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Prevalence of Transmitted HIV-1 Drug Resistance and the Role of Resistance Algorithms

Data From Seroconverters in the CASCADE Collaboration From 1987 to 2003

Masquelier, Bernard, PharmD, PhD*; Bhaskaran, Krishnan, MSc; Pillay, Deenan, MPH; Gifford, Robert, PhD; Balestre, Eric, MPH§; Jørgensen, Louise Bruun, MSc, PhD; Pedersen, Court, MD; van der Hoek, Lia, PhD#; Prins, Maria, PhD**; Balotta, Claudia, MD††; Longo, Benedetta, MD‡‡; Kücherer, Claudia, MD§§; Poggensee, Gabriele, MD§§; Ortiz, Marta, PhD¶¶; de Mendoza, Carmen, PhD∥∥; Gill, John, MD##; Fleury, Hervé, MD, PhD*; Porter, Kholoud, PhD on behalf of the CASCADE Collaboration

JAIDS Journal of Acquired Immune Deficiency Syndromes: December 15th, 2005 - Volume 40 - Issue 5 - p 505-511
doi: 10.1097/01.qai.0000186361.42834.61
Basic Science
Free

Objectives: To examine factors influencing the rate of transmitted drug resistance (TDR) among seroconverters, with particular emphasis on 3 widely used genotypic drug resistance algorithms.

Methods: The study used data from CASCADE (Concerted Action on Seroconversion to AIDS and Death in Europe), a collaboration of seroconverter cohorts in Europe and Canada. Genotypic resistance data were derived within 18 months of the last seronegative test or date of laboratory evidence of acute infection and before the initiation of antiretroviral therapy. The Stanford algorithm was used to analyze each individual's nucleotide sequence. A multivariate logistic model was used to assess independent relationships between the presence of TDR and exposure category, sex, age at seroconversion, and year of seroconversion. The paper also describes 3 alternative definitions of resistance: the Stanford algorithm, the key resistance mutations defined by the International AIDS Society, and the Agence Nationale de Recherches sur le Sida (ANRS) algorithm.

Results: Forty-five of 438 patients (10.3%) seroconverting between 1987 and 2003 were infected with a drug-resistant HIV-1 variant. Forty patients (9.1%) showed resistance mutations to only 1 class of antiretroviral drugs, 2 (0.5%) to 2 classes, and 3 (0.7%) to 3 classes of antiretroviral therapy. It was suggested that individuals seroconverting later in calendar time were more likely to have TDR (relative risk 3.89 and 95% CI: 0.84 to 18.02, and relative risk 4.69 and 95% CI: 1.03 to 21.31, for 1996-1999 and 2000-2003, respectively, compared with pre-1996; P trend = 0.08). This trend was apparent regardless of the definition of TDR used. The total estimated proportion of individuals with TDR varied between 10.3% and 15.5% according to which definition was used.

Conclusions: Evidence was found for the rise of TDR over time. A specific definition of what constitutes TDR rather than a simple list of mutations is needed.

From the *Département de Virologie et Immunologie Biologique, CHU Bordeaux, France; †MRC Clinical Trials Unit, London, UK; ‡Centre of Virology, Royal Free and University College Medical School, London, UK; §INSERM, U 593, Bordeaux, France; ¶Statens Serum Institut, Copenhagen, Denmark; ∥Odense University Hospital, Odense, Denmark; #Department of Human Retrovirology, Academic Medical Centre, Amsterdam, The Netherlands; **Municipal Health Service, Amsterdam, The Netherlands; ††Institute of Infectious and Tropical Diseases, University of Milan, Milan, Italy; ‡‡Istituto Superiore di Sanita, Rome, Italy; §§Robert Koch Institute, Berlin, Germany; ¶¶Servicio de Diagnostico y Referencia de Retrorivirus, Madrid, Spain; ∥∥Service of Infectious Diseases, Hospital Carlos III, Madrid, Spain; and ##Southern Alberta HIV Clinic, Calgary, Canada.

Received for publication June 2, 2005; accepted September 2, 2005.

