# American Institute of Mathematical Sciences

• Previous Article
Permanence and extinction of a stochastic SIS epidemic model with three independent Brownian motions
• DCDS-B Home
• This Issue
• Next Article
Carrying simplex in the Lotka-Volterra competition model with seasonal succession with applications

## Modeling within-host viral dynamics: The role of CTL immune responses in the evolution of drug resistance

 1 Department of Mathematics, Nanjing University of Science and Technology, Nanjing 210094, China 2 Department of Mathematics, Nanjing University of Science and Technology, Nanjing 210094, China, Department of Mathematics, University of Florida, Gainesville, FL 32611, USA

* Corresponding author: Zhipeng Qiu

Received  February 2020 Revised  June 2020 Published  August 2020

Fund Project: T. Guo was supported by the CSC (201806840119) and the NSFC (11971232). Z. Qiu was supported by the NSFC (11671206). L. Rong was supported by the NSF Grant DMS-1758290

To study the emergence and evolution of drug resistance during treatment of HIV infection, we study a mathematical model with two strains, one drug-sensitive and the other drug-resistant, by incorporating cytotoxic T lymphocyte (CTL) immune response. The reproductive numbers for each strain with and without the CTL immune response are obtained and shown to determine the stability of the steady states. By sensitivity analysis, we evaluate how the changes of parameters influence the reproductive numbers. The model shows that CTL immune response can suppress the development of drug resistance. There is a dynamic relationship between antiretroviral drug administration, the prevalence of drug resistance, the total level of viral production, and the strength of immune responses. We further investigate the scenario under which the drug-resistant strain can outcompete the wild-type strain. If drug efficacy is at an intermediate level, the drug-resistant virus is likely to arise. The slower the immune response wanes, the slower the drug-resistant strain grows. The results suggest that immunotherapy that aims to enhance immune responses, combined with antiretroviral drug treatment, may result in a functional control of HIV infection.

Citation: Qi Deng, Zhipeng Qiu, Ting Guo, Libin Rong. Modeling within-host viral dynamics: The role of CTL immune responses in the evolution of drug resistance. Discrete & Continuous Dynamical Systems - B, doi: 10.3934/dcdsb.2020245
##### References:

