Background: In 2005, free antiretroviral therapy (ART) was rolled-out as a national program in Viet Nam. The estimated population of people living with HIV reached 254,000 in 2010 and this leads to increasing demand of ART in the near future. By 2009, ART coverage reached 53.7% for adults and adolescents, and 49.7% for children. This study aims to describe the prevalence of HIV acquired drug resistance (ADR) among people receiving ART and prevalence of transmitted drug resistance (TDR) among recently HIV-infected persons in Vietnam, and their associated ART coverage, antiretroviral treatment adherence and risk behaviors.

Methods: We performed a comprehensive review of published English literature containing relevant epidemiological and behavioral indicators through internet searches.

Results: Twenty-one relevant publications were included in this review. TDR prevalence among people recently infected with HIV increased from below 5% in 2006 to a higher level of 5-15% during 2007-2008 in urban Vietnam, whereas TDR prevalence among chronic antiretroviral-naïve HIV-infected adults stabilized between 6-8% across the country. About half of all adults and children with clinical or immunological criteria of therapeutic failure had evidence of developing resistance to antiretroviral drugs. Non-adherence among adults on ART ranged between 25-32% and the level of viral suppression (< 1,000 copies/ml) fluctuated from 68% to more than 83% at 12 month after initiating ART. However, relevant data concerning children were mostly absent.

Conclusion: Increasing trend of transmission of HIV drug resistance was observed in urban Vietnam, suggesting an urgency of the establishment of regular surveillance for TDR. Viral load testing and availability of second or third line ART are recommended for the early diagnosis of drug resistance and prevention of its accumulation and transmission.

Objective: To describe the ARV resistance profiles of patients experiencing virological failure after at least 6 months on LPV/r-based 2nd line regimen in Cambodia. Design: Retrospective analysis of resistance testing of 89 patients with detectable viral load under LPV/r-based 2nd line regimen.

Methods: Bulk sequencing of HIV-1 protease, reverse transcriptase and integrase PCR products.

Results: Protease gene amplification was successful for 71/89 patients (80%). All were infected by CRF01_AE viruses. Among them, 42 did not present any resistance to PIs. A high level of resistance to PIs was observed for the 29 remaining patients. Twenty-six were resistant to LPV/r (8 possibly resistant). Twenty-eight, 21 and 20 were also found resistant to IDV, ATV/r and FPV/r, respectively. Twenty-six were resistant to NFV (11 possibly) and 22 to SQV/r (9 possibly). Finally, 22/29 (75.8%) were resistant to at least 3 PIs. Interestingly, 78.6% (22/29) were found sensitive to DRV/r. In this group, a high frequency of resistance to RTIs including ETV was also reported. No resistance to raltegravir (RAL) or elvitegravir (EVG) was observed (n=24). Detailed ARV histories documented for 15 patients revealed past exposition to multiple RTIs and PIs.

Conclusion: Almost 2/3 of patients (60/89) with virological failure on LPV/r-based 2nd line in our study were not in urgent need for treatment change. In contrast, switching treatment was clearly required for 1/3 (29/89) presenting high level of resistance to PIs and RTIs. For those patients, DRV, RAL/EVG, and potentially ETV, could be good candidates for 3rd line ARV regimen if available.

Background: The first antiretroviral drug (Truvada) to be used as a pre-exposure prophylaxis (PrEP) in preventing HIV transmission is about to be approved. Behavioral studies suggest that a portion of users may share anti-retroviral drugs with sex partners, family, or friends. Pill sharing will decrease PrEP efficacy and adherence level, and potentially create an environment favorable for the development of drug resistance. We aim to evaluate the potential impact of pill sharing on the PrEP effectiveness and on the rates of drug-resistance development in heterosexual populations.

Methods: A transmission dynamic model was used to assess the population-level impact of oral PrEP. The fractions of new HIV infections prevented (CPF), drug resistance prevalence and the proportion of new infections in which drug-resistant HIV is transmitted (TDR) are evaluated over fixed time periods. The influence of different factors on CPF and TDR is studied through simulations, using epidemic parameters representative of the countries in Sub-Saharan Africa.

Results: Without pill sharing, a 70% efficacious PrEP used consistently by 60% of uninfected individuals prevents 52.8% (95% CI 49.4%-56.4%) of all new HIV infections over ten years with drug-resistant HIV transmitted in 2.2% of the new infections. Absolute CPF may vary by 9% if up to 20% of the users share PrEP while the level of TDR and total resistance prevalence may increase by up to 6-fold due to pill sharing in some intervention scenarios.

Conclusion: Pill sharing may increase the PrEP coverage level achieved in the population but it also affects the PrEP efficacy for the users who do not follow the prescribed schedule. More importantly, it creates a pool of untracked users who remain unreached by the effort to avoid sub-optimal PrEP usage by infected people. This increases substantially the potential risk of drug resistance in the population

Background: Virologic failures and development of drug resistance can result in reduced treatment options in HIV infection.

