Collaborative Integration of HCV Molecular Virology and Mathematical Modeling

HCV 分子病毒学与数学建模的协同整合

• 批准号: 8461898
• 负责人: ALAN S PERELSON
• 金额: $ 36.98万
• 依托单位: LOYOLA UNIVERSITY CHICAGO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8461898
• 关键词: Acute Hepatitis; Address; Antiviral Agents; Binding; Biological Process; Biomedical Research; Blood; Cell Culture System; Cell Culture Techniques; Chronic; Chronic Hepatitis; Chronic Hepatitis C; Cirrhosis; Clinical Data; Complex; Consensus; Data; Development; Fostering; Genotype; Goals; Hepatitis C; Hepatitis C virus; Hepatocyte; Human; In Vitro; Infection; Infectious hepatitides; Information Sciences; Information Technology; Interferons; Kinetics; Knowledge; Liver diseases; Measures; Mediating; Methods; Modeling; Molecular; Molecular Virology; Pan Genus; Patients; Pharmaceutical Preparations; Play; Primary carcinoma of the liver cells; Process; Quantitative Evaluations; RNA replication; Regimen; Replicon; Research; Ribavirin; Role; Serum; System; Testing; Therapeutic; Time; Treatment Efficacy; United States National Institutes of Health; Vaccines; Viral; Virus; Virus Replication; anti-hepatitis C; base; clinical care; clinically relevant; design; experience; extracellular; improved; in vitro Model; in vivo; interferon therapy; mathematical model; programs; research study; response; statistics; tool; treatment effect; treatment response; viral RNA

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.

描述（由申请人提供）：丙型肝炎病毒（HCV）在全球范围内感染超过1.8亿人，导致急性和慢性肝炎和肝细胞癌癌，但是，没有保护性疫苗可用，只有受感染的患者的子集响应于当前对培根（IFN）以及ifterferon（IFN）以及ibravirin的治疗。统计数据预测，在2020年，在美国，如果没有改善治疗疗法，美国将患有与HCV相关的肝硬化。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感染动态和治疗反应反应的理解，并且在分析临床数据中起着重要作用。然而，缺乏感染性细胞培养系统阻碍了对HCV感染的完全理解和对治疗反应的机理基础。幸运的是，通过鉴定我们和其他人所显示的基因型2a HCV共识克隆（JFH-1），最近取得了重大进展，可以在体外复制并产生感染性HCV。因此，我们第一次可以有效地传播HCV并研究整个病毒生命周期以及潜在抗病毒药对感染过程的影响。体外研究HCV的能力提供了前所未有的跨学科机会，通过量化HCV感染动力学并在分子水平上制定HCV感染和治疗反应的数学模型，从而增加我们对HCV的了解。对细胞内和细胞外HCV感染和治疗动态的更定量理解将有助于确定感染所需的速率限制步骤，确定有效的抗病毒药靶标，并确定正在开发的抗病毒药物的作用机理（MOA），从而促进了改善的治疗剂的定位。因此，该提案的具体目的是：1）在体外量化HCV感染动力学，并开发新的数学模型来阐明调节HCV动力学从启动到稳态的过程。 2）通过表征已知MOA的抗病毒药物治疗期间的HCV抑制，验证和完善我们对HCV复制和感染的理解，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）使用HCV数学模型预测临床相关药物抑制HCV并经验检验这些假设的MOA。