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Dehingia, Kaushik
- An Analysis of the Dynamics of a Cancerous Tumour Model with Targeted Chemotherapy
Authors
1 Department of Mathematics, Gauhati University, Guwahati – 781014, Assam, IN
Source
Asian Journal of Pharmaceutical Research and Health Care, Vol 12, No 3 (2020), Pagination: 130-140Abstract
We have analyzed a model of Lotka-Volterra type interacting between immune cell-tumour cell-normal cells, where control policy is applied in terms of targeted chemotherapy. We determined conditions for the local stability of all the equilibrium points and global stability condition for the tumour free equilibrium point, including the feasibility of the solution. Further, we have discussed the possibility of Hopf bifurcation at each equilibrium point. Numerical simulation was carried out to observe the qualitative behaviour of the system as the control parameter is varied.
Keywords
Global Stability, Hopf Bifurcation, Lotka-Volterra Type, Targeted Chemotherapy.References
- Komarova NL, Wodarz D. Drug resistance in cancer: Principles of emergence and prevention. PNAS. 2005 Jul 5; 102(27):9714–9. PMid: 15980154 PMCid: PMC1172248. https://doi.org/10.1073/pnas.0501870102
- Malinzi J, Sibanda P, Mambili-Mamboundou H. Analysis of virotherapy in solid tumour invasion. MB. 2015 Feb; 263:102–10. PMid: 25725123. https://doi.org/10.1016/j.mbs.2015.01.015
- https://www.cancer.gov/about-cancer/treatment/types/targeted-therapies
- Belostotski G. A control theory model for cancer treatment by radiotherapy. IJPAM. 2005; 25(4):447–80.
- Isea R, Lonngren KE. A mathematical model of cancer under radiotherapy. IJPHR . 2015 Oct; 3(6):340–4.
- Adam JA, Panetta J. A simple mathematical model and alternative paradigm for certain chemotherapeutic regimens. MCM. 1995 Apr; 22(8):49–60. https://doi.org/10.1016/0895-7177(95)00154-T
- Eisen M. Mathematical models in cell biology and cancer chemotherapy. SV. 1979; 30:1–431. PMid: 447222. https://doi.org/10.1007/978-3-642-93126-0
- Martin RB, Fisher ME, Minchin R.F, Teo KL. A mathematical model of cancer chemotherapy with an optimal selection of parameters. MBIJ. 1990; 99(2):205–30. https://doi.org/10.1016/0025-5564(90)90005-J
- Isaeva O, Osipov V. Different strategies for cancer treatment: Mathematical modeling. CMMM. 2009; 10(4):253–72. https://doi.org/10.1080/17486700802536054
- Dehingia K, Nath KC, Sarmah HK. Mathematical modelling of tumour-immune dynamics and cure therapy: A review of literature.JNR. 2020; 21(1):26–41.
- Frascoli F, Kim PS, Hughes BD, Landman KA. A dynamical model of tumour immunotherapy. MB. 2014; 253:50–62. PMid: 24759513. https://doi.org/10.1016/j.mbs.2014.04.003
- Adam JA. The dynamics growth-factor-modified immune response to cancer growth: One dimensional models. MCM. 1993; 17(3):83–106. https://doi.org/10.1016/08957177(93)90041-V
- Owen M, Sherratt J. Modeling the macrophage invasion of tumours: Effects on growth and composition. IMAJMAMB. 1998; 15(2):165–85. https://doi.org/10.1093/imammb/15.2.165
- Wilson S, Levy D. A mathematical model of the enhancement of tumour vaccine efficacy by immune therapy. BMB. 2012 Mar; 74(7):1485–500. PMid: 22438084 PMCid: PMC3822329. https://doi.org/10.1007/s11538012-9722-4
- Albert A, Freedman M, Perelson AS. Tumours and the immune system: The effects of a tumour growth modulator. MB. 1980 Jul; 50(1-2):25–58. https://doi.org/10.1016/00255564(80)90120-0
- Liu P, Liu X. Dynamics of a tumour-immune model considering targeted chemotherapy. CSF. 2017 May; 98:7– 13. https://doi.org/10.1016/j.chaos.2017.03.002
- Padma VV. An overview of targeted cancer therapy. BM. 2015 Nov; 5(4):1–6. PMid: 26613930 PMCid: PMC4662664. https://doi.org/10.7603/s40681-015-0019-4
- Gerber DE. Targeted therapies: A new generation of cancer treatments. AFP. 2008 Feb; 77(3):311–9.
