Commenced in January 2007
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Edition: International
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HIV Treatment Planning on a case-by-CASE Basis

Authors: Marios M. Hadjiandreou, Raul Conejeros, Ian Wilson

Abstract:

This study presents a mathematical modeling approach to the planning of HIV therapies on an individual basis. The model replicates clinical data from typical-progressors to AIDS for all stages of the disease with good agreement. Clinical data from rapid-progressors and long-term non-progressors is also matched by estimation of immune system parameters only. The ability of the model to reproduce these phenomena validates the formulation, a fact which is exploited in the investigation of effective therapies. The therapy investigation suggests that, unlike continuous therapy, structured treatment interruptions (STIs) are able to control the increase in both the drug-sensitive and drug-resistant virus population and, hence, prevent the ultimate progression from HIV to AIDS. The optimization results further suggest that even patients characterised by the same progression type can respond very differently to the same treatment and that the latter should be designed on a case-by-case basis. Such a methodology is presented here.

Keywords: AIDS, chemotherapy, mathematical modeling, optimal control, progression.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071055

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References:


[1] S. Khalili and A. Armaou, "An extracellular stochastic model of early HIV infection and the formulation of optimal treatment policy", Chem. Eng. Sci., vol. 63, pp. 4361-4372, 2008.
[2] B. M. Adams, H. T. Banks, and H. Kwon, "Dynamic multidrug therapies for HIV: optimal and STI control approaches", Math. Biosci. Eng., vol. 1, pp. 223-241, 2004.
[3] O. Krakovska and L. M. Wahl, "Drug-sparing regimens for HIV combination therapy: benefits predicted for drug coasting", Bull. Math. Biol., vol. 69, pp. 2627-2647, 2007.
[4] L. Rong, M. A. Gilchrist, Z. Feng, "Modelling within-host HIV-1 dynamics and the evolution of drug resistance: trade-offs between viral enzyme function and drug susceptibility", J. Theor. Biol., vol. 247, pp. 804-818, 2007.
[5] S. H. Bajaria, G. Webb, M. Cloyd, "Dynamics of nave and memory CD4+ T lymphocytes in HIV-1 disease progression", JAIDS, vol. 30, pp. 41-58, 2002.
[6] S. H. Bajaria, G. Webb, and D. E. Kirschner, "Predicting differential responses to structured treatment interruptions during HAART", Bull. Math. Biol., vol. 66, pp. 1093-1118, 2004.
[7] T. W. Chun, R. Davey, and D. Engel, "Re-emergence of HIV after stopping therapy", Nature, vol. 401, pp. 874-875, 1999.
[8] M. A. Nowak and A. J. McMichael, "How HIV defeats the immune system", Scientific American, pp. 58-65, 1995.
[9] M. M. Hadjiandreou, R. Conejeros, and D. I. Wilson, "Long-term HIV dynamics subject to continuous therapy and structured treatment interruptions", Chem. Eng. Sci., vol. 64, pp. 1600-1617, 2009.
[10] M. A. Nowak, R. M. May, "Virus dynamics: Mathematical principles of immunology and virology", New York: Oxford University Press, 2000.
[11] T. Igarashi, C. R. Brown, Y. Endo, "Macrophages are the principal reservoir and sustain high virus loads in rhesus macaques following the depletion following the depletion of CD4+ T-cells by a highly pathogenic SHIV: implications for HIV-1 infections of man", PNAS, vol. 98, pp. 658-663, 2001.
[12] K. S. Dorman, A. H. Kaplan, K. Lange, "Mutation takes no vacation: can structured treatment interruptions increase the risk of drug-resistant HIV-1?", JAIDS, vol. 25, pp. 398-402, 2000.
[13] D. E. Kirschner and A. S. Perelson, "A model for the immune system response to HIV: AZT treatment studies", In: O. Arino, D. Axelrod, M. Kimmel, editors, Mathematical population dynamics: analysis of heterogeneity and theory of epidemics, Winnipeg: Wuerz Publishing, pp. 295, 1995.
[14] D. E. Kirschner and G. F. Webb, "A mathematical model of combined drug therapy of HIV infection", J. Theor. Med., vol. 1, pp. 25-34, 1997.
[15] J. Velasco-Hemandez, J. A. Garcia, and D. E. Kirschner, "Remarks on modeling host-viral dynamics and treatment", In: C. Chavez, S. Blower, P. Van Den Dreische, editors. Mathematical Approaches for Emerging and Reemerging Infectious Diseases: An Introduction to Models, Methods and Theory, vols. 1 and 2, New York: Springer-Verlag, 2001.
[16] Wolfram Research, Inc., Mathematica, Version 5.1, Champaign, IL, U.S, 2004.
[17] F. M. Campello de Souza, "Modeling the dynamics of HIV-1 and CD4 and CD8 lymphocytes", IEEE Eng. Med. Biol., vol. 18, pp. 21-24, 1999.
[18] A. S. Fauci, G. Pantaleo, S. Stanley, "Immunopathogenic mechanisms of HIV infection", Annals of Internal Medicine, vol. 124, pp. 654-663, 1996.
[19] J. B. Margolick, A. D. Donnenberg, and A. Munoz, "T Lymphocytes homeostasis after seroconversion", JAIDS, vol. 7, pp. 415-416, 1994.
