1
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Esmaeili S, Owens K, Wagoner J, Polyak SJ, White JM, Schiffer JT. A unifying model to explain high nirmatrelvir therapeutic efficacy against SARS-CoV-2, despite low post-exposure prophylaxis efficacy and frequent viral rebound. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.23.23294505. [PMID: 38352583 PMCID: PMC10862980 DOI: 10.1101/2023.08.23.23294505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
In a pivotal trial (EPIC-HR), a 5-day course of oral ritonavir-boosted nirmatrelvir, given early during symptomatic SARS-CoV-2 infection (within three days of symptoms onset), decreased hospitalization and death by 89.1% and nasal viral load by 0.87 log relative to placebo in high-risk individuals. Yet, nirmatrelvir/ritonavir failed as post-exposure prophylaxis in a trial, and frequent viral rebound has been observed in subsequent cohorts. We developed a mathematical model capturing viral-immune dynamics and nirmatrelvir pharmacokinetics that recapitulated viral loads from this and another clinical trial (PLATCOV). Our results suggest that nirmatrelvir's in vivo potency is significantly lower than in vitro assays predict. According to our model, a maximally potent agent would reduce the viral load by approximately 3.5 logs relative to placebo at 5 days. The model identifies that earlier initiation and shorter treatment duration are key predictors of post-treatment rebound. Extension of treatment to 10 days for Omicron variant infection in vaccinated individuals, rather than increasing dose or dosing frequency, is predicted to lower the incidence of viral rebound significantly.
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Affiliation(s)
- Shadisadat Esmaeili
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, WA, USA
| | - Katherine Owens
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, WA, USA
| | - Jessica Wagoner
- Department of Medicine, University of Washington; Seattle, WA, USA
| | | | - Judith M. White
- Department of Cell Biology, University of Virginia; Charlottesville, VA, USA
| | - Joshua T. Schiffer
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center; Seattle, WA, USA
- Department of Medicine, University of Washington; Seattle, WA, USA
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2
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Amidei A, Dobrovolny HM. Virus-mediated cell fusion of SARS-CoV-2 variants. Math Biosci 2024; 369:109144. [PMID: 38224908 DOI: 10.1016/j.mbs.2024.109144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/25/2023] [Accepted: 01/12/2024] [Indexed: 01/17/2024]
Abstract
SARS-CoV-2 has the ability to form large multi-nucleated cells known as syncytia. Little is known about how syncytia affect the dynamics of the infection or severity of the disease. In this manuscript, we extend a mathematical model of cell-cell fusion assays to estimate both the syncytia formation rate and the average duration of the fusion phase for five strains of SARS-CoV-2. We find that the original Wuhan strain has the slowest rate of syncytia formation (6.4×10-4/h), but takes only 4.0 h to complete the fusion process, while the Alpha strain has the fastest rate of syncytia formation (0.36 /h), but takes 7.6 h to complete the fusion process. The Beta strain also has a fairly fast syncytia formation rate (9.7×10-2/h), and takes the longest to complete fusion (8.4 h). The D614G strain has a fairly slow syncytia formation rate (2.8×10-3/h), but completes fusion in 4.0 h. Finally, the Delta strain is in the middle with a syncytia formation rate of 3.2×10-2/h and a fusing time of 6.1 h. We note that for these SARS-CoV-2 strains, there appears to be a tradeoff between the ease of forming syncytia and the speed at which they complete the fusion process.
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Affiliation(s)
- Ava Amidei
- Department of Chemistry & Biochemistry, Texas Christian University, Fort Worth, TX, USA
| | - Hana M Dobrovolny
- Department of Physics & Astronomy, Texas Christian University, Fort Worth, TX, USA.
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3
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González-Paz L, Lossada C, Hurtado-León ML, Vera-Villalobos J, Paz JL, Marrero-Ponce Y, Martinez-Rios F, Alvarado Y. Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data. ACS OMEGA 2024; 9:8923-8939. [PMID: 38434903 PMCID: PMC10905729 DOI: 10.1021/acsomega.3c06968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/20/2024] [Accepted: 02/05/2024] [Indexed: 03/05/2024]
Abstract
Recent reports have suggested that the susceptibility of cells to SARS-CoV-2 infection can be influenced by various proteins that potentially act as receptors for the virus. To investigate this further, we conducted simulations of viral dynamics using different cellular systems (Vero E6, HeLa, HEK293, and CaLu3) in the presence and absence of drugs (anthelmintic, ARBs, anticoagulant, serine protease inhibitor, antimalarials, and NSAID) that have been shown to impact cellular recognition by the spike protein based on experimental data. Our simulations revealed that the susceptibility of the simulated cell systems to SARS-CoV-2 infection was similar across all tested systems. Notably, CaLu3 cells exhibited the highest susceptibility to SARS-CoV-2 infection, potentially due to the presence of receptors other than ACE2, which may account for a significant portion of the observed susceptibility. Throughout the study, all tested compounds showed thermodynamically favorable and stable binding to the spike protein. Among the tested compounds, the anticoagulant nafamostat demonstrated the most favorable characteristics in terms of thermodynamics, kinetics, theoretical antiviral activity, and potential safety (toxicity) in relation to SARS-CoV-2 spike protein-mediated infections in the tested cell lines. This study provides mathematical and bioinformatic models that can aid in the identification of optimal cell lines for compound evaluation and detection, particularly in studies focused on repurposed drugs and their mechanisms of action. It is important to note that these observations should be experimentally validated, and this research is expected to inspire future quantitative experiments.
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Affiliation(s)
- Lenin González-Paz
- Centro
de Biomedicina Molecular (CBM). Laboratorio de Biocomputación
(LB),Instituto Venezolano de Investigaciones
Científicas (IVIC),Maracaibo, Zulia 4001, República Bolivariana de Venezuela
| | - Carla Lossada
- Centro
de Biomedicina Molecular (CBM). Laboratorio de Biocomputación
(LB),Instituto Venezolano de Investigaciones
Científicas (IVIC),Maracaibo, Zulia 4001, República Bolivariana de Venezuela
| | - María Laura Hurtado-León
- Facultad
Experimental de Ciencias (FEC). Departamento de Biología. Laboratorio
de Genética y Biología Molecular (LGBM),Universidad del Zulia (LUZ),Maracaibo 4001, República Bolivariana de Venezuela
| | - Joan Vera-Villalobos
- Facultad
de Ciencias Naturales y Matemáticas, Departamento de Química
y Ciencias Ambientales, Laboratorio de Análisis Químico
Instrumental (LAQUINS), Escuela Superior
Politécnica del Litoral, Guayaquil EC090112, Ecuador
| | - José L. Paz
- Departamento
Académico de Química Inorgánica, Facultad de
Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos. Cercado de Lima, Lima 15081, Perú
| | - Yovani Marrero-Ponce
- Grupo
de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias
de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades
Médicas; e Instituto de Simulación Computacional (ISC-USFQ),
Diego de Robles y vía Interoceánica, Universidad San Francisco de Quito (USFQ), Quito, Pichincha 170157, Ecuador
| | - Felix Martinez-Rios
- Universidad
Panamericana. Facultad de Ingeniería. Augusto Rodin 498, Ciudad de México 03920, México
| | - Ysaías.
