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，导致急性和慢性肝炎和肝细胞癌，然而，没有可用的保护性疫苗，只有一小部分感染患者对干扰素（IFN）加利巴韦林的治疗有反应。统计数据预测，如果不改进治疗方法，到2020年，美国将有100万人患有丙型肝炎相关肝硬化。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，并在临床数据分析中发挥重要作用。然而，感染性细胞培养系统的缺乏阻碍了对HCV感染的充分了解和对治疗反应的机制基础。幸运的是，随着基因型2a HCV共识克隆（JFH-1）的鉴定，我们和其他人已经证明可以在体外复制和产生感染性HCV，最近取得了重大进展。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和潜在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机会，通过量化HCV感染动力学和在分子水平上制定HCV感染和治疗反应的数学模型来增加我们对HCV的认识。更定量地了解细胞内和细胞外HCV感染和治疗动态，将有助于确定感染所需的限速步骤，确定有效的抗病毒靶点，并确定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)量化HCV体外感染动力学，并建立新的数学模型来阐明从初始到稳定状态的HCV动力学调节过程。2)验证和完善我们对HCV复制和感染的理解，通过描述已知MOA抗病毒药物治疗期间HCV抑制的特征，然后确定我们的数学模型是否准确地预测了那些经验测量的抑制动力学。3)利用HCV数学模型预测临床相关药物抑制HCV的MOA，并对这些假设进行实证检验。

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.