Bayesian network analyses of resistance pathways against efavirenz and nevirapine

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.

There is a disparity in the distribution of infections by HIV-1 subtype in the world. Subtype B is predominant in America, Australia and western and central Europe, and most therapeutic strategies are based on research and clinical studies on this subtype. However, non-B subtypes represent the majority of global HIV-1 infections; e.g., subtype C alone accounts for nearly half of all HIV-1 infections. We present a statistical framework enabling the comparison of patterns of accumulating mutations in different HIV-1 subtypes. Specifically, we compare the temporal ordering of lopinavir resistance mutations in HIV-1 subtypes B versus C. To this end, we combine the Hidden Conjunctive Bayesian Network (H-CBN) model with an approximate inference scheme enabling comparisons of larger networks. We show that the development of resistance to lopinavir differs significantly between subtypes B and C, such that findings based on subtype B sequences can not always be applied to sybtype C. The described methodology is suitable for comparing different subgroups in the context of other evolutionary processes.

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.

HCV1b genome evolution under selective pressure of the cyclophilin inhibitor alisporivir during the DEB-025-HCV-203 phase II clinical trial

Major advances have revolutionized the HCV antiviral treatment field, with interferon-free combinations of direct-acting antivirals (DAAs) resulting into success rates of >90% for all HCV genotypes. Nevertheless, viral eradication at a global level stills remains challenging, stimulating the continued search for new affordable pan-genotypic drugs. To overcome selection of drug resistant variants, targeting host proteins can be an attractive mechanism of action. Alisporivir (Debio 025) is a potent pan-genotypic host-targeting antiviral agent, acting on cyclophilin A, which is necessary for HCV replication. The efficacy and safety of three different oral doses of alisporivir in combination with pegylated interferon-α2a given over a period of four weeks, was investigated in a randomized, double-blind and placebo-controlled phase IIa clinical trial, in 90 treatment-naïve subjects infected with chronic hepatitis C, wherefrom 58 HCV1b samples were selected for genetic sequencing purposes. Sequencing results were used to study the HCV genome for amino acid changes potentially related with selective pressure and resistance to alisporivir. By comparing baseline and on-treatment sequences, a large variation in proportion of amino acid changes was detected in all treatment arms. The NS5A variant D320E, which was previously identified during in vitro resistance selection and resulted in 3.6-fold reduced alisporivir susceptibility, emerged in two subjects in the alisporivir monotherapy arm. However, emergence of D320E appeared to be associated only with concurrent viral load rebound in one subject with 0.8log10IU/ml increase in HCV RNA. In general, for all datasets, low numbers of positions under positive selective pressure were observed, with no significant differences between naïve and treated sequences. Additionally, incomplete sequence information for some of the 22 patients and the low number of individuals per treatment arm, is limiting the power to assess the association of alisporivir or interferon treatment with the observed amino acid changes.

Effectiveness of a Treatment Switch to Nevirapine plus Tenofovir and Emtricitabine (or Lamivudine) in Adults with HIV-1 Suppressed Viremia

BACKGROUND

Switching subjects with persistently undetectable HIV-1 viremia under antiretroviral treatment (ART) to once-daily tenofovir/emtricitabine (or lamivudine) + nevirapine is a cost-effective and well-tolerated strategy. However, the effectiveness of this approach has not been established.

METHODS

We performed a retrospective study evaluating the rates of treatment failure, virological failure (VF), and variables associated, in all subjects initiating this switch combination in our clinic since 2001. Analyses were performed by a modified intention to treat, where switch due to toxicity equalled failure. The main endpoint was plasma HIV-RNA < 50 copies/mL.

