Phenotypic or genotypic resistance testing for choosing antiretroviral therapy after treatment failure: a randomized trial

Web Service for HIV Drug Resistance Prediction Based on Analysis of Amino Acid Substitutions in Main Drug Targets

Predicting viral drug resistance is a significant medical concern. The importance of this problem stimulates the continuous development of experimental and new computational approaches. The use of computational approaches allows researchers to increase therapy effectiveness and reduce the time and expenses involved when the prescribed antiretroviral therapy is ineffective in the treatment of infection caused by the human immunodeficiency virus type 1 (HIV-1). We propose two machine learning methods and the appropriate models for predicting HIV drug resistance related to amino acid substitutions in HIV targets: (i) k-mers utilizing the random forest and the support vector machine algorithms of the scikit-learn library, and (ii) multi-n-grams using the Bayesian approach implemented in MultiPASSR software. Both multi-n-grams and k-mers were computed based on the amino acid sequences of HIV enzymes: reverse transcriptase and protease. The performance of the models was estimated by five-fold cross-validation. The resulting classification models have a relatively high reliability (minimum accuracy for the drugs is 0.82, maximum: 0.94) and were used to create a web application, HVR (HIV drug Resistance), for the prediction of HIV drug resistance to protease inhibitors and nucleoside and non-nucleoside reverse transcriptase inhibitors based on the analysis of the amino acid sequences of the appropriate HIV proteins from clinical samples.

Current ARTs, Virologic Failure, and Implications for AIDS Management: A Systematic Review

Antiretroviral therapies (ARTs) have revolutionized the management of human immunodeficiency virus (HIV) infection, significantly improved patient outcomes, and reduced the mortality rate and incidence of acquired immunodeficiency syndrome (AIDS). However, despite the remarkable efficacy of ART, virologic failure remains a challenge in the long-term management of HIV-infected individuals. Virologic failure refers to the persistent detectable viral load in patients receiving ART, indicating an incomplete suppression of HIV replication. It can occur due to various factors, including poor medication adherence, drug resistance, suboptimal drug concentrations, drug interactions, and viral factors such as the emergence of drug-resistant strains. In recent years, extensive efforts have been made to understand and address virologic failure in order to optimize treatment outcomes. Strategies to prevent and manage virologic failure include improving treatment adherence through patient education, counselling, and supportive interventions. In addition, the regular monitoring of viral load and resistance testing enables the early detection of treatment failure and facilitates timely adjustments in ART regimens. Thus, the development of novel antiretroviral agents with improved potency, tolerability, and resistance profiles offers new options for patients experiencing virologic failure. However, new treatment options would also face virologic failure if not managed appropriately. A solution to virologic failure requires a comprehensive approach that combines individualized patient care, robust monitoring, and access to a range of antiretroviral drugs.

Machine learning aided multiscale modelling of the HIV-1 infection in the presence of NRTI therapy

Human Immunodeficiency Virus (HIV) is one of the most common chronic infectious diseases in humans. Extending the expected lifetime of patients depends on the use of optimal antiretroviral therapies. Emergence of the drug-resistant strains can reduce the effectiveness of treatments and lead to Acquired Immunodeficiency Syndrome (AIDS), even with antiretroviral therapy. Investigating the genotype-phenotype relationship is a crucial process for optimizing the therapy protocols of the patients. Here, a mathematical modelling framework is proposed to address the impact of existing mutations, timing of initiation, and adherence levels of nucleotide reverse transcriptase inhibitors (NRTIs) on the evolutionary dynamics of the virus strains. For the first time, the existing Stanford HIV drug resistance data have been combined with a multi-strain within-host ordinary differential equation (ODE) model to track the dynamics of the most common NRTI-resistant strains. Overall, the D4T-3TC, D4T-AZT and TDF-D4T drug combinations have been shown to provide higher success rates in preventing treatment failure and further drug resistance. The results are in line with the genotype-phenotype data and pharmacokinetic parameters of the NRTI inhibitors. Moreover, we show that the undetectable mutant strains at the diagnosis have a significant effect on the success/failure rates of the NRTI treatments. Predictions on undetectable strains through our multi-strain within-host model yielded the possible role of viral evolution on the treatment outcomes. It has been recognized that the improvement of multi-scale models can contribute to the understanding of the evolutionary dynamics, and treatment options, and potentially increase the reliability of genotype-phenotype models.

Prediction of HIV-1 protease resistance using genotypic, phenotypic, and molecular information with artificial neural networks

Drug resistance is a primary barrier to effective treatments of HIV/AIDS. Calculating quantitative relations between genotype and phenotype observations for each inhibitor with cell-based assays requires time and money-consuming experiments. Machine learning models are good options for tackling these problems by generalizing the available data with suitable linear or nonlinear mappings. The main aim of this study is to construct drug isolate fold (DIF) change-based artificial neural network (ANN) models for estimating the resistance potential of molecules inhibiting the HIV-1 protease (PR) enzyme. Throughout the study, seven of eight protease inhibitors (PIs) have been included in the training set and the remaining ones in the test set. We have obtained 11,803 genotype-phenotype data points for eight PIs from Stanford HIV drug resistance database. Using the leave-one-out (LVO) procedure, eight ANN models have been produced to measure the learning capacity of models from the descriptors of the inhibitors. Mean R² value of eight ANN models for unseen inhibitors is 0.716, and the 95% confidence interval (CI) is [0.592-0.840]. Predicting the fold change resistance for hundreds of isolates allowed a robust comparison of drug pairs. These eight models have predicted the drug resistance tendencies of each inhibitor pair with the mean 2D correlation coefficient of 0.933 and 95% CI [0.930-0.938]. A classification problem has been created to predict the ordered relationship of the PIs, and the mean accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) values are calculated as 0.954, 0.791, 0.791, and 0.688, respectively. Furthermore, we have created an external test dataset consisting of 51 unique known HIV-1 PR inhibitors and 87 genotype-phenotype relations. Our developed ANN model has accuracy and area under the curve (AUC) values of 0.749 and 0.818 to predict the ordered relationships of molecules on the same strain for the external dataset. The currently derived ANN models can accurately predict the drug resistance tendencies of PI pairs. This observation could help test new inhibitors with various isolates.

From Sequence Data to Patient Result: A Solution for HIV Drug Resistance Genotyping With Exatype, End to End Software for Pol-HIV-1 Sanger Based Sequence Analysis and Patient HIV Drug Resistance Result Generation

Introduction:

With the rapid scale-up of antiretroviral therapy (ART) to treat HIV infection, there are ongoing concerns regarding probable emergence and transmission of HIV drug resistance (HIVDR) mutations. This scale-up has to lead to an increased need for routine HIVDR testing to inform the clinical decision on a regimen switch. Although the majority of wet laboratory processes are standardized, slow, labor-intensive data transfer and subjective manual sequence interpretation steps are still required to finalize and release patient results. We thus set out to validate the applicability of a software package to generate HIVDR patient results from raw sequence data independently.