show all references

##### References:
The schematic diagram of HIV transmission with wild-type and resistant virus
The dynamics of infection before converging to steady states. $(a)-(d)$ represent uninfected cells, wild-type virus, resistant virus, and CTL cells, respectively. The blue solid line is the case in which CTL cells are present. The parameters are from Table 1 and $b = 0.1$. We have $R_{s} = 3.13>R_{r} = 1.74>1$, $R_{cs} = 68.1>1$. Solution trajectories of the system converge to the positive equilibrium $E^{\star} = (415257, 14994.3, 586870, 1.01219, 26.4110, 44.9845)$. The red dotted line refers to the case in which CTL cells are absent. The parameter $c$ is 0. This yields $R_{s} = 3.13>R_{r} = 1.74>1$, $R_{cs} = 0<1$. Solution trajectories of the system converge to the CTL immune-free equilibrium $E^{f} = (318355, 23056.2, 902393, 1.55640, 40.6104, 0)$
Sensitivity analysis for four reproduction numbers. (a) The PRCC of $R_{s}$ for seven parameters, (b) the PRCC of $R_{r}$ for six parameters, (c) the PRCC of $R_{cs}$ for nine parameters, and (d) the PRCC of $R_{cr}$ for eight parameters
Predicted dynamics of system (4) under treatment. $(a)-(d)$ represent uninfected cells, wild-type virus, resistant virus, and CTL cells, respectively. The blue solid line represents the case in which CTL cells are present. The parameters given in Table 1 are used and $b=0.1$. This yields $R_{s}^{\prime}=1.57>R_{r}^{\prime}=1.54>1$ and $R_{cs}^{\prime}=36.30>1$. Thus, solutions trajectories of the system converge to the interior equilibrium $E^{\star\prime}=(733912, 7673.78, 243257, 13.8008, 346.384, 23.0628)$. The red dotted line refers to the case in which CTL cells are absent. Parameters from Table 1 are used and $b=0.1, c=0$. In this case, $R_{s}^{\prime}=1.57>R_{r}^{\prime}=1.54>1$ and $R_{cs}^{\prime}=0<1$. Solutions trajectories of the system converge to the CTL immune-free equilibrium $E^{f\prime}=(635966, 12174.6, 476875, 21.8671, 571.018, 0)$
Invasion of drug-resistant virus. (a) Effects of the infection rate $\beta_{s}$ and overall drug efficacy $\eta_{s}$ on $R_{s}^{\prime}$ and $R_{r}^{\prime}$. (b) The projection of (a) on the $\beta_{s}-\eta_{s}$ plane. (c) Part of (b) when $\eta_{s}$ is from 0.4 to 0.6. The vertical line in magenta represents $\eta_{s} = 0.51$. In the simulation, we assume that $\eta_{r} = 0.23\eta_{s}, \beta_{r} = 0.83\beta_{s}$. The other parameters are given in Table 1
Predicted dynamics of system (4) when drug resistance invades during treatment. Figure $(a)-(b)$ shows the wild-type virus and the resistant virus, respectively. $\eta_{s} = 0.6876, \eta_{r} = 0.1635$, $R_{s}^{\prime} = 0.9778<1<R_{r}^{\prime} = 1.455, R_{cr} = 312.7>1$. The solutions of the system converge to the infected equilibrium with drug-resistant strain and CTL immune response $E^{rc\prime} = (928780, 0, 0, 1756.85, 45817.3, 52.7080)$. Figure $(c)-(d)$ is similar to $(a)-(b)$ except $\eta_{s} = 0.4330, \eta_{r} = 0.0957$. In this case, $R_{r}^{\prime} = 1.573<R_{s}^{\prime} = 1.775, R_{cs}^{\prime} = 436.6>1$. Both strains of virus persist and the solutions of the system converge to the interior equilibrium $E^{\star\prime} = (872574, 2744.46, 86963.0, 0.722593, 18.1288, 82.2710)$. The parameters are from Table 1
Dynamics of system (4) with different values of $c$ (the proliferation rate of CTLs). Green dashed-dot line is for $c = 0$, black solid line is for $c = 0.00003$, blue dashed line is for $c = 0.0002$, and red dot line is for $c = 0.001$. We chose $\eta_{s} = 0.5328, \eta_{r} = 0.1221$ and the other parameters can be found in Table 1
Drug resistance dynamics under different drug efficacies. (a) The immune clearance rate is $b = 0.1$ and (b) $b = 0.01$. Green curve is for $\eta_{s} = 0.52, \eta_{r} = 0.12$, red curve is for $\eta_{s} = 0.69, \eta_{r} = 0.16$, and blue curve is for $\eta_{s} = 0.96, \eta_{r} = 0.29$. $c$ is fixed at $0.001$ and other parameters are given in Table 1
