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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Acquir Immune Defic Syndr. Author manuscript; available in PMC 2014 March 25.
Published in final edited form as:
PMCID: PMC3964197

Evaluating immunologic response and clinical deterioration in treatment-naïve patients initiating first-line therapies infected with HIV-1 CRF01_AE and subtype B

Rebecca A. Oyomopito, PhD,1,2 Patrick CK. Li, MBBS,3 Somnuek Sungkanuparph, MD,4 Praphan Phanuphak, MD, PhD,5 Kok Keng Tee, PhD, MMedSc,6 Thira Sirisanthana, MD,7 Pacharee Kantipong, MD,8 Shinichi Oka, MD, PhD,9 Chris KC. Lee, MBBS, MRCP,10 Adeeba Kamarulzaman, MBBS, FRACP, FAMM, FASc,6 Jun Yong Choi, MD, PhD,11 Annette H. Sohn, MD,12 Matthew Law, PhD,1 and Yi-Ming A. Chen, MD, MS, ScD13, on behalf of the TREAT Asia Studies to Evaluate Resistance (TASER) and the TREAT Asia HIV Observational Database



HIV-1 group M viruses diverge 25%–35% in envelope, important for viral attachment during infection, and 10–15% in the pol region, under selection pressure from common antiretrovirals. In Asia, subtypes B and CRF01_AE are common genotypes. Our objectives were to determine whether clinical, immunologic or virologic treatment responses differed by genotype in treatment-naïve patients initiating first-line therapy.


Prospectively collected, longitudinal data from patients in Thailand, Hong Kong, Malaysia, Japan, Taiwan and South Korea were provided for analysis. Covariates included demographics, hepatitis B and C coinfections, baseline CD4 T lymphocyte count and plasma HIV-1 RNA levels. Clinical deterioration (a new diagnosis of CDC category B/AIDS-defining illness or death) was assessed by proportional hazards models. Surrogate endpoints were 12-month change in CD4 cell count and virologic suppression post-therapy, evaluated by linear and logistic regression, respectively.


Of 1105 patients, 1036 (93.8%) infected with CRF01_AE or subtype B were eligible for inclusion in clinical deterioration analyses and contributed 1546.7 person-years of follow-up (median:413 days, IQR:169–672 days). Patients >40 years demonstrated smaller immunological increases (p=0.002) and higher risk of clinical deterioration (HR=2.17; p=0.008). Patients with baseline CD4 cell counts >200 cells/μL had lower risk of clinical deterioration (HR=0.373; p=0.003). A total of 532 patients (48.1% of eligible) had CD4 counts available at baseline and 12 months post-therapy for inclusion in immunolgic analyses. Patients infected with subtype B had larger increases in CD4 counts at 12 months (p=0.024). A total of 530 patients (48.0% of eligible) were included in virologic analyses with no differences in response found between genotypes.


Results suggest that patients infected with CRF01_AE have reduced immunologic response to therapy at 12 months, compared to subtype-B-infected counterparts. Clinical deterioration was associated with low baseline CD4 counts and older age. The lack of differences in virologic outcomes suggests that all patients have opportunities for virologic suppression.

Keywords: HIV-1, Asia, genotype, CRF01_AE, subtype B


HIV-1 group M viruses account for most infections internationally 1 and, based on genetic similarity, are classified into nine different subtypes (A–D, F–H, J, K). Variants, however, diverge 25%–35% in envelope (env), important for viral attachment to target cells during infection, and 10–15% in the pol region, under selection pressure from common antiretrovirals (ARVs) 13. Subtypes A and F are further divided into sub-subtypes A1, A2 and F1, F2, respectively. Although subtypes B and D are as similar as sub-subtypes, for historical reasons they maintain separate subtype classification 1. Formerly, HIV-1 genotype assignments were based on gene fragments. Later, when gag and pol regions were genotyped, subtype E env viruses were found to include subtype A sections in the other regions of the viral genome, resulting in subtype E’s reclassification as an A/E recombinant, CRF01_AE 4. Circulating recombinant forms (CRFs) result from recombination between HIV-1 genotypes within a dually infected person 1 but no complete subtype E genome has been found, leaving CRF01_AE’s recombinant status inconclusive.

