Collaborative Integration of HCV Molecular Virology and Mathematical Modeling

描述（由申请人提供）：丙型肝炎病毒（HCV）感染全球超过1.8亿人，引起急性和慢性肝炎以及肝细胞癌，然而，没有保护性疫苗可用，并且只有一部分感染患者对干扰素（IFN）加利巴韦林的当前治疗有反应。统计数据预测，如果没有改进的治疗方法，到2020年，美国将有100万人患有HCV相关肝硬化。在干扰素治疗期间慢性感染患者血清中HCV RNA水平的数学建模增加了我们对HCV感染动力学和治疗反应动力学的理解，并且在临床数据分析中发挥着重要作用。然而，感染性细胞培养系统的缺乏阻碍了对HCV感染和对治疗反应的机制基础的充分理解。幸运的是，最近随着基因型2a HCV共有克隆（JFH-1）的鉴定取得了重大进展，我们和其他人已经证明该基因型2a HCV共有克隆可以在体外复制和产生感染性HCV。因此，我们第一次可以有效地繁殖HCV并研究整个病毒生命周期以及潜在的抗病毒药物对感染过程的影响。体外研究HCV的能力提供了一个前所未有的跨学科的机会，以增加我们对HCV的知识，通过量化HCV感染动力学和制定数学模型的HCV感染和治疗反应在分子水平上。对细胞内和细胞外HCV感染和治疗动力学的更定量的理解将有助于定义感染所需的限速步骤，确定有效的抗病毒靶点，并定义正在开发的抗病毒药物的作用机制（MOA），从而促进改进治疗方法的设计。因此，本提案的具体目标是：1）体外定量HCV感染动力学并开发新的数学模型以阐明从起始到稳态调节HCV动力学的过程。2)通过表征已知MOA的抗病毒药物治疗期间的HCV抑制，然后确定我们的数学模型是否准确预测那些经验测量的抑制动力学，来验证和完善我们对HCV复制和感染的理解。3)使用HCV数学模型预测临床相关药物抑制HCV的MOA，并实证检验这些假设。 公共卫生相关性：丙型肝炎病毒（HCV）感染全球超过1.8亿人，引起急性和慢性肝炎和肝细胞癌，但没有保护性疫苗可用，只有一部分感染患者对目前的治疗方案有反应。为了设计更有效的抗病毒药物，研究HCV的生命周期以了解感染的动力学并确定感染的哪些步骤代表有效的抗病毒靶点至关重要。因此，我们建议病毒学家和数学家之间的合作努力，开发和利用数学模型来阐明调节HCV感染的生物过程，并确定病毒对治疗的反应。

DESCRIPTION (provided by applicant): Hepatitis C virus (HCV) infects more than 180 million people worldwide, causing acute and chronic hepatitis and hepatocellular carcinoma, however, no protective vaccine is available and only a subset of infected patients respond to the current treatment of interferon (IFN) plus ribavirin. Statistics predict that without improved therapeutics one million people in the US will suffer from HCV-related cirrhosis by 2020. Mathematical modeling of HCV RNA levels in the serum of chronically infected patients during interferon therapy has increased our understanding of HCV infection dynamics and treatment response kinetics, and is playing an important role in the analysis of clinical data. Nevertheless, the absence of infectious cell culture systems has impeded full understanding of HCV infection and the mechanistic basis of response to therapy. Fortunately, significant advances were made recently with the identification of a genotype 2a HCV consensus clone (JFH-1) that we and others have shown can replicate and produce infectious HCV in vitro. Hence, for the first time, we can efficiently propagate HCV and study the entire viral lifecycle and the effects of potential antiviral on the infection process. The ability to study HCV in vitro provides an unprecedented cross disciplinary opportunity to increase our knowledge of HCV by quantifying HCV infection kinetics and formulate mathematical models of HCV infection and treatment response at the molecular level. A more quantitative understanding of intracellular and extracellular HCV infection and treatment dynamics will help define rate limiting steps required for infection, identify effective antiviral targets and define the mechanism of action (MOA) of antivirals that are under development thus facilitating the design of improved therapeutics. Accordingly, the specific aims of this proposal are: 1) Quantify HCV infection kinetics in vitro and develop new mathematical models to elucidate processes that regulate HCV dynamics from initiation to steady state. 2) Validate and refine our understanding of HCV replication and infection by characterizing HCV inhibition during treatment with antiviral agents of known MOA and then determining if our mathematical models accurately predict those empirically measured inhibition dynamics. 3) Use HCV mathematical models to predict the MOA by which clinically relevant drugs inhibit HCV and empirically test those hypotheses. PUBLIC HEALTH RELEVANCE: Hepatitis C virus (HCV) infects more than 180 million people worldwide, causing acute and chronic hepatitis and hepatocellular carcinoma, however no protective vaccine is available and only a subset of infected patients respond to current treatment options. To design more effective antivirals it is crucial to study the HCV lifecycle to understand the dynamics of infection and identify which steps of infection represent effective antiviral targets. As such, we propose a collaborative effort between virologists and mathematicians to develop and utilize mathematical models to elucidate the biological processes that regulate HCV infection and determine viral response to treatment.