Determination of clinically relevant cutoffs for HIV-1 phenotypic resistance estimates through a combined analysis of clinical trial and cohort data

Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1

MOTIVATION

In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or latest available data analysis. This study analyses whether historical knowledge, which includes viral mutations detected in all genotypic tests before therapy, their temporal occurrence, and concomitant viral load measurements, can bring improvements. We introduce a method to weigh mutations, considering the previously enumerated factors and the reference mutation-drug Stanford resistance tables. We compare a model encompassing history (H) with one not using this information (NH).

RESULTS

The H-model demonstrates superior discriminative ability, with a higher ROC-AUC score (76.34%) than the NH-model (74.98%). Wilcoxon test results confirm significant improvement of predictive accuracy for treatment outcomes through incorporating historical information. The increased performance of the H-model might be attributed to its consideration of latent HIV reservoirs, probably obtained when leveraging historical information. The findings emphasize the importance of temporal dynamics in acquiring mutations. However, our result also shows that prediction accuracy remains relatively high even when no historical information is available.

AVAILABILITY AND IMPLEMENTATION

This analysis was conducted using the Euresist Integrated DataBase (EIDB). For further validation, we encourage reproducing this study with the latest release of the EIDB, which can be accessed upon request through the Euresist Network.

Tenofovir and emtricitabine resistance among antiretroviral-naive patients in the Canadian Observational Cohort Collaboration: implications for PrEP

BACKGROUND

The real-world effectiveness of pre-exposure prophylaxis (PrEP) may be influenced by circulating HIV strains resistant to either tenofovir or emtricitabine. Yet, few studies have examined rates of resistance to these drugs in clinical settings.

METHODS

We conducted a retrospective cohort study of antiretroviral-naive participants in the Canadian Observational Cohort collaboration who initiated antiretroviral therapy between 2006 and 2014. In separate analyses, we determined the prevalence of pretherapy resistance and cumulative incidence of follow-up resistance to tenofovir and emtricitabine. We used multivariable proportional hazards models to examine associations between baseline variables and the development of resistance.

RESULTS

We studied 6,622 antiretroviral-naive participants initiating therapy, of whom 5,428 (82.0%) had a baseline resistance test. Baseline resistance to tenofovir and emtricitabine was observed in 83 (1.5%) and 21 (0.4%) patients, respectively. Among patients without baseline resistance, the cumulative incidence of resistance to tenofovir and emtricitabine 5 years following treatment initiation was 0.0070 (95% CI 0.0046, 0.0095) and 0.033 (95% CI 0.028, 0.038), respectively. Following multivariable analysis, a baseline viral load ≥100,000 copies/ml was associated with emergence of tenofovir (hazard ratio [HR] 2.88; 95% CI 1.35, 6.15) and emtricitabine (HR 2.27; 95% CI 1.64, 3.15) resistance. Initiating an integrase inhibitor-based regimen and CD4⁺ T-cell count below 200 cells/mm³ were also associated with resistance to each drug.

CONCLUSIONS

We observed a low prevalence of baseline resistance and a low incidence of emergence of resistance to tenofovir and emtricitabine among antiretroviral-naive patients in routine clinical care.

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.

Collaborative update of a rule-based expert system for HIV-1 genotypic resistance test interpretation

INTRODUCTION

HIV-1 genotypic resistance test (GRT) interpretation systems (IS) require updates as new studies on HIV-1 drug resistance are published and as treatment guidelines evolve.

METHODS

An expert panel was created to provide recommendations for the update of the Stanford HIV Drug Resistance Database (HIVDB) GRT-IS. The panel was polled on the ARVs to be included in a GRT report, and the drug-resistance interpretations associated with 160 drug-resistance mutation (DRM) pattern-ARV combinations. The DRM pattern-ARV combinations included 52 nucleoside RT inhibitor (NRTI) DRM pattern-ARV combinations (13 patterns x 4 NRTIs), 27 nonnucleoside RT inhibitor (NNRTI) DRM pattern-ARV combinations (9 patterns x 3 NNRTIs), 39 protease inhibitor (PI) DRM pattern-ARV combinations (13 patterns x 3 PIs) and 42 integrase strand transfer inhibitor (INSTI) DRM pattern-ARV combinations (14 patterns x 3 INSTIs).

