Algorithms for the interpretation of HIV-1 genotypic drug resistance information

Checklist for studies of HIV drug resistance prevalence or incidence: rationale and recommended use

HIV drug resistance (HIVDR) is a major challenge to the effectiveness of antiretroviral therapy. Global efforts in addressing HIVDR require clear, transparent, and replicable reporting in HIVDR studies. We describe the rationale and recommended use of a checklist that should be included in reports of HIVDR incidence and prevalence. After preliminary consultations with experts on HIVDR and establishing the need for guidance on HIVDR reporting, we used a sequential, explanatory, mixed methods approach to create the checklist; together with the accompanying articles, the checklist was reviewed by the authors and validated externally. The checklist for studies on HIVDR prevalence or incidence (CEDRIC-HIV) includes 15 recommended items that would enhance transparency and facilitate interpretation, comparability, and replicability of HIVDR studies. CEDRIC-HIV will help authors of HIVDR studies prepare research reports and assist reviewers and editors in assessments of completeness of reporting. The checklist will also facilitate statistical pooling and interpretation of HIVDR data.

HIV-1 drug resistance profiling using amino acid sequence space cartography

MOTIVATION

Human immunodeficiency virus (HIV) drug resistance is a global healthcare issue. The emergence of drug resistance influenced the efficacy of treatment regimens, thus stressing the importance of treatment adaptation. Computational methods predicting the drug resistance profile from genomic data of HIV isolates are advantageous for monitoring drug resistance in patients. However, existing computational methods for drug resistance prediction are either not suitable for emerging HIV strains with complex mutational patterns or lack interpretability, which is of paramount importance in clinical practice. The approach reported here overcomes these limitations and combines high accuracy of predictions and interpretability of the models.

RESULTS

In this work, a new methodology based on generative topographic mapping (GTM) for biological sequence space representation and quantitative genotype-phenotype relationships prediction purposes was introduced. The GTM-based resistance landscapes allowed us to predict the resistance of HIV strains based on sequencing and drug resistance data for three viral proteins [integrase (IN), protease (PR) and reverse transcriptase (RT)] from Stanford HIV drug resistance database. The average balanced accuracy for PR inhibitors was 0.89 ± 0.01, for IN inhibitors 0.85 ± 0.01, for non-nucleoside RT inhibitors 0.73 ± 0.01 and for nucleoside RT inhibitors 0.84 ± 0.01. We have demonstrated in several case studies that GTM-based resistance landscapes are useful for visualization and analysis of sequence space as well as for treatment optimization purposes. Here, GTMs were applied for the in-depth analysis of the relationships between mutation pattern and drug resistance using mutation landscapes. This allowed us to predict retrospectively the importance of the presence of particular mutations (e.g. V32I, L10F and L33F in HIV PR) for the resistance development. This study highlights some perspectives of GTM applications in clinical informatics and particularly in the field of sequence space exploration.

AVAILABILITY AND IMPLEMENTATION

https://github.com/karinapikalyova/ISIDASeq.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development of HIV Drug Resistance in a Cohort of Adults on First-Line Antiretroviral Therapy in Tanzania during the Stavudine Era

As more HIV patients start combination antiretroviral therapy (cART), the emergence of HIV drug resistance (HIVDR) is inevitable. This will have consequences for the transmission of HIVDR, the success of ART, and the nature and trend of the epidemic. We recruited a cohort of 223 patients starting or continuing their first-line cART in Tanzania towards the end of the stavudine era in 2010. Patients were then followed for one year. Of those with a viral load test at baseline and follow-up time, 34% had a detectable viral load at the one-year endpoint. For 41 patients, protease and reverse transcriptase genotyping were successful. Eighteen samples were from cART-naïve patients, and 23 samples were taken under therapy either at baseline for cART-experienced patients or from follow-up samples for both cART–naïve and cART–experienced patients. The isolates were subtype A, followed by C and D in 41.5%, 22%, and 12.2% of the patients, respectively. No transmitted HIVDR was detected, as scored using the surveillance drug resistance mutations (DRMs) list. However, in 3 of the 18 samples from cART-naïve patients, the clinical Rega interpretation algorithm scored 44D or 138A as non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance-associated polymorphisms. The most observed nucleoside reverse transcriptase inhibitor (NRTI) mutation was 184V. The mutation was found in 16 patients, causing resistance to lamivudine and emtricitabine. Nineteen patients had NNRTI resistance mutations, the most common of which was 103N, observed in eight patients. These high levels of resistance call for regular drug resistance surveillance in Tanzania to inform the control of the emergence and transmission of HIVDR.

