Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2016 / Cilt: 6 - Sayı: 1

Free terminal time optimal control problem for the treatment of HIV infection

Pages
33–52
Publication Date
2022-10-11
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The main objectiveof this study is to be able to maximize the benefit based on number of healthy CD4+T-cells and CTLimmune cells and to minimize the infection level and the overall treatment cost while optimizing theduration of therapy.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:The main objectiveof this study is to be able to maximize the benefit based on number of healthy CD4+T-cells and CTLimmune cells and to minimize the infection level and the overall treatment cost while optimizing theduration of therapy.
💡
Conclusion & Impact:Thecorresponding optimality system with the additional transversality condition for the terminal time isderived and solved numerically using an adapted iterative method with a Runge-Kutta fourth orderscheme and a gradient method routine.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

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. Theoptimal treatment duration and the optimal medications amount are considered. The main objectiveof this study is to be able to maximize the benefit based on number of healthy CD4+T-cells and CTLimmune cells and to minimize the infection level and the overall treatment cost while optimizing theduration 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. Thecorresponding optimality system with the additional transversality condition for the terminal time isderived and solved numerically using an adapted iterative method with a Runge-Kutta fourth orderscheme and a gradient method routine.

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Cite This Article

HAMDACHEA, A., SAADİ, S., ELMOUKİ, I. (2022). Free terminal time optimal control problem for the treatment of HIV infection. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 6 - Sayı: 1, 33–52. https://ajindex.com/articles/440699

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References (16)

  1. Agosto, L. M., Zhong, P., Munro, J. and Mothes, W. Highly active antiretroviral therapies are effective 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 Vinter, R. B. Free time optimal control problems with time delays. In Decision and Control (CDC), 2013 IEEE 52nd Annual Conference, 520-525 (2013)
  4. Bonhoeffer, S., Coffin, 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 immunotherapy. Discrete and Continuous Dynamical Systems Series B, 4(1), 135-146 (2004)
  6. Butler, S., Kirschner, D. and Lenhart, S. Optimal control of chemotherapy affecting 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 Computation, 236, 621-627 (2014)
  8. Callaway, D. S. and Perelson, A. S. HIV-1 infection and low steady state viral loads. Bulletin of mathematical biology, 64(1), 29-64 (2002)
  9. Cassels, S., Jenness, S. M. and Khanna, A. S. Conceptual Framework and Research Methods for Migration and HIV Transmission Dynamics. AIDS and Behavior, 18(12), 2302- 2313 (2014)
  10. Cheney, E. and Kincaid, D. Numerical mathematics and computing. Cengage Learning, (2012)
  11. Coffin, J. M. HIV population dynamics in vivo: implications for genetic variation, pathogenesis, and therapy. Science, 267(5197), 483-489 (1995)
  12. Culshaw, R. V. and Ruan, S. A delaydifferential 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 immune response. Journal of Mathematical Biology, 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 Applications and Methods. (2015)
  15. Elmouki, I., Saadi, S. BCG immunotherapy 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 (19