RESULTS

There was universal agreement that a GRT report should include the NRTIs lamivudine, abacavir, zidovudine, emtricitabine, and tenofovir disoproxil fumarate; the NNRTIs efavirenz, etravirine, nevirapine, and rilpivirine; the PIs atazanavir/r, darunavir/r, and lopinavir/r (with "/r" indicating pharmacological boosting with ritonavir or cobicistat); and the INSTIs dolutegravir, elvitegravir, and raltegravir. There was a range of opinion as to whether the NRTIs stavudine and didanosine and the PIs nelfinavir, indinavir/r, saquinavir/r, fosamprenavir/r, and tipranavir/r should be included. The expert panel members provided highly concordant DRM pattern-ARV interpretations with only 6% of NRTI, 6% of NNRTI, 5% of PI, and 3% of INSTI individual expert interpretations differing from the expert panel median by more than one resistance level. The expert panel median differed from the HIVDB 7.0 GRT-IS for 20 (12.5%) of the 160 DRM pattern-ARV combinations including 12 NRTI, two NNRTI, and six INSTI pattern-ARV combinations. Eighteen of these differences were updated in HIVDB 8.1 GRT-IS to reflect the expert panel median. Additionally, HIVDB users are now provided with the option to exclude those ARVs not considered to be universally required.

CONCLUSIONS

The HIVDB GRT-IS was updated through a collaborative process to reflect changes in HIV drug resistance knowledge, treatment guidelines, and expert opinion. Such a process broadens consensus among experts and identifies areas requiring further study.

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.

Using drug exposure for predicting drug resistance - A data-driven genotypic interpretation tool

Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno_[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.

No Evidence That HIV-1 Subtype C Infection Compromises the Efficacy of Tenofovir-Containing Regimens: Cohort Study in the United Kingdom

Concern has been expressed that tenofovir-containing regimens may have reduced effectiveness in the treatment of human immunodeficiency virus type 1 (HIV-1) subtype C infections because of a propensity for these viruses to develop a key tenofovir-associated resistance mutation. We evaluated whether subtype influenced rates of virological failure in a cohort of 8746 patients from the United Kingdom who received a standard tenofovir-containing first-line regimen and were followed for a median of 3.3 years. In unadjusted analyses, the rate of failure was approximately 2-fold higher among patients infected with subtype C virus as compared to those with subtype B virus (hazard ratio [HR], 1.86; 95% confidence interval [CI], 1.50–2.31; P < .001). However, the increased risk was greatly attenuated in analyses adjusting for demographic and clinical factors (adjusted HR, 1.14; 95% CI, .83–1.58; P = .41). There were no differences between subtypes C and subtypes non-B and non-C in either univariate or multivariate analysis. These observations imply there is no intrinsic effect of viral subtype on the efficacy of tenofovir-containing regimens.

HIV drug resistance detected during low-level viraemia is associated with subsequent virologic failure

BACKGROUND

The clinical implications of emergent HIV drug resistance on samples with low-level viraemia (LLV <1000 copies/ml) remain unclear. We undertook the present analysis to evaluate the impact of emergent HIV drug resistance at LLV on the risk of subsequent virologic failure.

METHODS

One thousand, nine hundred and sixty-five patients had genotype results at LLV. Risk of virologic failure (≥1000 copies/ml) after LLV was evaluated by Kaplan-Meier analysis and Cox proportional hazards regression. Resistance was assessed using the Stanford algorithm or virtual phenotypes. Patients were grouped into four susceptibility categories ('GSS' or 'vPSS') during LLV, corresponding to the number of 'active' drugs prescribed: <1; 1-1.5; 2-2.5; and ≥3.

RESULTS

A total of 1702 patients with follow-up on constant therapy were eligible for analysis. Participants excluded due to changing therapy or loss to follow-up before their next observation had mostly similar characteristics to included participants. There was a 'dose-dependent' increase in the hazard ratio for virologic failure with susceptibility categories at LLV. Compared with a GSS of at least 3, hazard ratios for virologic failure were 1.4 for GSS 2-2.5; 2.0 for GSS 1-1.5; and 3.0 for GSS less than 1 (P < 0.001). Numerous sensitivity analyses confirmed these findings.

