The ability of four genotypic interpretation systems to predict virological response to ritonavir-boosted protease inhibitors

Human Immunodeficiency Virus Drug Resistance: 2018 Recommendations of the International Antiviral Society-USA Panel

Background

Contemporary antiretroviral therapies (ART) and management strategies have diminished both human immunodeficiency virus (HIV) treatment failure and the acquired resistance to drugs in resource-rich regions, but transmission of drug-resistant viruses has not similarly decreased. In low- and middle-income regions, ART roll-out has improved outcomes, but has resulted in increasing acquired and transmitted resistances. Our objective was to review resistance to ART drugs and methods to detect it, and to provide updated recommendations for testing and monitoring for drug resistance in HIV-infected individuals.

Methods

A volunteer panel of experts appointed by the International Antiviral (formerly AIDS) Society–USA reviewed relevant peer-reviewed data that were published or presented at scientific conferences. Recommendations were rated according to the strength of the recommendation and quality of the evidence, and reached by full panel consensus.

Results

Resistance testing remains a cornerstone of ART. It is recommended in newly-diagnosed individuals and in patients in whom ART has failed. Testing for transmitted integrase strand-transfer inhibitor resistance is currently not recommended, but this may change as more resistance emerges with widespread use. Sanger-based and next-generation sequencing approaches are each suited for genotypic testing. Testing for minority variants harboring drug resistance may only be considered if treatments depend on a first-generation nonnucleoside analogue reverse transcriptase inhibitor. Different HIV-1 subtypes do not need special considerations regarding resistance testing.

Conclusions

Testing for HIV drug resistance in drug-naive individuals and in patients in whom antiretroviral drugs are failing, and the appreciation of the role of testing, are crucial to the prevention and management of failure of ART.

Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response

Introduction

Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period.

Materials & Methods

9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.

Results

Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76–1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69–1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80–1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly.

Conclusion

Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.

Standardized representation, visualization and searchable repository of antiretroviral treatment-change episodes

BACKGROUND

To identify the determinants of successful antiretroviral (ARV) therapy, researchers study the virological responses to treatment-change episodes (TCEs) accompanied by baseline plasma HIV-1 RNA levels, CD4+ T lymphocyte counts, and genotypic resistance data. Such studies, however, often differ in their inclusion and virological response criteria making direct comparisons of study results problematic. Moreover, the absence of a standard method for representing the data comprising a TCE makes it difficult to apply uniform criteria in the analysis of published studies of TCEs.

RESULTS

To facilitate data sharing for TCE analyses, we developed an XML (Extensible Markup Language) Schema that represents the temporal relationship between plasma HIV-1 RNA levels, CD4 counts and genotypic drug resistance data surrounding an ARV treatment change. To demonstrate the adaptability of the TCE XML Schema to different clinical environments, we collaborate with four clinics to create a public repository of about 1,500 TCEs. Despite the nascent state of this TCE XML Repository, we were able to perform an analysis that generated a novel hypothesis pertaining to the optimal use of second-line therapies in resource-limited settings. We also developed an online program (TCE Finder) for searching the TCE XML Repository and another program (TCE Viewer) for generating a graphical depiction of a TCE from a TCE XML Schema document.

CONCLUSIONS

The TCE Suite of applications - the XML Schema, Viewer, Finder, and Repository - addresses several major needs in the analysis of the predictors of virological response to ARV therapy. The TCE XML Schema and Viewer facilitate sharing data comprising a TCE. The TCE Repository, the only publicly available collection of TCEs, and the TCE Finder can be used for testing the predictive value of genotypic resistance interpretation systems and potentially for generating and testing novel hypotheses pertaining to the optimal use of salvage ARV therapy.

Postpartum viral load rebound in HIV-1-infected women treated with highly active antiretroviral therapy: AIDS Clinical Trials Group Protocol A5150

BACKGROUND

Pregnancy may lead to increases in HIV-1 RNA levels postpartum. The AIDS Clinical Trials Group (ACTG) A5150 study was designed to characterize the incidence of viral load rebound during the immediate 24 weeks postpartum and explore factors associated with viral load rebound.

