Moderate Levels of Pre-Treatment HIV-1 Antiretroviral Drug Resistance Detected in the First South African National Survey

Kim Steegen; Sergio Carmona; Michelle Bronze; Maria A. Papathanasopoulos; Gert van Zyl; Dominique Goedhals; William MacLeod; Ian Sanne; Wendy S. Stevens

doi:10.1371/journal.pone.0166305

Abstract

Background

In order to assess the level of transmitted and/or pre-treatment antiretroviral drug resistance to HIV-1, the World Health Organization (WHO) recommends that regular surveys are conducted. This study’s objective was to assess the frequency of HIV-1 antiretroviral drug resistance in patients initiating antiretroviral treatment (ART) in the public sector throughout South Africa.

Methods

A prospective cross-sectional survey was conducted using probability proportional to size sampling. This method ensured that samples from each province were proportionally collected, based on the number of patients receiving ART in each region. Samples were collected between March 2013 and October 2014. Pol sequences were obtained using RT-PCR and Sanger sequencing and submitted to the Stanford Calibrated Population Resistance tool v6.0.

Results

A total of 277 sequences were available for analysis. Most participants were female (58.8%) and the median age was 34 years (IQR: 29–42). The median baseline CD4-count was 149 cells/mm³ (IQR: 62–249) and, based on self-reporting, participants had been diagnosed as HIV-positive approximately 44 days prior to sample collection (IQR: 23–179). Subtyping revealed that 98.2% were infected with HIV-1 subtype C. Overall, 25 out of 277 patients presented with ≥1 surveillance drug resistance mutation (SDRM, 9.0%, 95% CI: 6.1–13.0%). Non-nucleoside reverse transcriptase inhibitor (NNRTI) mutations were the most numerous mutations detected (n = 23). Only two patients presented with a protease inhibitor (PI) mutation. In four patients ≥4 SDRMs were detected, which might indicate that these patients were not truly ART-naïve or were infected with a multi-resistant virus.

Conclusions

These results show that the level of antiretroviral drug resistance in ART-naïve South Africans has reached moderate levels, as per the WHO classification. Therefore, regular surveys of pre-treatment drug resistance levels in all regions of South Africa is highly recommended to monitor the changing levels of pre-treatment antiretroviral drug resistance.

Citation: Steegen K, Carmona S, Bronze M, Papathanasopoulos MA, van Zyl G, Goedhals D, et al. (2016) Moderate Levels of Pre-Treatment HIV-1 Antiretroviral Drug Resistance Detected in the First South African National Survey. PLoS ONE 11(12): e0166305. https://doi.org/10.1371/journal.pone.0166305

Editor: Jason Blackard, University of Cincinnati College of Medicine, UNITED STATES

Received: May 3, 2016; Accepted: October 26, 2016; Published: December 1, 2016

Copyright: © 2016 Steegen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The pol nucleotide sequences were submitted to GenBank using Sequin v13.70 (www.ncbi.nlm.nih.gov/Sequin) and are available under accession numbers KT892975-KT893251.

Funding: The survey was funded by The Global Fund to Fight AIDS, Tuberculosis and Malaria (SAF-H-RTC, www.theglobalfund.org). The views described herein are the views of the researchers involved, and do not represent the views or opinions of the Global Fund to Fight AIDS, Tuberculosis & Malaria, nor is there any approval or authorisation of this material, express or implied, by the Global Fund to Fight AIDS, Tuberculosis and Malaria. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

South Africa has the largest antiretroviral treatment (ART) program in the world with an estimated cumulative number of individuals on treatment of 3.1 million[1]. Transmitted HIV drug resistance (TDR) implies that newly-infected individuals are infected with a drug-resistant virus. With increased treatment coverage, TDR is expected to increase, especially in settings relying on clinical and CD4 monitoring to detect treatment failure, whereas the early detection of failure through viral load monitoring coupled with a timely switch to second-line therapy may protect against an increase in TDR [2]. The presence of pre-treatment resistance can lead to an increased risk of virological failure and modelling predicts that increased TDR would have a significant impact on future HIV mortality [3–5]. Onward transmission of antiretroviral drug resistance from patients failing first-line therapy may therefore compromise South Africa’s success toward reaching the last 90% of the UNAIDS 90-90-90 target by 2020, which is achieving 90% viral suppression in patients on antiretroviral combination therapy [6].

The definition of TDR can theoretically only be applied to recently-infected individuals as transmitted mutations may revert back to wild type, or only be present at levels below the sensitivity of standard genotyping assays [7]. Since it is very challenging to identify recently-infected individuals, both recently- and chronically-infected individuals who start ART are often included in surveys.

The WHO has classified levels of TDR into three categories: low (<5%), moderate (5–15%) and high levels (>15%) [8]. The 2012 WHO HIV drug resistance report, which included survey data from 2004 to 2010, stated that most surveys (72.2%, 52/72) detected low prevalence (<5%) of TDR to all drug classes. However, 20 surveys showed a moderate level of TDR (5–15%) to one or more antiretroviral drug classes. Moderate levels of TDR were most commonly observed against the NNRTI drug class. Overall, 13 out of 20 surveys (65%) showing a moderate prevalence of TDR to any drug class were conducted in the African region, particularly east Africa [9]. A more recent meta-analysis suggested that the median overall level of TDR in sub-Saharan Africa was limited to 2.9% with a yearly 1.09-fold (95% CI: 1.05–1.14) increase in odds of TDR since national ARV scale-up, attributable to an increase in non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance [10]. Earlier mathematical models have suggested that the 5% threshold for TDR would only be breached 10 years after large-scale ART roll-out, or when at least 30% of HIV-infected patients are on ART [8]. As 2014 marked a decade of large-scale ART roll-out in South Africa and more than 80% of the HIV-infected South African individuals in need of ART, based on a CD4-count of 350 cells/mm³ were on treatment by the end of 2012, an increasing level of transmitted and/or pre-treatment drug resistance in the South African population can be expected.

