Drug use and phylogenetic clustering of hepatitis C virus infection among people who use drugs in Vancouver, Canada: A latent class analysis approach.

This study estimated latent classes (ie, unobserved subgroups in a population) of people who use drugs in Vancouver, Canada, and examined how these classes relate to phylogenetic clustering of hepatitis C virus (HCV) infection. HCV antibody-positive people who use drugs from two cohorts in Vancouver, Canada (1996-2012), with a Core-E2 sequence were included. Time-stamped phylogenetic trees were inferred, and phylogenetic clustering was determined by time to most common recent ancestor. Latent classes were estimated, and the association with the phylogenetic clustering outcome was assessed using an inclusive classify/analyse approach. Among 699 HCV RNA-positive participants (26% female, 24% HIV+), recent drug use included injecting cocaine (80%), injecting heroin (70%), injecting cocaine/heroin (ie, speedball, 38%) and crack cocaine smoking (28%). Latent class analysis identified four distinct subgroups of drug use typologies: (i) cocaine injecting, (ii) opioid and cocaine injecting, (iii) crack cocaine smoking and (iv) heroin injecting and currently receiving opioid substitution therapy. After adjusting for age and HIV infection, compared to the group defined by heroin injecting and currently receiving opioid substitution therapy, the odds of phylogenetic cluster membership was greater in the cocaine injecting group (adjusted OR [aOR]: 3.06; 95% CI: 1.73, 5.42) and lower in the crack cocaine smoking group (aOR: 0.06; 95% CI: 0.01, 0.48). Combining latent class and phylogenetic clustering analyses provides novel insights into the complex dynamics of HCV transmission. Incorporating differing risk profiles associated with drug use may provide opportunities to further optimize and target HCV treatment and prevention strategies.

Longitudinal trends of HIV drug resistance in a large Canadian cohort, 1996-2016.

OBJECTIVES: We aim to identify long-term trends in HIV drug resistance before and after combined antiretroviral therapy (cART) initiation. METHODS: IAS-USA (2015) mutations were identified in 23 271 HIV protease-reverse transcriptase sequences from 6543 treatment naïve adults in British Columbia. Participants who started cART between 1996 and 2014 were followed until April 2016. Equality of proportions test was used to compare the percentage of participants with acquired drug resistance (ADR) or transmitted drug resistance (TDR) in 1996, to those in 2014. Kaplan-Meier was used to estimate time to ADR in four drug resistance categories. Multivariable regression odds ratios (OR) of ADR for select clinical variables were determined by 5-year eras of cART initiation. RESULTS: The proportion of individuals with ADR declined from 39% (51/132) to 3% (8/322) in 1996-2014 (p <0.0001), while the proportion with TDR increased from 12% (16/132) to 18% (59/322) (p 0.14). The estimated proportions of individuals with ADR rose to 29% (NNRTI), 28% (3TC/FTC), 14% (other nRTI), and 7% (PI) after >16 years of therapy. After 5 years on therapy, participants initiating cART in 1996-2000 had 5.5-times more 3TC/FTC ADR, 5.3-times more other nRTI ADR, 4.7-times more NNRTI ADR, and 24-times more PI ADR than those starting in 2011-2014. The individuals with highest odds of developing ADR in 1996-2010 were adherent to regimens at levels between 60% and 80%, which shifted to <40% adherent in 2011-2014. CONCLUSIONS: HIV drug resistance transitioned from being primarily selected de-novo to being driven by TDR. Among those who started treatment in the past 5 years, ADR is rare and observed mostly in the lowest adherence strata.

An assessment of the relationship between the World Health Organization HIV drug resistance early warning indicators and HIV drug resistance acquisition.

