Open AccessHybrid optimal control for HIV multi-drug therapies: A finite set control transcription approach
Author(s) -
Divya Thakur,
Belinda G. Marchand
Publication year - 2012
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2012.9.899
Subject(s) - reverse transcriptase , drug , human immunodeficiency virus (hiv) , protease , medicine , boosting (machine learning) , immunology , antiretroviral treatment , viral load , virology , biology , antiretroviral therapy , pharmacology , computer science , machine learning , enzyme , rna , biochemistry , gene
In this study, the treatment of Human Immunodeficiency Virus (HIV) infection is investigated through an optimal structured treatment interruption (STI) schedule of two classes of antiretroviral drugs, mainly, reverse transcriptase inhibitors and protease inhibitors. An STI treatment strategy may be beneficial in lowering the risk of HIV mutating to drug-resistant strains, and could provide patients with respite from toxic side effects of HAART. A shorter treatment period is considered compared to previous studies and the solution to the HIV STI problem is obtained via the Finite Set Control Transcription (FSCT) formulation. The FSCT formulation offers a unique approach for handling multiple independent decision variables simultaneously, and, as is shown by the results of this study, is well-suited for an effective treatment of the optimal STI problem. The results obtained in the present investigation demonstrate that immune boosting and subsequent natural suppression of the viral load are possible even when a reduced STI therapy treatment duration is in consideration.