Since 2002, various surveys addressing the level of transmitted or pre-treatment drug resistance have been conducted in South Africa. For ease of reference no distinction was made between studies that targeted only recently-infected individuals versus any patient prior to ART initiation. Gauteng and KwaZulu-Natal were the most surveyed regions. The studies conducted in Gauteng, Free State and Mpumalanga consistently detected a low prevalence of TDR [11–14]. The detected prevalence in KwaZulu-Natal changed from low levels in 2005 and 2007 to moderate levels of TDR in 2008 and 2009 [13]. These moderate levels were confirmed by one study [15], but not by two additional studies from the region [16, 17]. A recent temporal analysis of transmitted drug resistance in KwaZulu-Natal showed an increase between 2010 and 2012 [18]. A single study from Limpopo also detected moderate levels of TDR [19]. Despite the prevalence of TDR remaining low in the Western Cape, the latest data suggest that this prevalence is on the rise [20–22].

This study therefore aimed to conduct the very first national pre-treatment drug resistance survey to assess the current situation in the whole of South Africa.

Materials and Methods

Survey design

In estimating the proportion of ART naïve patients that had transmitted or pre-treatment resistance at a national level, we had two considerations: the size of the sample and how to select samples to represent South Africa. For the size of the sample, with 277 sequences we had sufficient statistical power to detect a 10% prevalence with a margin of error of +/- 3.5%, which we deemed sufficient to estimate this proportion. We selected the samples from the provinces proportional to the HIV population of the sample in order to achieve a proportional distribution of ART naïve individuals using probability proportional to size sampling (PPSS). South Africa has 9 provinces and 52 districts. Only tier two facilities with at least 500 patients in ART care were considered, to ensure adequate sampling options. The number of patients in care per facility was obtained from the South African District Health Information System. Any patient initiating ART or attending a pre-treatment follow-up visit, without prior ART exposure, who was at least 18 years of age and had signed informed consent, was eligible to be included in the study. Due to the lack of research staff at the health care facilities, a non-random sample of patients was used for this survey.

Data and sample collection

Health care professionals at sentinel sites informed eligible patients of the HIV antiretroviral drug resistance survey. After obtaining written informed consent, a study questionnaire was completed. The study questionnaire included questions regarding basic demographics, ART history and CD4 counts. Subsequently, whole blood was collected in EDTA-vacutainers. The samples were then sent to one of three participating HIV genotyping laboratories: Tygerberg Hospital, Stellenbosch processed samples collected in the Western Cape; Universitas, Bloemfontein, processed samples collected in the Free State and the laboratory at Charlotte Maxeke Johannesburg Academic Hospital, Johannesburg processed the samples collected in the remaining provinces. To ensure sample integrity, it was advised that the specimens should reach the relevant laboratories within 72 hours after sample collection. Upon receipt, samples were centrifuged and plasma was stored at -80°C until processing. Each laboratory used their own validated population-based in-house genotyping method to obtain pol sequences. The in-house genotyping assay used at Tygerberg hospital covers protease (PR) amino acid (aa) 1 to 99 and reverse transcriptase (RT) aa 1 to 262 and has a lower limit of detection of 400 copies/ml [23] The in-house genotyping assay used at Universitas covers PR aa 1 to 99 and RT aa 1 to 240 and has a lower limit of detection of 1000 copies/ml. Finally, the in-house genotyping assay used at Charlotte Maxeke Johannesburg Academic Hospital covers PR aa 1 to 99 and RT aa 1 to 418 and has a lower limit of detection of 1000 copies/ml [24, 25].

Sequence analysis

All nucleotide sequences obtained were collated at Charlotte Maxeke Johannesburg Academic Hospital for analysis. In addition, the Stanford CPR v6.0 (Calibrated Population Resistance) tool (http://cpr.stanford.edu/cpr.cgi) was used to interpret the level of pre-treatment drug resistance in the ART naïve population. This tool is based on the 2009 WHO surveillance drug resistance mutation (SDRM) list [26]. The Stanford HIVdb v7.0.1 tool (http://sierra2.stanford.edu/sierra/servlet/JSierra) was used to generate predicted resistance profiles. Predicted antiretroviral drug resistance profiles were categorised as either being susceptible (including potential low-level resistance), intermediate (including low-level resistance) or high-level resistance.

Subtyping and cross-contamination check

A multiple alignment of the 277 nucleotide sequences with the 2010 subtype reference sequences from HIV-1 subtypes A to K, and several CRFs (http://hiv-web.lanl.gov), were generated using Clustal X (version 2.1) (http://www.clustal.org) to determine subtypes as well as confirm there was no cross-contamination amongst samples. The generated Clustal X alignment was used to construct a Neighbor-Joining phylogenetic tree in MEGA 7 (http://www.megasoftware.net) with the Kimura two-parameter model. The stability of the nodes was assessed by bootstrap analysis (1000 replicates), and bootstrap values greater than 70% were considered significant. An additional phylogenetic analysis was conducted for all sequences with at least one SDRM, to exclude clustering among resistant sequences.

In addition pol subtyping was confirmed by using the Rega HIV subtyping tool v2.0 (http://bioafrica.net/rega-genotype/html/subtypinghiv.html). Any sequence that was assigned to be a non-C subtype was also analysed using the NCBI genotyping tool (http://www.ncbi.nlm.nih.gov/projects/genotyping/) and the jumping profile HMM tool (http://jphmm.gobics.de/submission_hiv.html) to confirm the subtype.

Statistical analysis

Statistical analyses were performed using GraphPad Prism 6. For all prevalence calculations, 95% confidence intervals were calculated using the modified Wald Method.

Sequence Data

The pol nucleotide sequences were submitted to GenBank using Sequin v13.70 (www.ncbi.nlm.nih.gov/Sequin) and are available under accession numbers KT892975-KT893251.

Ethics Statement

This study was conducted with ethical clearance by the Research on Human Subjects (Medical) Committee at the University of the Witwatersrand (Clearance Number M120254). Written informed consent was obtained from all participants prior to sample collection.

