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

基本信息

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
项目类别：
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

项目摘要

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，乙型肝炎病毒 (HBV) 仍然在全球范围内造成巨大影响健康负担。全球有超过 2.6 亿人感染 HBV，导致慢性肝炎等每年有超过 400,000 人死于肝细胞癌。虽然目前可用的药物可以抑制乙型肝炎病毒复制只有一小部分患者被治愈。如此一来，更深层次的需要在分子水平上了解 HBV 感染动态，以便能够开发更有效（即治疗）的疗法。幸运的是，已经取得了重大进展最近，通过建立具有人源化肝脏的嵌合小鼠模型，保留对乙型肝炎病毒感染的许可和牛磺胆酸钠的鉴定共转运多肽（NTCP）作为 HBV 进入受体，当表达时外源性使肝癌细胞培养物能够在体外感染 HBV。因此，对于我们第一次可以在小鼠和细胞培养中进行 HBV 感染，以表征 HBV 生命周期和治疗反应。为此，本跨学科 R01 的目标是通过制定和测试数学/计算模型来增加我们对 HBV 的了解乙型肝炎病毒感染。前提是对乙型肝炎病毒感染有更定量的了解，治疗动态将有助于确定速率限制步骤，确定更有效的抗病毒靶点和预测当前药物和正在开发的药物的作用机制（MOA），从而促进改进疗法的设计。经验丰富的人之间独特的密切合作病毒学家和专家病毒动力学和计算科学家提出对于促进开发和利用数据驱动建模概念来阐明详细的调节 HBV 的分子生物学过程。具体来说，我们建议 (i) 量化 HBV 具有人源化肝脏的 uPA-SCID 嵌合小鼠的感染动力学并发展阐明调节 HBV 动力学过程的数学/计算模型，(ii) 通过表征 HBV 感染，在分子水平上完善我们对 HBV 感染的理解体外动力学并开发多室数学/计算模型阐明调节 HBV 动态的过程，(iii) 验证和完善我们的理解通过表征/建模 HBV 对已知抗病毒药物的治疗反应来评估 HBV 感染作用机制，以及 (iv) 使用 HBV 数学/计算模型来预测 MOA 通过临床相关药物抑制 HBV 并通过经验检验这些假设。