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

Effect of Peg-IFN on the viral kinetics of patients with HDV infection treated with bulevirtide

Background & Aims

Bulevirtide is a first-in-class entry inhibitor antiviral treatment for chronic hepatitis D. The viral kinetics during bulevirtide therapy and the effect of combining bulevirtide with pegylated-interferon (Peg-IFN) are unknown.

Methods

We used mathematical modelling to analyze the viral kinetics in two French observational cohorts of 183 patients receiving bulevirtide with or without Peg-IFN for 48 weeks.

Results

The efficacy of bulevirtide in blocking cell infection was estimated to 90.3%, whereas Peg-IFN blocked viral production with an efficacy of 92.4%, albeit with large inter-individual variabilities. The addition of Peg-IFN to bulevirtide was associated with a more rapid virological decline, with a rate of virological response (>2 log of decline or undetectability) at week 48 of 86.9% (95% prediction interval [PI] = [79.7-95.0]), compared with 56.1% (95% PI = [46.4-66.7]) with bulevirtide only. The model was also used to predict the probability to achieve a cure of viral infection, with a rate of 8.8% (95% PI = [3.5-13.2]) with bulevirtide compared with 18.8% (95% PI = [11.6-29.0]) with bulevirtide + Peg-IFN. Mathematical modelling suggests that after 144 weeks of treatment, the rates of viral cure could be 42.1% (95% PI = [33.3-52.6]) with bulevirtide and 66.7% (95% PI = [56.5-76.8]) with bulevirtide + Peg-IFN.

Conclusions

In this analysis of real-world data, Peg-IFN strongly enhanced the kinetics of viral decline in patients treated with bulevirtide. Randomized clinical trials are warranted to assess the virological and clinical benefit of this combination, and to identify predictors of poor response to treatment.

Impact and implications

Bulevirtide has been approved for chronic HDV infection by regulatory agencies in Europe based on its good safety profile and rapid virological response after treatment initiation, but the optimal duration of treatment and the chance to achieve a sustained virological response remain unknown. The results presented in this study have a high impact for clinicians and investigators as they provide important knowledge on the long-term virological benefits of a combination of Peg-IFN and bulevirtide in patients with CHD. Clinical trials are now warranted to confirm those predictions.

Mathematical Models of Early Hepatitis B Virus Dynamics in Humanized Mice

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 % 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.

Stationary distribution of a reaction-diffusion hepatitis B virus infection model driven by the Ornstein-Uhlenbeck process

A reaction-diffusion hepatitis B virus (HBV) infection model based on the mean-reverting Ornstein-Uhlenbeck process is studied in this paper. We demonstrate the existence and uniqueness of the positive solution by constructing the Lyapunov function. The adequate conditions for the solution's stationary distribution were described. Last but not least, the numerical simulation demonstrated that reversion rates and noise intensity influenced the disease and that there was a stationary distribution. It was concluded that the solution tends more toward the stationary distribution, the greater the reversion rates and the smaller the noise.

Making waves: Integrating wastewater surveillance with dynamic modeling to track and predict viral outbreaks

Wastewater surveillance has proved to be a valuable tool to track the COVID-19 pandemic. However, most studies using wastewater surveillance data revolve around establishing correlations and lead time relative to reported case data. In this perspective, we advocate for the integration of wastewater surveillance data with dynamic within-host and between-host models to better understand, monitor, and predict viral disease outbreaks. Dynamic models overcome emblematic difficulties of using wastewater surveillance data such as establishing the temporal viral shedding profile. Complementarily, wastewater surveillance data bypasses the issues of time lag and underreporting in clinical case report data, thus enhancing the utility and applicability of dynamic models. The integration of wastewater surveillance data with dynamic models can enhance real-time tracking and prevalence estimation, forecast viral transmission and intervention effectiveness, and most importantly, provide a mechanistic understanding of infectious disease dynamics and the driving factors. Dynamic modeling of wastewater surveillance data will advance the development of a predictive and responsive monitoring system to improve pandemic preparedness and population health.

