Resistance to ddI and Sensitivity to AZT Induced by a Mutation in HIV-1 Reverse Transcriptase

Clair, M H St; Martín, José Manuel García; Tudor‐Williams, Gareth; Bach, M. C.; Vavro, Cindy; King, David M.; Kellam, Paul; Kemp, Sharon D.; Larder, Brendan A.

doi:10.1126/science.1716788

Cited by 661 publications

(154 citation statements)

References 20 publications

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“…In addition, 8 non-expert-panel TSMs (K43E͞Q, V75M͞T, E203K, D218E, K219R, and L228H) decreased susceptibility to one or more NRTIs. As previously reported, M184V increased susceptibility to zidovudine (AZT), stavudine (d4T), and TDF; L74V increased susceptibility to AZT and TDF; and K65R increased susceptibility to AZT (11)(12)(13). The LSR coefficients for 24 NNRTI TSMs occurring in Ն2 sequences and associated with decreased susceptibility to one or more NNRTIs are shown in Table 10.…”

Section: Pi-resistance Mutationssupporting

confidence: 55%

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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Section: Pi-resistance Mutationssupporting

confidence: 55%

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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“…A62V, V75I, and F77L are associated with Q151M in a multidrug-resistant complex (16), and, as previously discussed, V75T causes slight resistance to D4T (46). Besides abacavir, L74V has been associated with resistance to ddI and ddC (18,45). Despite their importance, a mechanistic understanding of these mutations is for the most part lacking.…”

Section: Role For the Hydrophobic Nature Of Cbvtp's Ribose Ring Inmentioning

confidence: 97%

Mechanistic Studies to Understand the Progressive Development of Resistance in Human Immunodeficiency Virus Type 1 Reverse Transcriptase to Abacavir

Ray

Basavapathruni

Anderson

2002

Journal of Biological Chemistry

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Abacavir has been shown to select for multiple resistant mutations in the human immunodeficiency type 1 (HIV-1) pol gene. In an attempt to understand the molecular mechanism of resistance in response to abacavir, and nucleoside analogs in general, a set of reverse transcriptase mutants were studied to evaluate their kinetics of nucleotide incorporation and removal. It was found that, similar to the multidrug-resistant mutant reverse transcriptase (RT) Q151M, the mutations L74V, M184V, and a triple mutant containing L74V/Y115F/M184V all caused increased selectivity for dGTP over the active metabolite of abacavir (carbovir triphosphate). However, the magnitude of resistance observed in cell culture to abacavir in previous studies was less than that observed to other compounds. Our mechanistic studies suggest that this may be due to carbovir triphosphate decreasing the overall effect on its efficiency of incorporation by forming strong hydrophobic interactions in the RT active site. Unlike RT AZTR , no increase in the rate of ATP-or PP imediated chain terminator removal relative to RT WT could be detected for any of the mutants. However, marked decreases in the steady-state rate may serve as a mechanism for increased removal of a chain-terminating carbovir monophosphate by increasing the time spent at the primer terminus for some of the mutants studied. The triple mutant showed no advantage in selectivity over RT M184V and was severely impaired in its ability to remove a chain terminator, giving no kinetic basis for its increased resistance in a cellular system. Biochemical properties including percentage of active sites, fidelity, and processivity may suggest that the triple mutant's increased resistance to abacavir in cell culture is perhaps due to a fitness advantage, although further cellular studies are needed to verify this hypothesis. These data serve to further the understanding of how mutations in RT confer resistance to nucleoside analogs.

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“…Patient PBLs were also co-cultivated with donor (HIV seronegative) PBLs and DNA was extracted for PCR and DNA sequence analysis . To produce sitedirected mutant virus stocks, C8166 cells were co-transfected with a mixture of the RT-deleted proviral clone pHIVARTBstEII and a functional mutant RT coding sequence derived from M13 clones as described (Kellam et al, 1992;St Clair et al, 1991). Cell-free virus supernatants were harvested 12 to 14 days post-transfection and stored at -70 °C.…”

Section: Cells and Virusmentioning

confidence: 99%

“…Human immunodeficiency virus (HIV) isolates resistant to many compounds targeted to the virus-encoded enzyme reverse transcriptase (RT) have been identified during drug therapy or by selection in vitro (Fitzgibbon et al, 1992;Larder, 1992;Nunberg et al, 199 l ;Richman et al, 1991a;St Clair et al, 1991). Variants of HIV highly resistant to zidovudine (AZT, Retrovir) have been isolated from AIDS patients receiving prolonged treatment (Boucher et al, 1990;Land et al, 1990;Richman et al, 1990;Rooke et al, 1989).…”

Section: Introductionmentioning

confidence: 99%

Zidovudine treatment results in the selection of human immunodeficiency virus type 1 variants whose genotypes confer increasing levels of drug resistance

Kellam

Boucher

Tijnagel

et al. 1994

Journal of General Virology

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High level resistance to 3'-azido-3'-deoxythymidine (AZT, zidovudine or Retrovir) is conferred by the presence of four or five mutations (Met-41 ~ Leu; Asp-67 ~ Asn; Lys-70 ~ Arg; Thr-215 ~ Tyr or Phe; Lys-219 ~ Gin) in the human immunodeficiency virus (HIV) reverse transcriptase. The order of appearance of these five mutations in asymptomatic patients during therapy has been studied. This has enabled us to propose a model for the acquisition of zidovudine resistance mutations during the treatment of high-risk asymptomatic HIV-infeeted individuals. A consistent acquisition pattern of mutations at codons 41, 70 and 215 was observed in 17 individuals. Complex mixtures of HIV species containing different combinations of single and linked double resistance mutations were present early in zidovudine therapy in isolates from two patients studied in detail. From these mixtures the linked Leu-41/Tyr-215 genotype outgrew all others initially. The development of each new virus population is likely to be mediated primarily by the increase in the level of drug resistance rather than changes in the growth kinetics of the virus. This leads us to conclude that one major driving force in the outgrowth of different mutant viruses is the selective advantage conferred by higher levels of drug resistance.

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Resistance to ddI and Sensitivity to AZT Induced by a Mutation in HIV-1 Reverse Transcriptase

Cited by 661 publications

References 20 publications

Genotypic predictors of human immunodeficiency virus type 1 drug resistance

Genotypic predictors of human immunodeficiency virus type 1 drug resistance

Mechanistic Studies to Understand the Progressive Development of Resistance in Human Immunodeficiency Virus Type 1 Reverse Transcriptase to Abacavir

Zidovudine treatment results in the selection of human immunodeficiency virus type 1 variants whose genotypes confer increasing levels of drug resistance

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