title: Bioinformatics approach to predicting HIV drug resistance.
abstract: The emergence of drug resistance remains one of the most challenging issues in the treatment of HIV-1 infection. The extreme replication dynamics of HIV facilitates its escape from the selective pressure exerted by the human immune system and by the applied combination drug therapy. This article reviews computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genotypic and phenotypic data. Genotypic assays are based on the analysis of mutations associated with reduced drug susceptibility, but are difficult to interpret due to the numerous mutations and mutational patterns that confer drug resistance. Phenotypic resistance or susceptibility can be experimentally evaluated by measuring the inhibition of the viral replication in cell culture assays. However, this procedure is expensive and time consuming.
authors.affiliation: Division Scientific Computing, Department Numerical Analysis & Modeling, Konrad-Zuse-Zentrum, Takustr. 7, D-14195 Berlin-Dahlem, Germany. cordes@zib.de
authors.forename: Frank
authors.initials: F
authors.lastname: Cordes
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology*; D019295:Computational Biology / Q000379:methods*; D003362:Cost-Benefit Analysis / Q000379:methods; D024882:Drug Resistance, Viral; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D008958:Models, Molecular; D009154:Mutation; D010597:Pharmacogenetics / Q000379:methods; D010641:Phenotype; D011487:Protein Conformation; D012984:Software; D013329:Structure-Activity Relationship
chemical_list: D019380:Anti-HIV Agents