Skip Navigation

Antiretroviral Drug Resistance Testing in Adult HIV-1 Infection: 2008 Recommendations of an International AIDS Society-USA Panel

  1. Martin S. Hirsch1,
  2. Huldrych F. Günthard9,
  3. Jonathan M. Schapiro10,
  4. Françoise Brun Vézinet11,
  5. Bonaventura Clotet12,
  6. Scott M. Hammer2,
  7. Victoria A. Johnson3,4,
  8. Daniel R. Kuritzkes1,
  9. John W. Mellors5,
  10. Deenan Pillay13,
  11. Patrick G. Yeni11,
  12. Donna M. Jacobsen6, and
  13. Douglas D. Richman7,8
  1. 1Harvard Medical School, Boston, Massachusetts
  2. 2Columbia University College of Physicians and Surgeons, New York, New York
  3. 3Birmingham Veterans Affairs Medical Center, Birmingham, Alabama
  4. 4University of Alabama at Birmingham School of Medicine, Birmingham, Alabama
  5. 5University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
  6. 6International AIDS Society-USA, San Francisco, California
  7. 7University of California-San Diego, San Diego, California
  8. 8Veterans Affairs San Diego Healthcare System, San Diego, California
  9. 9University Hospital of Zürich, Zürich, Switzerland
  10. 10Sheba Medical Center, Tel Aviv, Israel
  11. 11Hôpital Bichat-Claude Bernard, Paris, France
  12. 12Hospital Universitari Germans Trias i Pujol, irsi Caixa Foundation, Barcelona, Catalonia, Spain
  13. 13Royal Free and University College Medical School, London, United Kingdom
  1. Reprints or correspondence: Dr. Martin S. Hirsch, Massachusetts General Hospital, Infectious Diseases, 65 Landsdowne St., Rm. 419, Cambridge, MA 02139 (mshirsch{at}partners.org).
 
Next Section

Abstract

Resistance to antiretroviral drugs remains an important limitation to successful human immunodeficiency virus type 1 (HIV-1) therapy. Resistance testing can improve treatment outcomes for infected individuals. The availability of new drugs from various classes, standardization of resistance assays, and the development of viral tropism tests necessitate new guidelines for resistance testing. The International AIDS Society-USA convened a panel of physicians and scientists with expertise in drug-resistant HIV-1, drug management, and patient care to review recently published data and presentations at scientific conferences and to provide updated recommendations. Whenever possible, resistance testing is recommended at the time of HIV infection diagnosis as part of the initial comprehensive patient assessment, as well as in all cases of virologic failure. Tropism testing is recommended whenever the use of chemokine receptor 5 antagonists is contemplated. As the roll out of antiretroviral therapy continues in developing countries, drug resistance monitoring for both subtype B and non-subtype B strains of HIV will become increasingly important.

A panel of the International AIDS Society-USA published recommendations for HIV-1 drug resistance testing in HIV-1-infected adults in 1998, 2000, and 2003 [1–3]. Since the 2003 publication, drug resistance testing has become widespread in the developed world and has been accepted as an important adjunct to the management of patients with detectable plasma viremia who are receiving antiretroviral therapy. Moreover, personto-person transmission of drug-resistant viruses occurs in a variety of settings, including between adults and from mother to child [4, 5], indicating that testing for drug resistance before initiating therapy may be useful even for treatment-naive patients. Novel resistance mutations that confer resistance to older drugs continue to be identified (figure 1), and newer-generation protease inhibitors (PIs) and reverse transcriptase inhibitors have been developed to counteract mutations that confer resistance to the older agents. Approval of agents from new classes, like integrase strand transfer inhibitors (INSTIs) and entry inhibitors, assure that drug resistance testing will become increasingly complex and important in case management in the years ahead. Testing methodologies have improved and are becoming more sensitive, and tests for viral coreceptor use (i.e., tropism) have been introduced.

With increasing patient access to antiretroviral drugs in the developing world, many of the same problems involving drug-resistant virus that are common in the developed world have begun to occur in the developing world, as well. In addition, resistance patterns among the non-subtype B strains of HIV-1, which are circulating more widely in developing countries, may differ from those seen among subtype B strains in North America and Europe. Although resistance testing is not yet widely employed in developing countries because of the costs involved, drug resistance will predictably emerge as antiretroviral therapy is more widely introduced, and the need for appropriate testing will increase. Expert advice is important in the management of patients with drug-resistant infection [8], but because it is not always available, this report will attempt to provide updated recommendations for HIV-1 drug resistance testing and tropism testing in developed countries, with the anticipation that, over time, such recommendations will also prove useful in developing countries.

Previous SectionNext Section

Methods

The panel was first convened by the International AIDS Society-USA (which is not related to the worldwide International AIDS Society) in 1997 to develop evidence-based recommendations for the assessment of HIV-1 drug susceptibility and the management of drug-resistant HIV-1 infection in clinical practices in the developed world [1–3]. Panel members are not compensated, and there is a process for panel member rotation. Updated reports are initiated when enough new published or presented information in the field accumulates to warrant revising previous recommendations.

The panel was convened by conference call in mid-2007 and met regularly thereafter to discuss new data published or presented at scientific conferences since its previous report [3]. Topic areas included new information about the prevalence of drug resistance worldwide; new data on mechanisms of resistance by drug class, including INSTI and entry inhibitor classes that have become available since the previous report; developments in assays to determine viral tropism and replication capacity; issues related to non-subtype B HIV-1; and updated recommendations for the clinical use of HIV-1 drug resistance and tropism testing. Individual panel members were appointed to review topics to be considered. In some cases, pharmaceutical or assay manufacturers were contacted to obtain relevant information in the public domain. Data on file, unpublished observations, personal communications, and other forms of data not previously published or presented in a scientific, public forum were not considered for this report. Discussions of drugs focused on those approved by the US Food and Drug Administration. Clinical recommendations were made by panel consensus.

The quality and strength of the evidence were rated according to a scale (table 1) that was modified in 2006 [9] and originally adapted from published rating scales used by other organizations (e.g., the American Heart Association [10], American Association for the Study of Liver Diseases [11], National Institutes of Health [12], and Infectious Diseases Society of America [13]).

Previous SectionNext Section

Transmission And Epidemiology Of Drug-Resistant Hiv-1

Transmission and prevalence of drug resistance in developed countries. Transmission of drug-resistant HIV-1 has been observed in most countries where antiretroviral treatment is available [14–22], and it jeopardizes the success of antiretroviral therapy. Indeed, transmitted drug resistance generally leads to a delay in virologic suppression [18, 23] and to an increased risk of earlier virologic failure [24, 25]. Long-term persistence of transmitted drug resistance in the absence of drug pressure has been documented for many types of mutations [26–30], as have specific revertant mutations for thymidine analogue reverse-transcriptase inhibitor-associated resistance mutations (TAMs) [29]. In contrast with patients with acquired drugresistant virus that emerged during therapy, patients with transmitted drug-resistant virus do not have a reservoir of drugsusceptible virus. Consequently, transmitted drug-resistant virus can only change to drug-susceptible virus by back mutation [30], and it will do so rapidly only if a substantial fitness benefit occurs with reversion of the drug resistance mutation, as in the case of the M184V mutation in reverse transcriptase. This mutation can revert relatively early after transmission [26], in contrast with the delayed reversion that occurs with most other mutations [26, 30]. This delayed reversion is different from the reversion associated with acquired drug resistance, in which archived, drug-susceptible wild-type virus reemerges within weeks of withdrawal of the selective pressure of drug treatment [31–34].

The prevalence of newly transmitted drug-resistant HIV-1 strains (primary HIV-1 drug resistance) varies widely with location, transmission risk group, and the sampling time after infection [14, 15, 18, 20–23, 35–37]. Variations in prevalence are multifactorial and reflect different treatment exposures at the population level, potential selection bias caused by nonrepresentative sampling of certain transmission risk groups, different definitions of resistance [38, 39], different sampling times after infection, and different risk behavior and access to therapy among transmission risk groups. A large increase in overall primary resistance, from 13.2% for the period 1995–1998 to 24.1% for the period 2003–2004, was reported in New York, New York, and the rate of transmitted multidrug resistance increased from 2.6% to 9.8% over the same period [36]. A British group also reported high rates of primary resistance in 2003: 19.2% for any drug, 12.4% for nucleoside analogue reverse-transcriptase inhibitors (NRTIs), 8.1% for nonnucleoside analogue reverse-transcriptase inhibitors (NNRTIs), and 6.6% for PIs. High-level resistance was found in 9.3%. In contrast, a representative 10-year transmission surveillance study (1996–2005), conducted by the Swiss HIV Cohort Study, showed considerably lower rates: 7.7% for any drug, 5.5% for NRTIs, 1.9% for NNRTIs, and 2.7% for PIs. Dual- or triple- drug class resistance was observed in only 2% of patients [21]. The rate of transmission, including the transmission of multidrug-resistant virus, was stable over a 10-year period, with the exception of NNRTI-resistant virus transmission, which—as has been reported by other groups—increased in 2005 [16, 18, 20, 36, 40]. These examples demonstrate that specific countries and regions require separate surveillance systems to monitor transmitted HIV drug resistance, because extrapolation from foreign data may be misleading.

The CASCADE study [41] has reported the longest followup time for patients with transmitted drug resistance to date; this study found higher initial CD4+ T cell counts in patients infected with drug-resistant virus than in patients infected with wild-type virus. This initial higher CD4+ T cell count was followed by a faster decrease in CD4+ T cell count, such that initial differences in CD4+ T cell counts were lost over the 5-year observation period. Thus, the effects of transmitted drug-resistant HIV on the infection's natural history before treatment are not great.

Drug resistance acquired during antiretroviral therapy is much more common than transmitted drug resistance. Crosssectional studies involving patients who have been treated but who are viremic yielded probabilities for the presence of at least 1 drug resistance mutation of 76%–90% [42–47]. However, considerable methodologic challenges exist in evaluating such prevalence data. Drug resistance testing can be reliably performed only if plasma HIV-1 RNA levels are >500 copies/mL. This is particularly important if a patient's plasma HIV-1 RNA level was suppressed to below the level of detection before the time that drug resistance testing became available, when patients initially received suboptimal treatment, such as singleor double-NRTI-only regimens. Estimates of prevalence are confounded, because the denominator of all treated patients is often not known, the practice of obtaining drug resistance test results has changed over time, and cross-sectional analyses may underestimate the prevalence of drug resistance [48]. In a recent 10-year longitudinal study from the Swiss HIV Cohort Study, which took into account shifts in population size and in which the denominator of treated subjects was known, the prevalence of drug-resistant virus among antiretroviral therapy-exposed patients was estimated to be 50%-60% in 1999 and decreased to 39%-53% in 2006 [49]. The prevalence of triple drug-resistant virus remained stable at 5%. These lower numbers are likely to be attributable to the improving efficacy of treatment.

Transmission and prevalence of drug resistance in developing countries. Access to antiretroviral drugs in the developing world is increasing rapidly, although only a fraction of individuals who need therapy are currently receiving it. As in the developed world, an increase in transmitted resistance will lag behind an increase in acquired drug resistance. Nevertheless, with (1) >3 million people receiving antiretroviral therapy; (2) treatment failure defined by clinical end points [50]; and (3) limited availability of assays for routine determination of plasma HIV-1 RNA levels and for detecting drug resistance, acquired and transmitted drug resistance in resource-limited settings will present formidable challenges. Even more extensive drug resistance may emerge in this setting than in the developed world [50]. Some studies have already demonstrated the presence of drug-resistant virus in patients with recent infections in developing countries [51]. The World Health Organization is developing a surveillance program to provide early warning of increasing rates of transmitted resistance and to facilitate additional treatment options [52].

Single-dose nevirapine is widely used in the developing world to prevent mother-to-child transmission of HIV-1, but it selects for nevirapine-resistant HIV-1 in 40%-60% of mothers, as detected by population sequencing within 6–8 weeks of administration [53], and this resistance may compromise subsequent response to nevirapine-containing regimens [51, 52, 54, 55]. Children who are born with infection despite nevirapine prophylaxis have a high risk of developing resistance to nevirapine, which limits their future treatment options [54]. Coadministration of other antiretroviral drugs with nevirapine may reduce the risk of drug-resistant infection in adults and children [56].

A number of characteristics of antiretroviral use in resourcepoor settings will affect the level of acquired drug resistance among treated patients. For example, in a Thai cohort receiving a fixed-dose combination of stavudine-lamivudine-nevirapine and infrequent monitoring, virologic failure was associated with more resistance (>90% of isolates had NNRTI-and lamivudine-resistance-associated mutations, and >30% had TAMs) [57] than would be expected in the developed world at the time of first viral rebound [50], although less resistance was observed in cohorts with more-intensive plasma HIV-1 RNA level monitoring [58, 59]. In the absence of real-time viral load monitoring, >55% of patients in Uganda who received zidovudinelamivudine-abacavir within the Development of Antiretroviral Therapy in Africa study and who had detectable viremia at week 48 had 1–4 TAMs, as well as the M184V mutation [60]. Limited access to antiretroviral drug programs may encourage some infected individuals to share their antiretroviral drugs with others, which may lead to suboptimal dosing. Such undisclosed therapy is likely to be the cause of drug resistance in certain populations entering antiretroviral therapy rollout programs. Pretreatment drug resistant infection was detected in ∼10% of a subset of the Development of Antiretroviral Therapy in Africa study recipients in Uganda and Zimbabwe [61].

Previous SectionNext Section

Mechanisms Of Antiretroviral Drug Resistance

Since the previous guidelines were published [3], several new drugs have been approved, and novel mechanisms of resistance have been elucidated. This section will review some of these advances.

Reverse-Transcriptase Inhibitors

Etravirine. Etravirine (TMC125) is a second-generation NNRTI that exhibits activity against many viruses that are resistant to first-line NNRTIs. Etravirine has favorable safety, pharmacokinetic, and antiviral activity profiles in heavily treatment-experienced HIV-1-infected patients [62–67]. The impact of pretreatment phenotype and genotype on the virologic response to etravirine at week 24 was examined in the DUET-1 and DUET-2 clinical trials [68–71]. Thirteen baseline HIV-1 reverse-transcriptase mutations were associated with resistance to etravirine in the DUET analyses: V90I, A98G, L100I, K101E/P, V106I, V179D/F, Y181C/I/V, and G190S/A [70, 71]; of note, V179T was also identified in a separate US Food and Drug Administration analysis [72]. In the pooled DUET study results, 70% of subjects had 0 or 1 baseline etravirine mutation, whereas 15% had ⩾3 baseline etravirine mutations. Notably, the reverse-transcriptase K103N mutation, which is often seen in virus obtained from patients who experience virologic failure during efavirenz and nevirapine treatment and which confers broad cross-resistance within the first-generation NNRTI class, was not associated with etravirine resistance [72].

Virologic responses were seen in the DUET trials despite the presence of single etravirine mutations [70, 71]. The impact of most of these etravirine mutations depended on the simultaneous presence of Y181C; however, Y181C had an impact only when present with ⩾1 additional mutation [70, 71, 73]. Having a greater number of baseline etravirine-related mutations was associated with a decreasing virologic response to etravirine, particularly when ⩾3 mutations were present [70, 74]. The impact of specific etravirine mutational patterns on clinical virologic responses has not been fully defined. No phenotypic “cutoff” levels for clinical responses to etravirine are currently available.

Antagonism among specific reverse-transcriptase mutations. A potentially relevant and mechanistically interesting antagonism among various thymidine analogue NRTIs, TAMs, and the emergence of the tenofovir-associated K65R mutation has been elucidated [75]. TAMs selected by zidovudine or stavudine counteract the selection of the K65R mutation, although TAMs and the K65R mutation do not appear on the same genome because of competing mutational pathways. Thymidine analogue NRTIs, such as zidovudine, may protect against the emergence of the K65R mutation when combined with tenofovir, leading some clinicians to combine these agents. In contrast to HIV-1 drug resistance patterns described for tenofovir and for the triple-NRTI regimen tenofovir-abacavir-lamivudine, which selects for the K65R mutation more frequently, a quadruple-drug regimen of tenofovir plus zidovudine-abacavirlamivudine in the COL40263 trial selected predominantly for NRTI-associated TAMs in virus from patients in whom therapy had failed [76].

