个体HCV宿主内变异的差异干扰素耐药性的数值检测、测量和分析及其对治疗反应的影响

Q2 Medicine
Pavel Skums, David S Campo, Zoya Dimitrova, Gilberto Vaughan, Daryl T Lau, Yury Khudyakov
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引用次数: 13

摘要

丙型肝炎病毒(HCV)是世界范围内肝脏疾病的主要病因。目前干扰素和利巴韦林(IFN/RBV)治疗对50%-60%的患者有效。HCV在感染患者体内以宿主内变异(准种)的大病毒群存在,可能对干扰素治疗有不同的耐药性。我们提出了一种基于数学模型和干扰素治疗前几个小时HCV种群动态分析的HCV准种差异干扰素耐药性测量方法。数学模型显示单个宿主内HCV变异在每个患者中对IFN治疗具有广泛的耐药性。分析宿主内HCV变异对干扰素耐药性的差异,可以准确预测对干扰素治疗的反应。这些模型强烈提示,在受感染宿主中,干扰素耐药性在密切相关的变异之间可能有很大差异,治疗结果可能由一种或几种变异来确定,而不管它们在治疗前在宿主内HCV人群中的频率如何。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Numerical detection, measuring and analysis of differential interferon resistance for individual HCV intra-host variants and its influence on the therapy response.

Hepatitis C virus (HCV) is a major cause of liver disease world-wide. Current interferon and ribavirin (IFN/RBV) therapy is effective in 50%-60% of patients. HCV exists in infected patients as a large viral population of intra-host variants (quasispecies), which may be differentially resistant to interferon treatment. We present a method for measuring differential interferon resistance of HCV quasispecies based on mathematical modeling and analysis of HCV population dynamics during the first hours of interferon therapy. The mathematical models showed that individual intra-host HCV variants have a wide range of resistance to IFN treatment in each patient. Analysis of differential IFN resistance among intra-host HCV variants allows for accurate prediction of response to IFN therapy. The models strongly suggest that resistance to interferon may vary broadly among closely related variants in infected hosts and therapy outcome may be defined by a single or a few variants irrespective of their frequency in the intra-host HCV population before treatment.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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