Suchergebnisse

7‐11 November 2010, Tenth International Congress on Drug Therapy in HIV Infection, Glasgow, UK

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.

Different diagnostic parameters may affect the tropism prediction reliability. The impact of usage of FPR cut‐offs<20%, use of viral RNA versus proviral DNA samples, single versus triple amplification, and presence of MVC resistance mutations on tropism prediction at baseline were analysed on 101 patients receiving maraviroc (MVC) and correlated with their clinical outcome. This was a non‐interventional, retrospective study. 82 RNA and 54 DNA samples from the 101 patients receiving MVC were obtained. The V3 region was sequenced and the tropism predicted using the geno2pheno[coreceptor] and T‐CUP tools with FPR cut‐offs of 5%, 7.5%, 10%, 15% and 20%. Additionally, 27/82 RNA and 28/54 DNA samples were analysed in triplicate and 34/82 samples with the ESTA assay. The influence of 16 MVC resistance mutations on clinical outcome was studied. The genotypic susceptibility score (GSS) of the concomitant drugs was mapped to numerical values: susceptible to 1 (or 0.5 for NRTIs), intermediate to 0.5 (0.25 for NRTIs) and resistant to 0. Detection of baseline R5 viruses in RNA (by geno2pheno[coreceptor] and T‐CUP) or DNA (by T‐CUP) samples correlated with MVC‐treatment success. Both tools performed very similarly, with PPVs close to 90%, even with FPR cut‐offs as low as 5%. The use of triple amplification did not improve the prediction value but reduced the number of patients elegible for MVC treatment. No influence of the GSS or MVC resistance mutations on the clinical outcome was detected. Genotypic tropism testing from viral RNA and proviral DNA using the geno2pheno[coreceptor] and T‐CUP systems is valid to select candidates for MVC treatment. Our data suggest that the use of 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 potent antiretroviral drug.

This work was funded by the BLUEPRINT project (European Union's Seventh Framework Programme grant 282510), the NIHR Cambridge Biomedical Research Centre, and the Austrian Academy of Sciences. F.A.C. is supported by a Medical Research Council Clinical Training Fellowship (grant MR/K024043/1). F.H. is supported by a postdoctoral fellowship of the German Research Council (DFG; grant HA 7723/1-1). J.K. is supported by a DOC Fellowship of the Austrian Academy of Sciences. W.H.O. is supported by the NIHR, BHF (grants PG-0310-1002 and RG/09/12/28096), and NHS Blood and Transplant. E.L. is supported by a Wellcome Trust Sir Henry Dale Fellowship (grant 107630/Z/15/Z) and core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. M. Frontini is supported by the BHF Cambridge Centre of Excellence (grant RE/13/6/30180). C.B. is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by a European Research Council (ERC) Starting Grant (European Union's Horizon 2020 research and innovation program; grant 679146). Supplement

Hematopoietic stem cells give rise to all blood cells in a differentiation process that involves widespread epigenome remodeling. Here we present genome-wide reference maps of the associated DNA methylation dynamics. We used a meta-epigenomic approach that combines DNA methylation profiles across many small pools of cells and performed single-cell methylome sequencing to assess cell-to-cell heterogeneity. The resulting dataset identified characteristic differences between HSCs derived from fetal liver, cord blood, bone marrow, and peripheral blood. We also observed lineage-specific DNA methylation between myeloid and lymphoid progenitors, characterized immature multi-lymphoid progenitors, and detected progressive DNA methylation differences in maturing megakaryocytes. We linked these patterns to gene expression, histone modifications, and chromatin accessibility, and we used machine learning to derive a model of human hematopoietic differentiation directly from DNA methylation data. Our results contribute to a better understanding of human hematopoietic stem cell differentiation and provide a framework for studying blood-linked diseases. ; This work was funded by the BLUEPRINT project (European Union's Seventh Framework Programme grant 282510), the NIHR Cambridge Biomedical Research Centre, and the Austrian Academy of Sciences. F.A.C. is supported by a Medical Research Council Clinical Training Fellowship (grant MR/K024043/1). F.H. is supported by a postdoctoral fellowship of the German Research Council (DFG; grant HA 7723/1-1). J.K. is supported by a DOC Fellowship of the Austrian Academy of Sciences. W.H.O. is supported by the NIHR, BHF (grants PG-0310-1002 and RG/09/12/28096), and NHS Blood and Transplant. E.L. is supported by a Wellcome Trust Sir Henry Dale Fellowship (grant 107630/Z/15/Z) and core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. M. Frontini is supported by the BHF Cambridge Centre of Excellence (grant RE/13/6/30180). C.B. is supported by a New Frontiers Group award of the Austrian Academy of Sciences and by a European Research Council (ERC) Starting Grant (European Union's Horizon 2020 research and innovation program; grant 679146).

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.