CASCADE (Concerted Action on Seroconversion to AIDS and Death in Europe) is funded through a grant from the European Union (QLK2-2000-01431). This work is funded through a parallel grant (QLRT-2001-01708).

See Appendix for CASCADE collaborators.

Correspondence: Bernard Masquelier, Département de Virologie et Immunologie Biologique, CHU de Bordeaux, place Amélie Raba Léon, 33076 Bordeaux cedex, France (e-mail: bernard.masquelier@chu-bordeaux.fr).

Reprints: Kholoud Porter, MRC Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK (e-mail: kp@ctu.mrc.ac.uk).

The ability of highly active antiretroviral therapy to suppress HIV-1 replication completely may be limited in viral strains with intrinsic resistance to antiretroviral drugs.1 Although the majority of HIV-1 drug resistance is observed in treated individuals, these drug-resistant variants are also being transmitted.2-6 Strains with mutations conferring drug resistance may be less fit than the wild-type (drug-sensitive) virus and, therefore, less likely to be transmitted,7 although there is evidence of increased fitness of drug-resistant HIV-1 strains.8

A wide variation has been reported in trends in the prevalence of transmitted drug-resistant (TDR) HIV-1 within European countries, the United States, and elsewhere.9-23 This is not surprising given that many factors affect the apparent prevalence of TDR HIV-1, such as the precise definition of resistance, time elapsed since HIV-1 infection, risk groups within the sampled population, sampling strategies, and testing policies. In addition, TDR must be viewed in the context of the source population, including the proportion of infected persons in the population who are on therapy (and by inference, the proportion of those with diagnosed infection), levels of adherence to antiretroviral therapy (ART), the proportion of treated individuals in whom therapy fails,24-26 HIV-1 incidence, the rate of unsafe sexual and nonsexual encounters, the efficacy of transmission of resistant and wild-type viruses, and the existence of possible clusters in the sampled population. Importantly, persons with genotypic information available may be a select group of individuals who do not represent the rest of the infected population. For example, some infected individuals may be selected for genotyping because there is particular concern that they are infected with drug-resistant HIV-1. Such a policy would increase the reported prevalence of transmitted resistance.

As the number of currently available anti-HIV drug classes is limited, infection with drug-resistant HIV-1 immediately restricts therapeutic options. We describe the characteristics of a large dataset of persons sampled close to the time of estimated HIV-1 seroconversion using data from a collaboration of seroconverter cohorts in Europe and Canada. We examined factors influencing the rate of TDR among seroconverters, with particular emphasis on 3 widely used genotypic drug resistance algorithms.

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MATERIALS AND METHODS

Study Population

Patients included in the study belonged to 11 cohorts of persons with HIV-1 infection from 7 countries in Western Europe and Canada. These cohorts were from the CASCADE Collaboration (Concerted Action on Seroconversion to AIDS and Death in Europe),27 and data were pooled in November 2004. Patients included in CASCADE are older than 15 years and have a reliably estimated date of HIV-1 seroconversion, either with a laboratory evidence of acute infection (antibody negative with polymerase chain reaction positivity or evolving antibody response) or with a previously seronegative test within 3 years of the 1st seropositive test.

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Genotypic Resistance Analysis

Genotypic resistance data were derived from the 1st available plasma sample within 18 months of the last seronegative test or date of laboratory evidence of acute infection and before the initiation of ART. Sequencing was performed by laboratory partners in the country of origin, using genotypic resistance assays: Viroseq HIV Genotyping System v2.0 (Abbott Diagnostics, Chicago, IL), TruGene HIV-1 Genotyping Test (Bayer, Berkeley, CA), and homemade procedures. Participating centers were asked to provide nucleotide sequence data spanning the entire protease gene and at least codons 41-236 of reverse transcriptase (RT).