J. Alvarado
- Centro
de Biomedicina Molecular (CBM). Laboratorio de Química Biofísica
Teórica y Experimental (LQBTE),Instituto
Venezolano de Investigaciones Científicas (IVIC),Maracaibo, Zulia 4001, República Bolivariana
de Venezuela
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4
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Bai Y, Du Z, Wang L, Lau EHY, Fung ICH, Holme P, Cowling BJ, Galvani AP, Krug RM, Meyers LA. Public Health Impact of Paxlovid as Treatment for COVID-19, United States. Emerg Infect Dis 2024; 30:262-269. [PMID: 38181800 PMCID: PMC10826746 DOI: 10.3201/eid3002.230835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
We evaluated the population-level benefits of expanding treatment with the antiviral drug Paxlovid (nirmatrelvir/ritonavir) in the United States for SARS-CoV-2 Omicron variant infections. Using a multiscale mathematical model, we found that treating 20% of symptomatic case-patients with Paxlovid over a period of 300 days beginning in January 2022 resulted in life and cost savings. In a low-transmission scenario (effective reproduction number of 1.2), this approach could avert 0.28 million (95% CI 0.03-0.59 million) hospitalizations and save US $56.95 billion (95% CI US $2.62-$122.63 billion). In a higher transmission scenario (effective reproduction number of 3), the benefits increase, potentially preventing 0.85 million (95% CI 0.36-1.38 million) hospitalizations and saving US $170.17 billion (95% CI US $60.49-$286.14 billion). Our findings suggest that timely and widespread use of Paxlovid could be an effective and economical approach to mitigate the effects of COVID-19.
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5
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Williams B, Carruthers J, Gillard JJ, Lythe G, Perelson AS, Ribeiro RM, Molina-París C, López-García M. The reproduction number and its probability distribution for stochastic viral dynamics. J R Soc Interface 2024; 21:20230400. [PMID: 38264928 PMCID: PMC10806437 DOI: 10.1098/rsif.2023.0400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/18/2023] [Indexed: 01/25/2024] Open
Abstract
We consider stochastic models of individual infected cells. The reproduction number, R, is understood as a random variable representing the number of new cells infected by one initial infected cell in an otherwise susceptible (target cell) population. Variability in R results partly from heterogeneity in the viral burst size (the number of viral progeny generated from an infected cell during its lifetime), which depends on the distribution of cellular lifetimes and on the mechanism of virion release. We analyse viral dynamics models with an eclipse phase: the period of time after a cell is infected but before it is capable of releasing virions. The duration of the eclipse, or the subsequent infectious, phase is non-exponential, but composed of stages. We derive the probability distribution of the reproduction number for these viral dynamics models, and show it is a negative binomial distribution in the case of constant viral release from infectious cells, and under the assumption of an excess of target cells. In a deterministic model, the ultimate in-host establishment or extinction of the viral infection depends entirely on whether the mean reproduction number is greater than, or less than, one, respectively. Here, the probability of extinction is determined by the probability distribution of R, not simply its mean value. In particular, we show that in some cases the probability of infection is not an increasing function of the mean reproduction number.
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Affiliation(s)
- Bevelynn Williams
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | | | - Joseph J. Gillard
- CBR Division, Defence Science and Technology Laboratory, Salisbury, UK
| | - Grant Lythe
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
| | - Alan S. Perelson
- T-6, Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Ruy M. Ribeiro
- T-6, Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Carmen Molina-París
- T-6, Theoretical Biology and Biophysics, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Martín López-García
- Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK
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6
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Jang H, Matsuoka M, Freire M. Oral mucosa immunity: ultimate strategy to stop spreading of pandemic viruses. Front Immunol 2023; 14:1220610. [PMID: 37928529 PMCID: PMC10622784 DOI: 10.3389/fimmu.2023.1220610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/11/2023] [Indexed: 11/07/2023] Open
Abstract
Global pandemics are most likely initiated via zoonotic transmission to humans in which respiratory viruses infect airways with relevance to mucosal systems. Out of the known pandemics, five were initiated by respiratory viruses including current ongoing coronavirus disease 2019 (COVID-19). Striking progress in vaccine development and therapeutics has helped ameliorate the mortality and morbidity by infectious agents. Yet, organism replication and virus spread through mucosal tissues cannot be directly controlled by parenteral vaccines. A novel mitigation strategy is needed to elicit robust mucosal protection and broadly neutralizing activities to hamper virus entry mechanisms and inhibit transmission. This review focuses on the oral mucosa, which is a critical site of viral transmission and promising target to elicit sterile immunity. In addition to reviewing historic pandemics initiated by the zoonotic respiratory RNA viruses and the oral mucosal tissues, we discuss unique features of the oral immune responses. We address barriers and new prospects related to developing novel therapeutics to elicit protective immunity at the mucosal level to ultimately control transmission.
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Affiliation(s)
- Hyesun Jang
- Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA, United States
| | - Michele Matsuoka
- Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA, United States
| | - Marcelo Freire
- Genomic Medicine and Infectious Diseases, J. Craig Venter Institute, La Jolla, CA, United States
- Division of Infectious Diseases and Global Public Health Department of Medicine, University of California San Diego, La Jolla, CA, United States
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7
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Quirouette C, Cresta D, Li J, Wilkie KP, Liang H, Beauchemin CAA. The effect of random virus failure following cell entry on infection outcome and the success of antiviral therapy. Sci Rep 2023; 13:17243. [PMID: 37821517 PMCID: PMC10567758 DOI: 10.1038/s41598-023-44180-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/04/2023] [Indexed: 10/13/2023] Open
Abstract
A virus infection can be initiated with very few or even a single infectious virion, and as such can become extinct, i.e. stochastically fail to take hold or spread significantly. There are many ways that a fully competent infectious virion, having successfully entered a cell, can fail to cause a productive infection, i.e. one that yields infectious virus progeny. Though many stochastic models (SMs) have been developed and used to estimate a virus infection's establishment probability, these typically neglect infection failure post virus entry. The SM presented herein introduces parameter [Formula: see text] which corresponds to the probability that a virion's entry into a cell will result in a productive cell infection. We derive an expression for the likelihood of infection establishment in this new SM, and find that prophylactic therapy with an antiviral reducing [Formula: see text] is at least as good or better at decreasing the establishment probability, compared to antivirals reducing the rates of virus production or virus entry into cells, irrespective of the SM parameters. We investigate the difference in the fraction of cells consumed by so-called extinct versus established virus infections, and find that this distinction becomes biologically meaningless as the probability of establishment approaches zero. We explain why the release of virions continuously over an infectious cell's lifespan, rather than as a single burst at the end of the cell's lifespan, does not result in an increased risk of infection extinction. We show, instead, that the number of virus released, not the timing of the release, affects infection establishment and associated critical antiviral efficacy.
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Affiliation(s)
| | - Daniel Cresta
- Department of Physics, Toronto Metropolitan University, Toronto, Canada
| | - Jizhou Li
- Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), RIKEN, Wako, Japan
| | - Kathleen P Wilkie
- Department of Mathematics, Toronto Metropolitan University, Toronto, Canada
| | - Haozhao Liang
- Nishina Center for Accelerator-Based Science (RNC), RIKEN, Wako, Japan
- Department of Physics, University of Tokyo, Tokyo, Japan
| | - Catherine A A Beauchemin
- Department of Physics, Toronto Metropolitan University, Toronto, Canada.
- Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), RIKEN, Wako, Japan.
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8
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Zhang S, Agyeman AA, Hadjichrysanthou C, Standing JF. SARS-CoV-2 viral dynamic modeling to inform model selection and timing and efficacy of antiviral therapy. CPT Pharmacometrics Syst Pharmacol 2023; 12:1450-1460. [PMID: 37534815 PMCID: PMC10583246 DOI: 10.1002/psp4.13022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 07/15/2023] [Accepted: 07/19/2023] [Indexed: 08/04/2023] Open
Abstract
Mathematical models of viral dynamics have been reported to describe adequately the dynamic changes of severe acute respiratory syndrome-coronavirus 2 viral load within an individual host. In this study, eight published viral dynamic models were assessed, and model selection was performed. Viral load data were collected from a community surveillance study, including 2155 measurements from 162 patients (124 household and 38 non-household contacts). An extended version of the target-cell limited model that includes an eclipse phase and an immune response component that enhances viral clearance described best the data. In general, the parameter estimates showed good precision (relative standard error <10), apart from the death rate of infected cells. The parameter estimates were used to simulate the outcomes of a clinical trial of the antiviral tixagevimab-cilgavimab, a monoclonal antibody combination which blocks infection of the target cells by neutralizing the virus. The simulated outcome of the effectiveness of the antiviral therapy in controlling viral replication was in a good agreement with the clinical trial data. Early treatment with high antiviral efficacy is important for desired therapeutic outcome.
