Non-active site mutants of HIV-1 protease influence resistance and sensitisation towards protease inhibitors.

BACKGROUND: HIV-1 can develop resistance to antiretroviral drugs, mainly through mutations within the target regions of the drugs. In HIV-1 protease, a majority of resistance-associated mutations that develop in response to therapy with protease inhibitors are found in the protease's active site that serves also as a binding pocket for the protease inhibitors, thus directly impacting the protease-inhibitor interactions. Some resistance-associated mutations, however, are found in more distant regions, and the exact mechanisms how these mutations affect protease-inhibitor interactions are unclear. Furthermore, some of these mutations, e.g. N88S and L76V, do not only induce resistance to the currently administered drugs, but contrarily induce sensitivity towards other drugs. In this study, mutations N88S and L76V, along with three other resistance-associated mutations, M46I, I50L, and I84V, are analysed by means of molecular dynamics simulations to investigate their role in complexes of the protease with different inhibitors and in different background sequence contexts. RESULTS: Using these simulations for alchemical calculations to estimate the effects of mutations M46I, I50L, I84V, N88S, and L76V on binding free energies shows they are in general in line with the mutations' effect on [Formula: see text] values. For the primary mutation L76V, however, the presence of a background mutation M46I in our analysis influences whether the unfavourable effect of L76V on inhibitor binding is sufficient to outweigh the accompanying reduction in catalytic activity of the protease. Finally, we show that L76V and N88S changes the hydrogen bond stability of these residues with residues D30/K45 and D30/T31/T74, respectively. CONCLUSIONS: We demonstrate that estimating the effect of both binding pocket and distant mutations on inhibitor binding free energy using alchemical calculations can reproduce their effect on the experimentally measured [Formula: see text] values. We show that distant site mutations L76V and N88S affect the hydrogen bond network in the protease's active site, which offers an explanation for the indirect effect of these mutations on inhibitor binding. This work thus provides valuable insights on interplay between primary and background mutations and mechanisms how they affect inhibitor binding.

HIV-1 persistent viremia is frequently followed by episodes of low-level viremia.

After the start of antiretroviral therapy (ART), plasma HIV-RNA levels should fall below the limit of detection (LOD) within 24 weeks. Hence, the prolonged decline of HIV-RNA after ART initiation is defined as persistent viremia (PV). In this retrospective study, we analyzed factors associated with PV. Next-generation sequencing of viral RNA/DNA was performed to study viral evolution and the emergence of drug-resistance mutations in HIV-infected patients with PV (n = 20). In addition, HIV-DNA species, immunological parameters, and clinical data of the patients were analyzed. We found that the possible causes for PV were divers, and both virologic and host parameters of this particular cohort were heterogeneous. We identified viruses with therapy-associated DRMs in six patients (30%); two of these were detected as minority variants. Five patients had sub-optimal drug levels (25%) and the baseline plasma viral loads were relatively high. Strikingly, we observed that >40% of the PV patients finally reaching HIV levels below the LOD later on showed up with episodes of low-level viremia (LLV). However, the amount of PBMC derived HIV-DNA species was not correlated with the likelihood of LLV after PV. According to our data, we conclude that drug-resistant viruses, sub-optimal drug level, and high baseline viral loads might be probable reasons for the prolonged RNA decline only in a sub-set of patients. In the absence of emerging DRMs and/or compliance issues, the clinical implications of PV remain unclear; however, PV appears to be a risk factor for episodes of LLV.

Targets for inhibition of HIV replication: entry, enzyme action, release and maturation.