Methods:

We assessed the performance characteristics of Hyrax Bioscience’s Exatype (a sequence data to patient result, fully automated sequence analysis software, which consolidates RECall, MEGA X and the Stanford HIV database) against the standard method (RECall and Stanford database). Exatype is a web-based HIV Drug resistance bioinformatic pipeline available at sanger.exatype.com. To validate the exatype, we used a test set of 135 remnant HIV viral load samples at the National HIV Reference Laboratory (NHRL).

Result:

We analyzed, and successfully generated results of 126 sequences out of 135 specimens by both Standard and Exatype software. Result production using Exatype required minimal hands-on time in comparison to the Standard (6 computation-hours using the standard method versus 1.5 Exatype computation-hours). Concordance between the 2 systems was 99.8% for 311,227 bases compared. 99.7% of the 0.2% discordant bases, were attributed to nucleotide mixtures as a result of the sequence editing in Recall. Both methods identified similar (99.1%) critical antiretroviral resistance-associated mutations resulting in a 99.2% concordance of resistance susceptibility interpretations. The Base-calling comparison between the 2 methods had Cohen’s kappa (0.97 to 0.99), implying an almost perfect agreement with minimal base calling variation. On a predefined dataset, RECall editing displayed the highest probability to score mixtures accurately 1 vs. 0.71 and the lowest chance to inaccurately assign mixtures to pure nucleotides (0.002–0.0008). This advantage is attributable to the manual sequence editing in RECall.

Conclusion:

The reduction in hands-on time needed is a benefit when using the Exatype HIV DR sequence analysis platform and result generation tool. There is a minimal difference in base calling between Exatype and standard methods. Although the discrepancy has minimal impact on drug resistance interpretation, allowance of sequence editing in Exatype as RECall can significantly improve its performance.

Predicting HIV drug resistance using weighted machine learning method at target protein sequence-level

Acquired immune deficiency syndrome (AIDS) is a fatal disease caused by human immunodeficiency virus (HIV). Although 23 different drugs have been available, the treatment of AIDS remains challenging because the virus mutates very quickly which can lead to drug resistance. Therefore, predicting drug resistance before treatment is crucial for individual treatments. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods. To transform target sequences into numeric vectors, seven physicochemical properties were used, which can well represent the interacting characteristics of target proteins. Then, principal component analysis (PCA) method was adopted to reduce the feature dimensionality. Random forest (RF) and support vector machine (SVM) based on three different kernel functions, including linear, polynomial and radial basis function (RBF), were all employed. By comparisons, we found that RBF-based SVM method gives a comparative performance with RF model. Further, we added the weight information to RBF-based SVM method by four different weight evaluation methods of RF, eXtreme Gradient Boosting (XGB), CfsSubsetEval and ReliefFAttributeEval, respectively. Results show that the RF-weighted RBF-based SVM yield the superior performance and 13 out of 21 drug models provide the correlation coefficients (R²) over 0.8 and 3 of them are higher than 0.9. Finally, position-specific importance analysis indicates that most of the mutation residues with high RF weight scores are proved to be closely related with drug resistance, which has been revealed in previous reports. Overall, we can expect that this method can be a supplementary tool for predicting HIV drug resistance for newly discovered mutations. Here, based on HIV target protein sequence information, we analyzed 21-drug resistance caused by mutated residues using machine learning (ML) methods by fusing the weight information of different mutation positions.

Management of Virologic Failure and HIV Drug Resistance

Approximately 20% of people with HIV in the United States prescribed antiretroviral therapy are not virally suppressed. Thus, optimal management of virologic failure has a critical role in the ability to improve viral suppression rates to improve long-term health outcomes for those infected and to achieve epidemic control. This article discusses the causes of virologic failure, the use of resistance testing to guide management after failure, interpretation and relevance of HIV drug resistance patterns, considerations for selection of second-line and salvage therapies, and management of virologic failure in special populations.

Antiretroviral resistance testing in HIV-positive people

BACKGROUND

Resistance to antiretroviral therapy (ART) among people living with human immunodeficiency virus (HIV) compromises treatment effectiveness, often leading to virological failure and mortality. Antiretroviral drug resistance tests may be used at the time of initiation of therapy, or when treatment failure occurs, to inform the choice of ART regimen. Resistance tests (genotypic or phenotypic) are widely used in high-income countries, but not in resource-limited settings. This systematic review summarizes the relative merits of resistance testing in treatment-naive and treatment-exposed people living with HIV.

OBJECTIVES

To evaluate the effectiveness of antiretroviral resistance testing (genotypic or phenotypic) in reducing mortality and morbidity in HIV-positive people.

SEARCH METHODS

We attempted to identify all relevant studies, regardless of language or publication status, through searches of electronic databases and conference proceedings up to 26 January 2018. We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), in the Cochrane Library, the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP), and ClinicalTrials.gov to 26 January 2018. We searched Latin American and Caribbean Health Sciences Literature (LILACS) and the Web of Science for publications from 1996 to 26 January 2018.

SELECTION CRITERIA

We included all randomized controlled trials (RCTs) and observational studies that compared resistance testing to no resistance testing in people with HIV irrespective of their exposure to ART.Primary outcomes of interest were mortality and virological failure. Secondary outcomes were change in mean CD4-T-lymphocyte count, clinical progression to AIDS, development of a second or new opportunistic infection, change in viral load, and quality of life.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed each reference for prespecified inclusion criteria. Two review authors then independently extracted data from each included study using a standardized data extraction form. We analysed data on an intention-to-treat basis using a random-effects model. We performed subgroup analyses for the type of resistance test used (phenotypic or genotypic), use of expert advice to interpret resistance tests, and age (children and adolescents versus adults). We followed standard Cochrane methodological procedures.

MAIN RESULTS

Eleven RCTs (published between 1999 and 2006), which included 2531 participants, met our inclusion criteria. All of these trials exclusively enrolled patients who had previous exposure to ART. We found no observational studies. Length of follow-up time, study settings, and types of resistance testing varied greatly. Follow-up ranged from 12 to 150 weeks. All studies were conducted in Europe, USA, or South America. Seven studies used genotypic testing, two used phenotypic testing, and two used both phenotypic and genotypic testing. Only one study was funded by a manufacturer of resistance tests.Resistance testing made little or no difference in mortality (odds ratio (OR) 0.89, 95% confidence interval (CI) 0.36 to 2.22; 5 trials, 1140 participants; moderate-certainty evidence), and may have slightly reduced the number of people with virological failure (OR 0.70, 95% CI 0.56 to 0.87; 10 trials, 1728 participants; low-certainty evidence); and probably made little or no difference in change in CD4 cell count (mean difference (MD) -1.00 cells/mm³, 95% CI -12.49 to 10.50; 7 trials, 1349 participants; moderate-certainty evidence) or progression to AIDS (OR 0.64, 95% CI 0.31 to 1.29; 3 trials, 809 participants; moderate-certainty evidence). Resistance testing made little or no difference in adverse events (OR 0.89, 95% CI 0.51 to 1.55; 4 trials, 808 participants; low-certainty evidence) and probably reduced viral load (MD -0.23, 95% CI -0.35 to -0.11; 10 trials, 1837 participants; moderate-certainty evidence). No studies reported on development of new opportunistic infections or quality of life. We found no statistically significant heterogeneity for any outcomes, and the I² statistic value ranged from 0 to 25%. We found no subgroup effects for types of resistance testing (genotypic versus phenotypic), the addition of expert advice to interpretation of resistance tests, or age. Results for mortality were consistent when we compared studies at high or unclear risk of bias versus studies at low risk of bias.