CONCLUSION

Our results demonstrate that emergent HIV drug resistance at LLV is strongly associated with subsequent virologic failure. Furthermore, we uncovered a 'dose-dependent' increase in the hazard ratio for virologic failure with decreasing GSS estimated at the time of LLV. On the basis of these findings, we propose that resistance genotyping be encouraged for HIV-infected individuals on antiretroviral therapy experiencing low-level viraemia.

Hypersusceptibility mechanism of Tenofovir-resistant HIV to EFdA

Background

The K65R substitution in human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) is the major resistance mutation selected in patients treated with first-line antiretroviral tenofovir disoproxil fumarate (TDF). 4'-ethynyl-2-fluoro-2'-deoxyadenosine (EFdA), is the most potent nucleoside analog RT inhibitor (NRTI) that unlike all approved NRTIs retains a 3'-hydroxyl group and has remarkable potency against wild-type (WT) and drug-resistant HIVs. EFdA acts primarily as a chain terminator by blocking translocation following its incorporation into the nascent DNA chain. EFdA is in preclinical development and its effect on clinically relevant drug resistant HIV strains is critically important for the design of optimal regimens prior to initiation of clinical trials.

Results

Here we report that the K65R RT mutation causes hypersusceptibility to EFdA. Specifically, in single replication cycle experiments we found that EFdA blocks WT HIV ten times more efficiently than TDF. Under the same conditions K65R HIV was inhibited over 70 times more efficiently by EFdA than TDF. We determined the molecular mechanism of this hypersensitivity using enzymatic studies with WT and K65R RT. This substitution causes minor changes in the efficiency of EFdA incorporation with respect to the natural dATP substrate and also in the efficiency of RT translocation following incorporation of the inhibitor into the nascent DNA. However, a significant decrease in the excision efficiency of EFdA-MP from the 3’ primer terminus appears to be the primary cause of increased susceptibility to the inhibitor. Notably, the effects of the mutation are DNA-sequence dependent.

Conclusion

We have elucidated the mechanism of K65R HIV hypersusceptibility to EFdA. Our findings highlight the potential of EFdA to improve combination strategies against TDF-resistant HIV-1 strains.

Comparison of predicted susceptibility between genotype and virtual phenotype HIV drug resistance interpretation systems among treatment-naive HIV-infected patients in Asia: TASER-M cohort analysis

Background

Accurate interpretation of HIV drug resistance (HIVDR) testing is challenging, yet important for patient care. We compared genotyping interpretation, based on the Stanford University HIV Drug Resistance Database (Stanford HIVdb), and virtual phenotyping, based on the Janssen Diagnostics BVBA’s vircoTYPE™ HIV-1, and investigated their level of agreement in antiretroviral (ARV) naive patients in Asia, where non-B subtypes predominate.

Methods

Sequences from 1301 ARV-naive patients enrolled in the TREAT Asia Studies to Evaluate Resistance – Monitoring Study (TASER-M) were analysed by both interpreting systems. Interpretations from both Stanford HIVdb and vircoTYPE™ HIV-1 were initially grouped into 2 levels: susceptible and non-susceptible. Discrepancy was defined as a discordant result between the susceptible and non-susceptible interpretations from the two systems for the same ARV. Further analysis was performed when interpretations from both systems were categorised into 3 levels: susceptible, intermediate and resistant; whereby discrepancies could be categorised as major discrepancies and minor discrepancies. Major discrepancy was defined as having a susceptible result from one system and resistant from the other. Minor discrepancy corresponded to having an intermediate interpretation in one system, with a susceptible or resistant result in the other. The level of agreement was analysed using the prevalence adjusted bias adjusted kappa (PABAK).

Results

Overall, the agreement was high, with each ARV being in “almost perfect agreement”, using Landis and Koch’s categorisation. Highest discordance was observed for efavirenz (75/1301, 5.8%), all arising from susceptible Stanford HIVdb versus non-susceptible vircoTYPE™ HIV-1 predictions. Protease Inhibitors had highest level of concordance with PABAKs all above 0.99, followed by Nucleoside Reverse Transcriptase Inhibitors with PABAKs above 0.97 and non-NRTIs with the lowest PABAK of 0.88. The 68/75 patients with discordant efavirenz results harboured the V179D/E mutations compared to 7/1226 with no efavirenz discrepancy (p-value <0.001). In the 3-level comparison, all but one of the discrepancies was minor.

