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Objectives: The objective of this study was to improve the prediction of the impact of HIV-1 protease mutations in different viral subtypes on virological response to darunavir., Methods: Darunavir-containing treatment change episodes (TCEs) in patients previously failing PIs were selected from large European databases. HIV-1 subtype B-infected patients were used as the derivation dataset and HIV-1 non-B-infected patients were used as the validation dataset. The adjusted association of each mutation with week 8 HIV RNA change from baseline was analysed by linear regression. A prediction model was derived based on best subset least squares estimation with mutational weights corresponding to regression coefficients. Virological outcome prediction accuracy was compared with that from existing genotypic resistance interpretation systems (GISs) (ANRS 2013, Rega 9.1.0 and HIVdb 7.0)., Results: TCEs were selected from 681 subtype B-infected and 199 non-B-infected adults. Accompanying drugs were NRTIs in 87%, NNRTIs in 27% and raltegravir or maraviroc or enfuvirtide in 53%. The prediction model included weighted protease mutations, HIV RNA, CD4 and activity of accompanying drugs. The model's association with week 8 HIV RNA change in the subtype B (derivation) set was R(2) = 0.47 [average squared error (ASE) = 0.67, P < 10(-6)]; in the non-B (validation) set, ASE was 0.91. Accuracy investigated by means of area under the receiver operating characteristic curves with a binary response (above the threshold value of HIV RNA reduction) showed that our final model outperformed models with existing interpretation systems in both training and validation sets., Conclusions: A model with a new darunavir-weighted mutation score outperformed existing GISs in both B and non-B subtypes in predicting virological response to darunavir., (© The Author 2016. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Objectives: The use of the NNRTI rilpivirine in low- and middle-income countries (LMICs) is under debate. The main objective of this study was to provide further clinical insights and biochemical evidence on the usefulness of rilpivirine in LMICs., Patients and Methods: Rilpivirine resistance was assessed in 5340 therapy-naive and 13,750 first-generation NNRTI-failed patients from Europe and therapy-naive HIV-1 subtype C (HIV-1C)-infected individuals from India (n = 617) and Ethiopia (n = 127). Rilpivirine inhibition and binding affinity assays were performed using patient-derived HIV-1C reverse transcriptases (RTs)., Results: Primary rilpivirine resistance was rare, but the proportion of patients with >100,000 HIV-1 RNA copies/mL pre-ART was high in patients from India and Ethiopia, limiting the usefulness of rilpivirine as a first-line drug in LMICs. In patients failing first-line NNRTI treatments, cross-resistance patterns suggested that 73% of the patients could benefit from switching to rilpivirine-based therapy. In vitro inhibition assays showed ∼ 2-fold higher rilpivirine IC50 for HIV-1C RT than HIV-1B RT. Pre-steady-state determination of rilpivirine-binding affinities revealed 3.7-fold lower rilpivirine binding to HIV-1C than HIV-1B RT. Structural analysis indicated that naturally occurring polymorphisms close to the NNRTI-binding pocket may reduce rilpivirine binding, leading to lower susceptibility of HIV-1C to rilpivirine., Conclusions: Our clinical and biochemical findings indicate that the usefulness of rilpivirine has limitations in HIV-1C-dominated epidemics in LMICs, but the drug could still be beneficial in patients failing first-line therapy if genotypic resistance testing is performed., (© The Author 2015. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: Resistance analysis from viral RNA is restricted to detectable viral load. Therefore, analysis from proviral DNA could help in cases with low-level or suppressed viremia., Methods: Viral plasma RNA and the corresponding cellular proviral DNA of 78 EDTA samples from 48 therapy-naïve (TN) and 30 therapy-experienced (TE) HIV-1-infected patients were isolated and analyzed for their resistance profiles in the protease and reverse transcriptase genes., Results: Overall, 175 drug-resistance mutations (DRMs) were detected in 25/30 TE (83.3%) and 5/48 TN (10.4%) samples. The TE patients displayed a mean number of 6.68 DRMs in RNA and 5.20 in DNA. In the TN patients, a mean of 0.8 DRMs was found in RNA and 1.0 in DNA; 75% of the DRMs were detected in RNA and DNA simultaneously. In the TE samples, 76% of the DRMs were detected simultaneously in RNA and DNA, 23% exclusively in RNA and 1% in DNA only. The TN samples revealed a significantly higher frequency of DRMs in DNA than in RNA., Conclusions: Proviral DNA resistance testing provides additional resistance information for TN patients. It is also a reliable alternative for TE patients with unsuccessful RNA testing and can provide valuable information when no records are available.