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Affiliation(s)
- Shengyuan Zhang
- Department of Pharmaceutics, School of PharmacyUniversity College LondonLondonUK
| | - Akosua A. Agyeman
- Infection, Immunity and Inflammation Research and Teaching Department, Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Christoforos Hadjichrysanthou
- Department of MathematicsUniversity of SussexBrightonUK
- Department of Infectious Disease Epidemiology, School of Public HealthImperial College LondonLondonUK
| | - Joseph F. Standing
- Infection, Immunity and Inflammation Research and Teaching Department, Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
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9
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Bai Y, Du Z, Wang L, Lau EHY, Fung ICH, Holme P, Cowling BJ, Galvani AP, Krug RM, Meyers LA. The public health impact of Paxlovid COVID-19 treatment in the United States. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.16.23288870. [PMID: 37732213 PMCID: PMC10508801 DOI: 10.1101/2023.06.16.23288870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The antiviral drug Paxlovid has been shown to rapidly reduce viral load. Coupled with vaccination, timely administration of safe and effective antivirals could provide a path towards managing COVID-19 without restrictive non-pharmaceutical measures. Here, we estimate the population-level impacts of expanding treatment with Paxlovid in the US using a multi-scale mathematical model of SARS-CoV-2 transmission that incorporates the within-host viral load dynamics of the Omicron variant. We find that, under a low transmission scenario R e ∼ 1.2 treating 20% of symptomatic cases would be life and cost saving, leading to an estimated 0.26 (95% CrI: 0.03, 0.59) million hospitalizations averted, 30.61 (95% CrI: 1.69, 71.15) thousand deaths averted, and US$52.16 (95% CrI: 2.62, 122.63) billion reduction in health- and treatment-related costs. Rapid and broad use of the antiviral Paxlovid could substantially reduce COVID-19 morbidity and mortality, while averting socioeconomic hardship.
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Affiliation(s)
- Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Isaac Chun-Hai Fung
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
| | - Petter Holme
- Department of Computer Science, Aalto University, Espoo, FI 00076, Finland
- Center for Computational Social Science, Kobe University, Nada, Kobe 657-8501, Japan
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Alison P. Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
- Santa Fe Institute, Santa Fe, NM 87507, USA
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10
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Phan T, Brozak S, Pell B, Oghuan J, Gitter A, Hu T, Ribeiro RM, Ke R, Mena KD, Perelson AS, Kuang Y, Wu F. Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks. WATER RESEARCH 2023; 243:120372. [PMID: 37494742 DOI: 10.1016/j.watres.2023.120372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 07/10/2023] [Accepted: 07/15/2023] [Indexed: 07/28/2023]
Abstract
Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.
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Affiliation(s)
- Tin Phan
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA
| | - Samantha Brozak
- School of Mathematical and Statistical Sciences, Arizona State University, AZ 85281, USA
| | - Bruce Pell
- Department of Mathematics and Computer Science, Lawrence Technological University, MI 48075, USA
| | - Jeremiah Oghuan
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Anna Gitter
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Tao Hu
- Department of Geography, Oklahoma State University, Stillwater, OK 74078, USA
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA
| | - Ruian Ke
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA
| | - Kristina D Mena
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Texas Epidemic Public Health Institute, Houston, TX 77030, USA
| | - Alan S Perelson
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, NM 87544, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, AZ 85281, USA
| | - Fuqing Wu
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Texas Epidemic Public Health Institute, Houston, TX 77030, USA.
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11
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Li Y, Zhu Y, Wang Y, Feng Y, Li D, Li S, Qin P, Yang X, Chen L, Zhao J, Zhang C, Li Y. Characterization of RNA G-quadruplexes in porcine epidemic diarrhea virus genome and the antiviral activity of G-quadruplex ligands. Int J Biol Macromol 2023; 231:123282. [PMID: 36657537 DOI: 10.1016/j.ijbiomac.2023.123282] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/08/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023]
Abstract
Porcine epidemic diarrhea virus (PEDV), an enteropathogenic coronavirus, has catastrophic impacts on the global pig industry. However, there are still no anti-PEDV drugs with accurate targets. G-quadruplexes (G4s) are non-canonical secondary structures formed within guanine-rich regions of DNA or RNA, and have attracted great attention as potential targets for antiviral strategy. In this study, we reported two putative G4-forming sequences (PQS) in S and Nsp5 genes of PEDV genome based on bioinformatic analysis, and identified that S-PQS and Nsp5-PQS were enabled to fold into G4 structure by using circular dichroism spectroscopy and fluorescence turn-on assay. Furthermore, we verified that both S-PQS and Nsp5-PQS PQS could form G4 structure in live cells by immunofluorescence microscopy. In addition, G4-specific compounds, such as TMPyP4 and PDS, could significantly inhibit transcription, translation and proliferation of PEDV in vitro. Importantly, these compounds exert antiviral activity at the post-entry step of PEDV infection cycle, by inhibiting viral genome replication and protein expression. Lastly, we demonstrated that TMPyP4 can inhibit reporter gene expression by targeting G4 structure in Nsp5. Taken together, these findings not only reinforce the presence of viral G-quadruplex sequences in PEDV genome but also provide new insights into developing novel antiviral drugs targeting PEDV RNA G-quadruplexes.
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Affiliation(s)
- Yaqin Li
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Yance Zhu
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Yue Wang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Yi Feng
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China; College of Veterinary Medicine, Huazhong Agricultural University, Wuhan 430070, China
| | - Dongliang Li
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Shuai Li
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Panpan Qin
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Xia Yang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Lu Chen
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Jun Zhao
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China
| | - Chao Zhang
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China.
| | - Yongtao Li
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450002, China.
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12
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Sharma S, Sarkar R, Mitra K, Giri L. Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies. Biotechnol Bioeng 2023; 120:1640-1656. [PMID: 36810760 DOI: 10.1002/bit.28358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 12/09/2022] [Accepted: 02/13/2023] [Indexed: 02/24/2023]
Abstract
Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.
