [PMID: 35247638 PMCID: PMC8889677 DOI: 10.1016/j.accpm.2022.101055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Lingas G, Néant N, Gaymard A, Belhadi D, Peytavin G, Hites M, Staub T, Greil R, Paiva JA, Poissy J, Peiffer-Smadja N, Costagliola D, Yazdanpanah Y, Wallet F, Gagneux-Brunon A, Mentré F, Ader F, Burdet C, Guedj J, Bouscambert-Duchamp M. OUP accepted manuscript. J Antimicrob Chemother 2022; 77:1404-1412. [PMID: 35233617 PMCID: PMC9383489 DOI: 10.1093/jac/dkac048] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 02/03/2022] [Indexed: 11/20/2022] Open
Abstract
Background The antiviral efficacy of remdesivir in COVID-19 hospitalized patients remains controversial. Objectives To estimate the effect of remdesivir in blocking viral replication. Methods We analysed nasopharyngeal normalized viral loads from 665 hospitalized patients included in the DisCoVeRy trial (NCT 04315948; EudraCT 2020-000936-23), randomized to either standard of care (SoC) or SoC + remdesivir. We used a mathematical model to reconstruct viral kinetic profiles and estimate the antiviral efficacy of remdesivir in blocking viral replication. Additional analyses were conducted stratified on time of treatment initiation (≤7 or >7 days since symptom onset) or viral load at randomization (< or ≥3.5 log10 copies/104 cells). Results In our model, remdesivir reduced viral production by infected cells by 2-fold on average (95% CI: 1.5–3.2-fold). Model-based simulations predict that remdesivir reduced time to viral clearance by 0.7 days compared with SoC, with large inter-individual variabilities (IQR: 0.0–1.3 days). Remdesivir had a larger impact in patients with high viral load at randomization, reducing viral production by 5-fold on average (95% CI: 2.8–25-fold) and the median time to viral clearance by 2.4 days (IQR: 0.9–4.5 days). Conclusions Remdesivir halved viral production, leading to a median reduction of 0.7 days in the time to viral clearance compared with SoC. The efficacy was larger in patients with high viral load at randomization.
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Affiliation(s)
- Guillaume Lingas
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- Corresponding author. E-mail:
| | - Nadège Néant
- Université de Paris, IAME, INSERM, F-75018 Paris, France
| | - Alexandre Gaymard
- Hospices Civils de Lyon, Département de Virologie, Institut des Agents Infectieux, Centre National de Référence des virus des infections respiratoires France Sud, F-69004, Lyon, France
- Université de Lyon, Virpath, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1, F-69372, Lyon, France
| | - Drifa Belhadi
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Département d’Épidémiologie, Biostatistique et Recherche Clinique, F-75018, Paris, France
- CIC-EC 1425, INSERM, F-75018, Paris, France
| | - Gilles Peytavin
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- AP-HP, Hôpital Bichat Claude Bernard, Laboratoire de Pharmacologie-toxicologie, F-75018 Paris, France
| | - Maya Hites
- Hôpital Universitaire de Bruxelles-Hôpital Erasme, Université Libre de Bruxelles, Clinique des maladies infectieuses, Brussels, Belgium
| | - Thérèse Staub
- Centre hospitalier de Luxembourg, Service des maladies infectieuses, L-1210 Luxembourg, Luxembourg
| | - Richard Greil
- Department of Internal Medicine III with Haematology, Medical Oncology, Haemostaseology, Infectiology and Rheumatology, Oncologic Center, Salzburg Cancer Research Institute - Laboratory for Immunological and Molecular Cancer Research (SCRI-LIMCR), Paracelsus Medical University Salzburg, 5020 Salzburg, Austria
- Cancer Cluster Salzburg, 5020, Salzburg, Austria
- AGMT, 5020 Salzburg, Austria
| | - Jose-Artur Paiva
- Centro Hospitalar São João, Emergency and Intensive Care Department, Porto, Portugal
- Universidade do Porto, Faculty of Medicine, Porto, Portugal
| | - Julien Poissy
- Université de Lille, Inserm U1285, CHU Lille, Pôle de réanimation, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, F-59000, Lille, France
| | - Nathan Peiffer-Smadja
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France
- National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
| | - Dominique Costagliola
- Sorbonne Université, Inserm, Institut Pierre-Louis d’Épidémiologie et de Santé Publique, F-75013, Paris, France
| | - Yazdan Yazdanpanah
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France
| | - Florent Wallet
- Service de Médecine Intensive Réanimation anesthésie, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Pierre-Benite, France
- Université Claude Bernard Lyon 1, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, F-69372, Lyon, France
| | - Amandine Gagneux-Brunon
- CHU de Saint-Etienne, Service d’Infectiologie, F-42055 Saint-Etienne, France
- Université Jean Monnet, Université Claude Bernard Lyon 1, GIMAP, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, F-42023 Saint-Etienne, France
- CIC 1408, INSERM, F-42055 Saint-Etienne, France
| | - France Mentré
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Département d’Épidémiologie, Biostatistique et Recherche Clinique, F-75018, Paris, France
- CIC-EC 1425, INSERM, F-75018, Paris, France
- AP-HP, Hôpital Bichat, Unité de Recherche Clinique, F-75018, Paris, France
| | - Florence Ader
- Université Claude Bernard Lyon 1, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, F-69372, Lyon, France
- Hospices Civils de Lyon, Département des maladies infectieuses et tropicales, F-69004, Lyon, France
| | - Charles Burdet
- Université de Paris, IAME, INSERM, F-75018 Paris, France
- AP-HP, Hôpital Bichat, Département d’Épidémiologie, Biostatistique et Recherche Clinique, F-75018, Paris, France
| | - Jérémie Guedj
- Université de Paris, IAME, INSERM, F-75018 Paris, France
| | - Maude Bouscambert-Duchamp
- Hospices Civils de Lyon, Département de Virologie, Institut des Agents Infectieux, Centre National de Référence des virus des infections respiratoires France Sud, F-69004, Lyon, France
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