Conclusions

The two systems agreed well with lowest concordance observed for efavirenz. When interpreting HIVDR, especially in non-B subtypes, clinical correlation is crucial, in particular when efavirenz resistance is interpreted based on V179D/E.

Development and performance of conventional HIV-1 phenotyping (Antivirogram®) and genotype-based calculated phenotyping assay (virco®TYPE HIV-1) on protease and reverse transcriptase genes to evaluate drug resistance

OBJECTIVES

A wide array of monitoring tests is commercially available to gauge HIV-1 disease progression and the overall health status of an HIV-1-infected patient. Viral load tests provide a picture of viral activity, while CD4 cell counts shed light on the immune status and can help physicians to prevent the development of opportunistic infections in patients. On the other hand, genotypic and phenotypic resistance testing and therapeutic drug monitoring help to optimize HIV-1 antiretroviral therapy. Resistance testing is currently recommended within the standard of care guidelines to aid the choice of new drug regimens following treatment failure(s).

METHODS

Genotypic testing described here is based on the amplification and sequencing of an HIV-1 protease (PR) and reverse transcriptase (RT) region from a patient sample to identify resistance mutations associated with PR and RT inhibitor resistance. A genotypic test takes a week to perform and the results are reported as a list of detected mutations. The virco®TYPE HIV-1 report uses genotypic data to predict phenotypic susceptibility by linear regression modeling that uses a large correlative database of genotype-phenotype pairs. Phenotypic testing measures the ability of the virus to replicate in the presence of a drug and provides a direct measurement of drug susceptibility in vitro. Since phenotypic analysis is laborious and time consuming (28 days), genotypic resistance testing is currently the standard reference method used for HIV-1 resistance testing. However, a phenotypic test is important when a patient harbors virus with complex genetic patterns, or when the mutational resistance profile for a particular drug is not well-characterized.

RESULTS AND CONCLUSIONS

Some of the currently used resistance tests are partially automated enabling laboratories to increase overall efficiency. However, maximum automation and standardization of the process, instruments and software that we have described here can overcome many of the problems encountered with current tests and aims at having a compliant, high-throughput, diagnostic laboratory, which can guarantee sample integrity from sample reception to result reporting. We also describe in detail the development and performance of virco®TYPE HIV-1 (genotype) and Antivirogram® (phenotype) assay on PR and RT genes to evaluate antiretroviral resistance.

Residual activity of two HIV antiretroviral regimens prescribed without virological monitoring

Virological residual activity (VRA) denotes the degree of HIV RNA suppression achieved by antiretroviral therapy in the presence of resistant virus. This concept is particularly important in resource-limited settings, where rapid switching after detection of virological failure may not be feasible. Using data from the NORA trial, we estimated VRA for two regimens-zidovudine-lamivudine-abacavir (ZDV-3TC-ABC) and zidovudine-lamivudine-nevirapine (ZDV-3TC-NVP)-and related this to the phenotypic drug sensitivity of the component drugs in the two regimens. Plasma samples at weeks 0, 48, and 96 were retrospectively assayed for HIV-1 RNA, and genotypic/phenotypic resistance testing was performed if HIV-1 RNA exceeded 1,000 copies/ml. Virological residual activity (VRA) was defined as the difference between log(10)(HIV RNA) at week 48 or 96 and week 0 and related to 50% inhibitory concentration (IC(50)) relative to wild-type virus for ZDV and ABC (fold change [FC]). Twenty-seven samples in the ZDV-3TC-NVP group and 56 in the ZDV-3TC-ABC group contributed to the analysis. Mean VRA was significantly higher in the ZDV-3TC-ABC group than in the ZDV-3TC-NVP at week 48 (1.62 versus 0.90) and week 96 (1.29 versus 0.78). There was a weak and nonsignificant relationship between VRA and ZDV FC, with VRA decreasing by 0.1 log(10) copies/ml per 2-fold increase in ZDV. The association with ABC FC was much stronger, with a marked reduction in VRA occurring at ABC FC values greater than approximately 2. This information should be considered in future treatment guidelines relevant to resource-poor settings.