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Definitions of Transmitted Drug Resistance

The Stanford algorithm was used to analyze each individual's nucleotide sequence (Stanford HIV Drug Resistance Database, http://hivdb.stanford.edu, updated October 2004). The algorithm is based on a matrix of scores for each drug-mutation combination, including negative values to represent hypersusceptibility. Drug-specific scores are summed across all mutations in the query sample, and drug susceptibility is classified, based on the total score, as “sensitive” (score 1), “potential low-level resistance” (score 2), “low-level resistance” (score 3), “intermediate resistance” (score 4), or “high-level resistance” (score 5). In our analysis, individuals were considered to have transmitted drug resistance if these sequence data implied “intermediate” or “high-level” resistance to at least 1 drug. This definition is referred to herein as “Stanford 4+.” We also describe 3 alternative definitions of resistance: “low-level resistance” or above to at least 1 drug using the Stanford algorithm (“Stanford 3+”); the presence of ≥1 of the key resistance mutations defined by the International AIDS Society (IAS; http://www.iasusa.org/resistance_mutations, updated October 2004) excluding those at position 118, and including any mutation at position 215 (“modified IAS”); and, finally, any resistance or possible resistance as defined by the Agence Nationale de Recherches sur le Sida (ANRS) algorithm (http://hivfrenchresistance.org, updated July 2004).

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Phylogenetic Analysis

To describe the phylogenetic relationships between viruses infecting individuals recruited to the CASCADE cohorts, we considered all virus sequences, including those from samples taken later than 18 months into infection. The derived HIV-1 sequence data were aligned across 862 nucleotides with reference sequences from the Los Alamos database representing various subtypes within the HIV-1 M group. The resulting alignment spanned codons 1-99 of protease and 40-228 of reverse transcriptase. We excluded sequences (n = 37) other than the reference sequences that were not supported by bootstrapped neighbor joining as subtype B. Phylogenetic analysis was performed using the neighbor-joining algorithm implemented in PAUP* (Swofford, D.L. PAUP* Phylogenetic analysis using parsimony (*and other methods), version 4.0 (1998, Sinauer Associates, Sunderland, MA) and the general time reversible substitution model with gamma rate heterogeneity and without a molecular clock, as selected by Modeltest.28 Statistical support for the branching relationships within the tree was obtained by carrying out 1000 bootstrap replicates of neighbor joining. The tree was rooted with non-B subtypes as an outgroup.

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Statistical Analysis

We performed simple descriptive analyses of the demographics and HIV-1 subtype of patients included in the study and on the prevalence and specific drug class mutations of TDR. A multivariate logistic model was used to assess independent relationships between the presence of TDR and exposure category, sex, age at seroconversion, and year of seroconversion, and P values are presented from Wald tests. We undertook sensitivity analyses to test the robustness of our conclusions to differing definitions of TDR. Changes in population characteristics between calendar periods were assessed using χ2 tests for categorical variables and nonparametric median tests for continuous variables. All analyses were performed using Stata version 8, 2003 (StataCorp., College Station, TX).

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RESULTS

Population Characteristics

Demographics

Of 6327 seroconverters enrolled in the participating 11 cohorts, a genotypic resistance analysis was available from plasma for 642 patients prior to any ART. Of these, 438 were sampled within 18 months of the last seronegative test (Table 1). The majority of individuals were exposed to HIV-1 through sex between men (328/438 patients, 75%) and the median year of seroconversion was 1999 (range: 1987-2003). The median age at seroconversion was 31.6 years (range: 17.2-66.2 years). Twenty percent (87/438) of patients had HIV-1 antibody negative and positive results <1 month apart. There was evidence of a change in the characteristics of the population over calendar time. The proportion of gay men increased from 54% of those seroconverting before 1996 to 83% in 1999-2003, whereas the proportion of injecting drug users decreased from 33% to 2% (P < 0.001). The proportions from the constituent cohorts also changed (P < 0.001); before 1996 the Netherlands cohorts accounted for 49% of seroconverters, whereas 42% were from cohorts in Italy, Denmark, and the United Kingdom. By 1999-2003, 52% of patients were from the United Kingdom and 25% from France. There was no evidence of a change over time in the proportion of individuals in whom acute infection was diagnosed, by age or sex (P = 0.85, 0.18, 0.13, respectively).

