×

Cellular automata approach for the dynamics of HIV infection under antiretroviral therapies: the role of the virus diffusion. (English) Zbl 1395.92094

Summary: We study a cellular automata model to test the timing of antiretroviral therapy strategies for the dynamics of infection with human immunodeficiency virus (HIV). We focus on the role of virus diffusion when its population is included in previous cellular automata model that describes the dynamics of the lymphocytes cells population during infection. This inclusion allows us to consider the spread of infection by the virus-cell interaction, beyond that which occurs by cell-cell contagion. The results show an acceleration of the infectious process in the absence of treatment, but show better efficiency in reducing the risk of the onset of AIDS when combined antiretroviral therapies are used even with drugs of low effectiveness. Comparison of results with clinical data supports the conclusions of this study.

MSC:

92C60 Medical epidemiology
68Q80 Cellular automata (computational aspects)
Full Text: DOI

References:

[1] W.H. Organization, Global HIV/AIDS response, Tech. rep., World Health Organization, 2011.
[2] Consortium, W. T.S., Timing of initiation of antiretroviral therapy in AIDS-free HIV-1-infected patients: a collaborative analysis of 18 HIV cohort studies, The Lancet, 373, 9672, 1352-1363, (2009)
[3] Abbas, A. K.; Lichtman, A. H.; Pober, J. S., Cellular and molecular immunology, (1994), W. B. Saunders Company
[4] Perelson, A. S., Modelling viral and immune system dynamics, Nature Reviews. Immunology, 2, 1, 28-36, (2002)
[5] Daar, E. S.; Moudgil, T.; Meyer, R. D.; Ho, D. D., Transient high levels of viremia in patients with primary human immunodeficiency virus type 1 infection, New England Journal of Medicine, 324, 14, 961-964, (1991)
[6] Pantaleo, G.; Graziozi, C.; Fauci, A. S., The immunopathogenesis of immunodeficiency virus infection, New England Journal of Medicine, 238, 327, (1993)
[7] Palmisano, L.; Vella, S., A brief history of antiretroviral therapy of HIV infection: success and challenges, Annali dell’Istituto Superiore di Sanità, 47, 1, 44-48, (2011)
[8] Perno, C. F., The discovery and development of HIV therapy: the new challenges, Annali dell’Istituto Superiore di Sanitá, 4, 1, 41-43, (2011)
[9] Herschhorn, A.; Hizi, A., Retroviral reverse transcriptases, Cellular and Molecular Life Sciences, 67, 16, 2717-2747, (2010)
[10] El Safadi, Y.; Vivet-Boudou, V.; Marquet, R., HIV-1 reverse transcriptase inhibitors, Applied Microbiology and Biotechnology, 75, 4, 723-737, (2007)
[11] Marchand, C.; Maddali, K.; Métifiot, M.; Pommier, Y., HIV-1 IN inhibitors: 2010 update and perspectives, Current Topics in Medicinal Chemistry, 9, 11, 1016-1037, (2009)
[12] Wensing, A. M.J.; van Maarseveen, N. M.; Nijhuis, M., Fifteen years of HIV protease inhibitors: raising the barrier to resistance, Antiviral Research, 85, 1, 59-74, (2010)
[13] Zorzenon dos Santos, R. M.; Coutinho, S., Dynamics of HIV infection: a cellular automata approach, Physical Review Letters, 87, 16, 168102, (2001)
[14] Smith, R. J.; Wahl, L. M., Distinct effects of protease and reverse transcriptase inhibition in an immunological model of HIV-1 infection with impulsive drug effects, Bulletin of Mathematical Biology, 66, 5, 1259-1283, (2004) · Zbl 1334.92239
[15] Nowak, M. A.; May, R. M., Virus dynamics — mathematical principles of immunology and virology, (2000), Oxford University Press · Zbl 1101.92028
[16] Perelson, A. S.; Nelson, P. W., Mathematical analysis of HIV-1 dynamics in vivo, SIAM Review, 41, 1, 3-44, (1999) · Zbl 1078.92502
