Resistance Patterns Selected by Nevirapine vs. Efavirenz in HIV-Infected Patients Failing First-Line Antiretroviral Treatment: A Bayesian Analysis

Background: WHO recommends starting therapy with a non-nucleoside reverse transcriptase inhibitor (NNRTI) and two nucleoside reverse transcriptase inhibitors (NRTIs), i.e. nevirapine or efavirenz, with lamivudine or emtricitabine, plus zidovudine or tenofovir. Few studies have compared resistance patterns induced by efavirenz and nevirapine in patients infected with the CRF01_AE Southeast Asian HIV-subtype. We compared patterns of NNRTI-and NRTI-associated mutations in Thai adults failing first-line nevirapine-and efavirenz-based combinations, using Bayesian statistics to optimize use of data. Methods and Findings: In a treatment cohort of HIV-infected adults on NNRTI-based regimens, 119 experienced virologic failure (<500 copies/mL), with resistance mutations detected by consensus sequencing. Mutations were analyzed in relation to demographic, clinical, and laboratory variables at time of genotyping. The Geno2Pheno system was used to evaluate second-line drug options. Eighty-nine subjects were on nevirapine and 30 on efavirenz. The NRTI backbone consisted of lamivudine or emtricitabine plus either zidovudine (37), stavudine (65), or tenofovir (19). The K103N mutation was detected in 83% of patients on efavirenz vs. 28% on nevirapine, whereas Y181C was detected in 56% on nevirapine vs. 20% efavirenz. M184V was more common with nevirapine (87%) than efavirenz (63%). Nevirapine favored TAM-2 resistance pathways whereas efavirenz selected both TAM-2 and TAM-1 pathways. Emergence of TAM-2 mutations increased with the duration of virologic replication (OR 1.25-1.87 per month increment). In zidovudine-containing regimens, the overall risk of resistance across all drugs was lower with nevirapine than with efavirenz, whereas in tenofovir-containing regimen the opposite was true. Conclusions: TAM-2 was the major NRTI resistance pathway for CRF01_ AE, particularly with nevirapine; it appeared late after virological failure. In patients who failed, there appeared to be more second-line drug options when zidovudine was combined with nevirapine or tenofovir with efavirenz than with alternative combinations

New-Onset Diabetes and Antiretroviral Treatments in HIV-Infected Adults in Thailand

BACKGROUND: Use of several antiretrovirals (ARVs) has been shown to be associated with a higher risk of diabetes in HIV-infected adults. We estimated the incidence of new-onset diabetes and assessed the association between individual ARVs and ARV combinations, and diabetes in a large cohort in Thailand. METHODS: We selected all HIV-1-infected, nondiabetic, antiretroviral-naive adults enrolled in the Program for HIV Prevention and Treatment cohort (NCT00433030) between January 2000 and December 2011. Diabetes was defined as confirmed fasting plasma glucose >/= 126 mg/dL or random plasma glucose >/= 2 00 mg/dL. Incidence was the number of cases divided by the total number of person-years of follow-up. Association between ARVs and ARV combinations, and new-onset diabetes was assessed using Cox proportional hazards models. RESULTS: Overall, 1594 HIV-infected patients (76% female) were included. Median age at antiretroviral therapy initiation was 32.5 years. The incidence rate of diabetes was 5.0 per 1000 person-years of follow-up (95% confidence interval: 3.8 to 6.6) (53 cases). In analyses adjusted for potential confounders, exposure to stavudine + didanosine [adjusted hazard ratio (aHR) = 3.9; P = 0.001] and cumulative exposure >/= 1 year to zidovudine (aHR = 2.3 vs. no exposure; P = 0.009) were associated with a higher risk of diabetes. Conversely, cumulative exposure >/= 1 year to tenofovir (aHR = 0.4 vs. no exposure; P = 0.02) and emtricitabine (aHR = 0.4 vs. no exposure; P = 0.03) were associated with a lower risk. CONCLUSIONS: The incidence of diabetes in this predominantly female, young, lean population was relatively low. Although stavudine and didanosine have now been phased out in most antiretroviral therapy programs, our analysis suggests a higher risk of diabetes with zidovudine, frequently prescribed today in resource-limited settings

Switching HIV treatment in adults based on CD4 count versus viral load monitoring: a randomized, non-inferiority trial in Thailand.