Concordance of genotypic resistance interpretation algorithms in HIV-1 infected patients: An exploratory analysis in Greece

PURPOSE

Genotypic resistance-related mutations in HIV-1 disease are often difficult to interpret. Different algorithms have been developed to provide meaningful application into clinical context. We aimed to compare, for the first time in Greece, the results of genotypic resistance derived from three interpretation algorithms.

METHODS

The sequences of 120 HIV 1-infected patients were tested for genotypic resistance to 19 antiretroviral (ARV) drugs (n = 2280 sequences). The interpretation results of Rega, ANRS and ViroSeq algorithms were compared.

RESULTS

Complete concordance was found for 2/19 ARV drugs, namely lamivudine and emptricitabine. Concordance was high for nucleoside reverse transcriptase inhibitors (NRTIs) and low for protease inhibitors (PIs). In inter-algorithm pairs, agreement was high between Rega and ViroSeq (kappa = 0.701), especially by ARV class, namely NRTIs (k = 0.869) and NNRTIs (k = 0.562). The only exception was noted for rilpivirine, where agreement was higher between ANRS and Rega (k = 0.410) compared to other inter-algorithm pairs (k = 0.018-0.055). By contrast, for PIs all comparisons yielded concordance equivalent to chance (k = 0.000).

CONCLUSIONS

Our exploratory analysis provided evidence of significant inter-algorithm discordances, especially for PIs and NNRTIs highlighting the importance of matching the results of different algorithms to achieve optimized risk stratification. Ongoing research could assist clinical physicians in interpreting complex genotypic resistance patterns.

Genotypic Methods for HIV Drug Resistance Monitoring: The Opportunities and Challenges Faced by China

AIDS is a globalized infectious disease. In 2014, UNAIDS launched a global project of "90-90-90" to end the HIV epidemic by 2030. The second and third 90 require 90% of HIV-1 infected individuals receiving antiretroviral therapy (ART) and durable virological suppression. However, wide use of ART will greatly increase the emergence and spreading of HIV drug resistance and current HIV drug resistance test (DRT) assays in China are seriously lagging behind, hindering to achieve virological suppression. Therefore, recommending an appropriate HIV DRT method is critical for HIV routine surveillance and prevention in China. In this review, we summarized the current existing HIV drug resistance genotypic testing methods around the world and discussed the advantages and disadvantages of these methods.

An Innovative Sequence-to-Structure-Based Approach to Drug Resistance Interpretation and Prediction: The Use of Molecular Interaction Fields to Detect HIV-1 Protease Binding-Site Dissimilarities

In silico methodologies have opened new avenues of research to understanding and predicting drug resistance, a pressing health issue that keeps rising at alarming pace. Sequence-based interpretation systems are routinely applied in clinical context in an attempt to predict mutation-based drug resistance and thus aid the choice of the most adequate antibiotic and antiviral therapy. An important limitation of approaches based on genotypic data exclusively is that mutations are not considered in the context of the three-dimensional (3D) structure of the target. Structure-based in silico methodologies are inherently more suitable to interpreting and predicting the impact of mutations on target-drug interactions, at the cost of higher computational and time demands when compared with sequence-based approaches. Herein, we present a fast, computationally inexpensive, sequence-to-structure-based approach to drug resistance prediction, which makes use of 3D protein structures encoded by input target sequences to draw binding-site comparisons with susceptible templates. Rather than performing atom-by-atom comparisons between input target and template structures, our workflow generates and compares Molecular Interaction Fields (MIFs) that map the areas of energetically favorable interactions between several chemical probe types and the target binding site. Quantitative, pairwise dissimilarity measurements between the target and the template binding sites are thus produced. The method is particularly suited to understanding changes to the 3D structure and the physicochemical environment introduced by mutations into the target binding site. Furthermore, the workflow relies exclusively on freeware, making it accessible to anyone. Using four datasets of known HIV-1 protease sequences as a case-study, we show that our approach is capable of correctly classifying resistant and susceptible sequences given as input. Guided by ROC curve analyses, we fined-tuned a dissimilarity threshold of classification that results in remarkable discriminatory performance (accuracy ≈ ROC AUC ≈ 0.99), illustrating the high potential of sequence-to-structure-, MIF-based approaches in the context of drug resistance prediction. We discuss the complementarity of the proposed methodology to existing prediction algorithms based on genotypic data. The present work represents a new step toward a more comprehensive and structurally-informed interpretation of the impact of genetic variability on the response to HIV-1 therapies.