- Seebacher NA, Stacy AE, Porter GM, Merlot AM. Clinical development of targeted and immune based anti-cancer therapies. JECCR. 2019 Apr; 38:156. PMid: 30975211 PMCid: PMC6460662. https://doi.org/10.1186/s13046019-1094-2
- Pento JT. Monoclonal antibodies for the treatment of cáncer. Monoclonal antibodies for the treatment of cancer. NIH. 2017 Nov; 37(11):5935–9. https://doi.org/10.21873/anticanres.12040
- de Pillis LG, Savage H, Radunskaya AE. Mathematical model of colorectal cancer with monoclonal antibody treatments. BJMCS. 2013 Dec; X(X):XX–XX.
- Smith K, Garman L, Wrammert J, Zheng NY, Capra JD, Ahmed R, Wilson PC. Rapid generation of fully human monoclonal antibodies specific to a vaccinating antigen. NIHPA. 2009; 4(3):372–84. PMid: 19247287 PMCid: PMC2750034. https://doi.org/10.1038/nprot.2009.3
- Anser W, Ghosh S. Monoclona antibodies: A tool in clinical Research. IJCM. 2013 Jul; 4: 9–21. https://doi.org/10.4137/IJCM.S11968
- Lia Y, Wang R, Chen X, Tang D. Emerging trends and new developments in monoclonal antibodies: A scientometric analysis. HVI. 2017 Jun; 13(6):1388–97. PMid: 28301271 PMCid: PMC5489293. https://doi.org/10.1080/21645515.2 017.1286433
- Kirschner D. On the global dynamics of a model for tumour immunotherapy. MBE. 2009 Jul; 6(3): 573–8. PMid: 19566127. https://doi.org/10.3934/mbe.2009.6.573
- Gurcan F, Kartal S, Ozturk I, Bozkurt F. Stability and bifurcation analysis of a mathematical model for tumourimmune interaction with piecewise constant arguments of delay. CSF. 2014 Jun; 38(9):169–79. https://doi.org/10.1016/j.chaos.2014.08.001
- Galach M. Dynamics of the tumour-immune system competition - the effect of time delay. IJAMCS. 2003; 13(3):395–406.
- Dibrov BF, Zhabotinsky AM, Neyfakh YA, Orlova MP, Churikova LI. Mathematical model of cancer chemotherapy. Periodic schedules of phase-specific cytotoxic- agent administration increasing the selectivity of therapy. MB.1985 Mar; 73(1):1–31. https://doi.org/10.1016/0025-5564(85)90073-2
- de Pillis LG de, Radunskaya AE. A mathematical tumour model with immune resistance and drug therapy: An optimal control approach. JTM. 2001; 3(2):79–100.https://doi.org/10.1080/10273660108833067
- de Pillis LG, Radunskaya AE. The dynamics of optimally controlled tumour model. A case ctudy. MCM. 2003 Jun; 37(11):1221–44. https://doi.org/10.1016/S08957177(03)00133-X
- Paul R, Das A, Bhattacharya D, Sarma HK. A policy to eradicate tumour in a discrete-continuous immune celltumour cell-drug administration model with the help of stability analysis and bifurcation analysis of the model.IJAER. 2019; 14(9):2192–7.
- Paul R, Das A, Sarma HK. Global stability analysis to control growth of tumour in an immune-tumour-normal cell model with drug administration in the form of chemotherapy. IJTP. 2017; 65(3–4)91–106.
- Novozhilov AS, Benezovskaya FS, Koonin EV. Mathematical modelling of tumour therapy with oncolytic viruses: Regimes with complete tumour elimination within the framework of deterministic models. BD. 2006 Feb; 1:6.
