Article

Genotypic predictors of human immunodeficiency virus type 1 drug resistance.

Division of Infectious Diseases, Department of Medicine, Stanford University, Stanford, CA 94305, USA.
Proceedings of the National Academy of Sciences (impact factor: 9.68). 11/2006; 103(46):17355-60. DOI:10.1073/pnas.0607274103 pp.17355-60
Source: PubMed

ABSTRACT Understanding the genetic basis of HIV-1 drug resistance is essential to developing new antiretroviral drugs and optimizing the use of existing drugs. This understanding, however, is hampered by the large numbers of mutation patterns associated with cross-resistance within each antiretroviral drug class. We used five statistical learning methods (decision trees, neural networks, support vector regression, least-squares regression, and least angle regression) to relate HIV-1 protease and reverse transcriptase mutations to in vitro susceptibility to 16 antiretroviral drugs. Learning methods were trained and tested on a public data set of genotype-phenotype correlations by 5-fold cross-validation. For each learning method, four mutation sets were used as input features: a complete set of all mutations in > or =2 sequences in the data set, the 30 most common data set mutations, an expert panel mutation set, and a set of nonpolymorphic treatment-selected mutations from a public database linking protease and reverse transcriptase sequences to antiretroviral drug exposure. The nonpolymorphic treatment-selected mutations led to the best predictions: 80.1% accuracy at classifying sequences as susceptible, low/intermediate resistant, or highly resistant. Least angle regression predicted susceptibility significantly better than other methods when using the complete set of mutations. The three regression methods provided consistent estimates of the quantitative effect of mutations on drug susceptibility, identifying nearly all previously reported genotype-phenotype associations and providing strong statistical support for many new associations. Mutation regression coefficients showed that, within a drug class, cross-resistance patterns differ for different mutation subsets and that cross-resistance has been underestimated.

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    Article: Phenotypic hypersusceptibility to multiple protease inhibitors and low replicative capacity in patients who are chronically infected with human immunodeficiency virus type 1.
    Javier Martinez-Picado, Terri Wrin, Simon D W Frost, Bonaventura Clotet, Lidia Ruiz, Andrew J Leigh Brown, Christos J Petropoulos, Neil T Parkin
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    ABSTRACT: Increased susceptibility to the protease inhibitors saquinavir and amprenavir has been observed in human immunodeficiency virus type 1 (HIV-1) with specific mutations in protease (V82T and N88S). Increased susceptibility to ritonavir has also been described in some viruses from antiretroviral agent-naive patients with primary HIV-1 infection in association with combinations of amino acid changes at polymorphic sites in the protease. Many of the viruses displaying increased susceptibility to protease inhibitors also had low replication capacity. In this retrospective study, we analyze the drug susceptibility phenotype and the replication capacity of virus isolates obtained at the peaks of viremia during five consecutive structured treatment interruptions in 12 chronically HIV-1-infected patients. Ten out of 12 patients had at least one sample with protease inhibitor hypersusceptibility (change </=0.4-fold) to one or more protease inhibitor. Hypersusceptibility to different protease inhibitors was observed at variable frequency, ranging from 38% to amprenavir to 11% to nelfinavir. Pairwise comparisons between susceptibilities for the protease inhibitors showed a consistent correlation among all pairs. There was also a significant relationship between susceptibility to protease inhibitors and replication capacity in all patients. Replication capacity remained stable over the course of repetitive cycles of structured treatment interruptions. We could find no association between in vitro replication capacity and in vivo plasma viral load doubling time and CD4(+) and CD8(+) T-cell counts at each treatment interruption. Several mutations were associated with hypersusceptibility to each protease inhibitor in a univariate analysis. This study extends the association between hypersusceptibility to protease inhibitors and low replication capacity to virus isolated from chronically infected patients and highlights the complexity of determining the genetic basis of this phenomenon. The potential clinical relevance of protease inhibitor hypersusceptibility and low replication capacity to virologic response to protease inhibitor-based therapies deserves to be investigated further.
    Journal of Virology 06/2005; 79(10):5907-13. · 5.40 Impact Factor
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Keywords

angle regression
 
antiretroviral drug exposure
 
classifying sequences
 
cross-resistance patterns
 
different mutation subsets
 
expert panel mutation
 
genetic basis
 
HIV-1 protease
 
Learning methods
 
least-squares regression
 
mutation patterns
 
Mutation regression coefficients
 
neural networks
 
nonpolymorphic treatment-selected mutations
 
quantitative effect
 
reverse transcriptase mutations
 
reverse transcriptase sequences
 
strong statistical support
 
support vector regression
 
three regression methods