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Abstract:
The acquired immunodeficiency syndrome (AIDS) is one of the biggest medical
challenges in the world today. Its causative pathogen, the human
immunodeficiency virus (HIV), is responsible for millions of deaths per year.
Although about two dozen antiviral drugs are currently available, progression
of the disease can only be delayed but patients cannot be cured.
In recent years, the new class of coreceptor antagonists has been added to the
arsenal of antiretroviral drugs. These drugs block viral cell-entry by binding
to one of the receptors the virus requires for infection of a cell. However,
some HIV variants can also use another coreceptor so that coreceptor usage has
to be tested before administration of the drug.
This thesis analyzes the use of statistical learning methods to infer HIV
coreceptor usage from viral genotype. Improvements over existing methods are
achieved by using sequence information of so far not used genomic regions, next
generation sequencing technologies, and by combining different existing
prediction systems.
In addition, HIV coreceptor usage prediction is analyzed with respect to
clinical outcome in patients treated with coreceptor antagonists. The results
demonstrate that inferring HIV coreceptor usage from viral genotype can be
reliably used in daily routine.
