Performance characteristics of Allele-Specific PCR (ASPCR) in detecting drug resistance mutations among non-B HIV-1 Variants

Allele-Specific Polymerase Chain Reaction (ASPCR) is an affordable point-mutation assay whose validation could improve the detection of HIV-1 drug resistance mutations (DRMs) in resource-limited settings (RLS). We assessed the performance of ASPCR onforty-four non-B HIV-1 plasma samples from patients who were ARV treated in failure in N'Djamena-Chad. Viral RNA was reverse-transcribed and amplified using LightCycler® FastStart DNA MasterPLUS SYBR Green I. Detection of six major DRMs (K70R, K103N, Y181C, M184V, T215F, T215Y) was evaluated on Roche LightCycler®480 automated system (with dilutions 0.01-100%). ASPCR-results were compared to Sanger-sequencing (gold-standard). Correlations of mutation curves were excellent (R2 >0.97); all DRMs were detected with desirable mutant/wild-type threshold differences (ΔCt≥9) except K70R(ΔCtK70R=6; ΔCtK103N=13; ΔCtM184V=9; ΔCtT215F=12; ΔCtT215Y=12; ΔCtY181C=9) and positive controls were below required thresholds. Also, ASPCR reproducibility on DRMs was assessed by using dilutions of intra-assay and inter-assay coefficient of variations respectively with a threshold of less than 50(i.e.<0.50 variation) which are;: K70R (0.02-0.28 vs. 0.12-0.37), K103N (0.08-0.42 vs. 0.12-0.37), Y181C (0.12-0.39 vs. 0.31-0.37), M184V (0.13-0.39 vs. 0.23-0.42), T215F (0.05-0.43 vs. 0.04-0.45) and T215Y (0.13-0.41 vs. 0.19-0.41). DRM detection-rate by ASPCR vs Sanger was respectively: M184V (63.6% vs. 38.6%); T215F (18.1% vs. 9.1%); T215Y (6.8% vs. 2.3%); K70R (4.5% vs. 2.3%). K103N (22.7% vs. 13.6%); Y181C (13.6% vs. 11.4%). Correlations of mutation curves were excellent (R2 >0.97); all DRMs were detected with desirable mutant/wild-type threshold differences (ΔCt≥9) except K70R(ΔCtK70R=6; ΔCtK103N=13; ΔCtM184V=9; ΔCtT215F=12; ΔCtT215Y=12; ΔCtY181C=9) and positive controls were below required thresholds. Also, ASPCR reproducibility on DRMs was assessed by using dilutions of intra-assay and inter-assay coefficient of variations respectively with a threshold of less than 50(i.e.<0.50 variation) which are;: K70R (0.02-0.28 vs. 0.12-0.37), K103N (0.08-0.42 vs. 0.12-0.37), Y181C (0.12-0.39 vs. 0.31-0.37), M184V (0.13-0.39 vs. 0.23-0.42), T215F (0.05-0.43 vs. 0.04-0.45) and T215Y (0.13-0.41 vs. 0.19-0.41). DRM detection-rate by ASPCR vs Sanger was respectively: M184V (63.6% vs. 38.6%); T215F (18.1% vs. 9.1%); T215Y (6.8% vs. 2.3%); K70R (4.5% vs. 2.3%). K103N (22.7% vs. 13.6%); Y181C (13.6% vs. 11.4%). ASPCR appears more efficient for detecting DRMs on diverse HIV-1 non-B circulating in RLS like Chad.

Need assessment for HIV drug resistance testing and landscape of current and future technologies in low- and middle-income countries

Resistance to antiretroviral drugs used to treat HIV is an important and evolving concern, particularly in low- and middle-income countries (LMICs) which have been impacted to the greatest extent by the HIV pandemic. Efforts to monitor the emergence and transmission of resistance over the past decade have shown that drug resistance-especially to the nucleoside analogue and non-nucleoside reverse transcriptase inhibitors-can (and have) increased to levels that can jeopardize the efficacy of available treatment options at the population level. The global shift to integrase-based regimens as the preferred first-line therapy as well as technological advancements in the methods for detecting resistance have had an impact in broadening and diversifying the landscape of and use case for HIV drug resistance testing. This review estimates the potential demand for HIV drug resistance tests, and surveys current testing methodologies, with a focus on their application in LMICs.

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.

HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data

Research and development of new antiretroviral agents are in great demand due to issues with safety and efficacy of the antiretroviral drugs. HIV reverse transcriptase (RT) is an important target for HIV treatment. RT inhibitors targeting early stages of the virus-host interaction are of great interest for researchers. There are a lot of clinical and biochemical data on relationships between the occurring of the single point mutations and their combinations in the pol gene of HIV and resistance of the particular variants of HIV to nucleoside and non-nucleoside reverse transcriptase inhibitors. The experimental data stored in the databases of HIV sequences can be used for development of methods that are able to predict HIV resistance based on amino acid or nucleotide sequences. The data on HIV sequences resistance can be further used for (1) development of new antiretroviral agents with high potential for HIV inhibition and elimination and (2) optimization of antiretroviral therapy. In our communication, we focus on the data on the RT sequences and HIV resistance, which are available on the Internet. The experimental methods, which are applied to produce the data on HIV-1 resistance, the known data on their concordance, are also discussed.

Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data

Supplemental Digital Content is Available in the Text.

Background:

HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to be divided into a suitable number of clinically meaningful intervals.

Methods:

We calculated a susceptible-to-intermediate and an intermediate-to-resistant cutoff per drug for RFs predicted by geno2pheno_[resistance]. Probability densities for therapeutic success and failure were estimated from 10,444 treatment episodes. The density estimation procedure corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. For estimating the probability of therapeutic success given an RF, we fit a sigmoid function. The cutoffs are given by the roots of the third derivative of the sigmoid function.

Results:

For performance assessment, we used geno2pheno_[resistance] RF predictions and the cutoffs for predicting therapeutic success in 2 independent sets of therapy episodes. HIVdb was used for performance comparison. On one test set (n = 807), our cutoffs and HIVdb performed equally well receiver operating characteristic curve [(ROC)–area under the curve (AUC): 0.68]. On the other test set (n = 917), our cutoffs (ROC–AUC: 0.63) and HIVdb (ROC–AUC: 0.65) performed comparatively well.

Conclusions:

Our method can be used for calculating clinically relevant cutoffs for (predicted) RFs. The method corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. Our method's performance is comparable with that of HIVdb. RF cutoffs for the latest version of geno2pheno_[resistance] have been estimated with this method.

Lack of impact of pre-existing T97A HIV-1 integrase mutation on integrase strand transfer inhibitor resistance and treatment outcome

T97A is an HIV-1 integrase polymorphism associated with integrase strand transfer inhibitor (INSTI) resistance. Using pooled data from 16 clinical studies, we investigated the prevalence of T97A (pre-existing and emergent) and its impact on INSTI susceptibility and treatment response in INSTI-naive patients who enrolled on elvitegravir (EVG)- or raltegravir (RAL)-based regimens. Prior to INSTI-based therapy, primary INSTI resistance-associated mutations (RAMs) were absent and T97A pre-existed infrequently (1.4%; 47 of 3367 integrase sequences); most often among non-B (5.3%) than B (0.9%) HIV-1 subtypes. During INSTI-based therapy, few patients experienced virologic failure with emergent INSTI RAMs (3%; 122 of 3881 patients), among whom T97A emerged infrequently in the presence (n = 6) or absence (n = 8) of primary INSTI RAMs. A comparison between pre-existing and emergent T97A patient populations (i.e., in the absence of primary INSTI RAMs) showed no significant differences in EVG or RAL susceptibility in vitro. Furthermore, among all T97A-containing viruses tested, only 38-44% exhibited reduced susceptibility to EVG and/or RAL (all of low magnitude; <11-fold), while all maintained susceptibility to dolutegravir. Of the patients with pre-existing T97A, 17 had available clinical follow-up: 16 achieved virologic suppression and 1 maintained T97A and INSTI sensitivity without further resistance development. Overall, T97A is an infrequent integrase polymorphism that is enriched among non-B HIV-1 subtypes and can confer low-level reduced susceptibility to EVG and/or RAL. However, detection of T97A does not affect response to INSTI-based therapy with EVG or RAL. These results suggest a very low risk of initiating INSTI-based therapy in patients with pre-existing T97A.

Advantages of predicted phenotypes and statistical learning models in inferring virological response to antiretroviral therapy from HIV genotype

Background

Inferring response to antiretroviral therapy from the viral genotype alone is challenging. The utility of an intermediate step of predicting in vitro drug susceptibility is currently controversial. Here, we provide a retrospective comparison of approaches using either genotype or predicted phenotypes alone, or in combination.

Methods

Treatment change episodes were extracted from two large databases from the USA (Stanford-California) and Europe (EuResistDB) comprising data from 6,706 and 13,811 patients, respectively. Response to antiretroviral treatment was dichotomized according to two definitions. Using the viral sequence and the treatment regimen as input, three expert algorithms (ANRS, Rega and HIVdb) were used to generate genotype-based encodings and VircoTYPE™ 4.0 (Virco BVBA, Mechelen, Belgium) was used to generate a predicted phenotype-based encoding. Single drug classifications were combined into a treatment score via simple summation and statistical learning using random forests. Classification performance was studied on Stanford- California data using cross-validation and, in addition, on the independent EuResistDB data.