(a) The difference between non-infectious and infectious wild-type virus concentrations. (b) The difference between non-infectious and infectious drug-resistant virus concentrations. Shortly after initiation of potent antiretroviral therapy, the difference between non-infectious and infectious viral levels (logarithm with base 10) approaches a constant. The parameters can be found in Table 1
Parameter descriptions and sources for their values
 Parameter Value Ranges Description Reference $\lambda$ $10^{4}ml^{-1}day^{-1}$ $100\sim200000$ Production rate of uninfected cells [42] $d$ $0.01day^{-1}$ $0.0001\sim 0.8$ Death rate of uninfected cells [34] $\beta_{s}$ $2.4\times10^{-8}ml\cdot day^{-1}$ $10^{-6}\sim2.4\times10^{-6}$ Infection rate of uninfected cells by wild-type virus [34] $\beta_{r}$ $2.0\times10^{-8}ml\cdot day^{-1}$ $10^{-6}\sim2.0\times10^{-6}$ Infection rate of uninfected cells by drug-resistant virus [42] $\delta$ $0.3day^{-1}$ $0.1\sim0.9$ Death rate of infected cells [42] $k$ $0.002mm^{3}day^{-1}$ - Clearance rate of infected cells by CTL killing [48, 1] $p_{s}$ $900day^{-1}$ $90\sim1000$ Generation rate of wild-type virus [8, 17] $p_{r}$ $600day^{-1}$ $60\sim1000$ Generation rate of drug-resistant virus [8, 17] $d_{1}$ $23day^{-1}$ $10\sim50$ Clearance rate of wild-type and resistant virus [39] $c$ $0.0003ml^{-1}day^{-1}$ $0\sim0.001$ generation rate of CTL [55, 61] $b$ $0.01day^{-1}$ $0.01\sim1$ death rate of CTL [48, 61] $\mu$ $3\times10^{-5}$ $10^{-6}\sim10^{-4}$ Single mutation rate [24] $\varepsilon_{RTI}$ Varied $0\sim1$ Efficacy of RTI see text $\varepsilon_{PI}$ Varied $0\sim1$ Efficacy of PI see text $\sigma_{RTI}$ Varied $0\sim1$ Resistance ratio of RTI see text $\sigma_{PI}$ Varied $0\sim1$ Resistance ratio of PI see text
 Parameter Value Ranges Description Reference $\lambda$ $10^{4}ml^{-1}day^{-1}$ $100\sim200000$ Production rate of uninfected cells [42] $d$ $0.01day^{-1}$ $0.0001\sim 0.8$ Death rate of uninfected cells [34] $\beta_{s}$ $2.4\times10^{-8}ml\cdot day^{-1}$ $10^{-6}\sim2.4\times10^{-6}$ Infection rate of uninfected cells by wild-type virus [34] $\beta_{r}$ $2.0\times10^{-8}ml\cdot day^{-1}$ $10^{-6}\sim2.0\times10^{-6}$ Infection rate of uninfected cells by drug-resistant virus [42] $\delta$ $0.3day^{-1}$ $0.1\sim0.9$ Death rate of infected cells [42] $k$ $0.002mm^{3}day^{-1}$ - Clearance rate of infected cells by CTL killing [48, 1] $p_{s}$ $900day^{-1}$ $90\sim1000$ Generation rate of wild-type virus [8, 17] $p_{r}$ $600day^{-1}$ $60\sim1000$ Generation rate of drug-resistant virus [8, 17] $d_{1}$ $23day^{-1}$ $10\sim50$ Clearance rate of wild-type and resistant virus [39] $c$ $0.0003ml^{-1}day^{-1}$ $0\sim0.001$ generation rate of CTL [55, 61] $b$ $0.01day^{-1}$ $0.01\sim1$ death rate of CTL [48, 61] $\mu$ $3\times10^{-5}$ $10^{-6}\sim10^{-4}$ Single mutation rate [24] $\varepsilon_{RTI}$ Varied $0\sim1$ Efficacy of RTI see text $\varepsilon_{PI}$ Varied $0\sim1$ Efficacy of PI see text $\sigma_{RTI}$ Varied $0\sim1$ Resistance ratio of RTI see text $\sigma_{PI}$ Varied $0\sim1$ Resistance ratio of PI see text
Summary of the stability results of system (1)
 Conditions System (1) $R_{0<1}$ $E^{0}$ $is$ $L.A.S$ $R_{0}>1$ $R_{s}1}$ $E^{rc}$ $is$ $L.A.S$ $R_{s}>R_{r}$ $R_{cs<1}$ $E^{f}$ $is$ $L.A.S$ $R_{cs>1}$ $E^{\star}$ $is$ $L.A.S$ $(\hbox{by simulation})$
 Conditions System (1) $R_{0<1}$ $E^{0}$ $is$ $L.A.S$ $R_{0}>1$ $R_{s}1}$ $E^{rc}$ $is$ $L.A.S$ $R_{s}>R_{r}$ $R_{cs<1}$ $E^{f}$ $is$ $L.A.S$ $R_{cs>1}$ $E^{\star}$ $is$ $L.A.S$ $(\hbox{by simulation})$
