MARVEL on protease mutations at position 23
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). Genotype-treatment correlation Mutation frequency according to subtype and drug-class experience. The frequency of each mutation at position 23 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 PI-naive (indicated in green). The second row shows the frequency of the mutation in persons who have received one or more PIs. The following rows show the frequency of the mutation in persons who have received only a single PI. Mutation rates that differ significantly between treated and untreated isolates are indicated in yellow.
Footnote: Data are not shown for TPV or DRV because there are no data available from persons who have developed virological failure after receiving just one of these PIs; 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. Genotype-phenotype correlation Phenotypes of top 10 common patterns of drug resistance mutations with mutations at position 23. Mutation pattern data is not available for L23.A complete summary of additional in vitro susceptibility data for viruses with L23 obtained using other assays including the Antivirogram can be found here.
Phenotypic coefficients using machine learning
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