Modeling hepatitis C virus kinetics during liver transplantation reveals the role of the liver in virus clearance

Quantitative methods for optimizing patient outcomes in liver transplantation

Liver transplantation (LT) is a lifesaving yet complex intervention with considerable challenges impacting graft and patient outcomes. Despite best practices, 5-year graft survival is only 70%. Sophisticated quantitative techniques offer potential solutions by assimilating multifaceted data into insights exceeding human cognition. Optimizing donor-recipient matching and graft allocation presents additional intricacies, involving the integration of clinical and laboratory data to select the ideal donor and recipient pair. Allocation must balance physiological variables with geographical and logistical constraints and timing. Quantitative methods can integrate these complex factors to optimize graft utilization. Such methods can also aid in personalizing treatment regimens, drawing on both pretransplant and posttransplant data, possibly using continuous immunological monitoring to enable early detection of graft injury or infected states. Advanced analytics is thus poised to transform management in LT, maximizing graft and patient survival. In this review, we describe quantitative methods applied to organ transplantation, with a focus on LT. These include quantitative methods for (1) utilizing and allocating donor organs equitably and optimally, (2) improving surgical planning through preoperative imaging, (3) monitoring graft and immune status, (4) determining immunosuppressant doses, and (5) establishing and maintaining the health of graft and patient after LT.

Hepatitis D Virus and HBsAg Dynamics in the era of new Antiviral Treatments

PURPOSE OF REVIEW

Hepatitis D virus (HDV) infection is the most severe form of chronic viral hepatitis, with no FDA-approved therapy. Progress in the development of effective HDV treatments is accelerating. This review highlights how mathematical modeling is improving understanding of HDV-HBsAg-host dynamics during antiviral therapy and generating insights into the efficacy and modes of action (MOA) of new antiviral agents.

RECENT FINDINGS

Clinical trials with pegylated-interferon-λ, bulevertide, nucleic acid polymers, and/or lonafarnib against various steps of the HDV-life cycle have revealed new viral-kinetic patterns that were not observed under standard treatment with pegylated-interferon-α. Modeling indicated that the half-lives of circulating HDV and HBsAg are ~ 1.7 d and ~ 1.3 d, respectively, estimated the relative response of HDV and HBsAg during different antiviral therapies, and provided insights into the efficacy and MOA of drugs in development for treating HDV, which can inform response-guided therapy to individualize treatment duration. Mathematical modeling of HDV and HBsAg kinetics provides a window into the HDV virus lifecycle, HDV-HBsAg-host dynamics during antiviral therapy, and the MOA of new drugs for HDV.

Advances in Parameter Estimation and Learning from Data for Mathematical Models of Hepatitis C Viral Kinetics

Mathematical models, some of which incorporate both intracellular and extracellular hepatitis C viral kinetics, have been advanced in recent years for studying HCV–host dynamics, antivirals mode of action, and their efficacy. The standard ordinary differential equation (ODE) hepatitis C virus (HCV) kinetic model keeps track of uninfected cells, infected cells, and free virus. In multiscale models, a fourth partial differential equation (PDE) accounts for the intracellular viral RNA (vRNA) kinetics in an infected cell. The PDE multiscale model is substantially more difficult to solve compared to the standard ODE model, with governing differential equations that are stiff. In previous contributions, we developed and implemented stable and efficient numerical methods for the multiscale model for both the solution of the model equations and parameter estimation. In this contribution, we perform sensitivity analysis on model parameters to gain insight into important properties and to ensure our numerical methods can be safely used for HCV viral dynamic simulations. Furthermore, we generate in-silico patients using the multiscale models to perform machine learning from the data, which enables us to remove HCV measurements on certain days and still be able to estimate meaningful observations with a sufficiently small error.