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Affiliation(s)
- Surbhi Sharma
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India
| | - Rahuldeb Sarkar
- Departments of Respiratory Medicine and Critical Care, Medway NHS Foundation Trust, Gillingham, Kent, UK.,Faculty of Life Sciences, King's College London, London, UK
| | - Kishalay Mitra
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India
| | - Lopamudra Giri
- Department of Chemical Engineering, Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, India
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13
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Morales-Salazar I, Montes-Enríquez FP, Garduño-Albino CE, García-Sánchez MA, Ibarra IA, Rojas-Aguirre Y, García-Hernández ME, Sarmiento-Silva RE, Alcaraz-Estrada SL, Díaz-Cervantes E, González-Zamora E, Islas-Jácome A. Synthesis of bis-furyl-pyrrolo[3,4- b]pyridin-5-ones via Ugi-Zhu reaction and in vitro activity assays against human SARS-CoV-2 and in silico studies on its main proteins. RSC Med Chem 2023; 14:154-165. [PMID: 36760742 PMCID: PMC9890515 DOI: 10.1039/d2md00350c] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 11/11/2022] [Indexed: 11/19/2022] Open
Abstract
An Ugi-Zhu three-component reaction (UZ-3CR) coupled in one pot manner to a cascade process (N-acylation/aza Diels-Alder cycloaddition/decarboxylation/dehydration) was performed to synthesize a series of bis-furyl-pyrrolo[3,4-b]pyridin-5-ones in 45 to 82% overall yields using ytterbium triflate as a catalyst, toluene as a solvent, and microwaves as a heat source. The synthesized molecules were evaluated in vitro against human SARS-CoV-2 through a time-of-addition approach, finding that compound 1e, at a concentration of 10.0 μM, exhibited a significant reduction at the initial infection stages, thus showing prophylactic potential. On the other hand, it was found that compound 1d, at the same concentration, was significantly active when applied post-infection, thus exhibiting a therapeutic profile. Moreover, compound 1f showed both, prophylactic and therapeutic activity. Then, to understand interactions between synthesized compounds and the main proteins related to the virus, docking studies were performed on spike-glycoprotein, main-protease, and Nsp3 protein, finding moderate to strong binding energies, matching accurately with the in vitro results. Additionally, a pharmacophore model was computed behind further rational drug design.
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Affiliation(s)
- Ivette Morales-Salazar
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A Sección Iztapalapa Ciudad de México C.P. 09310 Mexico
| | - Flora P Montes-Enríquez
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A Sección Iztapalapa Ciudad de México C.P. 09310 Mexico
| | - Carlos E Garduño-Albino
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A Sección Iztapalapa Ciudad de México C.P. 09310 Mexico
| | - M A García-Sánchez
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A Sección Iztapalapa Ciudad de México C.P. 09310 Mexico
| | - Ilich A Ibarra
- Laboratorio de Fisicoquímica y Reactividad de Superficies, Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México Circuito Exterior S/N, Ciudad Universitaria Coyoacán Ciudad de México C.P. 04510 Mexico
| | - Yareli Rojas-Aguirre
- Departamento de Polímeros, Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México Circuito Exterior S/N, Ciudad Universitaria Coyoacán Ciudad de México C.P. 04510 Mexico
| | - Montserrat Elemi García-Hernández
- Departamento de Microbiología e Inmunología, Facultad de Medicina, Veterinaria y Zootecnia, Universidad Nacional Autónoma de México Av. Universidad 3000, Ciudad Universitaria Coyoacán Ciudad de México C.P. 04510 Mexico
| | - Rosa Elena Sarmiento-Silva
- Laboratorio de Virología y Laboratorio Mixto Internacional ELDORADO, Facultad de Medicina, Veterinaria y Zootecnia, Universidad Nacional Autónoma de México Av. Universidad 3000, Ciudad Universitaria Coyoacán Ciudad de México C.P. 04510 Mexico
| | - Sofía Lizeth Alcaraz-Estrada
- División de Medicina Genómica, Centro Médico Nacional 20 de Noviembre, ISSSTE Félix Cuevas 540, Col. Del Valle Sur Benito Juárez Ciudad de México C.P. 03100 Mexico
| | - Erik Díaz-Cervantes
- Departamento de Alimentos, Centro Interdisciplinario del Noreste, Universidad de Guanajuato Tierra Blanca Guanajuato C.P. 37975 Mexico
| | - Eduardo González-Zamora
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A Sección Iztapalapa Ciudad de México C.P. 09310 Mexico
| | - Alejandro Islas-Jácome
- Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa Av. Ferrocarril San Rafael Atlixco 186, Col. Leyes de Reforma 1A Sección Iztapalapa Ciudad de México C.P. 09310 Mexico
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14
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Du Z, Wang S, Bai Y, Gao C, Lau EHY, Cowling BJ. Within-host dynamics of SARS-CoV-2 infection: A systematic review and meta-analysis. Transbound Emerg Dis 2022; 69:3964-3971. [PMID: 35907777 PMCID: PMC9353427 DOI: 10.1111/tbed.14673] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/23/2022] [Accepted: 07/28/2022] [Indexed: 02/04/2023]
Abstract
Within-host model specified by viral dynamic parameters is a mainstream tool to understand SARS-CoV-2 replication cycle in infected patients. The parameter uncertainty further affects the output of the model, such as the efficacy of potential antiviral drugs. However, gathering empirical data on these parameters is challenging. Here, we aim to conduct a systematic review of viral dynamic parameters used in within-host models by calibrating the model to the viral load data measured from upper respiratory specimens. We searched the PubMed, Embase and Web of Science databases (between 1 December 2019 and 10 February 2022) for within-host modelling studies. We identified seven independent within-host models from the above nine studies, including Type I interferon, innate response, humoral immune response or cell-mediated immune response. From these models, we extracted and analyse seven widely used viral dynamic parameters including the viral load at the point of infection or symptom onset, the rate of viral particles infecting susceptible cells, the rate of infected cells releasing virus, the rate of virus particles cleared, the rate of infected cells cleared and the rate of cells in the eclipse phase can become productively infected. We identified seven independent within-host models from nine eligible studies. The viral load at symptom onset is 4.78 (95% CI:2.93, 6.62) log(copies/ml), and the viral load at the point of infection is -1.00 (95% CI:-1.94, -0.05) log(copies/ml). The rate of viral particles infecting susceptible cells and the rate of infected cells cleared have the pooled estimates as -6.96 (95% CI:-7.66, -6.25) log([copies/ml]-1 day-1 ) and 0.92 (95% CI:-0.09, 1.93) day-1 , respectively. We found that the rate of infected cells cleared was associated with the reported model in the meta-analysis by including the model type as a categorical variable (p < .01). Joint viral dynamic parameters estimates when parameterizing within-host models have been published for SARS-CoV-2. The reviewed viral dynamic parameters can be used in the same within-host model to understand SARS-CoV-2 replication cycle in infected patients and assess the impact of pharmaceutical interventions.
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Affiliation(s)
- Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Shuqi Wang
- Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Chao Gao
- School of Artificial Intelligence, Optics, and Electronics (iOPEN)Northwestern Polytechnical UniversityXianChina
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of MedicineThe University of Hong Kong, Hong Kong Special Administrative RegionChina,Laboratory of Data Discovery for Health Limited, Hong Kong Science Park, Hong Kong Special Administrative RegionChina
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15
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Frank TD. Eigenvalue analysis of SARS-CoV-2 viral load data: illustration for eight COVID-19 patients. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2022; 15:281-290. [PMID: 35399335 PMCID: PMC8978770 DOI: 10.1007/s41060-022-00319-y] [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: 08/30/2021] [Accepted: 03/09/2022] [Indexed: 12/19/2022]
Abstract
Eigenvalue analysis is an important tool in economics and nonlinear physics to analyze industrial processes and instability phenomena, respectively. A model-based eigenvalue analysis of viral load data from eight symptomatic COVID-19 patients was conducted. The eigenvalues and eigenvectors of the instabilities were determined that give rise to COVID-19. For all eight patients, it was found that the virus dynamics followed the unstable eigenvectors until the viral load reached the respective peak values. At the peak virus values, the virus dynamics branched off from the directions specified by the eigenvectors. The temporal course of the unstable eigenvalues was determined as well. For all patients, it was found that the eigenvalues switched from positive to negative values just when the virus load reached peak values. These findings suggest that the fixed, instability-related eigenvalues and eigenvectors determine initial stages of SARS-CoV-2 infections during which virus load increases. In contrast, the time-dependent eigenvalues show a sign-switching phenomenon that indicates when the virus dynamics switches from the growth stage (increasing virus load) to the decay stage (decreasing virus load). The virus dynamics model was a standard three-variable virus dynamics model frequently used in the literature.
