Modeling the dynamics of hepatitis B infection, immunity, and drug therapy

Ciupe, Stanca M.

doi:10.1111/imr.12686

Cited by 34 publications

(25 citation statements)

References 118 publications

(318 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, HbxAg is the key factor and plays a central role in the development of HCC. HBV genome encodes four overlapping open reading frames (ORF), of which X ORF is one [14]. X ORF encodes a 16.5 kDa protein, HbxAg, which has multiple functions.…”

Section: Discussionmentioning

confidence: 99%

Survivin expression starts before hepatocellular cancer development in the liver of chronic hepatitis B patients: a pilot, cross-sectional study

Akpınar¹,

Şimşek²,

Aksoy³

et al. 2020

View full text Add to dashboard Cite

Introduction: Survivin expression is well known feature of hepatocellular carcinoma (HCC); however, there is no information about survivin expression in chronic hepatitis B (CHB). Aim: Investigating survivin expression in the liver of CHB patients. Material and methods: This is a single-centre, cross-sectional study. Seventy-five CHB patients and eight control patients were enrolled into the study between 2008 and 2018. Immunohistochemical study was performed by using anti-survivin antibody to evaluate survivin immunoreactivity. Results: Survivin immunoreactivity was significantly higher in CHB patients compared to controls (p = 0.008). Also, the degree of survivin immunoreactivity was significantly higher in CHB patients (p = 0.027). Between the anti-survivin-positive and anti-survivin-negative groups, baseline laboratory parameters and initial pathology features were not significantly different. Conclusions: This is the first study evaluating survivin expression in CHB patients. Understanding the possible relationship between survivin expression and HCC development in this population can promote new studies in terms of new therapies and treatment timing.

show abstract

Section: Discussionmentioning

confidence: 99%

Survivin expression starts before hepatocellular cancer development in the liver of chronic hepatitis B patients: a pilot, cross-sectional study

Akpınar¹,

Şimşek²,

Aksoy³

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Applications have been summarized in a recent issue of Immunological Reviews (3), showing their relevance for understanding the host-pathogen interactions in both chronic and acute infections (4)(5)(6). In the last decade, parameter estimation of these models has increasingly relied on nonlinear mixed effect models (NLMEM), a statistical approach that improves both precision and accuracy of estimates by explicitly taking into account the between-subjects variability in the model (7,8).…”

Section: Introductionmentioning

confidence: 99%

Model Averaging in Viral Dynamic Models

Gonçalves

Mentré

Lemenuel‐Diot

et al. 2020

AAPS J

View full text Add to dashboard Cite

The paucity of experimental data makes both inference and prediction particularly challenging in viral dynamic models. In presence of several candidate models, a common strategy is model selection (MS), in which models are fitted to the data but only results obtained with the "best model" are presented. However, this approach ignores model uncertainty, which may lead to inaccurate predictions. When several models provide a good fit to the data, another approach is model averaging (MA) that weights the predictions of each model according to its consistency to the data. Here we evaluated by simulations in a nonlinear mixed-effect model framework the performances of MS and MA in two realistic cases of acute viral infection: i) inference in presence of poorly identifiable parameters, namely initial viral inoculum and eclipse phase duration ii) uncertainty on the mechanisms of action of the immune response. MS was associated in some scenarios with a large rate of false selection. This led to a coverage rate lower than the nominal coverage rate of 0.95 in the majority of cases and below 0.50 in some scenarios. In contrast, MA provided better estimation of parameter uncertainty, with coverage rates ranging from 0.72 to 0.98 and mostly comprised within the nominal coverage rate. Finally, MA provided similar predictions than those obtained with MS. In conclusion, parameter estimates obtained with MS should be taken with caution, especially when several models well describe the data. In this situation, MA has better performances and could be performed to account for model uncertainty.