geno2pheno[ngs-freq]: a genotypic interpretation system for identifying viral drug resistance using next-generation sequencing data

Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.

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.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Comprehensive Mapping of HIV-1 Escape from a Broadly Neutralizing Antibody

Precisely defining how viral mutations affect HIV's sensitivity to antibodies is vital to develop and evaluate vaccines and antibody immunotherapeutics. Despite great effort, a full map of escape mutants has not been delineated for an anti-HIV antibody. We describe a massively parallel experimental approach to quantify how all single amino acid mutations to HIV Envelope (Env) affect neutralizing antibody sensitivity in the context of replication-competent virus. We apply this approach to PGT151, a broadly neutralizing antibody recognizing a combination of Env residues and glycans. We confirm sites previously defined by structural and functional studies and reveal additional sites of escape, such as positively charged mutations in the antibody-Env interface. Evaluating the effect of each amino acid at each site lends insight into biochemical mechanisms of escape throughout the epitope, highlighting roles for charge-charge repulsions. Thus, comprehensively mapping HIV antibody escape gives a quantitative, mutation-level view of Env evasion of neutralization.

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.

Virology

Molecular techniques have become indispensable in viral diagnostics. Current applications include: (1) The detection of (unknown) viral infections in clinical samples. (2) Quantitative monitoring of viral load. (3) Genotyping of viral strains. (4) Detection of mutations in the viral genome that are associated with viral resistance. Proper sample acquisition and sample transport, as well as accurate DNA or RNA isolation are a prerequisite for reliable test results in molecular diagnostics of viral infections. In-house as well as commercial assays can be used for the amplification of viral DNA or RNA for the detection of viral infections and viral load monitoring. Many virus species consist of several subspecies, genotypes or variants. This molecular variation has to be taken into account when applying molecular diagnostics. More complicated diagnostics for genotyping or the detection of mutations related to therapy failure often rely on sequencing, although for some viral targets commercial assays are available. In this chapter, applications are described in which molecular methods have become the most important form of viral diagnostics. Molecular test results have a direct impact on patient management and as such, results have to be reliable, standardized and reproducible. Therefore, quality control and standardization are important issues!

A simple and cost-saving phenotypic drug susceptibility testing of HIV-1

It is essential to monitor the occurrence of drug-resistant strains and to provide guidance for clinically adapted antiviral treatment of HIV/AIDS. In this study, an individual patient's HIV-1 pol gene encoding the full length of protease and part of the reverse transcriptase was packaged into a modified lentivirus carrying dual-reporters ZsGreen and luciferase. The optimal coefficient of correlation between drug concentration and luciferase activity was optimized. A clear-cut dose-dependent relationship between lentivirus production and luciferase activity was found in the phenotypic testing system. Fold changes (FC) to a wild-type control HIV-1 strain ratios were determined reflecting the phenotypic susceptibility of treatment-exposed patient's HIV-1 strains to 12 HIV-1 inhibitors including 6 nucleoside reverse-transcriptase inhibitors (NRTIs), 4 non-nucleoside reverse transcriptase inhibitors (NNRTIs) and 2 protease inhibitors (PIs). Phenotypic susceptibility calls from 8 HIV-1 infected patients were consistent with 80-90% genotypic evaluations, while phenotypic assessments rectified 10-20% genotypic resistance calls. By a half of replacement with ZsGreen reporter, the consumption of high cost Bright-Glo Luciferase Assay is reduced, making this assay cheaper when a large number of HIV-1 infected individuals are tested. The study provides a useful tool for interpreting meaningful genotypic mutations and guiding tailored antiviral treatment of HIV/AIDS in clinical practice.