Results

Patient samples were collected between March 2013 and October 2014. Only 320 samples out of the originally targeted 336 samples were collected. Samples from North West and Western Cape provinces were underrepresented due to the inability to motivate health care workers and/or patients to participate in the survey. For 43 samples, sequences could not be included due to the inability to obtain a PCR product (n = 31), inadequate completion of the consent form (n = 5) or participants being younger than 18 years (n = 7). Finally, a total of 277 sequences were available across all nine provinces (Table 1, S1 Fig). The median age of participants was 34 years (IQR: 29–42) and most participants were female (n = 163, 58.8%). Self-reported time of diagnosis of HIV-positive status was available for 258 participants at a median of 44 days (IQR: 23–179) prior to sample collection. Baseline CD4 counts were available for 195 participants and were obtained 16 days (median) before the date of sample collection (IQR: 8–36). The median baseline CD4 count was 149 cells/mm³ (IQR: 62–249).

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Table 1. Prevalence of surveillance drug resistance mutations (SDRM) amongst HIV-1 infected individuals throughout South Africa.

https://doi.org/10.1371/journal.pone.0166305.t001

The most common pol subtype was HIV-1 subtype C (98.6%). Five sequences belonged to non-C subtypes. One patient from the Eastern Cape was infected with HIV-1 subtype D; two patients from Gauteng were infected with HIV-1 subtype B and CRF02_AG, respectively. The last non-C subtype was detected in KwaZulu-Natal (recombinant between subtype C and A2).

Overall, 25 out of 277 surveyed patients presented with at least one antiretroviral drug resistance mutation. This finding translates into an overall moderate pre-treatment HIV-1 resistance prevalence of 9.0% (95% CI 6.1–13.0%, Table 1). Phylogenetic analysis of these 25 sequences did not reveal any clustering (S2 Fig).NNRTI mutations contributed to the majority of detected mutations, with 23 patients presenting with at least one NNRTI mutation (8.3%; 95% CI 5.6–12.2%). The most common NNRTI mutation was K103N (n = 16) followed by Y181C (n = 6) and V106M (n = 3). Seven (2.5%) participants presented with at least one NRTI mutation. The K65R mutation was the most frequently detected NRTI mutation (n = 4) followed by M184V (n = 3). Six individuals (2.2%) were infected with a virus harbouring a combination of at least one NNRTI and one NRTI mutation. Only two participants (0.7%, 95% CI 0.0–2.8%) presented with a PI mutation.

The presence of at least four mutations was observed in four patients. All of these patients presented with the M184V and/or the K65R mutation. Details about the SDRM detected in the viruses from these 25 patients are provided in Table 2.

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Table 2. Detailed antiretroviral drug resistance mutation profiles detected in 25 of the 277 pre-treatment patient samples.

https://doi.org/10.1371/journal.pone.0166305.t002

Although no data on the regimen initiation after sample collection was obtained, an assumption has been made that all patients included in the survey would be initiated on the standard first-line regimen comprising a combination of tenofovir (TDF), lamivudine/emtricitabine (3TC/FTC) and efavirenz (EFV) as per national guidelines [27]. Results of the sequence analyses imply that 91.0% (n = 252) of these patients, without any detected antiretroviral drug resistance, would be initiated on a fully-active triple ART combination. In two additional patients (0.7%), the detection of a resistance mutation would not affect the activity of the recommended first-line regimen. Seventeen patients (6.1%) showed resistance against NVP and EFV prior to ART initiation, which implies that only the NRTI backbone would be active in these patients. Essentially, these patients are started on a dual regimen. Finally, one patient (0.4%) would receive TDF monotherapy, whereas for five patients (1.8%) none of the three drugs in the standard first-line regimen would be active.

Discussion

This pre-treatment antiretroviral drug resistance survey was designed to assess the current prevalence of HIV-1 drug resistance among South African HIV-infected patients prior to ART initiation in all nine provinces for the first time. The obtained data is important to verify whether the currently recommended first-line ART regimens are still adequate to ensure a good treatment outcome in most HIV-infected patients initiating these regimens. This is the first survey which includes samples from every province in South Africa, based on the proportion of patients receiving ART in each region.

The observed pre-treatment resistance prevalence is classified as a moderate level of 9.0% (95% CI: 6.1% to 13.0%) according to the WHO classification. This level of pre-treatment drug resistance was only detected in three South African studies prior to this survey: 9.3% in Limpopo [19] and 7.4 [15] and 13.5% [13] in KwaZulu-Natal. However, the increasing prevalence over the years might be explained by the hypothesis that mature ART programmes can be a proxy for higher levels of pre-treatment drug resistance [11]. However, other factors, which were beyond the scope of this study, such as virological failure rates, adherence levels in the treated population and drug supply continuity, can also affect the levels of pre-treatment drug resistance. Earlier statistical models predicted that the 5% threshold for TDR would only be breached 10 years after large-scale ART roll-out or when at least 30% of HIV-infected patients are on ART [8]. As 2014 marks a decade of large-scale ART roll-out in South Africa and more than 80% of the HIV-infected South African individuals in need for ART were on treatment since the end of 2012, the increasing levels of transmitted and/or pre-treatment drug resistance in the South African population are not unexpected [3]. NNRTI mutations remain the most commonly observed pre-treatment resistance mutations. Non-TAM mutations such as K65R and M184V mutations were more commonly observed compared to TAMs, this is a reflection of the replacement of d4T and AZT by TDF in first-line regimens since 2010 [28]. To our knowledge it is also the first report on pre-treatment drug resistance that showed the K65R mutation to be the most commonly observed NRTI mutation.

Based on these findings, only 2.2% of the population initiating ART would likely be started on monotherapy, or a completely inactive regimen and 6.1% of the study population would be initiated on dual ART (active TDF and 3TC/FTC). Hamers et al. demonstrated in a large multi-centre study that pre-treatment drug resistance does have an impact on the outcome of first-line treatment with an odds ratios for increased virological failure of 2.13 (95% CI: 1.44–3.14). In addition, patients with pre-treatment drug resistance are more likely to develop acquired drug resistance as compared to those patients without pre-treatment drug resistance (OR 2.30, 95% CI: 1.55–3.40) [4].