Mutations in the connection and RNase H domains of reverse transcriptase. Mutations in the connection (E312Q, G335C/D, N348I, A360I/V, V365I, T369I, A371V, A376S, and E399D) and RNase H (Q509L) domains of reverse transcriptase are selected by NRTI therapy (in addition to TAMs); these newly recognized mutations, which are located outside of reverse transcriptase regions covered by standard genotype assays, substantially increase resistance to zidovudine when TAMs are also present [77–83]. They can increase cross-resistance to lamivudine, abacavir, and tenofovir (although to a much lesser extent) but do not increase resistance to stavudine or didanosine [77–79]. NNRTIs (mainly nevirapine) are also affected [80, 82].

The Q509L and A371V/Q509L mutations with TAMs impair the formation of RNase H cleavage products, which increases zidovudine-monophosphate excision on RNA/DNA duplexes by reducing template degradation. Q509L and A371V/Q509L also increase the efficiency of excision of short RNA/DNA duplexes [81]. The N348I mutation in the reverse-transcriptase connection domain confers dual zidovudine-nevirapine resistance via 2 interrelated mechanisms [82]. First, N348I decreases the ability of nevirapine to inhibit HIV-1 reverse transcriptase; second, N348I substantially decreases the rate of RNaseHcleavage, which increases zidovudine-monophosphate excision by reducing RNA/DNA template degradation. Furthermore, the ability of nevirapine to stimulate RNase H is substantially reduced, compared with the wild-type enzyme. The N348I and A360V mutations, in combination with TAMs, decrease the efficiency of RNase H cleavage and increase the amount of rescued reaction product after ATP-dependent excision. Mutations N348I and A360V promote reverse-transcriptase dissociation from an RNase H-competent complex, thereby reducing RNA/DNA template degradation [83]. The N348I mutation occurs relatively frequently and can emerge early during therapy with regimens containing zidovudine and nevirapine [82]. The clinical impact of connection and RNase H domain reverse-transcriptase mutations on virologic response has not been determined.

PIs

Improved understanding of the importance of drug exposure in PI activity and resistance has led to widespread use of lowdose ritonavir boosting to increase drug levels, resulting in more effective competition with viral substrates and reduced impact of single mutations on drug activity. Large cohort data confirm superior virologic suppression with ritonavir-boosted PI-containing regimens, compared with unboosted PI-containing regimens, in drug-naive patients [50]. The genetic barrier to resistance (i.e., the number of mutations required for resistance to develop combined with difficulty in their selection) is generally greater for ritonavir-boosted PI-containing regimens than it is for unboosted PI-containing regimens. Resistance to ritonavir-boosted PIs requires multiple mutations that vary among PIs, and the degree of resistance depends on the number, as well as the type, of mutations present [50, 73, 84, 85]. The large number of mutations required for resistance makes the selection of resistance to boosted PI regimens uncommon, compared with the selection of resistance to NNRTI-containing regimens, among drug-naive patients who are experiencing a first regimen failure [50, 86]. Recently approved drugs, such as darunavir and tipranavir, have improved virologic activity in patients harboring PI-resistant HIV-1 [87–89].

Studies relating baseline (i.e., pretreatment) PI susceptibility or mutations to virologic outcome have been performed for boosted PI-containing regimens. Predictions of virologic success can be made by measuring fold-change in susceptibility (phenotype) or number and type of mutations (genotype). Genotypic resistance scores and phenotypic clinical cutoff levels derived from virologic outcome data are now available for different boosted PI-containing regimens [87–93]. Among these regimens, the number and codon positions of PI mutations in the resistance scores vary, with partial overlap among different drugs. For example, the resistance scores of tipranavir-ritonavir and darunavir-ritonavir both include mutations I84V and L33F but differ with respect to many other mutations. Therefore, virus harboring multiple PI mutations will show some degree of reduced susceptibility to all boosted PI regimens, and the clinical usefulness of each regimen may vary greatly. Phenotypic resistance testing may be particularly beneficial in this setting. The extent of both resistance and drug exposure affects PI activity. Thus, considering both parameters might improve predictions of viral inhibition. The inhibitory quotient (i.e., the ratio of the measured plasma minimum concentration value divided by the IC50 value or IC90 value) characterizes this relationship. Nevertheless, studies evaluating whether assays for drug exposure add to the effectiveness of assays for resistance have yielded conflicting results [94–99]. Inhibitory quotient values better predict PI activity than do resistance or drug levels alone in some, but not all, clinical studies. No randomized studies have yet shown an overall benefit to using inhibitory quotient calculations for PI dose adjustment in place of standard resistance testing.

In addition to mutations in the protease gene associated with PI resistance, mutations in the gag cleavage site region, especially p7/p1 and p1/p6, can increase the viral fitness of viruses resistant to PIs [100–103]. These mutations typically occur in conjunction with drug resistance mutations in the protease gene [104–106]. Specific patterns of cleavage-site mutations have been described for certain PIs (e.g., A431V with L24I-M46I/LI54V-V82A, I437V with I54V-V82F/T/S, L449V with I54M/L/ S/T/A, and L449F/R452S/P453L with D30N-I84V). In contrast, mutation P453L and the emergence of V82A were negatively correlated [107]. Mutations in the C-terminal region of the viral gag gene (K436E and/or I437T/V), located outside the actual NC/p1 cleavage site, can be selected in vitro in the absence of conventional PI resistance mutations, and they can confer resistance by merely changing the substrate of protease and thereby increasing processivity of the enzyme [108]. These mutations were also present in clinical isolates that had reduced susceptibility to PIs but lacked major protease mutations. Whether these cleavage-site mutations are of clinical relevance and should be included in drug resistance assays remains to be determined.

Entry Inhibitors

HIV-1 entry involves the interaction of gp120 with its primary receptor, CD4, followed by binding to 1 of 2 chemokine receptors (chemokine receptor 5 [CCR5] or CXC chemokine receptor 4 [CXCR4]) that serve as coreceptors [109]. Engagement of the coreceptor triggers the assembly of the 2 heptad repeats (HR-1 and HR-2) in the trimeric gp41 into a 6-helix bundle that leads to the approximation and fusion of the cell and virus membranes [110]. The third variable (V3) loop is the major structural element of gp120 that determines coreceptor recognition and specificity, but regions outside of V3 contribute, as well [111–113].

Enfuvirtide. Enfuvirtide is a synthetic 36-amino acid oligopeptide that inhibits HIV-1 entry by preventing the assembly of HR-1 and HR-2 in the trimeric gp41 into a 6-helix bundle [114]. The drug binds to the trimeric HR-1 complex, thereby inhibiting fusion and blocking virus entry [115]. Resistance to enfuvirtide is mediated by amino acid substitutions within HR-1 at amino acid positions 36–45 of gp41 [116, 117]. The substitutions most frequently associated with resistance to enfuvirtide include G36D/S/V/E, V38A/E/M, Q40H, N42T, and N43D [118–120]. These mutations confer substantially reduced binding of enfuvirtide to HR-1 and a substantial decrease in antiviral activity in vitro [119]. In addition, the N126K and S138A mutations in HR-2 may contribute to reduced susceptibility to enfuvirtide [118]. Viruses carrying enfuvirtide resistance mutations show reduced viral fitness in vitro in the absence of enfuvirtide [121]. Clonal analysis of plasma HIV-1 RNA obtained from patients receiving enfuvirtide in the absence of a fully suppressive antiretroviral regimen showed rapid emergence of enfuvirtide resistance mutations [122]. The earlier emergence of mutants with gp41 substitutions at amino acid positions 36 and 38 suggests that these mutants may have an initial fitness advantage over mutants with substitutions at codons 40 and 43, which tended to emerge later.

Chemokine receptor antagonists. Small-molecule antagonists of the gp120-CCR5 interaction, such as maraviroc and the investigational drug vicriviroc, are allosteric, noncompetitive antagonists that bind to a similar site on CCR5 [123–125]. These drugs are potent inhibitors of HIV-1 [126, 127]. Maraviroc is now approved in the United States for use in treatment-experienced patients with exclusively R5 virus strains. Phase III trials of vicriviroc are under way.

Resistance to CCR5 antagonists selected in vitro is mediated by changes in HIV-1 gp120 that allow the envelope glycoprotein to interact with the drug-bound form of CCR5. A variety of amino acid substitutions associated with maraviroc and vicriviroc resistance have been described throughout the env gene; most involve V3, but their effect on drug susceptibility depends on the env backbone into which they are introduced [128, 129]. Phenotypically, resistance to the CCR5 antagonists manifests not as a classic rightward shift of the IC50 curve but, rather, as a plateau in the maximum achievable suppression of viral replication (figure 2). This plateau, referred to as the percent maximal inhibition, correlates with viral adaptation to use the inhibitor-bound form of CCR5 for entry [129, 130].

Few clinical isolates that are resistant to maraviroc or vicriviroc have been reported [131–133]. Overall, resistance of R5 virus appears to emerge slowly and is associated with mutations in the V3 loop stem (and possibly elsewhere in the env gene), similar to resistance that arises during in vitro passage experiments. The particular mutations observed vary from isolate to isolate. Thus, it is not yet possible to identify resistance to CCR5 antagonists on the basis of specific env mutations. Although no signature mutations associated with maraviroc resistance have yet been identified, changes at positions 13 and 26 in the middle of the V3 loop appear to be important [130, 132, 134]. Likewise, data are insufficient to determine the extent of crossresistance within the class. In clinical trials, virologic failure of CCR5 antagonists has been more frequently attributed to the emergence and outgrowth of CXCR4-using viruses that preexisted as minority populations before the initiation of CCR5 antagonist therapy.

INSTIs

The INSTIs are a new class of antiretroviral drugs that selectively target the HIV-1 integrase enzyme. Integrase catalyzes numerous steps within the cytoplasm and nucleus of host cells that allow proviral DNA to enter the nucleus and integrate into host cellular DNA. This entire process is required for productive HIV-1 replication. The approved drug raltegravir and other candidate drugs that are in development inhibit the strand transfer reaction.

Resistance to integrase inhibitors can emerge during treatment failure. Phenotypic and genotypic assays have been described that detect HIV-1 integrase resistance [135–137], but plasma-based phenotypic and genotypic assays to detect HIV-1 integrase inhibitor resistance are not yet clinically available. As with PI resistance mutations, INSTI resistance mutations are classified as either major or minor. Major mutations tend to be the primary contact residues for drug binding, based on crystal structures, and have an effect on the drug susceptibility phenotype. Major mutations are those selected first in the presence of the drug or those shown at the biochemical or virologic level to lead to an alteration in drug binding or an inhibition of viral activity or viral replication. Minor mutations generally emerge later than major mutations and enhance the degree of resistance or improve replicative fitness of the virus that contains major mutations [73].

Raltegravir is a hydroxypyrimidinone carboxamide derivative of the diketobutanoic acid family. It is approved in the United States and in Europe for use in treatment-experienced HIV-1-infected patients in combination antiretroviral regimens, based on favorable safety, pharmacokinetic, and efficacy parameters [138–143]. In phase II and III studies, virologic failure occurred infrequently and was generally associated with the emergence of resistance mutations [136]. In Merck protocol 005, a phase II study of HIV-1-infected subjects with triple-class drug resistance, genotyping by population sequencing was performed at treatment failure. Phenotyping of patient-derived sequences and site-directed mutants employed single-cycle infection assays on long-terminal repeat-responsive reporter cell lines.

Raltegravir failure was associated with integrase mutations in 2 distinct genetic pathways, defined by ⩾2 mutations, including (1) a signature (major) mutation at either Q148H/K/ R or N155H and (2) ⩾1 minor mutations unique to each pathway. The major mutations all reduced susceptibility to raltegravir and decreased viral replication capacity. Minor mutations described in the Q148H/K/R pathway included L74M, E138A, E138K, or G140S. These minor mutations consistently enhanced the level of resistance to raltegravir and, when combined with major mutations, improved viral replication capacity in a subset of combinations. The most common mutational pattern in this pathway is Q148H plus G140S. This Q148Hplus-G140S pattern exhibited the greatest decrease of drug susceptibility (>100-fold phenotypic resistance) and was the fittest variant seen (i.e., it had the highest replication capacity in both infectivity and multiple-cycle replication assays).

Mutations described in the N155H pathway include this major mutation plus 1 of either L74M, E92Q, T97A, E92Q plus T97A, Y143H, G163K/R, V151I, or D232N [73, 136]. Mutations observed in raltegravir protocols were similar to those selected with different integrase inhibitors in cell culture [144, 145]. The impact of specific raltegravir mutational patterns on clinical virologic responses has not been elucidated fully; thus, no phenotypic cutoff levels for clinical response have been determined for raltegravir. The most important prognostic factor that decreased the likelihood of virologic failure and drug resistance was having a genotypic susceptibility score or phenotypic susceptibility score >0 for optimized background regimen; thus, integrase inhibitors should always be paired with other active agents in an HIV-1 treatment regimen [136].

Previous SectionNext Section

HIV-1 Drug Resistance Assays

There are 2 general types of resistance assays used in clinical practice: genotypic assays (i.e., HIV-1 gene sequencing to detect mutations that confer HIV-1 drug resistance) and phenotypic assays (i.e., cell culture-based viral replication assays in the absence or presence of drugs). Genotypic testing can be performed with commercial assay kits or in-house protocols. Blinded quality assurance programs indicate a very high concordance between kits and in-house methods used [146–148]. The ability to detect drug-resistance mutations, however, can vary substantially among laboratories [146, 149]. This wide variation results from difficulties in recognizing viral mixtures, particularly in heavily treatment-experienced patient populations [147, 150, 151], sequencing specimens with low viral loads, and testing non-subtype B HIV [149]. Performance also relates to the level of experience among laboratory personnel [149], which suggests that appropriate operator training, certification, and periodic proficiency testing are important for accurate genotyping. Resistance testing laboratories, therefore, need to participate in quality assurance programs [146, 147, 149–152].

Despite numerous studies, appropriate interpretation of genotypic and phenotypic drug resistance testing remains challenging. Results of genotypic tests use lists of predefined drug resistance mutations [73] or classifications by computerized, rules-based algorithms to characterize virus as “susceptible,” “possibly resistant,” or “resistant” to each antiretroviral drug [153–156]. The creation of rule-based algorithms is a difficult and lengthy process, and the algorithms require frequent updating. Algorithms vary considerably in the classification of expected drug activity [150, 151, 156–159]. Differences appear to be lowest for lamivudine and NNRTIs and highest for NRTIs and PIs [150, 151]. The most stringent approach to building algorithms is to evaluate the impact of mutational patterns at the initiation of treatment with a specific drug with regard to treatment response (e.g., decrease of plasma HIV-1 RNA levels according to specific genotypic patterns). Considerable progress has been made in identifying mutational baseline patterns that predict clinical failure for specific drugs. These patterns are currently available for the combinations lopinavir-ritonavir, atazanavir-ritonavir, tipranavir-ritonavir, darunavir-ritonavir, and amprenavir-ritonavir, as well as for zidovudine, stavudine, didanosine, lamivudine, tenofovir, efavirenz, nevirapine, and etravirine [70–72, 91–93, 157, 160–168]. The vast majority of genotypic algorithms are based on data that were obtained using subtype B viruses. Although no large differences exist with regard to interpretation of drug resistance in non-subtype B HIV, discrepancies between genotype and phenotype have been observed for abacavir and subtype CRF02_AG, atazanavir and subtype C, and NNRTIs and subtype CRF01_AE [169]. HIV-1 proteases in drug-naive West African patients appear to be generally less sensitive to PIs [170]. More in vivo and in vitro resistance data are clearly needed for non-subtype B HIV.

