Free terminal time optimal control problem for the treatment of HIV infection
In this work, an optimal control approach is presented in order to propose an optimal therapy for the treatment HIV infection using a combination of two appropriate treatment strategies. The optimal treatment duration and the optimal medications amount are considered. The main objective of this study is to be able to maximize the benet based on number of healthy CD4+ T-cells and CTL immune cells and to minimize the infection level and the overall treatment cost while optimizing the duration of therapy. The free terminal time optimal control problem is formulated and the Pontryagin's maximum principle is employed to provide the explicit formulations of the optimal controls. The corresponding optimality system with the additional transversality condition for the terminal time is derived and solved numerically using an adapted iterative method with a Runge-Kutta fourth order scheme and a gradient method routine.
[1] Agosto, L. M., Zhong, P., Munro, J. and Mothes, W. Highly active antiretroviral ther- apies are efective against HIV-1 cell-to-cell transmission. PLoS Pathog, 10, e1003982 (2014).
[2] Arlinghaus, S. Practical handbook of curve fitting. CRC press (1994).
[3] Boccia, A., Falugi, P., Maurer, H. and Vin- ter, R. B. Free time optimal control problems with time delays. In Decision and Control (CDC), 2013 IEEE 52nd Annual Conference, 520-525 (2013).
[4] Bonhoefer, S., Co伍n, J. M. and Nowak, M. A. Human immunodeficiency virus drug therapy and virus load. Journal of Virology, 71(4),3275-3278 (1997).
[5] Burden, T. N., Ernstberger, J. and Fister, K. R. Optimal control applied to immunother- apy. Discrete and Continuous Dynamical Systems Series B, 4(1), 135-146 (2004).
[6] Butler, S., Kirschner, D. and Lenhart, S. Op- timal control of chemotherapy afecting the infectivity of HIV. Ann Arbor, 1001, 48109- 0620 (1997).
[7] Cai, L., Guo, S. and Wang, S. Analysis of an extended HIV/AIDS epidemic model with treatment. Applied Mathematics and Com- putation, 236, 621-627 (2014).
[8] Callaway, D. S. and Perelson, A. S. HIV-1 in- fection and low steady state viral loads. Bul- letin of mathematical biology, 64(1), 29-64 (2002).
[9] Cassels, S., Jenness, S. M. and Khanna, A. S. Conceptual Framework and Research Meth- ods for Migration and HIV Transmission Dy- namics. AIDS and Behavior, 18(12), 2302- 2313 (2014).
[10] Cheney, E. and Kincaid, D. Numerical math- ematics and computing. Cengage Learning, (2012).
[11] Co伍n, J. M. HIV population dynamics in vivo: implications for genetic varia- tion, pathogenesis, and therapy. Science, 267(5197), 483-489 (1995).
[12] Culshaw, R. V. and Ruan, S. A delay- diferential equation model of HIV infection of CD4+ T-cells. Mathematical biosciences, 165(1), 27-39 (2000).
[13] Culshaw, R. V., Ruan, S. and Spiteri, R. J. Optimal HIV treatment by maximising im- mune response. Journal of Mathematical Bi- ology, 48(5), 545-562 (2004).
[14] Elmouki, I., Saadi, S. Quadratic and linear controls developing an optimal treatment for the use of BCG immunotherapy in superfi- cial bladder cancer. Optimal Control Appli- cations and Methods. (2015).
[15] Elmouki, I., Saadi, S. BCG immunother- apy optimization on an isoperimetric optimal control problem for the treatment of superfi- cial bladder cancer. International Journal of Dynamics and Control. 1-7, (2014).
[16] Fauci, A.S., Desrosiers, R.C. Pathogenesis of HIV and SIV, 587-636. Cold Spring Harbor Laboratory Press, New York (1997).
[17] Fleming, W.H., Rishel, R.W. Deterministic and stochastic optimal control. Springer Ver- lag, New York (1975).
[18] Gray, C.M., Lawrence, J., Schapiro, J.M., Altman, J.D., Winters, M.A., Crompton, M., Loi, M., Kundu, S.K., Davis, M.M. and Merigan, T.C. Frequency of Class I HLA- Restricted anti-HIV CD8+ T-cells in individ- uals receiving Highly Active Antiretroviral Therapy (HAART). The journal of immunol- ogy. 162, 1780-1788 (1999).
