Phenotypic Hypersusceptibility to Multiple Protease Inhibitors and Low Replicative Capacity in Patients Who Are Chronically Infected with Human Immunodeficiency Virus Type 1

Martínez-Picado, Javier; Wrin, Terri; Frost, Simon D. W.; Clotet, Bonaventura; Ruiz, Lı́dia; Brown, Andrew; Petropoulos, Christos J.; Parkin, Neil

doi:10.1128/jvi.79.10.5907-5913.2005

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…Therefore, we speculate about the possibility that RT mutational patterns found in the clinical isolate could either affect Gag-Pol dimerization or reduce the stability of the viral polyprotein, causing reductions in the levels of active PR. These reductions could be responsible for PR inhibitor hypersusceptibility, a phenomenon that has been previously observed but whose underlying mechanism remains elusive (20,22). In summary, the 3-nucleotide deletion (⌬69) along with S163I in the context of an MDR RT genotype favored the ex vivo RC under drug pressure, in agreement with its in vivo emergence and evolution in a long-term-treated HIV-1-infected patient.…”

Section: Discussionsupporting

confidence: 61%

Relative Fitness and Replication Capacity of a Multinucleoside Analogue-Resistant Clinical Human Immunodeficiency Virus Type 1 Isolate with a Deletion of Codon 69 in the Reverse Transcriptase Coding Region

Villena

Prado

Puertas

et al. 2007

J Virol

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Deletions, insertions, and amino acid substitutions in the ␤3-␤4 hairpin loop-coding region of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) have been associated with resistance to nucleoside RT inhibitors when appearing in combination with other mutations in the RT-coding region. In this work, we have measured the in vivo fitness of HIV-1 variants containing a deletion of 3 nucleotides affecting codon 69 (⌬69) of the viral RT as well as the replication capacity (RC) ex vivo of a series of recombinant HIV-1 variants carrying an RT bearing the ⌬69 deletion or the T69A mutation in a multidrug-resistant (MDR) sequence background, including the Q151M complex and substitutions M184V, K103N, Y181C, and G190A. Patient-derived viral clones having RTs with ⌬69 together with S163I showed increased RCs under drug pressure. These data were consistent with the viral population dynamics observed in a long-term-treated HIV-1-infected patient. In the absence of drugs, viral clones containing T69A replicated more efficiently than those having ⌬69, but only when patient-derived sequences corresponding to RT residues 248 to 527 were present. These effects could be attributed to a functional interaction between the C-terminal domain of the p66 subunit (RNase H domain) and the DNA polymerase domain of the RT. Finally, recombinant HIV-1 clones bearing RTs with MDR-associated mutations, including deletions at codon 69, showed increased susceptibilities to protease inhibitors in phenotypic assays. These effects correlated with impaired Gag cleavage and could be attributed to delayed maturation and decreased production of active protease in those variants.

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Section: Discussionsupporting

confidence: 61%

Relative Fitness and Replication Capacity of a Multinucleoside Analogue-Resistant Clinical Human Immunodeficiency Virus Type 1 Isolate with a Deletion of Codon 69 in the Reverse Transcriptase Coding Region

Villena

Prado

Puertas

et al. 2007

J Virol

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“…Phenotypic hyper susceptibility to lopinavir or ritonavir and other protease inhibitors has been described in association with reduced replication capacity in viral isolates with wild-type protease. 26 …”

Section: Discussionmentioning

confidence: 99%

Raltegravir in second-line antiretroviral therapy in resource-limited settings (SELECT): a randomised, phase 3, non-inferiority study