Methods: RT sequence of HIV-1 subtype C isolates from 122 Antiretroviral Therapy (ART) naive and 13 virological and first line regimen failures from North India were analyzed. Mutations were defined according to Stanford Drug Resistance data base. A three dimensional HIV-1 subtype C specific computational model of RT was created from consensus sequences from naïve patients to analyze mutations in therapy failures. CD4 count and viral load were measured to analyze the disease status and subtyping was done using Genotyping NCBI HIV subtyping tool.

Results: All thirteen isolates from first-line ART-failure patients had mutations effecting susceptibility to RTI drugs when analyzed using Stanford DR HIV-1 database. The most common NRTI resistance mutations were in positions 118, 184, 210 and 215 indicating the possibility of high level resistance to Lamivudine (3TC) and Emtricitabine (FTC) in 92.3% of isolates. Common NNRTI resistance mutations were identified at position 98, 101, 103, 181 and 190 indicating a high level resistance to Nevirapine (NVP) in 100% therapy failures, while affecting the susceptibility to EFV in 76.92%. Energy scores were calculated after docking of various NRTIs on our newly proposed model based on the three dimensional structure of local wild type reverse transcriptase (RT) of subtype C. The presence of V75M mutation in one of the isolates (SK-206) seem to be partially neutralizing the resistance effect of mutations 118I, 184V, 210W, and 215Y for Stavudine (D4T), Didanosine (DDI) and FTC while 75M decreases the susceptibility of Zidovudine (AZT) (ΔG=0.87), causing high level of resistance.

Conclusion: The data suggest that the proposed model was successful in predicting the resistance/susceptibility to various RTIs based on docking energy scores taking into consideration the cumulative effect of all the mutations together.

A number of diseases including viral and bacterial infections, inflammatory and auto-immune disorders is associated with glucocorticoids resistance. Tissue resistance to glucocorticoids is observed in untreated HIV infected patients with hypercortisolism. These patients show a complete inability of glucocorticoids to exert their effect, full Addisonian symptoms and an impressive increase in type-1 Th-directed cellular immunity. This clearly suggests a severe receptor resistance to glucocorticoids. Removal of glucocorticoids by adrenal insufficiency or decreased glucocorticoid receptors sensitivity, likely induced by HIV RNA, endotoxin and lipopolysaccarides, significantly enhances morbidity and mortality in such patients. Highly active antiretroviral therapy is an important advance in the treatment of HIV infection since confers survival and acceptable conditions of life, but the suppression of viral replication is not associated with reconstitution of the immune function. This allows persistence of chronic inflammatory disease and activation of local 11β hydroxy steroid dehydrogenase type-1 and type-2 with associated increases in glucocorticoids and mineralcorticoids production. Glucocorticoids and their receptors have a central role in the control of innate and adaptive immune responses. In turn, cytokines signaling pathway may influence neuroendocrine function through changes in the receptors sensitivity and function. Evidence shows that glucocorticoids system works in concert with other systems, including the immune, metabolic and renin-angiotensin aldosterone systems. Imbalance of this network mainly drives to chronic inflammation and the metabolic syndrome with its component of insulin resistance, dyslipidemia and cardiovascular complications. Therapeutic strategies to restore the integrity of glucocorticoid receptor function and reconstitute the immune function are needed to keep HIV infected patients away from complications. Current therapeutic approaches tend to inhibit the 11β hydroxy steroid dehydrogenase type-1 activity and the renin-angiotensin-aldosterone system function. Both these treatments may reduce inflammation

Background: HIV drug resistance can cause viral re-bound in patients on combination antiretroviral therapy, requiring a change in therapy to re-establish virological control. The RDI has developed computational models that predict response to combination therapy based on the viral genotype, viral load, CD4 count and treatment history. Here we compare two sets of models developed with different levels of treatment history information and test their generalisability to new patient populations.

Methods: Two sets of five random forest models were trained to predict the probability of virological response (follow-up viral load <50 copies/ml viral RNA) following a change in antiretroviral therapy using the baseline viral load, CD4 count, genotype and treatment history from 7,263 treatment change episodes. One set used six treatment history variables and the other 18 - one for each drug. The accuracy of the models was assessed in terms of the area under the receiver-operator characteristic curve (AUC) during cross validation and with 375 TCEs from clinics that had not contributed data to the training set.

Results: The mean AUC achieved by the two sets of models during cross validation was 0·815 and 0.820. Mean overall accuracy was 75% and 76%, sensitivity 64% and 62% and specificity 81% and 84%. The AUC for each committee tested with the independent test set was 0.87 and 0.855. Mean overall accuracy was 89% and 87%, sensitivity 67% and 61% and specificity 90% and 87%. There were no significant differences between the two sets. The models correctly predicted 330 (92%) of the 357 treatment failures observed in practice and were able to identify alternative regimens that were predicted to be effective for up to 267 (75%) of the failures and regimens with a higher probability of response for all cases.

Conclusions: Computational models can predict accurately the virological response to antiretroviral therapy from a range of variables including genotype and treatment history even for patients from unfamiliar settings. This approach has potential utility as a useful aid to treatment decision-making and may reduce treatment failure