Gertjan Beheydt

Antiretroviral drug resistance in HIV-1 therapy-naive patients in Cuba

In Cuba, antiretroviral therapy rollout started in 2001 and antiretroviral therapy coverage has reached almost 40% since then. The objectives of this study were therefore to analyze subtype distribution, and level and patterns of drug resistance in therapy-naive HIV-1 patients. Four hundred and one plasma samples were collected from HIV-1 therapy-naive patients in 2003 and in 2007-2011. HIV-1 drug resistance genotyping was performed in the pol gene and drug resistance was interpreted according to the WHO surveillance drug-resistance mutations list, version 2009. Potential impact on first-line therapy response was estimated using genotypic drug resistance interpretation systems HIVdb version 6.2.0 and Rega version 8.0.2. Phylogenetic analysis was performed using Neighbor-Joining. The majority of patients were male (84.5%), men who have sex with men (78.1%) and from Havana City (73.6%). Subtype B was the most prevalent subtype (39.3%), followed by CRF20-23-24_BG (19.5%), CRF19_cpx (18.0%) and CRF18_cpx (10.3%). Overall, 29 patients (7.2%) had evidence of drug resistance, with 4.0% (CI 1.6%-4.8%) in 2003 versus 12.5% (CI 7.2%-14.5%) in 2007-2011. A significant increase in drug resistance was observed in recently HIV-1 diagnosed patients, i.e. 14.8% (CI 8.0%-17.0%) in 2007-2011 versus 3.8% (CI 0.9%-4.7%) in 2003 (OR 3.9, CI 1.5-17.0, p=0.02). The majority of drug resistance was restricted to a single drug class (75.8%), with 55.2% patients displaying nucleoside reverse transcriptase inhibitor (NRTI), 10.3% non-NRTI (NNRTI) and 10.3% protease inhibitor (PI) resistance mutations. Respectively, 20.7% and 3.4% patients carried viruses containing drug resistance mutations against NRTI+NNRTI and NRTI+NNRTI+PI. The first cases of resistance towards other drug classes than NRTI were only detected from 2008 onwards. The most frequent resistance mutations were T215Y/rev (44.8%), M41L (31.0%), M184V (17.2%) and K103N (13.8%). The median genotypic susceptibility score for the commonly prescribed first-line therapies was 2.5. This analysis emphasizes the need to perform additional surveillance studies to accurately assess the level of transmitted drug resistance in Cuba, as the extent of drug resistance might jeopardize effectiveness of first-line regimens prescribed in Cuba and might necessitate the implementation of baseline drug resistance testing.

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.

High frequency of antiviral drug resistance and non-B subtypes in HIV-1 patients failing antiviral therapy in Cuba

BACKGROUND Emergence of HIV-1 drug resistance may limit the sustained benefits of antiretroviral therapy (ART) in settings with limited laboratory monitoring and drug options. OBJECTIVES Surveillance of drug resistance and subtypes in HIV-1 patients failing ART in Cuba. STUDY DESIGN This study compiled data of ART-experienced HIV-1 patients attending a clinical center in Havana in 2003 and 2009-2011. The first period included results of a cross-sectional study, whereas in the second period genotyping was performed as part of routine care. Drug resistance mutations and levels were determined using HIVdb version 6.0.9. RESULTS Seventy-six percent received solely ART containing at least 3 drugs, of which 79.1% ever receiving unboosted protease inhibitors (PI). Patients from 2009 to 2011 were longer treated and exposed to more ART regimens. Subtype B (39%) and CRF19_cpx (18%) were the most prevalent genetic forms. Subtype distribution did not change significantly between both periods, except for BG recombinants that increased from 6% to 14%. Nucleoside reverse transcriptase inhibitor (NRTI), non-nucleoside RTI (NNRTI) and PI mutations were present in 69.5%, 54.8% and 44.4%. Full-class resistance (FCR) to NRTI, NNRTI, PI and multidrug resistance (MDR) were detected in 31.8%, 37.9%, 18.5% and 15.4%. FCR to NRTI, NNRTI, PI and MDR were present in 9.8%, 14.1%, 0%, 0% after first-line failure and in 19.8%, 20.8%, 2.9% and 2.9% after second-line failure. CONCLUSIONS Our study found a high prevalence of drug resistance and supports the need for appropriate laboratory monitoring in clinical practice and access to drug options in case of virological failure.

Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape.

BackgroundFailure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.ResultsWe investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).ConclusionsThus, variation in individualized genetic barrier to NFV resistance may impact effective treatment options available after treatment failure. If similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure.

Structural modifications induced by specific HIV-1 protease-compensatory mutations have an impact on the virological response to a first-line lopinavir/ritonavir-containing regimen

OBJECTIVES This study evaluates the impact of specific HIV-1 protease-compensatory mutations (wild-type amino acids in non-B subtypes) on virological response to a first-line lopinavir/ritonavir-containing regimen in an HIV-1 subtype B-infected population. PATIENTS AND METHODS The prevalence of protease-compensatory mutations from 1997 to 2011 was calculated in 3063 drug-naive HIV-1 B-infected patients. The role of these mutations on virological outcome is estimated in a subgroup of 201 patients starting their first lopinavir/ritonavir-containing regimen by covariation and docking analyses. RESULTS The number of HIV-1 B-infected patients with at least one protease-compensatory mutation increased over time (from 86.4% prior to 2001 to 92.6% after 2009, P = 0.02). Analysing 201 patients starting first-line lopinavir/ritonavir, the median time to virological failure was shorter in patients with at least one protease-compensatory mutation than in patients with no protease-compensatory mutations. By covariation and docking analyses, specific mutations were found to affect lopinavir affinity for HIV-1 protease and to impact virological failure. Specifically, the L10V + I13V + L63P + I93L cluster, related to fast virological failure, correlated with a decreased drug affinity for the enzyme in comparison with wild-type (ΔGmut = -30.0 kcal/mol versus ΔGwt = -42.3 kcal/mol). CONCLUSIONS Our study shows an increased prevalence of specific protease-compensatory mutations in an HIV-1 B-infected population and confirms that their copresence can affect the virological outcome in patients starting a lopinavir/ritonavir-containing 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.