RESULTS

341 patients were treated for a median of 176 (57; 308) weeks. At week 48, 306 (89.7%) subjects had HIV-1 RNA <50 copies/mL, 10 (2.9%) experienced VF, and 25 (7.4%) discontinued the treatment due to toxicity. During the whole follow-up 23 (6.7%) individuals (17 on lamivudine, 6 on emtricitabine; p = 0.034) developed VF and treatment modification due to toxicity occurred in 36 (10.7%). Factors independently associated with VF in a multivariate analysis were: intravenous drug use (HR 1.51; 95%CI 1.12, 2.04), time with undetectable viral load before the switch (HR 0.98; 0.97, 0.99), number of prior NRTIs (HR 1.49; 1.15, 1.93) or NNRTIs (HR 3.22; 1.64, 6.25), and previous NVP (HR 1.54; 1.10, 2.17) or efavirenz (HR 5.76; 1.11, 29.87) unscheduled interruptions. VF was associated with emergence of usual nevirapine mutations (Y181C/I/D, K103N and V106A/I), M184V (n = 16; 12 with lamivudine vs. 4 with emtricitabine, p = 0.04), and K65R (n = 7).

CONCLUSIONS

The rates of treatment failure at 48 weeks, or long-term toxicity or VF with this switch regimen are low and no unexpected mutations or patterns of mutations were selected in subjects with treatment failure.

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.

Predicting virus mutations through statistical relational learning

BACKGROUND

Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants.

RESULTS

We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones.

CONCLUSIONS

Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations.

Using systems approaches to address challenges for clinical implementation of pharmacogenomics

Many genetic variants have been shown to affect drug response through changes in drug efficacy and likelihood of adverse effects. Much of pharmacogenomic science has focused on discovering and clinically implementing single gene variants with large effect sizes. Given the increasing complexities of drug responses and their variability, a systems approach may be enabling for discovery of new biology in this area. Further, systems approaches may be useful in addressing challenges in moving these data to clinical implementation, including creation of predictive models of drug response phenotypes, improved clinical decision-making through complex biological models, improving strategies for integrating genomics into clinical practice, and evaluating the impact of implementation programs on public health.

Impact of Novel Resistance Profiles in HIV-1 Reverse Transcriptase on Phenotypic Resistance to NVP

Objective. To clarify the impact of H221Y mutation on drug resistance to NVP. Methods. 646 bp HIV-1 pol gene fragments (from 592 to 1237 nucleotide) with different NNRTIs mutation profiles from AIDS patients receiving antiretroviral therapy containing NVP regimens were introduced into pNL4-3 backbone plasmid. H221Y and (or) Y181C mutations were reverted to wild type amino acids by site-directed mutagenesis, then strains containing various mutation patterns were packaged. Phenotypic drug resistance was analyzed on TZM-bl cells. Results. 12 strains containing different drug-resistant mutation profiles were constructed, including the K101Q series (K101Q/Y181C/H221Y, K101Q/Y181C, K101Q/H221Y, and K101Q), the V179D series (V179D/Y181C/H221Y, V179D/Y181C, V179D/H221Y, and V179D), and the K103N series (K103N/Y181C/H221Y, K103N/Y181C, K103N/H221Y, K103N). For strains containing the mutation profiles (K101Q/Y181C, K101Q, V179D/Y181C, V179D, K103N/Y181C, and K103N), the presence of H221Y reduced NVP susceptibility by 2.1 ± 0.5 to 3.6 ± 0.5 fold. To the mutation profiles K101Q/H221Y, K101Q, V179D/H221Y, V179D, K103N/H221Y, and K103N, the presence of Y181C reduced NVP susceptibility by 41.9 ± 8.4 to 1297.0 ± 289.1 fold. For the strains containing K101Q, V179D, and K103N, the presence of Y181C/H221Y combination decreased NVP susceptibility by 100.6 ± 32.5 to 3444.6 ± 834.5 fold. Conclusion. On the bases of various NNRTIs mutation profiles, Y181C remarkably improved the IC₅₀ to NVP, although H221Ymutation alone just increases 2.1 ∼ 3.6-fold resistance to NVP, the mutation could improve 100.6 ∼ 3444.6-fold resistance to NVP when it copresent with Y181C, the phenotypic drug resistance fold was improved extremely. For strains containing the mutation profiles (K101Q/Y181C, K101Q, V179D/Y181C, V179D, K103N/Y181C, and K103N), the presence of H221Y reduced NVP susceptibility by 2.1 ± 0.5 to 3.6 ± 0.5 fold.