show abstract

“…In this study, we developed mathematical models that best reproduce observed HBV DNA, HBsAg and HBeAg kinetics following a single dose of ARC-520 in five HBeAg-positive patients from the Heparc-2001 study. Mathematical models of hepatitis B infection have been used to study the dynamics of acute, chronic, and occult HBV infections [20][21][22][23][24], anti-HBV therapy [14,[25][26][27][28][29][30], cell-to-cell transmission [31] , intracellular interactions [31][32][33], cellular immune responses [21,25,[34][35][36], antibody-mediated immune responses [11,33,37], HBeAg [33,38,39], and HBeAb [33] dynamics. We build on previous modeling work, consider the interaction between HBV DNA, HBsAg and HBeAg titers in the presence of a single dose RNAi-based therapy, and use the model to run in silico experiments to predict individual contributions of different drug effects on 2/23 the dynamics for HBsAg titers.…”

Section: Introductionmentioning

confidence: 99%

Understanding the antiviral effects of RNAi-based therapy on chronic hepatitis B infection

Ciupe

Dahari

Kadelka

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Reaching hepatitis B surface antigen (HBsAg) loss (called functional cure) with approved treatment with pegylated interferon-α (IFN) and/or nucleos(t)ide analogues (NAs) in chronic hepatitis B virus (HBV) infected patients is suboptimal. The RNA interference (RNAi) drug ARC-520 was shown to be effective in reducing serum HBV DNA, HBsAg and hepatitis B e antigen (HBeAg) in chimpanzees and small animals. A recent clinical study (Heparc-2001) showed reduction of serum HBV DNA, HBeAg and HBsAg in HBeAg-positive patients treated with a single dose of ARC-520 and daily NA (entecavir). To provide insights into HBV dynamics under ARC-520 treatment and its efficacy in blocking HBV DNA, HBsAg, and HBeAg production we developed a a multi-compartmental pharmacokinetic-pharamacodynamic model and calibrated it with measured HBV data. We showed that the time-dependent ARC-520 efficacies in blocking HBsAg and HBeAg are more than 96% effective around day 1, and slowly wane to 50% in 1-4 months. The combined ARC-520 and entecavir effect on HBV DNA is constant over time, with efficacy of more than 99.8%. HBV DNA loss is entecavir mediated and the strong but transient HBsAg and HBeAg decays are solely ARC-520 mediated. We added complexity to the model in order to reproduce current long-term therapy outcomes with NAs by considering the tradeoff between hepatocyte loss and hepatocyte division, and used it to make in-silico long-term predictions for virus, HBsAg and HBeAg titer dynamics. These results may help assess ongoing RNAi drug development for hepatitis B virus infection.Author summaryWith about 300 million persons infected worldwide and 800,000 deaths annually, chronic infection with hepatitis B virus (HBV) is a major public health burden with high endemic areas around the world. Current treatment options focus on removing circulating HBV DNA but are suboptimal in removing hepatitis B s- and e-antigens. ARC-520, a RNA interference drug, had induced substantial hepatitis B s- and e- antigen reductions in animals and patients receiving therapy. We study the effect of ARC-520 on hepatitis B s- and e-antigen decline by developing mathematical models for the dynamics of intracellular and serum viral replication, and compare it to patient HBV DNA, hepatitis B s- and e-antigen data from a clinical trial with one ARC-520 injection and daily nucleoside analogue therapy. We examine biological parameters describing the different phases of HBV DNA, s-antigen and e-antigen decline and rebound after treatment initiation, and estimate treatment effectiveness. Such approach can inform the RNA interference drug therapy.

show abstract

Modeling the dynamics of hepatitis B infection, immunity, and drug therapy

Cited by 34 publications

References 118 publications

Survivin expression starts before hepatocellular cancer development in the liver of chronic hepatitis B patients: a pilot, cross-sectional study

Survivin expression starts before hepatocellular cancer development in the liver of chronic hepatitis B patients: a pilot, cross-sectional study

Model Averaging in Viral Dynamic Models

Understanding the antiviral effects of RNAi-based therapy on chronic hepatitis B infection

Contact Info

Product

Resources

About