Novel method for simultaneous quantification of phenotypic resistance to maturation, protease, reverse transcriptase, and integrase HIV inhibitors based on 3'Gag(p2/p7/p1/p6)/PR/RT/INT-recombinant viruses: a useful tool in the multitarget era of antiretroviral therapy

Twenty-six antiretroviral drugs (ARVs), targeting five different steps in the life cycle of the human immunodeficiency virus type 1 (HIV-1), have been approved for the treatment of HIV-1 infection. Accordingly, HIV-1 phenotypic assays based on common cloning technology currently employ three, or possibly four, different recombinant viruses. Here, we describe a system to assess HIV-1 resistance to all drugs targeting the three viral enzymes as well as viral assembly using a single patient-derived, chimeric virus. Patient-derived p2-INT (gag-p2/NCp7/p1/p6/pol-PR/RT/IN) products were PCR amplified as a single fragment (3,428 bp) or two overlapping fragments (1,657 bp and 2,002 bp) and then recombined into a vector containing a near-full-length HIV-1 genome with the Saccharomyces cerevisiae uracil biosynthesis gene (URA3) replacing the 3,428 bp p2-INT segment (Dudley et al., Biotechniques 46:458-467, 2009). P2-INT-recombinant viruses were employed in drug susceptibility assays to test the activity of protease (PI), nucleoside/nucleotide reverse transcriptase (NRTI), nonnucleoside reverse transcriptase (NNRTI), and integrase strand-transfer (INSTI) inhibitors. Using a single standardized test (ViralARTS HIV), this new technology permits the rapid and automated quantification of phenotypic resistance for all known and candidate antiretroviral drugs targeting all viral enzymes (PR, RT, including polymerase and RNase H activities, and IN), some of the current and potential assembly inhibitors, and any drug targeting Pol or Gag precursor cleavage sites (relevant for PI and maturation inhibitors) This novel assay may be instrumental (i) in the development and clinical assessment of novel ARV drugs and (ii) to monitor patients failing prior complex treatment regimens.

Clinical management of HIV drug resistance

Combination antiretroviral therapy for HIV-1 infection has resulted in profound reductions in viremia and is associated with marked improvements in morbidity and mortality. Therapy is not curative, however, and prolonged therapy is complicated by drug toxicity and the emergence of drug resistance. Management of clinical drug resistance requires in depth evaluation, and includes extensive history, physical examination and laboratory studies. Appropriate use of resistance testing provides valuable information useful in constructing regimens for treatment-experienced individuals with viremia during therapy. This review outlines the emergence of drug resistance in vivo, and describes clinical evaluation and therapeutic options of the individual with rebound viremia during therapy.

"Dynamic range" of inferred phenotypic HIV drug resistance values in clinical practice

Background

‘Virtual’ or inferred phenotypes (vPhenotypes) are commonly used to assess resistance to antiretroviral agents in patients failing therapy. In this study, we provide a clinical context for understanding vPhenotype values.

Methods

All HIV-infected persons enrolled in the British Columbia Drug Treatment Program with a baseline plasma viral load (pVL) and follow-up genotypic resistance and pVL results were included up to October 29, 2008 (N = 5,277). Change from baseline pVL was determined as a function of Virco vPhenotype, and the “dynamic range” (defined here by the 10th and 90th percentiles for fold-change in IC₅₀ amongst all patients) was estimated from the distribution of vPhenotye fold-changes across the cohort.

Results

The distribution of vPhenotypes from a large cohort of HIV patients who have failed therapy are presented for all available antiretroviral agents. A maximum change in IC₅₀ of at least 13-fold was observed for all drugs. The dideoxy drugs, tenofovir and most PIs exhibited small “dynamic ranges” with values of <4-fold change observed in >99% of samples. In contrast, zidovudine, lamivudine, emtricitabine and the non-nucleoside reverse transcriptase inihibitors (excluding etravirine) had large dynamic ranges.

Conclusion

We describe the populational distribution of vPhenotypes such that vPhenotype results can be interpreted relative to other patients in a drug-specific manner.

