doi: 10.3934/dcdsb.2021278
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A delayed dynamical model for COVID-19 therapy with defective interfering particles and artificial antibodies

Department of Applied Mathematics, Shanghai Normal University, Road Guilin No.100, 200234, Shanghai, China

* Corresponding author: Yepeng Xing

Received  June 2021 Revised  October 2021 Early access November 2021

Fund Project: The authors were supported by National Natural Science Foundation of China (No.12071297, No.12171320)

In this paper, we use delay differential equations to propose a mathematical model for COVID-19 therapy with both defective interfering particles and artificial antibodies. For this model, the basic reproduction number $ \mathcal{R}_0 $ is given and its threshold properties are discussed. When $ \mathcal{R}_0<1 $, the disease-free equilibrium $ E_0 $ is globally asymptotically stable. When $ \mathcal{R}_0>1 $, $ E_0 $ becomes unstable and the infectious equilibrium without defective interfering particles $ E_1 $ comes into existence. There exists a positive constant $ R_1 $ such that $ E_1 $ is globally asymptotically stable when $ R_1<1<\mathcal{R}_0 $. Further, when $ R_1>1 $, $ E_1 $ loses its stability and infectious equilibrium with defective interfering particles $ E_2 $ occurs. There exists a constant $ R_2 $ such that $ E_2 $ is asymptotically stable without time delay if $ 1<R_1<\mathcal{R}_0<R_2 $ and it loses its stability via Hopf bifurcation as the time delay increases. Numerical simulation is also presented to demonstrate the applicability of the theoretical predictions.

Citation: Yanfei Zhao, Yepeng Xing. A delayed dynamical model for COVID-19 therapy with defective interfering particles and artificial antibodies. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2021278
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T. KajiwaraT. Sasaki and Y. Takeuchi, Construction of Lyapunov functionals for delay differential equations in virology and epidemiology, Nonlinear Anal. Real World Appl., 13 (2012), 1802-1826.  doi: 10.1016/j.nonrwa.2011.12.011.  Google Scholar

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A. C. Marriott and N. J. Dimmock, Defective interfering viruses and their potential as antiviral agents, Rev. Med. Virol., 20 (2010), 51-62.  doi: 10.1002/rmv.641.  Google Scholar

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[28]

S. Ruan and J. Wei, On the zeros of a third degree exponential polynomial with applications to a delayed model for the control of testosterone secretion, Math. Med. Biol., 18 (2001), 41-52.  doi: 10.1093/imammb/18.1.41.  Google Scholar

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[31]

Y. Sun, D. Jain, C. J. Koziol-White et al., Immunostimulatory defective viral genomes from respiratory syncytial virus promote a strong innate antiviral response during infection in mice and humans, Plos Pathog., 11 (2015), e1005122. doi: 10.1371/journal.ppat.1005122.  Google Scholar

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E. Szathmáry, Co-operation and defection: Playing the field in virus dynamics, J. Theoret. Biol., 165 (1993), 341-356.   Google Scholar

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F. Tapia, T. Laske, M. A. Wasik et al., Production of defective interfering particles of influenza a virus in parallel continuous cultures at two residence times-insights from qPCR measurements and viral dynamics modeling, Front. Bioeng. Biotech., 7 (2019), 275. doi: 10.3389/fbioe.2019.00275.  Google Scholar

[34]

Y. TianY. Bai and P. Yu, Impact of delay on HIV-1 dynamics of fighting a virus with another virus, Math. Biosci. Eng., 11 (2014), 1181-1198.  doi: 10.3934/mbe.2014.11.1181.  Google Scholar

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[41]

S. Yao, A. Narayanan, S. A. Majowicz, J. Jose and M. Archetti, A synthetic defective interfering SARS-CoV-2, PeerJ, 9 (2021), e11686. Google Scholar

[42]

M. Zhang, J. Xiao, A. Deng et al., Transmission dynamics of an outbreak of the COVID-19 delta variant B. 1.617. 2-Guangdong Province, China, May–June 2021, CCDC Weekly, 3 (2021), 584–586. Google Scholar

[43]

T. ZhangY. SongZ. Jiang and J. Wang, Dynamical analysis of a delayed HIV virus dynamic model with cell-to-cell transmission and apoptosis of bystander cells, Complexity, 2 (2020), 126-144.   Google Scholar