A novel tetra-peptide insertion was identified in Gag-p6 ALIX-binding region, which appeared in protease inhibitor failure Indian HIV-1C sequences (odds ratio=17.1, P < 0.001) but was naturally present in half of untreated Ethiopian HIV-1C sequences. The insertion is predicted to restore ALIX-mediated virus release pathway, which is lacking in HIV-1C. The clinical importance of the insertion needs to be evaluated in HIV-1C dominating regions wherein the use of protease inhibitor drugs are being scaled up.

Objective: We assessed trends in the proportion of transmitted (TDR) and acquired (ADR) HIV drug resistance and associated mutations between 2001 and 2011 in the German ClinSurv-HIV Drug Resistance Study., Method: The German ClinSurv-HIV Drug Resistance Study is a subset of the German ClinSurv-HIV Cohort. For the ClinSurv-HIV Drug Resistance Study all available sequences isolated from patients in five study centres of the long term observational ClinSurv-HIV Cohort were included. TDR was estimated using the first viral sequence of antiretroviral treatment (ART) naïve patients. One HIV sequence/patient/year of ART experienced patients was considered to estimate the proportion of ADR. Trends in the proportion of HIV drug resistance were calculated by logistic regression., Results: 9,528 patients were included into the analysis. HIV-sequences of antiretroviral naïve and treatment experienced patients were available from 34% (3,267/9,528) of patients. The proportion of TDR over time was stable at 10.4% (95% CI 9.1-11.8; p for trend = 0.6; 2001-2011). The proportion of ADR among all treated patients was 16%, whereas it was high among those with available HIV genotypic resistance test (64%; 1,310/2,049 sequences; 95% CI 62-66) but declined significantly over time (OR 0.8; 95% CI 0.77-0.83; p for trend<0.001; 2001-2011). Viral load monitoring subsequent to resistance testing was performed in the majority of treated patients (96%) and most of them (67%) were treated successfully., Conclusions: The proportion of TDR was stable in this study population. ADR declined significantly over time. This decline might have been influenced by broader resistance testing, resistance test guided therapy and the availability of more therapeutic options and not by a decline in the proportion of TDR within the study population.

Background: Superinfection with drug resistant HIV strains could potentially contribute to compromised therapy in patients initially infected with drug-sensitive virus and receiving antiretroviral therapy. To investigate the importance of this potential route to drug resistance, we developed a bioinformatics pipeline to detect superinfection from routinely collected genotyping data, and assessed whether superinfection contributed to increased drug resistance in a large European cohort of viremic, drug treated patients., Methods: We used sequence data from routine genotypic tests spanning the protease and partial reverse transcriptase regions in the Virolab and EuResist databases that collated data from five European countries. Superinfection was indicated when sequences of a patient failed to cluster together in phylogenetic trees constructed with selected sets of control sequences. A subset of the indicated cases was validated by re-sequencing pol and env regions from the original samples., Results: 4425 patients had at least two sequences in the database, with a total of 13816 distinct sequence entries (of which 86% belonged to subtype B). We identified 107 patients with phylogenetic evidence for superinfection. In 14 of these cases, we analyzed newly amplified sequences from the original samples for validation purposes: only 2 cases were verified as superinfections in the repeated analyses, the other 12 cases turned out to involve sample or sequence misidentification. Resistance to drugs used at the time of strain replacement did not change in these two patients. A third case could not be validated by re-sequencing, but was supported as superinfection by an intermediate sequence with high degenerate base pair count within the time frame of strain switching. Drug resistance increased in this single patient., Conclusions: Routine genotyping data are informative for the detection of HIV superinfection; however, most cases of non-monophyletic clustering in patient phylogenies arise from sample or sequence mix-up rather than from superinfection, which emphasizes the importance of validation. Non-transient superinfection was rare in our mainly treatment experienced cohort, and we found a single case of possible transmitted drug resistance by this route. We therefore conclude that in our large cohort, superinfection with drug resistant HIV did not compromise the efficiency of antiretroviral treatment.