A Semi-mechanistic Model to Characterize the Long-Term Dynamics of Hepatitis B Virus Markers During Treatment With Lamivudine and Pegylated Interferon

Antiviral treatments against hepatitis B virus (HBV) suppress viral replication but do not eradicate the virus, and need therefore to be taken lifelong to avoid relapse. Mathematical models can be useful to support the development of curative anti-HBV agents; however, they mostly focus on short-term HBV DNA data and neglect the complex host-pathogen interaction. This work aimed to characterize the effect of treatment with lamivudine and/or pegylated interferon (Peg-IFN) in 1,300 patients (hepatitis B envelope antigen (HBeAg)-positive and HBeAg-negative) treated for 1 year. A mathematical model was developed incorporating two populations of infected cells, namely I 1 , with a high transcriptional activity, that progressively evolve into I 2 , at a rate δ tr , representing cells with integrated HBV DNA that have a lower transcriptional activity. Parameters of the model were estimated in patients treated with lamivudine or Peg-IFN alone (N = 894), and the model was then validated in patients treated with lamivudine plus Peg-IFN (N = 436) to predict the virological response after a year of combination treatment. Lamivudine had a larger effect in blocking viral production than Peg-IFN (99.4-99.9% vs. 91.8-95.1%); however, Peg-IFN had a significant immunomodulatory effect, leading to an enhancement of the loss rates of I 1 (×1.7 in HBeAg-positive patients), I 2 (> ×7 irrespective of HBeAg status), and δ tr (×4.6 and ×2.0 in HBeAg-positive and HBeAg-negative patients, respectively). Using this model, we were able to describe the synergy of the different effects occurring during treatment with combination and predicted an effect of 99.99% on blocking viral production. This framework can therefore support the optimization of combination therapy with new anti-HBV agents.

Multiscale Model of Antiviral Timing, Potency, and Heterogeneity Effects on an Epithelial Tissue Patch Infected by SARS-CoV-2

We extend our established agent-based multiscale computational model of infection of lung tissue by SARS-CoV-2 to include pharmacokinetic and pharmacodynamic models of remdesivir. We model remdesivir treatment for COVID-19; however, our methods are general to other viral infections and antiviral therapies. We investigate the effects of drug potency, drug dosing frequency, treatment initiation delay, antiviral half-life, and variability in cellular uptake and metabolism of remdesivir and its active metabolite on treatment outcomes in a simulated patch of infected epithelial tissue. Non-spatial deterministic population models which treat all cells of a given class as identical can clarify how treatment dosage and timing influence treatment efficacy. However, they do not reveal how cell-to-cell variability affects treatment outcomes. Our simulations suggest that for a given treatment regime, including cell-to-cell variation in drug uptake, permeability and metabolism increase the likelihood of uncontrolled infection as the cells with the lowest internal levels of antiviral act as super-spreaders within the tissue. The model predicts substantial variability in infection outcomes between similar tissue patches for different treatment options. In models with cellular metabolic variability, antiviral doses have to be increased significantly (>50% depending on simulation parameters) to achieve the same treatment results as with the homogeneous cellular metabolism.

Machine learning for mathematical models of HCV kinetics during antiviral therapy

Mathematical models for hepatitis C virus (HCV) dynamics have provided a means for evaluating the antiviral effectiveness of therapy and estimating treatment outcomes such as the time to cure. Recently, a mathematical modeling approach was used in the first proof-of-concept clinical trial assessing in real-time the utility of response-guided therapy with direct-acting antivirals (DAAs) in chronic HCV-infected patients. Several retrospective studies have shown that mathematical modeling of viral kinetics predicts time to cure of less than 12 weeks in the majority of individuals treated with sofosbuvir-based as well as other DAA regimens. A database of these studies was built, and machine learning methods were evaluated for their ability to estimate the time to cure for each patient to facilitate real-time modeling studies. Data from these studies exploring mathematical modeling of HCV kinetics under DAAs in 266 chronic HCV-infected patients were gathered. Different learning methods were applied and trained on part of the dataset ('train' set), to predict time to cure on the untrained part ('test' set). Our results show that this machine learning approach provides a means for establishing an accurate time to cure prediction that will support the implementation of individualized treatment.

