Skip to main content
Log in

Multidrug Therapy for HIV Infection: Dynamics of Immune System

  • Regular Article
  • Published:
Acta Biotheoretica Aims and scope Submit manuscript

Abstract

A mathematical model of the dynamics of the immune system is considered to illustrate the effect of its response to HIV infection, i.e. on viral growth and on T-cell dynamics. The specific immune response is measured by the levels of cytotoxic lymphocytes in a human body. The existence and stability analyses are performed for infected steady state and uninfected steady state. In order to keep infection under control, roles of drug therapies are analyzed in the presence of efficient immune response. Numerical simulations are computed and exhibited to illustrate the support of the immune system to drug therapies, so as to ensure the decay of infection and to maintain the level of healthy cells.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Arnaout R, Nowak M, Wodarz D (2000) HIV-1 dynamics revisited: biphasic decay by cytotoxic lymphocyte killing? Proc R Soc Lond B 265:1347–1354

    Article  Google Scholar 

  • Bonhoeffer S, May RM, Shaw GM, Nowak MA (1997) Virus dynamics and drug therapy. Proc Natl Acad Sci USA 94:6971–6976

    Article  Google Scholar 

  • Callaway D, Perelson AS (2002) HIV-1 infection and low steady state viral loads. Bull Math Biol 64:29–64

    Article  Google Scholar 

  • Cuipe MS, Bivort BL, Bortz DM, Nelson PW (2006) Estimating kinetic parameters from HIV primary infection data through the eyes of three different mathematical models. Math Biosci 200:1–27

    Article  Google Scholar 

  • Culshaw RV, Raun S, Raymond JS (2004) Optimal HIV treatment by maximising immune response. J Math Biol 48:545–562

    Article  Google Scholar 

  • De Boer RJ, Perelson AS (1998) Target cell limited and immune control models of HIV infection: a comparison. J Theor Biol 190:201–214

    Article  Google Scholar 

  • De Leenheer P, Smith HL (2003) Virus dynamics: a global analysis. SIAM J Appl Math 63:1313–1327

    Article  Google Scholar 

  • Dixit NM, Perelson AS (2004) Complex patterns of viral load decay under antiretroviral therapy: influence of pharmacokinetics and intracellular delay. J Theor Biol 226:95–109

    Article  Google Scholar 

  • Dubey P, Dubey US, Dubey B (2018) Modeling the role of acquired immune response and antiretroviral therapy in the dynamics of HIV infection. Math Comput Simul 144:120–137

    Article  Google Scholar 

  • Essunger P, Perelson AS (1994) Modelling HIV infection of CD4+ T-cell subpopulations. J Theor Biol 170:367–391

    Article  Google Scholar 

  • Kamboj D, Sharma MD (2016) Effects of combined drug therapy on HIV-1 infection dynamics. Int J Biomath 09:1–23

    Article  Google Scholar 

  • Kirschner DE, Webb GF (1997) A mathematical model of combined drug therapy of HIV infection. J Theor Med 1:25–34

    Article  Google Scholar 

  • Mellors JW, Rinaldo CR Jr, Gupta P, White RM, Todd JA, Kingsley LA (1996) Prognosis in HIV-1 infection predicted by the quantity of virus in plasma. Science 272:1167–70

    Article  Google Scholar 

  • Mittler J, Sulzer B, Neumann A, Perelson AS (1998) Influence of delayed virus production on viral dynamics in HIV-1 infected patients. Math Biosci 152:143–163

    Article  Google Scholar 

  • Mohri H, Bohhoeffer S, Monard S, Perelson AS, Ho DD (1998) Rapid turnover of T-lymphocytes in SIV infected rhesus macaques. Science 279:1223–1227

    Article  Google Scholar 

  • Nelson PW, Murray JD, Perelson AS (2000) A model of HIV-1 pathogenesis that includes an intracellular delay. Math Biosci 163:201–215

    Article  Google Scholar 

  • Nelson P, Mittler J, Perelson AS (2001) Effect of drug efficacy and the eclipse phase of the viral life cycle on estimates of HIV-1 viral dynamic parameters. J Acquir Immune Defic Syndr 26:405–412

    Article  Google Scholar 

  • Nowak MA, Bangham C (1996) Population dynamics of immune response to persistent viruses. Science 272:74–79

    Article  Google Scholar 

  • Nowak MA, May RM (2000) Virus dynamics. Oxford University Press, Oxford

    Google Scholar 

  • Nowak MA, Bonhoeffer S, Shaw GM, May RM (1997) Anti-viral drug treatment: dynamics of resistance in free virus and infected cell populations. J Theor Biol 184:203–217

    Article  Google Scholar 

  • Perelson AS (1989) Modelling the interaction of the immune system with HIV. In: Castillo-Chavez C (ed) Mathematical and statistical approaches to AIDS epidemiology. Springer, Berlin, p 350

