Transmission of Human Immunodeficiency Virus I Drug Resistance - a Case Report. What are the Clinical Implications?

The success of first-line antiretroviral therapy can be challenged by the acquisition of primary drug resistance. Here we report a case where baseline genotypic resistance testing detected resistance conferring nucleoside/nucleotide reverse transcriptase inhibitor (NRTI)-associated mutations, but no primary mutations for protease inhibitor (PI). Subsequent PI-based HAART with boosted saquinavir led to virological treatment success with persistently undetectable viral load. After treatment simplification from saquinavir to an atazanavir based PI-therapy and no change in backbone therapy rapid virological breakthrough occurred. Retrospective analysis displayed preexisting gag cleavage site mutations which may have reduced the genetic barrier in a clinical relevant manner in combination with the already existing NRTI resistance mutations. Alternatively, this effect could be explained with a different antiviral potency for the respective PIs used

Endogenous or Exogenous Spreading of {HIV-1} in Nordrhein-Westfalen, Germany, Investigated by Phylodynamic Analysis of the {RESINA} Study Cohort

HIV's genetic instability means that sequence similarity can illuminate the underlying transmission network. Previous application of such methods to samples from the United Kingdom has suggested that as many as 86% of UK infections arose outside of the country, a conclusion contrary to usual patterns of disease spread. We investigated transmission networks in the Resina cohort, a 2,747 member sample from Nordrhein-Westfalen, Germany, sequenced at therapy start. Transmission networks were determined by thresholding the pairwise genetic distance in the pol gene at 96.8% identity. At first blush the results concurred with the UK studies. Closer examination revealed four large and growing transmission networks that encompassed all major transmission groups. One of these formed a supercluster containing 71% of the sex with men (MSM) subjects when the network was thresholded at levels roughly equivalent to those used in the UK studies, though methodological differences suggest that this threshold may be too generous in the current data. Examination of the endo- versus exogenesis hypothesis by testing whether infections that were exogenous to Cologne or to Dusseldorf were endogenous to the greater region supported endogenous spread in MSM subjects and exogenous spread in the endemic transmission group. In intravenous drug using group subjects, it depended on viral strain, with subtype B sequences appearing to have origin exogenous to the Resina data, while non-B sequences (primarily subtype A) were almost completely endogenous to their local community. These results suggest that, at least in Germany, the question of endogenous versus exogenous linkages depends on subject group

Proviral {DNA} as a Target for {HIV}-1 Resistance Analysis

Background: Resistance analysis from viral RNA is restricted to detectable viral load. Therefore, analysis from proviral DNA could help in cases with low-level or suppressed viremia. Methods: Viral plasma RNA and the corresponding cellular proviral DNA of 78 EDTA samples from 48 therapy-naive (TN) and 30 therapy-experienced (TE) HIV-1-infected patients were isolated and analyzed for their resistance profiles in the protease and reverse transcriptase genes. Results: Overall, 175 drug-resistance mutations (DRMs) were detected in 25/30 TE (83.3%) and 5/48 TN (10.4%) samples. The TE patients displayed a mean number of 6.68 DRMs in RNA and 5.20 in DNA. In the TN patients, a mean of 0.8 DRMs was found in RNA and 1.0 in DNA; 75% of the DRMs were detected in RNA and DNA simultaneously. In the TE samples, 76% of the DRMs were detected simultaneously in RNA and DNA, 23% exclusively in RNA and 1% in DNA only. The TN samples revealed a significantly higher frequency of DRMs in DNA than in RNA. Conclusions: Proviral DNA resistance testing provides additional resistance information for TN patients. It is also a reliable alternative for TE patients with unsuccessful RNA testing and can provide valuable information when no records are available. (C) 2015 S. Karger AG, Base

Efficacy of Antiretroviral Therapy Switch in HIV-Infected Patients: A 10-Year Analysis of the EuResist Cohort.

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

Predicting response to antiretroviral treatment by machine learning: the EuResist project.

For a long time, the clinical management of antiretroviral drug resistance was based on sequence analysis of the HIV genome followed by estimating drug susceptibility from the mutational pattern that was detected. The large number of anti-HIV drugs and HIV drug resistance mutations has prompted the development of computer-aided genotype interpretation systems, typically comprising rules handcrafted by experts via careful examination of in vitro and in vivo resistance data. More recently, machine learning approaches have been applied to establish data-driven engines able to indicate the most effective treatments for any patient and virus combination. Systems of this kind, currently including the Resistance Response Database Initiative and the EuResist engine, must learn from the large data sets of patient histories and can provide an objective and accurate estimate of the virological response to different antiretroviral regimens. The EuResist engine was developed by a European consortium of HIV and bioinformatics experts and compares favorably with the most commonly used genotype interpretation systems and HIV drug resistance experts. Next-generation treatment response prediction engines may valuably assist the HIV specialist in the challenging task of establishing effective regimens for patients harboring drug-resistant virus strains. The extensive collection and accurate processing of increasingly large patient data sets are eagerly awaited to further train and translate these systems from prototype engines into real-life treatment decision support tools