Transmission of HIV Drug Resistance and the Predicted Effect on Current First-line Regimens in Europe.

BACKGROUND: Numerous studies have shown that baseline drug resistance patterns may influence the outcome of antiretroviral therapy. Therefore, guidelines recommend drug resistance testing to guide the choice of initial regimen. In addition to optimizing individual patient management, these baseline resistance data enable transmitted drug resistance (TDR) to be surveyed for public health purposes. The SPREAD program systematically collects data to gain insight into TDR occurring in Europe since 2001. METHODS: Demographic, clinical, and virological data from 4140 antiretroviral-naive human immunodeficiency virus (HIV)-infected individuals from 26 countries who were newly diagnosed between 2008 and 2010 were analyzed. Evidence of TDR was defined using the WHO list for surveillance of drug resistance mutations. Prevalence of TDR was assessed over time by comparing the results to SPREAD data from 2002 to 2007. Baseline susceptibility to antiretroviral drugs was predicted using the Stanford HIVdb program version 7.0. RESULTS: The overall prevalence of TDR did not change significantly over time and was 8.3% (95% confidence interval, 7.2%-9.5%) in 2008-2010. The most frequent indicators of TDR were nucleoside reverse transcriptase inhibitor (NRTI) mutations (4.5%), followed by nonnucleoside reverse transcriptase inhibitor (NNRTI) mutations (2.9%) and protease inhibitor mutations (2.0%). Baseline mutations were most predictive of reduced susceptibility to initial NNRTI-based regimens: 4.5% and 6.5% of patient isolates were predicted to have resistance to regimens containing efavirenz or rilpivirine, respectively, independent of current NRTI backbones. CONCLUSIONS: Although TDR was highest for NRTIs, the impact of baseline drug resistance patterns on susceptibility was largest for NNRTIs. The prevalence of TDR assessed by epidemiological surveys does not clearly indicate to what degree susceptibility to different drug classes is affected.

HIV-1 subtype is an independent predictor of reverse transcriptase mutation K65R in HIV-1 patients treated with combination antiretroviral therapy including tenofovir.

Subtype-dependent selection of HIV-1 reverse transcriptase resistance mutation K65R was previously observed in cell culture and small clinical investigations. We compared K65R prevalence across subtypes A, B, C, F, G, and CRF02_AG separately in a cohort of 3,076 patients on combination therapy including tenofovir. K65R selection was significantly higher in HIV-1 subtype C. This could not be explained by clinical and demographic factors in multivariate analysis, suggesting subtype sequence-specific K65R pathways.

Decreasing population selection rates of resistance mutation K65R over time in HIV-1 patients receiving combination therapy including tenofovir.

OBJECTIVES: The use of tenofovir is highly associated with the emergence of mutation K65R, which confers broad resistance to nucleoside/nucleotide analogue reverse transcriptase inhibitors (NRTIs), especially when tenofovir is combined with other NRTIs also selecting for K65R. Although recent HIV-1 treatment guidelines discouraging these combinations resulted in reduced K65R selection with tenofovir, updated information on the impact of currently recommended regimens on the population selection rate of K65R is presently lacking. METHODS: In this study, we evaluated changes over time in the selection rate of resistance mutation K65R in a large population of 2736 HIV-1-infected patients failing combination antiretroviral treatment between 2002 and 2010. RESULTS: The K65R resistance mutation was detected in 144 patients, a prevalence of 5.3%. A large majority of observed K65R cases were explained by the use of tenofovir, reflecting its wide use in clinical practice. However, changing patterns over time in NRTIs accompanying tenofovir resulted in a persistent decreasing probability of K65R selection by tenofovir-based therapy. The currently recommended NRTI combination tenofovir/emtricitabine was associated with a low probability of K65R emergence. For any given dual NRTI combination including tenofovir, higher selection rates of K65R were consistently observed with a non-nucleoside reverse transcriptase inhibitor than with a protease inhibitor as the third agent. DISCUSSION: Our finding of a stable time trend of K65R despite elevated use of tenofovir illustrates increased potency of current HIV-1 therapy including tenofovir.

Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study).