METHODS

We enrolled pregnant women in the United States who were ≥13 years of age, between 22 to 30 weeks gestation, and who planned to be on stable highly active antiretroviral therapy (HAART) for ≥8 weeks predelivery and to continue this therapy after delivery for the duration of the study. Choice of antiretrovirals (ARVs) was determined by the primary HIV provider. Viral load rebound was defined as an increase of ≥0.7 log10 (5-fold) from the average of the weeks 34 and 36 gestation viral loads to week 24 postpartum or an absolute increase to ≯500 copies/mL for those with viral load <50 copies/mL.

RESULTS

Eighty-four women enrolled for postpartum follow-up. Sixty-three had follow-up and viral load obtained through week 24 postpartum. Overall, 18/63 (28.6%; 95% confidence interval [CI], 17.9-41.4) met criteria for viral load rebound. Nineteen of the 63 women made changes or discontinued their ARV regimen prior to week 24 postpartum. For those who remained on stable ARVs, rebound occurred in 8/44 (18.2%; 95% CI, 8.2-32.7) compared with 10/19 (52.6%; 95% CI, 28.9-75.5) who did not remain on a stable ARV regimen.

CONCLUSIONS

In the early postpartum period, HIV-1-infected women commonly have increases in viral load. Unplanned changes in ARV regimens and discontinuations of treatment are frequent.

Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time

BACKGROUND

Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford's HIVdb) to predict virological outcome at 12, 24, and 48 weeks.

METHODOLOGY/PRINCIPAL FINDINGS

Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8-16) weeks (2152 TCEs), 24 (16-32) weeks (2570 TCEs), and 48 (44-52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92+/-1.17, compared to Rega and ANRS, with 2.22+/-1.09 and 2.23+/-1.05, respectively. However, similar odds ratio's were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5-1.7] for HIVdb, 1.7 [1.5-1.8] for ANRS, and 1.7 [1.9-1.6] for Rega. Odds ratio's increased over time, but remained comparable (odds ratio's ranging between 1.9-2.1 at 24 weeks and 1.9-2.2 at 48 weeks). The Area under the curve of the ROC did not differ between the systems at all time points; p = 0.60 at week 12, p = 0.71 at week 24, and p = 0.97 at week 48.

CONCLUSIONS/SIGNIFICANCE

Three commonly used HIV drug resistance interpretation systems ANRS, Rega and HIVdb predict virological response at 12, 24, and 48 weeks, after change of treatment to the same extent.

Predicting tipranavir and darunavir resistance using genotypic, phenotypic, and virtual phenotypic resistance patterns: an independent cohort analysis of clinical isolates highly resistant to all other protease inhibitors

Genotypic interpretation systems (GISs) for darunavir and tipranavir susceptibility are rarely tested by the use of independent data sets. The virtual phenotype (the phenotype determined by Virco [the "Vircotype"]) was used to interpret all genotypes in Québec, Canada, and phenotypes were determined for isolates predicted to be resistant to all protease inhibitors other than darunavir and tipranavir. We used multivariate analyses to predict relative phenotypic susceptibility to darunavir and tipranavir. We compared the performance characteristics of the Agence Nationale de Recherche sur le Sida scoring algorithm, the Stanford HIV database scoring algorithm (with separate analyses of the discrete and numerical scores), the Vircotype, and the darunavir and tipranavir manufacturers' scores for prediction of the phenotype. Of the 100 isolates whose phenotypes were determined, 89 and 72 were susceptible to darunavir and tipranavir, respectively. In multivariate analyses, the presence of I84V and V82T and the lack of L10F predicted that the isolates would be more susceptible to darunavir than tipranavir. The presence of I54L, V32I, and I47V predicted that the isolates would be more susceptible to tipranavir. All GISs except the system that provided the Stanford HIV database discrete score performed well in predicting the darunavir resistance phenotype (R(2) = 0.61 to 0.69); the R(2) value for the Stanford HIV database discrete scoring system was 0.38. Other than the system that provided the Vircotype (R(2) = 0.80), all GISs performed poorly in predicting the tipranavir resistance phenotype (R(2) = 0.00 to 0.31). In this independent cohort harboring highly protease inhibitor-resistant HIV isolates, reduced phenotypic susceptibility to darunavir and tipranavir was rare. Generally, GISs predict susceptibility to darunavir substantially better than they predict susceptibility to tipranavir.