It can be argued that the patients with multiple antiretroviral drug resistance mutations, without recorded treatment history, were either not truly drug-naïve or might have been infected with a multidrug resistant virus. Several studies, especially cluster transmission studies, have shown that resistance mutations with minimal impact on viral fitness may be transmitted serially without the need for intervening drug pressure [29, 30]. However, the presence of K65R and/or M184V in each of these individuals might indicate these patients were not truly ART naïve due to the high fitness cost of both mutations [31–33]. Treatment histories of these patients with multiple SDRMs were verified and health care workers confirmed these patients did not disclose and prior ART exposure. The resistance profile of these patients is of concern as the likelihood of virological suppression on standard first-line regimens is limited. However, health care professionals would not be able to become aware of these mutations in routine care since HIV-1 drug resistance testing is currently not recommended prior to ART initiation.

Despite the adequate sample size and PPSS study design, the study had some limitations. Firstly, the enrolment of patients into the study was largely dependent on the willingness of clinicians and nurses to obtain consent from patients, complete study questionnaires and collect specimens, as there was no provision of study-specific staff to assist in the survey. These activities place a burden on clinics that are often already under-resourced and thus demonstrate the difficulties in conducting such surveys and the vast costs associated with their completion. These challenges resulted in a relatively long period to complete sample collection. Overall 82% of the planned sample size was collected and analysed. But there was under-sampling from North West, and Western Cape provinces.

Secondly, patients were not systematically sampled at the clinic level. This approach might have introduced a selection bias as sample selection was dependent on willingness of health care workers and patients to comply with the added burden of the study. However, we don’t believe that it is possible for clinicians to choose treatment naïve patients based upon demographic or clinical characteristics that would bias the proportion of SDRM.

Thirdly, data quality was dependant on the completeness and accuracy of the questionnaires. Moreover, no specific information regarding exposure to Prevention-of-Mother-to-Child-Transmission prophylaxis was obtained and may have potentially overestimated the level of pre-treatment drug resistance caused by the use of PMTCT regimens, especially since most patients were female. In addition, information regarding prior ART exposure could only be verified based on verbal confirmation from participants or notes in the patient’s hospital files, which might have led to the inclusion of patients with undisclosed ART exposure. Finally, resistance mutations were detected by population-based Sanger sequencing which might underestimate the prevalence of minority drug resistance mutations, due to sensitivity limitations of this approach, especially in patients who have been chronically infected before ART initiation. This study might have underestimated the level of pre-treatment resistance; since it is likely the sample included a substantial proportion of chronically infected patients, indicated by the low median CD4 count of 149 cells/mm³. It is known that TDR mutations can revert back to wild type in the bulk viral population, especially without the presence of drug pressure. It is therefore possible that archived mutations were missed in this analysis. However, these limitations are largely overcome by the large sample size of the study and the fact that samples were collected from all provinces, proportional to the number of patients on ART.

Overall, this study has shown that the level of antiretroviral drug resistance in ART-naïve South Africans is on the rise. Regional analyses indicate that there might be a difference in the level of drug resistance between provinces, although this trend needs to be confirmed by repeating surveys in each province with larger sample sizes. Despite the availability of routine viral load testing in the public sector, there is often a delayed response to changing management based on virological failure which could have contributed to the transmission of drug resistance [34]. These findings strongly suggest a renewed focus on first-line treatment success (the last 90% of the UNAIDS 90-90-90 strategy) with regular regional surveillance of pre-treatment drug resistance levels in all regions of the country.

Supporting Information

S1 Fig. Geographical map of South African indicating the health care facilities within the 9 provinces that contributed samples to the ART-naïve survey.

Each circle indicates a health care facility that contributed specimens to the survey. The size of the circles is proportional to the number of specimens collected at each health care facility. The grey scale indicates the number of patients on ART in each province.

https://doi.org/10.1371/journal.pone.0166305.s001

(DOCX)

S2 Fig. Phylogenetic tree including all 25 sequences with at least one SDRM.

No clustering is observed among these samples.

https://doi.org/10.1371/journal.pone.0166305.s002

(DOCX)

Acknowledgments

We would like to thank all study participants, health care workers and regional ART programme coordinators who made this study possible. In addition we would like to show our gratitude to E. Letsoalo, A. Lukhwareni, C. Mutuku, L. Skhosana, H. Zwane, A. Bester and M. Claassen, the staff at the HIV drug resistance testing laboratories that ensured all samples were processed in a timely manner. Thank you to L. Noble for assisting with the data capturing.

Author Contributions

Conceptualization: KS SC.
Data curation: KS MB.
Formal analysis: KS WM MP.
Funding acquisition: IS WS SC.
Investigation: KS GVZ DG.
Methodology: KS SC WM.
Project administration: KS.
Resources: KS SC GVZ DG.
Supervision: WS IS.
Validation: KS.
Visualization: KS SC WM.
Writing – original draft: KS.
Writing – review & editing: SC MB MP GVZ DG WM IS WS.