Evaluation and modeling of HIV based on communication theory in biological systems

Some forms of communication are used in biological systems such as HIV transmission in human beings. In this paper, we plan to get a unique insight into biological communication systems generally through the analogy between HIV infection and electrical communication system. The model established in this paper can be used to test and simulate various communication systems since it provides researchers with an opportunity. We interpret biological communication systems by using telecommunications exemplification from a layered communication protocol developed before and use the model to indicate HIV spreading. We also implement a simulation of HIV infection based on the layered communication protocol to predict the development of this disease and the results prove the validity of the model.

Comparison of susceptibility of HIV-1 variants to antiretroviral drugs by genotypic and recombinant virus phenotypic analyses

OBJECTIVE

This study utilized genotypic and in-house recombinant virus phenotypic assays to examine HIV-1 variant susceptibility to antiretroviral (ARV) drugs; comparisons were made between the analyses.

METHODS

A nested PCR was employed to amplify the HIV-1 gag-pol gene, which comprised the entire PR gene (codons 1-99) and the former RT gene (codons 1-312). Genetic resistance was determined by submitting the sequences to the Stanford University Network HIV-1 Database. Phenotypic susceptibilities to six ARV drugs were measured using a high-throughput, multi-cycle, recombinant virus phenotypic assay. Results were expressed in terms of the IC50 (half maximal inhibitory concentration) and fold-change values. The relationship between phenotypic drug resistance and genetic polymorphisms was determined.

RESULTS

Nineteen fragment sequences for which recombinant viruses were successfully constructed were translated and compared with the consensus B sequences in the Stanford University Network HIV-1 Database. No recognizable genotypic resistance-associated mutations were noted, except in one sample. Each homologous replication-competent recombinant viral fold-change in the presence of six ARV drugs used widely in China was measured. According to the clinical and statistical criteria, 16 of the 19 samples were susceptible to the six drugs tested. The majority of phenotypic and genotypic results obtained were in agreement, with a concordance rate of 97.4%. Both phenotypic and genotypic assays suggested that sample HN2009001 was resistant to all drugs tested. All phenotypic and genotypic results obtained regarding the susceptibility of the 19 recombinant viruses to nucleoside reverse transcriptase inhibitors (NRTIs) were in agreement. With regard to the genotypic results for the non-nucleoside reverse transcriptase inhibitors (NNRTIs), 7.9% (3/38) were inconsistent with the phenotypic results.

CONCLUSIONS

The in-house recombinant virus phenotypic assay was able to provide a straightforward quantitative assessment of resistance. In most cases, the genotypic and novel phenotypic assays yielded similar results. The disparity in HIV-1 susceptibility indicates a need to further investigate the clinical outcomes of antiretroviral therapy in certain individuals.

Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure

BACKGROUND

Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy.

RESULTS

In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance.

CONCLUSION

The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.

Comparison of ultra-deep versus Sanger sequencing detection of minority mutations on the HIV-1 drug resistance interpretations after virological failure

OBJECTIVE

Drug-resistance mutations are routinely detected using standard Sanger sequencing, which does not detect minor variants with a frequency below 20%. The impact of detecting minor variants generated by ultra-deep sequencing (UDS) on HIV drug-resistance interpretations has not yet been studied.

DESIGN

Fifty HIV-1 patients who experienced virological failure were included in this retrospective study.

METHODS

The HIV-1 UDS protocol allowed the detection and quantification of HIV-1 protease and reverse transcriptase variants related to genotypes A, B, C, F and G. DeepChek-HIV simplified drug-resistance interpretation software was used to compare Sanger sequencing and UDS.