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Affiliation(s)
- Till D. Frank
- Department of Psychological Sciences, University of Connecticut, 406 Babbidge Road, Storrs, CT 06269 USA
- Department of Physics, University of Connecticut, 196 Auditorium Road, Storrs, CT 06269 USA
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16
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SARS-CoV-2 Dynamics in the Mucus Layer of the Human Upper Respiratory Tract Based on Host–Cell Dynamics. SUSTAINABILITY 2022. [DOI: 10.3390/su14073896] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A thorough understanding of the inhalation dynamics of infectious aerosols indoors and infection dynamics within the host by inhaled viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays an important role in the assessment and control of infection risks indoors. Here, by combining computational fluid–particle dynamics (CFPD) and host–cell dynamics (HCD), SARS-CoV-2 infection dynamics in the mucus layer of the human upper airway were studied. To reproduce the diffusive and convective transport of the virus in the nasal cavity–nasopharynx by mucociliary motion, a three-dimensional (3D)-shell model with a mucus layer was developed. The initial virus concentrations for HCD calculation were estimated based on the deposition distribution of droplets with representative sizes analyzed by CFPD. To develop a new HCD model, the target-cell-limited model was integrated with the convection–diffusion equation. Additionally, the sensitivity of the infection rate β to the infection dynamics was systematically investigated. The results showed that the time series of SARS-CoV-2 concentration in the mucus layer strongly depended on diffusion, convection, and β. Although the SARS-CoV-2 dynamics obtained here have not been verified by corresponding clinical data, they can preliminarily reveal its transmission mode in the upper airway, which will contribute to the prevention and treatment of coronavirus disease 2019.
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17
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Treatment of Respiratory Viral Coinfections. EPIDEMIOLGIA (BASEL, SWITZERLAND) 2022; 3:81-96. [PMID: 36417269 PMCID: PMC9620919 DOI: 10.3390/epidemiologia3010008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/18/2022] [Accepted: 02/01/2022] [Indexed: 12/14/2022]
Abstract
With the advent of rapid multiplex PCR, physicians have been able to test for multiple viral pathogens when a patient presents with influenza-like illness. This has led to the discovery that many respiratory infections are caused by more than one virus. Antiviral treatment of viral coinfections can be complex because treatment of one virus will affect the time course of the other virus. Since effective antivirals are only available for some respiratory viruses, careful consideration needs to be given on the effect treating one virus will have on the dynamics of the other virus, which might not have available antiviral treatment. In this study, we use mathematical models of viral coinfections to assess the effect of antiviral treatment on coinfections. We examine the effect of the mechanism of action, relative growth rates of the viruses, and the assumptions underlying the interaction of the viruses. We find that high antiviral efficacy is needed to suppress both infections. If high doses of both antivirals are not achieved, then we run the risk of lengthening the duration of coinfection or even of allowing a suppressed virus to replicate to higher viral titers.
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18
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Frank T. SARS-coronavirus-2 infections: biological instabilities characterized by order parameters. Phys Biol 2022; 19. [PMID: 35108687 DOI: 10.1088/1478-3975/ac5155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 02/02/2022] [Indexed: 11/12/2022]
Abstract
A four-variable virus dynamics TIIV model was considered that involves infected cells in an eclipse phase. The state space description of the model was transferred into an amplitude space description which is the appropriate general, nonlinear physics framework to describe instabilities. In this context, the unstable eigenvector or order parameter of the model was determined. Subsequently, a model-based analysis of viral load data from eight symptomatic COVID-19 patients was conducted. For all patients, it was found that the initial SARS-CoV-2 infection evolved along the respective patient-specific order parameter, as expected by theoretical considerations. The order parameter amplitude that described the initial virus multiplication showed doubling times between 30 minutes and 3 hours. Peak viral loads of patients were linearly related to the amplitudes of the patient order parameters. Finally, it was found that the patient order parameters determined qualitatively and quantitatively the relationships between the increases in virus-producing infected cells and infected cells in the eclipse phase. Overall, the study echoes the 40 years old suggestion by Mackey and Glass to consider diseases as instabilities.
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Affiliation(s)
- Till Frank
- University of Connecticut, 406 Babbidge Road, Storrs, Connecticut, 06269, UNITED STATES
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19
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An age-structured model of hepatitis B viral infection highlights the potential of different therapeutic strategies. Sci Rep 2022; 12:1252. [PMID: 35075156 PMCID: PMC8786976 DOI: 10.1038/s41598-021-04022-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Hepatitis B virus (HBV) is a global health threat, and its elimination by 2030 has been prioritised by the World Health Organisation. Here we present an age-structured model for the immune response to an HBV infection, which takes into account contributions from both cell-mediated and humoral immunity. The model has been validated using published patient data recorded during acute infection. It has been adapted to the scenarios of chronic infection, clearance of infection, and flare-ups via variation of the immune response parameters. The impacts of immune response exhaustion and non-infectious subviral particles on the immune response dynamics are analysed. A comparison of different treatment options in the context of this model reveals that drugs targeting aspects of the viral life cycle are more effective than exhaustion therapy, a form of therapy mitigating immune response exhaustion. Our results suggest that antiviral treatment is best started when viral load is declining rather than in a flare-up. The model suggests that a fast antibody production rate always leads to viral clearance, highlighting the promise of antibody therapies currently in clinical trials.
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20
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Challenger JD, Foo CY, Wu Y, Yan AWC, Marjaneh MM, Liew F, Thwaites RS, Okell LC, Cunnington AJ. Modelling upper respiratory viral load dynamics of SARS-CoV-2. BMC Med 2022; 20:25. [PMID: 35022051 PMCID: PMC8755404 DOI: 10.1186/s12916-021-02220-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Accepted: 12/15/2021] [Indexed: 02/09/2023] Open
Abstract
Relationships between viral load, severity of illness, and transmissibility of virus are fundamental to understanding pathogenesis and devising better therapeutic and prevention strategies for COVID-19. Here we present within-host modelling of viral load dynamics observed in the upper respiratory tract (URT), drawing upon 2172 serial measurements from 605 subjects, collected from 17 different studies. We developed a mechanistic model to describe viral load dynamics and host response and contrast this with simpler mixed-effects regression analysis of peak viral load and its subsequent decline. We observed wide variation in URT viral load between individuals, over 5 orders of magnitude, at any given point in time since symptom onset. This variation was not explained by age, sex, or severity of illness, and these variables were not associated with the modelled early or late phases of immune-mediated control of viral load. We explored the application of the mechanistic model to identify measured immune responses associated with the control of the viral load. Neutralising antibodies correlated strongly with modelled immune-mediated control of viral load amongst subjects who produced neutralising antibodies. Our models can be used to identify host and viral factors which control URT viral load dynamics, informing future treatment and transmission blocking interventions.