References

1. UNAIDS. UNAIDS announces that the goal of 15 million people on life-saving HIV treatment by 2015 has been met nine months ahead of schedule 2015. Available from: http://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2015/july/20150714_PR_MDG6report.
2. Nichols BE, Sigaloff KC, Kityo C, Hamers RL, Baltussen R, Bertagnolio S, et al. Increasing the use of second-line therapy is a cost-effective approach to prevent the spread of drug-resistant HIV: a mathematical modelling study. Journal of the International AIDS Society. 2014;17:19164. Epub 2014/12/11. PubMed Central PMCID: PMC4260459. pmid:25491351
- View Article
- PubMed/NCBI
- Google Scholar
3. Cambiano V, Bertagnolio S, Jordan MR, Lundgren JD, Phillips A. Transmission of drug resistant HIV and its potential impact on mortality and treatment outcomes in resource-limited settings. The Journal of infectious diseases. 2013;207 Suppl 2:S57–62. Epub 2013/05/25.
- View Article
- Google Scholar
4. Hamers RL, Schuurman R, Sigaloff KC, Wallis CL, Kityo C, Siwale M, et al. Effect of pretreatment HIV-1 drug resistance on immunological, virological, and drug-resistance outcomes of first-line antiretroviral treatment in sub-Saharan Africa: a multicentre cohort study. The Lancet infectious diseases. 2012;12(4):307–17. Epub 2011/11/01. pmid:22036233
- View Article
- PubMed/NCBI
- Google Scholar
5. Li JZ, Paredes R, Ribaudo HJ, Svarovskaia ES, Kozal MJ, Hullsiek KH, et al. Relationship between minority nonnucleoside reverse transcriptase inhibitor resistance mutations, adherence, and the risk of virologic failure. AIDS. 2012;26(2):185–92. Epub 2011/12/20. PubMed Central PMCID: PMC3437231. pmid:22179227
- View Article
- PubMed/NCBI
- Google Scholar
6. UNAIDS. 90-90-90: An ambitious treatment target to help end the AIDS epidemic 2015 [cited 2015 07/10/2015].
7. Pingen M, Nijhuis M, de Bruijn JA, Boucher CA, Wensing AM. Evolutionary pathways of transmitted drug-resistant HIV-1. The Journal of antimicrobial chemotherapy. 2011;66(7):1467–80. Epub 2011/04/20. pmid:21502281
- View Article
- PubMed/NCBI
- Google Scholar
8. Bennett DE, Myatt M, Bertagnolio S, Sutherland D, Gilks CF. Recommendations for surveillance of transmitted HIV drug resistance in countries scaling up antiretroviral treatment. Antivir Ther. 2008;13 Suppl 2:25–36. Epub 2008/06/26.
- View Article
- Google Scholar
9. WHO. The HIV drug resistance report-2012. 2012.
10. Rhee SY, Blanco JL, Jordan MR, Taylor J, Lemey P, Varghese V, et al. Geographic and temporal trends in the molecular epidemiology and genetic mechanisms of transmitted HIV-1 drug resistance: an individual-patient- and sequence-level meta-analysis. PLoS medicine. 2015;12(4):e1001810. Epub 2015/04/08. PubMed Central PMCID: PMC4388826. pmid:25849352
- View Article
- PubMed/NCBI
- Google Scholar
11. Hamers RL, Wallis CL, Kityo C, Siwale M, Mandaliya K, Conradie F, et al. HIV-1 drug resistance in antiretroviral-naive individuals in sub-Saharan Africa after rollout of antiretroviral therapy: a multicentre observational study. The Lancet infectious diseases. 2011;11(10):750–9. Epub 2011/08/02. pmid:21802367
- View Article
- PubMed/NCBI
- Google Scholar
12. Huang KH, Goedhals D, Fryer H, van Vuuren C, Katzourakis A, De Oliveira T, et al. Prevalence of HIV type-1 drug-associated mutations in pre-therapy patients in the Free State, South Africa. Antivir Ther. 2009;14(7):975–84. Epub 2009/11/18. pmid:19918101
- View Article
- PubMed/NCBI
- Google Scholar
13. Hunt GM, Ledwaba J, Basson AE, Moyes J, Cohen C, Singh B, et al. Surveillance of transmitted HIV-1 drug resistance in Gauteng and KwaZulu-Natal Provinces, South Africa, 2005–2009. Clin Infect Dis. 2012;54 Suppl 4:S334–8. Epub 2012/05/11. PubMed Central PMCID: PMC3572869.
- View Article
- Google Scholar
14. Pillay V, Ledwaba J, Hunt G, Rakgotho M, Singh B, Makubalo L, et al. Antiretroviral drug resistance surveillance among drug-naive HIV-1-infected individuals in Gauteng Province, South Africa in 2002 and 2004. Antivir Ther. 2008;13 Suppl 2:101–7. Epub 2008/06/26.
- View Article
- Google Scholar
15. Parikh UM, Kiepiela P, Ganesh S, Gomez K, Horn S, Eskay K, et al. Prevalence of HIV-1 drug resistance among women screening for HIV prevention trials in KwaZulu-Natal, South Africa (MTN-009). PloS one. 2013;8(4):e59787. Epub 2013/04/16. PubMed Central PMCID: PMC3621859. pmid:23585827
- View Article
- PubMed/NCBI
- Google Scholar
16. Manasa J, Katzenstein D, Cassol S, Newell ML, de Oliveira T. Primary drug resistance in South Africa: data from 10 years of surveys. AIDS research and human retroviruses. 2012;28(6):558–65. Epub 2012/01/19. PubMed Central PMCID: PMC3358100. pmid:22251009
- View Article
- PubMed/NCBI
- Google Scholar
17. Parboosing R, Naidoo A, Gordon M, Taylor M, Vella V. Resistance to antiretroviral drugs in newly diagnosed, young treatment-naive HIV-positive pregnant women in the province of KwaZulu-Natal, South Africa. Journal of medical virology. 2011;83(9):1508–13. Epub 2011/07/09. pmid:21739439
- View Article
- PubMed/NCBI
- Google Scholar
18. Manasa J, Danaviah S, Lessells R, Elshareef M, Tanser F, Wilkinson E, et al. Increasing HIV-1 Drug Resistance Between 2010 and 2012 in Adults Participating in Population-Based HIV Surveillance in Rural KwaZulu-Natal, South Africa. AIDS research and human retroviruses. 2016. Epub 2016/03/24.
- View Article
- Google Scholar