RESULTS

The total time required for the UDS protocol was found to be approximately three times longer than Sanger sequencing with equivalent reagent costs. UDS detected all of the mutations found by population sequencing and identified additional resistance variants in all patients. An analysis of drug resistance revealed a total of 643 and 224 clinically relevant mutations by UDS and Sanger sequencing, respectively. Three resistance mutations with more than 20% prevalence were detected solely by UDS: A98S (23%), E138A (21%) and V179I (25%). A significant difference in the drug-resistance interpretations for 19 antiretroviral drugs was observed between the UDS and Sanger sequencing methods. Y181C and T215Y were the most frequent mutations associated with interpretation differences.

CONCLUSION

A combination of UDS and DeepChek software for the interpretation of drug resistance results would help clinicians provide suitable treatments. A cut-off of 1% allowed a better characterization of the viral population by identifying additional resistance mutations and improving the drug-resistance interpretation.

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.

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.

Novel two-round phenotypic assay for protease inhibitor susceptibility testing of recombinant and primary HIV-1 isolates

Antiretroviral drug susceptibility tests facilitate therapeutic management of HIV-1-infected patients. Although genotyping systems are affordable, inaccuracy in the interpretation of complex mutational patterns may limit their usefulness. Currently available HIV-1 phenotypic assays are based on the generation of recombinant viruses in which the specific viral gene of interest, derived from a patient plasma sample, is cloned into a susceptible genetic viral backbone prior to in vitro drug susceptibility evaluation. Nevertheless, in the case of protease inhibitors, not only are mutations in the HIV-1 protease-coding region involved in resistance, but the role of Gag in drug susceptibility has also recently been reported. In order to avoid the inherent limitations resulting from partial cloning of the viral genome, we designed and evaluated a new experimental strategy to test the in vitro susceptibility of primary viral isolates to protease inhibitors. Our protocol, which is based on a two-round infection protocol using the reporter TZM-bl cell line, showed a good correlation with genotypic resistance prediction and with the Antivirogram phenotypic assay, in both protease-recombinant viruses and primary viral isolates. The protocol is suitable for any HIV-1 subtype and enables rapid in-house measurement of protease inhibitor susceptibility, thus making it possible to evaluate the concomitant effects of both patient-derived gag and protease-coding regions.

HIV Drug-Resistant Patient Information Management, Analysis, and Interpretation

Introduction

The science of information systems, management, and interpretation plays an important part in the continuity of care of patients. This is becoming more evident in the treatment of human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS), the leading cause of death in sub-Saharan Africa. The high replication rates, selective pressure, and initial infection by resistant strains of HIV infer that drug resistance will inevitably become an important health care concern. This paper describes proposed research with the aim of developing a physician-administered, artificial intelligence-based decision support system tool to facilitate the management of patients on antiretroviral therapy.

Methods

This tool will consist of (1) an artificial intelligence computer program that will determine HIV drug resistance information from genomic analysis; (2) a machine-learning algorithm that can predict future CD₄ count information given a genomic sequence; and (3) the integration of these tools into an electronic medical record for storage and management.

Conclusion

The aim of the project is to create an electronic tool that assists clinicians in managing and interpreting patient information in order to determine the optimal therapy for drug-resistant HIV patients.

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.

Dynamical basis for drug resistance of HIV-1 protease

Background

Protease inhibitors designed to bind to protease have become major anti-AIDS drugs. Unfortunately, the emergence of viral mutations severely limits the long-term efficiency of the inhibitors. The resistance mechanism of these diversely located mutations remains unclear.

Results

Here I use an elastic network model to probe the connection between the global dynamics of HIV-1 protease and the structural distribution of drug-resistance mutations. The models for study are the crystal structures of unbounded and bound (with the substrate and nine FDA approved inhibitors) forms of HIV-1 protease. Coarse-grained modeling uncovers two groups that couple either with the active site or the flap. These two groups constitute a majority of the drug-resistance residues. In addition, the significance of residues is found to be correlated with their dynamical changes in binding and the results agree well with the complete mutagenesis experiment of HIV-1 protease.