An alternative approach for the interpretation of genotypic drug resistance information is to correlate genotypic data regarding the plasma HIV-1 RNA of a candidate gene with a large database of paired phenotypes and genotypes [171–174]. Such linkage then permits generation of a “virtual phenotype” by assigning calculated fold-changes in IC50. Although actual and virtual phenotypes show excellent correlation for most drugs, superiority of virtual phenotype over genotype alone could not be demonstrated in predicting clinical response to salvage regimens [173, 175, 176]. Virtual phenotype is an approach to genotype interpretation, and its main limitation is that predictive power depends on the number of matched datasets available. Thus, variation is frequently higher in smaller datasets; consequently, variation is frequently higher for newer drugs. Furthermore, matches are based on preselected codons, not on the entire nucleotide sequence.

Standard phenotypic testing, using recombinant virus assays, is performed by few commercial laboratories. Current assays amplify HIV-1 protease, a part of the HIV-1 reverse transcriptase, as well as the 3′-terminus of gag, as a unit from plasma virus, and they generate a recombinant virus pseudovirus with other HIV-1 genes derived from a laboratory construct [177-181]. A comparison between 2 different assays showed an overall concordance of 86.9%, with the highest concordance for PIs (93.4%) and the lowest concordance for NRTIs (79.8%) [150]. However, even within drug classes, concordance can vary widely among specific drugs (e.g., lamivudine has a very high concordance of 93%, but abacavir has a concordance of only 74% [150]). This recombinant technology is being modified so that it can also test for susceptibility to INSTIs, fusion inhibitors, and chemokine receptor antagonists [136, 182].

The results of phenotypic testing are usually presented as the fold-change in susceptibility of the test sample compared with a laboratory control isolate. The initial “technical” cutoff values, representing the interassay variation of cloned virus controls, did not accurately reflect the inherent variation in susceptibility encountered in circulating viruses from drug-naive patients. The normal distribution of susceptibility to a given drug for wild-type isolates from treatment-naive individuals (i.e., the “biologic” cutoff) was then adopted. Although clinical cutoffs have been defined for many drugs, the relationship between viral susceptibility and drug response is a continuum in which progressively reduced viral phenotypic susceptibility to a particular drug results in progressively blunted reductions in plasma HIV-1 RNA levels. For practical application, 2 different clinical phenotypic cutoff values should be defined: one above which clinical responses perceptibly diminish, compared with those of wild-type virus (“intermediate” resistance), and one above which no clinical response can be expected (“full” resistance). Even partial activity can be useful when treatment options are limited [183]. In evaluation of phenotypic cutoffs, drug-specific susceptibility needs to be compared at baseline, before switching to a new drug regimen, and with the drugspecific treatment response (e.g., decrease in plasma HIV-1 RNA levels) that occurs after initiation of new therapy. Since 2003, clinical trial and cohort data have led to a substantial increase in available clinical cutoff values, and such values are now available for most approved drugs.

In addition to standard genotypic and phenotypic testing used in clinical practice, other resistance testing assays may prove to be useful in the future. The allele-specific PCR assay [184–189] and the single-genome [190] and ultra-deep sequencing [191] assays are currently used to investigate the role of minority variants harboring drug resistance that are present below the level of detection by bulk plasma viral population sequencing approaches. Studies involving treatment-naive and treatment-experienced patients have shown strong associations between the detection of low-frequency drug-resistant variants, particularly those encoding resistance to NNRTIs, and subsequent treatment failure [192–194]. Further studies are required to define the clinically relevant frequency of variants in the virus population. Improvements in assay throughput and reductions in cost are necessary before such assays are available for patient management.

Replication capacity assays, which are designed to measure in vivo fitness of a virus, remain an interesting research tool, but they have not found a role in patient management [195]. A relatively simple and inexpensive alternative for estimating the reduction of in vivo fitness induced by a given nonsuppressive antiretroviral treatment regimen is to determine the differences in plasma HIV-1 RNA levels between pretreatment and on-treatment periods (e.g., for patients lacking fully suppressive treatment options); such information may be useful in optimizing a nonsuppressive treatment regimen [196]. Finally, the cost-effectiveness of using resistance testing assays for treatment-naive patients and for patients for whom antiretroviral treatment has failed has been demonstrated in various countries [197–202].

Previous SectionNext Section

Viral Coreceptor Use Testing

Phenotypic assays to determine coreceptor use (i.e., tropism testing) require the amplification of env sequences from plasma HIV-1 RNA and the construction of viral pseudotypes or infectious recombinant viruses that express the patient-derived env sequences along with a reporter gene [182]. These pseudotyped viruses or viral recombinants are then inoculated onto cells that express CD4 along with CCR5 or CXCR4. The presence of infection is detected by assays that determine reportergene activity. HIV-1 isolates that use CCR5 exclusively are termed R5 viruses, those that use only CXCR4 are termed X4 viruses, and those that use both are termed R5/X4, or dualtropic viruses. Because these assays do not distinguish between the presence of truly dual-tropic viruses and a mixture of R5 and X4 viruses, samples that can infect both CCR5- and CXCR4-expressing cells are often termed dual-mixed viruses. As with commercially available resistance tests, tropism testing generally requires a plasma sample with an HIV-1 level of ⩾1000 copies/mL. The assay used in most clinical trials of CCR5 antagonists can detect the presence of CXCR4-using virus when they constitute at least 5%-10% of the virus population as minor variants [182]. Assays with improved sensitivity for detection of 0.3% CXCR4-using or dual-mixed virus are now available [203].

Genotypic approaches to determining coreceptor use depend on sequencing the V3 loop and applying one of a variety of predictive algorithms. The 2 most commonly used measures for predicting CXCR4 use include (1) the presence of positively charged amino acids at positions 11 and 25, often referred to as the “11/25 rule,” and (2) the total charge of V3 loop amino acid residues of +5 or greater [204, 205]. Bioinformatic approaches include the use of position-specific scoring matrices [206], neural networks [207], or machine-learning techniques [208]. The heteroduplex tracking assay has also been used to detect the presence of CXCR4-using virus [209]. In this assay, the electrophoretic mobility of PCR-amplified env genes is assayed after hybridization to V3-coding sequences from viruses with phenotypically defined coreceptor use. When verified against phenotypic assays, genotypic approaches showed excellent specificity but poor sensitivity for detecting the presence of dual-mixed or CXCR4 viruses in clinical samples [210]. The low sensitivity of these methods is explained, in part, by the extensive heterogeneity of HIV-1 env genes in plasma virus populations, which makes it difficult to obtain coherent sequence data with population-based sequencing approaches. Another contributing factor is that not all determinants of viral tropism reside in the V3 loop. For these reasons, genotypic approaches cannot be recommended at present for identifying patients who may be suitable candidates for CCR5 antagonist therapy. Other limitations of genotyping for coreceptor tropism include the inability of population sequencing to detect variants that comprise <25% of the virus population and the lack of interpretation algorithms for sequences that are not from subtype B.

Drug Resistance in Non-Subtype B HIV

Antiretroviral drug design, resistance research, and interpretation systems have been largely based on HIV-1 subtype B viruses, which have historically been the most prevalent subtype in North America, Western Europe, and Australia. However, subtype B viruses account for only ∼12% of the worldwide HIV-1 infections, with subtype C viruses being the most prevalent, accounting for ∼50% of cases [211]. An increasing number of individuals who are infected with many of the non-subtype B virus strains now receive antiretroviral therapy because of rollout programs in the resource-limited world and because of increasing migration to the developed world, particularly to countries in Europe. It is essential to appreciate how HIV-1 genetic variation alters the characteristics of drug susceptibility and drug resistance. The differences in the natural polymorphisms between HIV-1 subtype B and non-subtype B viruses have been well documented [212, 213]. The effects of viral subtype on resistance are expressed in 2 broad cases: in genetic routes to, and frequency of, specific mutations and in the algorithms developed for interpreting drug resistance.

Genetic routes to and frequency of specific resistance mutations. Viruses from patients infected with subtypes C, G, or CRF_01 AE for whom a first-line nelfinavir-containing regimen is failing preferentially select the L90M mutation, rather than D30N, which more frequently occurs with subtype B virus [214, 215]. This preferential selection of L90M in protease may stem from methionine polymorphism in these non-subtype B viruses at position 89, rather than from lysine in subtype B [216].

Synonymous differences are also responsible for different resistance mutations. For example, valine at position 106 of reverse transcriptase is coded by a GTG codon in subtype C, in contrast with coding by a GTA codon in subtype B. Thus, subtype C viruses more readily select methionine in the presence of efavirenz (a 1-nucleotide change), whereas subtype B viruses change to an alanine (a 1-nucleotide change) [217]. The V106M mutation is responsible for broad cross-resistance to older NNRTIs [218]. A further example is at reverse transcriptase position 210; subtype F viruses require 2 nucleotide substitutions for L210W to emerge, compared with the 1 mutation required by other subtypes. This difference explains the lower prevalence of L210W in cohorts of patients with subtype F infection [219]. An even more intriguing finding is the increased prevalence of K65R in reverse transcriptase from subtype C virus-infected patients receiving tenofovir or didanosine-containing regimens, compared with the prevalence in reverse transcriptase from patients infected with other virus subtypes. Although synonymous differences at reverse transcriptase position 65 exist, these do not change the number of substitutions required for the K65R mutation. One possible explanation for this is that the longer string of adenines in the reverse-transcriptase gene preceding the codon that codes for K65 in subtype C viruses favors increased slippage of the reverse-transcriptase enzyme during transcription, thus encouraging mutations at position 65 [220, 221].

Drug resistance algorithms for non-subtype B HIV. The consensus sequence used for many algorithms, against which changes are identified in sequences from tested patients, is a subtype B sequence. This becomes problematic for subtypes in which some “resistance” mutations actually reflect the wildtype consensus for that subtype. This disparity is particularly apparent for minor protease mutations. For instance, 6 subtypespecific polymorphisms in protease (at positions 10, 20, 33, 36, 82, and 93) occur at sites known to be associated with drug resistance in subtype B viruses. These amino acid substitutions may represent the consensus sequence for more than 1 non-subtype B virus, such as M36I in subtypes A, C, D, F, G, CRF01, and CRF02. Most of the polymorphisms are located outside the active site of the protease [222], such as M36I, which is situated in the hinge region of the enzyme flap. Only in subtype G is a polymorphism present in the active site of the enzyme (V82I).

Several reports have suggested similar in vitro susceptibilities to antiretroviral drugs among different group M subtypes [223, 224]. No differences in response to therapy among these subtypes have been reported [195, 225]. The practical implication of these polymorphisms is that resistance algorithms may overinterpret resistance to specific drugs because of the inclusion of these polymorphisms in the total number of identified resistance mutations. This applies particularly to PIs, for which the total number of mutations is often used to ascribe susceptibility. Therefore, subtype determination should be included in genotypic resistance testing. Because the selection of mutations is incompletely characterized in non-subtype B viruses, it would be beneficial to compare their sequences before treatment and at treatment failure.

Previous SectionNext Section

Clinical Applications and Recommendations

Clinical applications and recommendations are shown in table 2. The essential strategy behind the use of resistance testing for individual patient management is to provide information to assist in the selection of antiretroviral regimens that achieve and maintain virologic suppression—that is, plasma HIV-1 RNA levels below the lower limits of detection of the most sensitive assays available for routine clinical use (50 copies/mL).

Untreated, Established HIV-1 Infection

Because of the prevalence of primary HIV-1 resistance to antiretroviral drugs in developed countries, resistance testing is recommended for all patients at the time of diagnosis of HIV-1 infection as part of the initial, comprehensive assessment [AII]. This recommendation is not restricted to patients for whom initiation of antiretroviral treatment is being considered on the basis of clinical, immunologic, or virologic criteria. The persistence of detectable mutations acquired at the time of HIV-1 infection varies, with some (e.g., the reverse-transcriptase K103N mutation) having the potential to persist for years and others (e.g., the reverse-transcriptase M184V mutation) having a greater potential for reversion [30]. Thus, establishing a drugsusceptibility profile for a patient's virus at the time of diagnosis can be helpful in antiretroviral decision-making years later because of the ability of archived drug-resistant virus to reemerge in cases of suboptimal therapy. Genotypic testing is recommended [AIII] for the initial evaluation, because mutations that may not yet have accumulated enough to affect phenotypic susceptibility can be detected, HIV-1 subtype can be determined, and the cost of genotypic testing is lower than that for phenotypic testing.

Treatment Failure

First or second treatment failure. Because of the high prevalence of infection due to drug-resistant virus among antiretroviral-treated patients with confirmed, detectable plasma virus, drug resistance testing should be performed in all cases of treatment failure [AI] (defined as an insufficient decrease or an increase in plasma HIV-1 RNA level after 1–2 months of treatment or a confirmed viral breakthrough in a patient with previously undetectable virus). However, genotypic and phenotypic resistance assays both have low amplification success rates in specimens with plasma HIV-1 RNA levels <500 copies/mL. Resistance to specific drugs may not always be detected if treatment with the drug has been discontinued before the sample is obtained, because resistant strains are often less fit than wildtype virus and may persist only as undetectable minor subspecies in the absence of drug pressure. Therefore, a specimen for resistance testing should be obtained before treatment has been discontinued or changed, and if treatment has been discontinued, the specimen should be obtained as quickly as possible thereafter. In addition, antiretroviral treatment history is important, along with resistance data, in choosing the subsequent treatment regimen, especially if the treatment has been discontinued for several weeks.

Resistance is not the only cause of treatment failure. Insufficient drug exposure, often resulting from incomplete adherence to therapy, is the most common reason for failure of an initial treatment regimen. However, continuing therapy in the context of treatment failure will often lead to the emergence and accumulation of additional resistance mutations. This applies particularly to drugs with low genetic barriers to resistance (e.g., the NNRTIs, lamivudine, emtricitabine, enfuvirtide, and raltegravir). Clinical resistance to ritonavir-boosted PIs often emerges at a slower pace, requiring several mutations. Therefore, rapid evaluation and prompt action should occur at the time of treatment failure. For first and second treatment failures, genotypic resistance testing usually suffices, unless the patient had initially acquired multidrug-resistant virus. Current commercially available resistance assays report results for NRTIs, NNRTIs, and PIs. Viral tropism assays that determine whether a patient's virus population is predominantly R5, X4, or dual-mixed are used to indicate whether CCR5 antagonists (e.g., maraviroc) may be an appropriate choice for patients with treatment failure. Tropism determination may be useful in this circumstance [BIII]. It is expected that susceptibility testing for INSTIs (e.g., raltegravir) will be clinically available in the near future.

Multiple treatment failures (advanced treatment failure). In patients who experience multiple treatment failures, the virus is often resistant to NRTIs, NNRTIs, and PIs. Until recently, constructing potent alternative regimens that combined 2 or 3 fully active drugs was often impossible, despite the use of enfuvirtide, because of the high level of intraclass cross-resistance. The increased availability of new drugs—including drugs from existing classes but with low levels of intraclass cross-resistance, such as darunavir and etravirine, and drugs from new classes, such as maraviroc and raltegravir—have made the goal of attaining undetectable viral loads more realistic for patients with numerous treatment failures [9]. In this situation, tropism determination is recommended [AI], and phenotypic testing may be a useful addition to genotypic testing, because the number of mutations and the complexity of mutational interactions may make genotypic interpretation challenging. However, phenotypic testing may not be available because of cost. Genotypic testing is recommended for patients with multiple treatment failures [AI], along with phenotypic testing, if available [AI]. The decision to change therapy and the selection of the new regimen should be discussed with experts who are knowledgeable in antiretroviral therapy, antiretroviral pharmacology, and resistance patterns.