19. Nwobegahay JM, Bessong PO, Masebe TM, Mavhandu LG, Iweriebor BC, Selabe G. Prevalence of antiretroviral drug resistance mutations and HIV-I subtypes among newly-diagnosed drug-naive persons visiting a voluntary testing and counselling centre in northeastern South Africa. Journal of health, population, and nutrition. 2011;29(4):303–9. Epub 2011/10/01. PubMed Central PMCID: PMC3190360. pmid:21957668
- View Article
- PubMed/NCBI
- Google Scholar
20. Jacobs GB, Laten A, van Rensburg EJ, Bodem J, Weissbrich B, Rethwilm A, et al. Phylogenetic diversity and low level antiretroviral resistance mutations in HIV type 1 treatment-naive patients from Cape Town, South Africa. AIDS research and human retroviruses. 2008;24(7):1009–12. Epub 2008/07/03. pmid:18593350
- View Article
- PubMed/NCBI
- Google Scholar
21. Jacobs GB, Wilkinson E, Isaacs S, Spies G, de Oliveira T, Seedat S, et al. HIV-1 subtypes B and C unique recombinant forms (URFs) and transmitted drug resistance identified in the Western Cape Province, South Africa. PloS one. 2014;9(6):e90845. Epub 2014/03/13. PubMed Central PMCID: PMC3946584. pmid:24609015
- View Article
- PubMed/NCBI
- Google Scholar
22. Orrell C, Walensky RP, Losina E, Pitt J, Freedberg KA, Wood R. HIV type-1 clade C resistance genotypes in treatment-naive patients and after first virological failure in a large community antiretroviral therapy programme. Antivir Ther. 2009;14(4):523–31. Epub 2009/07/07. pmid:19578237
- View Article
- PubMed/NCBI
- Google Scholar
23. Claassen M, Van Zyl GU. The improvement, validation and quality assurance of in-house HIV-1 antiretroviral resistance tests for practical use in resistance monitoring of patients receiving antiretroviral combination theray at Tygerberg Hospital Family Clinic. 2008. Available from: http://scholar.sun.ac.za/handle/10019.1/42809.
24. Bronze M, Moodley K, Steegen K, Papathanasopolous MA, Carmona S, W. S, editors. Development and validation of a one-step genotyping protocol encompassing HIV-1 protease and reverse transcriptase for patient antiretroviral drug resistance monitoring. 1st International Conference African Society for Laboratory Medicine 2012; 1st International Conference African Society for Laboratory Medicine, Cape Town, South Africa.
25. Lukhwareni A, Bronze M, Ledwaba J, Steegen K, Rossouw I, Hunt H, et al., editors. Validation of a robust in-house genotyping assay for the detection of HIV-1 antiretroviral drug resistance. Pathpoint Conference; 2012; Pathpoint Conference, Cape Town, South Africa.
26. Bennett DE, Camacho RJ, Otelea D, Kuritzkes DR, Fleury H, Kiuchi M, et al. Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PloS one. 2009;4(3):e4724. Epub 2009/03/07. PubMed Central PMCID: PMC2648874. pmid:19266092
- View Article
- PubMed/NCBI
- Google Scholar
27. Richard N, Juntilla M, Abraha A, Demers K, Paxinos E, Galovich J, et al. High prevalence of antiretroviral resistance in treated Ugandans infected with non-subtype B human immunodeficiency virus type 1. AIDS Res Hum Retroviruses. 2004;20(4):355–64. pmid:15157354
- View Article
- PubMed/NCBI
- Google Scholar
28. The South African Antiretroviral Treatment Guidelines 2010. Available from: http://www.sahivsoc.org/upload/documents/Clinical_Guidelines_for_the_Management_of_HIV_AIDS_in_Adults_Adolescents_2010.pdf.
29. Brenner BG, Roger M, Stephens D, Moisi D, Hardy I, Weinberg J, et al. Transmission clustering drives the onward spread of the HIV epidemic among men who have sex with men in Quebec. The Journal of infectious diseases. 2011;204(7):1115–9. Epub 2011/09/02. PubMed Central PMCID: PMC3164430. pmid:21881127
- View Article
- PubMed/NCBI
- Google Scholar
30. Chalmet K, Staelens D, Blot S, Dinakis S, Pelgrom J, Plum J, et al. Epidemiological study of phylogenetic transmission clusters in a local HIV-1 epidemic reveals distinct differences between subtype B and non-B infections. BMC infectious diseases. 2010;10:262. Epub 2010/09/09. PubMed Central PMCID: PMC2940905. pmid:20822507
- View Article
- PubMed/NCBI
- Google Scholar
31. Wainberg MA. The impact of the M184V substitution on drug resistance and viral fitness. Expert review of anti-infective therapy. 2004;2(1):147–51. Epub 2004/10/16. pmid:15482179
- View Article
- PubMed/NCBI
- Google Scholar
32. Weber J, Chakraborty B, Weberova J, Miller MD, Quinones-Mateu ME. Diminished replicative fitness of primary human immunodeficiency virus type 1 isolates harboring the K65R mutation. Journal of clinical microbiology. 2005;43(3):1395–400. Epub 2005/03/08. PubMed Central PMCID: PMC1081293. pmid:15750116
- View Article
- PubMed/NCBI
- Google Scholar
33. White KL, Margot NA, Wrin T, Petropoulos CJ, Miller MD, Naeger LK. Molecular mechanisms of resistance to human immunodeficiency virus type 1 with reverse transcriptase mutations K65R and K65R+M184V and their effects on enzyme function and viral replication capacity. Antimicrobial agents and chemotherapy. 2002;46(11):3437–46. Epub 2002/10/18. PubMed Central PMCID: PMC128721. pmid:12384348
- View Article
- PubMed/NCBI
- Google Scholar
34. Johnston V, Fielding KL, Charalambous S, Churchyard G, Phillips A, Grant AD. Outcomes following virological failure and predictors of switching to second-line antiretroviral therapy in a South African treatment program. J Acquir Immune Defic Syndr. 2012;61(3):370–80. Epub 2012/07/24. PubMed Central PMCID: PMC3840925. pmid:22820803
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. UNAIDS. UNAIDS announces that the goal of 15 million people on life-saving HIV treatment by 2015 has been met nine months ahead of schedule 2015. Available from: http://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2015/july/20150714_PR_MDG6report.

