HIV RNA and genotype in resource-limited settings: can we do better?

Optimizing viral load suppression in Kenyan children on antiretroviral therapy (Opt4Kids)

Background

As many as 40% of the 1 million children living with HIV (CLHIV) receiving antiretroviral treatment (ART) in resource limited settings have not achieved viral suppression (VS). Kenya has a large burden of pediatric HIV with nearly 140,000 CLHIV. Feasible, scalable, and cost-effective approaches to ensure VS in CLHIV are urgently needed. The goal of this study is to determine the feasibility and impact of point-of-care (POC) viral load (VL) and targeted drug resistance mutation (DRM) testing to improve VS in children on ART in Kenya.

Methods

We are conducting a randomized controlled study to evaluate the use of POC VL and targeted DRM testing among 704 children aged 1-14 years on ART at health facilities in western Kenya. Children are randomized 1:1 to intervention (higher frequency POC VL and targeted DRM testing) vs. control (standard-of-care) arms and followed for 12 months. Our primary outcome is VS (VL < 1000 copies/mL) 12 months after enrollment by study arm. Secondary outcomes include time to VS and the impact of targeted DRM testing on VS. In addition, key informant interviews with patients and providers will generate an understanding of how the POC VL intervention functions. Finally, we will model the cost-effectiveness of POC VL combined with targeted DRM testing.

Discussion

This study will provide critical information on the impact of POC VL and DRM testing on VS among CLHIV on ART in a resource-limited setting and directly address the need to find approaches that maximize VS among children on ART.

Trials registration

NCT03820323.

High rate of HIV-1 drug resistance in treatment failure patients in Taiwan, 2009-2014

Background

Drug resistance to nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs) has been associated with loss of viral suppression measured by a rise in HIV-1 RNA levels, a decline in CD4 cell counts, persistence on a failing treatment regimen, and lack of adherence to combination antiretroviral therapy.

Objectives

This study aimed to monitor the prevalence and risk factors associated with drug resistance in Taiwan after failure of first-line therapy.

Materials and methods

Data from the Veterans General Hospital Surveillance and Monitor Network for the period 2009–2014 were analyzed. Plasma samples from patients diagnosed with virologic failure and an HIV-1 RNA viral load >1000 copies/mL were analyzed by the ViroSeq^™ HIV-1 genotyping system for drug susceptibility. Hazard ratios (HRs) for drug resistance were calculated using a Cox proportional hazard model.

Results

From 2009 to 2014, 359 patients were tested for resistance. The median CD4 count and viral load (log) were 214 cells/μL (interquartile range [IQR]: 71–367) and 4.5 (IQR: 3.9–5.0), respectively. Subtype B HIV-1 strains were found in 90% of individuals. The resistance rate to any of the three classes of antiretroviral drugs (NRTI, NNRTI, and PI) was 75.5%. The percentage of NRTI, NNRTI, and PI resistance was 58.6%, 61.4%, and 11.4%, respectively. The risk factors for any class of drug resistance included age ≤35 years (adjusted HR: 2.30, CI: 1.48–3.56; p<0.0001), initial NNRTI-based antiretroviral regimens (adjusted HR: 1.70, CI: 1.10–2.63; p=0.018), and current NNRTI-based antiretroviral regimens when treatment failure occurs (odds ratio: 4.04, CI: 2.47–6.59; p<0.001). There was no association between HIV-1 subtype, viral load, and resistance.

Conclusion

This study demonstrated a high level of resistance to NRTI and NNRTI in patients with virologic failure to first-line antiretroviral therapy despite routine viral load monitoring. Educating younger men who have sex with men to maintain good adherence is crucial, as PI use is associated with lower possibility of drug resistance.

Baseline HIV-1 resistance, virological outcomes, and emergent resistance in the SECOND-LINE trial: an exploratory analysis

BACKGROUND

WHO-recommended second-line antiretroviral therapy (ART) of a pharmacologically enhanced (boosted) protease inhibitor plus nucleoside or nucleotide reverse transcriptase inhibitors (NtRTIs) might be compromised by resistance. Results of the 96 week SECOND-LINE randomised trial showed that NtRTI-sparing ART with ritonavir-boosted lopinavir and raltegravir (raltegravir-group) provided non-inferior efficacy to ritonavir-boosted lopinavir and two or three NtRTIs (NtRTI-group) in participants with virological failure composed of a first-line regimen of a non-nucleoside reverse transcriptase inhibitor plus two NtRTIs. We report the relation of baseline virological resistance with virological failure and emergent resistance on study.

METHODS

As part of the randomised open-label SECOND-LINE trial, second-line ART NtRTI selection was made by either genotype (local laboratory) or algorithm. Genotypic resistance for the entire cohort at baseline was assessed on stored samples at a central laboratory. Virological failure was defined as plasma viral load greater than 200 copies per mL. Baseline viral isolates were assigned genotypic sensitivity scores (GSSs) by use of the Stanford HIV Database version 6.3.1: a global GSS (gGSS), defined as the combined GSS for lamivudine or emtricitabine, abacavir, zidovudine, stavudine, didanosine, and tenofovir and a specific GSS (sGSS) defined as the GSS for the ART regimen initiated by a specific participant. Emergent resistance was reported on samples with a viral load greater than 500 copies per mL. We used multivariate logistic regression with backward elimination to assess predictors of virological failure and emergent resistance.

FINDINGS

From April 19, 2010, to July 22, 2013, 271 patients were included in the NtRTI group and and 270 in the raltegravir group. In the NtRTI group 215 had available baseline sequence data, and 240 had viral load measurements at 96 weeks; in the raltegravir group 236 had baseline sequence data and 255 had viral load measurements at 96 weeks. Median (IQR) gGSS was 3.0 (1.3-4.3) in the NtRTI group and 3.0 (1.0-4.3) in the raltegravir group. The median sGSS in the NtRTI group was 1.0 (0.5-1.8). Multivariate analysis showed significant associations between virological failure and less than complete adherence at week 4 (odds ratio [OR] 2.18, 95%CI 1.07-4.47; p=0·03) and week 48 (2.49, 1.09-5.69; p=0.03), baseline plasma viral load greater than 100,000 copies per mL (3.43, 1.70-6.94; p=0.0006), baseline gGSS >4.25 (4.73, 1.94-11.6; p=0.0007), and being Hispanic (3.13, 1.21-8.13; p=0.02) or African (3.49, 1.68-7.28; p=0.0008) rather than Asian. We observed emergent major mutations in one (1%) of 129 participants for protease (both groups), eight (13%) of 64 for reverse transcriptase (NtRTI group) and 16 (20%) of 79 for integrase. Emergent resistance was associated with the raltegravir group (OR 2.47, 95% CI 1.02-5.99; p=0.05), baseline log10 viral load (1.83, 1.12-2.97; p=0.02), and absence of the Lys65Arg (K65R) or Lys70Glu (K70E) mutation at baseline (3.18, 1.12-9.02; p=0.03).

INTERPRETATION

Poor adherence was a major determinant of virological failure in people on second-line ART. In settings with limited resources, investment in optimisation of adherence rather than implementation of drug resistance testing might be advisable.

FUNDING

University of New South Wales Australia, Merck, AbbVie, and the Foundation for AIDS Research.

A comparison of computational models with and without genotyping for prediction of response to second-line HIV therapy

OBJECTIVES

We compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure.

METHODS

Two sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared.

RESULTS

The rankings of alternative regimens by the two sets of models were significantly correlated in 60-69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping.

CONCLUSIONS

The two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.