et al. 2016

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Summary Background For second-line antiretroviral therapy, WHO recommends a boosted protease inhibitor plus nucleoside or nucleotide reverse transcriptase inhibitors (NRTIs). However, concerns about toxicity and cross-resistance motivated a search for regimens that do not contain NRTIs. We aimed to assess whether boosted lopinavir plus raltegravir would be non-inferior to boosted lopinavir plus NRTIs for virological suppression in resource-limited settings. Methods A5273 was a randomised, open-label, phase 3, non-inferiority study at 15 AIDS Clinical Trials Group (ACTG) research sites in nine resource-limited countries (three sites each in India and South Africa, two each in Malawi and Peru, and one each in Brazil, Kenya, Tanzania, Thailand, and Zimbabwe). Adults with plasma HIV-1 RNA concentrations of at least 1000 copies per mL after at least 24 weeks on a regimen based on a non-NRTI inhibitor were randomly assigned (1:1) to receive oral ritonavir-boosted lopinavir (100 mg ritonavir, 400 mg lopinavir) plus 400 mg raltegravir twice a day (raltegravir group) or to ritonavir-boosted lopinavir plus two or three NRTIs selected from an algorithm (eg, zidovudine after failure with tenofovir and vice versa; NRTI group). Randomised group assignment was done with a computer algorithm concealed to site personnel, and stratified by HIV-1 RNA viral load, CD4 cell count, and intention to use zidovudine, with the groups balanced by each site. The primary endpoint was time to confirmed virological failure (two measurements of HIV-1 RNA viral load >400 copies per mL) at or after week 24 in the intention-to-treat population. Non-inferiority (10% margin) was assessed by comparing the cumulative probability of virological failure by 48 weeks. This trial was registered with ClinicalTrials.gov, NCT01352715. Findings Between March 13, 2012, and Oct 2, 2013, we randomly assigned 515 participants: 260 to the raltegravir group and 255 to the NRTI group; two participants in the raltegravir group and one in the NRTI group were excluded from analyses because of ineligibility. By the end of follow-up (October, 2014), 96 participants had virological failure (46 in the raltegravir group and 50 in the NRTI group). By 48 weeks, the cumulative probability of virological failure was 10·3% (95% CI 6·5–14·0) in the raltegravir group and 12·4% (8·3–16·5) in the NRTI group, with a weighted difference of −3·4% (−8·4 to 1·5), indicating that raltegravir was non-inferior, but not superior, to NRTIs. 62 (24%) participants in the raltegravir group and 81 (32%) in the NRTI group had grade 3 or higher adverse events; 19 (7%) and 29 (11%), respectively, had serious adverse events. Three participants in each group died, all from HIV-related causes. Interpretation In settings with extensive NRTI resistance but no available resistance testing, our data support WHO’s recommendation for ritonavir-boosted lopinavir plus NRTI for second-line antiretroviral therapy. Ritonavir-boosted lopinavir plus raltegravir is an appropriate alternative, especially if NRT...

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“…The learning curves show that for most drugs, Ϸ400 genotype-phenotype examples were required for optimal predictive accuracy and that the MSE then saturates at 0.15-0.20, suggesting that factors other than TSMs influence susceptibility. For example, polymorphic sites influence susceptibility either directly or indirectly by affecting virus replication capacity (16). In addition, protease cleavage site mutations, primarily those in the gag gene, also influence viral replication capacity and drug susceptibility (17).…”

Section: Discussionmentioning

confidence: 99%

Genotypic predictors of human immunodeficiency virus type 1 drug resistance

Rhee

Taylor²,

Wadhera³

et al. 2006

Proc. Natl. Acad. Sci. U.S.A.

219

211

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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 crossresistance 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 >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, low͞intermediate 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, crossresistance patterns differ for different mutation subsets and that cross-resistance has been underestimated. antiviral therapy ͉ HIV ͉ linear regression ͉ machine learning T wenty antiretroviral drugs are approved for treating HIV-1 infection: eight protease inhibitors (PIs), seven nucleoside and one nucleotide reverse transcriptase (RT) inhibitors (NRTIs), three nonnucleoside RT inhibitors (NNRTIs), and one fusion inhibitor. Resistance to these drugs is caused by mutations in their molecular targets. Understanding the genetic basis of cross-resistance is essential for designing new antiviral drugs and for using genotypic drug resistance testing to select optimal therapy. Despite the large number of PIs and RT inhibitors, therapy is challenging because drug resistance arises from complex patterns of mutations and because of the high degree of cross-resistance within each drug class.Approaches for using HIV-1 drug resistance mutations to predict changes in drug susceptibility have included decision trees (1), linear regression (2), linear discriminant analysis (3), neural networks (4), and support vector regression (SVR) (5). Here, we compare five statistical learning methods each using four different sets of input mutations to develop quantitative models associating HIV-1 protease and RT mutations with changes in susce...

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Phenotypic Hypersusceptibility to Multiple Protease Inhibitors and Low Replicative Capacity in Patients Who Are Chronically Infected with Human Immunodeficiency Virus Type 1

Cited by 14 publications

References 29 publications

Relative Fitness and Replication Capacity of a Multinucleoside Analogue-Resistant Clinical Human Immunodeficiency Virus Type 1 Isolate with a Deletion of Codon 69 in the Reverse Transcriptase Coding Region

Relative Fitness and Replication Capacity of a Multinucleoside Analogue-Resistant Clinical Human Immunodeficiency Virus Type 1 Isolate with a Deletion of Codon 69 in the Reverse Transcriptase Coding Region

Raltegravir in second-line antiretroviral therapy in resource-limited settings (SELECT): a randomised, phase 3, non-inferiority study

Genotypic predictors of human immunodeficiency virus type 1 drug resistance

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