aid for diagnosis; AIDS; apoptosis; dynamical systems; HIV; modeling; non linear control; pharmacodynamics; pharmacokinetics
Abstract :
[en] This review shows the potential ground-breaking impact that mathematical tools may have in the analysis and the understanding of the HIV dynamics. In the first part, early diagnosis of immunological failure is inferred from the estimation of certain parameters of a mathematical model of the HIV infection dynamics. This method is supported by clinical research results from an original clinical trial: data just after 1 month following therapy initiation are used to carry out the model identification. The diagnosis is shown to be consistent with results from monitoring of the patients after 6 months. In the second part of this review, prospective research results are given for the design of individual anti-HIV treatments optimizing the recovery of the immune system and minimizing side effects. In this respect, two methods are discussed. The first one combines HIV population dynamics with pharmacokinetics and pharmacodynamics models to generate drug treatments using impulsive control systems. The second one is based on optimal control theory and uses a recently published differential equation to model the side effects produced by highly active antiretroviral therapy therapies. The main advantage of these revisited methods is that the drug treatment is computed directly in amounts of drugs, which is easier to interpret by physicians and patients.
Disciplines :
Computer science Immunology & infectious disease
Author, co-author :
Rivadeneira, Pablo
Moog, Claude
Stan, Guy-Bart
Brunet, Cécile
Raffi, François
Ferré, Virginie
Costanza, Vicente
Mhawej, Marie-José
Biafore, Federico
Ouattara, Djomangan
Ernst, Damien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
Fonteneau, Raphaël ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Ho DD, Neumann AU, Perelson AS, et al. Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature. 1995;373:123-126.
Perelson AS, Essunger P, Cao Y, et al. Decay characteristics of HIV-1-infected compartments during combination therapy. Nature. 1997;387:188-191.
Perelson AS, Nelson PW. Mathematical analysis of HIV-1 dynamics in vivo. SIAM Review. 1999;41:3-44.
Mhawej MJ, Brunet-François C, Fonteneau R, et al. Apop-tosis characterizes immunological failure of HIV infected patients. Control Engineering Practice. 2009;17:798-804.
Ouattara DA. Mathematical analysis of the HIV-1 infection: Parameter estimation, therapies effectiveness, and therapeutical failures. In 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, China, September 2005.
Ouattara DA, Mhawej MJ, Moog CH. IRCCyN web software for the computation of HIV infection parameters. 2007. Available at http://vih.irccyn.ec-nantes.fr
Stan GB, Belmudes F, Fonteneau R, et al. Modelling the influence of activation-induced apoptosis of CD4+ and CD8 + T-cells on the immune system response of a HIV infected patient. IET Syst Biol. 2008;2:94-102.
Ouattara DA, Mhawej MJ, Moog CH. Clinical tests of therapeutical failures based on mathematical modeling of the HIV infection. Joint special issue of IEEE Transactions on Circuits and Systems and IEEE Transactions on Automatic Control, Special issue on Systems Biology 2008;53: 230-241.
Costanza V, Rivadeneira PS, Biafore FL, et al. Taking into account side effects for HIV medication. IEEE Trans Biomed Eng. 2010;57:2079-2089.
Mhawej MJ, Moog CH, Biafore F, et al. Control of the HIV infection and drug dosage. Biomedical Signal Processing and Control. 2010;5:45-52.
Rivadeneira PS, Moog CH. Impulsive control of single-input nonlinear systems with application to HIV dynamics. Appl Math Comput. 2012;18:8462-8474.
Adams BM, Banks HT, Kwon HD, et al. Dynamic multi-drug therapies for HIV: optimal and STI approaches. Math Biosci Eng. 2004;1:223-41.
Chang H, Astolfi A. Control of HIV infection dynamics. IEEE Control Systems. 2008;28:28-39.
Ahr B, Hebmann VR, Devaux C, et al. Apoptosis of unin-fected cells induced by HIV envelope glycoproteins. Retro-virology. 2004;1:12-34.
Craig I, Xia X. Can HIV/AIDS be controlled? IEEE Control Systems. 2005;80-83.
Israel-Ballard K, Ziermann R, Leutenegger C, et al. Taqman RT-PCR and Versant HIV-1 RNA 3.0 (bDNA) assay quantification of HIV-1 RNA viral load in breast milk. J Clin Virol. 2005;34:253-256.
Delfraissy JF. Prise en charge des personnes infectées par le VIH: Recommandations du groupe d'experts. Flammarion, Médecine-Sciences: Paris; 2004.
U.S. Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents, May 2010. Available at http://www.aidsinfo.nih.gov/guidelines
Badley AD. Cell Death During HIV Infection. CRC Press: Boca Raton, FL; 2005.
Gougeon ML, Montagnier L. Programmed cell death as a mechanism of CD4 and CD8 T cell depletion in AIDS: molecular control and effect of highly active anti-retroviral therapy. Ann NY Acad Sci. 1999;887:199-212.
Herbein G, Mahlknecht U, Batliwalla F, et al. Apoptosis of CD8 + T cells is mediated by macrophages through interaction of HIV gp120 with chemokine receptor CXCR4. Nature. 1998;395:189-194.