Inhibition of HIV replication initially targeted viral enzymes, which are exclusively expressed by the virus and not present in the human cell. The development of reverse transcriptase (RT) inhibitors started with the discovery of antiretroviral activity of the nucleoside analog zidovudine in March 1987. Currently, six major classes of antiretroviral drugs are used for the treatment of HIV-infected patients: the RT inhibitors, nucleoside inhibitors and nonnucleoside inhibitors, the protease inhibitors, the integrase inhibitor raltegravir, the fusion inhibitor enfuvirtide (T-20), and the chemokine receptor 5 antagonist maraviroc. A seventh class, the maturation inhibitors, has not yet been approved as their effectiveness is impaired by HIV-1 polymorphisms naturally occurring in 30-40% of HIV-1 therapy-naive isolates. The use of antiretroviral combination therapy has proven to be effective in delaying progression to AIDS and to reconstitute the immune system of HIV-infected individuals. During the last 5 years, the introduction of the newest antiretrovirals has increased treatment efficacy tremendously. However, the development and accumulation of resistance to all antiretroviral drug classes are still a major problem. Additional targets will have to be defined to achieve the ultimate goal: the eradication of the virus from the infected human body.

HIV-GRADE: a publicly available, rules-based drug resistance interpretation algorithm integrating bioinformatic knowledge.

BACKGROUND: Genotypic drug resistance testing provides essential information for guiding treatment in HIV-infected patients. It may either be used for identifying patients with transmitted drug resistance or to clarify reasons for treatment failure and to check for remaining treatment options. While different approaches for the interpretation of HIV sequence information are already available, no other available rules-based systems specifically have looked into the effects of combinations of drugs. HIV-GRADE (Genotypischer Resistenz Algorithmus Deutschland) was planned as a countrywide approach to establish standardized drug resistance interpretation in Germany and also to introduce rules for estimating the influence of mutations on drug combinations. The rules for HIV-GRADE are taken from the literature, clinical follow-up data and from a bioinformatics-driven interpretation system (geno2pheno([resistance])). HIV-GRADE presents the option of seeing the rules and results of other drug resistance algorithms for a given sequence simultaneously. METHODS: The HIV-GRADE rules-based interpretation system was developed by the members of the HIV-GRADE registered society. For continuous updates, this expert committee meets twice a year to analyze data from various sources. Besides data from clinical studies and the centers involved, published correlations for mutations with drug resistance and genotype-phenotype correlation data information from the bioinformatic models of geno2pheno are used to generate the rules for the HIV-GRADE interpretation system. A freely available online tool was developed on the basis of the Stanford HIVdb rules interpretation tool using the algorithm specification interface. Clinical validation of the interpretation system was performed on the data of treatment episodes consisting of sequence information, antiretroviral treatment and viral load, before and 3 months after treatment change. Data were analyzed using multiple linear regression. RESULTS: As the developed online tool allows easy comparison of different drug resistance interpretation systems, coefficients of determination (R(2)) were compared for the freely available rules-based systems. HIV-GRADE (R(2) = 0.40), Stanford HIVdb (R(2) = 0.40), REGA algorithm (R(2) = 0.36) and ANRS (R(2) = 0.35) had a very similar performance using this multiple linear regression model. CONCLUSION: The performance of HIV-GRADE is comparable to alternative rules-based interpretation systems. While there is still room for improvement, HIV-GRADE has been made publicly available to allow access to our approach regarding the interpretation of resistance against single drugs and drug combinations.

The SnoB study: frequency of baseline raltegravir resistance mutations prevalence in different non-B subtypes.

The SnoB study analysed the variability of the integrase (IN) gene of non-B viruses from treatment-naïve patients to determine whether non-B subtypes carry natural resistance mutations to raltegravir (RAL). Plasma viral RNA from 427 patients was gained, and IN sequences were subtyped and screened for subtype-specific highly-variable residues. Seven viruses of different subtypes were phenotypically tested for RAL susceptibility; 359/427 samples could be sequenced. One hundred and seventy samples (47%) were classified as non-B subtypes. No primary RAL resistance-associated mutations (RRAMs) were detected. Certain secondary mutations were found, mostly related to specific non-B subtypes. L74 M was significantly more prevalent in subtype 02_AG, T97A in A and 06_cpx, V151I in 06_cpx, and G163R in 12_BF. Various additional mutations were also detected and could be associated with the subtype too. While K156 N and S230 N were correlated with B subtype, V72I, L74I, T112I, T125A, V201I and T206S were more frequent in certain non-B subtypes. The resistance factors (RF) of 7 viral strains of different subtypes ranged from 1.0 to 1.9. No primary or secondary but subtype-associated additional RRAMs were present. No correlation between RF and additional RRAMs was found. The prevalence of RRAMs was higher in non-B samples. However, the RFs for the analysed non-B subtypes showed lower values to those reported relevant to clinical failure. As the role of baseline secondary and additional mutations on RAL therapy failure is actually not known, baseline IN screening is necessary.

