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

Abstract

While the liver, specifically hepatocytes, are widely accepted as the main source of hepatitis C virus (HCV) production, the role of the liver/hepatocytes in clearance of circulating HCV remains unknown. Frequent HCV kinetic data were recorded and mathematically modeled from five liver transplant patients throughout the anhepatic (absence of liver) phase and for 4 hr post-reperfusion. During the anhepatic phase, HCV remained at pre-anhepatic levels (n = 3) or declined (n = 2) with t1/2~1 hr. Immediately post-reperfusion, virus declined in a biphasic manner in four patients consisting of a rapid decline (t1/2 = 5 min) followed by a slower decline (t1/2 = 67 min). Consistent with the majority of patients in the anhepatic phase, when we monitored HCV clearance at 37°C from culture medium in the absence/presence of chronically infected hepatoma cells that were inhibited from secreting HCV, the HCV t1/2 in cell culture was longer in the absence of chronically HCV-infected cells. The results suggest that the liver plays a major role in the clearance of circulating HCV and that hepatocytes may be involved.

Quantitative methods for optimizing patient outcomes in liver transplantation

“…[20] Mechanistic models of liver dynamics have been used to study regeneration in living donors [21] and the role of allografts in clearing hepatitis viruses after transplantation. [22,23] Mathematical models have been built to help understand the factors that influence transplant outcomes. For example, agent-based models (ABMs) have been developed to simulate the posttransplant immune system, offering insights into allograft rejection and its mitigation by immunosuppression.…”

“…Mechanistic models of liver dynamics have been used to study regeneration in living donors 21 and the role of allografts in clearing hepatitis viruses after transplantation 22,23 . Mathematical models have been built to help understand the factors that influence transplant outcomes.…”

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

“…Viral dynamic mathematical models are useful in our understanding of the in vivo kinetics of a variety of viruses that trigger both persistent infection (e.g., HIV-1 [1][2][3][4][5], HBV [6][7][8], HDV [9][10][11][12], Theiler's encephalomyelitis virus [13], herpes [14] and HCV [15][16][17]) and acute infection (e.g., SARS-CoV-2, influenza A [18] and ebola [19,20]). Mathematical models are also improving our understanding of intracellular viral genome dynamics.…”