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Abstract 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.

Objectives: The study aimed to survey maraviroc use and assess effectiveness and durability of maraviroc-containing antiretroviral treatment (ART) in routine practice across Europe., Methods: Data were retrieved from 26 cohorts in 8 countries comprising adults who started maraviroc in 2005-2016 and had ≥1 follow-up visit. Available V3 sequences were re-analysed centrally for tropism determination by geno2pheno[coreceptor]. Treatment failure (TF) was defined as either virological failure (viral load >50 copies/mL) or maraviroc discontinuation for any reason over 48 weeks. Predictors of TF were explored by logistic regression analysis. Time to maraviroc discontinuation was estimated by Kaplan-Meier survival analysis., Results: At maraviroc initiation (baseline), among 1,381 patients, 67.1% had experienced ≥3 ART classes and 45.6% had a viral load <50 copies/mL. Maraviroc was occasionally added to the existing regimen as a single agent (7.3%) but it was more commonly introduced alongside other new agents, and was often (70.4%) used with protease inhibitors. Accompanying drugs comprised 1 (40.2%), 2 (48.6%) or ≥3 (11.2%) ART classes. Among 1,273 patients with available tropism data, 17.6% showed non-R5 virus. Non-standard maraviroc use also comprised reported once daily dosing (20.0%) and a total daily dose of 150mg (12.1%). Over 48 weeks, 41.4% of patients met the definition of TF, although the 1-year estimated retention on maraviroc was 82.1% (95% confidence interval 79.9-84.2). Among 1,010 subjects on maraviroc at week 48, the viral load was >50 copies/mL in 19.9% and >200 copies/mL in 10.7%. Independent predictors of TF comprised a low nadir CD4 count, a detectable baseline viral load, previous PI experience, non-R5 tropism, having ≥3 active drugs in the accompanying regimen, and a more recent calendar year of maraviroc initiation., Conclusions: This study reports on the largest observation cohort of patients who started maraviroc across 8 European countries. In this overall highly treatment-experienced population, with a small but appreciable subset that received maraviroc outside of standard treatment guidelines, maraviroc was safe and reasonably effective, with relatively low rates of discontinuation over 48 weeks and only 2 cases of serum transaminase elevations reported as reasons for discontinuation., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Dr. Boucher reports personal fees and research grant from ViiV, outside the submitted work. Dr. Geretti reports grants from BMS, grants and personal fees from Gilead Sciences, personal fees from Cepheid, grants and personal fees from Roche Pharma, grants and personal fees from ViiV Healthcare, grants and personal fees from Janssen, outside the submitted work; Dr. Kaiser reports grants from Gilead Sciences and ViiV Healthcare, personal fees from Janssen-Cilag, MSD, ROCHE, ABBVIE, Siemens and ViiV Healthcare, outside the submitted work; Dr. Vandamme received a personal fee from Gilead outside the submitted work; Dr. Zazzi reports grants from Gilead Sciences and ViiV Healthcare, personal fees from Janssen-Cilag and ViiV Healthcare, outside the submitted work; Dr. Incardona reports grants from Gilead Sciences, Janssen and ViiV Healthcare, outside the submitted work; she received salary from InformaPRO, Rome. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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.

Introduction: The health condition of HIV-1 infected patients has improved during the last years, but lifelong antiretroviral treatment is still needed. However resistance, multiple side effects and drug to drug interactions of antiretrovirals challenge the establishment of a long lasting regimen. The average running time of each antiretroviral drug composing the therapy episodes combination antiretroviral therapy (cART) may be seen as an indicator of effectiveness and tolerability., Materials and Methods: To evaluate the running time of each drug used in HIV-1 treatment, we extracted therapy episodes from the latest release of the EuResist database (www.euresist.org). The evaluation period was from Oct 2006 to Oct 2012. Inclusion criteria for this analysis were continuous patient monitoring for at least two years (i.e. latest therapy start in Oct 2010), and the extraction of at least 100 cases per drug analyzed. Drug intake interruptions of less than a month were ignored., Results: At the time of data extraction (Feb 2013), the EuResist database contained data from 61,953 patients of which 11,499 fulfilled the inclusion criteria. We obtained 37,035 drug treatment lines from 38,153 cARTs and the overall average length of drug intake was 18.7 months. For each single drug these average durations measured in months were: 18.3 (3TC); 20.8 (ABC); 12.3 (d4T); 14.3 (ddI); 23.2 (FTC); 23.0 (TDF); 13.4 (ZDV); 19.8 (EFV); 21.9 (ETR); 17.7 (NVP); 19.2 (ATV); 22.7 (DRV); 18.7 (FPV); 17.9 (LPV); 15.2 (SQV); 14.6 (TPV); 22.6 (RAL); 21.9 (MVC) and 8.9 (T20). Overall drug discontinuation rates at one, two and three years were 35.0, 48.8 and 95.8%, respectively. Average discontinuation rates for the different drug classes at two years these were: 46.2% for NRTIs; 49.7% for NNRTIs; 55.4% for PIs and 37.6% for Raltegravir/Maraviroc., Conclusions: In this cohort the overall frequency of therapy changes is high. After two years of treatment, on average 49% of the patients change at least one drug in their cART. Thus, we have to expect numerous changes in the long term perspective of treatments. The observed differences in durations suggest that newer drugs might have advantages over older ones. However possible reasons and confounding factors (such as number of past treatment lines, co-medication, risk group, etc.) were not addressed at this time of the analysis.

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.

Subtype-dependent selection of HIV-1 reverse transcriptase resistance mutation K65R was previously observed in cell culture and small clinical investigations. We compared K65R prevalence across subtypes A, B, C, F, G, and CRF02&#95;AG separately in a cohort of 3,076 patients on combination therapy including tenofovir. K65R selection was significantly higher in HIV-1 subtype C. This could not be explained by clinical and demographic factors in multivariate analysis, suggesting subtype sequence-specific K65R pathways.