AUTHORS' CONCLUSIONS

Resistance testing probably improved virological outcomes in people who have had virological failure in trials conducted 12 or more years ago. We found no evidence in treatment-naive people. Resistance testing did not demonstrate important patient benefits in terms of risk of death or progression to AIDS. The trials included very few participants from low- and middle-income countries.

Machine Learning to Improve the Effectiveness of ANRS in Predicting HIV Drug Resistance

OBJECTIVES

Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV/AIDS) is one of the major burdens of disease in developing countries, and the standard-of-care treatment includes prescribing antiretroviral drugs. However, antiretroviral drug resistance is inevitable due to selective pressure associated with the high mutation rate of HIV. Determining antiretroviral resistance can be done by phenotypic laboratory tests or by computer-based interpretation algorithms. Computer-based algorithms have been shown to have many advantages over laboratory tests. The ANRS (Agence Nationale de Recherches sur le SIDA) is regarded as a gold standard in interpreting HIV drug resistance using mutations in genomes. The aim of this study was to improve the prediction of the ANRS gold standard in predicting HIV drug resistance.

METHODS

A genome sequence and HIV drug resistance measures were obtained from the Stanford HIV database (http://hivdb.stanford.edu/). Feature selection was used to determine the most important mutations associated with resistance prediction. These mutations were added to the ANRS rules, and the difference in the prediction ability was measured.

RESULTS

This study uncovered important mutations that were not associated with the original ANRS rules. On average, the ANRS algorithm was improved by 79% ± 6.6%. The positive predictive value improved by 28%, and the negative predicative value improved by 10%.

CONCLUSIONS

The study shows that there is a significant improvement in the prediction ability of ANRS gold standard.

Ensemble Classifiers for Predicting HIV-1 Resistance from Three Rule-Based Genotypic Resistance Interpretation Systems

Resistance to antiretrovirals (ARVs) is a major problem faced by HIV-infected individuals. Different rule-based algorithms were developed to infer HIV-1 susceptibility to antiretrovirals from genotypic data. However, there is discordance between them, resulting in difficulties for clinical decisions about which treatment to use. Here, we developed ensemble classifiers integrating three interpretation algorithms: Agence Nationale de Recherche sur le SIDA (ANRS), Rega, and the genotypic resistance interpretation system from Stanford HIV Drug Resistance Database (HIVdb). Three approaches were applied to develop a classifier with a single resistance profile: stacked generalization, a simple plurality vote scheme and the selection of the interpretation system with the best performance. The strategies were compared with the Friedman's test and the performance of the classifiers was evaluated using the F-measure, sensitivity and specificity values. We found that the three strategies had similar performances for the selected antiretrovirals. For some cases, the stacking technique with naïve Bayes as the learning algorithm showed a statistically superior F-measure. This study demonstrates that ensemble classifiers can be an alternative tool for clinical decision-making since they provide a single resistance profile from the most commonly used resistance interpretation systems.

Improving fold resistance prediction of HIV-1 against protease and reverse transcriptase inhibitors using artificial neural networks

Background

Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs.

Results

Trained regression ANNs were optimized for eight protease inhibitors, six nucleoside reverse transcriptase (RT) inhibitors and four non-nucleoside RT inhibitors by experimenting combinations of rare variant filtering (none versus 1 residue occurrence) and ANN topologies (1–3 hidden layers with 2, 4, 6, 8 and 10 nodes per layer). Single hidden layers (5–20 nodes) were used for training where overfitting was detected. 5-fold cross-validation produced mean R² values over 0.95 and standard deviations lower than 0.04 for all but two antiretrovirals.

Conclusions

Overall, higher accuracies and lower variances (compared to results published in 2016) were obtained by experimenting with various preprocessing methods, while focusing on the most prevalent subtype in the raw dataset (subtype B).We thus highlight the need to develop and make available subtype-specific datasets for developing higher accuracy in drug-resistance prediction methods.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1782-x) contains supplementary material, which is available to authorized users.

Role of phenotypic investigation in the era of routine genotypic HIV-1 drug resistance testing

The emergence of drug resistance can seriously compromise HIV type-1 therapy and decrease therapeutic options. Resistance testing is highly recommended to guide treatment decisions and drug activity can be accurately predicted in the clinical setting through genotypic assays. While phenotypic systems are not suitable for monitoring drug resistance in routine laboratory practice, genotyping can misclassify unusual or complex mutational patterns, particularly with recently approved antivirals. In addition, phenotypic assays remain fundamental for characterizing candidate antiretroviral compounds. This review aims to discuss how phenotypic assays contributed to and still play a role in understanding the mechanisms of resistance of both licensed and investigational HIV type-1 inhibitors.

Sub-Epidemics Explain Localized High Prevalence of Reduced Susceptibility to Rilpivirine in Treatment-Naive HIV-1-Infected Patients: Subtype and Geographic Compartmentalization of Baseline Resistance Mutations

Objective: The latest nonnucleoside reverse transcriptase inhibitor (NNRTI) rilpivirine (RPV) is indicated for human immunodeficiency virus type-1 (HIV-1) patients initiating antiretroviral treatment, but the extent of genotypic RPV resistance in treatment-naive patients outside clinical trials is poorly defined.

Study Design: This retrospective observational study of clinical data from Belgium and Portugal evaluates genotypic information from HIV-1 drug-naive patients obtained for the purpose of drug resistance testing. Rilpivirine resistance-associated mutations (RPV-RAMs) were defined based on clinical trials, phenotypic studies, and expert-based resistance algorithms. Viral susceptibility to RPV alone and to the single-tablet regimen was estimated using expert-based resistance algorithms.

Results: In 4,631 HIV-1 treatment-naive patients infected with diverse HIV-1 subtypes, major RPV-RAMs were detected in 4.6%, while complete viral susceptibility to RPV was estimated in 95% of patients. Subtype C- and F1-infected patients displayed the highest levels of reduced viral susceptibility at baseline, respectively 13.2% and 9.3%, mainly due to subtype- and geographic-dependent occurrence of RPV-RAMs E138A and A98G as natural polymorphisms. Strikingly, a founder effect in Portugal resulted in a 138A prevalence of 13.2% in local subtype C-infected treatment-naive patients. The presence of transmitted drug resistance did not impact our estimates.