Conclusions

The dynamic study of HIV-1 protease elucidates the functional importance of common drug-resistance mutations and suggests a unifying mechanism for drug-resistance residues based on their dynamical properties. The results support the robustness of the elastic network model as a potential predictive tool for drug resistance.

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.

Prediction of response to antiretroviral therapy by human experts and by the EuResist data-driven expert system (the EVE study)

OBJECTIVES

The EuResist expert system is a novel data-driven online system for computing the probability of 8-week success for any given pair of HIV-1 genotype and combination antiretroviral therapy regimen plus optional patient information. The objective of this study was to compare the EuResist system vs. human experts (EVE) for the ability to predict response to treatment.

METHODS

The EuResist system was compared with 10 HIV-1 drug resistance experts for the ability to predict 8-week response to 25 treatment cases derived from the EuResist database validation data set. All current and past patient data were made available to simulate clinical practice. The experts were asked to provide a qualitative and quantitative estimate of the probability of treatment success.

RESULTS

There were 15 treatment successes and 10 treatment failures. In the classification task, the number of mislabelled cases was six for EuResist and 6-13 for the human experts [mean±standard deviation (SD) 9.1±1.9]. The accuracy of EuResist was higher than the average for the experts (0.76 vs. 0.64, respectively). The quantitative estimates computed by EuResist were significantly correlated (Pearson r=0.695, P<0.0001) with the mean quantitative estimates provided by the experts. However, the agreement among experts was only moderate (for the classification task, inter-rater κ=0.355; for the quantitative estimation, mean±SD coefficient of variation=55.9±22.4%).

CONCLUSIONS

With this limited data set, the EuResist engine performed comparably to or better than human experts. The system warrants further investigation as a treatment-decision support tool in clinical practice.

Extracting causal relations on HIV drug resistance from literature

BACKGROUND

In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.

RESULTS

In this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each <drug, mutation> pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project http://www.virolab.org to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation.

CONCLUSIONS

The proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation.

Resistance and viral subtypes: how important are the differences and why do they occur?

PURPOSE OF REVIEW

The global HIV-1 pandemic has evolved to include 11 subtypes and 34 circulating recombinant forms. Our knowledge of HIV-1 response to antiretroviral drugs and emergent drug resistance has, however, been limited to subtype B infections circulating in Europe and North America, with little comparative information on non-B subtypes representing approximately 90% of worldwide epidemics. This review summarizes publications in the past year that highlight intersubtype differences influencing viral susceptibility to antiretroviral drugs and emergent drug resistance.

RECENT FINDINGS

Cumulative findings from clinical studies suggest that antiretroviral therapy will be of benefit in the overall treatment of non-B subtype infections, and result in drug-resistance profiles comparable to those observed for subtype B infections. Nevertheless, the 10-15% sequence diversity in the Pol region contributes to intersubtype differences in response to particular nucleoside and non-nucleoside analogues, as well as protease inhibitors. Distinct signature mutations and mutational pathways are identified for specific non-B subtypes. The implications of subtype on clinical outcome and interpretative algorithms are described.

SUMMARY

Understanding intersubtype differences in drug resistance is important in optimizing treatment strategies in resource-poor settings. Hopefully, this may assist in the design of prophylactic approaches to prevent HIV-1 horizontal and vertical HIV-1 transmission.

Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment

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.

Predicting drug resistance of the HIV-1 protease using molecular interaction energy components

Drug resistance significantly impairs the efficacy of AIDS therapy. Therefore, precise prediction of resistant viral mutants is particularly useful for developing effective drugs and designing therapeutic regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV-1 protease resistant to the seven FDA approved drugs. We analyzed the energetic pattern of the protease-drug interaction by calculating the molecular interaction energy components (MIECs) between the drug and the protease residues. Support vector machines (SVMs) were trained on MIECs to classify protease mutants into resistant and nonresistant categories. The high prediction accuracies for the test sets of cross-validations suggested that the MIECs successfully characterized the interaction interface between drugs and the HIV-1 protease. We conducted a proof-of-concept study on a newly approved drug, darunavir (TMC114), on which no drug resistance data were available in the public domain. Compared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistance. To quantitatively estimate binding affinities of drugs and study the contributions of protease residues to causing resistance, linear regression models were trained on MIECs using partial least squares (PLS). The MIEC-PLS models also achieved satisfactory prediction accuracy. Analysis of the fitting coefficients of MIECs in the regression model revealed the important resistance mutations and shed light into understanding the mechanisms of these mutations to cause resistance. Our study demonstrated the advantages of characterizing the protease-drug interaction using MIECs. We believe that MIEC-SVM and MIEC-PLS can help design new agents or combination of therapeutic regimens to counter HIV-1 protease resistant strains.