OBJECTIVES: The World Health Organization (WHO)'s HIV drug resistance (HIVDR) early warning indicators (EWIs) measure antiretroviral therapy (ART)-site factors associated with HIVDR prevention, without HIVDR laboratory testing. We assessed the relationship between EWIs and HIVDR acquisition using data from British Columbia, Canada. METHODS: Eligible patients were ART-naïve, were ≥ 19 years old, had initiated ART between 1 January 2000 and 31 December 2012, had ≥ 15 months of follow-up, and were without transmitted HIVDR. Patients were followed for acquired HIVDR until 31 March 2014, the last contact date, or death. We built logistic regression models to assess the associations and predictive ability of individual indicators and of the EWI Score (the number of indicators for which a patient did not meet the criteria) on HIVDR acquisition (to any class of HIVDR, lamivudine (3TC)/emtricitabine (FTC), nonnucleoside reverse transcriptase inhibitors (NNRTIs), nucleoside reverse transcriptase inhibitors (NRTIs) or protease inhibitors (PIs)]). RESULTS: All explored EWIs were associated with at least one class of HIVDR, with the exception of 'ART prescribing practices'. We observed a dose-response relationship between acquiring HIVDR to any antiretroviral class and an increasing EWI score in our predictive logistic regression model. The area under the curve was 0.848 (excellent discrimination). The adjusted odds ratios for acquiring any class of HIVDR for an EWI score of 1, 2 and ≥ 3 versus 0 were 2.30 [95% confidence Interval (CI) 1.21-4.38], 3.35 (95% CI: 1.86-6.03) and 7.26 (95% CI: 4.18-12.61), respectively. CONCLUSIONS: Several EWIs were associated with and predictive of HIVDR, supporting the WHO EWIs as a component of the HIVDR prevention method in settings where HIVDR testing is not routinely or widely available.

A single untimed plasma drug concentration measurement during low-level HIV viremia predicts virologic failure.

Suboptimal untimed plasma drug levels (UDL) have been associated with lower rates of virologic suppression and the emergence of drug resistance. Our aim was to evaluate whether UDL among patients with low-level viremia (LLV) while receiving highly active antiretroviral therapy (HAART) can predict subsequent virologic failure (plasma viral load ≥1000 copies/mL) and emergence of resistance. The first documented LLV episode of 328 consenting patients was analysed in terms of drug levels, viral load and resistance, which were monitored while patients were on a consistent HAART regimen. UDL of protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs), were categorized as 'therapeutic' or 'subtherapeutic' based on predefined target trough concentrations. Drug resistance genotype was assessed using the Stanford algorithm. Time to virologic failure was evaluated by Kaplan-Meier analysis and Cox proportional hazards regression. We found 78 of 328 patients (24%) with subtherapeutic drug levels at time of first detectable LLV, while 19% harboured drug-resistant virus. Both subtherapeutic UDL and drug resistance independently increased the risk of subsequent virologic failure (p <0.001 and p 0.04, respectively). In a multivariable model, variables associated with LLV and virologic failure included subtherapeutic UDL, elevated plasma viral load, and drug resistance. Patients with subtherapeutic UDL accumulated further drug resistance faster during follow-up (p 0.03). Together, resistance and UDL variables can explain a higher proportion of virologic failure than either measure alone. Our results support further prospective evaluation of UDL in the management of low-level viremia.

HIV drug resistance testing by high-multiplex "wide" sequencing on the MiSeq instrument.

Limited access to HIV drug resistance testing in low- and middle-income countries impedes clinical decision-making at the individual patient level. An efficient protocol to address this issue must be established to minimize negative therapeutic outcomes for HIV-1-infected individuals in such settings. This is an observational study to ascertain the potential of newer genomic sequencing platforms, such as the Illumina MiSeq instrument, to provide accurate HIV drug resistance genotypes for hundreds of samples simultaneously. Plasma samples were collected from Canadian patients during routine drug resistance testing (n = 759) and from a Ugandan study cohort (n = 349). Amplicons spanning HIV reverse transcriptase codons 90 to 234 were sequenced with both MiSeq sequencing and conventional Sanger sequencing methods. Sequences were evaluated for nucleotide concordance between methods, using coverage and mixture parameters for quality control. Consensus sequences were also analyzed for disparities in the identification of drug resistance mutations. Sanger and MiSeq sequencing was successful for 881 samples (80%) and 892 samples (81%), respectively, with 832 samples having results from both methods. Most failures were for samples with viral loads of <3.0 log10 HIV RNA copies/ml. Overall, 99.3% nucleotide concordance between methods was observed. MiSeq sequencing achieved 97.4% sensitivity and 99.3% specificity in detecting resistance mutations identified by Sanger sequencing. Findings suggest that the Illumina MiSeq platform can yield high-quality data with a high-multiplex "wide" sequencing approach. This strategy can be used for multiple HIV subtypes, demonstrating the potential for widespread individual testing and annual population surveillance in resource-limited settings.