The L76V mutation in HIV-1 protease is potentially associated with hypersusceptibility to protease inhibitors Atazanavir and Saquinavir: is there a clinical advantage?

BACKGROUND: Although being considered as a rarely observed HIV-1 protease mutation in clinical isolates, the L76V-prevalence increased 1998-2008 in some European countries most likely due to the approval of Lopinavir, Amprenavir and Darunavir which can select L76V. Beside an enhancement of resistance, L76V is also discussed to confer hypersusceptibility to the drugs Atazanavir and Saquinavir which might enable new treatment strategies by trying to take advantage of particular mutations. RESULTS: Based on a cohort of 47 L76V-positive patients, we examined if there might exist a clinical advantage for L76V-positive patients concerning long-term success of PI-containing regimens in patients with limited therapy options.Genotypic- and phenotypic HIV-resistance tests from 47 mostly multi-resistant, L76V-positive patients throughout Germany were accomplished retrospectively 1999-2009. Five genotype-based drug-susceptibility predictions received from online interpretation-tools for Atazanavir, Saquinavir, Amprenavir and Lopinavir, were compared to phenotype-based predictions that were determined by using a recombinant virus assay along with a Virtual Phenotype™(Virco). The clinical outcome of the L76V-adapted follow-up therapy was determined by monitoring viral load for 96 weeks. CONCLUSIONS: In this analysis, the mostly used interpretation systems overestimated the L76V-mutation concerning Atazanavir- and SQV resistance. In fact, a clear benefit in drug susceptibility for these drugs was observed in phenotype analysis after establishment of L76V. More importantly, long-term therapy success was significantly higher in patients receiving Atazanavir and/or Saquinavir plus one L76V-selecting drug compared to patients without L76V-selecting agents (p = 0.002).In case of L76V-occurrence ATV and/or SQV may represent encouraging options for patients in deep salvage situations.

Comparison of G-to-A mutation frequencies induced by APOBEC3 proteins in H9 cells and peripheral blood mononuclear cells in the context of impaired processivities of drug-resistant human immunodeficiency virus type 1 reverse transcriptase variants.

APOBEC3 proteins can inhibit human immunodeficiency virus type 1 (HIV-1) replication by inducing G-to-A mutations in newly synthesized viral DNA. However, HIV-1 is able to overcome the antiretroviral activity of some of those enzymes by the viral protein Vif. We investigated the impact of different processivities of HIV-1 reverse transcriptases (RT) on the frequencies of G-to-A mutations introduced by APOBEC3 proteins. Wild-type RT or the M184V, M184I, and K65R+M184V RT variants, which are increasingly impaired in their processivities, were used in the context of a vif-deficient molecular HIV-1 clone to infect H9 cells and peripheral blood mononuclear cells (PBMCs). After two rounds of infection, a part of the HIV-1 env gene was amplified, cloned, and sequenced. The M184V mutation led to G-to-A mutation frequencies that were similar to those of the wild-type RT in H9 cells and PBMCs. The frequencies of G-to-A mutations were increased after infection with the M184I virus variant. This effect was augmented when using the K65R+M184V virus variant (P < 0.001). Overall, the G-to-A mutation frequencies were lower in PBMCs than in H9 cells. Remarkably, 38% +/- 18% (mean +/- standard deviation) of the env clones derived from PBMCs did not harbor any G-to-A mutation. This was rarely observed in H9 cells (3% +/- 3%). Our data imply that the frequency of G-to-A mutations induced by APOBEC3 proteins can be influenced by the processivities of HIV-1 RT variants. The high number of nonmutated clones derived from PBMCs leads to several hypotheses, including that additional antiretroviral mechanisms of APOBEC3 proteins other than their deamination activity might be involved in the inhibition of vif-deficient viruses.