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Affiliation(s)
- Joseph D Challenger
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
| | - Cher Y Foo
- School of Medicine, Imperial College London, London, UK
| | - Yue Wu
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Ada W C Yan
- Department of Infectious Disease, Imperial College London, London, UK
| | - Mahdi Moradi Marjaneh
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
| | - Felicity Liew
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Ryan S Thwaites
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Lucy C Okell
- Medical Research Council Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Aubrey J Cunnington
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.,Centre for Paediatrics and Child Health, Imperial College London, London, UK
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21
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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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22
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Zhang L, Wang J, von Kleist M. Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes. PLoS Comput Biol 2021; 17:e1009295. [PMID: 34941864 PMCID: PMC8741042 DOI: 10.1371/journal.pcbi.1009295] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 01/07/2022] [Accepted: 12/03/2021] [Indexed: 11/18/2022] Open
Abstract
Pre-exposure prophylaxis (PrEP) is an important pillar to prevent HIV transmission. Because of experimental and clinical shortcomings, mathematical models that integrate pharmacological, viral- and host factors are frequently used to quantify clinical efficacy of PrEP. Stochastic simulations of these models provides sample statistics from which the clinical efficacy is approximated. However, many stochastic simulations are needed to reduce the associated sampling error. To remedy the shortcomings of stochastic simulation, we developed a numerical method that allows predicting the efficacy of arbitrary prophylactic regimen directly from a viral dynamics model, without sampling. We apply the method to various hypothetical dolutegravir (DTG) prophylaxis scenarios. The approach is verified against state-of-the-art stochastic simulation. While the method is more accurate than stochastic simulation, it is superior in terms of computational performance. For example, a continuous 6-month prophylactic profile is computed within a few seconds on a laptop computer. The method’s computational performance, therefore, substantially expands the horizon of feasible analysis in the context of PrEP, and possibly other applications. Pre-exposure prophylaxis (PrEP) is an important tool to prevent HIV transmission. However, experimental identification of parameters that determine prophylactic efficacy is extremely difficult. Clues about these parameters could prove essential for the design of next-generation PrEP compounds. Integrative mathematical models can fill this void: Based on stochastic simulation, a sample statistic can be generated, from which the prophylactic efficacy is estimated. However, for this sample statistic to be accurate, many simulations need to be performed. Here, we introduce a numerical method to directly compute the prophylactic efficacy from a viral dynamics model, without the need for sampling. Based on several examples with dolutegravir (DTG) -based short- and long-term PrEP, as well as post-exposure prophylaxis we demonstrate the correctness of the new method and its outstanding computational performance. Due to the method’s computational performance, a number of analyses, including formal sensitivity analysis, are becoming feasible with the proposed method.
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Affiliation(s)
- Lanxin Zhang
- Project group 5 “Systems Medicine of Infectious Disease”, Robert Koch Institute, Berlin, Germany
| | - Junyu Wang
- Project group 5 “Systems Medicine of Infectious Disease”, Robert Koch Institute, Berlin, Germany
| | - Max von Kleist
- Project group 5 “Systems Medicine of Infectious Disease”, Robert Koch Institute, Berlin, Germany
- * E-mail:
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23
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Fatehi F, Bingham RJ, Dechant PP, Stockley PG, Twarock R. Therapeutic interfering particles exploiting viral replication and assembly mechanisms show promising performance: a modelling study. Sci Rep 2021; 11:23847. [PMID: 34903795 PMCID: PMC8668974 DOI: 10.1038/s41598-021-03168-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/22/2021] [Indexed: 11/09/2022] Open
Abstract
Defective interfering particles arise spontaneously during a viral infection as mutants lacking essential parts of the viral genome. Their ability to replicate in the presence of the wild-type (WT) virus (at the expense of viable viral particles) is mimicked and exploited by therapeutic interfering particles. We propose a strategy for the design of therapeutic interfering RNAs (tiRNAs) against positive-sense single-stranded RNA viruses that assemble via packaging signal-mediated assembly. These tiRNAs contain both an optimised version of the virus assembly manual that is encoded by multiple dispersed RNA packaging signals and a replication signal for viral polymerase, but lack any protein coding information. We use an intracellular model for hepatitis C viral (HCV) infection that captures key aspects of the competition dynamics between tiRNAs and viral genomes for virally produced capsid protein and polymerase. We show that only a small increase in the assembly and replication efficiency of the tiRNAs compared with WT virus is required in order to achieve a treatment efficacy greater than 99%. This demonstrates that the proposed tiRNA design could be a promising treatment option for RNA viral infections.
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Affiliation(s)
- Farzad Fatehi
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
| | - Richard J Bingham
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
- Department of Biology, University of York, York, YO10 5DD, UK
| | - Pierre-Philippe Dechant
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK
- Department of Mathematics, University of York, York, YO10 5DD, UK
- School of Science, Technology and Health, York St John University, York, YO31 7EX, UK
| | - Peter G Stockley
- Astbury Centre for Structural Molecular Biology, University of Leeds, Leeds, LS2 9JT, UK.
| | - Reidun Twarock
- York Cross-disciplinary Centre for Systems Analysis, University of York, York, YO10 5GE, UK.
- Department of Mathematics, University of York, York, YO10 5DD, UK.
- Department of Biology, University of York, York, YO10 5DD, UK.
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24
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Ke R, Zitzmann C, Ho DD, Ribeiro RM, Perelson AS. In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness. Proc Natl Acad Sci U S A 2021; 118:e2111477118. [PMID: 34857628 PMCID: PMC8670484 DOI: 10.1073/pnas.2111477118] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2021] [Indexed: 01/11/2023] Open
Abstract
The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person's infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop viral dynamic models of SARS-CoV-2 infection and fit them to data to estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking viral load (VL) to infectiousness and show a person's infectiousness increases sublinearly with VL and that the logarithm of the VL in the upper respiratory tract is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and RT-PCR tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies.
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Affiliation(s)
- Ruian Ke
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
- New Mexico Consortium, Los Alamos, NM 87544
| | - Carolin Zitzmann
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - David D Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032
| | - Ruy M Ribeiro
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545
| | - Alan S Perelson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545;
- New Mexico Consortium, Los Alamos, NM 87544
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25
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Zhang L, Li R, Song G, Scholes GD, She ZS. Impairment of T cells' antiviral and anti-inflammation immunities may be critical to death from COVID-19. ROYAL SOCIETY OPEN SCIENCE 2021; 8:211606. [PMID: 34950497 PMCID: PMC8692966 DOI: 10.1098/rsos.211606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/25/2021] [Indexed: 05/02/2023]
Abstract
Clarifying dominant factors determining the immune heterogeneity from non-survivors to survivors is crucial for developing therapeutics and vaccines against COVID-19. The main difficulty is quantitatively analysing the multi-level clinical data, including viral dynamics, immune response and tissue damages. Here, we adopt a top-down modelling approach to quantify key functional aspects and their dynamical interplay in the battle between the virus and the immune system, yielding an accurate description of real-time clinical data involving hundreds of patients for the first time. The quantification of antiviral responses gives that, compared to antibodies, T cells play a more dominant role in virus clearance, especially for mild patients (96.5%). Moreover, the anti-inflammatory responses, namely the cytokine inhibition and tissue repair rates, also positively correlate with T cell number and are significantly suppressed in non-survivors. Simulations show that the lack of T cells can lead to more significant inflammation, proposing an explanation for the monotonic increase of COVID-19 mortality with age and higher mortality for males. We propose that T cells play a crucial role in the immunity against COVID-19, which provides a new direction-improvement of T cell number for advancing current prevention and treatment.