    Chapter  Google Scholar 

  • Perelson AS, Nelson PW (1999) Mathematical analysis of HIV-1 dynamics in vivo. SIAM Rev 41:3–44

    Article  Google Scholar 

  • Perelson AS, Kirschner DE, De Boer R (1993) Dynamics of HIV Infection of CD4+ T-cells. Math Biosci 114:81–125

    Article  Google Scholar 

  • Perelson AS, Neumann AU, Markowitz M, Leonard JM, Ho DD (1996) HIV-1 dynamics in vivo: virion clearance rate, infected cell life span, and viral generation time. Science 271:1582–1586

    Article  Google Scholar 

  • Rong L, Feng Z, Perelson AS (2007) Mathematical analysis of age-structured HIV-1 dynamics with combination antiretroviral therapy. SIAM J Appl Math 67:731–756

    Article  Google Scholar 

  • Srivastava PK, Chandra P (2008) Hopf bifurcation and periodic solutions in model for the dynamics of HIV and immune response. Differ Equ Dyn Syst 16:77–100

    Article  Google Scholar 

  • Srivastava PK, Banerjee M, Chandra P (2009) Modeling the drug therapy for HIV infection. J Biol Syst 17:213–223

    Article  Google Scholar 

  • Srivastava PK, Banerjee M, Chandra P (2012) Dynamical model of inhost HIV infection: with drug therapy and multi viral strains. J Biol Syst 20:303–325

    Article  Google Scholar 

  • Wang L, Ellermeyer S (2006) HIV infection and CD4+ T-cell dynamics, Discrete. Contin Dyn Syst B6:1417–1430

    Google Scholar 

  • Wang L, Li M (2006) Mathematical analysis of the global dynamics of a model for HIV infection of CD4+ T-cells. Math Biosci 200:44–57

    Article  Google Scholar 

  • Wang Y, Zhou Y, Wu J, Heffernan J (2009) Oscillatory viral dynamics in a delayed HIV pathogenesis model. Math Biosci 219:104–112

    Article  Google Scholar 

  • Wang Y, Zhou Y, Brauer F, Heffernan JM (2013) Viral dynamics model with CTL immune response incorporating antiretroviral therapy. J Math Biol 67:901–934

    Article  Google Scholar 

  • Willems JL (1970) Stability theory of dynamical systems. Wiley, New York

    Google Scholar 

  • Wodarz D, Hamer DH (2007) Infection dynamic in HIV-specific CD4+ T-cells. Math Biosci 209:14–29

    Article  Google Scholar 

  • Wodarz D, Nowak MA (1999) Specific therapy regimes could lead to a long-term immunological control of HIV. Proc Natl Acad Sci 96:14464–14469

    Article  Google Scholar 

  • Zhu H, Zou X (2009) Dynamics of a HIV-1 infection model with cell-mediated immune response and intracellular delay. Discrete Contin Dyn Syst Ser-B 12:511–524

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deepmala Kamboj.

Appendix

Appendix

Parameter

Definition

Value (unit)

References

s

Rate at which new T-cells are created from sources within the body, such as thymus

10 (mm−3 day−1)

Perelson et al. (1993)

k

Rate constant for CD4+ cells becoming infected by free virus

0.000024 (mm3 day−1)

Perelson et al. (1993)

N

Average number of viral particles produced by an infected cell

Varies

Perelson et al. (1993)

\(T_{max}\)

Maximum CD4+ cell population level

1500 (mm−3)

Perelson et al. (1993)

b

Reverting rate of infected cells to uninfected class due to the non-completion of reverse transcription

0.05 (day−1)

Essunger and Perelson (1994)

\(\alpha\)

Transition rate from pre-RT class to post-RT infected class

0.4 (day−1)

Essunger and Perelson (1994)

\(\delta\)

Death rate of productively infected cells (\(T_2\))

0.24 (day−1)

Perelson et al. (1993)

r

Rate of growth for the CD4+ cell population

0.03 (day−1)

Perelson et al. (1993)

\(\mu\)

Death rate of uninfected cells

0.01 (day−1)

Mohri et al. (1998)

\(\mu _1\)

Death rate of infected cells

0.015 (day−1)

Mohri et al. (1998)

\(\mu _v\)

Clearance rate of virus

2.4 (day−1)

Perelson et al. (1993)

p

Proliferation rate of CTLs

1.02 (day−1)

Srivastava and Chandra (2008)

\(d_x\)

Rate of clearance of infected cells (\(T_{2}\)) by CTLs

0.01 (mm3 day−1)

Culshaw et al. (2004), Arnaout et al. (2000), Nowak and Bangham (1996)

\(d_E\)

Death rate of CTLs

0.1 (day−1)

Srivastava and Chandra (2008)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kamboj, D., Sharma, M.D. Multidrug Therapy for HIV Infection: Dynamics of Immune System. Acta Biotheor 67, 129–147 (2019). https://doi.org/10.1007/s10441-018-9340-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10441-018-9340-0

Keywords

Navigation