Special Circumstances

Acute and early phase HIV-1 infection. Genotypic resistance testing is recommended for any patient who presents within several months after HIV-1 infection because of the high reported rates of transmitted drug resistance [AII]. If immediate antiretroviral intervention is indicated, the initiation of treatment should not be delayed until this result is available, because the turnaround time may be ⩾2 weeks; rather, treatment should be modified if the result demonstrates resistance to ⩾1 component of the regimen. The initial choice of treatment should also take into account the treatment history of the source patient and resistance data for that patient's virus, if available. If treatment is not initiated during the acute or early phases of infection, the resistance test results will still be helpful in the future, because early testing provides the best opportunity to detect transmitted drug resistant virus that has been archived and replaced by more-susceptible virus but which could emerge later during treatment.

Pregnancy. Genotypic resistance testing is recommended for all HIV-1-infected pregnant women with detectable plasma virus, both for their own health and for the health of their infants [AII]. This information will assist with treatment choices for the mother, as well as with choices to prevent mother-to-child HIV-1 transmission (including multidrug-resistant HIV-1 transmission) by selecting a regimen that will be effective and safe for the fetus.

Postexposure prophylaxis. The question of whether to initiate postexposure prophylaxis occurs primarily in 2 settings: accidental exposure of a health care worker and high-risk sexual exposure. In the former circumstance, drug resistance data from the source may be useful in constructing a prophylactic regimen. In the case of high-risk sexual exposure, data on the source are usually not available.

Previous SectionNext Section

Summary and Future Directions

Antiretroviral drug resistance is present wherever antiretroviral drugs are widely used, and as treatment rollout continues in developing countries, the range of resistance will expand. The incidence, prevalence, and transmission of drug-resistant viruses vary from country to country, and programs for worldwide drug resistance surveillance should increase rapidly to meet the emerging need. In developing countries, methods to simplify specimen collection, storage, and testing should be explored to facilitate better monitoring of individual patients and community patterns.

The approval of several new antiretroviral drugs from different classes since our previous recommendations [3] has increased the complexity of resistance testing and interpretation in the developed world. Resistance testing for entry inhibitors and INSTIs should be incorporated into routine management when such testing becomes available and validated.

Techniques for resistance testing have become more standardized since our previous recommendations and have been widely incorporated into routine management in the developed world. Coreceptor tropism testing has also become available. Future efforts should be made to increase the sensitivity of these assays to better detect minor variants that may be of clinical significance. Moreover, as resistance testing becomes increasingly employed in developing countries, attention should be given to detecting resistance patterns for non-subtype B strains of HIV and to establishing algorithms for evaluating their importance.

Previous SectionNext Section

Figures and Tables

Figure 1.
View larger version:
Figure 1.

Mutations in HIV-1 that affect susceptibility to antiretroviral drugs, by HIV gene target. The letter above each position is the wild-type amino acid and the letter(s) below each position indicate the substitution(s) that are associated with drug resistance. Reprinted with permission from [6]. Detailed user notes and regular updates are available at the International AIDS Society-USA Web site [7]. FDA, Food and Drug Administration.

Figure 2.
View larger version:
Figure 2.

The left panel shows a typical inhibition curve of a susceptible virus (solid line) with a typical competitive inhibitor (e.g., a protease inhibitor). The IC50 value of the resistant virus (dotted line) is shifted to the right (arrow). The right panel shows an example of a noncompetitive inhibitor (e.g., a chemokine receptor 5 antagonist). The susceptible virus (solid line) shows a typical inhibition curve, but in this case, the resistant virus (dotted line) reaches a plateau. The maximum achievable percent inhibition is shifted downward (arrow), but the curve does not shift to the right; hence, the IC50 value remains unchanged.

Table 1.
View larger version:
Table 1.

Strength of recommendation and quality of evidence rating scale.

Table 2.
View larger version:
Table 2.

Summary of clinical situations in which resistance testing is recommended.

Previous SectionNext Section

Acknowledgments

We thank Michelle Tayag and Ann McGuire for administrative and editorial support in preparing the manuscript.

Financial support. The International AIDS Society-USA.

Potential conflicts of interest. F.B.-V. has received grants and research support from GlaxoSmithKline and Tibotec, has served as a consultant to Merck and Tibotec, and has served as a paid lecturer for Bristol-Myers Squibb, GlaxoSmithKline, and Tibotec. S.M.H. has received grants and research support from Merck and has served as a scientific advisor to Boehringer Ingelheim Pharmaceuticals, Progenics, Pfizer, and Tibotec-Virco and has served on a database-monitoring board for Bristol-Myers Squibb. D.R.K. has served as a consultant to and has received honoraria from Abbott Laboratories, Avexa, Boehringer Ingelheim Pharmaceuticals, Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Human Genome Sciences, Idenix, Merck, Monogram Biosciences, Pfizer, Roche, Schering-Plough, Siemens, and Trimeris and has received research grant support from GlaxoSmithKline, Human Genome Sciences, Merck, and Schering-Plough. J.W.M. serves as a consultant to Gilead Sciences, Merck, Idenix Pharmaceutical, and Panacos and owns stock in Pharmasset. P.G.Y. has received grants and research support from Bristol-Myers Squibb, GlaxoSmithKline, Merck, Roche, and Tibotec and has served as a consultant to Bristol-Myers Squibb, Gilead, GlaxoSmithKline, Merck, and Tibotec. B.C. has served on scientific and marketing advisory boards and has received honoraria for lectures from Abbott Laboratories, Boehringer Ingelheim Pharmaceuticals, Merck Sharp & Dohme, Bristol-Myers Squibb, Gilead Sciences, Glaxo-SmithKline, Panacos Pharmaceuticals, Pfizer, Roche Pharmaceuticals, and Tibotec Therapeutics. H.F.G. has served as a scientific and medical advisor for Abbott Laboratories, Bristol-Myers Squibb, Boehringer Ingelheim Pharmaceuticals, GlaxoSmithKline, Novartis Pharmaceuticals, and Tibotec Therapeutics and has received unrestricted research and travel grants from Abbott Laboratories, Boehringer Ingelheim Pharmaceuticals, Bristol-Myers Squibb, Gilead Sciences, Merck Sharp & Dohme, and Roche Pharmaceuticals. M.S.H. has served on data safety monitoring boards for Merck and TaiMed Biologics. V.A.J. has received grant and research support from Agouron Pharmaceuticals, Bristol-Myers Squibb, GlaxoSmithKline, Monogram Biosciences, and Visible Genetics (later Bayer, now Siemens Medical Solutions Diagnostics); has served on the speaker's bureaus or received honoraria from Abbott Laboratories, GlaxoSmithKline, and Monogram Biosciences; and has served on medical or clinical advisory boards of Bristol-Myers Squibb, GlaxoSmithKline, Monogram Biosciences, and Virco Lab. D.D.R. has served as a consultant to Anadys Pharmaceuticals, Biota, Bristol-Myers Squibb, Boehringer Ingelheim Pharmaceuticals, Gilead Sciences, Idenix, Koronis Pharmaceuticals, Merck, Monogram Biosciences, Pfizer, Roche Pharmaceuticals, and Tobira Therapeutics. J.M.S. has served as a consultant, advisor, or speaker for Abbott Laboratories, Ambrilia Biopharma, Bristol-Myers Squibb, Boehringer Ingelheim Pharmaceuticals, Gilead Sciences, GlaxoSmithKline, Merck, Monogram Biosciences, Pfizer, Roche Pharmaceuticals, Siemens, Tibotec Therapeutics, Virco Lab, and Virology Education and has received research support from Glaxo-SmithKline, Monogram Biosciences, Roche Pharmaceuticals, and Tibotec Therapeutics. P.G.Y. has served as a scientific advisor for Abbott, Boehringer Ingelheim, Gilead Sciences, Merck, Pfizer, and Tibotec Therapeutics and has received grants and research support from Abbott, Roche Pharmaceuticals, Tibotec Therapeutics, GlaxoSmithKline, Bristol-Myers Squibb, and Merck. D.M.J.: no conflicts.