Superiority of infectivity-based over particle-based methods for quantitation of drug resistant HIV-1 as inocula for cell cultures.

Performance of phenotypic assays and replication capacity assays require normalization of virus input. Therefore, quantitation of HIV-1 in supernatants to inoculate cell cultures is an important step. Since the gold standard for the determination of infectivity, the tissue culture infectious dose 50% (TCID50) is time-consuming, several other methods are in use. This study evaluated methods for the quantitation of drug resistant viruses in cell culture supernatants. The compared methods were based on the detection of viral structural components like genomic RNA or p24 antigen (CA-p24) (particle-based), the determination of reverse transcriptase (RT) activity, and methods based on the detection of viral infectivity like LTR-induced beta-galactosidase (beta-gal) activity and the TCID50 (infectivity-based). Significant correlations were observed between beta-gal activity and TCID50, and between CA-p24 and viral RNA. RT activity did not correlate with any other method. However, RT activity correlated significantly with infectivity when non-resistant subtype-B isolates were analyzed. In contrast to viral infectivity, CA-p24 exhibited a long half life and accumulated in cell culture, resulting in decreasing ratios of infectious virions to CA-p24 over time. As a consequence, relative replication capacities of drug resistant viruses were only determined reliably if the input virus was normalized according to infectivity. In conclusion, RT activity seems to be feasible for non-resistant subtype-B viruses but may be of limited use for non-B subtypes and for drug resistant viruses. Methods determining infectivity are most suitable for quantitation of cell culture inocula, whereas particle-based assays are more appropriate for quantitation of virus production during an experiment.

Determination of HIV-1 coreceptor tropism in clinical practise.

Several studies showed that the upcoming drug class of CCR5 coreceptor antagonists have potent virological and immunological activity in treatment experienced patients. In patients failing a CCR5 antagonists-based regimen, the emergence of CXCR4-tropic viral variants has been demonstrated. Clonal analysis of viral isolates from a limited number of patients revealed that these CXCR4-tropic strains did not develop by mutation of a CCR5-tropic virus during therapy, but emerged from a minor population of CXCR4-tropic variants already present in the patients at baseline. Obviously, screening for CXCR4-tropic strains with a functional assay and subsequent exclusion of positive individuals from clinical studies could not completely avoid the selection of CXCR4-tropic strains during failure. But emergence of CXCR4-tropic viruses on therapy may require a critical threshold of CXCR4 viral load at baseline, which may not be the case in patients with a very low proportion of CXCR4-using variants. Therefore, this review addresses to what extent currently available methods are suitable to detect CXCR4-tropic strains in clinical settings. Available functional assays are based on recombinant viruses. These assays are generally restricted to a few laboratories and cannot be easily included in daily clinical settings. Whereas minority detection limits of sequence analyses are generally high with 15 to 30%, functional assays achieve lower detection limits for minorities of 5%. Sequence analyses require an additional interpretation step, and the accuracy of interpretation from clinical samples by current predictions systems has to be improved. In consequence, new methods are arising: genotyping may be improved by hybridisation assays, which quantify CXCR4-tropic viruses by their homology down to 1% minorities, and functional non-infectious cell fusion assays may overcome security restrictions and make phenotypic methods suitable for routine clinical laboratory practise. The highly sensitive detection of CXCR4-tropic viruses may provide the opportunity to clarify the conditions of clinical relevance for CXCR4-tropic minorities.

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.

Results of the first international HIV-1 coreceptor proficiency panel test.