BACKGROUND: Viral load (VL) is recommended for monitoring the response to highly active antiretroviral therapy (HAART) but is not routinely available in most low- and middle-income countries. The purpose of the study was to determine whether a CD4-based monitoring and switching strategy would provide a similar clinical outcome compared to the standard VL-based strategy in Thailand. METHODS AND FINDINGS: The Programs for HIV Prevention and Treatment (PHPT-3) non-inferiority randomized clinical trial compared a treatment switching strategy based on CD4-only (CD4) monitoring versus viral-load (VL). Consenting participants were antiretroviral-naïve HIV-infected adults (CD4 count 50-250/mm(3)) initiating non-nucleotide reverse transcriptase inhibitor (NNRTI)-based therapy. Randomization, stratified by site (21 public hospitals), was performed centrally after enrollment. Clinicians were unaware of the VL values of patients randomized to the CD4 arm. Participants switched to second-line combination with confirmed CD4 decline >30% from peak (within 200 cells from baseline) in the CD4 arm, or confirmed VL >400 copies/ml in the VL arm. Primary endpoint was clinical failure at 3 years, defined as death, new AIDS-defining event, or CD4 400 copies/ml at switch was 7.2 months (5.8-8.0) in VL versus 15.8 months (8.5-20.4) in CD4 (p=0.002). FDO scores were not significantly different at time of switch. No adverse events related to the monitoring strategy were reported. CONCLUSIONS: The 3-year rates of clinical failure and loss of treatment options did not differ between strategies although the longer-term consequences of CD4 monitoring would need to be investigated. These results provide reassurance to treatment programs currently based on CD4 monitoring as VL measurement becomes more affordable and feasible in resource-limited settings. TRIAL REGISTRATION: ClinicalTrials.govNCT00162682 Please see later in the article for the Editors' Summary

Number of primary outcomes by arm.

a<p>Including nine cases followed by death.</p>b<p>Tuberculosis (8), cryptococcal meningitis (3), <i>P. jirovecii</i> pneumonia (2), systemic <i>P. marneffei</i> (2), disseminated <i>Mycobacterium avium intracellulare</i> (1), sepsis (2).</p>c<p>Tuberculosis (4), cryptococcal meningitis (4), <i>P. jirovecii</i> pneumonia (2), systemic <i>P. marneffei</i> (2), sepsis (3).</p>d<p>Sepsis (3), cerebrovascular accidents (2), heart failure (1), asthma attack (1), <i>P. jirovecii</i> pneumonia (1), hepatic failure (1), unknown cause (2).</p>e<p>Heart failure (3), cancer (2, 1 breast cancer, 1 liver cancer), suicide (2), renal failure (1), hepatic failure (1), pneumonia (1), sepsis (1).</p

Primary outcomes (clinical failure) at 3 y: death, new AIDS-defining events, or confirmed CD4 <50/mm³.

a<p>The 95% CI of the difference, −0.6% was −4.5% to 3.4%. The upper limit of this CI was below the pre-determined criterion for non-inferiority, 7.4%.</p

Serious adverse events by trial arm.

<p>Serious adverse events by trial arm.</p

Characteristics of the patients at baseline, according to randomization arm.

a<p>There were eight protocol deviations reported related to inclusion criteria: One patient was not ARV naive, one woman was pregnant, one was chronically infected with hepatitis C, two had hemoglobin level <8.0 g/dl, two had absolute neutrophil count <1,000 cells/mm³, one had serum creatinine above 1.0× ULN.</p

Treatment switch to second-line, PI-based treatment.

a<p>Calculated starting in the middle of the time period between the last VL <400 copies/ml and first VL above.</p>b<p>Calculated starting in the middle of the time period between the last VL <50 copies/ml and first VL above.</p

Risk factors for clinical failure.

a<p>HR, hazard ratio.</p>b<p>Adjusted HR (AHR, Cox proportional analysis) adjusted for hemoglobin, CDC stage, CD4 count, VL, BMI, sex, and randomization arm.</p