Search

Recent studies indicate that treatment of chronic hepatitis D virus (HDV) with either pegylated interferon (IFN)λ or pegylated IFNα monotherapy leads to a dramatic decline in HDV RNA. Herein, we investigated the innate antiviral efficacy of IFNλ and IFNα in humanized mice that lack an adaptive immune response. Humanized mice were either co-infected with hepatitis B virus (HBV) and HDV simultaneously, or HDV infection was performed subsequent to HBV infection (i.e., superinfected). After steady viral replication was achieved, mice received either IFNλ (n = 6) or IFNα (n = 7) for 12 (or 13) weeks. Pretreatment median levels of serum HBV DNA (8.8 [IQR:0.2] log IU/ml), HDV RNA (9.8 [0.5] log IU/ml), HBsAg (4.0 [0.4] log IU/ml) and human albumin, hAlb (6.9 [0.1] log ng/mL) were similar between mice treated with IFNα or IFNλ and between those coinfected versus superinfected. Compared to mice treated with IFNλ, mice treated with IFNα had a significantly greater decline in HBV, HDV, and HBsAg levels. In conclusion, IFNα induces stronger inhibition of HBV and HDV than IFNλ in humanized mice that lack an adaptive immune response. Further studies are needed to assess the respective role of the combined innate-and adaptive-immune systems in the treatment of HBV and HDV with IFNα and IFNλ.

Access to treatment and medication for opioid use disorder (MOUD) is essential in reducing opioid use and associated behavioral risks, such as syringe sharing among persons who inject drugs (PWID). Syringe sharing among PWID carries high risk of transmission of serious infections such as hepatitis C and HIV. MOUD resources, such as methadone provider clinics, however, are often unavailable to PWID due to barriers like long travel distance to the nearest methadone provider and the required frequency of clinic visits. The goal of this study is to examine the uncertainty in the effects of travel distance in initiating and continuing methadone treatment and how these interact with different spatial distributions of methadone providers to impact co-injection (syringe sharing) risks. A baseline scenario of spatial access was established using the existing locations of methadone providers in a geographical area of metropolitan Chicago, Illinois, USA. Next, different counterfactual scenarios redistributed the locations of methadone providers in this geographic area according to the densities of both the general adult population and according to the PWID population per zip code. We define different reasonable methadone access assumptions as the combinations of short, medium, and long travel distance preferences combined with three urban/suburban travel distance preference. Our modeling results show that when there is a low travel distance preference for accessing methadone providers, distributing providers near areas that have the greatest need (defined by density of PWID) is best at reducing syringe sharing behaviors. However, this strategy also decreases access across suburban locales, posing even greater difficulty in regions with fewer transit options and providers. As such, without an adequate number of providers to give equitable coverage across the region, spatial distribution cannot be optimized to provide equitable access to all PWID. Our study has important implications for increasing interest in methadone as a resurgent treatment for MOUD in the United States and for guiding policy toward improving access to MOUD among PWID.

Background & Aims: Bulevirtide (BLV) was approved for the treatment of compensated chronic hepatitis D virus (HDV) infection in Europe in 2020. However, research into the effects of the entry inhibitor BLV on HDV-host dynamics is in its infancy. Methods: Eighteen patients with HDV under nucleos(t)ide analogue treatment for hepatitis B, with compensated cirrhosis and clinically significant portal hypertension, received BLV 2 mg/day. HDV RNA, alanine aminotransferase (ALT), and hepatitis B surface antigen (HBsAg) were measured at baseline, weeks 4, 8 and every 8 weeks thereafter. A mathematical model was developed to account for HDV, HBsAg and ALT dynamics during BLV treatment. Results: Median baseline HDV RNA, HBsAg, and ALT were 4.9 log IU/ml [IQR: 4.4-5.8], 3.7 log IU/ml [IQR: 3.4-3.9] and 106 U/L [IQR: 81-142], respectively. During therapy, patients fit into four main HDV kinetic patterns: monophasic (n = 2), biphasic (n = 10), flat-partial response (n = 4), and non-responder (n = 2). ALT normalization was achieved in 14 (78%) patients at a median of 8 weeks (range: 4-16). HBsAg remained at pre-treatment levels. Assuming that BLV completely (∼100%) blocks HDV entry, modeling indicated that two HDV-infected cell populations exist: fast HDV clearing (median t1/2 = 13 days) and slow HDV clearing (median t1/2 = 44 days), where the slow HDV-clearing population consisted of ∼1% of total HDV-infected cells, which could explain why most patients exhibited a non-monophasic pattern of HDV decline. Moreover, modeling explained ALT normalization without a change in HBsAg based on a non-cytolytic loss of HDV from infected cells, resulting in HDV-free HBsAg-producing cells that release ALT upon death at a substantially lower rate compared to HDV-infected cells. Conclusion: The entry inhibitor BLV provides a unique opportunity to understand HDV, HBsAg, ALT, and host dynamics. Impact and implications: Mathematical modeling of hepatitis D virus (HDV) treatment with the entry inhibitor bulevirtide (BLV) provides a novel window into the dynamics of HDV RNA and alanine aminotransferase. Kinetic data from patients treated with BLV monotherapy can be explained by hepatocyte populations with different basal HDV clearance rates and non-cytolytic clearance of infected cells. While further studies are needed to test and refine the kinetic characterization described here, this study provides a new perspective on viral dynamics, which could inform evolving treatment strategies for HDV.