Systems biology data analysis methodology in pharmacogenomics

Pharmacogenetics aims to elucidate the genetic factors underlying the individual's response to pharmacotherapy. Coupled with the recent (and ongoing) progress in high-throughput genotyping, sequencing and other genomic technologies, pharmacogenetics is rapidly transforming into pharmacogenomics, while pursuing the primary goals of identifying and studying the genetic contribution to drug therapy response and adverse effects, and existing drug characterization and new drug discovery. Accomplishment of both of these goals hinges on gaining a better understanding of the underlying biological systems; however, reverse-engineering biological system models from the massive datasets generated by the large-scale genetic epidemiology studies presents a formidable data analysis challenge. In this article, we review the recent progress made in developing such data analysis methodology within the paradigm of systems biology research that broadly aims to gain a 'holistic', or 'mechanistic' understanding of biological systems by attempting to capture the entirety of interactions between the components (genetic and otherwise) of the system.

Resistance patterns selected by nevirapine vs. efavirenz in HIV-infected patients failing first-line antiretroviral treatment: a bayesian analysis

Background

WHO recommends starting therapy with a non-nucleoside reverse transcriptase inhibitor (NNRTI) and two nucleoside reverse transcriptase inhibitors (NRTIs), i.e. nevirapine or efavirenz, with lamivudine or emtricitabine, plus zidovudine or tenofovir. Few studies have compared resistance patterns induced by efavirenz and nevirapine in patients infected with the CRF01_AE Southeast Asian HIV-subtype. We compared patterns of NNRTI- and NRTI-associated mutations in Thai adults failing first-line nevirapine- and efavirenz -based combinations, using Bayesian statistics to optimize use of data.

Methods and Findings

In a treatment cohort of HIV-infected adults on NNRTI-based regimens, 119 experienced virologic failure (>500 copies/mL), with resistance mutations detected by consensus sequencing. Mutations were analyzed in relation to demographic, clinical, and laboratory variables at time of genotyping. The Geno2Pheno system was used to evaluate second-line drug options. Eighty-nine subjects were on nevirapine and 30 on efavirenz. The NRTI backbone consisted of lamivudine or emtricitabine plus either zidovudine (37), stavudine (65), or tenofovir (19). The K103N mutation was detected in 83% of patients on efavirenz vs. 28% on nevirapine, whereas Y181C was detected in 56% on nevirapine vs. 20% efavirenz. M184V was more common with nevirapine (87%) than efavirenz (63%). Nevirapine favored TAM-2 resistance pathways whereas efavirenz selected both TAM-2 and TAM-1 pathways. Emergence of TAM-2 mutations increased with the duration of virologic replication (OR 1.25–1.87 per month increment). In zidovudine-containing regimens, the overall risk of resistance across all drugs was lower with nevirapine than with efavirenz, whereas in tenofovir-containing regimen the opposite was true.

Conclusions

TAM-2 was the major NRTI resistance pathway for CRF01_AE, particularly with nevirapine; it appeared late after virological failure. In patients who failed, there appeared to be more second-line drug options when zidovudine was combined with nevirapine or tenofovir with efavirenz than with alternative combinations.

HIV-1 mutational pathways under multidrug therapy

BACKGROUND

Genotype-derived drug resistance profiles are a valuable asset in HIV-1 therapy decisions. Therapy decisions could be further improved, both in terms of predicting length of current therapy success and in preserving followup therapy options, through better knowledge of mutational pathways- here defined as specific locations on the viral genome which, when mutant, alter the risk that additional specific mutations arise. We limit the search to locations in the reverse transcriptase region of the HIV-1 genome which host resistance mutations to nucleoside (NRTI) and non-nucleoside (NNRTI) reverse transcriptase inhibitors (as listed in the 2008 International AIDS Society report), or which were mutant at therapy start in 5% or more of the therapies studied.