- Abbott LH, Michor F. Mathematical models of targeted cancer therapy. BJC. 2006; 95:1136–41. PMid: 17031409 PMCid: PMC2360553. https://doi.org/10.1038/sj.bjc.6603310
- Yu P. Closed form conditions of bifurcation points for general differential equations. IJBC. 2005; 15(4):1467–83. https://doi.org/10.1142/S0218127405012582
- A Study on an HIV Pathogenesis Model with Different Growth rates of Uninfected and Infected CD4+T cells
Authors
1 Department of Mathematics, Barnagar College, Sorbhog – 781317, Barpeta, Assam, IN
2 Department of Mathematics, Gauhati University, Guwahati – 781014, Assam, IN
Source
Asian Journal of Pharmaceutical Research and Health Care, Vol 12, No 4 (2020), Pagination: 198-212Abstract
The objective of this paper is to discuss the dynamics of an HIV pathogenesis model with full logistic target cell growth of uninfected T cells and cure rate of infected T cells. Local and global dynamics of both infection-free and infected equilibrium points are rigorously established. It is found that if basic reproduction number R0≤1, the infection is cleared from T cells and if R0>1, the HIV infection persists. Also, we have carried out numerical simulations to verify the results. The existence of non-trivial periodic solution is also studied by means of numerical simulation. Therefore, we find a parameter region where infected equilibrium point is globally stable to make the model biologically significant. From the overall study, it is found that proliferation of T cells cannot be ignored during the study of HIV dynamics for better results and we can focus on a treatment policy which can control the parameters of the model in such a way that the basic reproduction number remains less than or equal to one.
Keywords
HIV, Local and Global Stability, Periodic Solution, Treatment2010 AMS classification: 34A34, 34D23, 37C25
References
- World Health Organisation HIV/AIDS Key facts. 2020 Jul https://www.who.int/newsroom/fact-sheets/detail/hiv
- Douek DC, Brenchley JM, Betts MR, Ambrozak DR, Hill BJ, Okamoto Y, Casazza JP, Kuruppu J, Kunstman K, Wolinsky S, Grossman Z. HIV preferentially infects HIV-specific CD4+ T cells. Nature. 2002 May; 417(6884):95–8. PMid: 11986671. https://doi.org/10.1038/417095a
- Perelson AS, Kirschner DE, Boer R De. Dynamics of HIV infection of CD4+ T cells. Math Biosci. 1993; 114:81–125. https://doi.org/10.1016/0025-5564(93)90043-A
- Essunger P, Perelson AS. Modelling HIV infection CD4+ T - subpopulations. J Theoret Biol. 1994; 170:367–91. PMid: 7996863. https://doi.org/10.1006/jtbi.1994.1199
- Wang L, Li MY. Mathematical analysis of the global dynamics of a model for HIV infection of CD4+ T cells. Math Biosci. 2006; 200:44–57. PMid: 16466751. https://doi.org/10.1016/j.mbs.2005.12.026
- Rong L, Gilchrist MA, Feng Z, Perelson AS. Modeling within host HIV-1 dynamics and the evolution of drug resistance: Trade-offs between viral enzyme function and drug susceptibility. J Theoret Biol. 2007; 247(4):804–18. PMid: 17532343 PMCid: PMC2265667. https://doi.org/10.1016/j.jtbi.2007.04.014
- Perelson AS, Nelson Patrick W. Mathematical Analysis of HIV-1 dynamics in Vivo. SIAM Review. 1999; 41(1):3–44. https://doi.org/10.1137/S0036144598335107
- Leenheer P. De, Smith HL. Virus dynamics: A global analysis. SIAM J Appl Math. 2003; 63:1313–27. https://doi.org/10.1137/S0036139902406905
- Gao T, Wang W, Liu X. Mathematical analysis of an HIV model with impulsive antiretroviral drug doses. Math Comput Simul. 2011 Dec; 82(4):653–65. https://doi.org/10.1016/j.matcom.2011.10.007
- Srivastava PK, Chandra P. Modeling the dynamics of HIV and CD4+ T cells during primary infection.
- Nonlinear Anal Real World Appl. 2010 Apr; 11(2):612–8. https://doi.org/10.1016/j.nonrwa.2008.10.037
- Chandra P. Mathematical modeling of HIV dynamics: In Vivo. Mathematics Student-India. 2009; 78(1):7.
