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.
Despite availability of an effective vaccine and drugs that can suppress viral replication, hepatitis B virus (HBV) continues to impose an enormous global health burden. With the availability of new HBV infection experimental model systems, we propose to characterize HBV infection in mice with humanized livers and cell culture to develop new stochastic multiscale mathematical/ computational models to provide insight into HBV infection dynamics and ultimately treatment response.