Conclusion: RPV is the first HIV-1 inhibitor for which, in the absence of transmitted drug resistance, intermediate or high-level genotypic resistance can be detected in treatment-naive patients. The extent of RPV susceptibility in treatment-naive patients differs depending on the HIV-1 subtype and dynamics of local compartmentalized epidemics. The highest prevalence of reduced susceptibility was found to be 15.7% in Portuguese subtype C-infected treatment-naive patients. In this context, even in the absence of transmitted HIV-1 drug resistance (TDR), drug resistance testing at baseline should be considered extremely important before starting treatment with this NNRTI.

Discordant predictions of residual activity could impact dolutegravir prescription upon raltegravir failure

BACKGROUND

Dolutegravir is approved for the treatment of HIV-1 patients exposed to other integrase inhibitors, but the decision to use dolutegravir in this setting should be informed by drug resistance testing.

OBJECTIVES

This study determined the extent of disagreement in predicted residual dolutegravir activity after raltegravir use, and identified individual mutational patterns for which uncertainty exists among HIV-1 expert systems.

STUDY DESIGN

Mutation patterns were classified in raltegravir signature pathways including positions 143, 148 and 155, and interpreted into clinically informative resistance levels using genotypic drug resistance interpretation systems ANRS v24, HIVdb v7.0 and Rega v9.1.0, and instructions of dolutegravir use as approved by the Food and Drug Administration and the European Medicines Agency.

RESULTS

In 216HIV-1 patients failing raltegravir-therapy, 87% patients displayed mutations associated with resistance towards integrase inhibitors. A total of 141 unique mutational patterns were observed, with N155H (25.4%), Q148H (16.2%) and Y143R (8.3%) the most prevalent signature mutations. The Q148 pathway occurred almost exclusively in HIV-1 subtype B viruses. Concordances in predicted dolutegravir susceptibility scores among 5 systems were obtained in 57.8% of patients, and concordant intermediate resistant and concordant resistant scores were only observed in 6.5% and 0.9% of patients, respectively. However, systems individually scored higher levels of dolutegravir intermediate resistance and resistance, ranging from 4.2% to 10.2% and from 14.8% to 22.7% of patients, respectively. A consensus on interpreting the extent of residual activity was lacking in 34.7% of patients and was highly resistance pathway-specific.

CONCLUSIONS

Dolutegravir may potentially be effective in the majority of HIV-1 patients failing raltegravir, but concern over the uncertainty in predicted residual activity could withhold clinicians from prescribing dolutegravir during its clinical assessment.

Genotype-guided drug prescribing: a systematic review and meta-analysis of randomized control trials

AIM

Adverse drug events lead to increased morbidity, mortality and health care costs. Pharmacogenetic testing that guides drug prescribing has the potential to reduced adverse drug events and increase drug effectiveness. Our aim was to quantify the clinical effectiveness of genotype-guided prescribing.

METHODS

Three electronic databases were searched from January 1980 through December 2013. Studies were eligible if they were RCTs comparing genotype-guided prescribing with non-genetic informed prescribing, reported drug specific adverse drug events and clinical effectiveness outcomes. Two reviewers independently screened titles and abstracts, extracted data and assessed study quality. Meta-analyses of specific outcomes were conducted where data allowed.

RESULTS

Fifteen studies, involving 5688 patients and 19 drugs, met the inclusion and exclusion criteria. Eight studies had statistically significant results for their primary outcome in favour of genotype-guided prescribing. Nine studies evaluated genotype-guided warfarin dosing. Analysis of percentage of time in therapeutic international normalized ratio range (1952 individuals) showed a statistically significant benefit in favour of genotype-guided warfarin dosing (mean difference = 6.67; 95% CI 1.34, 12.0, I(2) = 80%). There was a statistically significant reduction in numbers of warfarin-related minor bleeding, major bleeding and thromboembolisms associated with genotype guided warfarin dosing, relative risk 0.57 (95% CI 0.33, 0.99; I(2) = 60%). It was not possible to meta-analyze genotype-guided dosing for other drugs. Of the six non-warfarin genotype-guided trials, two demonstrated a statistically significant benefit for their primary outcome, odds ratio 0.03 (95% CI 0.00, 0.62, P < 0.001) for abacavir.

CONCLUSIONS

There is evidence of improved clinical effectiveness associated with genotype-guided warfarin dosing.

Predicted residual activity of rilpivirine in HIV-1 infected patients failing therapy including NNRTIs efavirenz or nevirapine

Rilpivirine is a second-generation nonnucleoside reverse-transcriptase inhibitor (NNRTI) currently indicated for first-line therapy, but its clinical benefit for HIV-1 infected patients failing first-generation NNRTIs is largely undefined. This study quantified the extent of genotypic rilpivirine resistance in viral isolates from 1212 patients upon failure of efavirenz- or nevirapine-containing antiretroviral treatment, of whom more than respectively 80% and 90% showed high-level genotypic resistance to the failing NNRTI. Of all study patients, 47% showed a rilpivirine resistance-associated mutation (RPV-RAM), whereas preserved residual rilpivirine activity was predicted in half of the patients by three genotypic drug resistance interpretation algorithms. An NNRTI-dependent impact on rilpivirine resistance was detected. Compared with the use of nevirapine, the use of efavirenz was associated with a 32% lower risk of having a RPV-RAM and a 50% lower risk of predicted reduced rilpivirine susceptibility. Most prevalent RPV-RAMs after nevirapine experience were Y181C and H221Y, whereas L100I+K103N, Y188L and K101E occurred most in efavirenz-experienced patients. Predicted rilpivirine activity was not affected by HIV-1 subtype, although frequency of individual mutations differed across subtypes. In conclusion, this genotypic resistance analysis strongly suggests that the latest NNRTI, rilpivirine, may retain activity in a large proportion of HIV-1 patients in whom resistance failed while they were on an efavirenz- or nevirapine-containing regimen, and may present an attractive option for second-line treatment given its good safety profile and dosing convenience. However, prospective clinical studies assessing the effectiveness of rilpivirine for NNRTI-experienced patients are warranted to validate knowledge derived from genotypic and phenotypic drug resistance studies.

Recent advances in the treatment of HIV/AIDS

In industrialized countries, highly active antiretroviral therapy has resulted in significant reductions in morbidity and mortality in patients with HIV/AIDS. At the same time, the management of the HIV-infected individual has become exceedingly complex due to the increasing number of antiretroviral medications and resistance to them. New medications are needed that are effective against the drug-resistant virus. The key advances in the management of HIV/AIDS as seen through the eyes of a front-line HIV physician who has been actively involved in patient care, clinical drug trials and as an educator for the past 15 years will be discussed.