K70Q adds high-level tenofovir resistance to "Q151M complex" HIV reverse transcriptase through the enhanced discrimination mechanism

HIV-1 carrying the “Q151M complex” reverse transcriptase (RT) mutations (A62V/V75I/F77L/F116Y/Q151M, or Q151Mc) is resistant to many FDA-approved nucleoside RT inhibitors (NRTIs), but has been considered susceptible to tenofovir disoproxil fumarate (TFV-DF or TDF). We have isolated from a TFV-DF-treated HIV patient a Q151Mc-containing clinical isolate with high phenotypic resistance to TFV-DF. Analysis of the genotypic and phenotypic testing over the course of this patient's therapy lead us to hypothesize that TFV-DF resistance emerged upon appearance of the previously unreported K70Q mutation in the Q151Mc background. Virological analysis showed that HIV with only K70Q was not significantly resistant to TFV-DF. However, addition of K70Q to the Q151Mc background significantly enhanced resistance to several approved NRTIs, and also resulted in high-level (10-fold) resistance to TFV-DF. Biochemical experiments established that the increased resistance to tenofovir is not the result of enhanced excision, as K70Q/Q151Mc RT exhibited diminished, rather than enhanced ATP-based primer unblocking activity. Pre-steady state kinetic analysis of the recombinant enzymes demonstrated that addition of the K70Q mutation selectively decreases the binding of tenofovir-diphosphate (TFV-DP), resulting in reduced incorporation of TFV into the nascent DNA chain. Molecular dynamics simulations suggest that changes in the hydrogen bonding pattern in the polymerase active site of K70Q/Q151Mc RT may contribute to the observed changes in binding and incorporation of TFV-DP. The novel pattern of TFV-resistance may help adjust therapeutic strategies for NRTI-experienced patients with multi-drug resistant (MDR) mutations.

Connection domain mutations in treatment-experienced patients in the OPTIMA trial

OBJECTIVES

To determine the frequency of mutations in the connection domain (CD) of HIV reverse transcriptase in treatment-experienced patients in the Options in Management with Antiretrovirals trial, their impact on susceptibility to antiretroviral (ARV) drugs, and their impact on virologic outcomes.

METHODS

Baseline plasma ARV genotypes and inferred resistance phenotypes were obtained. Frequencies of E312Q, Y318F, G333D, G333E, G335C, G335D, N348I, A360I, A360V, V365I, A371V, A376S, and E399G were compared with a treatment-naive population. The association of CD mutations with inferred IC50 fold changes to nucleos(t)ide reverse transcriptase inhibitors was evaluated. Univariate and multivariate analyses examined the association of CD mutations with a >1 log10 per milliliter decrease in HIV viral load after 24 weeks on a new ARV regimen.

RESULTS

Higher CD mutation rates were seen in Options in Management with Antiretrovirals patients (n = 345) compared with a treatment-naive population. CD mutations were associated with increased inferred IC50 fold changes to abacavir, stavudine, tenofovir, and zidovudine. On univariate analysis, A371V was associated with lack of virologic response, as was having any CD mutation on multivariate analysis.

CONCLUSIONS

CD mutations are frequent in treatment-experienced populations. They are associated with reduced susceptibility to some nucleos(t)ide reverse transcriptase inhibitors and with a diminished response to ARV therapy.

A comparison of HIV-1 drug susceptibility as provided by conventional phenotyping and by a phenotype prediction tool based on viral genotype

Concordance between the conventional HIV-1 phenotypic drug resistance assay, PhenoSense (PS), and vircoTYPE HIV-1 (vT), a drug resistance assay based on prediction of the phenotype, was investigated in a data set from the Stanford HIV Resistance database (hivdb). Depending on the drug, between 287 and 902 genotype-phenotype data pairs were available for comparisons. Test results (fold-change values) in the two assays were highly correlated, with an overall mean correlation coefficient of 0.90 using single PS measurements. This coefficient rose to 0.94 when the vT results were compared to the mean of repeat PS measurements. These results are comparable with the corresponding correlation coefficients of 0.87 and 0.95, calculated using single measurements, and the mean of repeat measurements, respectively, as obtained in the Antivirogram assay, the conventional HIV-1 phenotypic drug resistance test on which vT is based. The proportion of resistance calls resulting in a "major" discordance (fully susceptible or maximal response by one assay but fully resistant or minimal response by the other) ranged from 0% to 8.1% for drugs for which two clinical test cut-offs were available in both assays (didanosine, abacavir, tenofovir, saquinavir/r, fosamprenavir/r, and lopinavir/r), from 2.4% to 8.1% for the drugs for which two clinical test cut-offs were available in the vT assay and one clinical test cut-off in the PS assay (lamivudine, stavudine, indinavir/r, and atazanavir/r) and from 3.1% to 10.3% for drugs for which biological test cut-offs were used (zidovudine, nevirapine, delavirdine, efavirenz, indinavir, ritonavir, nelfinavir, saquinavir, and fosamprenavir). Our analyses suggest that these assays provide comparable resistance information, which will be of value to physicians who may be presented with either or both types of test report in their practice.

