Telaprevir or boceprevir based therapy for chronic hepatitis C infection: Development of resistance-associated variants in treatment failure

The use of triple-therapy, pegylated-interferon, ribavirin and either of the first generation hepatitis C virus (HCV) protease inhibitors telaprevir or boceprevir, is the new standard of care for treating genotype 1 chronic HCV. Clinical trials have shown response rates of around 70–80%, but there is limited data from the use of this combination outside this setting. Through an expanded access programme, we treated 59 patients, treatment naïve and experienced, with triple therapy. Baseline factors predicting treatment response or failure during triple therapy phase were identified in 58 patients. Thirty seven (63.8%) of 58 patients had undetectable HCV RNA 12 weeks after the end of treatment. Genotype 1a (p = 0.053), null-response to previous treatment (p = 0.034), the rate of viral load decline after 12 weeks of previous interferon-based treatment (p = 0.033) were all associated with triple-therapy failure. The most common cause of on-treatment failure for telaprevir-based regimens was the development of resistance-associated variants (RAVs) at amino acids 36 and/or 155 of HCV protease (p = 0.027) whereas in boceprevir-based regimens mutations at amino acid 54 were significant (p = 0.015). SVR12 rates approaching 64% were achieved using triple therapy outside the clinical trial setting, in a patient cohort that included cirrhotics.

Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies

Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such asMRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat–muscle quantitation system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRATM Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variationwas 1.4%for IAAT and 1.2%for ASAT, the intra-observer correlationwas 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.