Subtype B is the genotype historically common in developed countries and nucleotide substitutions (mutations or naturally occurring polymorphisms), insertions and deletions in the HIV-1 genome are made in reference to the earliest characterised subtype B wild-type strain, HXB2 5,6. ARVs, commonly designed on subtype B, are classified based on where the HIV-1 life cycle is interrupted. Synergistic combinations of ARVs, known as highly active antiretroviral therapy (HAART), suppress viral load (VL) thereby reducing the risk of opportunistic infections and death 7,8. However, natural drug-resistant polymorphisms may exist in patients pre-therapy with higher frequencies being found in non-B subtypes 9.

In vitro studies suggest differences in viral transmission characteristics between genotypes and viral heterogeneity may have implications for disease progression. HIV-1 infection depends on the interaction of env gp120 with the target cell CD4 receptor 10 and this interaction promotes binding to a coreceptor, viral tropism being determined by the env amino acid sequence and structure. Most genotypes use R5 coreceptors during transmission and in early stages of infection with X4-using syncytium inducing variants emerging later 11,12. Subtype C studies generally report a lack of coreceptor switching from R5 to X4, possibly affecting transmission 11, and dual tropic virus (X4/R5) found in other genotypes have not been reported in subtype D viruses 13. Where subtypes A and D co-circulate, more rapid disease progression has been found for subtype D compared with subtype A 14 although the literature suggests that subtype A infections are outpacing subtype D 15. A retrospective analysis found faster rates of CD4 decline and virologic failure in subtype D infection compared to subtypes A, B or C suggesting differences in HIV-1 genotypes with respect to patient response to therapy 16.

In Asia, predominant genotypes are subtypes B and C, CRF01_AE and their recombinants, with country-specific epidemics featuring different group M genotypes. During 2000–2007, in India, approximately 97% of infections were from subtype C while four Mekong River countries (Cambodia, Myanmar, Thailand and Viet Nam) reported almost 80% of infections were from CRF01_AE 17. Subtype B infections are primarily reported in Japan and the Republic of Korea (South Korea) 1720. In China’s Special Administrative Region of Hong Kong and in Malaysia, subtype B and CRF01_AE co-circulate 17,21,22 while in Taiwan, subtype B, CRF01_AE and CRF07_BC have been found 23,24. Epidemic distributions differ depending on the sub-populations at risk with subtype B frequently found in injecting drug users and men-who-have-sex-with-men (MSM), whereas CRF01_AE is more commonly found in heterosexual populations 25.

Previously we reported that mainly CRF01_AE and subtype B were infecting patients from Thailand, Hong Kong and Malaysia 26. The objectives were to determine whether treatment responses (clinical deterioration, immunologic response or virologic suppression) differed between these genotypes in treatment-naïve patients initiating a first-line HAART regimen.


Patient data

Patients providing data were enrolled in either the TREAT Asia Studies to Evaluate Resistance monitoring protocol (TASER-M) 26 or the TREAT Asia HIV Observational Database (TAHOD) 27. Data for these longitudinal, cohort studies are collected prospectively. Most TASER-M sites are selected from TAHOD sites which consist of government- or university-based clinics and hospitals or private clinics, situated in major cities and other urban areas. Pre-treatment drug resistance prevalence for the TASER-M cohort has been published elsewhere 28. Clinical interventions and testing procedures were implemented according to local practices, excepting HIV-1 genotyping in TASER-M which was collected under the protocol.

