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

尽管有一种有效的疫苗，但乙肝病毒(HBV)继续在全球范围内造成巨大的 健康负担。全世界超过2.6亿人感染了乙肝病毒，导致慢性肝炎和更多 每年死于肝细胞癌的人数超过40万。虽然目前可用的药物可以 抑制乙肝病毒复制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乙肝病毒感染的动态，以使 开发更有效的(即治愈的)疗法。幸运的是，重大进展已经 最近建立了具有人化肝脏的嵌合小鼠模型， 对乙肝病毒感染的保留容许性及牛磺胆酸钠的鉴定 共转运多肽作为乙肝病毒进入受体的表达 外源性使肝癌细胞培养在体外允许乙肝病毒感染。因此，对于 第一次，我们可以在小鼠身上进行乙肝病毒感染和细胞培养，以表征乙肝病毒的生命周期 和治疗反应。为此，这一跨学科R01的目标是 通过建立和测试数学/计算模型来增加我们对乙肝病毒的了解 乙肝病毒感染的可能性。前提是更定量地了解乙肝病毒感染和 治疗动态将有助于确定限速步骤，确定更有效的抗病毒靶点，并 预测当前药物和正在开发的药物的作用机制，从而促进 改进疗法的设计。经验丰富的人之间独特的密切合作努力 病毒学家和病毒动力学和计算专家提出的建议对于促进 详细阐述了开发和利用数据驱动建模的概念 调节乙肝病毒的分子生物学过程。具体地说，我们建议(I)量化乙肝病毒 人肝uPA-SCID嵌合小鼠的感染动力学及发育 数学/计算模型以阐明调节乙肝病毒动态的过程，(Ii) 通过对乙肝病毒感染的描述，在分子水平上加深对乙肝病毒感染的理解 体外动力学和开发多室数学/计算模型以 阐明调节乙肝病毒动态的过程，(Iii)验证和完善我们的理解 通过表征/模拟对已知抗病毒药物的乙肝治疗反应来了解乙肝病毒感染 作用机制，以及(Iv)使用乙肝数学/计算模型来预测MOA 通过哪些临床相关药物抑制乙肝病毒，并经验性地检验这些假说。

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.