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      STOP-HCV - Stratified Medicine to Optimise Treatment for Hepatitis C Virus Infection



      Science Impact, Ltd.

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          With funding from the Medical Research Council we have established a consortium ? STOP-HCV ? to explore the use of personalised medicine as a tool to improve clinical outcomes and patient management for patients with HCV infection. STOP-HCV brings together experts in the fields of Hepatitis C, genomics and big data from all across the UK. It is a national collaboration between researchers, clinicians, patient groups and industrial representatives.Approximately 300,000 people in the UK are infected with HCV, only half of whom have been diagnosed as carrying the virus. The virus has a high tendency to persist, as the body's immune system is usually unable to clear infection. HCV exists in different genetic forms called genotypes. In the UK, most infections are caused by either genotype 1 (gt1) or 3 (gt3), which occur at about equal frequency. HCV infects the liver, causing liver cirrhosis (scarring), liver failure and liver cancer. In the past, treatment for HCV consisted of two drugs: interferon and ribavirin. This was typically a long-course treatment (24 weeks), with unpleasant side-effects, and only a ~60% chance of cure. The development and launch of direct-acting antiviral (DAA) drugs in 2012 revolutionised treatment for Hepatitis C. These DAAs (now in their third generation) are an all-oral treatment regime, which can be taken for shorter durations of time and have cure rates of over 95%. Third generation DAAs are effective against all genotypes of HCV. However, HCV gt3 infection remains notoriously more difficult to treat, especially in patients that have severe liver disease. A small, but significant, proportion of patients (especially those with severe liver disease) continue to fail DAA treatment.STOP-HCV?s objectives are 4 fold: 1) Through the use of stratified medicine, understand how factors such as viral genetics, viral load, immune parameters, host genetics and other biomarkers affect treatment of hepatitis C and the management of liver disease; 2) Develop clinical studies and prognostic models to predict treatment response and utilise patient information to establish the most effective and cost-effective hepatitis C treatment regimes; 3) Optimise treatment regimes to patients? individual characteristics in a bid to ultimately eradicate the hepatitis C virus and 4) To use stratified medicine technologies to predict the progression or regression of liver disease and the development of liver cancer after viral clearance.

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          Science Impact, Ltd.
          August 08 2017
          August 08 2017
          : 2017
          : 6
          : 81-83
          © 2017

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Earth & Environmental sciences, Medicine, Computer science, Agriculture, Engineering


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