[44]

H. Zhao, K. K. To, H. Chu et al., Dual-functional peptide with defective interfering genes effectively protects mice against avian and seasonal influenza, Nat. Commun., 9 (2018), 1–14. doi: 10.1038/s41467-018-04792-7.  Google Scholar

[45]

X. ZhouX. Song and X. Shi, A differential equation model of HIV infection of CD4+ T-cells with cure rate, J. Math. Anal. Appl., 342 (2008), 1342-1355.  doi: 10.1016/j.jmaa.2008.01.008.  Google Scholar

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H. Zhu and X. Zou, Dynamics of HIV-1 infection model with cell-mediated immune response and intracellular delay, Discrete Contin. Dyn. Syst. Ser. B, 12 (2009), 511-524.  doi: 10.3934/dcdsb.2009.12.511.  Google Scholar

show all references

References:
[1]

D. Adam, What scientists know about new, fast-spreading coronavirus variants, Nature, 594 (2021), 19-20.   Google Scholar

[2]

C. M. Bangham and T. B. L. Kirkwood, Defective interfering particles: Effects in modulating virus growth and persistence, Virology, 179 (1990), 821-826.  doi: 10.1016/0042-6822(90)90150-P.  Google Scholar

[3]

A. Baum, D. Ajithdoss, R. Copin et al., REGN-COV2 antibodies prevent and treat SARSCoV-2 infection in rhesus macaques and hamsters, Science, 370 (2020), 1110-1115. doi: 10.1126/science.abe2402.  Google Scholar

[4]

F. Campbell, B. Archer, H. Laurenson-Schafer et al., Increased transmissibility and global spread of SARS-CoV-2 variants of concern as at June 2021, Eurosurveillance, 26 (2021), 2100509. Google Scholar

[5]

X. Cao, COVID-19: Immunopathology and its implications for therapy, Nat. Rev. Immunol., 20 (2020), 269-270.   Google Scholar

[6]

P. Chen, A. Nirula, B. Heller et al., SARS-CoV-2 neutralizing antibody LY-CoV555 in outpatients with Covid-19, N. Engl. J. Med., 384 (2021), 229–237. Google Scholar

[7]

N. J. Dimmock and A. J. Easton, Defective interfering influenza virus RNAs: Time to reevaluate their clinical potential as broad-spectrum antivirals, J. Virol., 88 (2014), 5217-5227.  doi: 10.1128/JVI.03193-13.  Google Scholar

[8]

S. A. Frank, Within-host spatial dynamics of viruses and defective interfering particles, J. Theoret. Biol., 206 (2000), 279-290.  doi: 10.1006/jtbi.2000.2120.  Google Scholar

[9]

T. Frensing, F. S. Heldt, A. Pflugmacher et al., Continuous influenza virus production in cell culture shows a periodic accumulation of defective interfering particles, Plos One, 8 (2013), e72288. doi: 10.1371/journal.pone.0072288.  Google Scholar

[10]

S. A. GourleyY. Kuang and J. D. Nagy, Dynamics of a delay differential equation model of hepatitis B virus infection, J. Biol. Dyn., 2 (2008), 140-153.  doi: 10.1080/17513750701769873.  Google Scholar

[11]

J. K. Hale and S. M. Verduyn Lunel, Introduction to Functional Differential Equations, Springer, New York, 1993. doi: 10.1007/978-1-4612-4342-7.  Google Scholar

[12]

A. S. Huang and D. Baltimore, Defective viral particles and viral disease processes, Nature, 226 (1970), 325-327.  doi: 10.1038/226325a0.  Google Scholar

[13]

C. Huang, Y. Wang, X. Li et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, 395 (2020), 497–506. Google Scholar

[14]

T. KajiwaraT. Sasaki and Y. Takeuchi, Construction of Lyapunov functionals for delay differential equations in virology and epidemiology, Nonlinear Anal. Real World Appl., 13 (2012), 1802-1826.  doi: 10.1016/j.nonrwa.2011.12.011.  Google Scholar

[15]

