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.

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.

Characterization of amino acids Arg, Ser and Thr at position 70 within HIV-1 reverse transcriptase.

OBJECTIVES: The amino acid position 70 in HIV-1 reverse transcriptase (RT) plays an important role in nucleoside RT inhibitor (NRTI) resistance. K70R is part of the thymidine analog mutations, but also other amino acid changes have been associated with NRTI resistance, such as K70E and K70G. In this study, we investigated the in vivo selection of the HIV-1 RT mutations K70S and K70T and their in vitro effect on drug resistance and replication capacity. METHODS: Recombinant viruses with RT mutations were generated to measure the in vitro drug susceptibility and replication capacity. Bayesian network analysis and three-dimensional modeling were performed to understand the selection and impact of the RT70 mutations. RESULTS: K70S and K70T were found at a low frequency in RTI-experienced HIV-1 patients (0.10% and 0·20%). Baeyesian network learning identified no direct association with the in vivo exposure to any specific RTI. However, direct associations of K70S with mutations within the Q151M-complex and of K70T with K65R were observed. In vitro phenotypic testing revealed only minor effects of K70R/S/T as single mutations, associated with Q151M and within the context of the Q151M-complex. DISCUSSION: These results suggest that the selection of K70S/T and their phenotypic impact are influenced by the presence of other mutations in RT. However, the low impact on in vitro phenotype here observed, alongside with the low in vivo prevalence, the exclusive direct association with known major RTI mutations and the unknown correlation with in vivo response, do not yet necessitate the inclusion of K70S/T in drug resistance interpretation systems.

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.

European guidelines on the clinical management of HIV-1 tropism testing.

Viral tropism is the ability of viruses to enter and infect specific host cells and is based on the ability of viruses to bind to receptors on those cells. Testing for HIV tropism is recommended before prescribing a chemokine receptor blocker. In most European countries, HIV tropism is identified with tropism phenotype testing. New data support genotype analysis of the HIV third hypervariable loop (V3) for the identification of tropism. The European Consensus Group on clinical management of tropism testing was established to make recommendations to clinicians and clinical virologists. The panel recommends HIV-tropism testing for the following groups: drug-naive patients in whom toxic effects are anticipated or for whom few treatment options are available; patients who have poor tolerability to or toxic effects from current treatment or who have CNS pathology; and patients for whom therapy has failed and a change in treatment is considered. In general, an enhanced sensitivity Trofile assay and V3 population genotyping are the recommended methods. Genotypic methods are anticipated to be used more frequently in the clinical setting because of their greater accessibility, lower cost, and faster turnaround time than other methods. For the interpretation of V3 loop genotyping, clinically validated systems should be used when possible. Laboratories doing HIV tropism tests should have adequate quality assurance measures. Similarly, close collaboration between HIV clinicians and virologists is needed to ensure adequate diagnostic and treatment decisions.

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.

HIV-1 drug-resistance patterns among patients on failing treatment in a large number of European countries.

BACKGROUND: Information about patterns of HIV-1 drug resistance among treatment-exposed patients is crucial for the development of novel effective drugs. Currently no system exists that monitors patterns of resistance in patients failing therapy. METHODS: The study included 1,988 HIV-1 sequences from patients experiencing therapy failure collected between 2000 and 2004 in 15 European countries. Genotypic resistance was interpreted using the ANRS algorithm. Phenotypic resistance was predicted using the Virco geno- to phenotype system. RESULTS: 80.7% of the sequences included at least one drug-resistance mutation. Mutations were found for NRTIs (73.5%), NNRTIs (48.5%), and protease inhibitors (35.8%). Ninety percent of sequences with genotypic resistance harbored M184V, M41L, K103N, D67N, and/or T215Y. Among NRTIs, resistance was most frequently predicted for lamivudine. About half of all sequences had reduced susceptibility for NNRTIs. Resistance to most boosted protease inhibitors was found in < 25%. No sequence had resistance to all currently available drugs. CONCLUSION: Levels of resistance among patients with therapy failure were high. The patterns of resistance reflect resistance to drugs available for a longer time. Fully suppressive regimens can be designed even for the most mutated HIV because boosted protease inhibitors have remained active against most circulating viruses and new drug classes have become available.

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.

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.

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/.

HIV forensics: pitfalls and acceptable standards in the use of phylogenetic analysis as evidence in criminal investigations of HIV transmission.

BACKGROUND: Phylogenetic analysis - the study of the genetic relatedness between HIV strains - has recently been used in criminal prosecutions as evidence of responsibility for HIV transmission. In these trials, the expert opinion of virologists has been of critical importance. PITFALLS: Phylogenetic analysis of HIV gene sequences is complex and its findings do not achieve the levels of certainty obtained with the forensic analysis of human DNA. Although two individuals may carry HIV strains that are closely related, these will not necessarily be unique to the two parties and could extend to other persons within the same transmission network. ACCEPTABLE STANDARDS: For forensic purposes, phylogenetic analysis should be conducted under strictly controlled conditions by laboratories with relevant expertise applying rigorous methods. It is vitally important to include the right controls, which should be epidemiologically and temporally relevant to the parties under investigation. Use of inappropriate controls can exaggerate any relatedness between the virus strains of the complainant and defendant as being strikingly unique. It will be often difficult to obtain the relevant controls. If convenient but less appropriate controls are used, interpretation of the findings should be tempered accordingly. CONCLUSIONS: Phylogenetic analysis cannot prove that HIV transmission occurred directly between two individuals. However, it can exonerate individuals by demonstrating that the defendant carries a virus strain unrelated to that of the complainant. Expert witnesses should acknowledge the limitations of the inferences that might be made and choose the correct language in both written and verbal testimony.

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.

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.

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.