  • Received March 21, 2008.
  • Accepted March 28, 2008.
Previous Section
 

References

  1. 1.↵
    1. Hirsch MS,
    2. Conway B,
    3. D'Aquila RT,
    4. et al
    . Antiretroviral drug resistance testing in adults with HIV infection: implications for clinical management. International AIDS Society-USA Panel. JAMA 1998;279:1984-91.
    Abstract/FREE Full Text
  2. 2.
    1. Hirsch MS,
    2. Brun-Vézinet F,
    3. D'Aquila RT,
    4. et al
    . Antiretroviral drug resistance testing in adult HIV-1 infection: recommendations of an International AIDS Society-USA Panel. JAMA 2000;283:2417-26. (updated guidelines are in press).
    Abstract/FREE Full Text
  3. 3.↵
    1. Hirsch MS,
    2. Brun-Vézinet F,
    3. Clotet B,
    4. et al
    . Antiretroviral drug resistance testing in adults infected with human immunodeficiency virus type I: 2003 recommendations of an International AIDS Society-USA panel. Clin Infect Dis 2003;37:113-28.
    Abstract/FREE Full Text
  4. 4.↵
    1. Booth CL,
    2. Geretti AM
    . Prevalence and determinants of transmitted antiretroviral drug resistance in HIV-1 infection. J Antimicrob Chemother 2007;59:1047-56.
    Abstract/FREE Full Text
  5. 5.↵
    1. Arrive E,
    2. Newell ML,
    3. Ekouevi DK,
    4. et al
    . Prevalence of resistance to nevirapine in mothers and children after single-dose exposure to prevent vertical transmission of HIV-1: a meta-analysis. Int J Epidemiol 2007;36:1009-21.
    Abstract/FREE Full Text
  6. 6.↵
    1. Johnson VA,
    2. Brun-Vézinet F,
    3. Clotet B,
    4. et al
    . Update of the drug resistance mutations in HIV-1: 2008. Top HIV Med 2008;16:62-8.
    Medline
  7. 7.↵
    International AIDS Society-USA Web page. [Accessed 4 June 2008]. Available at: http://www.iasusa.org.
  8. 8.↵
    1. Tural C,
    2. Ruiz L,
    3. Holtzer C,
    4. et al
    . Clinical utility of HIV-1 genotyping and expert advice: the Havana trial. AIDS 2002;16:209-18.
    CrossRefMedlineWeb of Science
  9. 9.↵
    1. Hammer SM,
    2. Saag MS,
    3. Schechter M,
    4. et al
    . Treatment for adult HIV infection: 2006 recommendations of the International AIDS Society-USA panel. JAMA 2006;296:827-43.
    Abstract/FREE Full Text
  10. 10.↵
    1. Mosca L,
    2. Appel LJ,
    3. Benjamin EJ,
    4. et al
    . Evidence-based guidelines for cardiovascular disease prevention in women. Circulation 2004;109:672-93.
    FREE Full Text
  11. 11.↵
    1. Strader DB,
    2. Wright T,
    3. Thomas DL,
    4. Seeff LB
    . American Association for the Study of Liver Diseases. Diagnosis, management, and treatment of hepatitis C. Hepatology 2004;39:1147-71.
    CrossRefMedlineWeb of Science
  12. 12.↵
    National Institutes of Health. Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) Express. [Accessed 3 February 2006]. Available at: http://www.nhlbi.nih.gov/guidelines/hypertension/express.pdf.
  13. 13.↵
    1. Gupta SK,
    2. Eustace JA,
    3. Winston JA,
    4. et al
    . Guidelines for the management of chronic kidney disease in HIV-infected patients: recommendations of the HIV Medicine Association of the Infectious Diseases Society of America. Clin Infect Dis 2005;40:1559-85.
    FREE Full Text
  14. 14.↵
    1. Brenner B,
    2. Wainberg MA,
    3. Salomon H,
    4. et al
    . Resistance to antiretroviral drugs in patients with primary HIV-1 infection. Investigators of the Quebec Primary Infection Study. Int J Antimicrob Agents 2000;16:429-34.
    CrossRefMedlineWeb of Science
  15. 15.↵
    1. Chaix ML,
    2. Descamps D,
    3. Harzic M,
    4. et al
    . Stable prevalence of genotypic drug resistance mutations but increase in non-B virus among patients with primary HIV-1 infection in France. AIDS 2003;17:2635-43.
    CrossRefMedlineWeb of Science
  16. 16.↵
    1. Descamps D,
    2. Chaix ML,
    3. Andre P,
    4. et al
    . French national sentinel survey of antiretroviral drug resistance in patients with HIV-1 primary infection and in antiretroviral-naive chronically infected patients in 2001–2002. J Acquir Immune Defic Syndr 2005;38:545-52.
    CrossRefMedlineWeb of Science
  17. 17.
    1. Grant RM,
    2. Kuritzkes DR,
    3. Johnson VA,
    4. et al
    . Accuracy of the TRUGENE HIV-1 genotyping kit. J Clin Microbiol 2003;41:1586-93.
    Abstract/FREE Full Text
  18. 18.↵
    1. Little SJ,
    2. Holte S,
    3. Routy JP,
    4. et al
    . Antiretroviral-drug resistance among patients recently infected with HIV. N Engl J Med 2002;347:385-94.
    CrossRefMedlineWeb of Science
  19. 19.
    1. Masquelier B,
    2. Bhaskaran K,
    3. Pillay D,
    4. et al
    . Prevalence of transmitted HIV-1 drug resistance and the role of resistance algorithms: data from seroconverters in the CASCADE collaboration from 1987 to 2003. J Acquir Immune Defic Syndr 2005;40:505-11.
    CrossRefMedlineWeb of Science
  20. 20.↵
    1. Wensing AM,
    2. van de Vijver DA,
    3. Angarano G,
    4. et al
    . Prevalence of drug-resistant HIV-1 variants in untreated individuals in Europe: implications for clinical management. J Infect Dis 2005;192:958-66.
    Abstract/FREE Full Text
  21. 21.↵
    1. Yerly S,
    2. Von Wyl V,
    3. Ledergerber B,
    4. et al
    . Transmission of HIV-1 drug resistance in Switzerland: a 10-year molecular epidemiology survey. AIDS 2007;21:2223-9.
    MedlineWeb of Science
  22. 22.↵
    1. Yerly S,
    2. Kaiser L,
    3. Race E,
    4. Bru JP,
    5. Clavel F,
    6. Perrin L
    . Transmission of antiretroviral-drug-resistant HIV-1 variants. Lancet 1999;354:729-33.
    CrossRefMedlineWeb of Science
  23. 23.↵
    1. Grant RM,
    2. Hecht FM,
    3. Warmerdam M,
    4. et al
    . Time trends in primary HIV-1 drug resistance among recently infected persons. JAMA 2002;288:181-8.
    Abstract/FREE Full Text
  24. 24.↵
    1. Violin M,
    2. Cozzi-Lepri A,
    3. Velleca R,
    4. et al
    . Risk of failure in patients with 215 HIV-1 revertants starting their first thymidine analog-containing highly active antiretroviral therapy. AIDS 2004;18:227-35.
    CrossRefMedlineWeb of Science
  25. 25.↵
    1. Kuritzkes DR,
    2. Lalama CM,
    3. Ribaudo HJ,
    4. et al
    . Preexisting resistance to nonnucleoside reverse-transcriptase inhibitors predicts virologic failure of an efavirenz-based regimen in treatment-naive HIV-1-infected subjects. J Infect Dis 2008;197:867-70.
    Abstract/FREE Full Text
  26. 26.↵
    1. Barbour JD,
    2. Hecht FM,
    3. Wrin T,
    4. et al
    . Persistence of primary drug resistance among recently HIV-1 infected adults. AIDS 2004;18:1683-9.
    CrossRefMedlineWeb of Science
  27. 27.
    1. Delaugerre C,
    2. Marcelin AG,
    3. Soulie C,
    4. et al
    . Transmission of multidrug-resistant HIV-1: 5 years of immunological and virological survey. AIDS 2007;21:1365-7.
    CrossRefMedlineWeb of Science
  28. 28.
    1. Smith DM,
    2. Wong JK,
    3. Shao H,
    4. et al
    . Long-term persistence of transmitted HIV drug resistance in male genital tract secretions: implications for secondary transmission. J Infect Dis 2007;196:356-60.
    Abstract/FREE Full Text
  29. 29.↵
    1. Yerly S,
    2. Rakik A,
    3. De Loes SK,
    4. et al
    . Switch to unusual amino acids at codon 215 of the human immunodeficiency virus type 1 reverse transcriptase gene in seroconvertors infected with zidovudine-resistant variants. J Virol 1998;72:3520-3.
    Abstract/FREE Full Text
  30. 30.↵
    1. Little SJ,
    2. Frost SD,
    3. Wong JK,
    4. et al
    . The persistence of transmitted drug resistance among subjects with primary HIV infection. J Virol 2008;82:5510-8.
    Abstract/FREE Full Text
  31. 31.↵
    1. Strain MC,
    2. Gunthard HF,
    3. Havlir DV,
    4. et al
    . Heterogeneous clearance rates of long-lived lymphocytes infected with HIV: intrinsic stability predicts lifelong persistence. Proc Natl Acad Sci USA 2003;100:4819-24.
    Abstract/FREE Full Text
  32. 32.
    1. Deeks SG,
    2. Wrin T,
    3. Liegler T,
    4. et al
    . Virologic and immunologic consequences of discontinuing combination antiretroviral-drug therapy in HIV-infected patients with detectable viremia. N Engl J Med 2001;344:472-80.
    CrossRefMedlineWeb of Science
  33. 33.
    1. Wirden M,
    2. Delaugerre C,
    3. Marcelin AG,
    4. et al
    . Comparison of the dynamics of resistance-associated mutations to nucleoside reverse transcriptase inhibitors, nonnucleoside reverse transcriptase inhibitors, and protease inhibitors after cessation of antiretroviral combination therapy. Antimicrob Agents Chemother 2004;48:644-7.
    Abstract/FREE Full Text
  34. 34.↵
    1. Lawrence J,
    2. Mayers DL,
    3. Hullsiek KH,
    4. et al
    . Structured treatment interruption in patients with multidrug-resistant human immunodeficiency virus. N Engl J Med 2003;349:837-46.
    CrossRefMedlineWeb of Science
  35. 35.↵
    1. Cane P,
    2. Chrystie I,
    3. Dunn D,
    4. et al
    . Time trends in primary resistance to HIV drugs in the United Kingdom: multicentre observational study. BMJ 2005;331:1368.
    Abstract/FREE Full Text
  36. 36.↵
    1. Shet A,
    2. Berry L,
    3. Mohri H,
    4. et al
    . Tracking the prevalence of transmitted antiretroviral drug-resistant HIV-1: a decade of experience. J Acquir Immune Defic Syndr 2006;41:439-46.
    CrossRefMedlineWeb of Science
  37. 37.↵
    1. Violin M,
    2. Velleca R,
    3. Cozzi-Lepri A,
    4. et al
    . Prevalence of HIV-1 primary drug resistance in seroconverters of the ICoNA cohort over the period 1996–2001. J Acquir Immune Defic Syndr 2004;36:761-4.
    CrossRefMedlineWeb of Science
  38. 38.↵
    1. Shafer RW,
    2. Rhee SY,
    3. Pillay D,
    4. et al
    . HIV-1 protease and reverse transcriptase mutations for drug resistance surveillance. AIDS 2007;21:215-23.
    MedlineWeb of Science
  39. 39.↵
    1. Liu L,
    2. May S,
    3. Richman DD,
    4. et al
    . Comparison of algorithms that interpret genotypic HIV-1 drug resistance to determine the prevalence of transmitted drug resistance. AIDS 2008;22:835.
    CrossRefMedlineWeb of Science
  40. 40.↵
    1. Truong HM,
    2. Grant RM,
    3. McFarland W,
    4. et al
    . Routine surveillance for the detection of acute and recent HIV infections and transmission of antiretroviral resistance. AIDS 2006;20:2193-7.
    MedlineWeb of Science
  41. 41.↵
    1. Pillay D,
    2. Bhaskaran K,
    3. Jurriaans S,
    4. et al
    . The impact of transmitted drug resistance on the natural history of HIV infection and response to first-line therapy. AIDS 2006;20:21-28.
    MedlineWeb of Science
  42. 42.↵
    1. Costagliola D,
    2. Descamps D,
    3. Assoumou L,
    4. et al
    . Prevalence of HIV-1 drug resistance in treated patients: a French nationwide study. J Acquir Immune Defic Syndr 2007;46:12-8.
    MedlineWeb of Science
  43. 43.
    1. de Mendoza C,
    2. Garrido C,
    3. Corral A,
    4. et al
    . Changing rates and patterns of drug resistance mutations in antiretroviral-experienced HIV-infected patients. AIDS Res Hum Retroviruses 2007;23:879-85.
    CrossRefMedlineWeb of Science
  44. 44.
    1. Pillay D,
    2. Green H,
    3. Matthias R,
    4. et al
    . Estimating HIV-1 drug resistance in antiretroviral-treated individuals in the United Kingdom. J Infect Dis 2005;192:967-73.
    CrossRefMedlineWeb of Science
  45. 45.
    1. Scott P,
    2. Arnold E,
    3. Evans B,
    4. et al
    . Surveillance of HIV antiretroviral drug resistance in treated individuals in England: 1998–2000. J Antimicrob Chemother 2004;53:469-73.
    Abstract/FREE Full Text
  46. 46.
    1. Tozzi V,
    2. Zaccarelli M,
    3. Bonfigli S,
    4. et al
    . Drug-class-wide resistance to antiretrovirals in HIV-infected patients failing therapy: prevalence, risk factors and virological outcome. Antivir Ther 2006;11:553-60.
    MedlineWeb of Science
  47. 47.↵
    1. Richman DD,
    2. Bozzette S,
    3. Morton S,
    4. et al
    . The prevalence of antiretroviral drug resistance in the United States. AIDS 2004;18:1393-401.
    CrossRefMedlineWeb of Science
  48. 48.↵
    1. Harrigan PR,
    2. Wynhoven B,
    3. Brumme ZL,
    4. et al
    . HIV-1 drug resistance: degree of underestimation by a cross-sectional versus a longitudinal testing approach. J Infect Dis 2005;191:1325-30.
    Abstract/FREE Full Text
  49. 49.↵
    1. von Wyl V,
    2. Yerly S,
    3. Boni J,
    4. et al
    . The proportion of individuals without further treatment options has stabilized at low levels in the Swiss HIV Cohort Study. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 896].
  50. 50.↵
    1. von Wyl V,
    2. Yerly S,
    3. Boni J,
    4. et al
    . Emergence of HIV-1 drug resistance in previously untreated patients initiating combination antiretroviral treatment: a comparison of different regimen types. Arch Intern Med 2007;167:1782-90.
    Abstract/FREE Full Text
  51. 51.↵
    1. Toni T,
    2. Masquelier B,
    3. Minga A,
    4. et al
    . HIV-1 antiretroviral drug resistance in recently infected patients in Abidjan, Côte d'Ivoire: a 4-year survey, 2002–2006. AIDS Res Hum Retroviruses 2007;23:1155-60.
    CrossRefMedlineWeb of Science
  52. 52.↵
    World Health Organization (WHO). WHO consultation on technical and operational recommendations for scale-up of laboratory services and monitoring HIV antiretroviral therapy in resource-limited settings. [Accessed 9 January 2007.]. Available at: http://www.who.int/hiv/pub/meetingreports/scaleup/en/index.html.
  53. 53.↵
    1. Lockman S,
    2. McIntyre JA
    . Reduction of HIV-1 drug resistance after intrapartum single-dose nevirapine. Lancet 2007;370:1668-70.
    CrossRefMedlineWeb of Science
  54. 54.↵
    1. Lockman S,
    2. Shapiro RL,
    3. Smeaton LM,
    4. et al
    . Response to antiretroviral therapy after a single, peripartum dose of nevirapine. N Engl J Med 2007;356:135-47.
    CrossRefMedlineWeb of Science
  55. 55.↵
    1. Coffie PA,
    2. Ekouevi DK,
    3. Chaix ML,
    4. et al
    . Maternal 12-month response to antiretroviral therapy following prevention of mother-to-child transmission of HIV type 1, Ivory Coast, 2003–2006. Clin Infect Dis 2008;46:611-21.
    Abstract/FREE Full Text
  56. 56.↵
    1. Chi BH,
    2. Sinkala M,
    3. Mbewe F,
    4. et al
    . Single-dose tenofovir and emtricitabine for reduction of viral resistance to non-nucleoside reverse transcriptase inhibitor drugs in women given intrapartum nevirapine for perinatal HIV prevention: an open-label randomised trial. Lancet 2007;370:1698-705.
    CrossRefMedlineWeb of Science
  57. 57.↵
    1. Sungkanuparph S,
    2. Manosuthi W,
    3. Kiertiburanakul S,
    4. Piyavong B,
    5. Chumpathat N,
    6. Chantratita W
    . Options for a second-line antiretroviral regimen for HIV type 1-infected patients whose initial regimen of a fixed-dose combination of stavudine, lamivudine, and nevirapine fails. Clin Infect Dis 2007;44:447-52.
    Abstract/FREE Full Text
  58. 58.↵
    1. Kamya MR,
    2. Mayanja-Kizza H,
    3. Kambugu A,
    4. et al
    . Predictors of longterm viral failure among Ugandan children and adults treated with antiretroviral therapy. J Acquir Immune Defic Syndr 2007;46:187-93.
    CrossRefMedlineWeb of Science
  59. 59.↵
    1. Marcelin AG,
    2. Jarrousse B,
    3. Derache A,
    4. et al
    . HIV drug resistance after the use of generic fixed-dose combination stavudine/lamivudine/nevirapine as standard first-line regimen. AIDS 2007;21:2341-3.
    CrossRefMedlineWeb of Science
  60. 60.↵
    1. Ndenbi N,
    2. Pillay D,
    3. Goodall R,
    4. et al