TABLE 1

TABLE 1

Baseline CD4 cell counts (defined as the 1st available count within 1 year of seroconversion and before therapy) were available for 413 individuals; the median CD4 cell count was 522 cells/mm3 (interquartile range 389-690). The mean baseline plasma HIV-1 RNA (based on n = 364 individuals) was 4.68 log10 copies/mL (SD = 1.06).

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Phylogenetic Relationships Among Cohorts

Of 642 sequences included within this analysis, 605 were retained as subtype B. Statistical support for branching relationships could generally only be seen at the tips of the phylogeny. A total of 153 taxa (25%) grouped into clusters with at least 1 other virus and with bootstrap support >65%. Bootstrapping revealed that >90% of supported clusters consisted of sequences obtained from samples within the same cohort. Thus, detectable relationships between novel infections generally occurred only within localities (data not shown). There was no clear evidence of clusters of drug-resistant viruses in this dataset, possibly due to the relatively low frequency of such viruses. However, this obviously does not exclude the possibility of founder effects of viruses with specific mutational patterns.

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HIV Subtype

Of 438 individuals, 401 (91.6%) with viruses obtained within 18 months of last seronegative test carried HIV-1 subtype B. The frequency of B subtype was high in men who had sex with men (97.3%) and in injecting drug users (97.8%) but lower in those infected through sex between men and women (50.0%). The 37 non-B viruses clustered with CRF02_AG sequences in 6 patients, with CRF01_AE in 7 patients, and with subtypes A, C, D, and G in 6, 7, 1, and 1 patients, respectively; 8 patients were probably infected with complex recombinants, with different clustering for RT and protease sequences. The overall proportion of B subtypes tended to decrease over time, with 98.5% of those seroconverting before 1996 carrying B subtype, compared with 92.7% in 1996-1998 and 88.0% in 1999-2003.

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Genotypic Resistance Analysis

Of 438 sampled within 18 months of the last seronegative test, 45 (10.3%) were infected with a drug-resistant HIV-1 variant (using Stanford 4+). Estimated seroconversion for these 45 patients ranged between 1995 and 2002. There was no evidence to suggest that the prevalence of TDR differed by cohort (P = 0.21). The proportion of TDR did not differ between those sampled at the stage of acute HIV-1 infection (ie, within 3 months of their last seronegative HIV-1 test, n = 117) and those sampled at 3-18 months after seroconversion (10.3% in both cases). Forty-four of these patients carried HIV-1 subtype B, and one carried subtype G. Twenty-five patients (5.7%) had a virus considered resistant to ≥1 NRTIs; 15 (3.4%) carried resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs); and 13 (3.0%) had resistance to protease inhibitors (PIs). Forty patients (9.1%) showed resistance mutations to only 1 class of antiretroviral drugs (20 resistance to NRTIs, 12 to NNRTIs, and 8 to PIs); 2 patients (0.5%) had a virus with resistance mutations to 2 classes of ART; and 3 (0.7%) patients, reported from 2 cohorts, were infected by variants with resistance to drugs within 3 classes of ART.

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Factors Associated With Transmitted Drug Resistance

The prevalence of TDR was highest among those exposed to HIV-1 through sex between men (11.6% compared with 5.6% and 6.7% among those infected through sex between men and women, and injecting drug users, respectively) (Table 1). Men appeared to have more TDR than women (10.9% compared with 5.6%). There was no difference in the median age at seroconversion among those with and those without TDR, though the median year of seroconversion was slightly later among those with TDR (1999, interquartile range 1997-2001 compared with 1998, 1996-2001). In a multivariate logistic model (Table 2), there was no evidence to suggest that TDR was associated with exposure category (P = 0.88), sex (P = 0.86), or age at seroconversion (P = 0.99). There was a suggestion that individuals seroconverting later in calendar time were more likely to have TDR (relative risk 3.89 and 95% CI: 0.84 to 18.02, and relative risk 4.69 and 95% CI: 1.03 to 21.31, for 1996-1999 and 2000-2003, respectively, compared with pre-1996; P trend = 0.08).