Clinical management of HIV-1 resistance

Antiretroviral drug resistance is a fundamental survival strategy for the virus that stems from its vast capacity to generate diversity. With the recent availability of new ARV drugs and classes, it is now possible to prescribe fully active ART to most HIV-infected subjects and achieve viral suppression even in those with multidrug-resistant HIV. It is uncertain, however, if this scenario will endure. Given that ART must be given for life, and new compounds other than second-generation integrase inhibitors may not reach the clinic soon, all efforts must be done to avoid the development of resistance to the new agents. Here, we discuss relevant aspects for the clinical management of antiretroviral drug resistance, leaving detailed explanations of mechanisms and mutation patterns to other articles in this issue. This article forms part of a special issue of Antiviral Research marking the 25th anniversary of antiretroviral drug discovery and development, vol. 85, issue 1, 2010.

Mutation T74S in HIV-1 subtype B and C proteases resensitizes them to ritonavir and indinavir and confers fitness advantage

OBJECTIVES

Several drug resistance and secondary mutations have been described in HIV-1 viruses from patients undergoing antiretroviral therapy. In this study, we assessed the impact of the protease substitution T74S on the phenotype and on the replicative fitness in HIV-1 subtypes B and C.

METHODS

HIV-1 molecular clones carrying subtype B or C proteases had these coding regions subjected to site-directed mutagenesis to include T74S alone or in combination with four known protease inhibitor (PI) primary drug resistance mutations. All clones were used in a phenotypic assay to evaluate their susceptibility to most commercially available PIs. The impact of T74S on virus fitness was also assessed for all viruses through head-to-head competitions and oligonucleotide ligation assays to measure the proportion of each virus in culture.

RESULTS

Viruses of both subtypes carrying T74S did not have their susceptibility altered to any tested PI. Viruses with the four resistance mutations showed strong resistance to most PIs with fold changes ranging from 5 to 300 times compared with their wild-type counterparts. Surprisingly, the addition of T74S to the multiresistant clones restored their susceptibilities to indinavir and ritonavir and partially to lopinavir, close to those of wild-type viruses. Most 74S-containing viruses were more fit than their 74T counterparts.

CONCLUSIONS

Our results suggest that T74S is not a major drug resistance mutation, but it resensitizes multiresistant viruses to certain PIs. T74S is a bona fide accessory mutation, restoring fitness of multidrug-resistant viruses in both subtypes B and C. T74S should be further studied in clinical settings and considered in drug resistance interpretation algorithms.

Clinical cut-offs for HIV-1 phenotypic resistance estimates: update based on recent pivotal clinical trial data and a revised approach to viral mixtures

The clinical utility of HIV-1 resistance testing is dependent upon accurate interpretation and application of results. The development of clinical cut-offs (CCOs) for most HIV antiretroviral drugs assessed by the vircoTYPE HIV-1 resistance test has been described previously. Updated CCOs based on new methodology and new data from clinical cohorts and pivotal clinical studies are presented in this communication. Data for analysis included the original records for CCO derivation from eight clinical trials and two cohort studies plus new records from the clinical cohorts and from the TITAN, POWER, and DUET clinical studies. Drug-specific linear regression models were developed to describe the relationship between baseline characteristics (phenotypic resistance as estimated by virtualPhenotype-LM using methods revised recently for handling mixed viral sequences; viral load; and treatment history), new treatment regimen, and 8-week virologic outcome. The clinical cut-offs were defined as the estimated phenotypic resistance levels (fold change, FC) associated with a 20% and 80% loss of drug activity. The development dataset included 6550 records with an additional 2299 reserved for validation. The updated, v.4.2 CCOs were generally close to the v4.1 values, with a trend observed toward marginally higher cut-offs for the NRTIs. These results suggest that the updated CCOs provide a relevant tool for estimating the contribution to virological response of individual antiviral drugs in antiretroviral drug combinations as used currently in clinical practice.

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.