METHODS

A Cox proportional hazards model was fit to each location with the hazard of a mutation at that location during therapy proportional to the presence/absence of mutations at the remaining locations at therapy start. A pathway from preexisting to occurring mutation was indicated if the covariate was both selected as important via smoothly clipped absolute deviation (a form of regularized regression) and had a small p-value. The Cox model also allowed controlling for non-genetic parameters and potential nuisance factors such as viral resistance and number of previous therapies. Results were based on 1981 therapies given to 1495 distinct patients drawn from the EuResist database.

RESULTS

The strongest influence on the hazard of developing NRTI resistance was having more than four previous therapies, not any one existing resistance mutation. Known NRTI resistance pathways were shown, and previously speculated inhibition between the thymidine analog pathways was evidenced. Evidence was found for a number of specific pathways between NRTI and NNRTI resistance sites. A number of common mutations were shown to increase the hazard of developing both NRTI and NNRTI resistance. Viral resistance to the therapy compounds did not materially effect the hazard of mutation in our model.

CONCLUSIONS

The accuracy of therapy outcome prediction tools may be increased by including the number of previous treatments, and by considering locations in the HIV genome which increase the hazard of developing resistance mutations.

Genotypic impact of prolonged detectable HIV type 1 RNA viral load after HAART failure in a CRF01_AE-infected cohort

HIV subtype-specific data on mutation type, rate, and accumulation following HAART treatment failure are limited. We studied patterns and accrual of drug resistance mutations in a Cambodian CRF01_AE-infected cohort continuing a virologically failing first-line, nonnucleoside reverse transcriptase inhibitor- (NNRTI-) based, HAART. Between 2005 and 2007, 837 adult HIV-infected patients had regular plasma HIV-1 RNA viral load measurements at Sihanouk Hospital Centre of Hope (SHCH), Cambodia. Drug resistance testing was performed in all patients with HIV-1 RNA >1000 copies/ml after at least 6 months of HAART. Seventy-one patients with a mean age of 34 years, of whom 68% were male, were retrospectively assessed at virological failure. The median duration of antiretroviral therapy was 12.3 (IQR 7.1-18.23) months, the median CD4 cell count was 173 (IQR 118-256) cells/mm(3), and the mean plasma HIV-1 RNA viral load was 3.9 log (SD 0.72) at failure. NNRTI mutations, M184I/V mutation, thymidine analogue mutations, and K65R were observed in 78.9%, 69%, 20%, and 12.7% of patients, respectively. For 33 patients, genotypic testing was carried out on at least two occasions before the switch to second-line HAART after a median duration of 5.8 (IQR 4.3-6.1) months of virological failure: 54.5% of patients accumulated new mutations with a rate of 1.6 mutations per person-year. Accumulation was seen both for nucleoside and nonnucleoside reverse transcriptase inhibitors, and also in patients with low-level viremia. Subtype-specific data on mutation type, rate, and accumulation after HAART failure are urgently needed to optimize treatment strategies in resource-limited settings.

Constrained patterns of covariation and clustering of HIV-1 non-nucleoside reverse transcriptase inhibitor resistance mutations

Objectives

We characterized pairwise and higher order patterns of non-nucleoside reverse transcriptase inhibitor (NNRTI)-selected mutations because multiple mutations are usually required for clinically significant resistance to second-generation NNRTIs.

Patients and methods

We analysed viruses from 13 039 individuals with sequences containing at least one of 52 published NNRTI-selected mutations, including 1133 viruses from individuals who received efavirenz but no other NNRTI and 1510 viruses from individuals who received nevirapine but no other NNRTI. Of the 17 reported etravirine resistance-associated mutations (RAMs), Y181C/I/V, L100I, K101P and M230L were considered major based on published in vitro susceptibility data.