TABLE 2

TABLE 2

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Comparison of Differing Definitions of Resistance

We then considered and compared the 4 different definitions of TDR more closely (Table 3). The trend of increasing prevalence of TDR over calendar time was apparent regardless of the definition of TDR used. Notably, however, there was little increase in the proportion of NRTI resistance in the latest calendar period (and indeed this proportion fell from 7.6% to 5.4% and 9.7% to 8.0% using the Stanford 4+ and ANRS definitions, respectively). This was in contrast with the increasing prevalence of resistance to other classes of ART.

TABLE 3

TABLE 3

The total estimated proportion of individuals with TDR was highest using the Stanford 3+ and modified IAS definitions (15.1% and 15.5%, respectively) and lowest using the Stanford 4+ and ANRS definitions (10.3% and 11.4%, respectively). Differences in prevalence of resistance between the definitions were most evident when considering NRTI and PI resistance; estimates of NNRTI resistance were similar by all definitions.

Among the 45 individuals with resistance on the Stanford 4+ definition, 42 (93%) carried ≥1 key mutation from the modified IAS list (Table 4). The most common mutations in RT were at codon 215, with 9 patients with mutations T215Y/F, 13 with 215 revertants (T215C/D/E/L/S), and M41L (18 individuals, all of whom also had a mutation at codon 215). The most prevalent NNRTI and PI resistance mutations were K103N and L90M, present in 9 and 8 patients, respectively.

TABLE 4

TABLE 4

Twenty-six patients carried 1 mutation conferring resistance according to the modified IAS definition but not by the Stanford 4+ definition, the most common being L33I (7 patients) and T215D/S/V (8 patients). All 8 patients with resistance by ANRS but not Stanford 4+ carried a mutation at codon 215 (S/C/D/V). One specimen found to be resistant to the 3 classes of ART according to the Stanford 4+ algorithm carried the protease mutations L10I, L63P, V77I, and V82C and was considered to have intermediate resistance to nelfinavir by Stanford 4+ but to be sensitive to PIs according to the other definitions.

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DISCUSSION

Studying TDR HIV-1 over time in recently infected antiretroviral-naive patients enrolled into 11 cohorts from Western Europe and Canada, we find a prevalence of 10.3% of drug-resistant HIV-1 variants between 1995 and 2002. This frequency is similar to those recently reported from large drug-naive cohort databases in Europe9,13,29 and the United States12,18 but lower than frequencies reported from New York City,17 Spain,22 Israel,20 and Quebec.30 In our study, the transmission of HIV-1 variants resistant to NRTIs was more frequent than variants resistant to NNRTIs or PIs; the resistance to at least 2 classes of antiretrovirals was rare (1.2% of the patients). Furthermore, we identified a suggestion of an increase in TDR over time. This finding is not biased by the unknown duration of infection as might be the case in cohorts of chronically infected individuals. However, this trend is contrary to other reports showing no change13 or a decrease16,31 of TDR over time but is in accordance with previous studies showing increasing prevalences in United States.12,17 However, it must be noted that there were individual variations over calendar time between the contributing cohorts, but as numbers were low for some, and cannot be regarded as typical of the countries from which they were drawn, individual cohort data are not shown. Furthermore, we cannot rule out sampling bias that may have led to a greater proportion of persons with TDR strains being genotyped in later calendar periods.

Most of the patients followed up in prospective cohorts such as those included here are likely to have access to ART, as probably do their sexual partners. It is, therefore, possible that the viruses from the infecting partner of our patients may have been submitted to a growing selective pressure of ART, explaining an increase in TDR over time, particularly concerning resistance to NNRTIs and PIs. Indeed, the majority of patients enrolled into our study were gay men, although we found no evidence to suggest that risk group significantly influenced TDR rates. Nevertheless, we would expect different results if studying a drug-naive, chronically infected population. An increasing proportion of new HIV-1 diagnoses in Europe represents individuals infected elsewhere, often in resource-poor countries with little access to ART. Such infections would contribute to HIV-1 diversity (clade distribution) but not necessarily to TDR.32 Indeed, phylogenetic analyses of viruses represented in this study demonstrate the presence of localized epidemics within cohorts, including specific clusters. These data therefore support the theory that primary infections are a significant source of further new drug-resistant infections, due to the high infectivity at this period.33