T. B. Kirkwood and C. R. Bangham, Cycles, chaos, and evolution in virus cultures: A model of defective interfering particles, Proc. Natl. Acad. Sci., 91 (1994), 8685-8689.  doi: 10.1073/pnas.91.18.8685.  Google Scholar

[16]

R. L. Kruse, Therapeutic strategies in an outbreak scenario to treat the novel coronavirus originating in Wuhan, China, F1000Research, 9 (2020), 72.   Google Scholar

[17]

J. P. La Salle, The Stability of Dynamical Systems, Society for Industrial and Applied Mathematics, 1976.  Google Scholar

[18]

Q. Li, X. Guan, P. Wu et al., Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia, N. Engl. J. Med., 382 (2020), 1199–1207. doi: 10.1056/NEJMoa2001316.  Google Scholar

[19]

W. Li, M. J. Moore, N. Vasilieva et al., Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus, Nature, 426 (2003), 450–454. doi: 10.1038/nature02145.  Google Scholar

[20]

X. Li and J. Wei, On the zeros of a fourth degree exponential polynomial with applications to a neural network model with delays, Chaos Solitons Fractals, 26 (2005), 519-526.  doi: 10.1016/j.chaos.2005.01.019.  Google Scholar

[21]

C. C. MacDuffee, The Theory of Matrices, Springer, New York, 2012. Google Scholar

[22]

T. B. Manzoni and C. B. López, Defective (interfering) viral genomes re-explored: Impact on antiviral immunity and virus persistence, Future Virol., 13 (2018), 493-503.  doi: 10.2217/fvl-2018-0021.  Google Scholar

[23]

A. C. Marriott and N. J. Dimmock, Defective interfering viruses and their potential as antiviral agents, Rev. Med. Virol., 20 (2010), 51-62.  doi: 10.1002/rmv.641.  Google Scholar

[24]

G. W. Nelson and A. S.Perelson, Modeling defective interfering virus therapy for AIDS: Conditions for DIV survival, Math. Biosci., 125 (1995), 127-153.  doi: 10.1016/0025-5564(94)00021-Q.  Google Scholar

[25]

Y. Pan, J. Du, J. Liu et al., Screening of potent neutralizing antibodies against SARS-CoV-2 using convalescent patients-derived phage-display libraries, Cell Discov., 7 (2021), 1–19. Google Scholar

[26]

A. S. Perelson and P. W. Nelson, Mathematical analysis of HIV-1 dynamics in vivo., SIAM Rev., 41 (1999), 3-44.  doi: 10.1137/S0036144598335107.  Google Scholar

[27]

U. Rand, S. Y. Kupke, H. Shkarlet et al., Antiviral activity of influenza A virus defective interfering particles against SARS-CoV-2 replication in vitro through stimulation of innate immunity, Cells, 10 (2021), 1756. Google Scholar

[28]

S. Ruan and J. Wei, On the zeros of a third degree exponential polynomial with applications to a delayed model for the control of testosterone secretion, Math. Med. Biol., 18 (2001), 41-52.  doi: 10.1093/imammb/18.1.41.  Google Scholar

[29]

H. L. Smith, Monotone dynamical systems: An introduction to the theory of competitive and cooperative systems, Bull. Amer. Math. Soc., 33 (1996), 203-209.   Google Scholar

[30]

X. Sun and J. Wei, Stability and bifurcation analysis in a viral infection model with delays, Adv. Differential Equations, 2015 (2015), Article number: 332, 22 pp. doi: 10.1186/s13662-015-0664-7.  Google Scholar

[31]

Y. Sun, D. Jain, C. J. Koziol-White et al., Immunostimulatory defective viral genomes from respiratory syncytial virus promote a strong innate antiviral response during infection in mice and humans, Plos Pathog., 11 (2015), e1005122. doi: 10.1371/journal.ppat.1005122.  Google Scholar

[32]

E. Szathmáry, Co-operation and defection: Playing the field in virus dynamics, J. Theoret. Biol., 165 (1993), 341-356.   Google Scholar

[33]

F. Tapia, T. Laske, M. A. Wasik et al., Production of defective interfering particles of influenza a virus in parallel continuous cultures at two residence times-insights from qPCR measurements and viral dynamics modeling, Front. Bioeng. Biotech., 7 (2019), 275. doi: 10.3389/fbioe.2019.00275.  Google Scholar