HIV's genetic instability means that sequence similarity can illuminate the underlying transmission network. Previous application of such methods to samples from the United Kingdom has suggested that as many as 86% of UK infections arose outside of the country, a conclusion contrary to usual patterns of disease spread. We investigated transmission networks in the Resina cohort, a 2,747 member sample from Nordrhein-Westfalen, Germany, sequenced at therapy start. Transmission networks were determined by thresholding the pairwise genetic distance in the pol gene at 96.8% identity. At first blush the results concurred with the UK studies. Closer examination revealed four large and growing transmission networks that encompassed all major transmission groups. One of these formed a supercluster containing 71% of the sex with men (MSM) subjects when the network was thresholded at levels roughly equivalent to those used in the UK studies, though methodological differences suggest that this threshold may be too generous in the current data. Examination of the endo- versus exogenesis hypothesis by testing whether infections that were exogenous to Cologne or to Dusseldorf were endogenous to the greater region supported endogenous spread in MSM subjects and exogenous spread in the endemic transmission group. In intravenous drug using group subjects, it depended on viral strain, with subtype B sequences appearing to have origin exogenous to the Resina data, while non-B sequences (primarily subtype A) were almost completely endogenous to their local community. These results suggest that, at least in Germany, the question of endogenous versus exogenous linkages depends on subject group.

Sustained suppression of viral replication in HIV-1 infected patients is especially hampered by the emergence of HIV-1 drug resistance. The mechanisms of drug resistance mainly involve mutations directly altering the interaction of viral enzymes and inhibitors. However, protease inhibitors do not only select for mutations in the protease but also for mutations in the precursor Gag and Pol proteins. In this study, we analysed the frameshift-regulating site of HIV-1 subtype B isolates, which also encodes for Gag and Pol proteins, classified as either treatment-naïve (TN) or protease inhibitor resistant (PI-R). HIV-1 Gag cleavage site mutations (G435E, K436N, I437V, L449F/V) especially correlated with protease inhibitor resistance mutations, but also Pol cleavage site mutations (D05G, D05S) could be assigned to specific protease resistance profiles. Additionally, two Gag non-cleavage site mutations (S440F, H441P) were observed more often in HIV-1 isolates carrying protease resistance mutations. However, in dual luciferase assays, the frameshift efficiencies of specific clones did not reveal any effect from these mutations. Nevertheless, two patterns of mutations modestly increased the frameshift rates in vitro, but were not specifically accumulating in PI-resistant HIV-1 isolates. In summary, HIV-1 Gag cleavage site mutations were dominantly selected in PI-resistant HIV-1 isolates but also Pol cleavage site mutations influenced resistance profiles in the protease. Additionally, Gag non-cleavage site mutations accumulated in PI-resistant HIV-1 isolates, but were not related to an increased frameshift efficiency.

For a long time, the clinical management of antiretroviral drug resistance was based on sequence analysis of the HIV genome followed by estimating drug susceptibility from the mutational pattern that was detected. The large number of anti-HIV drugs and HIV drug resistance mutations has prompted the development of computer-aided genotype interpretation systems, typically comprising rules handcrafted by experts via careful examination of in vitro and in vivo resistance data. More recently, machine learning approaches have been applied to establish data-driven engines able to indicate the most effective treatments for any patient and virus combination. Systems of this kind, currently including the Resistance Response Database Initiative and the EuResist engine, must learn from the large data sets of patient histories and can provide an objective and accurate estimate of the virological response to different antiretroviral regimens. The EuResist engine was developed by a European consortium of HIV and bioinformatics experts and compares favorably with the most commonly used genotype interpretation systems and HIV drug resistance experts. Next-generation treatment response prediction engines may valuably assist the HIV specialist in the challenging task of establishing effective regimens for patients harboring drug-resistant virus strains. The extensive collection and accurate processing of increasingly large patient data sets are eagerly awaited to further train and translate these systems from prototype engines into real-life treatment decision support tools., (Copyright © 2012 S. Karger AG, Basel.)

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&#95;AG, T97A in A and 06&#95;cpx, V151I in 06&#95;cpx, and G163R in 12&#95;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., (© Springer-Verlag 2011)

The high number of Turkish immigrants in the German state North-Rhine Westphalia (NRW) compelled us to look for HIV-infected patients with Turkish nationality. In the AREVIR database, we found 127 (107 men, 20 women) Turkish HIV patients living in NRW. In order to investigate transmission clusters and their correlation to gender, nationality and self-reported transmission mode, a phylogenetic analysis including pol gene sequences was performed. Subtype distribution and the number of HIV drug resistance mutations in the Turkish patient group were found to be similar to the proportion in the non-Turkish patients. Great differences were observed in self-reported mode of transmission in the heterosexual Turkish male subgroup. Neighbour-joining tree of pol gene sequences gave indication that 59% of these reported heterosexual transmissions cluster with those of men having sex with men in the database. This is the first study analysing HIV type distribution, drug resistance mutations and transmission mode in a Turkish immigrant population., (© Springer-Verlag 2011)

Objectives: The EuResist expert system is a novel data-driven online system for computing the probability of 8-week success for any given pair of HIV-1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment., Methods: The EuResist system was compared with 10 HIV-1 drug resistance experts for the ability to predict 8-week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success., Results: There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6-13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter-rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%)., Conclusions: With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment-decision support tool in clinical practice., (© 2010 British HIV Association.)