[ref2] 2. Nichols BE, Sigaloff KC, Kityo C, Hamers RL, Baltussen R, Bertagnolio S, et al. Increasing the use of second-line therapy is a cost-effective approach to prevent the spread of drug-resistant HIV: a mathematical modelling study. Journal of the International AIDS Society. 2014;17:19164. Epub 2014/12/11. PubMed Central PMCID: PMC4260459. pmid:25491351
View Article
PubMed/NCBI
Google Scholar

[3] View Article

[4] PubMed/NCBI

[5] Google Scholar

[ref3] 3. Cambiano V, Bertagnolio S, Jordan MR, Lundgren JD, Phillips A. Transmission of drug resistant HIV and its potential impact on mortality and treatment outcomes in resource-limited settings. The Journal of infectious diseases. 2013;207 Suppl 2:S57–62. Epub 2013/05/25.
View Article
Google Scholar

[7] View Article

[8] Google Scholar

[ref4] 4. Hamers RL, Schuurman R, Sigaloff KC, Wallis CL, Kityo C, Siwale M, et al. Effect of pretreatment HIV-1 drug resistance on immunological, virological, and drug-resistance outcomes of first-line antiretroviral treatment in sub-Saharan Africa: a multicentre cohort study. The Lancet infectious diseases. 2012;12(4):307–17. Epub 2011/11/01. pmid:22036233
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref5] 5. Li JZ, Paredes R, Ribaudo HJ, Svarovskaia ES, Kozal MJ, Hullsiek KH, et al. Relationship between minority nonnucleoside reverse transcriptase inhibitor resistance mutations, adherence, and the risk of virologic failure. AIDS. 2012;26(2):185–92. Epub 2011/12/20. PubMed Central PMCID: PMC3437231. pmid:22179227
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref6] 6. UNAIDS. 90-90-90: An ambitious treatment target to help end the AIDS epidemic 2015 [cited 2015 07/10/2015].

[ref7] 7. Pingen M, Nijhuis M, de Bruijn JA, Boucher CA, Wensing AM. Evolutionary pathways of transmitted drug-resistant HIV-1. The Journal of antimicrobial chemotherapy. 2011;66(7):1467–80. Epub 2011/04/20. pmid:21502281
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref8] 8. Bennett DE, Myatt M, Bertagnolio S, Sutherland D, Gilks CF. Recommendations for surveillance of transmitted HIV drug resistance in countries scaling up antiretroviral treatment. Antivir Ther. 2008;13 Suppl 2:25–36. Epub 2008/06/26.
View Article
Google Scholar

[23] View Article

[24] Google Scholar

[ref9] 9. WHO. The HIV drug resistance report-2012. 2012.

[ref10] 10. Rhee SY, Blanco JL, Jordan MR, Taylor J, Lemey P, Varghese V, et al. Geographic and temporal trends in the molecular epidemiology and genetic mechanisms of transmitted HIV-1 drug resistance: an individual-patient- and sequence-level meta-analysis. PLoS medicine. 2015;12(4):e1001810. Epub 2015/04/08. PubMed Central PMCID: PMC4388826. pmid:25849352
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref11] 11. Hamers RL, Wallis CL, Kityo C, Siwale M, Mandaliya K, Conradie F, et al. HIV-1 drug resistance in antiretroviral-naive individuals in sub-Saharan Africa after rollout of antiretroviral therapy: a multicentre observational study. The Lancet infectious diseases. 2011;11(10):750–9. Epub 2011/08/02. pmid:21802367
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref12] 12. Huang KH, Goedhals D, Fryer H, van Vuuren C, Katzourakis A, De Oliveira T, et al. Prevalence of HIV type-1 drug-associated mutations in pre-therapy patients in the Free State, South Africa. Antivir Ther. 2009;14(7):975–84. Epub 2009/11/18. pmid:19918101
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref13] 13. Hunt GM, Ledwaba J, Basson AE, Moyes J, Cohen C, Singh B, et al. Surveillance of transmitted HIV-1 drug resistance in Gauteng and KwaZulu-Natal Provinces, South Africa, 2005–2009. Clin Infect Dis. 2012;54 Suppl 4:S334–8. Epub 2012/05/11. PubMed Central PMCID: PMC3572869.
View Article
Google Scholar

[39] View Article

[40] Google Scholar

[ref14] 14. Pillay V, Ledwaba J, Hunt G, Rakgotho M, Singh B, Makubalo L, et al. Antiretroviral drug resistance surveillance among drug-naive HIV-1-infected individuals in Gauteng Province, South Africa in 2002 and 2004. Antivir Ther. 2008;13 Suppl 2:101–7. Epub 2008/06/26.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref15] 15. Parikh UM, Kiepiela P, Ganesh S, Gomez K, Horn S, Eskay K, et al. Prevalence of HIV-1 drug resistance among women screening for HIV prevention trials in KwaZulu-Natal, South Africa (MTN-009). PloS one. 2013;8(4):e59787. Epub 2013/04/16. PubMed Central PMCID: PMC3621859. pmid:23585827
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref16] 16. Manasa J, Katzenstein D, Cassol S, Newell ML, de Oliveira T. Primary drug resistance in South Africa: data from 10 years of surveys. AIDS research and human retroviruses. 2012;28(6):558–65. Epub 2012/01/19. PubMed Central PMCID: PMC3358100. pmid:22251009
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref17] 17. Parboosing R, Naidoo A, Gordon M, Taylor M, Vella V. Resistance to antiretroviral drugs in newly diagnosed, young treatment-naive HIV-positive pregnant women in the province of KwaZulu-Natal, South Africa. Journal of medical virology. 2011;83(9):1508–13. Epub 2011/07/09. pmid:21739439
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref18] 18. Manasa J, Danaviah S, Lessells R, Elshareef M, Tanser F, Wilkinson E, et al. Increasing HIV-1 Drug Resistance Between 2010 and 2012 in Adults Participating in Population-Based HIV Surveillance in Rural KwaZulu-Natal, South Africa. AIDS research and human retroviruses. 2016. Epub 2016/03/24.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref19] 19. Nwobegahay JM, Bessong PO, Masebe TM, Mavhandu LG, Iweriebor BC, Selabe G. Prevalence of antiretroviral drug resistance mutations and HIV-I subtypes among newly-diagnosed drug-naive persons visiting a voluntary testing and counselling centre in northeastern South Africa. Journal of health, population, and nutrition. 2011;29(4):303–9. Epub 2011/10/01. PubMed Central PMCID: PMC3190360. pmid:21957668
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref20] 20. Jacobs GB, Laten A, van Rensburg EJ, Bodem J, Weissbrich B, Rethwilm A, et al. Phylogenetic diversity and low level antiretroviral resistance mutations in HIV type 1 treatment-naive patients from Cape Town, South Africa. AIDS research and human retroviruses. 2008;24(7):1009–12. Epub 2008/07/03. pmid:18593350
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref21] 21. Jacobs GB, Wilkinson E, Isaacs S, Spies G, de Oliveira T, Seedat S, et al. HIV-1 subtypes B and C unique recombinant forms (URFs) and transmitted drug resistance identified in the Western Cape Province, South Africa. PloS one. 2014;9(6):e90845. Epub 2014/03/13. PubMed Central PMCID: PMC3946584. pmid:24609015
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref22] 22. Orrell C, Walensky RP, Losina E, Pitt J, Freedberg KA, Wood R. HIV type-1 clade C resistance genotypes in treatment-naive patients and after first virological failure in a large community antiretroviral therapy programme. Antivir Ther. 2009;14(4):523–31. Epub 2009/07/07. pmid:19578237
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref23] 23. Claassen M, Van Zyl GU. The improvement, validation and quality assurance of in-house HIV-1 antiretroviral resistance tests for practical use in resistance monitoring of patients receiving antiretroviral combination theray at Tygerberg Hospital Family Clinic. 2008. Available from: http://scholar.sun.ac.za/handle/10019.1/42809.