Because differences between drug resistance algorithms may explain the differences in TDR prevalence published in the literature, including those published from the individual studies included in CASCADE, we interpreted the combined data by use of 4 different definitions of resistance. The Stanford 4+ and ANRS definitions lead to lower estimates of TDR than do the modified IAS list or the Stanford 3+ definitions. This could be explained because the Stanford low-level resistance level (score 3) considers isolated mutations or associations of mutations leading to only a low level of resistance. TDR prevalence could also be higher with the modified IAS definition because of inclusion of patterns with only 1 mutation, which are not considered as resistant with interpretation algorithms (eg, RT K70R, or PR L33I). Finally, other discordances, including those between Stanford 4+ and ANRS, were due to different scorings for the revertant genotypes at RT codon 215, which are related to resistance to thymidine analogues with the ANRS algorithm.

Taken together, these findings indicate that there is evidence of a rise in the rate of TDR in the populations sampled within this collaboration. However, there is a need to harmonize across the various interpretation algorithms currently available to lead to a specific definition of what constitutes TDR rather than a simple list of mutations. Using 2 of the most currently used algorithms led to close prevalences of TDR, suggesting that a consensus definition of TDR could possibly be constructed but would need regular updating according to the evolution of the knowledge of ART resistance. Further, the identification of algorithms that predict treatment response following transmission of drug resistance will assist in moving to such a consensus. Such a definition, and well-defined study populations, would undoubtedly help to identify the different dynamics of the transmission of resistant HIV-1 isolates in the future.

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ACKNOWLEDGMENTS

The authors thank all clinical colleagues who have contributed data to the individual cohorts. Special thanks to Ms. Demelza Nock for help with the manuscript.

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REFERENCES

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22. de Mendoza C, Rodriguez C, Colomina J, et al, on behalf of the Spanish Seroconverter Study Group. Transmission of drug-resistant viruses in recent HIV seroconverters in Spain. Abstract 672. Paper presented at: 12th Conference on Retroviruses and Opportunistic Infections; February 22-25, 2005; Boston, MA.
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APPENDIX

Coordinating Centers

MRC Clinical Trials Unit (Abdel Babiker, Krishnan Bhaskaran, Janet Darbyshire, Kholoud Porter, A. Sarah Walker) and Royal Free and University College Medical School Windeyer Institute (Rob Gifford and Deenan Pillay).

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Collaborators Contributing Data to Virology Study

Aquitaine cohort, France (Eric Balestre, Sophie Capdepont, Geneviève Chêne, Francois Dabis, Hervé Fleury, Bernard Masquelier, Rodolphe Thiébaut); German cohort, Germany (Osamah Hamouda, Claudia Kücherer, Gabriele Poggensee); Italian Seroconversion Study, Italy (Claudia Balotta, Benedetta Longo, Giovanni Rezza, Lorenzo Deho); GEMES, Spain (Carmen Rodriguez, Vicente Soriano, Alfredo García-Saiz, Julia del Amo, Jorge del Romero, Marta Ortiz, Carmen de Mendoza); Amsterdam Cohort Studies among homosexual men and drug users, the Netherlands (Nicole Back, Roel Coutinho, Maria Prins, Lia van der Hoek); Copenhagen cohort, Denmark (Louise Bruun Jørgensen, Claus Nielsen, Court Pedersen, Niels Obel); UK Register of HIV Seroconverters, United Kingdom (Abdel Babiker, Janet H. Darbyshire, Noël Gill, Anne M. Johnson, Andrew N. Phillips, Kholoud Porter); South Alberta clinic, Canada: (M. John Gill, Sonia Gingues).

Keywords:

HIV-1 drug resistance; HIV-1 transmission; genotype; prevalence of resistance

© 2005 Lippincott Williams & Wilkins, Inc.