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

尽管有有效的疫苗，但乙型肝炎病毒（HBV）继续在全球范围内造成巨大的影响。B型肝炎病毒（HBV） 健康负担。全世界有超过2.6亿人感染HBV，导致慢性肝炎等 每年有超过40万人死于肝细胞癌。虽然现有药物可以 只有一小部分患者被治愈。因此，更深层次的 需要在分子水平上了解HBV感染动力学， 开发更有效（即治愈性）的治疗方法。幸运的是， 最近建立了具有人源化肝脏的嵌合小鼠模型， 保留对HBV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， 外源性地使肝癌细胞培养物允许体外HBV感染。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的生命周期 和治疗反应。为此，本跨学科R 01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 乙肝病毒感染。前提是对HBV感染有更定量的了解， 治疗动力学将有助于确定限速步骤，确定更有效的抗病毒靶点， 预测当前药物和正在开发的药物的作用机制，从而促进 改进治疗方法的设计。在经验丰富的人之间进行独特的密切合作 病毒学家和病毒动力学专家以及计算科学家提出， 开发和利用数据驱动的建模概念，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）定量HBV 具有人源化肝脏和发育的uPA-SCID嵌合小鼠中的感染动力学 数学/计算模型，以阐明调节HBV动力学的过程，（ii） 通过描述HBV感染，在分子水平上完善我们对HBV感染的理解 体外动力学和开发多房室数学/计算模型， 阐明调节HBV动力学的过程，（iii）更新和完善我们的理解 通过表征/建模对已知抗病毒药物的HBV治疗反应， 作用机制，以及（iv）使用HBV数学/计算模型预测MOA 临床相关药物抑制HBV的机制，并对这些假设进行实证检验。

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.