Treatment-naive patients were eligible for inclusion if they were initiating first-line HAART regimens and had HIV-1 genotype available. Eligible patients enrolled at March 2010 from 11 clinic locations in Thailand (4), Hong Kong (China) (2), Malaysia (2), Japan (1), Taiwan (1) and South Korea (1) provided prospective and retrospective data (TAHOD) for analysis. Patient covariates included demographics (age at entry to cohort, gender, HIV source exposure), hepatitis B (HBV) and hepatitis C (HCV) coinfections and baseline indices of illness severity (CD4 lymphocyte count, HIV-1 RNA and CDC classification 29). The most severe pre-therapy CDC category recorded was used as the baseline clinical status. HBV (HCV) positive status was defined as having any HBsAg (HCV-Ab) positive result prior to enrolment. HIV-1 genotypes were determined by Virco BVBA, Belgium. For assessing associations between patient covariates and genotype, patients were restricted to those infected with subtype B or CRF01_AE whose sequences passed Virco’s quality control procedures. Due to small numbers, patients reporting injecting drug use exposure, receipt of blood products, perinatal transmission or unknown exposure were collapsed into an “Other” transmission category.

Analysis endpoints

Patients were required to have at least one clinic visit or test procedure recorded post-therapy initiation for inclusion. Clinical deterioration was determined as a new diagnosis of a CDC B or C (AIDS-defining) illness or death from any cause. Patient follow-up commenced at HAART initiation and ended at earliest clinical deterioration endpoint or censored at the most recent contact. Surrogate endpoints were plasma HIV-1 RNA viral suppression (< 400 copies/mL) and change in CD4 cell count from baseline at 12 months post-HAART. For calculating the 12-month immunologic change, the surrogate marker value closest to the 12-month target date was chosen from windows of 9–15 months and the CD4 count sampled within the 91 days prior, and closest to therapy initiation, was selected as the baseline value.

Statistical analysis

For eligible patients, baseline comparisons by country (χ2, Fisher’s exact or Cochrane-Armitage test for trend) were performed, as appropriate. Determinants of change in CD4 cell count and 12-month HIV-1 RNA suppression were assessed via linear regression and logistic regression, respectively. Proportional hazards models were used to evaluate predictors of time to progression to a new clinical deterioration endpoint. Analyses were based on an intention-to-continue treatment approach in that we did not take into account regimen changes or interruptions post-therapy. Forward stepwise techniques were used to determine the best fitting models. Binary covariate p-values and multi-categorical parameter p-values (from tests for trend/heterogeneity) of <0.1, in univariate analyses, were considered for inclusion in multivariate patient covariate models. Final models consisted of patient covariates remaining significant at the 0.05 level. Then, because of our a priori interest in the effect of HIV-1 genotype on outcomes, we assessed the effect of HIV-1 genotype, adjusting for any significant patient covariates, and tested for interactions between genotype and cohort. Analyses were performed using SAS version 9.2 (SAS Institute Inc., Cary, NC, USA) and STATA version 10 (STATA Corp., College Station, TX, USA).


A total of 1105 ARV-naïve patients had HIV-1 genotype information available [TASER-M: n=922 (83.4%); Thailand: n=675 (73.2%); Hong Kong: n=160 (17.4%); Malaysia: n=87 (9.4%); TAHOD: n=183 (16.6%); Japan: n=65 (35.5%); Hong Kong: n=49 (26.8%); Taiwan: n=43 (23.5%); South Korea: n=15 (8.2%)]. Differences in ethnicity reflected population distributions within countries contributing data [TASER-M; Thai: n=675 (73.2%), Chinese: n=177 (19.2%), Malay: n=37 (4.0%), Indian: n=11 (1.2%), Caucasian: n=5 (0.5%); TAHOD; Thai: n=14 (7.7%), Chinese: n=88 (48.1%), Japanese: n=65 (35.5%), Korean: n=15 (8.2%), Caucasian: n=1 (0.5%)].