Modeling the dynamics of Usutu virus infection in birds

Usutu virus is an emerging zoonotic flavivirus causing high avian mortality rates and occasional severe neurological disorders in humans. Several virus strains are co-circulating and the differences in their characteristics and avian pathogenesis levels are still unknown. In this study, we use within-host mathematical models to characterize the mechanisms responsible for virus expansion and clearance in juvenile chickens challenged with four Usutu virus strains. We find heterogeneity between the virus strains, with the time between cell infection and viral production varying between 16 h and 23 h, the infected cell lifespan varying between 48 min and 9.5 h, and the basic reproductive number R₀ varying between 12.05 and 19.49. The strains with high basic reproductive number have short infected cell lifespan, indicative of immune responses. The virus strains with low basic reproductive number have lower viral peaks and longer lasting viremia, due to lower infection rates and high infected cell lifespan. We discuss how the host and virus heterogeneities may differently impact the public health threat presented by these virus strains.

Early events in hepatitis B infection: the role of inoculum dose

The relationship between the inoculum dose and the ability of the pathogen to invade the host is poorly understood. Experimental studies in non-human primates infected with different inoculum doses of hepatitis B virus have shown a non-monotonic relationship between dose magnitude and infection outcome, with high and low doses leading to 100% liver infection and intermediate doses leading to less than 0.1% liver infection, corresponding to CD4 T-cell priming. Since hepatitis B clearance is CD8 T-cell mediated, the question of whether the inoculum dose influences CD8 T-cell dynamics arises. To help answer this question, we developed a mathematical model of virus-host interaction following hepatitis B virus infection. Our model explains the experimental data well, and predicts that the inoculum dose affects both the timing of the CD8 T-cell expansion and the quality of its response, especially the non-cytotoxic function. We find that a low-dose challenge leads to slow CD8 T-cell expansion, weak non-cytotoxic functions, and virus persistence; high- and medium-dose challenges lead to fast CD8 T-cell expansion, strong cytotoxic and non-cytotoxic function, and virus clearance; while a super-low-dose challenge leads to delayed CD8 T-cell expansion, strong cytotoxic and non-cytotoxic function, and virus clearance. These results are useful for designing immune cell-based interventions.

Understanding the antiviral effects of RNAi-based therapy in HBeAg-positive chronic hepatitis B infection

The RNA interference (RNAi) drug ARC-520 was shown to be effective in reducing serum hepatitis B virus (HBV) DNA, hepatitis B e antigen (HBeAg) and hepatitis B surface antigen (HBsAg) in HBeAg-positive patients treated with a single dose of ARC-520 and daily nucleosidic analogue (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 multi-compartmental pharmacokinetic-pharamacodynamic model and calibrated it with frequent measured HBV kinetic data. We showed that the time-dependent single dose 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 single dose ARC-520 and entecavir effect on HBV DNA was constant over time, with efficacy of more than 99.8%. The observed continuous HBV DNA decline is entecavir mediated, the strong but transient HBsAg and HBeAg decays are ARC-520 mediated. The modeling framework may help assess ongoing RNAi drug development for hepatitis B virus infection.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Efficient Methods for Parameter Estimation of Ordinary and Partial Differential Equation Models of Viral Hepatitis Kinetics

Parameter estimation in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation that addresses all cases of data points available presents a formidable challenge and efficiency considerations need to be employed in order for the method to become practical. In the case of age-structured models of viral hepatitis dynamics under antiviral treatment that deal with partial differential equations, a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derivative approximations. The newly efficient methods that were developed as a result of the above approach are described for hepatitis C virus kinetic models during antiviral therapy. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for both the ordinary and partial differential equation models.