OBJECTIVES: The EuResist expert system is a novel data-driven online system for computing the probability of 8-week success for any given pair of HIV-1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment. METHODS: The EuResist system was compared with 10 HIV-1 drug resistance experts for the ability to predict 8-week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success. RESULTS: There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6-13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter-rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%). CONCLUSIONS: With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment-decision support tool in clinical practice.

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine.

A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, an in vivo fitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly with in vitro resistance phenotype in genotype-phenotype pairs (R(2)=0.70). Variation in predicted evolution under selective pressure correlated significantly with observed in vivo evolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

Estimation of an in vivo fitness landscape experienced by HIV-1 under drug selective pressure useful for prediction of drug resistance evolution during treatment.

MOTIVATION: HIV-1 antiviral resistance is a major cause of antiviral treatment failure. The in vivo fitness landscape experienced by the virus in presence of treatment could in principle be used to determine both the susceptibility of the virus to the treatment and the genetic barrier to resistance. We propose a method to estimate this fitness landscape from cross-sectional clinical genetic sequence data of different subtypes, by reverse engineering the required selective pressure for HIV-1 sequences obtained from treatment naive patients, to evolve towards sequences obtained from treated patients. The method was evaluated for recovering 10 random fictive selective pressures in simulation experiments, and for modeling the selective pressure under treatment with the protease inhibitor nelfinavir. RESULTS: The estimated fitness function under nelfinavir treatment considered fitness contributions of 114 mutations at 48 sites. Estimated fitness correlated significantly with the in vitro resistance phenotype in 519 matched genotype-phenotype pairs (R(2) = 0.47 (0.41 - 0.54)) and variation in predicted evolution under nelfinavir selective pressure correlated significantly with observed in vivo evolution during nelfinavir treatment for 39 mutations (with FDR = 0.05). AVAILABILITY: The software is available on request from the authors, and data sets are available from http://jose.med.kuleuven.be/~kdforc0/nfv-fitness-data/.

Fas 670 promoter polymorphism is associated to susceptibility, clinical presentation, and survival in adult T cell leukemia.

Fas (TNFRSF6/Apo-1/CD95) is a type I transmembrane receptor, which mediates apoptosis. Fas gene mutations, aberrant transcripts, and abundant expression of Fas have been reported in adult T cell leukemia (ATL). To further elucidate the role of Fas in ATL pathogenesis, we investigated whether the -670 FAS promoter A/G polymorphism (STAT1-binding site) might contribute to susceptibility and clinical outcome in ATL. Thirty-one patients with ATL, 33 healthy, human T lymphotropic virus type 1-infected individuals, and 70 healthy, uninfected controls were genotyped for the FAS -670 polymorphism by PCR-restriction fragment-length polymorphism. The AA genotype was significantly over-represented in ATL patients in comparison with healthy controls (P=0.006), as well as asymptomatics (P=0.037), corresponding to an odds ratio (OR) of 3.79 [95% confidence intervals (CI; 1.28-11.41)] and 4.58 [95% CI (1.13-20.03)], respectively. The AA group also comprised significantly more aggressive (acute and lymphoma) clinical subtypes [P=0.012; OR=8.40; 95% CI (1.60-44.12)]. In addition, we observed a statistically significant association between GG genotype and survival (log rank test, P=0.032). Finally, IFN-gamma-induced but not basal FAS mRNA levels were increased significantly (P=0.049) in PBMCs from AA versus GG individuals, demonstrating the IFN-dependent functionality of the -670 polymorphism. In conclusion, our results demonstrate that a functional Fas promoter polymorphism is significantly associated to susceptibility, clinical manifestation, and survival in ATL.

The epidemic history of hepatitis C among injecting drug users in Flanders, Belgium.

We employed recently developed statistical methods to explore the epidemic behaviour of hepatitis C subtype 1a and subtype 3a among injecting drug users (IDUs) in Flanders, Belgium, using new gene sequence data sampled among two geographically distinct populations of IDUs. First the extent of hepatitis C transmission across regions/countries was studied through calculation of association indices. It was shown that viral exchange had occurred between both populations in Flanders as well as across international borders. Furthermore, evidence was found suggestive of subtypes 1a and 3a predominantly circulating in subpopulations of Flemish IDUs, exhibiting different degrees of travelling/migration behaviour. Secondly, through coalescent-based analysis the viral epidemic history of the hepatitis C subtype 1a and 3a epidemics was inferred. Evidence was found for different dynamic forces driving both epidemics. Moreover, results suggested that the hepatitis C subtype 3a epidemic has reached a steady state, while the hepatitis C 1a epidemic has not, which therefore might become the predominant subtype among IDUs.