[20] E. Pennisi, J. Cohen, "Eradicating HIV from a patient: not just a dream?", Science, vol. 272, pp. 1884, 1996.
[21] E. Vergu, A. Mallet, and J. Golmard, "A modeling approach to the impact of HIV mutations on the immune system", Comput. Biol. Med., vol. 35, pp. 1-24, 2005.
[22] R. F. Stengel, "Mutation and control of the human immunodeficiency virus", Math. Biosci., vol. 213, pp. 93-102, 2008.
[23] M. M. Hadjiandreou, R. Conejeros, and V. S. Vassiliadis, "Towards a long-term model construction for the dynamic simulation of HIV infection", Math. Biosci. Eng., vol. 4, pp. 489-504, 2007.
[24] R. A. Filter, X. Xia, and C. M. Gray, "Dynamic HIV/AIDS parameter estimation with application to a vaccine readiness study in Southern Africa", IEEE Trans. Biomed. Eng., vol. 52, pp. 784-791, 2005.
[25] C. A. Sabin, H. Devereux, A. N. Phillips, "Course of viral load throughout HIV-1 infection", JAIDS, vol. 23, pp. 172-177, 2000.
[26] S. LeBlanc, "The long and the short of AIDS Progression", The Bay Area Reporter, 1996.
[27] M. Comar, C. Simonelli, S. Zanussi, "Dynamics of HIV-1 mRNA expression in patients with long-term nonprogressive HIV-1 infection", J. Clin. Invest., vol. 100, pp. 893-900, 1997.
[28] T. E. Yamashita, J. P. Phair, A. Munoz, "Immunologic and virologic response to highly active antiretroviral therapy in the Multicenter AIDS Cohort Study", AIDS, vol. 15, pp. 735-746, 2001.
[29] R. B. Markham, W. Wang, A. E. Weisstein, "Patterns of HIV-1 evolution in individuals with differing rates of CD4 T cell decline", PNAS, vol. 95, pp. 12568-12573, 1998.
[30] T. C. Greenough, D. B. Brettler, F. Kirchhoff, "Long-term nonprogressive infection with human immunodeficiency virus type in a hemophilia cohort", J. Infect. Dis., vol. 180, pp. 1790-1802, 1999.
[31] The Body Health Resources Corporation, Drug Side Effects Chart,
[Online] Available: http://www.thebody.com/pinf/sideeffectchart.html (accessed in 2007).
[32] M. Joly and J. M. Pinto, "Role of mathematical modeling on the optimal control of HIV-1 pathogenesis", AIChEJ, vol. 52, pp. 856-885, 2006.
[33] gPROMS Advanced User Guide, Release 2.3. 2004. Process System Enterprise Ltd, United Kingdom.
[34] R. S. Braithwaite, A. C. Justice, C. C. Chang, "Estimating the proportion of patients infected with HIV who will die of comorbid diseases", Am. J. Med., vol. 118, pp. 890-898, 2005.
[35] C. T. Fang, H. M. Hsu, S. J. Twu, "Decreased HIV transmission after policy of providing free access to highly active antiretroviral therapy in Taiwan", J. Infect. Dis., vol. 190, pp. 879-885, 2004.
[36] A. D. Paltiel, M. C. Weinstein, A. D. Kimmel, "The qualitative nature of the primary immune response to HIV infection is a prognosticator of disease progression independent of the initial level of plasma viremia", PNAS, vol. 94, pp. 254-258, 1997.
[37] "Antiretroviral Guidelines 2006". Department of Health and Human Services, pp. 1-113.
[Online] Available: http://www.aidsinfo.nih.gov/ContentFiles/ AdultandAdolescentsGL.pdf. Accessed (March 2007).
[38] J. Lawrence, D. L. Mayers, K. H. Hullsiek, "Structured treatment interruption in patients with multidrug-resistant human immunodeficiency virus", N. Engl. J. Med., vol. 349, pp. 837-846, 2003.
[39] L. Ruiz, E. Ribera, A. Bonjoch, "Role of structured treatment interruption before a 5-drug salvage antiretroviral regimen: the retrogene study", J. Infect. Dis., vol. 188, pp. 977-985, 2003.
[40] T. Hraba, J. Dolezal, "A mathematical model and CD4+ lymphocyte dynamics in HIV infection", Em. Infect. Dis., pp. 299-305, 1996.
[41] UK Group on Transmitted HIV Drug Resistance, "Time trends in primary resistance to HIV drugs in the United Kingdom: multicentre observational study", BMJ, vol. 331, pp. 1368-1371, 2005.
[42] Y. Huang, S. L. Rosenkranz, H. Wu, "Modeling HIV dynamics and antiviral response with consideration of time-varying drug exposures, adherence, and phenotypic sensitiviry", Math. Biosci., vol. 184, pp. 165- 186, 2003.
[43] V. Novk, I. Perfilieva, and J. Mockor, Mathematical principles of fuzzy logic, USA: Kluwer Academic Publishers, 1999.
[44] GlaxoSmithKline, Product Information. 2005.
[Online] Available: http://www.gsk.com/products/prescriptionmedicines.shtml.
[45] Abbott Laboratories, Product Information. 2006.
[Online] Available: http://www.norvir.com/hiv/hiv0044.htm.
[46] F. T. Aweeka, M. Kang, J-Y Yu, "Pharmacokinetic evaluation of the effects of ribavirin on zidovudine triphosphate formation: ACTG 5092s Study Team", HIV Medicine, vol. 8, pp. 288-294, 2007.
[47] B. S. Kappelhoff, A. D. R. Huitema, K. M. L. Crommentuyn, "Development and validation of a population pharmacokinetic model for ritonavir used as a booster or as an antiviral agent in HIV-1-infected patients", Br. J. Clin. Pharmacol., vol. 59, pp. 174-182, 2004.
[48] M. Legrand, E. Comets, G. Aymard, "An in vivo pharmacokinetic/ pharmacodynamic model for antiretroviral combination", HIV Clin. Trials, vol. 4, pp. 170/183, 2003.