 [1] Xin Zhao, Tao Feng, Liang Wang, Zhipeng Qiu. Threshold dynamics and sensitivity analysis of a stochastic semi-Markov switched SIRS epidemic model with nonlinear incidence and vaccination. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2021010 [2] Jianquan Li, Xin Xie, Dian Zhang, Jia Li, Xiaolin Lin. Qualitative analysis of a simple tumor-immune system with time delay of tumor action. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020341 [3] Do Lan. Regularity and stability analysis for semilinear generalized Rayleigh-Stokes equations. Evolution Equations & Control Theory, 2021  doi: 10.3934/eect.2021002 [4] A. M. Elaiw, N. H. AlShamrani, A. Abdel-Aty, H. Dutta. Stability analysis of a general HIV dynamics model with multi-stages of infected cells and two routes of infection. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020441 [5] Shujing Shi, Jicai Huang, Yang Kuang. Global dynamics in a tumor-immune model with an immune checkpoint inhibitor. Discrete & Continuous Dynamical Systems - B, 2021, 26 (2) : 1149-1170. doi: 10.3934/dcdsb.2020157 [6] Adam Glick, Antonio Mastroberardino. Combined therapy for treating solid tumors with chemotherapy and angiogenic inhibitors. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020343 [7] Hui Zhao, Zhengrong Liu, Yiren Chen. Global dynamics of a chemotaxis model with signal-dependent diffusion and sensitivity. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2021011 [8] Shigui Ruan. Nonlinear dynamics in tumor-immune system interaction models with delays. Discrete & Continuous Dynamical Systems - B, 2021, 26 (1) : 541-602. doi: 10.3934/dcdsb.2020282 [9] Yi-Hsuan Lin, Gen Nakamura, Roland Potthast, Haibing Wang. Duality between range and no-response tests and its application for inverse problems. Inverse Problems & Imaging, , () : -. doi: 10.3934/ipi.2020072 [10] Hongyu Cheng, Shimin Wang. Response solutions to harmonic oscillators beyond multi–dimensional brjuno frequency. Communications on Pure & Applied Analysis, , () : -. doi: 10.3934/cpaa.2020222 [11] He Zhang, John Harlim, Xiantao Li. Estimating linear response statistics using orthogonal polynomials: An RKHS formulation. Foundations of Data Science, 2020, 2 (4) : 443-485. doi: 10.3934/fods.2020021 [12] George W. Patrick. The geometry of convergence in numerical analysis. Journal of Computational Dynamics, 2021, 8 (1) : 33-58. doi: 10.3934/jcd.2021003 [13] Yi-Ming Tai, Zhengyang Zhang. Relaxation oscillations in a spruce-budworm interaction model with Holling's type II functional response. Discrete & Continuous Dynamical Systems - B, 2021  doi: 10.3934/dcdsb.2021027 [14] Skyler Simmons. Stability of broucke's isosceles orbit. Discrete & Continuous Dynamical Systems - A, 2021  doi: 10.3934/dcds.2021015 [15] Min Chen, Olivier Goubet, Shenghao Li. Mathematical analysis of bump to bucket problem. Communications on Pure & Applied Analysis, 2020, 19 (12) : 5567-5580. doi: 10.3934/cpaa.2020251 [16] Bernard Bonnard, Jérémy Rouot. Geometric optimal techniques to control the muscular force response to functional electrical stimulation using a non-isometric force-fatigue model. Journal of Geometric Mechanics, 2020  doi: 10.3934/jgm.2020032 [17] Eduard Feireisl, Elisabetta Rocca, Giulio Schimperna, Arghir Zarnescu. Weak sequential stability for a nonlinear model of nematic electrolytes. Discrete & Continuous Dynamical Systems - S, 2021, 14 (1) : 219-241. doi: 10.3934/dcdss.2020366 [18] Meihua Dong, Keonhee Lee, Carlos Morales. Gromov-Hausdorff stability for group actions. Discrete & Continuous Dynamical Systems - A, 2021, 41 (3) : 1347-1357. doi: 10.3934/dcds.2020320 [19] Qianqian Han, Xiao-Song Yang. Qualitative analysis of a generalized Nosé-Hoover oscillator. Discrete & Continuous Dynamical Systems - B, 2020  doi: 10.3934/dcdsb.2020346 [20] Laurence Cherfils, Stefania Gatti, Alain Miranville, Rémy Guillevin. Analysis of a model for tumor growth and lactate exchanges in a glioma. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020457

2019 Impact Factor: 1.27