BACKGROUND: Quality Assurance (QA) programs are essential to evaluate performance in diagnostics laboratories. OBJECTIVES: We present the results from the first QA for HIV-1 genotypic tropism testing, organized and coordinated by the Institute of Virology at the University of Cologne. STUDY DESIGN: 12 cell culture-derived viral strains of different HIV-1 clades from the NIH AIDS Reagent Program were sent to the participants to be processed with their standard diagnostic methods Fasta files containing the V3 region sequence were centrally analyzed at the Institute of Virology, Cologne. All samples were sent in parallel for phenotypic testing. RESULTS: 36 laboratories from 16 countries reported genotypic results. The sequence-generation efficacy was 95.1%, while the phenotypic assays ESTA® and PhenXR only achieved results for 58.3% of the samples. All four X4 samples were identified by 31/36 laboratories, two laboratories amplified 3/4×4 samples, and three detected 2/4×4 samples. There was high concordance among the genotypic and phenotypic results, although differences in FPR values were detected. Most deficiencies in sequence editing did not result in wrong classification of X4 viruses as R5, with the exception of sample NRZ05 by laboratory 38, but in an overestimation of CXCR4 use. CONCLUSIONS: This demonstrates that genotypic tropism prediction is a safe procedure for clinical purposes. As we used homogeneous cell culture samples and all sequence fasta files were centrally analyzed, variations in FPR values can only be attributed to sample preparation, RT-PCR or sequence editing protocols.

Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data.

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.

Antiviral activity of Arthrospira-derived spirulan-like substances.

Natural substances offer interesting pharmacological perspectives for antiviral drug development in regard to broad-spectrum antiviral properties and novel modes of action. In this study we analyzed polysaccharide fractions isolated from Arthrospira platensis. Fractions containing intracellular or extracellular spirulan-like molecules showed a pronounced antiviral activity in the absence of cytotoxic effects. Using specific assays for the quantification of viral replication in vitro, these substances exhibited strong inhibition of human cytomegalovirus, herpes simplex virus type 1, human herpesvirus type 6 and human immunodeficiency virus type 1, while only weak or no inhibition was noted for Epstein-Barr virus and influenza A virus. Considering herpesviruses, antiviral effects were most pronounced when the cells were preincubated with the substances prior to the addition of virus, indicating that antiviral action may be primarily targeted to virus entry. However, an inspection of the inhibition of human cytomegalovirus protein synthesis clearly demonstrated that intracellular steps also contributed to the antiviral effect. In the case of human immunodeficiency virus, inhibition occurred at a stage later than viral entry. Thus, spirulan-like substances possess a marked antiherpesviral and anti-HIVactivity based on different modes of action. Further development of these substances might yield novel candidates of broad-spectrum antiviral drugs.

Geno2pheno: Estimating phenotypic drug resistance from HIV-1 genotypes.

Therapeutic success of anti-HIV therapies is limited by the development of drug resistant viruses. These genetic variants display complex mutational patterns in their pol gene, which codes for protease and reverse transcriptase, the molecular targets of current antiretroviral therapy. Genotypic resistance testing depends on the ability to interpret such sequence data, whereas phenotypic resistance testing directly measures relative in vitro susceptibility to a drug. From a set of 650 matched genotype-phenotype pairs we construct regression models for the prediction of phenotypic drug resistance from genotypes. Since the range of resistance factors varies considerably between different drugs, two scoring functions are derived from different sets of predicted phenotypes. Firstly, we compare predicted values to those of samples derived from 178 treatment-naive patients and report the relative deviance. Secondly, estimation of the probability density of 2000 predicted phenotypes gives rise to an intrinsic definition of a susceptible and a resistant subpopulation. Thus, for a predicted phenotype, we calculate the probability of membership in the resistant subpopulation. Both scores provide standardized measures of resistance that can be calculated from the genotype and are comparable between drugs. The geno2pheno system makes these genotype interpretations available via the Internet (http://www.genafor.org/).

Development of a phenotypic susceptibility assay for HIV-1 integrase inhibitors.