Years of enrolment differed as a function of cohort recruitment and 80% of TAHOD patients were enrolled prior to opening of the TASER-M cohort. Patients initiated therapy from 2003–2010 (TASER-M: 2007–2010; TAHOD: 2003–2010) and significant differences between cohorts were noted for covariates as shown in Table 1. All Table 1 covariates were evaluated for significance in endpoint analyses.

Table 1
Patient characteristics at first-line therapy initiation by cohort

Most first-line regimens included lamivudine (3TC) as a nucleoside/nucleotide reverse transcriptase inhibitor (NRTI) backbone component [n=1013 (91.7%)]. Regimens for the cohorts differed in the second NRTI component [TASER-M; stavudine (d4T): n=479 (52.0%); zidovudine (AZT): n=221 (24.0%); abacavir (ABC): n=67 (7.3%); TAHOD; d4T: n=37 (20.2%); AZT: n=84 (45.9%); ABC: n=26 (14.2%), p<0.001]. Most regimens were based on non-nucleoside reverse transcriptase inhibitors (NNRTIs) with more TAHOD patients being prescribed efavirenz (EFV) and TASER-M patients’ regimens including higher proportions of nevirapine (NVP) [TASER; NVP: n=484 (58.9%), EFV: n=338 (41.1%); TAHOD; EFV: n=77 (82.8%), NVP: n=16 (17.2%); p<0.001]. Of protease inhibitor (PI) regimens, most included ritonavir-boosted atazanavir (ATZ) or lopinavir (LPV), proportions of which marginally differed between cohorts [TASER-M; ATZ/r: n=28 (38.9%), LPV/r: n=44 (61.1%); TAHOD; ATZ/r: n=17 (22.7%), LPV/r: n=58 (77.3%); p=0.048].

In East Asia, there is a higher odds of CRF01_AE infecting heterosexual populations and subtype B is more frequently found in MSM. We found differences in genotype proportions consistent with patient-reported HIV source exposures (Table 1) [TASER-M; CRF01_AE: n=740 (86.8%), subtype B: n=113 (13.2%); TAHOD; CRF01_AE: n=38 (20.8%); subtype B: n=145 (79.2)%; p<0.001]. In TASER-M, both HIV-1 pol protease (PR) and reverse transcriptase (RT) genotypes are recorded and 59 (6.4%) of TASER-M patients were infected with discordant PR and RT genotypes, reflecting possible dual infection and/or recombination. Of discordant genotypes, 28 (47.5%) included subtype B components (assessed as including subtype B, CRF08_BC, CRF08_BC or CRF15_01B) and 23 (39.0%) were CRF01_AE recombinants (assessed including CRF01_AE or CRF15_01B). The remaining discordant genomes [n=8 (13.6%)] included both B and AE components. Discordant genotypes and subtypes CRF02_AG (n=1), CRF 07_BC (n=3), C (n=5), subtype D (n=1) were excluded from further evaluation.

Progression to CDC B or CDC C (AIDS-defining) illness or death

A total of 1036 patients (93.8%) infected with CRF01_AE or subtype B were eligible for inclusion in clinical deterioration analyses (Table 2) and contributed 1546.7 person-years of retrospective and prospective follow-up (median: 413 days, IQR: 169–672 days). During this time, there were a total of 104 events (22 CDC B diagnoses, 63 AIDS diagnoses and 19 deaths) giving an event rate of 6.7 per 100 person-years [95% CI: 5.5–8.1]. Clinical deterioration endpoints were recorded between 2003 and 2010 (TASER: n=76, range: 2007–2010; TAHOD: n=28, range: 2003–2009). Significant univariate associations were found with age group, baseline CD4 count and HIV-1 RNA viral load. After adjustment for Table 1 covariates, patients older than 40 years had higher risk of clinical deterioration (HR=2.17; p=0.008) while patients having baseline CD4 cell counts greater than 200 cells/μL had lower risk of clinical deterioration (HR= 0.373; p<0.003). A total of 450 (43.4%) patients contributing to the clinical deterioration analyses were also included in immunologic and virologic analyses.