Pantaleo G, Fauci AS. Apoptosis in HIV infection. Nat Med. 1995;1:118-120.
Vassena L, Proschan M, Fauci AS, et al. Interleukin 7 reduces the level of spontaneous apoptosis in CD4 + and CD8 + T cells from HIV-1 infected individuals. Proc Natl Acad Sci USA. 2007;104:2355-2360.
Yue FY, Kovacs CM, Dimayuga RC, et al. Preferential ap-optosis of HIV-1 specific CD4+ T cells. J Immunol. 2005; 174:2196-2204.
Berger A, Scherzed L, Stürmer M, et al. Comparative evaluation of the COBAS Amplicor HIV-1 MonitorTM Ultrasensitive Test, the new COBAS AmpliPrep/COBAS Amplicor HIV-1 Moni-torTM and the Versant HIV RNA 3.0 assays for quantitation of HIV-1 RNA in plasma samples. J Clin Virol. 2005;33:43-51.
Firme Roche. Fiches techniques, 2003. Available at http://www.roche-diagnostics.fr
Galli R, Merrick L, Friesenhahn M, et al. Comprehensive comparison of the Versant HIV-1 RNA 3.0 (bDNA) and COBAS Amplicor HIV-1 Monitor 1.5 assays on 1000 clinical specimens. J Clin Virol. 2005;34:245-252.
Prud'homme IT, Kim JE, Pilon RG, et al. Amplicor HIV monitor, NASBA HIV-1 RNA QT and quantiplex HIV RNA version 2.0 viral load assays: a Canadian evaluation. J Clin Virol. 1998;11:189-202.
Rivadeneira PS, Moog CH, Stan GB, et al. Mathematical modeling of HIV dynamics after antiretroviral therapy initiation: a clinical research study. AIDS Res Hum Retrovi-ruses. 2014;30:831-834.
Xia X, Moog CH. Identifiability of nonlinear systems with application to HIV/AIDS models. IEEE Transactions on Automatic Control. 2003;48:330-336.
Alvarez-Ramirez J, Meraz M, Velasco-Hernandez JX. Feedback control of the chemotherapy of HIV. Int J Bifurcation Chaos. 2000;10:2207-2219.
Costanza V, Rivadeneira PS, Biafore F, et al. A closed-loop approach to antiretroviral therapies for HIV infection. Bio-medical Signal Processing and Control. 2009;4:139-148.
Jeffrey MA, Xia X, Craig I. When to initiate HIV therapy: a control theoretic approach. IEEE Trans Biomed Eng. 2003; 50:1213-1220.
Ouattara DA, Moog CH. Identification, linéarisation et com-mande optimale du modèle 3D de l'infection VIH-1. In Conférence Internationale Francophone d'Automatique, CIFA 2006, Bordeaux, France, May 2006.
Shim H, Han SJ, Chung CC, et al. Optimal scheduling of drug treatment for HIV infection: continuous dose control and receding horizon control. International Journal of Control, Automation, and Systems 2003;1:282-288.
Zurakowski R, Teel RA. A model predictive control based scheduling method for HIV therapy. J Theoret Biol. 2006; 238:368-382.
Hernandez-Vargas E, Colaneri P, Middleton R, et al. Discrete-time control for switched positive systems with application to mitigating viral escape. International Journal of Robust Nonlinear Control. 2011;21:93-111.
Legrand M, Comets E, Aymard G, et al. An in vivo pharma-cokinetic/pharmacodynamic model for antiretroviral combination. HIV Clin Trials. 2003;4:170-183.
Mhawej MJ, Moog CH, Biafore F. The HIV dynamicsisasingle input system. In Proceedings of the 13th International Conference on Biomedical Engineering, Singapore, December 2008.
Nordic Medical Research Council's HIV Therapy Group. Double blind dose-response study of zidovudine in AIDS and advanced HIV infection. BMJ. 1992;304:13-17.
Siegel L, El-Sadr W. New perspectives in HIV treatment interruption: the SMART study. In: The PRN Notebook. Braun JF, Pozo MD (eds.) Physicians' Research Network: New York; vol. 11, pp. 8-9; 2006.
Hoffmann C, Rockstroh JK, Kamps BS (eds.). HIV Medicine 2007. Flying Publishers, 2007. Available at http://hivmedicine.com/hivmedicine2007.pdf
Ramratnam BS, Bonhoeffer S, Binley J, et al. Rapid production and clearance of HIV-1 and hepatitis C virus assessed by large volume plasma apheresis. Lancet. 1999;354: 1782-1785.
Markowitz M, Louie M, Hurley A, et al. A novel antiviral intervention results in more accurate assessment of human immunodeficiency virus type 1 replication dynamics and T-cell decay in vivo. J Virol. 2003;77:5037-5038.
Luo R, Piovoso MJ, Martinez-Picado J, et al. HIV model parameter estimates from interruption trial data including drug efficacy and reservoir dynamics. PLoS ONE. 2012;7: e40198.
Putter H, Heisterkamp SH, Lange JMA, et al. A Bayesian approach to parameter estimation in HIV dynamical models. Stat Med. 2002;21:2199-2214.
Huang Y, Wu H, Acosta EP. Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors. Biomed J. 2010;52: 470-486.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.