Hepatitis B envelope antigen increases Tregs by converting CD4+CD25- T cells into CD4+CD25+Foxp3+ Tregs

Hepatitis B virus (HBV) can establish a lifelong chronic infection in humans, leading to liver cirrhosis, liver failure and hepatocellular carcinoma. Patients with chronic hepatitis B (CHB) exhibit a weak virus-specific immune response. Regulatory T cells (Tregs) play a key role in regulating the immune response in patients with CHB. Patients with hepatitis B envelope antigen (HBeAg)-positive CHB harbored a higher percentage of Tregs in their peripheral blood than those with HBeAg-negative CHB. However, whether and how HBeAg manipulates the host immune system to increase the population of Tregs remains to be elucidated. The present manuscript describes a preliminary immunological study of HBeAg in a mouse model. Multiple potential CD4⁺ T cell epitopes in HBeAg were identified using Immune Epitope Database consensus binding prediction. It was demonstrated that HBeAg treatment increased the numbers of Tregs in mouse spleens in vitro and in vivo. Furthermore, it was indicated that the HBeAg-mediated increase in Tregs occurred through the conversion of CD4⁺CD25^- T cells into CD4⁺CD25⁺Foxp3⁺ Tregs. Additionally, in vitro study illustrated that HBeAg stimulated murine spleen cells to produce increased transforming growth factor-β, which is required to enable HBeAg to convert T cells into Tregs. The results of the present study may provide further evidence of the effect of HBeAg on Tregs and aid in the development of novel HBeAg-based immunotherapy for CHB.

Model Averaging in Viral Dynamic Models

The paucity of experimental data makes both inference and prediction particularly challenging in viral dynamic models. In the 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.e., (1) inference in the presence of poorly identifiable parameters, namely, initial viral inoculum and eclipse phase duration, (2) 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.

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

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.

Validated Models of Immune Response to Virus Infection

Viruses are a main cause of disease worldwide and many are without effective therapeutics or vaccines. A lack of understanding about how host responses work to control viral spread is one factor limiting effective management. How different immune components regulate infection dynamics is beginning to be better understood with the help of mathematical models. These models have been key in discriminating between hypotheses and in identifying rates of virus growth and clearance, dynamical control by different host factors and antivirals, and synergistic interactions during multi-pathogen infections. A recent focus in evaluating model predictions in the laboratory and clinic has illuminate the accuracy of models for a variety of viruses and highlighted the critical nature of theoretical approaches in virology. Here, I discuss recent model-driven exploration of host-pathogen interactions that have illustrated the importance of model validation in establishing the model's predictive capability and in defining new biology.

Multifunctional Chitosan Inverse Opal Particles for Wound Healing

Wound healing is one of the most important and basic issues faced by the medical community. In this paper, we present biomass-composited inverse opal particles with a series of advanced features for drug delivery and wound healing. The particles were derived by using chitosan biomass to negatively replicate spherical colloid crystal templates. Because of the interconnected porous structures, various forms of active drugs, including fibroblast growth factor could be loaded into the void spaces of the inverse opal particles and encapsulated by temperature-responsive hydrogel. This endowed the composited particles with the capability of intelligent drug release through the relatively high temperature caused by the inflammation reaction at wound sites. Because the structural colors and characteristic reflection peaks of the composited inverse opal particles are blue-shifted during the release process, the drug delivery can be monitored in real time. It was demonstrated that the biomass-composited microcarriers were able to promote angiogenesis, collagen deposition, and granulation-tissue formation as well as reduce inflammation and thus significantly contributed to wound healing. These features point to the potential value of multifunctional biomass inverse opal particles in biomedicine.