HIV-1 genotyping: comparison of two commercially available assays

HIV-1 resistance testing has become an increasingly important feature in antiretroviral treatment and is commonly accomplished by genotyping. Currently, two different systems are being marketed and, despite being far from easy to use, have achieved a high degree of sophistication. Modifications of the standard-kit protocols may be advantageous in certain situations. Although resistance reports are issued by these systems through interpretation software based on decision rules, it nevertheless requires considerable knowledge and skills by the user to make useful clinical data out of detected resistance patterns. This review describes both systems in detail and discusses their advantages and disadvantages. A final decision on which system to use must be based on an individual's requirements. The future of this field may lie with the use of microarray systems.

Sparse Representation for Prediction of HIV-1 Protease Drug Resistance

HIV rapidly evolves drug resistance in response to antiviral drugs used in AIDS therapy. Estimating the specific resistance of a given strain of HIV to individual drugs from sequence data has important benefits for both the therapy of individual patients and the development of novel drugs. We have developed an accurate classification method based on the sparse representation theory, and demonstrate that this method is highly effective with HIV-1 protease. The protease structure is represented using our newly proposed encoding method based on Delaunay triangulation, and combined with the mutated amino acid sequences of known drug-resistant strains to train a machine-learning algorithm both for classification and regression of drug-resistant mutations. An overall cross-validated classification accuracy of 97% is obtained when trained on a publically available data base of approximately 1.5×10⁴ known sequences (Stanford HIV database http://hivdb.stanford.edu/cgi-bin/GenoPhenoDS.cgi). Resistance to four FDA approved drugs is computed and comparisons with other algorithms demonstrate that our method shows significant improvements in classification accuracy.

[Consensus Statement by GeSIDA/National AIDS Plan Secretariat on antiretroviral treatment in adults infected by the human immunodeficiency virus (Updated January 2013)]

OBJECTIVE

This consensus document is an update of combined antiretroviral therapy (cART) guidelines for HIV-1 infected adult patients.

METHODS

To formulate these recommendations a panel composed of members of the GeSIDA/National AIDS Plan Secretariat (Grupo de Estudio de Sida and the Secretaría del Plan Nacional sobre el Sida) reviewed the efficacy and safety advances in clinical trials, cohort and pharmacokinetic studies published in medical journals (PubMed and Embase) or presented in medical scientific meetings. The strength of the recommendations and the evidence which support them are based on a modification of the criteria of Infectious Diseases Society of America.

RESULTS

cART is recommended in patients with symptoms of HIV infection, in pregnant women, in serodiscordant couples with high risk of transmission, in hepatitisB co-infection requiring treatment, and in HIV nephropathy. cART is recommended in asymptomatic patients if CD4 is <500cells/μl. If CD4 are >500cells/μl cART should be considered in the case of chronic hepatitisC, cirrhosis, high cardiovascular risk, plasma viral load >100.000 copies/ml, proportion of CD4 cells <14%, neurocognitive deficits, and in people aged >55years. The objective of cART is to achieve an undetectable viral load. The first cART should include 2 reverse transcriptase inhibitors (RTI) nucleoside analogs and a third drug (a non-analog RTI, a ritonavir boosted protease inhibitor, or an integrase inhibitor). The panel has consensually selected some drug combinations, for the first cART and specific criteria for cART in acute HIV infection, in tuberculosis and other HIV related opportunistic infections, for the women and in pregnancy, in hepatitisB or C co-infection, in HIV-2 infection, and in post-exposure prophylaxis.

CONCLUSIONS

These new guidelines update previous recommendations related to first cART (when to begin and what drugs should be used), how to monitor, and what to do in case of viral failure or adverse drug reactions. cART specific criteria in comorbid patients and special situations are similarly updated.

The individualized genetic barrier predicts treatment response in a large cohort of HIV-1 infected patients

The success of combination antiretroviral therapy is limited by the evolutionary escape dynamics of HIV-1. We used Isotonic Conjunctive Bayesian Networks (I-CBNs), a class of probabilistic graphical models, to describe this process. We employed partial order constraints among viral resistance mutations, which give rise to a limited set of mutational pathways, and we modeled phenotypic drug resistance as monotonically increasing along any escape pathway. Using this model, the individualized genetic barrier (IGB) to each drug is derived as the probability of the virus not acquiring additional mutations that confer resistance. Drug-specific IGBs were combined to obtain the IGB to an entire regimen, which quantifies the virus' genetic potential for developing drug resistance under combination therapy. The IGB was tested as a predictor of therapeutic outcome using between 2,185 and 2,631 treatment change episodes of subtype B infected patients from the Swiss HIV Cohort Study Database, a large observational cohort. Using logistic regression, significant univariate predictors included most of the 18 drugs and single-drug IGBs, the IGB to the entire regimen, the expert rules-based genotypic susceptibility score (GSS), several individual mutations, and the peak viral load before treatment change. In the multivariate analysis, the only genotype-derived variables that remained significantly associated with virological success were GSS and, with 10-fold stronger association, IGB to regimen. When predicting suppression of viral load below 400 cps/ml, IGB outperformed GSS and also improved GSS-containing predictors significantly, but the difference was not significant for suppression below 50 cps/ml. Thus, the IGB to regimen is a novel data-derived predictor of treatment outcome that has potential to improve the interpretation of genotypic drug resistance tests.

Dynamical models of biomarkers and clinical progression for personalized medicine: the HIV context

Mechanistic models, based on ordinary differential equation systems, can exhibit very good predictive abilities that will be useful to build treatment monitoring strategies. In this review, we present the potential and the limitations of such models for guiding treatment (monitoring and optimizing) in HIV-infected patients. In the context of antiretroviral therapy, several biological processes should be considered in addition to the interaction between viruses and the host immune system: the mechanisms of action of the drugs, their pharmacokinetics and pharmacodynamics, as well as the viral and host characteristics. Another important aspect to take into account is clinical progression, although its implementation in such modelling approaches is not easy. Finally, the control theory and the use of intrinsic properties of mechanistic models make them very relevant for dynamic treatment adaptation. Their implementation would nevertheless require their evaluation through clinical trials.

RegaDB: community-driven data management and analysis for infectious diseases

Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface.

Availability and implementation: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.

Contact:pieter.libin@rega.kuleuven.be

Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response

Introduction

Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period.

Materials & Methods

9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.

Results

Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76–1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69–1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80–1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly.

Conclusion

Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.

