MARVEL on RT mutations at position 103
HIVdb Algorithm: Comments & Scores
Footnote:Mutation scores on the left are derived from published literature linking mutations and ARVs (the complete details can be found in the HIVdb Release Notes).
Mutation frequency according to subtype and drug-class experience.The frequency of each mutation at position 103 according to subtype and drug-class experience. Data are shown for the 8 most common subtypes. The number of persons in each subtype/treatment category is shown beneath the subtype. Mutations occurring at a frequency >0.5% are shown. Each mutation is also a hyper-link to a separate web page with information on each isolate, including literature references with PubMed abstracts, the GenBank accession number, and complete sequence and treatment records.
Mutation frequency according to treatment with individual ARVs.The first row shows the frequency of the mutation in persons who are RTI-naive (indicated in green). The second row shows the frequency of the mutation in persons who have received one or more NRTIs (No NNRTIs). The third row shows the frequency of the mutation in persons who have received one or more NNRTIs (+/- NRTIs). The following rows show the frequency of the mutation in persons who have received only a single NNRTI. Mutation rates that differ significantly between treated and untreated isolates are indicated in yellow.
Footnote: About one-half of the untreated isolates belong to non-subtype B isolates; About 20% of the treated isolates belong to non-subtype B isolates; A page containing summaries for all of the mutations at this position can be found here.
Phenotypes of top 10 common patterns of drug resistance mutations with mutations at position 103.Mutation patterns are listed in the frequency with which they have been reported in the published literature. The median level of fold resistance (compared with wildtype) for viruses with the mutation pattern in the first column are indicated when available. The subscripts indicate the number of viruses that were phenotyped. The drug susceptibility assay used was the PhenoSense assay (Monogram, South San Francisco). A hyperlink for each individual pattern is provided to access a complete list of mutations and fold resistances for each sequence matching the pattern of mutation.
A complete summary of additional in vitro susceptibility data for viruses with K103 obtained using other assays including the Antivirogram can be found here. A complete list of all mutation patterns with K103 (not just the top 10 most frequent patterns) can be found at this page.
Phenotypic coefficients using machine learning