HIV Clinical Trial
Official title:
Antiretroviral Resistance Detection by Ultrasensitive Pyrosequencing of the HIV-1 Genome and Virological Response to Antiretroviral Rescue Treatment
This study aims to analyze the association between the baseline detection of resistance mutations through Ultra deep Sequencing (UDS) and the virological outcome of salvage antiretroviral therapy, in comparison with conventional genotypic resistance tests. Based on the data generated in this study, new resistance interpretation tools and algorithms will be developed to improve the prediction of antiretroviral therapy outcomes. The final aim of the study is to improve the clinical care of HIV-1-infected patients through the incorporation of improved new antiretroviral resistances tests in the clinical practice.
Antiretroviral resistance testing is an essential tool for the clinical management of Human
Immunodeficiency Virus (HIV-1)-infected persons and HIV surveillance in the community. In
the absence of appropriate treatment, HIV-1 infection leads toward the collapse of the
immune system and death of most HIV-1-infected persons. Adequate antiretroviral treatment
usually reverses this process. Nevertheless, HIV-1 easily develops treatment resistance
through the accumulation of mutations in its genome. This causes treatment failure, and the
requirement of increasingly complex, toxic and expensive treatments. Resistant viruses can
be transmitted to other persons. More than 10 % of HIV-1-positive persons in Spain become
infected with viruses that are already resistant to at least one antiretroviral drug.
Antiretroviral treatment regimens designed on the basis of drug resistance information are
more efficacious, effective and efficient.
In spite of their clinical relevance, however, conventional resistance tests are insensitive
and underestimate antiretroviral resistance. By means of new ultrasensitive resistance tests
it is possible to detect resistant viruses in minority populations that remain undetected by
conventional genotypic tests. Ultrasensitive resistance tests thus double or triple the
number of patients in whom antiretroviral resistance is detected. It is important to
emphasize that detection of minority resistant mutants in antiretroviral naïve patients
increases more than triples the risk of virological failure. The clinical impact of
detecting minority resistant variants in treatment-experienced patients with therapeutic
failure remains unknown.
Recently developed techniques of parallel emulsion sequencing of thousands of amplicon
clones (Ultra deep Sequencing (UDS), Roche Diagnostics/454 Life Sciences) increase the
sensibility to detect polymorphisms and mutations in highly variable genomes such as in
HIV-1 in several orders of magnitude. In addition, this technique allows stablishing genetic
linkage of such mutations and polymorphisms in thousands of HIV-1 clonal sequences for every
patient and point of follow-up. This generates an unprecedented opportunity to characterize
the nature of HIV-1 variability and the physiopathology of antiretroviral resistance in
depth. Ultrasensitive resistance tests hold the promise of improving the clinical management
of HIV-1 seropositive patients, avoiding unnecessary toxicities, improving epidemiological
estimations of antiretroviral resistance, improving the knowledge of the pathogenesis of
HIV-1 infection and antiretroviral resistance, and improving the cost-efficiency of
HIV-related pharmaceutical cost.
This study aims to analyze the association between the baseline detection of resistance
mutations through UDS and the virological outcome of salvage antiretroviral therapy, in
comparison with conventional genotypic resistance tests. Based on the data generated in this
study, new resistance interpretation tools and algorithms will be developed to improve the
prediction of antiretroviral therapy outcomes. The final aim of the study is to improve the
clinical care of HIV-1-infected patients through the incorporation of improved new
antiretroviral resistances tests in the clinical practice.
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Observational Model: Cohort, Time Perspective: Retrospective
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