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance.

Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.

Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors.

Interpretation of Human Immunodeficiency Virus 1 (HIV-1) genotypic drug resistance is still a major challenge in the follow-up of antiviral therapy in infected patients. Because of the high degree of HIV-1 natural variation, complex interactions and stochastic behaviour of evolution, the role of resistance mutations is in many cases not well understood. Using Bayesian network learning of HIV-1 sequence data from diverse subtypes (A, B, C, F and G), we could determine the specific role of many resistance mutations against the protease inhibitors (PIs) nelfinavir (NFV), indinavir (IDV), and saquinavir (SQV). Such networks visualize relationships between treatment, selection of resistance mutations and presence of polymorphisms in a graphical way. The analysis identified 30N, 88S, and 90M for nelfinavir, 90M for saquinavir, and 82A/T and 46I/L for indinavir as most probable major resistance mutations. Moreover we found striking similarities for the role of many mutations against all of these drugs. For example, for all three inhibitors, we found that the novel mutation 89I was minor and associated with mutations at positions 90 and 71. Bayesian network learning provides an autonomous method to gain insight in the role of resistance mutations and the influence of HIV-1 natural variation. We successfully applied the method to three protease inhibitors. The analysis shows differences with current knowledge especially concerning resistance development in several non-B subtypes.

The simian origins of the pathogenic human T-cell lymphotropic virus type I.

At least four, and possibly six, molecular subtypes of human T-cell lymphotropic virus type I (HTLV-I) exist: one is confined to Melanesia/Australia, one is ubiquitous, and the others are found only in Africa. Molecular epidemiology suggests that all subtypes arose from separate interspecies transmissions from simians to humans.

Analysis of complex HIV-1 intersubtype recombinants using a Bayesian scanning method.

The increased complexity of HIV-1 genetic heterogeneity raises the issue for reliable classification and analysis of these sequences. Until now, bootscanning analysis has been the main method used for the analysis of potential HIV-1 intersubtype recombinants. We show evidence that in some cases of complex recombinants, where three or more segments with discordant phylogenetic signal may exist in protease (PR) and partial reverse transcriptase (RT) region, Bayesian scanning provides a clearer picture than bootscanning plots about the boundaries of potential recombination. Thus, a recently developed Bayesian scanning tool can facilitate the analysis and classification of HIV-1 mosaic sequences.

Re-analysis of 34 full-length HIV-1 intersubtype recombinant sequences.

One of the main characteristics of the HIV-1 is its extensive genetic heterogeneity. Intersubtype recombination was first described in 1995 and since then a significant proportion of the HIV-1 isolates was found to comprise mosaic sequences. Re-analysis of 34 full-length HIV-1 intersubtype recombinants, including all "pure" HIV-1 subtypes revealed that 19 of the 34 analyzed mosaics consist of a more complex mosaic pattern than initially described. These findings indicate that the complexity of the HIV-1 recombinants is much greater than previously estimated.

HIV-1 genetic variants circulation in the North of Angola.

Few molecular epidemiological data on HIV-1 in Angola are available. In this study, we analysed 37 pol sequences from patients originated from Luanda and Cabinda in Angola. It was our objective to investigate the circulation of different HIV-1 subtypes in this country. We found a high HIV-1 genetic diversity. The predominant subtypes were C and F, while subtypes A, D, G and H were also detected. Three sequences were untypable and may possibly belong to new subtypes or recombinants of unknown subtypes. Moreover, 13 recombinant sequences were found, most of them with very complex patterns including untypable fragments.

Optimization of a genotypic assay applicable to all human immunodeficiency virus type 1 protease and reverse transcriptase subtypes.