- Nowak MA, May RM. Virus dynamics. UK: Oxford University Press; 2000.
- LaSalle JP. The stability of dynamical systems. SIAM; 1976. PMCid: PMC1411100. https://doi.org/10.21236/ADA031020
- Ho DD, Neumann AU, Perelson AS, Chen W, Leonard JM, Markowitz M. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature. 1995; 373:123–6. PMid: 7816094. https://doi.org/10.1038/373123a0
- Sachsenberg N, Perelson AS, Yerly S, Schockmel GA, Leduc D, Hirschel B, Perrin L. Turnover of CD4+ and CD8+ T lymphocytes in HIV-1 infection as measured by ki-67 antigen. J Exp Med. 1998; 187:1295–303. PMid: 9547340 PMCid: PMC2212238. https://doi.org/10.1084/jem.187.8.1295
- Fan X, Brauner CM,Wittkop L. Mathematical analysis of a HIV model with quadratic logistic growth term. Discrete and Continuous Dynamical Systems Series-B. 2012; 17(7):2359–85. https://doi.org/10.3934/dcdsb.2012.17.2359
- Song X, Neumann AU. Global stability and periodic solution of the viral dynamics. J Math Anal Appl. 2007 May; 329(1):281–97. https://doi.org/10.1016/j.jmaa.2006.06.064
- Wang X, Song X. Global stability and periodic solution of a model for HIV infection of CD4+ T cells.
- Appl Math Comput. 2007 Jun; 189(2):1331–40. https://doi.org/10.1016/j.amc.2006.12.044
- Culshaw RV, Ruan S. A delay-differential equation model of HIV infection of CD4+ T-cells. Math Biosci.
- May; 165(1):27–39. https://doi.org/10.1016/S00255564(00)00006-7
- Merdan M, Gokdogan A, Yildirim A. On the numerical solution of the model for HIV infection of CD4+ T cells. Comput Math. Appl. 2011 Jul; 62(1):118–23. https://doi.org/10.1016/j.camwa.2011.04.058
- Zack JA, Arrigo SJ, Weitsman SR, Go AS, Haislip A, Chen IS. HIV-1 entry into quiescent primary lymphocytes: molecular analysis reveals a labile, latent viral structure. Cell. 1990 Apr; 61(2):213–22. https://doi.org/10.1016/00928674(90)90802-L
- Zack JA, Haislip AM, Krogstad P, Chen IS. Incompletely reverse-transcribed human immunodeficiency virus type 1 genomes in quiescent cells can function as intermediates in the retroviral life cycle. J Virol. 1992 Mar; 66(3):1717– 25. PMid: 1371173 PMCid: PMC240919. https://doi.org/10.1128/JVI.66.3.1717-1725.1992
- Gradshteyn IS, Ryzhik IM. Routh-Hurwitz theorem, Tables of Integrals, Series and Products. San Diego: Academic Press; 2000.
- Li MY, Muldowney JS. A geometric approach to global-stability problems. SIAM J Math Anal. 1996 Jul; 27(4):1070–83. https://doi.org/10.1137/S0036141094266449
- Coppel WA. Stability and asymptotic behaviour of differential equations. Health, Boston; 1965.
- Fiedler M. Additive compound matrices and inequalities for eigen values of stochastic matrices. Czech. Math J. 1974; 24(3): 392–402. https://doi.org/10.21136/CMJ.1974.101253
- Muldowney JS. Compound matrices and ordinary differential equations. Rocky Mount. J Math. 1990 Oct: 857-72. https://doi.org/10.1216/rmjm/1181073047
- Van den Driessche P, Watmough J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosci. 2002 Nov; 180(1-2):29–48. https://doi.org/10.1016/S00255564(02)00108-6
- Bani-Yaghoub M, Gautam R, Shuai Z, Van Den Driessche P, Ivanek R. Reproduction numbers for infections with free-living pathogens growing in the environment. J Biol Dyn. 2012 Mar; 6(2):923–40. PMid: 22881277. https://doi.org/10.1080/17513758.2012.693206