    . Differences in the dynamics of viral rebound and evolution of resistance between CBV/NVP and CBV/ABC uncovered in the absence of viral load monitoring real time: NORA substudy of the DART. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 889].
  61. 61.↵
    DART Virology Group and Trial Team. Virological response to a triple nucleoside/nucleotide analogue regimen over 48 weeks in HIV-1-infected adults in Africa. AIDS 2006;20:1391-9.
    Medline
  62. 62.↵
    1. Sankatsing SU,
    2. Weverling GJ,
    3. Peeters M,
    4. et al
    . TMC125 exerts similar initial antiviral potency as a five-drug/triple class antiretroviral regimen. AIDS 2003;17:2623-7.
    CrossRefMedlineWeb of Science
  63. 63.
    1. Kakuda T,
    2. Wade J,
    3. Snoeck E,
    4. et al
    . Pharmacokinetics and pharmacodynamics of the NNRTI TMC125 in treatment-experienced HIV-1-infected patients: pooled 24-week results of DUET-1 and DUET-2. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 762].
  64. 64.
    1. Haubrich R,
    2. Cahn P,
    3. Grinsztein B,
    4. et al
    . DUET-1: week-48 results of a phase III randomized double-blind trial to evaluate the efficacy and safety of TMC125 vs placebo in 612 treatment-experienced HIV-1-infected patients. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 790].
  65. 65.
    1. Johnson M,
    2. Campbell T,
    3. Clotet B,
    4. et al
    . DUET-2: 48-week results of a phase III randomized double-blind trial to evaluate the efficacy and safety of TMC125 vs placebo in 591 treatment-experienced HIV-1-infected patients. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 791].
  66. 66.
    1. Sungkanuparph S,
    2. Manosuthi W,
    3. Kiertiburanakul S,
    4. Piyavong B,
    5. Chantratita W
    . Evaluating the role of etravirine in the second-line ART after failing an initial NNRTI-based regimens in a resourcelimited setting. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 865].
  67. 67.↵
    1. Taiwo B,
    2. Chaplin B,
    3. Stanton J
    . Etravirine-resistance mutations in patients with virologic failure on nevirapine or efavirenz-based HAART. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 867].
  68. 68.↵
    1. Lazzarin A,
    2. Campbell T,
    3. Clotet B,
    4. et al
    . Efficacy and safety of TMC125 (etravirine) in treatment-experienced HIV-1-infected patients in DUET-2: 24-week results from a randomised, double-blind, placebocontrolled trial. Lancet 2007;370:39-48.
    CrossRefMedlineWeb of Science
  69. 69.
    1. Madruga JV,
    2. Cahn P,
    3. Grinsztejn B,
    4. et al
    . Efficacy and safety of TMC125 (etravirine) in treatment-experienced HIV-1-infected patients in DUET-1: 24-week results from a randomised, double-blind, placebocontrolled trial. Lancet 2007;370:29-38.
    CrossRefMedlineWeb of Science
  70. 70.↵
    1. Vingerhoets J,
    2. Buelens A,
    3. Peeters M,
    4. et al
    . Impact of baseline NNRTI mutations on the virological response to TMC125 in the phase III clinical trials DUET-1 and DUET-2. Antivir Ther 2007;12:S34.
    Web of Science
  71. 71.↵
    1. Cahn P,
    2. Haubrich R,
    3. Leider J,
    4. et al
    . Pooled 24-week results of DUET-1 and DUET-2: TMC125 (etravirine; ETR) versus placebo in treatment-experienced HIV-1-infected patients. Program and abstracts of the 47th Interscience Conference on Antimicrobial Agents and Chemotherapy; Chicago, IL. 2007. [abstract H-717].
  72. 72.↵
    Tibotec. INTELENCE (etravirine) tablets. Raritan, NJ: Tibotec; 2008. [package insert].
  73. 73.↵
    1. Johnson VA,
    2. Brun-Vézinet F,
    3. Clotet B,
    4. et al
    . Update of the drug resistance mutations in HIV-1: 2007. Top HIV Med 2007;15:119-25.
    Medline
  74. 74.↵
    1. Picchio G,
    2. Vingerhoets J,
    3. Staes M,
    4. et al
    . Prevalence of TMC125 resistance-associated mutations in a large panel of clinical isolates. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 866].
  75. 75.↵
    1. vonWyl V,
    2. Yerly S,
    3. Boni J,
    4. et al
    . Factors associated with the emergence of K65R in patients with HIV-1 infection treated with combination antiretroviral therapy containing tenofovir. Clin Infect Dis 2008;46:1299-309.
    Abstract/FREE Full Text
  76. 76.↵
    1. Elion R,
    2. Cohen C,
    3. Dejesus E,
    4. et al
    . Once-daily abacavir/lamivudine/zidovudine plus tenofovir for the treatment of HIV-1 infection in antiretroviral-naive subjects: a 48-week pilot study. HIV Clin Trials 2006;7:324-33.
    CrossRefMedlineWeb of Science
  77. 77.↵
    1. Brehm JH,
    2. Koontz D,
    3. Meteer JD,
    4. Pathak V,
    5. Sluis-Cremer N,
    6. Mellors JW
    . Selection of mutations in the connection and RNase H domains of human immunodeficiency virus type 1 reverse transcriptase that increase resistance to 3′-azido-3′-dideoxythymidine. J Virol 2007;81:7852-9.
    Abstract/FREE Full Text
  78. 78.
    1. Nikolenko GN,
    2. Delviks-Frankenberry KA,
    3. Palmer S,
    4. et al
    . Mutations in the connection domain of HIV-1 reverse transcriptase increase 3′-zido-3′-deoxythymidine resistance. Proc Natl Acad Sci USA 2007;104:317-22.
    Abstract/FREE Full Text
  79. 79.↵
    1. Delviks-Frankenberry KA,
    2. Nikolenko GN,
    3. Barr R,
    4. Pathak VK
    . Mutations in human immunodeficiency virus type 1 RNase H primer grip enhance 3′-azido-3′-deoxythymidine resistance. J Virol 2007;81:6837-45.
    Abstract/FREE Full Text
  80. 80.↵
    1. Gupta S,
    2. Fransen S,
    3. Paxinos EE,
    4. et al
    . Infrequent occurrence of mutations in the C-terminal region of reverse transcriptase modulates susceptibility to RT inhibitors. Antivir Ther 2006;11:S143. [abstract 127].
    Web of Science
  81. 81.↵
    1. Brehm J,
    2. Sluis-Cremer N,
    3. Mellors J
    . Molecular mechanisms for 3′-azido-3′-dideoxythymidine-resistance conferred by mutations in the connection and RNase H domains of HIV-1 reverse transcriptase. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 80].
  82. 82.↵
    1. Yap SH,
    2. Radzio J,
    3. Sluis-Cremer N,
    4. Tachedjian G
    . Mechanism by which N348I in HIV-1 reverse transcriptase confers dual zidovudine/nevirapine resistance. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 79].
  83. 83.↵
    1. Beilhartz G,
    2. Eichelbaum M,
    3. Scarth B,
    4. Tchesnokov E,
    5. Gotte M
    . Connection domain mutations N348I and A360V in HIV-1 RT selectively facilitate excision of AZT by improving access to transiently formed RNA/DNA hybrids. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 81].
  84. 84.↵
    1. Liu TF,
    2. Shafer RW
    . Web resources for HIV type 1 genotypic-resistance test interpretation. Clin Infect Dis 2006;42:1608-18.
    Abstract/FREE Full Text
  85. 85.↵
    1. Shafer RW,
    2. Schapiro JM
    . Drug resistance and antiretroviral drug development. J Antimicrob Chemother 2005;55:817-20.
    Abstract/FREE Full Text
  86. 86.↵
    1. Haubrich RH,
    2. Riddler SA,
    3. DiRienzo AG,
    4. et al
    . Drug resistance at virological failure in a randomized, phase III trial of NRTI-, PI- and NNRTI-sparing regimens for initial treatment of HIV-1 infection (ACTG 5142). Antivir Ther 2007;12:S66.
  87. 87.↵
    1. Baxter JD,
    2. Schapiro JM,
    3. Boucher CA,
    4. et al
    . Genotypic changes in human immunodeficiency virus type 1 protease associated with reduced susceptibility and virologic response to the protease inhibitor tipranavir. J Virol 2006;80:10794-801.
    Abstract/FREE Full Text
  88. 88.
    1. De Meyer S,
    2. Azijn H,
    3. Surleraux D,
    4. et al
    . TMC114, a novel human immunodeficiency virus type 1 protease inhibitor active against protease inhibitor-resistant viruses, including a broad range of clinical isolates. Antimicrob Agents Chemother 2005;49:2314-21.
    Abstract/FREE Full Text
  89. 89.↵
    1. De Meyer S,
    2. Vangeneugden T,
    3. Lefebvre E,
    4. et al
    . Phenotypic and genotypic determinants of resistance to TMC114: pooled analysis of POWER 1, 2, and 3. Antivir Ther 2006;11:S83.
    Web of Science
  90. 90.
    1. Petropolous C,
    2. Coakley E,
    3. Chappey C,
    4. et al
    . Defining the upper and lower phenotypic clinical cut-offs: defining darunavir/r activity within the POWER 1, 2, and 3 trials as exemplary datasets by the PhenoSense assay. Program and abstracts of the 5th European HIV Drug Resistance Workshop; Cascais, Portugal. 2007. [abstract 61].
  91. 91.↵
    1. Vora S,
    2. Marcelin AG,
    3. Gunthard HF,
    4. et al
    . Clinical validation of atazanavir/ritonavir genotypic resistance score in protease inhibitor-experienced patients. AIDS 2006;20:35-40.
    MedlineWeb of Science
  92. 92.
    1. Marcelin AG,
    2. Flandre P,
    3. de Mendoza C,
    4. et al
    . Clinical validation of saquinavir/ritonavir genotypic resistance score in protease-inhibitor-experienced patients. Antivir Ther 2007;12:247-52.
    MedlineWeb of Science
  93. 93.↵
    1. King MS,
    2. Rode R,
    3. Cohen-Codar I,
    4. et al
    . Predictive genotypic algorithm for virologic response to lopinavir-ritonavir in protease inhibitor-experienced patients. Antimicrob Agents Chemother 2007;51:3067-74.
    Abstract/FREE Full Text
  94. 94.↵
    1. Pellegrin I,
    2. Breilh D,
    3. Coureau G,
    4. et al
    . Interpretation of genotype and pharmacokinetics for resistance to fosamprenavir-ritonavir-based regimens in antiretroviral-experienced patients. Antimicrob Agents Chemother 2007;51:1473-80.
    Abstract/FREE Full Text
  95. 95.
    1. Winston A,
    2. Patel N,
    3. Back D,
    4. et al
    . Different methods to calculate the inhibitory quotient of boosted single protease inhibitors and their association with virological response. J Acquir Immune Defic Syndr 2006;41:675-6.
    CrossRefMedlineWeb of Science
  96. 96.
    1. Marcelin AG,
    2. Dalban C,
    3. Peytavin G,
    4. et al
    . Clinically relevant interpretation of genotype and relationship to plasma drug concentrations for resistance to saquinavir-ritonavir in human immunodeficiency virus type 1 protease inhibitor-experienced patients. Antimicrob Agents Chemother 2004;48:4687-92.
    Abstract/FREE Full Text
  97. 97.
    1. Maillard A,
    2. Chapplain JM,
    3. Tribut O,
    4. et al
    . The use of drug resistance algorithms and genotypic inhibitory quotient in prediction of lopinavir-ritonavir treatment response in human immunodeficiency virus type 1 protease inhibitor-experienced patients. J Clin Virol 2007;38:131-8.
    CrossRefMedlineWeb of Science
  98. 98.
    1. Marcelin AG,
    2. Cohen-Codar I,
    3. King MS,
    4. et al
    . Virological and pharmacological parameters predicting the response to lopinavir-ritonavir in heavily protease inhibitor-experienced patients. Antimicrob Agents Chemother 2005;49:1720-6.
    Abstract/FREE Full Text
  99. 99.↵
    1. Marcelin AG,
    2. Lamotte C,
    3. Delaugerre C,
    4. et al
    . Genotypic inhibitory quotient as predictor of virological response to ritonavir-amprenavir in human immunodeficiency virus type 1 protease inhibitor-experienced patients. Antimicrob Agents Chemother 2003;47:594-600.
    Abstract/FREE Full Text
  100. 100.↵
    1. Bleiber G,
    2. Munoz M,
    3. Ciuffi A,
    4. Meylan P,
    5. Telenti A
    . Individual contributions of mutant protease and reverse transcriptase to viral infectivity, replication, and protein maturation of antiretroviral drug-resistant human immunodeficiency virus type 1. J Virol 2001;75:3291-300.
    Abstract/FREE Full Text
  101. 101.
    1. Myint L,
    2. Matsuda M,
    3. Matsuda Z,
    4. et al
    . Gag non-cleavage site mutations contribute to full recovery of viral fitness in protease inhibitor-resistant human immunodeficiency virus type 1. Antimicrob Agents Chemother 2004;48:444-52.
    Abstract/FREE Full Text
  102. 102.
    1. Robinson LH,
    2. Myers RE,
    3. Snowden BW,
    4. Tisdale M,
    5. Blair ED
    . HIV type 1 protease cleavage site mutations and viral fitness: implications for drug susceptibility phenotyping assays. AIDS Res Hum Retroviruses 2000;16:1149-56.
    CrossRefMedlineWeb of Science
  103. 103.↵
    1. Feher A,
    2. Weber IT,
    3. Bagossi P,
    4. et al
    . Effect of sequence polymorphism and drug resistance on two HIV-1 Gag processing sites. Eur J Biochem 2002;269:4114-20.
    MedlineWeb of Science
  104. 104.↵
    1. Bally F,
    2. Martinez R,
    3. Peters S,
    4. Sudre P,
    5. Telenti A
    . Polymorphism of HIV type 1 Gag p7/p1 and p1/p6 cleavage sites: clinical significance and implications for resistance to protease inhibitors. AIDS Res Hum Retroviruses 2000;16:1209-13.
    CrossRefMedlineWeb of Science
  105. 105.
    1. Cote HC,
    2. Brumme ZL,
    3. Harrigan PR
    . Human immunodeficiency virus type 1 protease cleavage site mutations associated with protease inhibitor cross-resistance selected by indinavir, ritonavir, and/or saquinavir. J Virol 2001;75:589-94.
    Abstract/FREE Full Text
  106. 106.↵
    1. Kaufmann GR,
    2. Suzuki K,
    3. Cunningham P,
    4. et al
    . Impact of HIV type 1 protease, reverse transcriptase, cleavage site and p6 mutations on the virological response to quadruple therapy with saquinavir, ritonavir, and two nucleoside analogs. AIDS Res Hum Retroviruses 2001;17:487-97.
    CrossRefMedlineWeb of Science
  107. 107.↵
    1. Verheyen J,
    2. Litau E,
    3. Sing T,
    4. et al
    . Compensatory mutations at the HIV cleavage sites p7/p1 and p1/p6-gag in therapy-naive and therapyexperienced patients. Antivir Ther 2006;11:879-87.
    MedlineWeb of Science
  108. 108.↵
    1. Nijhuis M,
    2. Van Maarseveen NM,
    3. Lastere S,
    4. et al
    . A novel substratebased HIV-1 protease inhibitor drug resistance mechanism. PLoS Med 2007;4:e36.
    CrossRefMedline
  109. 109.↵
    1. Este JA,
    2. Telenti A
    . HIV entry inhibitors. Lancet 2007;370:81-8.
    CrossRefMedlineWeb of Science
  110. 110.↵
    1. Chan DC,
    2. Kim PS
    . HIV entry and its inhibition. Cell 1998;93:681-4.
    CrossRefMedlineWeb of Science
  111. 111.↵
    1. O'Brien WA,
    2. Koyanagi Y,
    3. Namazie A,
    4. et al
    . HIV-1 tropism for mononuclear phagocytes can be determined by regions of gp120 outside the CD4-binding domain. Nature 1990;348:69-73.
    CrossRefMedline
  112. 112.
    1. Rizzuto CD,
    2. Wyatt R,
    3. Hernandez-Ramos N,
    4. et al
    . A conserved HIV gp120 glycoprotein structure involved in chemokine receptor binding. Science 1998;280:1949-53.
    Abstract/FREE Full Text
  113. 113.↵
    1. Shioda T,
    2. Levy JA,
    3. Cheng-Mayer C
    . Small amino acid changes in the V3 hypervariable region of gp120 can affect the T cell line and macrophage tropisms of human immunodeficiency virus type 1. Proc Natl Acad Sci USA 1992;89:9434-8.
    Abstract/FREE Full Text
  114. 114.↵
    1. Chan DC,
    2. Fass D,
    3. Berger JM,
    4. Kim PS
    . Core structure of gp41 from the HIV envelope glycoprotein. Cell 1997;89:263-73.
    CrossRefMedlineWeb of Science
  115. 115.↵
    1. Wild CT,
    2. Shugrass DC,
    3. Greenwell TK,
    4. McDanal CB,
    5. Matthews TJ
    . Peptides corresponding to a predictive alpha-helical domain of human immunodeficiency virus type 1 gp41 are potent inhibitors of virus infection. Proc Natl Acad Sci USA 1994;91:9770-4.
    Abstract/FREE Full Text
  116. 116.↵
    1. Rimsky LT,
    2. Shugars DC,
    3. Matthews TJ
    . Determinants of human immunodeficiency virus type 1 resistance to gp41-derived inhibitory peptides. J Virol 1998;72:986-93.
    Abstract/FREE Full Text
  117. 117.↵
    1. Sista PR,
    2. Melby T,
    3. Davison D,
    4. et al