Understanding the molecular epidemiology of foot-and-mouth-disease virus

The use of molecular epidemiology is an important tool in understanding and consequently controlling FMDV. In this review I will present basic information about the disease, needed to perform molecular epidemiology. I will give a short introduction to the history and impact of foot-and-mouth disease, clinical picture, infection route, subclinical and persistent infections, general aspects of the transmission of FMDV, serotype-specific epidemiological characteristics, field epidemiology of FMDV, evolution and molecular epidemiology of FMDV. This is followed by two chapters describing the molecular epidemiology of foot-and-mouth disease in global surveillance and molecular epidemiology of foot-and-mouth disease in outbreak investigation.

Antiretroviral drug resistance testing in adult HIV-1 infection: 2008 recommendations of an International AIDS Society-USA panel

Resistance to antiretroviral drugs remains an important limitation to successful human immunodeficiency virus type 1 (HIV-1) therapy. Resistance testing can improve treatment outcomes for infected individuals. The availability of new drugs from various classes, standardization of resistance assays, and the development of viral tropism tests necessitate new guidelines for resistance testing. The International AIDS Society-USA convened a panel of physicians and scientists with expertise in drug-resistant HIV-1, drug management, and patient care to review recently published data and presentations at scientific conferences and to provide updated recommendations. Whenever possible, resistance testing is recommended at the time of HIV infection diagnosis as part of the initial comprehensive patient assessment, as well as in all cases of virologic failure. Tropism testing is recommended whenever the use of chemokine receptor 5 antagonists is contemplated. As the roll out of antiretroviral therapy continues in developing countries, drug resistance monitoring for both subtype B and non-subtype B strains of HIV will become increasingly important.

Virtual Physiological Human research initiative: the future for rational HIV treatment design?

The clinical management of HIV-positive patients receiving existing drug regimens is performed largely as a clinician’s best guess, based on available viral and patient data. The development of new antiretroviral therapies for HIV is at an impasse. How can we progress beyond this? ViroLab, an example of a new ‘style’ of research project, is attempting to answer this question by stepping into the realm of prediction. A method is in development that can model and predict the efficacy of antiretroviral treatment and may enhance clinical decision support. If extended, this method can potentially target any other pathology where a ligand–substrate interaction is concerned. This type of research falls within the remit of a new initiative: the Virtual Physiological Human (VPH). The potential impact of VPH on current and future rational treatment design is discussed.

A study of seven rule-based algorithms for the interpretation of HIV-1 genotypic resistance data in Thailand

Since the free therapy program was started by the Thai government, the number of patients infected by HIV-1 with access to antiretroviral drugs has increased. The selection of effective interpretation algorithms for antiretroviral drug resistance has become even more important for clinical management. In this retrospective study, the level of agreement was evaluated in 721 antiretroviral-therapy failing HIV-1 subjects. Regarding genetic diversity, about 89% was recognized as non-B variants (CRF01_AE). The level of complete concordant interpretation score in all seven algorithms was recognized in non-nucleoside reverse transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs) (67%), but not in nucleoside reverse transcriptase inhibitors (NRTIs) (52%). Over 10% of the major discordance score with TRUGENE was revealed in didanosine (Agence Nationale de Recherches sur le SIDA[ANRS]; Detroit Medical Centre [DMC]), abacavir (ANRS; Centre Hospitalier de Luxembourg [CHL]), and also with delavirdine, indinavir and amprenavir (Grupo de Aconselhamento Virológico [GAV]). A good to excellent agreement range of kappa scores was detected for most antiretroviral drugs. However, poor agreement with the TRUGENE system (k<0.40) was seen in the ANRS system with didanosine, abacavir and lopinavir; GAV system in indinavir and amprenavir; and DMC system in ritonavir. These might be an option for resource limited countries when selecting the use of a low cost or free algorithm interpretation, which has excellent agreement as the U.S. Food and Drug Administration (FDA)-approved TRUGENE commercial system.