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Affiliation(s)
- Luhao Zhang
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- Department of Chemistry, Princeton University, Princeton, NJ 08540, USA
| | - Rong Li
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing 100871, People's Republic of China
| | - Gang Song
- Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, People's Republic of China
| | | | - Zhen-Su She
- Institute of Health System Engineering, College of Engineering, Peking University, Beijing 100871, People's Republic of China
- State Key Laboratory for Turbulence and Complex Systems, Peking University, Beijing 100871, People's Republic of China
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26
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Maisonnasse P, Aldon Y, Marc A, Marlin R, Dereuddre-Bosquet N, Kuzmina NA, Freyn AW, Snitselaar JL, Gonçalves A, Caniels TG, Burger JA, Poniman M, Bontjer I, Chesnais V, Diry S, Iershov A, Ronk AJ, Jangra S, Rathnasinghe R, Brouwer PJM, Bijl TPL, van Schooten J, Brinkkemper M, Liu H, Yuan M, Mire CE, van Breemen MJ, Contreras V, Naninck T, Lemaître J, Kahlaoui N, Relouzat F, Chapon C, Ho Tsong Fang R, McDanal C, Osei-Twum M, St-Amant N, Gagnon L, Montefiori DC, Wilson IA, Ginoux E, de Bree GJ, García-Sastre A, Schotsaert M, Coughlan L, Bukreyev A, van der Werf S, Guedj J, Sanders RW, van Gils MJ, Le Grand R. COVA1-18 neutralizing antibody protects against SARS-CoV-2 in three preclinical models. Nat Commun 2021; 12:6097. [PMID: 34671037 PMCID: PMC8528857 DOI: 10.1038/s41467-021-26354-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/24/2021] [Indexed: 01/01/2023] Open
Abstract
Effective treatments against Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) are urgently needed. Monoclonal antibodies have shown promising results in patients. Here, we evaluate the in vivo prophylactic and therapeutic effect of COVA1-18, a neutralizing antibody highly potent against the B.1.1.7 isolate. In both prophylactic and therapeutic settings, SARS-CoV-2 remains undetectable in the lungs of treated hACE2 mice. Therapeutic treatment also causes a reduction in viral loads in the lungs of Syrian hamsters. When administered at 10 mg kg-1 one day prior to a high dose SARS-CoV-2 challenge in cynomolgus macaques, COVA1-18 shows very strong antiviral activity in the upper respiratory compartments. Using a mathematical model, we estimate that COVA1-18 reduces viral infectivity by more than 95% in these compartments, preventing lymphopenia and extensive lung lesions. Our findings demonstrate that COVA1-18 has a strong antiviral activity in three preclinical models and could be a valuable candidate for further clinical evaluation.
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Affiliation(s)
- Pauline Maisonnasse
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Yoann Aldon
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | | | - Romain Marlin
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Nathalie Dereuddre-Bosquet
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Natalia A Kuzmina
- Department of Pathology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
- Galveston National Laboratory, Galveston, TX, USA
| | - Alec W Freyn
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonne L Snitselaar
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | | | - Tom G Caniels
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Judith A Burger
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Meliawati Poniman
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Ilja Bontjer
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | | | | | | | - Adam J Ronk
- Department of Pathology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
- Galveston National Laboratory, Galveston, TX, USA
| | - Sonia Jangra
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Raveen Rathnasinghe
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Philip J M Brouwer
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Tom P L Bijl
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Jelle van Schooten
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Mitch Brinkkemper
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Hejun Liu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Meng Yuan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Chad E Mire
- Galveston National Laboratory, Galveston, TX, USA
- Department of Microbiology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Mariëlle J van Breemen
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Vanessa Contreras
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Thibaut Naninck
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Julien Lemaître
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Nidhal Kahlaoui
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Francis Relouzat
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Catherine Chapon
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Raphaël Ho Tsong Fang
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France
| | - Charlene McDanal
- Duke Human Vaccine Institute & Department of Surgery, Durham, NC, USA
| | | | | | | | | | - Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | | | - Godelieve J de Bree
- Internal Medicine of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
| | - Adolfo García-Sastre
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Schotsaert
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lynda Coughlan
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- University of Maryland School of Medicine, Department of Microbiology and Immunology and Center for Vaccine Development and Global Health (CVD), Baltimore, MD, USA
| | - Alexander Bukreyev
- Department of Pathology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
- Galveston National Laboratory, Galveston, TX, USA
- Department of Microbiology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Sylvie van der Werf
- Molecular Genetics of RNA Viruses, Department of Virology, Institut Pasteur, CNRS UMR 3569, Université de Paris, Paris, France
- National Reference Center for Respiratory Viruses, Institut Pasteur, Paris, France
| | | | - Rogier W Sanders
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands.
- Department of Microbiology and Immunology, Weill Medical College of Cornell University, New York, NY, USA.
| | - Marit J van Gils
- Departments of Medical Microbiology of the Amsterdam UMC, University of Amsterdam, Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands.
| | - Roger Le Grand
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, Paris, France.
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27
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Vaidya NK, Bloomquist A, Perelson AS. Modeling Within-Host Dynamics of SARS-CoV-2 Infection: A Case Study in Ferrets. Viruses 2021; 13:1635. [PMID: 34452499 PMCID: PMC8402735 DOI: 10.3390/v13081635] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/27/2021] [Accepted: 08/09/2021] [Indexed: 12/26/2022] Open
Abstract
The pre-clinical development of antiviral agents involves experimental trials in animals and ferrets as an animal model for the study of SARS-CoV-2. Here, we used mathematical models and experimental data to characterize the within-host infection dynamics of SARS-CoV-2 in ferrets. We also performed a global sensitivity analysis of model parameters impacting the characteristics of the viral infection. We provide estimates of the viral dynamic parameters in ferrets, such as the infection rate, the virus production rate, the infectious virus proportion, the infected cell death rate, the virus clearance rate, as well as other related characteristics, including the basic reproduction number, pre-peak infectious viral growth rate, post-peak infectious viral decay rate, pre-peak infectious viral doubling time, post-peak infectious virus half-life, and the target cell loss in the respiratory tract. These parameters and indices are not significantly different between animals infected with viral strains isolated from the environment and isolated from human hosts, indicating a potential for transmission from fomites. While the infection period in ferrets is relatively short, the similarity observed between our results and previous results in humans supports that ferrets can be an appropriate animal model for SARS-CoV-2 dynamics-related studies, and our estimates provide helpful information for such studies.
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Affiliation(s)
- Naveen K. Vaidya
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA;
- Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Angelica Bloomquist
- Department of Mathematics and Statistics, San Diego State University, San Diego, CA 92182, USA;
- Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA
- Viral Information Institute, San Diego State University, San Diego, CA 92182, USA
| | - Alan S. Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, NM 87545, USA;
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28
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Ke R, Zitzmann C, Ho DD, Ribeiro RM, Perelson AS. In vivo kinetics of SARS-CoV-2 infection and its relationship with a person's infectiousness. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.06.26.21259581. [PMID: 34230935 PMCID: PMC8259912 DOI: 10.1101/2021.06.26.21259581] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The within-host viral kinetics of SARS-CoV-2 infection and how they relate to a person's infectiousness are not well understood. This limits our ability to quantify the impact of interventions on viral transmission. Here, we develop data-driven viral dynamic models of SARS-CoV-2 infection and estimate key within-host parameters such as the infected cell half-life and the within-host reproductive number. We then develop a model linking VL to infectiousness, showing that a person's infectiousness increases sub-linearly with VL. We show that the logarithm of the VL in the upper respiratory tract (URT) is a better surrogate of infectiousness than the VL itself. Using data on VL and the predicted infectiousness, we further incorporated data on antigen and reverse transcription polymerase chain reaction (RT-PCR) tests and compared their usefulness in detecting infection and preventing transmission. We found that RT-PCR tests perform better than antigen tests assuming equal testing frequency; however, more frequent antigen testing may perform equally well with RT-PCR tests at a lower cost, but with many more false-negative tests. Overall, our models provide a quantitative framework for inferring the impact of therapeutics and vaccines that lower VL on the infectiousness of individuals and for evaluating rapid testing strategies. SIGNIFICANCE Quantifying the kinetics of SARS-CoV-2 infection and individual infectiousness is key to quantitatively understanding SARS-CoV-2 transmission and evaluating intervention strategies. Here we developed data-driven within-host models of SARS-CoV-2 infection and by fitting them to clinical data we estimated key within-host viral dynamic parameters. We also developed a mechanistic model for viral transmission and show that the logarithm of the viral load in the upper respiratory tract serves an appropriate surrogate for a person's infectiousness. Using data on how viral load changes during infection, we further evaluated the effectiveness of PCR and antigen-based testing strategies for averting transmission and identifying infected individuals.