Table 2
Factors associated with clinical deterioration after initiating first-line therapy

Change in CD4 cell count at 12 months following HAART

For immunologic analyses, 532 patients (48.1% of eligible) had CD4 counts available at baseline and at 12 months, with a median increase of 187.2 cells/μL over the period (Table 3). To calculate the change in CD4 over the period, baseline CD4 was subtracted from the 12-month result. In unadjusted analyses, smaller increases in CD4 counts were associated with age older than 40 years while larger improvements were associated with being infected with subtype B. Excluding patients with unknown baseline VL, compared to patients with less than 10,000 copies/mL, patients with higher VLs evidenced larger increases. These associations were maintained after adjustment for other covariates (Age>40 years; p=0.002; Subtype B; p=0.024, HIV-1 RNA >=10,000 copies/mL; p=0.024). There was no interaction between HIV-1 genotype and cohort membership (Change in CD4: TASER-M; median: 168 cells/μL, IQR: 100 – 252 cells/μL, TAHOD: median: 166 cells/μL, IQR: 101 – 250 cells/μL; Interaction; p<0.402). As shown in Table 3, 459 (86.3%) patients had greater than 10,000 copies/mL at study entry. Median CD4 count increases for these patients, in all age categories, were higher for subtype B-infected patients (Age<30 years; Subtype B: median: 185 cells/μL, IQR: 138–289, CRF01_AE: median: 178.5 cells/μL, IQR: 120–276; Age 30–40 years; Subtype B: median: 251 cells/μL, IQR: 165–299, CRF01_AE: median: 176 cells/μL, IQR: 99–250; Age>40 years; Subtype B: median: 157.5 cells/μL, IQR: 101.5–217, CRF01_AE: median: 152.5 cells/μL, IQR: 76–218). Most patients infected with CRF01_AE came from Thailand [Thailand: n=288 (75.0%), Hong Kong: n=68 (17.7%), Malaysia: n=27 (7.0%), Taiwan: n=1 (0.3%)] whereas the majority of subtype B patients came from high-income economies [Hong Kong: n=74 (50.0%), Taiwan: n=32 (21.6%), Japan: n=13 (8.8%) South Korea: n=11(7.4%) vs. Thailand: n=15 (10.1%), Malaysia: n=3 (2.0%)]. TAHOD patients from Japan and South Korea were only infected with subtype B but excluding these patients from analyses did not impact upon interpretations.

Table 3
Factors associated with 12-month change in CD4 cell counts after initiating first-line therapy

HIV RNA at 12 months following HAART

Due to the heterogeneity of virology assays and associated dynamic ranges across sites, we defined the lower limit of detection (LLD) as 400 copies/mL. Analyses included 530 patients (48.0% of eligible) who had an HIV-1 RNA result available at 12 months and 92.6% of patients were virologically suppressed below the LLD (TASER: n=383 (94.3%), TAHOD n=108 (87.1%). Multivariate analyses showed no associations between the patient characteristics shown in Table 1 and the virologic outcome.


Subtype B and CRF01_AE have been circulating in Asia for more than 10 years 30 and we report on the first evaluation of treatment responses in these genotypes in ARV-naïve patients. Patients initiated therapy from 2003–2010 and findings from adjusted analyses demonstrated that patients infected with subtype B had increased immunological response to therapy, compared to CRF01_AE. A retrospective, cross-sectional study of mainly treated patients also found lower immunologic response in CRF01_AE patients compared to subtype B 9. However, our finding in treatment-naive patients is uncomplicated by genomic variation attributable to drug selection pressures. A study from Singapore found increased CD4+ T-cell loss in predominantly Chinese males infected with CRF01_AE 31 and, as mentioned previously, studies in other cohorts have reported differences in HIV-1 transmission and disease progression. Several in vitro studies have suggested structural reasons for these differences.