[ref24] 24. Bronze M, Moodley K, Steegen K, Papathanasopolous MA, Carmona S, W. S, editors. Development and validation of a one-step genotyping protocol encompassing HIV-1 protease and reverse transcriptase for patient antiretroviral drug resistance monitoring. 1st International Conference African Society for Laboratory Medicine 2012; 1st International Conference African Society for Laboratory Medicine, Cape Town, South Africa.

[ref25] 25. Lukhwareni A, Bronze M, Ledwaba J, Steegen K, Rossouw I, Hunt H, et al., editors. Validation of a robust in-house genotyping assay for the detection of HIV-1 antiretroviral drug resistance. Pathpoint Conference; 2012; Pathpoint Conference, Cape Town, South Africa.

[ref26] 26. Bennett DE, Camacho RJ, Otelea D, Kuritzkes DR, Fleury H, Kiuchi M, et al. Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update. PloS one. 2009;4(3):e4724. Epub 2009/03/07. PubMed Central PMCID: PMC2648874. pmid:19266092
View Article
PubMed/NCBI
Google Scholar

[79] View Article

[80] PubMed/NCBI

[81] Google Scholar

[ref27] 27. Richard N, Juntilla M, Abraha A, Demers K, Paxinos E, Galovich J, et al. High prevalence of antiretroviral resistance in treated Ugandans infected with non-subtype B human immunodeficiency virus type 1. AIDS Res Hum Retroviruses. 2004;20(4):355–64. pmid:15157354
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref28] 28. The South African Antiretroviral Treatment Guidelines 2010. Available from: http://www.sahivsoc.org/upload/documents/Clinical_Guidelines_for_the_Management_of_HIV_AIDS_in_Adults_Adolescents_2010.pdf.

[ref29] 29. Brenner BG, Roger M, Stephens D, Moisi D, Hardy I, Weinberg J, et al. Transmission clustering drives the onward spread of the HIV epidemic among men who have sex with men in Quebec. The Journal of infectious diseases. 2011;204(7):1115–9. Epub 2011/09/02. PubMed Central PMCID: PMC3164430. pmid:21881127
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref30] 30. Chalmet K, Staelens D, Blot S, Dinakis S, Pelgrom J, Plum J, et al. Epidemiological study of phylogenetic transmission clusters in a local HIV-1 epidemic reveals distinct differences between subtype B and non-B infections. BMC infectious diseases. 2010;10:262. Epub 2010/09/09. PubMed Central PMCID: PMC2940905. pmid:20822507
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref31] 31. Wainberg MA. The impact of the M184V substitution on drug resistance and viral fitness. Expert review of anti-infective therapy. 2004;2(1):147–51. Epub 2004/10/16. pmid:15482179
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref32] 32. Weber J, Chakraborty B, Weberova J, Miller MD, Quinones-Mateu ME. Diminished replicative fitness of primary human immunodeficiency virus type 1 isolates harboring the K65R mutation. Journal of clinical microbiology. 2005;43(3):1395–400. Epub 2005/03/08. PubMed Central PMCID: PMC1081293. pmid:15750116
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref33] 33. White KL, Margot NA, Wrin T, Petropoulos CJ, Miller MD, Naeger LK. Molecular mechanisms of resistance to human immunodeficiency virus type 1 with reverse transcriptase mutations K65R and K65R+M184V and their effects on enzyme function and viral replication capacity. Antimicrobial agents and chemotherapy. 2002;46(11):3437–46. Epub 2002/10/18. PubMed Central PMCID: PMC128721. pmid:12384348
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref34] 34. Johnston V, Fielding KL, Charalambous S, Churchyard G, Phillips A, Grant AD. Outcomes following virological failure and predictors of switching to second-line antiretroviral therapy in a South African treatment program. J Acquir Immune Defic Syndr. 2012;61(3):370–80. Epub 2012/07/24. PubMed Central PMCID: PMC3840925. pmid:22820803
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

Figures

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and Methods

Survey design

Data and sample collection

Sequence analysis

Subtyping and cross-contamination check

Statistical analysis

Sequence Data

Ethics Statement

Results

Discussion

Supporting Information

S1 Fig. Geographical map of South African indicating the health care facilities within the 9 provinces that contributed samples to the ART-naïve survey.

S2 Fig. Phylogenetic tree including all 25 sequences with at least one SDRM.

Acknowledgments

Author Contributions

References