Development and Validation of Two Screening Assays for the Hepatitis C Virus NS3 Q80K Polymorphism Associated with Reduced Response to Combination Treatment Regimens Containing Simeprevir.

Persons with hepatitis C virus (HCV) genotype 1a (GT1a) infections harboring a baseline Q80K polymorphism in nonstructural protein 3 (NS3) have a reduced virologic response to simeprevir in combination with pegylated interferon-alfa and ribavirin. We aimed to develop, validate, and freely disseminate an NS3 clinical sequencing assay to detect the Q80K polymorphism and potentially other HCV NS3 drug resistance mutations. HCV RNA was extracted from frozen plasma using a NucliSENS easyMAG automated nucleic acid extractor, amplified by nested reverse transcription-PCR, and sequenced using Sanger and/or next-generation (MiSeq) methods. Sanger chromatograms were analyzed using in-house software (RECall), and nucleotide mixtures were called automatically. MiSeq reads were iteratively mapped to the H77 reference genome, and consensus NS3 sequences were generated with nucleotides present at >20% called as mixtures. The accuracy, precision, and sensitivity for detecting the Q80K polymorphism were assessed in 70 samples previously sequenced by an external laboratory. A comparison of the sequences generated by the Sanger and MiSeq methods with those determined by an external lab revealed >98.5% nucleotide sequence concordance and zero discordant calls of the Q80K polymorphism. The results were both highly repeatable and reproducible (>99.7% nucleotide concordance and 100% Q80K concordance). The limits of detection (>2 and ∼5 log10 IU/ml for the Sanger and MiSeq assays, respectively) are sufficiently low to allow genotyping in nearly all chronically infected treatment-naive persons. No systematic bias in the under- or overamplification of minority variants was observed. Coinfection with other viruses (e.g., HIV and hepatitis B virus [HBV]) did not affect the assay results. The two independent HCV NS3 sequencing assays with the automated analysis procedures described here are useful tools to screen for the Q80K polymorphism and other HCV protease inhibitor drug resistance mutations.

Phylogenetic clustering of hepatitis C virus among people who inject drugs in Vancouver, Canada.

UNLABELLED: Little is known about factors associated with hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Phylogenetic clustering and associated factors were evaluated among PWID in Vancouver, Canada. Data were derived from the Vancouver Injection Drug Users Study. Participants who were HCV antibody-positive at enrolment and those with HCV antibody seroconversion during follow-up (1996 to 2012) were tested for HCV RNA and sequenced (Core-E2 region). Phylogenetic trees were inferred using maximum likelihood analysis and clusters were identified using ClusterPicker (90% bootstrap threshold, 0.05 genetic distance threshold). Factors associated with clustering were assessed using logistic regression. Among 655 eligible participants, HCV genotype prevalence was: G1a: 48% (n=313), G1b: 6% (n=41), G2a: 3% (n=20), G2b: 7% (n=46), G3a: 33% (n=213), G4a: <1% (n=4), G6a: 1% (n=8), G6e: <1% (n=1), and unclassifiable: 1% (n=9). The mean age was 36 years, 162 (25%) were female, and 164 (25%) were HIV+. Among 501 participants with HCV G1a and G3a, 31% (n=156) were in a pair/cluster. Factors independently associated with phylogenetic clustering included: age <40 (versus age≥40, adjusted odds ratio [AOR]=1.64; 95% confidence interval [CI] 1.03, 2.63), human immunodeficiency virus (HIV) infection (AOR=1.82; 95% CI 1.18, 2.81), HCV seroconversion (AOR=3.05; 95% CI 1.40, 6.66), and recent syringe borrowing (AOR 1.59; 95% CI 1.07, 2.36). CONCLUSION: In this sample of PWID, one-third demonstrated phylogenetic clustering. Factors independently associated with phylogenetic clustering included younger age, recent HCV seroconversion, prevalent HIV infection, and recent syringe borrowing. Strategies to enhance the delivery of prevention and/or treatment strategies to those with HIV and recent HCV seroconversion should be explored, given an increased likelihood of HCV transmission in these subpopulations.

Differential escape patterns within the dominant HLA-B*57:03-restricted HIV Gag epitope reflect distinct clade-specific functional constraints.