Significantly improved HIV inhibitor efficacy prediction employing proteochemometric models generated from antivirogram data

Infection with HIV cannot currently be cured; however it can be controlled by combination treatment with multiple anti-retroviral drugs. Given different viral genotypes for virtually each individual patient, the question now arises which drug combination to use to achieve effective treatment. With the availability of viral genotypic data and clinical phenotypic data, it has become possible to create computational models able to predict an optimal treatment regimen for an individual patient. Current models are based only on sequence data derived from viral genotyping; chemical similarity of drugs is not considered. To explore the added value of chemical similarity inclusion we applied proteochemometric models, combining chemical and protein target properties in a single bioactivity model. Our dataset was a large scale clinical database of genotypic and phenotypic information (in total ca. 300,000 drug-mutant bioactivity data points, 4 (NNRTI), 8 (NRTI) or 9 (PI) drugs, and 10,700 (NNRTI) 10,500 (NRTI) or 27,000 (PI) mutants). Our models achieved a prediction error below 0.5 Log Fold Change. Moreover, when directly compared with previously published sequence data, derived models PCM performed better in resistance classification and prediction of Log Fold Change (0.76 log units versus 0.91). Furthermore, we were able to successfully confirm both known and identify previously unpublished, resistance-conferring mutations of HIV Reverse Transcriptase (e.g. K102Y, T216M) and HIV Protease (e.g. Q18N, N88G) from our dataset. Finally, we applied our models prospectively to the public HIV resistance database from Stanford University obtaining a correct resistance prediction rate of 84% on the full set (compared to 80% in previous work on a high quality subset). We conclude that proteochemometric models are able to accurately predict the phenotypic resistance based on genotypic data even for novel mutants and mixtures. Furthermore, we add an applicability domain to the prediction, informing the user about the reliability of predictions.

Infection with the human immunodeficiency virus (HIV) currently cannot be cured. It can however be contained through treatment with a combination of several anti-viral drugs. Yet, during treatment resistance can occur which leads to drugs becoming ineffective. Through a combination of drugs, this resistance can be deferred indefinitely. The optimal combination of drugs depends on the specific strain of HIV with which the patient is infected. Previously, methods have been developed that predict a personalized treatment regimen based on the genetic sequence (genotype) of the virus via the use of computer modeling, corner stone of the methods is drug affinity prediction. Here we have applied proteochemometric modeling which takes this genetic information into account, but also includes chemical description of the drugs that are now clinically available. We show that this combined technique performs better than models that only include genetic information. Our approach leads to personalized treatment predictions with a higher reliability compared to the current state of the art. In addition, we include a reliability measure which allows each prediction to be assessed for reliability. Finally we describe mutations of the HIV genome that were not previously described in literature and lead to resistance to treatment.

From base pair to bedside: molecular simulation and the translation of genomics to personalized medicine

Despite the promises made that genomic sequencing would transform therapy by introducing a new era of personalized medicine, relatively few tangible breakthroughs have been made. This has led to the recognition that complex interactions at multiple spatial, temporal, and organizational levels may often combine to produce disease. Understanding this complexity requires that existing and future models are used and interpreted within a framework that incorporates knowledge derived from investigations at multiple levels of biological function. It also requires a computational infrastructure capable of dealing with the vast quantities of data generated by genomic approaches. In this review, we discuss the use of molecular modeling to generate quantitative and qualitative insights at the smallest scales of the systems biology hierarchy, how it can play an important role in the development of a systems understanding of disease and in the application of such knowledge to help discover new therapies and target existing ones on a personal level.

Human Immunodeficiency Virus Gag and protease: partners in resistance

Human Immunodeficiency Virus (HIV) maturation plays an essential role in the viral life cycle by enabling the generation of mature infectious virus particles through proteolytic processing of the viral Gag and GagPol precursor proteins. An impaired polyprotein processing results in the production of non-infectious virus particles. Consequently, particle maturation is an excellent drug target as exemplified by inhibitors specifically targeting the viral protease (protease inhibitors; PIs) and the experimental class of maturation inhibitors that target the precursor Gag and GagPol polyproteins. Considering the different target sites of the two drug classes, direct cross-resistance may seem unlikely. However, coevolution of protease and its substrate Gag during PI exposure has been observed both in vivo and in vitro. This review addresses in detail all mutations in Gag that are selected under PI pressure. We evaluate how polymorphisms and mutations in Gag affect PI therapy, an aspect of PI resistance that is currently not included in standard genotypic PI resistance testing. In addition, we consider the consequences of Gag mutations for the development and positioning of future maturation inhibitors.

[Consensus document of Gesida and Spanish Secretariat for the National Plan on AIDS (SPNS) regarding combined antiretroviral treatment in adults infected by the human immunodeficiency virus (January 2012)]

This consensus document has been prepared by a panel consisting of members of the AIDS Study Group (Gesida) and the Spanish Secretariat for the National Plan on AIDS (SPNS) after reviewing the efficacy and safety results of clinical trials, cohort and pharmacokinetic studies published in medical journals, or presented in medical scientific meetings. Gesida has prepared an objective and structured method to prioritise combined antiretroviral treatment (cART) in naïve patients. Recommendations strength (A, B, C) and the evidence which supports them (I, II, III) are based on a modification of the Infectious Diseases Society of America criteria. The current antiretroviral treatment (ART) of choice for chronic HIV infection is the combination of three drugs. ART is recommended in patients with symptomatic HIV infection, in pregnancy, in serodiscordant couples with high transmission risk, hepatitis B fulfilling treatment criteria, and HIV nephropathy. Guidelines on ART treatment in patients with concurrent diagnosis of HIV infection and an opportunistic type C infection are included. In asymptomatic patients ART is recommended on the basis of CD4 lymphocyte counts, plasma viral load and patient co-morbidities, as follows: 1) therapy should be started in patients with CD4 counts <350 cells/μL; 2) when CD4 counts are between 350 and 500 cells/μL, therapy will be recommended and only delayed if patient is reluctant to take it, the CD4 are stabilised, and the plasma viral load is low; 3) therapy could be deferred when CD4 counts are above 500 cells/μL, but should be considered in cases of cirrhosis, chronic hepatitis C, high cardiovascular risk, plasma viral load >10(5) copies/mL, proportion of CD4 cells <14%, and in people aged >55 years. ART should include 2 reverse transcriptase inhibitors nucleoside analogues and a third drug (non-analogue reverse transcriptase inhibitor, ritonavir boosted protease inhibitor or integrase inhibitor). The panel has consensually selected and given priority to using the Gesida score for some drug combinations, some of them co-formulated. The objective of ART is to achieve an undetectable viral load. Adherence to therapy plays an essential role in maintaining antiviral response. Therapeutic options are limited after ART failures, but an undetectable viral load may be possible nowadays. Adverse events are a fading problem of ART. Guidelines in acute HIV infection, in women, in pregnancy, and to prevent mother-to-child transmission and pre- and post-exposition prophylaxis are commented upon. Management of hepatitis B or C co-infection, other co-morbidities, and the characteristics of ART in HIV-2 infection are included.

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.