[19] Gumel, A.B. Spread and control of HIV: a mathematical model. Accromath. 26(8), (2013).
[20] Hamdache, A., Saadi, S., Elmouki, I., Zouhri, S. Two Therapeutic Approaches for the Treatment of HIV Infection in AIDS Stage. Journal of Applied Mathematical sci- ences. 7(105), 5243-5257 (2013).
[21] Hamdache, A., Elmouki, I., Saadi, S. Op- timal Control with an Isoperimetric Con- straint Applied to Cancer Immunotherapy. International Journal of Computer Applica- tions. 94(15), 31-37 (2014).
[22] Hamdache, A., Saadi, S. and Elmouki, I. Nominal and neighboring-optimal control ap- proaches to the adoptive immunotherapy for cancer. International Journal of Dynamics and Control, 1-16 (2015).
[23] Hlavacek, W.S., Wofsy, C. and Perelson, A.S. Dissociation of HIV-1 from follicular dendritic cells during HAART: mathematical analysis. Proceedings of the National Acad- emy of Sciences. 96(26), 14681-14686 (1999).
[24] Iversen, A.K., Shafer, R.W., Wehrly, K.,Winters, M.A., Mullins, J.I., Chesebro, B. and Merigan, T.C. Multidrug-resistant hu- man immunodeficiency virus type 1 strains resulting from combination antiretroviral therapy. Journal of Virology. 70(2), 1086- 1090 (1996).
[25] Jacobson, E.L., Pilaro, F. and Smith, A.K. Rationnal IL-2 therapy for HIV positifs in- dividuals: daily low doses enhance immune function without toxicity. Proc. Natl. Acad. Sci USA. 93, 10405-10410 (1996).
[26] Janeway, C., Murphy, K. P., Travers, P. and Walport, M. Janeway’s immunobiology, 530-
535. Garland Science, London (2008).
[27] Jang, T., Kwon, H. D. and Lee, J. Free ter- minal time optimal control problem of an HIV model based on a conjugate gradient method. Bulletin of mathematical biology, 73(10), 2408-2429 (2011).
[28] Jiang, C., Lin, Q., Yu, C., Teo, K. L. and Duan, G. R. An exact penalty method for free terminal time optimal control problem with continuous inequality constraints. Jour- nal of Optimization Theory and Applica- tions, 154(1), 30-53 (2012).
[29] Joshi, H. R. Optimal control of an HIV im- munology model. Optimal control applica- tions and methods, 23(4), 199-213 (2002).
[30] Kirschner, D.E., Webb, G.F. Immunother- apy of HIV-1 infection. Journal of Biological Systems. 6(1), 71-83 (1998).
[31] Khanna, A. S., Dimitrov, D. T. and Goodreau, S. M. What can mathematical models tellus about the relationship between circular migrations and HIV transmission dy- namics?. Mathematical biosciences and engi- neering: MBE, 11(5), 1065-1090 (2014).
[32] Klatzmann, D. and Abbas, A. K. The promise of low-dose interleukin-2 therapy for autoimmune and in丑ammatory diseases. Na- ture Reviews Immunology, (2015).
[33] Lenhart, S., Workman, T. Optimal control applied to biological models, 49-55. Chap- man and Hall/CRC Mathematical and Com- putational Biology Series, New York (2007).
[34] L/evy, Y. Immunoth/erapie de l’infection par le VIH par l’utilisation de cytokines: un /etat des lieux. M/S: m/edecine sciences. 22(8-9), 751-754 (2006).
[35] Lukes, D. L. Diferential Equations: Clas- sical to Controlled, Mathematics in Science and Engineering, Academic Press, New York, (1982).
[36] Maartens, G., Celum, C. and Lewin, S. R. HIV infection: epidemiology, pathogene- sis, treatment, and prevention. The Lancet, 384(9939), 258-271 (2014).
[37] MacArthur, R.D., Novak, R.M. Maraviroc: The First of a New Class of Antiretrovi- ral Agents. Oxford journals. 47(2), 236-241 (2008).
[38] McAsey, M., Mou, L., Han, W. Convergence of the Forward-Backward Sweep Method in optimal control. Comput Optim Appl. 3, (2012).