Phenotypic resistance analysis is an indispensable method for determination of HIV-1 resistance and cross-resistance to novel drug compounds. Since integrase inhibitors are essential components of recent antiretroviral combination therapies, phenotypic resistance data, in conjunction with the corresponding genotypes, are needed for improving rules-based and data-driven tools for resistance prediction, such as HIV-Grade and geno2pheno[integrase]. For generation of phenotypic resistance data to recent integrase inhibitors, a recombinant phenotypic integrase susceptibility assay was established. For validation purposes, the phenotypic resistance to raltegravir, elvitegravir and dolutegravir of nine subtype-B virus strains, isolated from integrase inhibitor-naïve and raltegravir-treated patients was determined. Genotypic resistance analysis identified four virus strains harbouring RAL resistance-associated mutations. Phenotypic resistance analysis was performed as follows. The HIV-1 integrase genes were cloned into a modified pNL4-3 vector and transfected into 293T cells for the generation of recombinant virus. The integrase-inhibitor susceptibility of the recombinant viruses was determined via an indicator cell line. While raltegravir resistance profiles presented a high cross-resistance to elvitegravir, dolutegravir maintained in-vitro activity in spite of the Y143R and N155H mutations, confirming the strong activity of dolutegravir against raltegravir-resistant viruses. Solely a Q148H+G140S variant presented reduced susceptibility to dolutegravir. In conclusion, our phenotypic susceptibility assay permits resistance analysis of the integrase gene of patient-derived viruses for integrase inhibitors by replication-competent recombinants. Thus, this assay can be used to analyze phenotypic drug resistance of integrase inhibitors in vitro. It provides the possibility to determine the impact of newly appearing mutational patterns to drug resistance of recent integrase inhibitors.

Geno2pheno[HCV] - A Web-based Interpretation System to Support Hepatitis C Treatment Decisions in the Era of Direct-Acting Antiviral Agents.

The face of hepatitis C virus (HCV) therapy is changing dramatically. Direct-acting antiviral agents (DAAs) specifically targeting HCV proteins have been developed and entered clinical practice in 2011. However, despite high sustained viral response (SVR) rates of more than 90%, a fraction of patients do not eliminate the virus and in these cases treatment failure has been associated with the selection of drug resistance mutations (RAMs). RAMs may be prevalent prior to the start of treatment, or can be selected under therapy, and furthermore they can persist after cessation of treatment. Additionally, certain DAAs have been approved only for distinct HCV genotypes and may even have subtype specificity. Thus, sequence analysis before start of therapy is instrumental for managing DAA-based treatment strategies. We have created the interpretation system geno2pheno[HCV] (g2p[HCV]) to analyse HCV sequence data with respect to viral subtype and to predict drug resistance. Extensive reviewing and weighting of literature related to HCV drug resistance was performed to create a comprehensive list of drug resistance rules for inhibitors of the HCV protease in non-structural protein 3 (NS3-protease: Boceprevir, Paritaprevir, Simeprevir, Asunaprevir, Grazoprevir and Telaprevir), the NS5A replicase factor (Daclatasvir, Ledipasvir, Elbasvir and Ombitasvir), and the NS5B RNA-dependent RNA polymerase (Dasabuvir and Sofosbuvir). Upon submission of up to eight sequences, g2p[HCV] aligns the input sequences, identifies the genomic region(s), predicts the HCV geno- and subtypes, and generates for each DAA a drug resistance prediction report. g2p[HCV] offers easy-to-use and fast subtype and resistance analysis of HCV sequences, is continuously updated and freely accessible under http://hcv.geno2pheno.org/index.php. The system was partially validated with respect to the NS3-protease inhibitors Boceprevir, Telaprevir and Simeprevir by using data generated with recombinant, phenotypic cell culture assays obtained from patients' virus variants.

Detection of minor populations of drug-resistant HIV-1 in acute seroconverters.