Genotypic susceptibility scores and HIV type 1 RNA responses in treatment-experienced subjects with HIV type 1 infection

This study compared the role of genotypic susceptibility scores (GSS) as a predictor of virologic response in a group (n = 234) of HIV-infected, protease inhibitor (PI)-experienced subjects. Two scoring methods [discrete genotypic susceptibility score (dGSS) and continuous genotypic susceptibility score (cGSS)] were developed. Each drug in the subject's regimen was given a binary susceptibility score using Stanford inferred drug resistance scores to calculate the dGSS. In contrast to the dGSS, the cGSS model was designed to reflect partial susceptibility to a drug. Both GSS were independent predictors of week 16 virologic response. We also compared the GSS to a phenotypic susceptibility score (PSS) model on a subset of subjects that had both GSS and PSS performed, and found that both models were predictive of virologic response. Genotypic analyses at enrollment showed that subjects who were virologic nonresponders at week 16 revealed enrichment of several mutated codons associated with nucleoside reverse transcriptase inhibitors (NRTI) (codons 67, 69, 70, 118, 215, and 219) or PI resistance (codons 10, 24, 71, 73, and 88) compared to subjects who were virologic responders. Regression analyses revealed that protease mutations at codons 24 and 90 were most predictive of poor virologic response, whereas mutations at 82 were associated with enhanced virologic response. Certain NNRTI-associated mutations, such as K103N, were rapidly selected in the absence of NRTIs. These data indicate that GSS may be a useful tool in selecting drug regimens in HIV-1-infected subjects to maximize virologic response and improve treatment outcomes.

Prevalence and epidemiology of HIV type 1 drug resistance among newly diagnosed therapy-naive patients in Belgium from 2003 to 2006

This study is the first prospective study to assess the prevalence, epidemiology, and risk factors of HIV-1 drug resistance in newly diagnosed HIV-infected patients in Belgium. In January 2003 it was initiated as part of the pan-European SPREAD program, and continued thereafter for four inclusion rounds until December 2006. Epidemiological, clinical, and behavioral data were collected using a standardized questionnaire and genotypic resistance testing was done on a sample taken within 6 months of diagnosis. Two hundred and eighty-five patients were included. The overall prevalence of transmitted HIV-1 drug resistance in Belgium was 9.5% (27/285, 95% CI: 6.6-13.4). Being infected in Belgium, which largely coincided with harboring a subtype B virus, was found to be significantly associated with transmission of drug resistance. The relatively high rate of baseline resistance might jeopardize the success of first line treatment as more than 1 out of 10 (30/285, 10.5%) viruses did not score as fully susceptible to one of the recommended first-line regimens, i.e., zidovudine, lamivudine, and efavirenz. Our results support the implementation of genotypic resistance testing as a standard of care in all treatment-naive patients in Belgium.

A pharmacogenomics primer

The human genome project and related research initiatives have enabled the identification of a significant number of genetic variants that are predictive of drug response and outcome (pharmacogenomic biomarkers). As yet, incorporation of routine pharmacogenomic testing into clinical practice has been relatively modest. Potential barriers to adoption include a relative lack of prospective controlled trials establishing the benefits of such testing, economic constraints, and ethical concerns, among others. Clinicians considering the use of pharmacogenomic testing in their practice also may be unfamiliar with the concepts and principles underlying this rapidly evolving discipline. Consequently, the purpose of this review is to provide the clinical pharmacologist with a primer on the principles and molecular mechanisms underlying pharmacogenomics. In addition, the methods currently being used to discover novel pharmacogenomic biomarkers and then apply these to clinical practice will be described.