[39] Mastroberardino, A., Cheng, Y., Abdel- razec, A. and Liu, H. Mathematical modeling of the HIV/AIDS epidemic in Cuba. Interna- tional Journal of Biomathematics, 1550047 (2015).
[40] Merry, C., Barry, M.G., Mulcahy, F., Ryan, M., Heavey, J., Tjia, J.F., Gibbons, S.E., Breckenridge, A.M. and Back, D.J. Saquinavir pharmacokinetics alone and in combination with ritonavir in HIV-infected patients. AIDS. 11(4), (1997).
[41] Palanki, S., Kravaris, C. and Wang, H. Y. Optimal feedback control of batch reactors with a state inequality constraint and free terminal time. Chemical engineering science, 49(1), 85-97 (1994).
[42] Perelson, A. S., Neumann, A. U., Markowitz, M., Leonard, J. M. and Ho, D. D. HIV-1 dy- namics in vivo: virion clearance rate, infected cell life-span, and viral generation time. Sci- ence, 271(5255), 1582-1586 (1996).
[43] Perelson, A. S., Kirschner, D. E. and De Boer, R. Dynamics of HIV infection of CD4+ T cells. Mathematical biosciences, 114(1), 81- 125 (1993).
[44] Pontryagin, L. S. Mathematical theory of op- timal processes. CRC Press, (1987).
[45] Pontryagin, L. S., Boltyanskii, V. G. and Gamkrelidze, R. V. EF Mishchenko The Mathematical Theory of Optimal Processes. New York: Interscience (1962).
[46] Pooseh, S., Almeida, R. and Torres, D. F. Fractional order optimal control prob- lems with free terminal time. arXiv preprint arXiv: 1302.1717 (2013).
[47] Qun, L., Loxton, R., Teo, K. L. and Wu, Y. H. A new computational method for a class of free terminal time optimal control prob- lems. Pacific Journal of Optimization. 7(1), 63-81 (2011).
[48] Ra伍, F.: Enfuvirtide, premier inhibiteur de fusion dans le traitement de l’infection par le virus de l’immunod/eficience humaine: m/ecanisme d’action et pharmacocintique. M/edecine et maladies infectieuses. 34, 3-7 (2004).
[49] Roshanfekr, M., Farahi, M. H. and Rah- barian, R. A diferent approach of optimal control on an HIV immunology model. Ain Shams Engineering Journal, 5(1), 213-219 (2014).
[50] Roy, P.K., Chatterjee, A.N. T-cell prolifera- tion in amathematical model of CTL activity through HIV-1 infection. Proceedings of the World Congress on Engineering. 1, (2010).
[51] Roy, P. K., Saha, S. and Al Basir, F. Efect of awareness programs in controlling the dis- ease HIV/AIDS: an optimal control theoretic approach. Advances in Diference Equations, 2015(1), 1-18 (2015).
[52] Saadi, S., Elmouki, I. and Hamdache, A. Im- pulsive control dosing BCG immunotherapy for non-muscle invasive bladder cancer. Inter- national Journal of Dynamics and Control, 1-11 (2015).
[53] Stengel, R. F., Ghigliazza, R. M. and Kulka- rni, N. V. Optimal enhancement of im- mune response Bioinformatics,18(9), 1227- 1235 (2002).
[54] Su, B., Lederle, A., Laumond, G., Schmidt, S., Decoville, T., Ducloy, C. and Moog, C. Antibody Inhibition of HIV-1 Transmis- sion from Antigen-presenting Cells to CD4 T Lymphocytes Involves Immune Cell Activa- tion. AIDS research and human retroviruses, 30(S1), A154-A154 (2014).
[55] Tr/elat, E. Controle optimal: th/eorie et ap- plications. Paris: Vuibert (2005).
[56] Wang, L. and Li, M. Y. Mathematical anal- ysis of the global dynamics of a model for HIV infection of CD4+ T-cells. Mathemati- cal Biosciences, 200(1), 44-57 (2006).
[57] Zhou, X., Song, X. and Shi, X. A diferential equation model of HIV infection of CD4+ T- cells with cure rate. Journal of Mathematical Analysis and Applications, 342(2), 1342-1355 (2008).
[58] Zurakowski, R. and Teel, A. R. A model pre- dictive control based scheduling method for HIV therapy. Journal of Theoretical Biology, 238(2), 368-382 (2006).