ABSTRACT Chronic infection with hepatitis B and delta viruses (HDV) is the most serious form of viral hepatitis due to more severe manifestations of an accelerated progression to liver fibrosis, cirrhosis, and hepatocellular carcinoma. We characterized early HDV kinetics post-inoculation and incorporated mathematical modeling to provide insights into host-HDV dynamics. We analyzed HDV RNA serum viremia in 192 immunocompetent (C57BL/6) and immunodeficient (NRG) mice that did or did not transgenically express the HDV receptor—human sodium taurocholate co-transporting polypeptide (hNTCP). Kinetic analysis indicates an unanticipated biphasic decline consisting of a sharp first-phase and slower second-phase decline regardless of immunocompetence. HDV decline after re-inoculation again followed a biphasic decline; however, a steeper second-phase HDV decline was observed in NRG-hNTCP mice compared to NRG mice. HDV-entry inhibitor bulevirtide administration and HDV re-inoculation indicated that viral entry and receptor saturation are not major contributors to clearance, respectively. The biphasic kinetics can be mathematically modeled by assuming the existence of a non-specific-binding compartment with a constant on/off-rate and the steeper second-phase decline by a loss of bound virus that cannot be returned as free virus to circulation. The model predicts that free HDV is cleared with a half-life of 35 minutes (standard error, SE: 6.3), binds to non-specific cells with a rate of 0.05 per hour (SE: 0.01), and returns as free virus with a rate of 0.11 per hour (SE: 0.02). Characterizing early HDV-host kinetics elucidates how quickly HDV is either cleared or bound depending on the immunological background and hNTCP presence. IMPORTANCE The persistence phase of HDV infection has been studied in some animal models; however, the early kinetics of HDV in vivo is incompletely understood. In this study, we characterize an unexpectedly HDV biphasic decline post-inoculation in immunocompetent and immunodeficient mouse models and use mathematical modeling to provide insights into HDV-host dynamics.

Abstract 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.

Progress toward hepatitis C virus (HCV) elimination in the United States is not on track to meet targets set by the World Health Organization, as the opioid crisis continues to drive both injection drug use and increasing HCV incidence. A pragmatic approach to achieving this is using a microelimination approach of focusing on high-risk populations such as people who inject drugs (PWID). Computational models are useful in understanding the complex interplay of individual, social, and structural level factors that might alter HCV incidence, prevalence, transmission, and treatment uptake to achieve HCV microelimination. However, these models need to be informed with realistic sociodemographic, risk behavior and network estimates on PWID. We conducted a meta-analysis of research studies spanning 20 years of research and interventions with PWID in metropolitan Chicago to produce parameters for a synthetic population for realistic computational models (e.g., agent-based models). We then fit an exponential random graph model (ERGM) using the network estimates from the meta-analysis in order to develop the network component of the synthetic population.

Hepatitis C virus (HCV) infection is a leading cause of chronic liver disease and mortality worldwide. Direct-acting antiviral (DAA) therapy leads to high cure rates. However, persons who inject drugs (PWID) are at risk for reinfection after cure and may require multiple DAA treatments to reach the World Health Organization’s (WHO) goal of HCV elimination by 2030. Using an agent-based model (ABM) that accounts for the complex interplay of demographic factors, risk behaviors, social networks, and geographic location for HCV transmission among PWID, we examined the combination(s) of DAA enrollment (2.5%, 5%, 7.5%, 10%), adherence (60%, 70%, 80%, 90%) and frequency of DAA treatment courses needed to achieve the WHO’s goal of reducing incident chronic infections by 90% by 2030 among a large population of PWID from Chicago, IL and surrounding suburbs. We also estimated the economic DAA costs associated with each scenario. Our results indicate that a DAA treatment rate of >7.5% per year with 90% adherence results in 75% of enrolled PWID requiring only a single DAA course; however 19% would require 2 courses, 5%, 3 courses and

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.

Hepatitis D virus is an infectious subviral agent that can only propagate in people infected with hepatitis B virus. In this study, we modified and further developed a recent model for early hepatitis D virus and hepatitis B virus kinetics to better reproduce hepatitis D virus and hepatitis B virus kinetics measured in infected patients during anti-hepatitis D virus treatment. The analytical solutions were provided to highlight the new features of the modified model. The improved model offered significantly better prospects for modeling hepatitis D virus and hepatitis B virus interactions.

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.