The success of first-line antiretroviral therapy can be challenged by the acquisition of primary drug resistance. Here we report a case where baseline genotypic resistance testing detected resistance conferring nucleoside/nucleotide reverse transcriptase inhibitor (NRTI)-associated mutations, but no primary mutations for protease inhibitor (PI). Subsequent PI-based HAART with boosted saquinavir led to virological treatment success with persistently undetectable viral load. After treatment simplification from saquinavir to an atazanavir based PI-therapy and no change in backbone therapy rapid virological breakthrough occurred. Retrospective analysis displayed preexisting gag cleavage site mutations which may have reduced the genetic barrier in a clinical relevant manner in combination with the already existing NRTI resistance mutations. Alternatively, this effect could be explained with a different antiviral potency for the respective PIs used.

Recently, first-line lopinavir failure was observed due to protease mutation 76V. In the present study, we found 76V associated with protease mutation 46I and gag cleavage-site mutation 431V. Longitudinal analysis of patients failing protease inhibitor therapies demonstrated that 76V strictly occurs either together with 46I and/or 431V or in HIV isolates already harbouring one of both mutations. Therefore, all three mutations seem to cooperate in terms of protease inhibitor resistance.

Objectives: We investigated the prevalence of raltegravir resistance-associated mutations at baseline and their evolution during raltegravir therapy in patients infected with different HIV-1 subtypes., Methods: At pre-treatment screening, the integrase gene from plasma samples from patients infected with subtype B and non-B viruses was analysed. Raltegravir resistance evolution was further evaluated in 10 heavily pre-treated patients., Results: Two hundred and nine plasma samples from 94 subtype B and 115 non-B patients were sequenced. No signature/primary raltegravir resistance mutations were detected at baseline. The secondary mutations L74M, T97A, V151I and G163R were observed with a frequency of <4%. The primary mutations N155H, Q148R/H or Q143R were observed during raltegravir therapy. The Q148R/H was detected only in subtype B. A switch of the primary mutation during raltegravir treatment was not restricted to the subtype B viruses. The prevalence of each primary mutation varied depending on the length of the raltegravir therapy. The Q148R/H was mostly detected after short exposure to raltegravir, while the Y143R was observed only after prolonged raltegravir exposure. We detected an association between the presence of the T206S in the baseline genotype and the absence of the primary Q148R/H mutation or any secondary mutation accompanying the N155H following raltegravir failure., Conclusions: A number of secondary and additional mutations were found in baseline genotypes. During therapy, when the virus was not optimally suppressed, resistance mutations developed, which were dependent on subtype and time on raltegravir.

Background: Expert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure., Methods: We retrospectively validated the statistical model used by g2p-THEO in approximately 7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega., Results: The difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P < .001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed., Conclusion: Finding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.org.

Background: The extreme flexibility of the HIV type-1 (HIV-1) genome makes it challenging to build the ideal antiretroviral treatment regimen. Interpretation of HIV-1 genotypic drug resistance is evolving from rule-based systems guided by expert opinion to data-driven engines developed through machine learning methods., Methods: The aim of the study was to investigate linear and non-linear statistical learning models for classifying short-term virological outcome of antiretroviral treatment. To optimize the model, different feature selection methods were considered. Robust extra-sample error estimation and different loss functions were used to assess model performance. The results were compared with widely used rule-based genotypic interpretation systems (Stanford HIVdb, Rega and ANRS)., Results: A set of 3,143 treatment change episodes were extracted from the EuResist database. The dataset included patient demographics, treatment history and viral genotypes. A logistic regression model using high order interaction variables performed better than rule-based genotypic interpretation systems (accuracy 75.63% versus 71.74-73.89%, area under the receiver operating characteristic curve [AUC] 0.76 versus 0.68-0.70) and was equivalent to a random forest model (accuracy 76.16%, AUC 0.77). However, when rule-based genotypic interpretation systems were coupled with additional patient attributes, and the combination was provided as input to the logistic regression model, the performance increased significantly, becoming comparable to the fully data-driven methods., Conclusions: Patient-derived supplementary features significantly improved the accuracy of the prediction of response to treatment, both with rule-based and data-driven interpretation systems. Fully data-driven models derived from large-scale data sources show promise as antiretroviral treatment decision support tools.