OBJECTIVE: The transmission of drug-resistant HIV-1 is a major health concern. To date, most clinical studies have relied on sequencing techniques for genotypic analyses which do not allow quantification of minority viral populations below 25%. As minor populations of drug-resistant HIV-1 could impact the efficiency of antiretroviral therapy, this study was performed to determine the prevalence of minor populations of drug-resistant HIV-1 in acute seroconverters. DESIGN AND METHODS: Forty-nine acute seroconverters from two clinical centers in Germany were included in the study. Individuals were identified between June 1999 and March 2003, and none had received antiretroviral therapy prior to sampling. Minor populations of drug-resistant variants were detected by quantitative real-time polymerase chain reaction using allele-discriminating oligonucleotides for three key resistance mutations: L90M (protease), K103N and M184V (reverse transcriptase). The approximate discriminative power was between 0.01 and 0.2%. RESULTS: Drug-resistant variants were detected in 10 of 49 patients (20.4%). The L90M mutation was found in one of 49 (2%), the K103N mutation in five of 49 (10.2%) and the M184V mutation in six of 49 (12.2%) patients, respectively. In five of the 10 individuals with detectable drug-resistant virus (50%), the detected population represented a minor viral quasi-species (< 25% of viruses) and was not detected by direct sequencing. CONCLUSIONS: The prevalence of minor populations of drug-resistant HIV-1 in acute seroconverters can be frequently detected and may impact the success of antiretroviral therapy.

Genotypic drug resistance interpretation systems--the cutting edge of antiretroviral therapy.

The technical quality of genotypic and phenotypic drug resistance testing has considerably improved, and therefore the major challenge now lies in the interpretation of drug resistance. This is due to several facts: (i) in times of combination therapy, the effect of drug resistance-associated mutations cannot be considered independently, (ii) many additive and subtractive interactions between mutations exist, and resistant strains may exhibit varying degrees of cross-resistance, (iii) the phenotype cannot adequately determine slight, but clinically relevant, differences for those drugs with a narrow range of resistance, and (iv) pharmacokinetic interactions may shift relevant levels of drug resistance. Genotypic drug resistance interpretation systems are designed to solve these problems. Rule-based systems incorporate current knowledge about correlations between genotype, phenotype and clinical response. Database-driven systems use the information provided by paired geno- and phenotypic data, applying database matching search or bioinformatic approaches. For detailed comparison, 11 interpretation systems were selected which present a comprehensive system for most of the available drugs, can easily be accessed via the Internet and are regularly updated. The systems were characterized for the source data, access, input, output, and availability of clinical studies. For further comparison, existing clinical databases should be merged into one large database to allow competition between the systems. This may also solve the burning problem of clinically relevant cut-offs. Head-to-head comparisons of interpretation systems require large prospective randomized trials in which only the interpretation system is different between groups, before a consensus can be achieved for the best antiretroviral therapy of the individual patient.

Distinct cross-resistance profiles of the new protease inhibitors amprenavir, lopinavir, and atazanavir in a panel of clinical samples.

A panel of 245 clinical samples with known treatment histories was retrospectively evaluated for cross-resistance to new protease inhibitors (PI). Samples with resistance to previously approved PI displayed high cross-resistance to atazanavir, whereas cross-resistance to amprenavir was considerably lower. A similar cross-resistance profile was observed for lopinavir, if a higher cut-off for resistance (9.5-fold) was applied. The enhanced efficacy of boosted PI is discussed with respect to clinically relevant cut-offs for drug resistance.

Technologies for measuring HIV-1 drug resistance.

Drug resistance testing significantly improves response to antiretroviral treatment in HIV-1-infected patients, therefore it has recently been implemented into current guidelines for the management of antiretroviral therapy. Knowledge about technologies for measuring drug resistance is important for several reasons: (a) differences exist between different technologies and also between assays based on the same technology; (b) the results of resistance testing are strongly dependent on the reliability and precision of the technology used; and (c) technical aspects have to be considered for a clinically relevant interpretation of drug resistance. The spectrum of genotypic and phenotypic technologies as well as the technical quality is increasing, which shifts the emphasis to the interpretation of resistance profiles. The interpretation is based on the knowledge of drug resistance-associated mutations as well as correlations between genotype and phenotype and clinical response, which are incorporated into rules-based systems. Bioinformatic techniques are used to generate mathematical models for the prediction of drug resistance from genotype. Both approaches are converging toward the prediction of clinical response. Because therapy response is dependent on many additional variables, further efforts are required for the generation of a large clinical database. This will be the basis of a prediction system that will optimize the antiretroviral therapy for each individual patient.