Clinical management of HIV drug resistance

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

The global status of HIV drug resistance: clinical and public-health approaches for detection, treatment and prevention

Antiretroviral therapy (ART) scale-up in resource limited settings (RLS) has been successful, utilizing a standardized population-based approach to ART delivery. An unintended consequence of treatment scale-up is the inevitable emergence of HIV drug resistance (HIV DR) in populations even when patient adherence to ART is optimally supported. HIV DR has the potential to undermine the dramatic gains that ART has had in reducing the morbidity and mortality of HIV-infected patients in RLS. Sustaining and expanding ART coverage in RLS will depend upon the ability of ART programs to deliver ART in a way that minimizes the emergence of HIVDR. Fortunately, current evidence demonstrates that HIVDR in RLS has neither emerged nor been transmitted to the degree that had initially been feared. However, due to a lack of standardized methodologies, HIVDR data from RLS can be difficult to interpret and may not provide the programmatic evidence necessary for public health action. The World Health Organization has developed simple, standardized surveys that generate comparable results to assess acquired and transmitted HIVDR for routine public health implementation in RLS. These HIVDR surveys are designed to be implemented in conjunction with annual monitoring of program and site factors known to create situations favorable to the developments of HIV DR.

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

Background

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

Methods

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

Results

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

Conclusion

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

Replicative phenotyping adds value to genotypic resistance testing in heavily pre-treated HIV-infected individuals--the Swiss HIV Cohort Study

Background

Replicative phenotypic HIV resistance testing (rPRT) uses recombinant infectious virus to measure viral replication in the presence of antiretroviral drugs. Due to its high sensitivity of detection of viral minorities and its dissecting power for complex viral resistance patterns and mixed virus populations rPRT might help to improve HIV resistance diagnostics, particularly for patients with multiple drug failures. The aim was to investigate whether the addition of rPRT to genotypic resistance testing (GRT) compared to GRT alone is beneficial for obtaining a virological response in heavily pre-treated HIV-infected patients.

Methods

Patients with resistance tests between 2002 and 2006 were followed within the Swiss HIV Cohort Study (SHCS). We assessed patients' virological success after their antiretroviral therapy was switched following resistance testing. Multilevel logistic regression models with SHCS centre as a random effect were used to investigate the association between the type of resistance test and virological response (HIV-1 RNA <50 copies/mL or ≥1.5log reduction).

Results

Of 1158 individuals with resistance tests 221 with GRT+rPRT and 937 with GRT were eligible for analysis. Overall virological response rates were 85.1% for GRT+rPRT and 81.4% for GRT. In the subgroup of patients with >2 previous failures, the odds ratio (OR) for virological response of GRT+rPRT compared to GRT was 1.45 (95% CI 1.00-2.09). Multivariate analyses indicate a significant improvement with GRT+rPRT compared to GRT alone (OR 1.68, 95% CI 1.31-2.15).

Conclusions

In heavily pre-treated patients rPRT-based resistance information adds benefit, contributing to a higher rate of treatment success.

Genetic variation in the HR region of the env gene of HIV: A perspective for resistance to HIV fusion inhibitors

HIV fusion inhibitors may be classified into three groups. Peptides binding to HR1 include T1249, C30, and T20 (enfuvirtide). Peptides binding to HR2 include 5-helix. XTT formazan, NB-2, and NB-64 are nonpeptide fusion inhibitors. Genotypic testing for drug resistance is used more commonly than phenotypic testing because of its lower cost, wider availability, and shorter turnaround time. The aim of the study was to predict the efficacy of fusion inhibitors for AIDS patients in our population. A total of 100 specimens were collected. The viral RNA was isolated and nucleotides of the required regions were sequenced using the BigDye terminator method. It is concluded from this study that viruses in our population may show resistance to C30 and T20 whereas the other fusion inhibitors may be effectively used for our population.

Comparison of HIV-1 genotypic resistance test interpretation systems in predicting virological outcomes over time

BACKGROUND

Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford's HIVdb) to predict virological outcome at 12, 24, and 48 weeks.

METHODOLOGY/PRINCIPAL FINDINGS

Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8-16) weeks (2152 TCEs), 24 (16-32) weeks (2570 TCEs), and 48 (44-52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92+/-1.17, compared to Rega and ANRS, with 2.22+/-1.09 and 2.23+/-1.05, respectively. However, similar odds ratio's were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5-1.7] for HIVdb, 1.7 [1.5-1.8] for ANRS, and 1.7 [1.9-1.6] for Rega. Odds ratio's increased over time, but remained comparable (odds ratio's ranging between 1.9-2.1 at 24 weeks and 1.9-2.2 at 48 weeks). The Area under the curve of the ROC did not differ between the systems at all time points; p = 0.60 at week 12, p = 0.71 at week 24, and p = 0.97 at week 48.

CONCLUSIONS/SIGNIFICANCE

Three commonly used HIV drug resistance interpretation systems ANRS, Rega and HIVdb predict virological response at 12, 24, and 48 weeks, after change of treatment to the same extent.

[AIDS Study Group/Spanish AIDS Plan consensus document on antiretroviral therapy in adults with human immunodeficiency virus infection (updated January 2010)]

OBJECTIVE

This consensus document is an update of antiretroviral therapy recommendations for adult patients with human immunodeficiency virus infection.

METHODS

To formulate these recommendations a panel made up of members of the Grupo de Estudio de Sida (Gesida, AIDS Study Group) and the Plan Nacional sobre el Sida (PNS, Spanish AIDS Plan) reviewed the advances in the current understanding of the pathophysiology of human immunodeficiency virus (HIV) infection, the efficacy and safety of clinical trials, and cohort and pharmacokinetic studies published in biomedical journals or presented at scientific meetings. Three levels of evidence were defined according to the data source: randomized studies (level A), cohort or case-control studies (level B), and expert opinion (level C). The decision to recommend, consider or not to recommend ART was established in each situation.

RESULTS

Currently, the treatment of choice for chronic HIV infection is the combination of three drugs of two different classes, including 2 nucleosides or nucleotide analogs (NRTI) plus 1 non-nucleoside (NNRTI) or 1 boosted protease inhibitor (PI/r), but other combinations are possible. Initiation of ART is recommended in patients with symptomatic HIV infection. In asymptomatic patients, initiation of ART is recommended on the basis of CD4 lymphocyte counts, plasma viral load and patient co-morbidities, as follows: 1) therapy should be started in patients with CD4 counts below 350 cells/microl; 2) When CD4 counts are between 350 and 500 cells/microl, therapy should be started in case of cirrhosis, chronic hepatitis C, high cardiovascular risk, HIV nephropathy, HIV viral load above 100,000 copies/ml, proportion of CD4 cells under 14%, and in people aged over 55; 3) Therapy should be deferred when CD4 are above 500 cells/microl, but could be considered if any of previous considerations concurs. Treatment should be initiated in case of hepatitis B requiring treatment and should be considered for reduce sexual transmission. The objective of ART is to achieve an undetectable viral load. Adherence to therapy plays an essential role in maintaining antiviral response. Therapeutic options are limited after ART failures but undetectable viral loads maybe possible with the new drugs even in highly drug experienced patients. Genotype studies are useful in these situations. Drug toxicity of ART therapy is losing importance as benefits exceed adverse effects. Criteria for antiretroviral treatment in acute infection, pregnancy and post-exposure prophylaxis are mentioned as well as the management of HIV co-infection with hepatitis B or C.