    . Characterization of determinants of genotypic and phenotypic resistance to enfuvirtide in baseline and on-treatment HIV-1 isolates. AIDS 2004;18:1787-94.
    CrossRefMedlineWeb of Science
  118. 118.↵
    1. Xu L,
    2. Pozniak A,
    3. Wildfire A,
    4. et al
    . Emergence and evolution of enfuvirtide resistance following long-term therapy involves heptad repeat 2 mutations within gp41. Antimicrob Agents Chemother 2005;49:1113-9.
    Abstract/FREE Full Text
  119. 119.↵
    1. Mink M,
    2. Mosier SM,
    3. Janumpalli S,
    4. et al
    . Impact of human immunodeficiency virus type 1 gp41 amino acid substitutions selected during enfuvirtide treatment on gp41 binding and antiviral potency of enfuvirtide in vitro. J Virol 2005;79:12447-54.
    Abstract/FREE Full Text
  120. 120.↵
    1. Marcelin AG,
    2. Reynes J,
    3. Yerly S,
    4. et al
    . Characterization of genotypic determinants in HR-1 and HR-2 gp41 domains in individuals with persistent HIV viraemia under T-20. AIDS 2004;18:1340-2.
    CrossRefMedlineWeb of Science
  121. 121.↵
    1. Lu J,
    2. Sista P,
    3. Giguel F,
    4. Greenberg M,
    5. Kuritzkes DR
    . Relative replicative fitness of human immunodeficiency virus type 1 mutants resistant to enfuvirtide (T-20). J Virol 2004;78:4628-37.
    Abstract/FREE Full Text
  122. 122.↵
    1. Lu J,
    2. Deeks SG,
    3. Hoh R,
    4. et al
    . Rapid emergence of enfuvirtide resistance in HIV-1-infected patients: results of a clonal analysis. J Acquir Immune Defic Syndr 2006;43:60-4.
    CrossRefMedlineWeb of Science
  123. 123.↵
    1. Watson C,
    2. Jenkinson S,
    3. Kazmierski W,
    4. Kenakin T
    . The CCR5 receptor-based mechanism of action of 873140, a potent allosteric noncompetitive HIV entry inhibitor. Mol Pharmacol 2005;67:1268-82.
    Abstract/FREE Full Text
  124. 124.
    1. Strizki JM,
    2. Tremblay C,
    3. Xu S,
    4. et al
    . Discovery and characterization of vicriviroc (SCH 417690), a CCR5 antagonist with potent activity against human immunodeficiency virus type 1. Antimicrob Agents Chemother 2005;49:4911-9.
    Abstract/FREE Full Text
  125. 125.↵
    1. Dorr P,
    2. Westby M,
    3. Dobbs S,
    4. et al
    . Maraviroc (UK-427,857), a potent, orally bioavailable, and selective small-molecule inhibitor of chemokine receptor CCR5 with broad-spectrum anti-human immunodeficiency virus type 1 activity. Antimicrob Agents Chemother 2005;49:4721-32.
    Abstract/FREE Full Text
  126. 126.↵
    1. Fatkenheuer G,
    2. Pozniak AL,
    3. Johnson MA,
    4. et al
    . Efficacy of shortterm monotherapy with maraviroc, a new CCR5 antagonist, in patients infected with HIV-1. Nat Med 2005;11:1170-2.
    CrossRefMedlineWeb of Science
  127. 127.↵
    1. Gulick RM,
    2. Su Z,
    3. Flexner C,
    4. et al
    . Phase 2 study of the safety and efficacy of vicriviroc, a CCR5 inhibitor, in HIV-1-infected, treatment-experienced patients: AIDS clinical trials group 5211. J Infect Dis 2007;196:304-12.
    Abstract/FREE Full Text
  128. 128.↵
    1. Kuhmann SE,
    2. Pugach P,
    3. Kunstman KJ,
    4. et al
    . Genetic and phenotypic analyses of human immunodeficiency virus type 1 escape from a small-molecule CCR5 inhibitor. J Virol 2004;78:2790-807.
    Abstract/FREE Full Text
  129. 129.↵
    1. Pugach P,
    2. Marozsan AJ,
    3. Ketas TJ,
    4. Landes EL,
    5. Moore JP,
    6. Kuhmann SE
    . HIV-1 clones resistant to a small molecule CCR5 inhibitor use the inhibitor-bound form of CCR5 for entry. Virology 2007;361:212-28.
    CrossRefMedlineWeb of Science
  130. 130.↵
    1. Westby M,
    2. Smith-Burchnell C,
    3. Mori J,
    4. et al
    . Reduced maximal inhibition in phenotypic susceptibility assays indicates that viral strains resistant to the CCR5 antagonist maraviroc utilize inhibitor-bound receptor for entry. J Virol 2007;81:2359-71.
    Abstract/FREE Full Text
  131. 131.↵
    1. Tsibris AMN,
    2. Sagar M,
    3. Gulick RM,
    4. et al
    . In vivo emergence of vicriviroc resistance in an HIV-1 subtype C-infected subject. J Virol 2008. [Epub ahead of print].
  132. 132.↵
    1. Mori J,
    2. Mosley M,
    3. Lewis M,
    4. et al
    . Characterization of maraviroc resistance in patients failing treatment with CCR5-tropic virus in MOTIVATE 1 and MOTIVATE 2. Antivir Ther 2007;12:S12.
  133. 133.↵
    1. Landowitz R,
    2. Faetkenheuer G,
    3. Hoffmann C,
    4. et al
    . Characterization of susceptibility profiles for the CCR5 antagonist vicriviroc in treatment- naive HIV-infected subjects. Antivir Ther 2006;11:S23.
    Web of Science
  134. 134.↵
    1. Lewis M,
    2. Mori J,
    3. Simpson P,
    4. et al
    . Changes in V3 loop sequence associated with failure of maraviroc treatment in patients enrolled in the MOTIVATE 1 and 2 trials. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 871].
  135. 135.↵
    1. Fransen S,
    2. Gupta S,
    3. Paxinos E,
    4. et al
    . Integrase inhibitor susceptibility can be measured using recombinant viruses that express patient virus integrase alone, or in combination with protease and reverse transcriptase. Program and abstracts of the 12th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2005. [abstract 725].
  136. 136.↵
    1. Hazuda DF,
    2. Miller MD,
    3. Nguyen BY,
    4. Zhao J,
    5. for the P005 Study Team
    . Resistance to the HIV-integrase inhibitor raltegravir: analysis of protocol 005, a phase II study in patients with triple-class resistant HIV-1 infection. Antivir Ther 2007;12:S10.
  137. 137.↵
    1. Shimura K,
    2. Kodama E,
    3. Sakagami Y,
    4. et al
    . Broad antiretroviral activity and resistance profile of the novel human immunodeficiency virus integrase inhibitor elvitegravir (JTK-303/GS-9137). J Virol 2008;82:764-74.
    Abstract/FREE Full Text
  138. 138.↵
    1. Markowitz M,
    2. Morales-Ramirez JO,
    3. Nguyen BY,
    4. et al
    . Antiretroviral activity, pharmacokinetics, and tolerability of MK-0518, a novel inhibitor of HIV-1 integrase, dosed as monotherapy for 10 days in treatment-naive HIV-1-infected individuals. J Acquir Immune Defic Syndr 2006;43:509-15.
    CrossRefMedlineWeb of Science
  139. 139.
    1. Markowitz M,
    2. Nguyen BY,
    3. Gotuzzo E,
    4. et al
    . Rapid and durable antiretroviral effect of the HIV-1 integrase inhibitor raltegravir as part of combination therapy in treatment-naive patients with HIV-1 infection: results of a 48-week controlled study. J Acquir Immune Defic Syndr 2007;46:125-33.
    CrossRefMedlineWeb of Science
  140. 140.
    1. Cooper D,
    2. Gatell J,
    3. Rockstroh J,
    4. et al
    . Results of BENCHMRK-1, a phase III study evaluating the efficacy and safety of MK-0518, a novel HIV-1 integrase inhibitor, in patients with triple-class resistant virus. Program and abstracts of the 14th Conference on Retroviruses and Opportunistic Infections; Los Angeles, CA. 2007. [abstract 105aLB].
  141. 141.
    1. Steigbigel R,
    2. Kumar P,
    3. Eron J,
    4. et al
    . Results of BENCHMRK-2, a phase III study evaluating the efficacy and safety of MK-0518, a novel HIV-1 integrase inhibitor, in patients with triple-class resistant virus. Program and abstracts of the 14th Conference on Retroviruses and Opportunistic Infections; Los Angeles, CA. 2007. [abstract 105bLB].
  142. 142.
    1. Teppler H,
    2. Azrolan N,
    3. Chen J
    . Differential effect of MK-0518 and efavirenz on serum lipids and lipoproteins in antiretroviral therapy (ART)-naive patients. Program and abstracts of the 46th Interscience Conference on Antimicrobial Agents and Chemotherapy; San Francisco, CA. 2007. [abstract H-256a].
  143. 143.↵
    1. Kassahun K,
    2. McIntosh I,
    3. Hreniuk D,
    4. et al
    . Absorption, metabolism and excretion of MK-0518, a potent HIV-1 integrase inhibitor, in healthy male volunteers. Program and abstracts of the 46th Interscience Conference on Antimicrobial Agents and Chemotherapy; San Francisco, CA. 2006. [abstract A-372].
  144. 144.↵
    1. Cooper D,
    2. Gatell J,
    3. Rockstroh J,
    4. et al
    . 48-week results from BENCHMRK-1, a phase III study of raltegravir in patients faiing ART with triple-class resistant HIV-1. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 788].
  145. 145.↵
    1. Steigbigel R,
    2. Kumar P,
    3. Eron J,
    4. et al
    . 48-week results from BENCHMRK-2, a phase III study of raltegravir in patients failing ART with triple-class resistant HIV. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 789].
  146. 146.↵
    1. Sayer DC,
    2. Land S,
    3. Gizzarelli L,
    4. et al
    . Quality assessment program for genotypic antiretroviral testing improves detection of drug resistance mutations. J Clin Microbiol 2003;41:227-36.
    Abstract/FREE Full Text
  147. 147.↵
    1. Schuurman R,
    2. Demeter L,
    3. Reichelderfer P,
    4. Tijnagel J,
    5. de Groot T,
    6. Boucher C
    . Worldwide evaluation of DNA sequencing approaches for identification of drug resistance mutations in the human immunodeficiency virus type 1 reverse transcriptase. J Clin Microbiol 1999;37:2291-6.
    Abstract/FREE Full Text
  148. 148.↵
    1. Maes B,
    2. Schrooten Y,
    3. Snoeck J,
    4. et al
    . Performance of ViroSeq HIV-1 Genotyping System in routine practice at a Belgian clinical laboratory. J Virol Methods 2004;119:45-9.
    CrossRefMedlineWeb of Science
  149. 149.↵
    1. Descamps D,
    2. Delaugerre C,
    3. Masquelier B,
    4. et al
    . Repeated HIV-1 resistance genotyping external quality assessments improve virology laboratory performance. J Med Virol 2006;78:153-60.
    CrossRefMedlineWeb of Science
  150. 150.↵
    1. Ross L,
    2. Boulme R,
    3. Fisher R,
    4. et al
    . A direct comparison of drug susceptibility to HIV type 1 from antiretroviral experienced subjects as assessed by the antivirogram and PhenoSense assays and by seven resistance algorithms. AIDS Res Hum Retroviruses 2005;21:933-9.
    CrossRefMedlineWeb of Science
  151. 151.↵
    1. Ross L,
    2. Boulme R,
    3. Fusco G,
    4. Scarsella A,
    5. Florance A
    . Comparison of HIV type 1 protease inhibitor susceptibility results in viral samples analyzed by phenotypic drug resistance assays and by six resistance algorithms: an analysis of a subpopulation of the CHORUS cohort. AIDS Res Hum Retroviruses 2005;21:696-701.
    CrossRefMedlineWeb of Science
  152. 152.↵
    1. Korn K,
    2. Reil H,
    3. Walter H,
    4. Schmidt B
    . Quality control trial for human immunodeficiency virus type 1 drug resistance testing using clinical samples reveals problems with detecting minority species and interpretation of test results. J Clin Microbiol 2003;41:3559-65.
    Abstract/FREE Full Text
  153. 153.↵
    ANRS AC11 Resistance Study Group. PCR and sequencing procedures: HIV-1. Version March 2005. [Accessed 12 October 2007]. Available at: http://www.hivfrenchresistance.org/tab2005.html.
  154. 154.
    1. MacArthur RD
    . An updated guide to genotype interpretation. AIDS Read 2004;14:256-8. 261–4, 266.
    Medline
  155. 155.
    1. Shafer RW,
    2. Rhee SY,
    3. Zioni R,
    4. Liu T,
    5. Kiuchi M
    . Stanford University HIV drug resistance database. [Accessed 12 October 2007]. Available at: http://hivdb.stanford.edu/pages/asi/releaseNotes/.
  156. 156.↵
    1. Vercauteren J,
    2. Vandamme AM
    . Algorithms for the interpretation of HIV-1 genotypic drug resistance information. Antiviral Res 2006;71:335-42.
    CrossRefMedlineWeb of Science
  157. 157.↵
    1. Fox ZV,
    2. Geretti AM,
    3. Kjaer J,
    4. et al
    . The ability of four genotypic interpretation systems to predict virological response to ritonavirboosted protease inhibitors. AIDS 2007;21:2033-42.
    MedlineWeb of Science
  158. 158.
    1. Ravela J,
    2. Betts BJ,
    3. Brun-Vezinet F,
    4. et al
    . HIV-1 protease and reverse transcriptase mutation patterns responsible for discordances between genotypic drug resistance interpretation algorithms. J Acquir Immune Defic Syndr 2003;33:8-14.
    MedlineWeb of Science
  159. 159.↵
    1. Zazzi M,
    2. Romano L,
    3. Venturi G,
    4. et al
    . Comparative evaluation of three computerized algorithms for prediction of antiretroviral susceptibility from HIV type 1 genotype. J Antimicrob Chemother 2004;53:356-60.
    Abstract/FREE Full Text
  160. 160.↵
    1. Meynard JL,
    2. Vray M,
    3. Morand-Joubert L,
    4. et al
    . Phenotypic or genotypic resistance testing for choosing antiretroviral therapy after treatment failure: a randomized trial. AIDS 2002;16:727-36.
    CrossRefMedlineWeb of Science
  161. 161.
    1. Marcelin AG,
    2. Chazallon C,
    3. Gerard L,
    4. et al
    . External validation of atazanavir/ritonavir genotypic score in HIV-1 protease inhibitor-experienced patients. J Acquir Immune Defic Syndr 2006;42:127-8.
    MedlineWeb of Science
  162. 162.
    1. Marcelin AG,
    2. Flandre P,
    3. Pavie J,
    4. et al
    . Clinically relevant genotype interpretation of resistance to didanosine. Antimicrob Agents Chemother 2005;49:1739-44.
    Abstract/FREE Full Text
  163. 163.
    1. Winters B,
    2. Van der Borght K,
    3. van Craenenbroeck E,
    4. et al
    . Darunavir phenotype predicts response better than genotype. Program and abstracts of the 5th European HIV Drug Resistance Workshop; Cascais, Portugal. 2007.
  164. 164.
    1. Picard V,
    2. Angelini E,
    3. Maillard A,
    4. et al
    . Comparison of genotypic and phenotypic resistance patterns of human immunodeficiency virus type 1 isolates from patients treated with stavudine and didanosine or zidovudine and lamivudine. J Infect Dis 2001;184:781-4.
    Abstract/FREE Full Text
  165. 165.
    1. Loutfy MR,
    2. Raboud JM,
    3. Walmsley SL,
    4. et al
    . Predictive value of HIV-1 protease genotype and virtual phenotype on the virological response to lopinavir/ritonavir-containing salvage regimens. Antivir Ther 2004;9:595-602.
    MedlineWeb of Science
  166. 166.
    1. Marcelin AG,
    2. Masquelier B,
    3. Descamps D,
    4. et al
    . Mutations associated with response to boosted tipranavir in HIV-1-infected PI-experienced patients. Program and abstracts of the 14th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2007. [abstract 612].
  167. 167.
    1. Naeger LK,
    2. Struble KA
    . Effect of baseline protease genotype and phenotype on HIV response to atazanavir/ritonavir in treatment-experienced patients. AIDS 2006;20:847-53.
    MedlineWeb of Science
  168. 168.↵
    1. Naeger LK,
    2. Struble KA
    . Food and Drug Administration analysis of tipranavir clinical resistance in HIV-1-infected treatment-experienced patients. AIDS 2007;21:179-85.
    MedlineWeb of Science
  169. 169.↵
    1. Fleury HJ,
    2. Toni T,
    3. Lan NT,
    4. et al
    . Susceptibility to antiretroviral drugs of CRF01_AE, CRF02_AG, and subtype C viruses from untreated patients of Africa and Asia: comparative genotypic and phenotypic data. AIDS Res Hum Retroviruses 2006;22:357-66.
    CrossRefMedlineWeb of Science
  170. 170.↵
    1. Kinomoto M,
    2. Appiah-Opong R,
    3. Brandful JA,
    4. et al
    . HIV-1 proteases from drug-naive West African patients are differentially less susceptible to protease inhibitors. Clin Infect Dis 2005;41:243-51.
    Abstract/FREE Full Text
  171. 171.↵
    1. Beerenwinkel N,
    2. Daumer M,
    3. Oette M,
    4. et al
    . Geno2pheno: estimating phenotypic drug resistance from HIV-1 genotypes. Nucleic Acids Res 2003;31:3850-5.
    Abstract/FREE Full Text
  172. 172.
    1. Bacheler L,
    2. Jeffrey S,