Patients older than 40 years had reduced immunologic response at 12-months while baseline HIV-1 RNA greater than 10,000 copies/mL at study entry was predictive of larger CD4 counts increases, compared patients with lower viral burdens. Older patients with low CD4 counts pre-therapy had increased risk of clinical deterioration, consistent with the literature 32. Comparisons of virologic suppression in other genotypes have yielded mixed results 16,33 but we found no differences in virologic suppression post-HAART and approximately 90% of patients achieved virologic suppression at 12-months post-therapy.

Patients being followed under protocol at funded study sites or with HIV-1 genotype recorded in observational data suggest that site clinicians have diagnostic technologies available to guide patient treatment. Consequently, treatment outcomes for our patients may be better than those experienced in general clinic populations. Adherence information was not available and limited follow-up for TASER-M patients may have contributed to our nonsignificant finding in relation to clinical deterioration. Country differences were not specifically controlled for although cohort membership may serve as a surrogate for these effects. Separate PR and RT genotypes are not reported in TAHOD and we noted 6.4% of discordant genotypes among the TASER-M patients. Therefore, a small proportion of TAHOD patients with discordant genotypes may have been misclassified.

ARVs are commonly designed on subtype B. If immunologic response in the year following HAART affects patient prognosis, our findings of a reduced response for patients infected with CRF01_AE may possibly translate to a higher burden on country health systems, for these patients than for their subtype-B infected counterparts. Studies of longer duration in representative patient populations, including socio-economic information and in vitro studies are required to investigate this hypothesis. Patients starting therapy with low CD4 counts have been infected for some time and are commencing therapy later than recommended by international guidelines 34,35. Late therapy initiation for patients from developing economies generally reflects resourcing issues. However, for patients from high-income Asian economies, this may be due to ignorance of HIV-positive status. Increased testing to alert of HIV infection, before CD4 counts decrease substantially, should be encouraged, particularly in high-risk groups.

Our finding of no differences in virologic response to treatment suggests that with appropriate diagnostic testing, all patients have opportunities to suppress circulating virus to non-detectable levels, thereby potentially increasing disease-free-survival. In addition to being a welcome outcome for individual patients, levels of onward transmission are reduced in virologically suppressed individuals 36.

The HIV pandemic is of increasing complexity and where genotypes co-circulate, individuals coinfected with multiple variants provide HIV-1 opportunities for recombination, augmenting viral diversity 37. We found discordant PR and RT genotypes in 6.4% of our patients, reflecting possible dual infection and/or recombination. Strategies such as serosorting, where same HIV-status partners are sought for unprotected sex, have been reported in MSM, as have higher frequencies of multi-variant transmission 38. Serosorting is not supported as a risk reduction strategy and increases opportunities for recombination 39, further complicating vaccine initiatives which seek to target transmitted virus.

Assays which evaluate patient circulating viral sequence for the presence of drug resistant mutations also determine the circulating viral genotype. Although phylogenetic investigations cannot determine the direction of HIV evolution, and, consequently the direction of transmission in humans 40, mechanisms to capture genotypes resulting from HIVDR testing at country-level may contribute to monitoring and quantification of HIV-1 diversity and genotypic proliferation in at-risk population networks 41. Genotyping sequencing is expensive but there have been recent improvements in dried blood spot methodologies, a less expensive alternative for specimen collection 42. Increased availability of lower cost genotyping may contribute to local surveillance efforts.

In summary, our finding of reduced immunologic response in CRF01_AE-infected patients, compared to subtype B, suggests that genotypic diversity impacts upon patient response to treatment. Evidence of dual infection and recombination in our patients may suggest a need for regional epidemic surveillance. Tracking of local variants may help to identify increasing incidence of HIV-1 genotypes in at-risk groups and contribute to monitoring HIV-1 diversity and proliferation in the region.


