Alejandro Pironti

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 (R2) were compared for the freely available rules-based systems. HIV-GRADE (R2 = 0.40), Stanford HIVdb (R2 = 0.40), REGA algorithm (R2 = 0.36) and ANRS (R2 = 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.

HIV-2EU: Supporting Standardized HIV-2 Drug Resistance Interpretation in Europe

Considering human immunodeficiency virus type 2 (HIV-2) phenotypic data and experience from HIV type 1 and from the follow-up of HIV-2-infected patients, a panel of European experts voted on a rule set for interpretation of mutations in HIV-2 protease, reverse transcriptase, and integrase and an automated tool for HIV-2 drug resistance analyses freely available on the Internet (http://www.hiv-grade.de).

Metabolic flux analysis gives an insight on verapamil induced changes in central metabolism of HL-1 cells

Verapamil has been shown to inhibit glucose transport in several cell types. However, the consequences of this inhibition on central metabolism are not well known. In this study we focused on verapamil induced changes in metabolic fluxes in a murine atrial cell line (HL-1 cells). These cells were adapted to serum free conditions and incubated with 4 μM verapamil and [U-¹³C₅] glutamine. Specific extracellular metabolite uptake/production rates together with mass isotopomer fractions in alanine and glutamate were implemented into a metabolic network model to calculate metabolic flux distributions in the central metabolism. Verapamil decreased specific glucose consumption rate and glycolytic activity by 60%. Although the HL-1 cells show Warburg effect with high lactate production, verapamil treated cells completely stopped lactate production after 24 h while maintaining growth comparable to the untreated cells. Calculated fluxes in TCA cycle reactions as well as NADH/FADH₂ production rates were similar in both treated and untreated cells. This was confirmed by measurement of cell respiration. Reduction of lactate production seems to be the consequence of decreased glucose uptake due to verapamil. In case of tumors, this may have two fold effects; firstly depriving cancer cells of substrate for anaerobic glycolysis on which their growth is dependent; secondly changing pH of the tumor environment, as lactate secretion keeps the pH acidic and facilitates tumor growth. The results shown in this study may partly explain recent observations in which verapamil has been proposed to be a potential anticancer agent. Moreover, in biotechnological production using cell lines, verapamil may be used to reduce glucose uptake and lactate secretion thereby increasing protein production without introduction of genetic modifications and application of more complicated fed-batch processes.

HIV-2EU—Supporting Standardized HIV-2 Drug- Resistance Interpretation in Europe: An Update

1IAME, UMR 1137, Univ Paris Diderot, Sorbonne Paris Cite, F-75018 Paris, France 2IAME, UMR 1137, INSERM, F-75018 Paris, France 3AP-HP, Hopital Bichat-Claude Bernard, Laboratoire de Virologie, F-75018 Paris, France 4Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology and Immunology, KU Leuven, Leuven, Belgium 5Universite catholique de Louvain, AIDS Reference Laboratory, Brussels, Belgium 6Max von Pettenkofer Institute, Ludwig Maximilian University Munich, Munich, Germany 7Max Planck Institute for Informatics, Saarbrucken, Germany 8Johann Wolfgang Goethe-University Hospital, Institute for Medical Virology, German National Reference Centre for Retroviruses, Frankfurt, Germany 9Institute of Virology, University of Cologne, Germany 10Medizinisches Labor Dr. Berg, Berlin, Germany

Using Drug Exposure for Predicting Drug Resistance – A data-driven Genotypic Interpretation Tool

Antiretroviral treatment history and past HIV-1 genotypes have been shown to be useful predictors for the success of antiretroviral therapy. However, this information may be unavailable or inaccurate, particularly for patients with multiple treatment lines often attending different clinics. We trained statistical models for predicting drug exposure from current HIV-1 genotype. These models were trained on 63,742 HIV-1 nucleotide sequences derived from patients with known therapeutic history, and on 6,836 genotype-phenotype pairs (GPPs). The mean performance regarding prediction of drug exposure on two test sets was 0.78 and 0.76 (ROC-AUC), respectively. The mean correlation to phenotypic resistance in GPPs was 0.51 (PhenoSense) and 0.46 (Antivirogram). Performance on prediction of therapy-success on two test sets based on genetic susceptibility scores was 0.71 and 0.63 (ROC-AUC), respectively. Compared to geno2pheno[resistance], our novel models display a similar or superior performance. Our models are freely available on the internet via www.geno2pheno.org. They can be used for inferring which drug compounds have previously been used by an HIV-1-infected patient, for predicting drug resistance, and for selecting an optimal antiretroviral therapy. Our data-driven models can be periodically retrained without expert intervention as clinical HIV-1 databases are updated and therefore reduce our dependency on hard-to-obtain GPPs.