    3. Hanna G,
    4. et al
    . Genotypic correlates of phenotypic resistance to efavirenz in virus isolates from patients failing nonnucleoside reverse transcriptase inhibitor therapy. J Virol 2001;75:4999-5008.
    Abstract/FREE Full Text
  173. 173.↵
    1. Mazzotta F,
    2. Lo Caputo S,
    3. Torti C,
    4. et al
    . Real versus virtual phenotype to guide treatment in heavily pretreated patients: 48-week follow-up of the Genotipo-Fenotipo di Resistenza (GenPheRex) trial. J Acquir Immune Defic Syndr 2003;32:268-80.
    CrossRefMedlineWeb of Science
  174. 174.↵
    1. Perez-Elias MJ,
    2. Garcia-Arota I,
    3. Munoz V,
    4. et al
    . Phenotype or virtual phenotype for choosing antiretroviral therapy after failure: a prospective, randomized study. Antivir Ther 2003;8:577-84.
    Medline
  175. 175.↵
    1. Hales G,
    2. Birch C,
    3. Crowe S,
    4. et al
    . A randomised trial comparing genotypic and virtual phenotypic interpretation of HIV drug resistance: the CREST study. PLoS Clin Trials 2006;1:e18.
    CrossRefMedline
  176. 176.↵
    1. Torti C,
    2. Quiros-Roldan E,
    3. Regazzi M,
    4. et al
    . A randomized controlled trial to evaluate antiretroviral salvage therapy guided by rules-based or phenotype-driven HIV-1 genotypic drug-resistance interpretation with or without concentration-controlled intervention: the Resistance and Dosage Adapted Regimens (RADAR) study. Clin Infect Dis 2005;40:1828-36.
    Abstract/FREE Full Text
  177. 177.↵
    1. Race E,
    2. Dam E,
    3. Obry V,
    4. Paulous S,
    5. Clavel F
    . Analysis of HIV crossresistance to protease inhibitors using a rapid single-cycle recombinant virus assay for patients failing on combination therapies. AIDS 1999;13:2061-8.
    CrossRefMedlineWeb of Science
  178. 178.
    1. Schmidt B,
    2. Walter H,
    3. Moschik B,
    4. et al
    . Simple algorithm derived from a geno-/phenotypic database to predict HIV-1 protease inhibitor resistance. AIDS 2000;14:1731-8.
    CrossRefMedlineWeb of Science
  179. 179.
    1. Walter H,
    2. Schmidt B,
    3. Korn K,
    4. Vandamme AM,
    5. Harrer T,
    6. Uberla K
    . Rapid, phenotypic HIV-1 drug sensitivity assay for protease and reverse transcriptase inhibitors. J Clin Virol 1999;13:71-80.
    CrossRefMedlineWeb of Science
  180. 180.
    1. Hertogs K,
    2. de Bethune MP,
    3. Miller V,
    4. et al
    . A rapid method for simultaneous detection of phenotypic resistance to inhibitors of protease and reverse transcriptase in recombinant human immunodeficiency virus type 1 isolates from patients treated with antiretroviral drugs. Antimicrob Agents Chemother 1998;42:269-76.
    Abstract/FREE Full Text
  181. 181.↵
    1. Petropoulos CJ,
    2. Parkin NT,
    3. Limoli KL,
    4. et al
    . A novel phenotypic drug susceptibility assay for human immunodeficiency virus type 1. Antimicrob Agents Chemother 2000;44:920-8.
    Abstract/FREE Full Text
  182. 182.↵
    1. Whitcomb JM,
    2. Huang W,
    3. Fransen S,
    4. et al
    . Development and characterization of a novel single-cycle recombinant-virus assay to determine human immunodeficiency virus type 1 coreceptor tropism. Antimicrob Agents Chemother 2007;51:566-75.
    Abstract/FREE Full Text
  183. 183.↵
    1. Haubrich RH,
    2. Kemper CA,
    3. Hellmann NS,
    4. et al
    . A randomized, prospective study of phenotype susceptibility testing versus standard of care to manage antiretroviral therapy: CCTG 575. AIDS 2005;19:295-302.
    MedlineWeb of Science
  184. 184.↵
    1. Charpentier C,
    2. Dwyer DE,
    3. Mammano F,
    4. Lecossier D,
    5. Clavel F,
    6. Hance AJ
    . Role of minority populations of human immunodeficiency virus type 1 in the evolution of viral resistance to protease inhibitors. J Virol 2004;78:4234-47.
    Abstract/FREE Full Text
  185. 185.
    1. Halvas EK,
    2. Aldrovandi GM,
    3. Balfe P,
    4. et al
    . Blinded, multicenter comparison of methods to detect a drug-resistant mutant of human immunodeficiency virus type 1 at low frequency. J Clin Microbiol 2006;44:2612-4.
    Abstract/FREE Full Text
  186. 186.
    1. Hance AJ,
    2. Lemiale V,
    3. Izopet J,
    4. et al
    . Changes in human immunodeficiency virus type 1 populations after treatment interruption in patients failing antiretroviral therapy. J Virol 2001;75:6410-7.
    Abstract/FREE Full Text
  187. 187.
    1. Johnson JA,
    2. Li LF,
    3. Wei X,
    4. et al
    . Simple PCR assays improve the sensitivity of HIV-1 subtype B drug resistance testing and allow linking of resistance mutations. PLoS ONE 2007;2:e638.
    CrossRefMedline
  188. 188.
    1. Metzner KJ,
    2. Bonhoeffer S,
    3. Fischer M,
    4. et al
    . Emergence of minor populations of human immunodeficiency virus type 1 carrying the M184V and L90M mutations in subjects undergoing structured treatment interruptions. J Infect Dis 2003;188:1433-43.
    Abstract/FREE Full Text
  189. 189.↵
    1. Metzner KJ,
    2. Rauch P,
    3. Walter H,
    4. et al
    . Detection of minor populations of drug-resistant HIV-1 in acute seroconverters. AIDS 2005;19:1819-25.
    MedlineWeb of Science
  190. 190.↵
    1. Palmer S,
    2. Kearney M,
    3. Maldarelli F,
    4. et al
    . Multiple, linked human immunodeficiency virus type 1 drug resistance mutations in treatment-experienced patients are missed by standard genotype analysis. J Clin Microbiol 2005;43:406-13.
    Abstract/FREE Full Text
  191. 191.↵
    1. Simen BB,
    2. Huppler-Hullsiek K,
    3. Novak RM,
    4. et al
    . Prevalence of low abundant drug resistant variants by ultra-deep sequencing in chronically HIV-infected antiretroviral naive patients and the impact on virologic outcomes. Antivir Ther 2007;12:S149.
  192. 192.↵
    1. Mellors J,
    2. Palmer S,
    3. Nissley D,
    4. et al
    . Low-frequency NNRTI-resistant variants contribute to failure of efavirenz-containing regimens. Program and abstracts of the 11th Conference on Retroviruses and Opportunistic Infections; San Francisco, CA. 2004. [abstract 39].
  193. 193.
    1. Johnson VA,
    2. Li LF,
    3. Wei X,
    4. et al
    . Baseline detection of low-frequency drug resistance-associated mutations is strongly associated with virologic failure among previous ART naive HIV-1-infected persons. Antivir Ther 2006;11:S79.
    Web of Science
  194. 194.↵
    1. Paredes R,
    2. Lalama C,
    3. Ribaudo H,
    4. et al
    . Presence of minor populations of Y181C mutants detected by alle-specific PCR and risk of efavirenz failure in treatment-naive patients: results of an ACTG 5095 casecohort study. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 83].
  195. 195.↵
    1. Bannister WP,
    2. Ruiz L,
    3. Loveday C,
    4. et al
    . HIV-1 subtypes and response to combination antiretroviral therapy in Europe. Antivir Ther 2006;11:707-15.
    MedlineWeb of Science
  196. 196.↵
    1. Ledergerber B,
    2. Lundgren JD,
    3. Walker AS,
    4. et al
    . Predictors of trend in CD4-positive T-cell count and mortality among HIV-1-infected individuals with virological failure to all three antiretroviral-drug classes. Lancet 2004;364:51-62.
    CrossRefMedlineWeb of Science
  197. 197.↵
    1. Weinstein MC,
    2. Goldie SJ,
    3. Losina E,
    4. et al
    . Use of genotypic resistance testing to guide HIV therapy: clinical impact and cost-effectiveness. Ann Intern Med 2001;134:440-50.
    Abstract/FREE Full Text
  198. 198.
    1. Sax PE,
    2. Islam R,
    3. Walensky RP,
    4. et al
    . Should resistance testing be performed for treatment-naive HIV-infected patients? A cost-effectiveness analysis. Clin Infect Dis 2005;41:1316-23.
    Abstract/FREE Full Text
  199. 199.
    1. Corzillius M,
    2. Muhlberger N,
    3. Sroczynski G,
    4. Jaeger H,
    5. Wasem J,
    6. Sibert U
    . Cost effectiveness analysis of routine use of genotypic antiretroviral resistance testing after failure of antiretroviral treatment for HIV. Antivir Ther 2004;9:27-36.
    MedlineWeb of Science
  200. 200.
    1. Chaix C,
    2. Grenier-Sennelier C,
    3. Clevenbergh P,
    4. et al
    . Economic evaluation of drug resistance genotyping for the adaptation of treatment in HIV-infected patients in the VIRADAPT study. J Acquir Immune Defic Syndr 2000;24:227-31.
    MedlineWeb of Science
  201. 201.
    1. Simcock M,
    2. Sendi P,
    3. Ledergerber B,
    4. et al
    . A longitudinal analysis of healthcare costs after treatment optimization following genotypic antiretroviral resistance testing: does resistance testing pay off? Antivir Ther 2006;11:305-14.
    MedlineWeb of Science
  202. 202.↵
    1. Sendi P,
    2. Gunthard HF,
    3. Simcock M,
    4. et al
    . Cost-effectiveness of genotypic antiretroviral resistance testing in HIV-infected patients with treatment failure. PLoS ONE 2007;2:e173.
    CrossRefMedline
  203. 203.↵
    1. Reeves J,
    2. Han D,
    3. Wilkin T,
    4. et al
    . An enhanced version of the trofile HIV co-receptor tropism assay predicts emergence of CXCR4 use in ACTG5211 vicriviroc trial samples. Program and abstracts of the 15th Conference on Retroviruses and Opportunistic Infections; Boston, MA. 2008. [abstract 869].
  204. 204.↵
    1. Hoffman NG,
    2. Seillier-Moiseiwitsch F,
    3. Ahn J,
    4. Walker JM,
    5. Swanstrom R
    . Variability in the human immunodeficiency virus type 1 gp120 Env protein linked to phenotype-associated changes in the V3 loop. J Virol 2002;76:3852-64.
    Abstract/FREE Full Text
  205. 205.↵
    1. Xiao L,
    2. Owen SM,
    3. Goldman I,
    4. et al
    . CCR5 coreceptor usage of non-syncytium-inducing primary HIV-1 is independent of phylogenetically distinct global HIV-1 isolates: delineation of consensus motif in the V3 domain that predicts CCR-5 usage. Virology 1998;240:83-92.
    CrossRefMedlineWeb of Science
  206. 206.↵
    1. Jensen MA,
    2. Li FS,
    3. van 't Wout AB,
    4. et al
    . Improved coreceptor usage prediction and genotypic monitoring of R5-to-X4 transition by motif analysis of human immunodeficiency virus type 1 env V3 loop sequences. J Virol 2003;77:13376-88.
    Abstract/FREE Full Text
  207. 207.↵
    1. Resch W,
    2. Hoffman N,
    3. Swanstrom R
    . Improved success of phenotype prediction of the human immunodeficiency virus type 1 from envelope variable loop 3 sequence using neural networks. Virology 2001;288:51-62.
    CrossRefMedlineWeb of Science
  208. 208.↵
    1. Pillai S,
    2. Good B,
    3. Richman D,
    4. Corbeil J
    . A new perspective on V3 phenotype prediction. AIDS Res Hum Retroviruses 2003;19:145-9.
    CrossRefMedlineWeb of Science
  209. 209.↵
    1. Nelson JA,
    2. Fiscus SA,
    3. Swanstrom R
    . Evolutionary variants of the human immunodeficiency virus type 1 V3 region characterized by using a heteroduplex tracking assay. J Virol 1997;71:8750-8.
    Abstract/FREE Full Text
  210. 210.↵
    1. Sing T,
    2. Low AJ,
    3. Beerenwinkel N,
    4. et al
    . Predicting HIV coreceptor usage on the basis of genetic and clinical covariates. Antivir Ther 2007;12:1097-106.
    MedlineWeb of Science
  211. 211.↵
    1. Hemelaar J,
    2. Gouws E,
    3. Ghys PD,
    4. Osmanov S
    . Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004. AIDS 2006;20:W13-23.
    MedlineWeb of Science
  212. 212.↵
    1. Kantor R,
    2. Katzenstein D
    . Polymorphism in HIV-1 non-subtype B protease and reverse transcriptase and its potential impact on drug susceptibility and drug resistance evolution. AIDS Rev 2003;5:25-35.
    Medline
  213. 213.↵
    1. Kantor R,
    2. Katzenstein DA,
    3. Efron B,
    4. et al
    . Impact of HIV-1 subtype and antiretroviral therapy on protease and reverse transcriptase genotype: results of a global collaboration. PLoS Med 2005;2:e112.
    CrossRefMedline
  214. 214.↵
    1. Grossman Z,
    2. Paxinos EE,
    3. Averbuch D,
    4. et al
    . Mutation D30N is not preferentially selected by human immunodeficiency virus type 1 subtype C in the development of resistance to nelfinavir. Antimicrob Agents Chemother 2004;48:2159-65.
    Abstract/FREE Full Text
  215. 215.↵
    1. Ariyoshi K,
    2. Matsuda M,
    3. Miura H,
    4. Tateishi S,
    5. Yamada K,
    6. Sugiura W
    . Patterns of point mutations associated with antiretroviral drug treatment failure in CRF01_AE (subtype E) infection differ from subtype B infection. J Acquir Immune Defic Syndr 2003;33:336-42.
    MedlineWeb of Science
  216. 216.↵
    1. Abecasis AB,
    2. Deforche K,
    3. Snoeck J,
    4. et al
    . Protease mutation M89I/V is linked to therapy failure in patients infected with the HIV-1 non-B subtypes C, F or G. AIDS 2005;19:1799-806.
    MedlineWeb of Science
  217. 217.↵
    1. Brenner B,
    2. Turner D,
    3. Oliveira M,
    4. et al
    . A V106M mutation in HIV-1 clade C viruses exposed to efavirenz confers cross-resistance to nonnucleoside reverse transcriptase inhibitors. AIDS 2003;17:F1-5.
    CrossRefMedlineWeb of Science
  218. 218.↵
    1. Cane PA,
    2. de Ruiter A,
    3. Rice P,
    4. Wiselka M,
    5. Fox R,
    6. Pillay D
    . Resistanceassociated mutations in the human immunodeficiency virus type 1 subtype C protease gene from treated and untreated patients in the United Kingdom. J Clin Microbiol 2001;39:2652-4.
    Abstract/FREE Full Text
  219. 219.↵
    1. Dumans AT,
    2. Soares MA,
    3. Machado ES,
    4. et al
    . Synonymous genetic polymorphisms within Brazilian human immunodeficiency virus type 1 subtypes may influence mutational routes to drug resistance. J Infect Dis 2004;189:1232-8.
    Abstract/FREE Full Text
  220. 220.↵
    1. Doualla-Bell F,
    2. Avalos A,
    3. Brenner B,
    4. et al
    . High prevalence of the K65R mutation in human immunodeficiency virus type 1 subtype C isolates from infected patients in Botswana treated with didanosinebased regimens. Antimicrob Agents Chemother 2006;50:4182-5.
    Abstract/FREE Full Text
  221. 221.↵
    1. Brenner BG,
    2. Oliveira M,
    3. Doualla-Bell F,
    4. et al
    . HIV-1 subtype C viruses rapidly develop K65R resistance to tenofovir in cell culture. AIDS 2006;20:F9-13.
    MedlineWeb of Science
  222. 222.↵
    1. Velazquez-Campoy A,
    2. Vega S,
    3. Fleming E,
    4. et al
    . Protease inhibition in African subtypes of HIV-1. AIDS Rev 2003;5:165-71.
    Medline
  223. 223.↵
    1. Shafer RW,
    2. Chuang TK,
    3. Hsu P,
    4. White CB,
    5. Katzenstein DA
    . Sequence and drug susceptibility of subtype C protease from human immunodeficiency virus type 1 seroconverters in Zimbabwe. AIDS ResHum Retroviruses 1999;15:65-9.
    CrossRef
  224. 224.↵
    1. Tanuri A,
    2. Vicente AC,
    3. Otsuki K,
    4. et al
    . Genetic variation and susceptibilities to protease inhibitors among subtype B and F isolates in Brazil. Antimicrob Agents Chemother 1999;43:253-8.
    Abstract/FREE Full Text
  225. 225.↵
    1. Montes B,
    2. Vergne L,
    3. Peeters M,
    4. Reynes J,
    5. Delaporte E,
    6. Segondy M
    . Comparison of drug resistance mutations and their interpretation in patients infected with non-B HIV-1 variants and matched patients infected with HIV-1 subtype B. J Acquir Immune Defic Syndr 2004;35:329-36.
    CrossRefMedlineWeb of Science
| Table of Contents

This Article

  1. Clin Infect Dis. (2008) 47 (2): 266-285. doi: 10.1086/589297
  1. AbstractFree
  2. » Full Text (HTML)Free
  3. Full Text (PDF)Free

Classifications

Share

  1. Email this article

Navigate This Article

Current Issue

  1. March 1, 2012 54 (5)
  1. Clinical Infectious Diseases
  1. Alert me to new issues

Most