Source of Funding

The TREAT Asia HIV Observational Database, TREAT Asia Studies to Evaluate Resistance, and the Australian HIV Observational Database are initiatives of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the Dutch Ministry of Foreign Affairs through a partnership with Stichting Aids Fonds, and the U.S. National Institutes of Health’s National Institute of Allergy and Infectious Diseases, Eunice Kennedy Shriver National Institute of Child Health and Human Development, and National Cancer Institute, as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA; U01AI069907). Queen Elizabeth Hospital and the Integrated Treatment Centre received additional support from the Hong Kong Council for AIDS Trust Fund. The Kirby Institute is funded by the Australian Government Department of Health and Ageing, and is affiliated with the Faculty of Medicine, The University of New South Wales. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above.

TAHOD-TASER study members

A Kamarulzaman, A Kajindran, and LY Ong, University Malaya Medical Center, Kuala Lumpur, Malaysia;

C KC Lee, R David, and B LH Sim, Hospital Sungai Buloh, Kuala Lumpur, Malaysia;

CV Mean, V Saphonn, and K Vohith, National Center for HIV/AIDS, Dermatology and STDs, Phnom Penh, Cambodia;

E Yunihastuti, Working Group on AIDS Faculty of Medicine, University of Indonesia/Ciptomangunkusumo Hospital, Jakarta, Indonesia;

FJ Zhang, HX Zhao, and N Han, Beijing Ditan Hospital, Capital Medical University, Beijing, China;

JY Choi, SH Han, and JM Kim, Division of Infectious Diseases, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea;

M Mustafa and N Nordin, Hospital Raja Perempuan Zainab II, Kota Bharu, Malaysia;

N Kumarasamy, S Saghayam, and C Ezhilarasi, YRG Centre for AIDS Research and Education, Chennai, India;

OT Ng, A Chua, LS Lee, and A Loh, Tan Tock Seng Hospital, Singapore;

PCK Li and MP Lee, Queen Elizabeth Hospital and KH Wong, Integrated Treatment Centre, Hong Kong, China;

P Kantipong and P Kambua, Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand;

P Phanuphak, K Ruxrungtham, M Khongphattanayothin, and S Sirivichayakul, HIV-NAT/Thai Red Cross AIDS Research Centre, Bangkok, Thailand;

R Ditangco, E Uy, and R Bantique, Research Institute for Tropical Medicine, Manila, Philippines;

R Kantor, Brown University, Rhode Island, U.S.A.;

S Oka, J Tanuma, and T Nishijima, National Center for Global Health and Medicine, Tokyo, Japan;

S Pujari, K Joshi, and A Makane, Institute of Infectious Diseases, Pune, India;

S Sungkanuparph, S Kiertiburanakul, L Chumla, and N Sanmeema, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand;

TP Merati, DN Wirawan, and F Yuliana, Faculty of Medicine, Udayana University and Sanglah Hospital, Bali, Indonesia;

T Sirisanthana, R Chaiwarith, W Kotarathititum, and J Praparattanapan, Research Institute for Health Sciences, Chiang Mai University, Chiang Mai, Thailand;

TT Pham, DD Cuong, and HL Ha, Bach Mai Hospital, Hanoi, Vietnam;

VK Nguyen, VH Bui, and TT Cao, National Hospital for Tropical Diseases, Hanoi, Vietnam;

W Ratanasuwan and R Sriondee, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand;

YMA Chen, WW Wong, YJ Chen, LH Kuo, and YT Lin, Taipei Veterans General Hospital and AIDS Prevention and Research Centre, National Yang-Ming University, Taipei, Taiwan;

AH Sohn, N Durier, B Petersen, and T Singtoroj, TREAT Asia, amfAR - The Foundation for AIDS Research, Bangkok, Thailand;

DA Cooper, MG Law, and A Jiamsakul, The Kirby Institute, University of New South Wales, Sydney, Australia.


Current Steering Committee Chairs;


Presented at:

National Medicines Symposium, Sydney, Australia, May 2012.

Conflicts of Interest:

None reported


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