UNLABELLED: HLA-B*57:01 and HLA-B*57:03, the most prevalent HLA-B*57 subtypes in Caucasian and African populations, respectively, are the HLA alleles most protective against HIV disease progression. Understanding the mechanisms underlying this immune control is of critical importance, yet they remain unclear. Unexplained differences are observed in the impact of the dominant cytotoxic T lymphocyte (CTL) response restricted by HLA-B*57:01 and HLA-B*57:03 in chronic infection on the Gag epitope KAFSPEVIPMF (KF11; Gag 162 to 172). We previously showed that the HLA-B*57:03-KF11 response is associated with a >1-log-lower viral setpoint in C clade virus infection and that this response selects escape mutants within the epitope. We first examined the relationship of KF11 responses in B clade virus-infected subjects with HLA-B*57:01 to immune control and observed that a detectable KF11 response was associated with a >1-log-higher viral load (P = 0.02). No evidence of HLA-B*57:01-KF11-associated selection pressure was identified in previous comprehensive analyses of >1,800 B clade virus-infected subjects. We then studied a B clade virus-infected cohort in Barbados, where HLA-B*57:03 is highly prevalent. In contrast to findings for B clade virus-infected subjects expressing HLA-B*57:01, we observed strong selection pressure driven by the HLA-B*57:03-KF11 response for the escape mutation S173T. This mutation reduces recognition of virus-infected cells by HLA-B*57:03-KF11 CTLs and is associated with a >1-log increase in viral load in HLA-B*57:03-positive subjects (P = 0.009). We demonstrate functional constraints imposed by HIV clade relating to the residue at Gag 173 that explain the differential clade-specific escape patterns in HLA-B*57:03 subjects. Further studies are needed to evaluate the role of the KF11 response in HLA-B*57:01-associated HIV disease protection. IMPORTANCE: HLA-B*57 is the HLA class I molecule that affords the greatest protection against disease progression in HIV infection. Understanding the key mechanism(s) underlying immunosuppression of HIV is of importance in guiding therapeutic and vaccine-related approaches to improve the levels of HIV control occurring in nature. Numerous mechanisms have been proposed to explain the HLA associations with differential HIV disease outcome, but no consensus exists. These studies focus on two subtypes of HLA-B*57 prevalent in Caucasian and African populations, HLA-B*57:01 and HLA-B*57:03, respectively. These alleles appear equally protective against HIV disease progression. The CTL epitopes presented are in many cases identical, and the dominant response in chronic infection in each case is to the Gag epitope KF11. However, there the similarity ends. This study sought to better understand the reasons for these differences and what they teach us about which immune responses contribute to immune control of HIV infection.

Performance of HIV-1 drug resistance testing at low-level viremia and its ability to predict future virologic outcomes and viral evolution in treatment-naive individuals.

BACKGROUND: Low-level viremia (LLV; human immunodeficiency virus [HIV-1] RNA 50-999 copies/mL) occurs frequently in patients receiving antiretroviral therapy (ART), but there are few or no data available demonstrating that HIV-1 drug resistance testing at a plasma viral load (pVL) <1000 copies/mL provides potentially clinically useful information. Here, we assess the ability to perform resistance testing by genotyping at LLV and whether it is predictive of future virologic outcomes in patients beginning ART. METHODS: Resistance testing by genotyping at LLV was attempted on 4915 plasma samples from 2492 patients. A subset of previously ART-naive patients was analyzed who achieved undetectable pVL and subsequently rebounded with LLV (n = 212). A genotypic sensitivity score (GSS) was calculated based on therapy and resistance testing results by genotyping, and stratified according to number of active drugs. RESULTS: Eighty-eight percent of LLV resistance assays produced useable sequences, with higher success at higher pVL. Overall, 16 of 212 (8%) patients had pretherapy resistance. Thirty-eight of 196 (19%) patients without pretherapy resistance evolved resistance to 1 or more drug classes, primarily the nucleoside reverse transcriptase (14%) and/or nonnucleoside reverse transcriptase (9%) inhibitors. Patients with resistance at LLV (GSS <3) had a 2.1-fold higher risk of virologic failure (95% confidence interval, 1.2- to 3.7-fold) than those without resistance (P = .007). Progressively lower GSS scores at LLV were associated with a higher increase in pVL over time (P < .001). Acquisition of additional resistance mutations to a new class of antiretroviral drugs during LLV was not found in a subset of patients. CONCLUSIONS: Routine HIV-1 genotyping of LLV samples can be performed with a reasonably high success rate, and the results appear predictive of future virologic outcomes.