Genotypic assays are used often to guide clinicians in decisions concerning the treatment of patients. An optimized sequence-based genotypic assay was used to determine the whole protease and reverse transcriptase (RT) gene, including the gag cleavage site region and RNase H region. Since non-B subtypes are increasing in countries where subtype B was the most prevalent subtype, and treatment becomes more available in developing countries where the epidemic is characterized by a high prevalence of non-B subtypes, it was important that the genotypic test was evaluated using a panel of different subtypes. Amplification was successful for different subtypes: A, B, C, D, F, G, H, J, CRF01_AE, CRF02_AG, CRF11_cpx, CRF13_cpx and an uncharacterized recombinant sample. The detection limit of the PCR was 1000 copies/ml, except for 1 subtype C sample (PL3) and 1 CRF02_AG sample (PL8). The detection limit for these samples was 5000 copies/ml. A sequence could be obtained in both directions for most of the samples.

Predicted residual activity of rilpivirine in HIV-1 infected patients failing therapy including NNRTIs efavirenz or nevirapine.

Rilpivirine is a second-generation nonnucleoside reverse-transcriptase inhibitor (NNRTI) currently indicated for first-line therapy, but its clinical benefit for HIV-1 infected patients failing first-generation NNRTIs is largely undefined. This study quantified the extent of genotypic rilpivirine resistance in viral isolates from 1212 patients upon failure of efavirenz- or nevirapine-containing antiretroviral treatment, of whom more than respectively 80% and 90% showed high-level genotypic resistance to the failing NNRTI. Of all study patients, 47% showed a rilpivirine resistance-associated mutation (RPV-RAM), whereas preserved residual rilpivirine activity was predicted in half of the patients by three genotypic drug resistance interpretation algorithms. An NNRTI-dependent impact on rilpivirine resistance was detected. Compared with the use of nevirapine, the use of efavirenz was associated with a 32% lower risk of having a RPV-RAM and a 50% lower risk of predicted reduced rilpivirine susceptibility. Most prevalent RPV-RAMs after nevirapine experience were Y181C and H221Y, whereas L100I+K103N, Y188L and K101E occurred most in efavirenz-experienced patients. Predicted rilpivirine activity was not affected by HIV-1 subtype, although frequency of individual mutations differed across subtypes. In conclusion, this genotypic resistance analysis strongly suggests that the latest NNRTI, rilpivirine, may retain activity in a large proportion of HIV-1 patients in whom resistance failed while they were on an efavirenz- or nevirapine-containing regimen, and may present an attractive option for second-line treatment given its good safety profile and dosing convenience. However, prospective clinical studies assessing the effectiveness of rilpivirine for NNRTI-experienced patients are warranted to validate knowledge derived from genotypic and phenotypic drug resistance studies.

Activity of non-nucleoside reverse transcriptase inhibitors against HIV-2 and SIV.

BACKGROUND: After the initial discovery of 1-(2-hydroxyethoxymethyl)-6-(phenylthio)thymine (HEPT) and tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepin-2(1H)-one and thione (TIBO) derivatives, several other non-nucleoside reverse transcriptase (RT) inhibitors (NNRTI), including nevirapine (BI-RG-587), pyridinone derivatives (L-696,229 and L-697,661), delavirdine (U-90152), alpha-anilinophenylacetamides (alpha-APA) and various other classes of NNRTI have been described. The hallmark of NNRTI has been based on their ability to interact with a specific site ('pocket') of HIV-1 RT. OBJECTIVE: To investigate whether, in addition to HIV-1, different strains of HIV-2 (ROD and EHO) and SIV (mac251, agm3 and mndGB1) are sensitive to a selection of NNRTI i.e. delavirdine, the HEPT derivative I-EBU (MKC-442), 8-chloro-TIBO (tivirapine), alpha-APA (loviride), nevirapine and the pyridinone derivative L-697,661. METHODS AND RESULTS: The NNRTI tested inhibited the replication of the different strains of HIV-2 and SIV at micromolar concentrations. The inhibitory effects of the NNRTI on HIV-2-induced cytopathicity correlated well with their inhibitory effects on HIV-2 RT activity. Drug-resistant HIV-2 (EHO) variants containing the Ser102Leu and/or Glu219Asp mutations in their RT were selected after passaging the virus in MT-4 cells in the presence of increasing concentrations of delavirdine. The EHO virus mutants were at least 20-fold less susceptible to the antiviral effects of delavirdine. Some cross-resistance, depending on the mutant strain, was observed with the other NNRTI tested (i.e. MKC-442, tivirapine, loviride and pyridinone L-697,661). CONCLUSIONS: Our data demonstrate that NNRTI are not exclusively specific for HIV-1 but are also inhibitory to different HIV-2 and SIV strains. These observations will have important implications for the development of new NNRTI with higher activity against both HIV-1 and HIV-2. Furthermore, in view of their anti-SIV activity, NNRTI could be evaluated further for their in vivo anti-retrovirus efficacy in non-human primate models.