The use of resistance testing in the management of HIV-1-infected patients

PURPOSE OF REVIEW

Resistance testing has become an important component of the recommended care for treatment-naive and treatment-experienced HIV-infected patients in the developed world, and their use has been shown to improve clinical outcomes. Despite the widespread use of resistance testing, the clinician faces a number of challenges in optimally applying these technologies to antiretroviral management.

RECENT FINDINGS

Even with the aid of a genotypic interpretation system, the interpretation of a genotype is complex and benefits from expert input. Phenotypic resistance testing is limited by cost and availability for many patients. Standard resistance testing (both genotypes and phenotypes) is unable to detect minority species. The presence of resistant minority populations has been associated with virologic failure. However, the current techniques available to detect their presence are cumbersome and not soon likely to become part of routine clinical care. The development of the chemokine (C-C motif) receptor 5 antagonists has provided new challenges in quantifying antiretroviral resistance.

SUMMARY

Resistance testing plays a central role in the management of treatment-experienced patients. Further progress in the interpretation of resistance testing, especially as new agents are developed, will continue to add value to the care of HIV-infected patients.

Predictive value of HIV-1 genotypic resistance test interpretation algorithms

BACKGROUND

Interpreting human immunodeficiency virus type 1 (HIV-1) genotypic drug-resistance test results is challenging for clinicians treating HIV-1-infected patients. Multiple drug-resistance interpretation algorithms have been developed, but their predictive value has rarely been evaluated using contemporary clinical data sets.

METHODS

We examined the predictive value of 4 algorithms at predicting virologic response (VR) during 734 treatment-change episodes (TCEs). VR was defined as attaining plasma HIV-1 RNA levels below the limit of quantification. Drug-specific genotypic susceptibility scores (GSSs) were calculated by applying each algorithm to the baseline genotype. Weighted GSSs were calculated by multiplying drug-specific GSSs by antiretroviral (ARV) potency factors. Regimen-specific GSSs (rGSSs) were calculated by adding unweighted or weighted drug-specific GSSs for each salvage therapy ARV. The predictive value of rGSSs were estimated by use of multivariate logistic regression.

RESULTS

Of 734 TCEs, 475 (65%) were associated with VR. The rGSSs for the 4 algorithms were the variables most strongly predictive of VR. The adjusted rGSS odds ratios ranged from 1.6 to 2.2 (P < .001). Using 10-fold cross-validation, the averaged area under the receiver operating characteristic curve for all algorithms increased from 0.76 with unweighted rGSSs to 0.80 with weighted rGSSs.

CONCLUSIONS

Unweighted and weighted rGSSs of 4 genotypic resistance algorithms were the strongest independent predictors of VR. Optimizing ARV weighting may further improve VR predictions.

