Optimizing background therapy in treatment-experienced HIV-1 patients by rules-based algorithms and bioinformatics

In HIV-1-infected patients with extensive drug resistance, the optimization of background antiretroviral therapy is essential when changing drugs after treatment failure. The genotypic sensitivity score (GSS) and phenotypic sensitivity score (PSS), determined by rules-based algorithms, are employed...

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Bibliographic Details
Published in:Future virology Vol. 7; no. 8; pp. 749 - 757
Main Authors: Svard, J, Sonnerborg, A
Format: Journal Article Book Review
Language:English
Published: London Future Medicine Ltd 01.08.2012
Subjects:
Algorithms
Antiretroviral drugs
antiretroviral treatment
Antiviral agents
Bioinformatics
Care and treatment
Clinical trials
Computational biology
Drug resistance
Drug therapy
EuResist
GSS
HIV
HIV (Viruses)
HIV infection
HIV patients
Human immunodeficiency virus
Medical research
Medicine, Experimental
optimized background therapy
Pharmaceutical industry
predictive engine
PSS
ISSN:1746-0794, 1746-0808
Online Access:Get full text
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Summary:In HIV-1-infected patients with extensive drug resistance, the optimization of background antiretroviral therapy is essential when changing drugs after treatment failure. The genotypic sensitivity score (GSS) and phenotypic sensitivity score (PSS), determined by rules-based algorithms, are employed to predict which drugs to select in a background therapy in order to receive the best treatment response when a new drug will be used, both in investigational trials of new agents and in clinical care. However, the outcome of the GSS/PSS approach for the purpose of assessing antiretroviral efficacy in patients with multiresistance has become more problematic, despite improvements such as drug potency weighting and adding information on treatment history. Bioinformatics-based methods are more recent attractive alternatives that have demonstrated equal or better precision compared with rules-based algorithms. This review aims to discuss the usefulness of GSS/PSS and bioinformatics, respectively, for the optimization of anti-HIV background therapy in heavily treatment-experienced patients.
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ISSN:1746-0794
1746-0808
DOI:10.2217/fvl.12.66