CONCLUSIONS

CD4 cells counts, viral load and patient co-morbidities are the most important reference factors to consider when initiating ART in asymptomatic patients. The large number of available drugs, the increased sensitivity of tests to monitor viral load, and the ability to determine viral resistance is leading to a more individualized therapy approach in order to achieve undetectable viral load under any circumstances.

Predicting tipranavir and darunavir resistance using genotypic, phenotypic, and virtual phenotypic resistance patterns: an independent cohort analysis of clinical isolates highly resistant to all other protease inhibitors

Genotypic interpretation systems (GISs) for darunavir and tipranavir susceptibility are rarely tested by the use of independent data sets. The virtual phenotype (the phenotype determined by Virco [the "Vircotype"]) was used to interpret all genotypes in Québec, Canada, and phenotypes were determined for isolates predicted to be resistant to all protease inhibitors other than darunavir and tipranavir. We used multivariate analyses to predict relative phenotypic susceptibility to darunavir and tipranavir. We compared the performance characteristics of the Agence Nationale de Recherche sur le Sida scoring algorithm, the Stanford HIV database scoring algorithm (with separate analyses of the discrete and numerical scores), the Vircotype, and the darunavir and tipranavir manufacturers' scores for prediction of the phenotype. Of the 100 isolates whose phenotypes were determined, 89 and 72 were susceptible to darunavir and tipranavir, respectively. In multivariate analyses, the presence of I84V and V82T and the lack of L10F predicted that the isolates would be more susceptible to darunavir than tipranavir. The presence of I54L, V32I, and I47V predicted that the isolates would be more susceptible to tipranavir. All GISs except the system that provided the Stanford HIV database discrete score performed well in predicting the darunavir resistance phenotype (R(2) = 0.61 to 0.69); the R(2) value for the Stanford HIV database discrete scoring system was 0.38. Other than the system that provided the Vircotype (R(2) = 0.80), all GISs performed poorly in predicting the tipranavir resistance phenotype (R(2) = 0.00 to 0.31). In this independent cohort harboring highly protease inhibitor-resistant HIV isolates, reduced phenotypic susceptibility to darunavir and tipranavir was rare. Generally, GISs predict susceptibility to darunavir substantially better than they predict susceptibility to tipranavir.

Clinical management of treatment-experienced, HIV/AIDS patients in the combination antiretroviral therapy era

Despite the success of combination antiretroviral therapy (ART) in improving clinical outcomes, treatment failure remains a significant challenge, particularly for highly treatment-experienced patients. This review evaluates current issues in the management of HIV-infected, treatment-experienced patients. It may provide guidance in selecting active, tolerable drug combinations that promote a reasonable quality of life, full adherence and a durable treatment response. Current treatment guidelines and clinical trial data were reviewed to identify reasons for treatment failure and to summarize therapy options for treatment-experienced and highly treatment-experienced patients. Current treatment options include nucleoside reverse transcriptase inhibitors (NRTIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), and inhibitors of viral fusion, entry and integration. The use of NRTIs may be limited by resistance and short- and long-term toxicities. Resistance has restricted the NNRTI class with cross-resistance preventing their sequential use. Etravirine, a next-generation NNRTI, however, demonstrates effective virological suppression in patients with baseline NNRTI resistance. Boosted PIs are key components of ART for treatment-experienced patients. The newer boosted PIs tipranavir and darunavir have demonstrated impressive activity in patients with resistance to NRTIs, NNRTIs and PIs, as well as in less treatment-experienced patients for darunavir. The fusion inhibitor enfuvirtide has demonstrated efficacy in heavily treatment-experienced patients, although injection-site reactions can be problematical. The recently approved integrase inhibitor raltegravir has also shown impressive potency and tolerability in highly treatment-experienced patients. Finally, the entry inhibitor maraviroc has also been approved recently, although its use is somewhat limited by the need for HIV tropism testing. The availability of potent next-generation PIs, NNRTIs, integrase and entry-inhibitors may offer improved therapy for treatment-experienced patients, including those with multiresistant virus. These new drugs may reduce HIV immunological and clinical progression and in doing so may also reduce treatment costs.

Selective-advantage profile of human immunodeficiency virus type 1 integrase mutants explains in vivo evolution of raltegravir resistance genotypes

The emergence of human immunodeficiency virus type 1 resistance to raltegravir, an integrase strand transfer inhibitor, follows distinct and independent genetic pathways, among which the N155H and Q148HKR pathways are the most frequently encountered in treated patients. After prolonged viral escape, mutants of the N155H pathway are replaced by mutants of the Q148HKR pathway. We have examined the mechanisms driving this evolutionary pattern using an approach that assesses the selective advantage of site-directed mutant viruses as a function of drug concentration. These selective-advantage curves revealed that among single mutants, N155H had the highest and the widest (1 to 500 nM) selective-advantage profile. Despite the higher 50% inhibitory concentration, Q148H displayed a lower and narrower (10 to 100 nM) selective-advantage profile. Among double mutants, the highest and widest selective-advantage profile was seen with G140S+Q148H. This finding likely explains why N155H can be selected early in the course of RAL resistance evolution in vivo but is later replaced by genotypes that include Q148HKR.

Running a tightrope: regulatory challenges in the development of antiretrovirals

Since the approval of Retrovir, (zidovudine, AZT) in 1987 by the Food and Drug Administration, a number of regulatory initiatives were codified into regulation which contributed to the rapid development of new treatments for HIV-1 infection. These initiatives are a testament to the efforts of AIDS activists and regulators to improve access to drugs for serious and life-threatening diseases. Currently, 28 antiretroviral drugs and combinations of antiretrovirals are available to treat HIV-1 infection. The broadening armamentarium of approved antiretroviral drugs provides new options and more choices for physicians and HIV patients. Importantly, the introduction of these newly approved HIV drugs has shown that the majority of HIV-1-infected treatment-naïve and treatment-experienced patients can achieve maximal virologic suppression (less than 50 copies/mL HIV-1 RNA). This article describes the past and current regulatory challenges in the development of new HIV treatments and provides an overview of the drug regulations that were required for the approval of HIV drugs. This article forms part of a special issue of Antiviral Research marking the 25th anniversary of antiretroviral drug discovery and development, Vol 85, issue 1, 2010.

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

Background

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

Methods

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

Results

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

Conclusions

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