Results

Efavirenz preferentially selected for 16 mutations, including L100I (14% versus 0.1%, P < 0.001), K101P (3.3% versus 0.4%, P < 0.001) and M230L (2.8% versus 1.3%, P = 0.004), whereas nevirapine preferentially selected for 12 mutations, including Y181C/I/V (48% versus 6.9%, P < 0.001). Twenty-nine pairs of NNRTI-selected mutations covaried significantly, including Y181C with seven other mutations (A98G, K101E/H, V108I, G190A/S and H221Y), L100I with K103N, and K101P with K103S. Two pairs (Y181C + V179F and Y181C + G190S) were predicted to confer >10-fold decreased etravirine susceptibility. Seventeen percent of sequences had three or more NNRTI-selected mutations, mostly in clusters of covarying mutations. Many clusters had Y181C plus a non-major etravirine RAM; few had more than one major etravirine RAM.

Conclusions

Although major etravirine RAMs rarely occur in combination, 2 of 29 pairs of covarying mutations were associated with >10-fold decreased etravirine susceptibility. Viruses with three or more NNRTI-selected mutations often contained Y181C in combination with one or more minor etravirine RAMs; however, phenotypic and clinical correlates for most of these higher order combinations have not been published.

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.

[Etravirine: genetic barrier and resistance development]

Unlike first-generation non-nucleoside reverse transcriptase inhibitors (NNRTI), to develop complete resistance to etravirine (ETR), various mutations must be accumulated. This drug shows an intermediate barrier against partial resistance and a high barrier to complete resistance. Some mutations selected by nevirapine or efavirenz affect the activity of ETR, the most frequent being Y181C, G190A/S, K101E, L100I, Y188L and V90I. The grade of resistance conferred by each mutation differs. Currently, there are at least three lists of mutations that confer an exact score to each mutation. These lists have been validated with the grade of resistance observed in paired phenotypes and with clinical response in the DUET studies. The three scores show a high degree of agreement. ETR is currently one of the antiretroviral drugs whose activity can be calculated simply and accurately on the basis of genotypic data. The mutations selected after failure to nucleoside reverse transcriptase inhibitors, thymidine analogue, T69D/N and M184I/V, confer hypersusceptibility to ETR (fold change < 0.4) in up to 1 out of every 3 samples analyzed. The early withdrawal of first-generation NNRTIs in patients with virological failure is essential to avoid the accumulation of mutations that could compromise the activity of this drug.

The K65R mutation in HIV-1 reverse transcriptase: genetic barriers, resistance profile and clinical implications

Resistance to antiviral therapy is the limiting factor in the successful management of HIV. In general, the K65R mutation is rarely selected (1.7-4%) with tenofovir disoproxil fumarate (TDF), abacavir (ABC), didanosine (ddI), and stavudine (d4T), as compared with the high incidence (>40%) of thymidine analog mutations associated with zidovudine and d4T. The high barrier to the development of K65R may reflect a combination of factors, including the high potency of K65R-selecting drugs, including recommended TDF/emtricitabine and ABC/lamivudine (ABC/3TC) combinations; the partial (low-intermediate level) profile of cross-resistance conferred by K65R to TDF, ABC and 3TC; the favorable viral fitness constraint imposed by K65R and the 3TC/emtricitabine-associated M184V mutations; the bidirectional antagonism between the K65R and thymidine analog mutation pathways; and unique RNA structural considerations in the region surrounding codon 65. Nevertheless, surprisingly high levels of treatment failures and K65R resistance may be associated with triple nucleoside analog regimens. The use of TDF + ABC, TDF + ddI and ABC + d4T in combination with 3TC or emtricitabine should be avoided. This selection of K65R may be reduced by the inclusion of zidovudine in two-four nucleoside reverse-transcriptase regimens. Clinical studies have demonstrated an increased frequency of K65R in association with suboptimal d4T and ddI regimens, as well as nevirapine and its resistance mutations Y181C and G190A. The potential for the development of the K65R mutation in subtype C is particularly problematic wherein a signature KKK nucleotide motif, at codons 64, 65 and 66 in reverse transcriptase, appear to lead to template pausing, facilitating the selection of K65R. Optimizing regimens may attenuate the emergence of K65R, leading to better long-term treatment management in different geographic settings. TDF-based regimens are the leading candidates for first- and second-line therapy, microbicides and chemoprophylaxis strategies.