TREAT Asia Quality Assessment Scheme (TAQAS) to standardize the outcome of HIV genotypic resistance testing in a group of Asian laboratories

The TREAT Asia (Therapeutics, Research, Education, and AIDS Training in Asia) Network is building capacity for Human Immunodeficiency Virus Type-1 (HIV-1) drug resistance testing in the region. The objective of the TREAT Asia Quality Assessment Scheme - designated TAQAS - is to standardize HIV-1 genotypic resistance testing (HIV genotyping) among laboratories to permit rigorous comparison of results from different clinics and testing centres. TAQAS has evaluated three panels of HIV-1-positive plasma from clinical material or low-passage, culture supernatant for up to 10 Asian laboratories. Laboratory participants used their standard protocols to perform HIV genotyping. Assessment was in comparison to a target genotype derived from all participants and the reference laboratory's result. Agreement between most participants at the edited nucleotide sequence level was high (>98%). Most participants performed to the reference laboratory standard in detection of drug resistance mutations (DRMs). However, there was variation in the detection of nucleotide mixtures (0-83%) and a significant correlation with the detection of DRMs (p<0.01). Interpretation of antiretroviral resistance showed approximately 70% agreement among participants when different interpretation systems were used but >90% agreement with a common interpretation system, within the Stanford University Drug Resistance Database. Using the principles of external quality assessment and a reference laboratory, TAQAS has demonstrated high quality HIV genotyping results from Asian laboratories.

Virologic response to lopinavir-ritonavir-based antiretroviral regimens in a multicenter international clinical cohort: comparison of genotypic interpretation scores

Several genotypic interpretation scores have been proposed for the evaluation of susceptibility to lopinavir/ritonavir (LPV/r) but have not been compared using an independent data set. This study was a retrospective multicenter cohort of patients initiating LPV/r-based therapy. The virologic response (VR) was defined as a viral load of <500 copies/ml at week 24. The genotypic interpretation scores surveyed were the LPV mutation score, the ViroLogic score, the ATU score, the Stanford database score, and the International AIDS Society-USA mutation list. Of the 103 patients included in the analysis, 76% achieved VR at 24 weeks. For scores with clinical breakpoints defined (LPV mutation, ATU, ViroLogic, and Stanford), over 80% of the patients below the breakpoints achieved VR, while 50% or less above the breakpoints responded. Protease mutations at positions 10, 54, and 82 and at positions 54, 84, and 90 were associated with a lack of VR in the univariate and multivariate analyses, respectively. The area under the receiver-operator characteristic curves for the five genotypic interpretation scores studied ranged from 0.73 to 0.76. The study confirms that the currently available genotypic interpretation scores which are widely used by clinicians performed similarly well and can be effectively used to predict the virologic activity of LPV/r in treatment-experienced patients.

Modelled in vivo HIV Fitness under drug Selective Pressure and Estimated Genetic Barrier Towards Resistance are Predictive for Virological Response

Background

A method has been developed to estimate a fitness landscape experienced by HIV-1 under treatment selective pressure as a function of the genotypic sequence thereby also estimating the genetic barrier to resistance.

Methods

We evaluated the performance of two estimated fitness landscapes (nelfinavir [NFV] and zidovudine [AZT] plus lamivudine [3TC]) to predict week 12 viral load (VL) change for 176 treatment change episodes (TCEs) and probability of week 48 virological failure for 90 TCEs, in treatment experienced patients starting these drugs in combination.

Results

A higher genetic barrier for AZT plus 3TC, (quantified per additional mutation required to develop resistance against these drugs) was associated with a 0.54 (95% confidence interval [CI] 0.30–0.77) larger log10 VL reduction at 12 weeks ( P<0.0001) and a 0.39 (95% CI 0.23–0.66) lower odds of virological failure at 48 weeks ( P=0.0005), in analyses adjusting for the pre-TCE VL and the exact time-lag between the TCE and the date of determining response VL. The strength of these associations was comparable with those seen with expert interpretation systems (Rega, ANRS and HIVDB). A higher genetic barrier to NFV resistance was the only genotypic predictor that tended to be associated with a 0.19 (95% CI 0–0.39) higher log10 VL reduction at 12 weeks ( P=0.05) and a 0.63 (95% CI 0.36–1.09) lower odds of virological failure at 48 weeks ( P=0.10) per additional mutation.

Conclusions

These results suggest that an estimated genetic barrier derived from fitness landscapes may contribute to an improvement of predicted treatment outcome for NFV and this approach should be explored for other drugs.