HIV-1 dynamics and coreceptor usage in Maraviroc-treated patients with ongoing replication.

There is evidence that HIV-1 evolution under maraviroc (MVC) pressure can lead to the selection of either X4-tropic variants and/or R5-tropic, MVC-resistant isolates. However, the viral dynamics of HIV-1 variants in patients with virological failure (VF) on MVC-containing regimens remain poorly studied. Here, we investigated the V3 loop evolution of HIV-1 on MVC in relation to coreceptor usage and the nature of HIV-1 quasispecies before MVC therapy using bulk population sequences and ultradeep sequencing. The majority of patients had no detectable minority X4 variant at baseline. The evolution of tropism was followed up until VF and showed three possibilities for viral evolution in these patients: emergence of preexisting X4 variants, de novo selection of R5 variants presenting V3 loop mutations, or replication of R5 variants without selection of known mutations.

Correlation of the virological response to short-term maraviroc monotherapy with standard and deep-sequencing-based genotypic tropism prediction methods.

Genotypic tropism testing methods are emerging as the first step before prescription of the CCR5 antagonist maraviroc (MVC) to HIV-infected patients in Europe. Studies validating genotypic tests have included other active drugs that could have potentially convoluted the effects of MVC. The maraviroc clinical test (MCT) is an in vivo drug sensitivity test based on the virological response to a short-term exposure to MVC monotherapy. Thus, our aim was to compare the results of genotypic tropism testing methods with the short-term virological response to MVC monotherapy. A virological response in the MCT was defined as a ≥ 1-log(10) decrease in HIV RNA or undetectability after 8 days of drug exposure. Seventy-three patients undergoing the MCT were included in this study. We used both standard genotypic methods (n = 73) and deep sequencing (n = 27) on MCT samples at baseline. For the standard methods, the most widely used genotypic algorithms for analyzing the V3 loop sequence, geno2pheno and PSSM, were used. For deep sequencing, the geno2pheno algorithm was used with a false-positive rate cutoff of 3.5. The discordance rates between the standard genotypic methods and the virological response were approximately 20% (including mostly patients without a virological response). Interestingly, these discordance rates were similar to that obtained from deep sequencing (18.5%). The discordance rates between the genotypic methods (tropism assays predictive of the use of the CCR5 coreceptor) and the MCT (in vivo MVC sensitivity assay) indicate that the algorithms used by genotypic methods are still not sufficiently optimized.

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.

Performance of a World Health Organization first-line regimen (stavudine/lamivudine/nevirapine) in antiretroviral-naïve individuals in a Western setting.

OBJECTIVES: In 2003, the World Health Organization (WHO) and Joint United Nations Programme on HIV/AIDS (UNAIDS) introduced the '3 by 5 Initiative' to treat 3 million individuals by the end of 2005. This study evaluates the time to treatment termination, viral load suppression, and detection of drug resistance among antiretroviral-naïve individuals initiating stavudine/lamivudine/nevirapine (d4T/3TC/NVP) in British Columbia, Canada, to provide a context for future programme planning. METHODS: Primary outcome was time to treatment termination. Secondary outcome was time to viral suppression. Accumulation of drug resistance mutations was followed systematically in the first 145 individuals over 30 months. Cox proportional hazard regression identified factors associated with termination and suppression. RESULTS: 312 antiretroviral-naïve individuals initiated d4T/3TC/NVP between August 1996 and September 2003. Median follow-up time was 26.5 months (interquartile range [IQR] 6.8-46.5). At a median of 12.4 months (IQR 4.3-33.3), 132 (42.3%) patients switched treatment, 53 (17.0%) stopped therapy and 26 (8.3%) died. Of 308 subjects with baseline viral load >500 copies/mL, 223 (72.4%) suppressed to

Superior virological response to boosted protease inhibitor-based highly active antiretroviral therapy in an observational treatment programme.