Results

In all experiments, predicted phenotype was among the most sensitive approaches. Combining single drug classifications by statistical learning was significantly superior to unweighted summation ( P<2.2x10-16). Classification performance could be increased further by combining predicted phenotypes and expert encodings but not by combinations of expert encodings alone. These results were confirmed on an independent test set comprising data solely from EuResistDB.

Conclusions

This study demonstrates consistent performance advantages in utilizing predicted phenotype in most scenarios over methods based on genotype alone in inferring virological response. Moreover, all approaches under study benefit significantly from statistical learning for merging single drug classifications into treatment scores.

Genotypic Drug Resistance and Long-Term Mortality in Patients with Triple-Class Antiretroviral Drug Failure

Objective

To examine the prevalence of drug-resistance-associated mutations in HIV patients with triple-drug class virological failure (TCF) and their association with long-term mortality.

Design

Population-based study from the Danish HIV Cohort Study (DHCS).

Methods

We included all patients in the DHCS who experienced TCF between January 1995 and November 2004, and we performed genotypic resistance tests for International AIDS Society (IAS)-USA primary mutations on virus from plasma samples taken around the date of TCF. We computed time to all-cause death from date of TCF. The relative risk of death according to the number of mutations and individual mutations was estimated by Cox regression analysis and adjusted for potential confounders.

Results

Resistance tests were done for 133 of the 179 patients who experienced TCF. The median number of resistance mutations was eight (interquartile range 2–10), and 81 (61%) patients had mutations conferring resistance towards all three major drug classes. In a regression model adjusted for CD4+ T-cell count, HIV RNA, year of TCF, age, gender and previous inferior antiretroviral therapy, harbouring ≥9 versus ≤8 mutations was associated with increased mortality (mortality rate ratio [MRR] 2.3 [95% confidence interval (CI) 1.1–4.8]), as were the individual mutations T215Y (MRR 3.4 [95% CI 1.6–7.0]), G190A/S (MRR 3.2 [95% CI 1.6–6.6]) and V82F/A/T/S (MRR 2.5 [95% CI 1.2–5.3]).

Conclusions

In HIV patients with TCF, the total number of genotypic resistance mutations and specific single mutations predicted mortality.

Virological Responses to Atazanavir–Ritonavir-Based Regimens: Resistance-Substitutions Score and Pharmacokinetic Parameters (Reyaphar Study)

Objective

To assess the impact of baseline HIV-1 substitutions, individual pharmacokinetic (PK) parameters (Cmin, Cmax, area under the curve [AUC0→24 h]) and genotype-inhibitory quotient (GIQ) on virological responses (VR) to atazanavir-ritonavir (300 mg/100 mg)-based highly active antiretroviral therapy (HAART) in 71 antiretroviral-experienced, atazanavir-naive patients in virological failure (VF) on HAART.

Methodology

VR was defined as HIV RNA <1.7 log10 copies/ml at week 12 (W12). A clinically relevant genotype-substitutions score for atazanavir-ritonavir was developed and validated (Reyaphar substitutions score). Previously published substitutions scores were also tested.

Results

Patients had a median (Q1; Q3) of 6 (3; 8) previous treatment lines during 9 (7; 11) years. Baseline (W0) values were as follows: 262 (187; 435) CD4+/μl, 3.9 (2.6; 4.9) log10 HIV-1 RNA copies/ml, 4 (2; 6) protease substitutions and 3 (1; 4) NRTI-related substitutions. Respective steady-state Cmin, Cmax and AUC0→24h were 300 (200; 700) ng/ml, 620 (430; 750) ng/ml and 78,000 (61,000; 94,000) ng.h/ml. At W12, 49% of the patients had VR with a median decrease of -1.2 (-0.5; -2.3) log10 HIV-1 RNA copies/ml. The Reyaphar score included 12 baseline protease substitutions from the International AIDS Society USA list that were associated with poorer VR: L10I/F/R/V, K20I/M/R, L24I, M46I/L, I54L/M/T/V, L63P, A71I/L/V/T, G73A/C/F/T, V77I, V82A/F/S/T, I84V, L90M and the polymorphism substitution Q58E. Comparing <5 versus ≥5 Reyaphar substitutions, the W12-W0 HIV-1 RNA decrease was -1.4 (-0.7; -2.3) versus -0.5 (-1.2; +0.5) log10 copies/ml ( P=0.009) with VR in 63% versus 11% ( P<10–4), respectively. This score predicted VF at W12 with 46% sensitivity, compared to 33% and 28% for the ANRS 2004 and 2005 scores. PK parameters alone were not associated with VR, but GIQ was associated with virological outcome ( P=0.04). I50L, known to be correlated with atazanavir-specific resistance, emerged in 2 (8%) of the 24 failing patients with paired genotypes at W0 and VF.

Conclusions

These findings highlight the need to cross-validate genotype-based algorithms to interpret substitution impact on virological outcome using different patient databases before their implementation in routine clinical practice.