[17] Landi, A.; Mazzoldi, A.; Andreoni, C.; Bianchi, M.; Cavallini, A.; Laurino, M.; Ricotti, L.; Iuliano, R.; Matteoli, B.; Ceccherini-Nelli, L., Modelling and control of HIV dynamics, Computer Methods and Programs in Biomedicine, 89, 2, 162-168, (2008)
[18] Rong, L.; Perelson, A. S., Modeling HIV persistence, the latent reservoir, and viral blips, Journal of Theoretical Biology, 260, 2, 308-331, (2009) · Zbl 1402.92409
[19] Wasserstein-Robbins, F., A mathematical model of HIV infection: simulating T4, T8, macrophages, antibody, and virus via specific anti-HIV response in the presence of adaptation and tropism, Bulletin of Mathematical Biology, 1208-1253, (2010) · Zbl 1197.92028
[20] Sloot, P. M.A.; Chen, F.; Boucher, C., Cellular automata model of drug therapy for HIV infection, (Proceedings of the 5th International Conference on Cellular Automata for Research and Industry, ACRI ’01, (2002), Springer-Verlag London, UK), 282-293 · Zbl 1027.92504
[21] Benyoussef, A.; HafidAllh, N. E.; ElKenz, A.; Loulidi, H. E.-Z. M., Dynamics of HIV infection on 2D cellular automata, Physica A, 322, 506-520, (2003) · Zbl 1018.92012
[22] Peer, M. A.; Shan, N. A.; Khan, K. A., Cellular automata and its advances to drug therapy for HIV infection, Indian Journal of Experimental Biology, 42, 2, 131-137, (2004)
[23] Shi, V.; Tridane, A.; Kuang, Y., A viral load-based cellular automata approach to modeling hiv dynamics and drug treatment, Journal of Theoretical Biology, 253, 1, 24-35, (2008) · Zbl 1398.92253
[24] Burkhead, E.; Hawkins, J.; Molinek, D., A dynamical study of a cellular automata model of the spread of HIV in a lymph node, Bulletin of Mathematical Biology, 71, 1, 25-74, (2009) · Zbl 1169.92023
[25] Precharattana, M.; Nokkeaw, A.; Triampo, W.; Triampo, D.; Lenbury, Y., Stochastic cellular automata model and Monte Carlo simulations of CD4^{+} T cell dynamics with a proposed alternative leukapheresis treatment for HIV/AIDS, Computers in Biology and Medicine, 41, 7, 546-558, (2011)
[26] Bangsberg, D. R.; Charlebois, E. D.; Grant, R. M.; Holodniy, M.; Deeks, S. G.; Perry, S.; Conroy, K. N.; Clark, R.; Guzman, D.; Zolopa, A.; Moss, A., High levels of adherence do not prevent accumulation of HIV drug resistance mutations, AIDS (London, England), 17, 13, 1925-1932, (2003)
[27] Huang, Y.; Lu, T., Modeling long-term longitudinal HIV dynamics with application to an AIDS clinical study, The Annals of Applied Statistics, 2, 4, 1384-1408, (2008) · Zbl 1169.92026
[28] Acosta, E. P.; Wu, H.; Hammer, S. M.; Yu, S.; Kuritzkes, D. R.; Walawander, A.; Eron, J. J.; Fichtenbaum, C. J.; Pettinelli, C.; Neath, D.; Ferguson, E.; Saah, A. J.; Gerber, J. G., Comparison of two indinavir/ritonavir regimens in the treatment of HIV-infected individuals, Journal of Acquired Immune Deficiency Syndromes, 37, 3, 1358-1366, (2004)
[29] Novitsky, V.; Wang, R.; Bussmann, H.; Lockman, S.; Baum, M.; Shapiro, R.; Thior, I.; Wester, C.; Wester, C. W.; Ogwu, A.; Asmelash, A.; Musonda, R.; Campa, A.; Moyo, S.; van Widenfelt, E.; Mine, M.; Moffat, C.; Mmalane, M.; Makhema, J.; Marlink, R.; Gilbert, P.; Seage, G. R.; DeGruttola, V.; Essex, M., HIV-1 subtype C-infected individuals maintaining high viral load as potential targets for the “test-and-treat” approach to reduce HIV transmission, PloS one, 5, 4, e10148, (2010)
[30] Cavert, W., Kinetics of response in lymphoid tissues to antiretroviral therapy of HIV-1 infection, Science, 276, 5314, 960-964, (1997)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.