Collaborative Integration of Hepatitis B Molecular Virology and Mathematical/Computational Modeling

乙型肝炎分子病毒学与数学/计算模型的协作整合

• 批准号: 10322437
• 负责人: Harel Dahari
• 金额: $ 41.4万
• 依托单位: LOYOLA UNIVERSITY CHICAGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322437
• 关键词: Address; Animals; Antiviral Agents; Biological Models; Biological Process; Biomedical Computing; Biomedical Research; Capsid; Cell Culture System; Cell Culture Techniques; Cell Nucleus; Cessation of life; Chronic Hepatitis; Circular DNA; Communities; Complex; Computer Models; Computer software; Core Protein; Cytoplasm; DNA; Data; Development; Disease; Experimental Models; Health; Hepatitis B; Hepatitis B Virus; Hepatocyte; Human; In Vitro; Infection; Information Sciences; Information Technology; Kinetics; Knowledge; Life Cycle Stages; Liver; Mathematics; Mission; Modeling; Molecular; Molecular Biology; Molecular Virology; Mus; Na(+)-taurocholate-cotransporting peptide; Outcome; Pan Genus; Patients; Pharmaceutical Preparations; Poly A; Poly I; Primary carcinoma of the liver cells; Process; RNA; RNA Transport; Recycling; Research; Scientist; Serum; System; Testing; Therapeutic; Time; Transgenic Organisms; Translations; United States National Institutes of Health; Vaccines; Viral; Viral Genome; Viral Proteins; Virus; Virus Diseases; Virus Replication; clinically relevant; curative treatments; data-driven model; design; experience; global health; hepatoma cell; improved; innovation; insight; mathematical model; molecular dynamics; mouse model; patient subsets; permissiveness; prevent; programs; receptor; tool; treatment response; viral DNA; viral RNA

Despite an effective vaccine, hepatitis B virus (HBV) continues to impose an enormous global health burden. Over 260 million are HBV infected worldwide, causing chronic hepatitis and more than 400,000 death per year due to hepatocellular carcinoma. While currently available drugs can suppress HBV replication only a small subset of patients are cured. As such, a deeper understanding of HBV infection dynamics at the molecular level is needed to enable the development of more effective (i.e. curative) therapeutics. Fortunately, significant advances have been made recently with the establishment of chimeric mouse models with humanized livers that retain permissiveness to HBV infection and the identification of sodium taurocholate cotransporting polypeptide (NTCP) as the HBV entry receptor which when expressed exogenously renders hepatoma cell cultures permissive to HBV infection in vitro. Hence, for the first time, we can perform HBV infections in mice and cell culture to characterizing HBV lifecycle and treatment response. Towards this end, the objective of this cross disciplinary R01 is to increase our knowledge of HBV by formulating and testing mathematical/computational models of HBV infection. The premise is that a more quantitative understanding of HBV infection and treatment dynamics will help define rate limiting steps, identify more effective antiviral targets and predict mechanism of action (MOA) of current drugs and those under development thus facilitating the design of improved therapeutics. The uniquely close collaborative effort among experienced virologists and expert viral dynamic and computational scientists proposed is critical for facilitating the development and utilization of data-driven modeling concepts to elucidate the detailed molecular biological processes that regulate HBV. Specifically, we propose to (i) Quantify HBV infection kinetics in uPA-SCID chimeric mice with humanized livers and develop mathematical/computational models to elucidate the processes that regulate HBV dynamics, (ii) Refine our understanding of HBV infection at the molecular level by characterizing HBV infection kinetics in vitro and developing multi-compartmental mathematical/computational models to elucidate the processes that regulate HBV dynamics, (iii) Validate and refine our understanding of HBV infection by characterizing/ modeling HBV treatment response to antivirals of known mechanism of action, and (iv) Use HBV mathematical/computational models to predict the MOA by which clinically relevant drugs inhibit HBV and empirically test those hypotheses.

尽管有有效的疫苗，但乙型肝炎病毒（HBV）仍继续施加巨大的全球 健康负担。超过2.6亿的HBV在全球范围内感染了HBV，导致慢性肝炎等 每年由于肝细胞癌而导致的40万人死亡。虽然目前可用的毒品可以 抑制HBV复制只有一小部分患者被治愈。因此，更深层次 需要了解分子水平的HBV感染动力学 开发更有效（即治愈）治疗剂。幸运的是，重大进展已有 最近是通过建立带有人性化肝的嵌合小鼠模型来制作的 保留对HBV感染的允许性和能旋钠的鉴定 共转运多肽（NTCP）作为HBV进入受体 外源性使肝癌细胞培养物在体外允许感染HBV。因此，对于 第一次，我们可以在小鼠和细胞培养中进行HBV感染来表征HBV生命周期 和治疗反应。为此，该交叉学科R01的目的是 通过制定和测试数学/计算模型来提高我们对HBV的了解 HBV感染。前提是对HBV感染和 治疗动态将有助于定义限制速率步骤，确定更有效的抗病毒靶标，并 预测当前药物和正在开发的药物的作用机理（MOA），从而促进 改进的治疗剂的设计。经验丰富的独特紧密合作 病毒学家和专家病毒动态和计算科学家提出的对促进至关重要 数据驱动的建模概念的开发和利用以阐明详细的 调节HBV的分子生物学过程。具体而言，我们建议（i）量化HBV 带有人源化肝脏的UPA-SCID嵌合小鼠中的感染动力学并发展 数学/计算模型以阐明调节HBV动力学的过程，（ii） 通过表征HBV感染来完善我们对分子水平上HBV感染的理解 体外动力学和开发多室数学/计算模型的动力学 阐明调节HBV动态的过程，（iii）验证和完善我们的理解 通过表征/建模对已知抗病毒药的HBV治疗反应来感染HBV感染 作用机理，（iv）使用HBV数学/计算模型来预测MOA 通过临床相关的药物抑制HBV并经验检验这些假设。