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Affiliation(s)
- Ruian Ke
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, 4200 West Jemez Road, Los Alamos, NM 87544
| | - Carolin Zitzmann
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - David D. Ho
- Aaron Diamond AIDS Research Center, Columbia University Vagelos College of Physicians and Surgeons, New York, NY 10032
| | - Ruy M. Ribeiro
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Alan S. Perelson
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, 4200 West Jemez Road, Los Alamos, NM 87544
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29
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Single-chain variable fragment antibody constructs neutralize measles virus infection in vitro and in vivo. Cell Mol Immunol 2021; 18:1835-1837. [PMID: 34007030 DOI: 10.1038/s41423-021-00691-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
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30
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Perelson AS, Ke R. Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics. Clin Pharmacol Ther 2021; 109:829-840. [PMID: 33410134 DOI: 10.1002/cpt.2160] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022]
Abstract
Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.
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Affiliation(s)
- Alan S Perelson
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
| | - Ruian Ke
- Los Alamos National Laboratory, Theoretical Biology and Biophysics Group, Los Alamos, New Mexico, USA.,New Mexico Consortium, Los Alamos, New Mexico, USA
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Gonçalves A, Maisonnasse P, Donati F, Albert M, Behillil S, Contreras V, Naninck T, Marlin R, Solas C, Pizzorno A, Lemaitre J, Kahlaoui N, Terrier O, Ho Tsong Fang R, Enouf V, Dereuddre-Bosquet N, Brisebarre A, Touret F, Chapon C, Hoen B, Lina B, Rosa Calatrava M, de Lamballerie X, Mentré F, Le Grand R, van der Werf S, Guedj J. SARS-CoV-2 viral dynamics in non-human primates. PLoS Comput Biol 2021; 17:e1008785. [PMID: 33730053 PMCID: PMC8007039 DOI: 10.1371/journal.pcbi.1008785] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 03/29/2021] [Accepted: 02/11/2021] [Indexed: 01/08/2023] Open
Abstract
Non-human primates infected with SARS-CoV-2 exhibit mild clinical signs. Here we used a mathematical model to characterize in detail the viral dynamics in 31 cynomolgus macaques for which nasopharyngeal and tracheal viral load were frequently assessed. We identified that infected cells had a large burst size (>104 virus) and a within-host reproductive basic number of approximately 6 and 4 in nasopharyngeal and tracheal compartment, respectively. After peak viral load, infected cells were rapidly lost with a half-life of 9 hours, with no significant association between cytokine elevation and clearance, leading to a median time to viral clearance of 10 days, consistent with observations in mild human infections. Given these parameter estimates, we predict that a prophylactic treatment blocking 90% of viral production or viral infection could prevent viral growth. In conclusion, our results provide estimates of SARS-CoV-2 viral kinetic parameters in an experimental model of mild infection and they provide means to assess the efficacy of future antiviral treatments.
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Affiliation(s)
| | - Pauline Maisonnasse
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Flora Donati
- Unité de Génétique Moléculaire des Virus à ARN, GMVR: Institut Pasteur, UMR CNRS 3569, Université de Paris, Paris, France
- Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut Pasteur, Paris, France
| | - Mélanie Albert
- Unité de Génétique Moléculaire des Virus à ARN, GMVR: Institut Pasteur, UMR CNRS 3569, Université de Paris, Paris, France
- Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut Pasteur, Paris, France
| | - Sylvie Behillil
- Unité de Génétique Moléculaire des Virus à ARN, GMVR: Institut Pasteur, UMR CNRS 3569, Université de Paris, Paris, France
- Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut Pasteur, Paris, France
| | - Vanessa Contreras
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Thibaut Naninck
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Romain Marlin
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Caroline Solas
- Aix-Marseille Univ, APHM, Unité des Virus Emergents (UVE) IRD 190, INSERM 1207, Laboratoire de Pharmacocinétique et Toxicologie, Hôpital La Timone, Marseille, France
| | - Andres Pizzorno
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Julien Lemaitre
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Nidhal Kahlaoui
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Olivier Terrier
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Raphael Ho Tsong Fang
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Vincent Enouf
- Unité de Génétique Moléculaire des Virus à ARN, GMVR: Institut Pasteur, UMR CNRS 3569, Université de Paris, Paris, France
- Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut Pasteur, Paris, France
- Plate-forme de microbiologie mutualisée (P2M), Pasteur International Bioresources Network (PIBnet), Institut Pasteur, Paris, France
| | - Nathalie Dereuddre-Bosquet
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Angela Brisebarre
- Unité de Génétique Moléculaire des Virus à ARN, GMVR: Institut Pasteur, UMR CNRS 3569, Université de Paris, Paris, France
- Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut Pasteur, Paris, France
| | - Franck Touret
- Unité des Virus Emergents, UVE: Aix Marseille Univ, IRD 190, INSERM 1207, IHU Méditerranée Infection, Marseille, France
| | - Catherine Chapon
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Bruno Hoen
- Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France
| | - Bruno Lina
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
- Laboratoire de Virologie, Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut des Agents Infectieux, Groupement Hospitalier Nord, Hospices Civils de Lyon, Lyon, France
| | - Manuel Rosa Calatrava
- CIRI, Centre International de Recherche en Infectiologie, (Team VirPath), Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France
| | - Xavier de Lamballerie
- Unité des Virus Emergents, UVE: Aix Marseille Univ, IRD 190, INSERM 1207, IHU Méditerranée Infection, Marseille, France
| | | | - Roger Le Grand
- Université Paris-Saclay, Inserm, CEA, Center for Immunology of Viral, Auto-immune, Hematological and Bacterial diseases (IMVA-HB/IDMIT), Fontenay-aux-Roses & Le Kremlin-Bicêtre, France
| | - Sylvie van der Werf
- Unité de Génétique Moléculaire des Virus à ARN, GMVR: Institut Pasteur, UMR CNRS 3569, Université de Paris, Paris, France
- Centre National de Référence des Virus des infections respiratoires (dont la grippe), Institut Pasteur, Paris, France
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Kim KS, Ejima K, Iwanami S, Fujita Y, Ohashi H, Koizumi Y, Asai Y, Nakaoka S, Watashi K, Aihara K, Thompson RN, Ke R, Perelson AS, Iwami S. A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2. PLoS Biol 2021; 19:e3001128. [PMID: 33750978 PMCID: PMC7984623 DOI: 10.1371/journal.pbio.3001128] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2-3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.
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Affiliation(s)
- Kwang Su Kim
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Keisuke Ejima
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health–Bloomington, Bloomington, Indiana, United States of America
| | - Shoya Iwanami
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Yasuhisa Fujita
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
| | - Hirofumi Ohashi
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
| | - Yoshiki Koizumi
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Yusuke Asai
- National Center for Global Health and Medicine, Tokyo, Japan
| | - Shinji Nakaoka
- Faculty of Advanced Life Science, Hokkaido University, Sapporo, Japan
| | - Koichi Watashi
- Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan
- Department of Applied Biological Science, Tokyo University of Science, Noda, Japan
- Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan
- JST-Mirai, Japan Science and Technology Agency, Saitama, Japan
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, University of Tokyo Institutes for Advanced Study, University of Tokyo, Tokyo, Japan
| | - Robin N. Thompson
- Mathematics Institute, University of Warwick, Coventry, United Kingdom
- Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry, United Kingdom
| | - Ruian Ke
- New Mexico Consortium, Los Alamos, New Mexico, United States of America
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Alan S. Perelson
- New Mexico Consortium, Los Alamos, New Mexico, United States of America
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
| | - Shingo Iwami
- Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan
- JST-Mirai, Japan Science and Technology Agency, Saitama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo, Japan
- Science Groove, Fukuoka, Japan
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