Parameters Influencing Baseline HIV-1 Genotypic Tropism Testing Related to Clinical Outcome in Patients on Maraviroc

Objectives We analysed the impact of different parameters on genotypic tropism testing related to clinical outcome prediction in 108 patients on maraviroc (MVC) treatment. Methods 87 RNA and 60 DNA samples were used. The viral tropism was predicted using the geno2pheno[coreceptor] and T-CUP tools with FPR cut-offs ranging from 1%-20%. Additionally, 27 RNA and 28 DNA samples were analysed in triplicate, 43 samples with the ESTA assay and 45 with next-generation sequencing. The influence of the genotypic susceptibility score (GSS) and 16 MVC-resistance mutations on clinical outcome was also studied. Results Concordance between single-amplification testing compared to ESTA and to NGS was in the order of 80%. Concordance with NGS was higher at lower FPR cut-offs. Detection of baseline R5 viruses in RNA and DNA samples by all methods significantly correlated with treatment success, even with FPR cut-offs of 3.75%-7.5%. Triple amplification did not improve the prediction value but reduced the number of patients eligible for MVC. No influence of the GSS or MVC-resistance mutations but adherence to treatment, on the clinical outcome was detected. Conclusions Proviral DNA is valid to select candidates for MVC treatment. FPR cut-offs of 5%-7.5% and single amplification from RNA or DNA would assure a safe administration of MVC without excluding many patients who could benefit from this drug. In addition, the new prediction system T-CUP produced reliable results.

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

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

Etravirine in Protease Inhibitor-Free Antiretroviral Combination Therapies

Etravirine (ETR) is a next generation non‐nucleoside reverse transcriptase inhibitor (NNRTI). The studies for ETR EMA approval were almost exclusively performed together with the protease inhibitor (PI) darunavir. However the fact that ETR can be active against NNRTI‐pretreated HIV variants and that it is well tolerated suggests its application in PI‐free antiretroviral combination therapies. Although approved only for PI‐containing therapies, a number of ETR treatments without PIs are performed currently. To evaluate the performance of ETR in PI‐free regimens, we analyzed the EURESIST database. We observed a total of 70 therapy switches to a PI‐free, ETR containing antiretroviral combination with detectable baseline viral load. 50/70 switches were in male patients and 20/70 in females. The median of previous treatments was 10. The following combinations were detected in the EURESIST database: ETR+MVC+RAL (20.0%); ETR+FTC+TDF (18.6%); 3TC+ETR+RAL (7.1%); 3TC+ABC+ETR (5.7%); other combinations (31.4%). A switch was defined as successful when either ≤50 copies/mL or a decline of the viral load of 2 log10, both at week 24 (range 18–30) were achieved. The overall success rate (SR) was 77% (54/70), and for the different combinations: ETR+MVC+RAL=78.6% (11/14); ETR+FTC+TDF=92.3% (12/13); 3TC+ETR+RAL =80.0% (4/5), 3TC+ABC+ETR=100% (SR 4/4); and for other combinations=67.6% (23/34). These SR values are comparable to those for other therapy combinations in such pretreated patients.

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

Abstract 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.

Determination of Phenotypic Resistance Cutoffs From Routine Clinical Data.

Background: HIV-1 drug resistance can be measured with phenotypic drug-resistance tests. However, the output of these tests, the resistance factor (RF), requires interpretation with respect to the in vivo activity of the tested variant. Specifically, the dynamic range of the RF for each drug has to be divided into a suitable number of clinically meaningful intervals. Methods: We calculated a susceptible-to-intermediate and an intermediate-to-resistant cutoff per drug for RFs predicted by geno2pheno[resistance]. Probability densities for therapeutic success and failure were estimated from 10,444 treatment episodes. The density estimation procedure corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. For estimating the probability of therapeutic success given an RF, we fit a sigmoid function. The cutoffs are given by the roots of the third derivative of the sigmoid function. Results: For performance assessment, we used geno2pheno[resistance] RF predictions and the cutoffs for predicting therapeutic success in 2 independent sets of therapy episodes. HIVdb was used for performance comparison. On one test set (n = 807), our cutoffs and HIVdb performed equally well receiver operating characteristic curve [(ROC)–area under the curve (AUC): 0.68]. On the other test set (n = 917), our cutoffs (ROC–AUC: 0.63) and HIVdb (ROC–AUC: 0.65) performed comparatively well. Conclusions: Our method can be used for calculating clinically relevant cutoffs for (predicted) RFs. The method corrects for the activity of the backbone drug compounds and for therapy failure without drug resistance. Our methods performance is comparable with that of HIVdb. RF cutoffs for the latest version of geno2pheno[resistance] have been estimated with this method.