[34]

Y. TianY. Bai and P. Yu, Impact of delay on HIV-1 dynamics of fighting a virus with another virus, Math. Biosci. Eng., 11 (2014), 1181-1198.  doi: 10.3934/mbe.2014.11.1181.  Google Scholar

[35]

M. N. Tortorici and D. Veesler, Structural insights into coronavirus entry, Adv. Virus. Res., 105 (2019), 93-116.  doi: 10.1016/bs.aivir.2019.08.002.  Google Scholar

[36]

P. van den Driessche and J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29-48.  doi: 10.1016/S0025-5564(02)00108-6.  Google Scholar

[37]

M. Vignuzzi and C. B. López, Defective viral genomes are key drivers of the virus–host interaction, Nat. Microbiol., 4 (2019), 1075-1087.  doi: 10.1038/s41564-019-0465-y.  Google Scholar

[38]

D. Wang, B. Hu, C. Hu et al., Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China, JAMA, 323 (2020), 1061–1069. Google Scholar

[39]

D. Wodarz, Hepatitis C virus dynamics and pathology: The role of CTL and antibody responses, J. Gen. Virol., 84 (2003), 1743-1750.  doi: 10.1099/vir.0.19118-0.  Google Scholar

[40]

Y. WuC. Chen and Y. Chan, The outbreak of COVID-19: An overview, J. Chin. Med. Assoc., 83 (2020), 217-220.  doi: 10.1097/JCMA.0000000000000270.  Google Scholar

[41]

S. Yao, A. Narayanan, S. A. Majowicz, J. Jose and M. Archetti, A synthetic defective interfering SARS-CoV-2, PeerJ, 9 (2021), e11686. Google Scholar

[42]

M. Zhang, J. Xiao, A. Deng et al., Transmission dynamics of an outbreak of the COVID-19 delta variant B. 1.617. 2-Guangdong Province, China, May–June 2021, CCDC Weekly, 3 (2021), 584–586. Google Scholar

[43]

T. ZhangY. SongZ. Jiang and J. Wang, Dynamical analysis of a delayed HIV virus dynamic model with cell-to-cell transmission and apoptosis of bystander cells, Complexity, 2 (2020), 126-144.   Google Scholar

[44]

H. Zhao, K. K. To, H. Chu et al., Dual-functional peptide with defective interfering genes effectively protects mice against avian and seasonal influenza, Nat. Commun., 9 (2018), 1–14. doi: 10.1038/s41467-018-04792-7.  Google Scholar

[45]

X. ZhouX. Song and X. Shi, A differential equation model of HIV infection of CD4+ T-cells with cure rate, J. Math. Anal. Appl., 342 (2008), 1342-1355.  doi: 10.1016/j.jmaa.2008.01.008.  Google Scholar

[46]

H. Zhu and X. Zou, Dynamics of HIV-1 infection model with cell-mediated immune response and intracellular delay, Discrete Contin. Dyn. Syst. Ser. B, 12 (2009), 511-524.  doi: 10.3934/dcdsb.2009.12.511.  Google Scholar

Figure 1.  Artificial antibodies block SARS-CoV-2 from infecting cells
Figure 2.  Pathogen viral particles $ V $ infect normal cells $ T $ producing infected cells $ I $; $ W $ can produce in infected cells; artificial antibodies $ F $ bind to virus, infected cells are able to produce virus $ V $ and defective interfering particles $ W $
Figure 3.  When $ \mathcal{R}_0<1 $, $ \tau = 1 $, the disease-free equilibrium $ E_0 $ is globally asymptotically stable
Figure 4.  When $ R_1<1<\mathcal{R}_0 $, $ \tau = 0.8, 1,1.5 $, the infectious equilibrium without defective interfering particles $ E_1 $ is globally asymptotically stable
Figure 5.  When $ 1<R_1<\mathcal{R}_0 $, $ \tau = 1.6 $, the infectious equilibrium with defective intefering particles $ E_2 $ is locally asymptotically stable
Figure 6.  When $ 1<R_1<\mathcal{R}_0 $, $ \tau = 1.6 $, the infectious equilibrium with defective interfering particles $ E_2 $ showing bifurcation to a stable limit cycle
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