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.)

Introduction: Highly active antiretroviral therapy (HAART) has been shown to be effective in many recent trials. However, there is limited data on time trends of HAART efficacy after treatment change., Methods: Data from different European cohorts were compiled within the EuResist Project. The efficacy of HAART defined by suppression of viral replication at 24 weeks after therapy switch was analyzed considering previous treatment modifications from 1999 to 2008., Results: Altogether, 12,323 treatment change episodes in 7,342 patients were included in the analysis. In 1999, HAART after treatment switch was effective in 38.0% of the patients who had previously undergone 1-5 therapies. This figure rose to 85.0% in 2008. In patients with more than 5 previous therapies, efficacy rose from 23.9 to 76.2% in the same time period. In patients with detectable viral load at therapy switch, the efficacy rose from 23.3 to 66.7% with 1-5 previous treatments and from 14.4 to 55.6% with more than 5 previous treatments., Conclusion: The results of this large cohort show that the outcome of HAART switch has improved considerably over the last years. This result was particularly observed in the context after viral rebound. Thus, changing HAART is no longer associated with a high risk of treatment failure., (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)

Background: Although genotypic resistance testing (GRT) is recommended to guide combination antiretroviral therapy (cART), funding and/or facilities to perform GRT may not be available in low to middle income countries. Since treatment history (TH) impacts response to subsequent therapy, we investigated a set of statistical learning models to optimise cART in the absence of GRT information., Methods and Findings: The EuResist database was used to extract 8-week and 24-week treatment change episodes (TCE) with GRT and additional clinical, demographic and TH information. Random Forest (RF) classification was used to predict 8- and 24-week success, defined as undetectable HIV-1 RNA, comparing nested models including (i) GRT+TH and (ii) TH without GRT, using multiple cross-validation and area under the receiver operating characteristic curve (AUC). Virological success was achieved in 68.2% and 68.0% of TCE at 8- and 24-weeks (n = 2,831 and 2,579), respectively. RF (i) and (ii) showed comparable performances, with an average (st.dev.) AUC 0.77 (0.031) vs. 0.757 (0.035) at 8-weeks, 0.834 (0.027) vs. 0.821 (0.025) at 24-weeks. Sensitivity analyses, carried out on a data subset that included antiretroviral regimens commonly used in low to middle income countries, confirmed our findings. Training on subtype B and validation on non-B isolates resulted in a decline of performance for models (i) and (ii)., Conclusions: Treatment history-based RF prediction models are comparable to GRT-based for classification of virological outcome. These results may be relevant for therapy optimisation in areas where availability of GRT is limited. Further investigations are required in order to account for different demographics, subtypes and different therapy switching strategies.

Objectives: To analyse HIV Gag cleavage site (CS) and non-CS mutations in HIV non-B isolates from patients failing antiretroviral therapy., Patients and Methods: Twenty-one HIV isolates were obtained from patients infected with HIV subtype G during an outbreak in Russia 20 years ago. Most patients were failing antiretroviral therapy when genotyping was performed., Results: HIV Gag CS mutations accumulated in protease inhibitor (PI)-resistant HIV isolates and were correlated with the presence of three or more PI resistance mutations. Only 1 of 11 HIV isolates carrying major protease mutations did not harbour treatment-associated CS mutations. Natural polymorphism 453T, often found in HIV non-B subtypes, seems to favour the selection of CS mutation 453I rather than treatment-associated CS mutation 453L. Resistance-associated non-CS mutations (123E and 200I) were also observed in PI-resistant clinical isolates. Non-CS mutations in the frameshift-regulating site, which controls the synthesis of Gag-Pol, did not affect frameshift efficiency in dual luciferase assays. Of note, one of four HIV isolates from patients failing PI therapies without protease mutations harboured Gag mutations associated with PI resistance (123E and 436R) and reverse transcriptase inhibitor mutations conferring resistance to the backbone drug., Conclusions: HIV Gag CS mutations commonly occurred in HIV isolates from patients failing PI therapies and natural polymorphisms at the same position influence their emergence. Non-CS mutations previously associated with PI resistance were also observed in clinical isolates. Gag mutations might indicate the evolution of PI resistance even in the absence of protease mutations.

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.

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.

Motivation: Optimizing HIV therapies is crucial since the virus rapidly develops mutations to evade drug pressure. Recent studies have shown that genotypic information might not be sufficient for the design of therapies and that other clinical and demographical factors may play a role in therapy failure. This study is designed to assess the improvement in prediction achieved when such information is taken into account. We use these factors to generate a prediction engine using a variety of machine learning methods and to determine which clinical conditions are most misleading in terms of predicting the outcome of a therapy., Results: Three different machine learning techniques were used: generative-discriminative method, regression with derived evolutionary features, and regression with a mixture of effects. All three methods had similar performances with an area under the receiver operating characteristic curve (AUC) of 0.77. A set of three similar engines limited to genotypic information only achieved an AUC of 0.75. A straightforward combination of the three engines consistently improves the prediction, with significantly better prediction when the full set of features is employed. The combined engine improves on predictions obtained from an online state-of-the-art resistance interpretation system. Moreover, engines tend to disagree more on the outcome of failure therapies than regarding successful ones. Careful analysis of the differences between the engines revealed those mutations and drugs most closely associated with uncertainty of the therapy outcome., Availability: The combined prediction engine will be available from July 2008, see http://engine.euresist.org.

Background: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers., Principal Findings: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system., Conclusion: The combined EuResist prediction engine is freely available at http://engine.euresist.org.