BACKGROUND: The use of boosted protease inhibitor (PI)-based antiretroviral therapy has become increasingly recommended in international HIV treatment consensus guidelines based on the results of randomized clinical trials. However, the impact of this new treatment strategy has not yet been evaluated in community-treated cohorts. METHODS: We evaluated baseline characteristics and plasma HIV RNA responses to unboosted and boosted PI-based highly active antiretroviral therapy (HAART) among antiretroviral-naïve HIV-infected patients in British Columbia, Canada who initiated HAART between August 1997 and September 2003 and who were followed until September 2004. We evaluated time to HIV-1 RNA suppression (<500 HIV-1 RNA copies/mL) and HIV-1 RNA rebound (>or=500 copies/mL), while stratifying patients into those that received boosted and unboosted PI-based HAART as the initial regimen, using Kaplan-Meier methods and Cox proportional hazards regression. RESULTS: During the study period, 682 patients initiated therapy with unboosted PI and 320 individuals initiated HAART with a boosted PI. Those who initiated therapy with a boosted PI were more likely to have a CD4 cell count <200 cells/muL and to have a plasma HIV RNA>100 000 copies/mL, and to have AIDS at baseline (all P<0.001). However, when we examined virological response rates, those who initiated HAART with a boosted PI achieved more rapid virological suppression [relative hazard 1.26, 95% confidence interval (CI) 1.06-1.51, P=0.010]. CONCLUSIONS: Patients prescribed boosted PIs achieved superior virological response rates despite baseline factors that have been associated with inferior virological responses to HAART. Despite the inherent limitations of observational studies which require this study be interpreted with caution, these findings support the use of boosted PIs for initial HAART therapy.

Assay of HIV gp41 amino acid sequence to identify baseline variation and mutation development in patients with virologic failure on enfuvirtide.

In this study, we retrospectively assessed a gp41 genotypic assay in 404 enfuvirtide-naïve individuals (340 clade B, 64 non-B clade) to determine the prevalence of baseline polymorphisms and in 41 patients virologically failing enfuvirtide to determine correlates of resistance to this agent. Conserved and polymorphic regions of gp41 were identified in clade B isolates, with 127 of 328 codons (38.7%) being highly conserved (<1.0% variation) and 74 of 328 codons (22.6%) being partially conserved (1.0-5.0% variation). Polymorphisms were observed throughout gp41 in non-B clade virus sequences compared to the clade B reference strain, ranging from 53 natural substitutions in clade D to 76 in clade A. Insertions were common at positions 3, 105, 215 and 276. In the patients failing enfuvirtide, mutations were detected in the 10 amino acid region at positions 36-45 in all plasma virus sequences. Six additional mutations were selected outside of the common region which may be clinically significant at positions 33, 73, 75, 126, and 138. Two or three mutations at positions 36-45 were observed in the majority of plasma virus sequences from patients with virologic failure following the use of enfuvirtide. Further study is required to determine the clinical relevance of the clade related polymorphisms and the new mutations identified in the patients with virologic failure.

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.

Explaining variability in the relationship between antiretroviral adherence and HIV mutation accumulation.

OBJECTIVES: Determining the relationship between antiretroviral adherence and resistance accumulation is important for the design and evaluation of adherence interventions. Our objective was to explain heterogeneity observed in this relationship. METHODS: We first conducted a systematic review to locate published reports describing the relationship between adherence and resistance. We then used a validated computer simulation to simulate the patient populations in these reports, exploring the impact of changes in individual patient characteristics (age, CD4, viral load, prior antiretroviral experience) on the shape of the adherence-resistance (A-R) curve. RESULTS: The search identified 493 titles, of which 3 contained relevant primary data and 2 had sufficient follow-up for inclusion (HOMER and REACH cohorts). When simulating HOMER, the A-R curve had a high peak with a greatly increased hazard ratio (HR) of accumulating mutations at partial compared to complete adherence (simulation, HR 2.9; HOMER, HR 2.7). When simulating REACH, the A-R curve had a shallow peak with a slightly increased hazard of accumulating mutations at partial adherence (simulation, HR 1.2; REACH, HR 1.4). This heterogeneity was primarily attributable to differences in antiretroviral experience between the cohorts. CONCLUSIONS: Our computer simulation was able to explain much of the heterogeneity in observed A-R curves.