Modeling suggests that virion production cycles within individual cells is key to understanding acute hepatitis B virus infection kinetics

Hailegiorgis, Atesmachew; Ishida, Yuji; Collier, Nicholson; Imamura, Michio; Shi, Zhenzhen; Reinharz, Vladimir; Tsuge, Masataka; Barash, Danny; Hiraga, Nobuhiko; Yokomichi, Hiroshi; Tateno, Chise; Ozik, Jonathan; Uprichard, Susan L.; Chayama, Kazuaki; Dahari, Harel

doi:10.1371/journal.pcbi.1011309

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…More data is needed to determine the shape of such a continuous antiviral effect and overall sHBV and HBsAg dynamics, which can have complex patterns of decay, as seen recently in HBV infected severe combined immunodeficient mice Hailegiorgis et al. ( 2023 ); Ishida et al. ( 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Mathematical Models of Early Hepatitis B Virus Dynamics in Humanized Mice

Ciupe,

Dahari,

Ploss

2024

Bull Math Biol

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Analyzing the impact of the adaptive immune response during acute hepatitis B virus (HBV) infection is essential for understanding disease progression and control. Here we developed mathematical models of HBV infection which either lack terms for adaptive immune responses, or assume adaptive immune responses in the form of cytolytic immune killing, non-cytolytic immune cure, or non-cytolytic-mediated block of viral production. We validated the model that does not include immune responses against temporal serum hepatitis B DNA (sHBV) and temporal serum hepatitis B surface-antigen (HBsAg) experimental data from mice engrafted with human hepatocytes (HEP). Moreover, we validated the immune models against sHBV and HBsAg experimental data from mice engrafted with HEP and human immune system (HEP/HIS). As expected, the model that does not include adaptive immune responses matches the observed high sHBV and HBsAg concentrations in all HEP mice. By contrast, while all immune response models predict reduction in sHBV and HBsAg concentrations in HEP/HIS mice, the Akaike Information Criterion cannot discriminate between non-cytolytic cure (resulting in a class of cells refractory to reinfection) and antiviral block functions (of up to $$99\%$$ 99 % viral production 1–3 weeks following peak viral load). We can, however, reject cytolytic killing, as it can only match the sHBV and HBsAg data when we predict unrealistic levels of hepatocyte loss.

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Section: Discussionmentioning

confidence: 99%

Mathematical Models of Early Hepatitis B Virus Dynamics in Humanized Mice

Ciupe,

Dahari,

Ploss

2024

Bull Math Biol

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“…Mathematical modeling has a long history in studying the dynamics of many viruses causing acute and chronic infections, including West Nile virus [27], dengue [28][29][30][31][32], Zika [33][34][35], respiratory syncytial virus [36,37], influenza [25,[38][39][40][41][42], SARS-CoV-2 [1,12,16,[22][23][24][43][44][45][46][47][48][49][50], hepatitis C virus [51][52][53][54][55][56][57][58], hepatitis B virus [59][60][61][62][63][64][65][66] and HIV [67][68][69]…”

Section: Introductionmentioning

confidence: 99%

How robust are estimates of key parameters in standard viral dynamic models?

Zitzmann,

Ke,

Ribeiro

et al. 2024

PLoS Comput Biol

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Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.

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