Simple algorithm derived from a geno-/phenotypic database to predict HIV-1 protease inhibitor resistance.

BACKGROUND: Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance. OBJECTIVE: Development of an algorithm to predict PI phenotype from genotypic data. METHODS: Recombinant viruses containing patient-derived protease genes were analysed for sensitivity to indinavir, saquinavir, ritonavir and nelfinavir. Drug resistance-associated mutations were determined by direct sequencing. geno- and phenotypic data were compared for 119 samples from 97 HIV-1 infected patients. RESULTS: Samples with one or two mutations in the gene for the protease were phenotypically sensitive in 74.3%, whereas 83.6% of samples with five or more mutations were resistant against all PI tested. Some mutations (361, 63P, 71V/T, 771) were frequent both in sensitive and resistant samples, whereas others (241, 30N, 461/L, 48V, 54V, 82A/F/T/S, 84V, 90M) were predominantly present in resistant samples. Therefore, the presence or absence of a single drug resistance-associated mutation predicted phenotypic PI resistance with high sensitivity (96.5-100%) but low specificity (13.3-57.4%). A more specific algorithm was obtained by taking into account the total number of drug resistance-associated mutations in the gene for the protease and restricting these to certain key positions for the PI. The algorithm was subsequently validated by analysis of 72 independent samples. CONCLUSION: With an optimized algorithm, phenotypic PI resistance can be predicted by viral genotype with good sensitivity (89.1-93.0%) and specificity (82.6-93.3%). The reliability and relevance of this algorithm should be further evaluated in clinical practice.

Resistance-related mutations in the HIV-1 protease gene of patients treated for 1 year with the protease inhibitor ritonavir (ABT-538).

OBJECTIVE: To define genotypic and phenotypic resistance patterns following prolonged therapy with the protease inhibitor ritonavir (ABT-538). DESIGN: Seven HIV-1-infected patients, all but one previously treated with dideoxynucleoside analogues (zidovudine, didanosine, zalcitabine), were treated for 1 year with ritonavir. METHODS: Direct solid-phase sequencing of the protease gene starting from plasma derived viral RNA followed by comparison to phenotypic drug resistance data. RESULTS: The most frequent amino-acid substitutions occurring upon administration of the protease inhibitor were V82A/F (substrate binding site), I54V (flap region), A71V and L10I. Additional mutations found in more than one patient were I15V, M36I, I84V and I93L. Mutation L63P was found both in pre- and post-ritonavir samples. Phenotypic drug resistance assays confirmed resistance to ritonavir in post-treatment samples (approximately 170-fold) and showed cross-resistance to indinavir (approximately 30-fold) and partially to saquinavir (approximately fivefold). At 1 year of treatment, one patient without known resistance-associated mutations in the protease gene still showed a substantial rise in CD4 cell count accompanied by a more than 2.4 log decrease in RNA viral load. However, at week 78, mutations R8Q, E34K, R57K, L63P and I84V were detected and the treatment benefit was partially lost. CONCLUSIONS: Long-term treatment with ritonavir is associated with the emergence of multiple mutations in the HIV-1 protease gene. The mutations L10I, I54V, L63P, A71V, V82A/F and I84V correspond to known drug-resistance mutations for ritonavir and other protease inhibitors. Phenotypic resistance to ritonavir was detected in a majority of ritonavir-treated patients at 1 year of treatment. In addition, long-term ritonavir treatment selects for cross-resistance to the protease inhibitors indinavir and saquinavir. This argues against sequential therapy with several protease inhibitors. Delayed resistance in one patient was accompanied with a prolonged increase in CD4 cell count and decrease in viral load suggesting a temporary benefit of treatment.