Title |
Optimal Antiviral Switching to Minimize Resistance Risk in HIV Therapy
|
---|---|
Published in |
PLOS ONE, November 2011
|
DOI | 10.1371/journal.pone.0027047 |
Pubmed ID | |
Authors |
Rutao Luo, Michael J. Piovoso, Javier Martinez-Picado, Ryan Zurakowski |
Abstract |
The development of resistant strains of HIV is the most significant barrier to effective long-term treatment of HIV infection. The most common causes of resistance development are patient noncompliance and pre-existence of resistant strains. In this paper, methods of antiviral regimen switching are developed that minimize the risk of pre-existing resistant virus emerging during therapy switches necessitated by virological failure. Two distinct cases are considered; a single previous virological failure and multiple virological failures. These methods use optimal control approaches on experimentally verified mathematical models of HIV strain competition and statistical models of resistance risk. It is shown that, theoretically, order-of-magnitude reduction in risk can be achieved, and multiple previous virological failures enable greater success of these methods in reducing the risk of subsequent treatment failures. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 3 | 60% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 3% |
United States | 1 | 3% |
Unknown | 27 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
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Professor | 6 | 21% |
Student > Bachelor | 5 | 17% |
Student > Ph. D. Student | 4 | 14% |
Researcher | 4 | 14% |
Other | 3 | 10% |
Other | 6 | 21% |
Unknown | 1 | 3% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 7 | 24% |
Medicine and Dentistry | 7 | 24% |
Biochemistry, Genetics and Molecular Biology | 4 | 14% |
Engineering | 2 | 7% |
Immunology and Microbiology | 2 | 7% |
Other | 4 | 14% |
Unknown | 3 | 10% |