Modeling shows that the NS5A inhibitor daclatasvir has two modes of action and yields a shorter estimate of the hepatitis C virus half-life. Proc Natl Acad Sci U S A. 2013;110:3991-3996. [PMID: 23431163 DOI: 10.1073/pnas.1203110110]

Incorporating Intracellular Processes in Virus Dynamics Models

In-host models have been essential for understanding the dynamics of virus infection inside an infected individual. When used together with biological data, they provide insight into viral life cycle, intracellular and cellular virus-host interactions, and the role, efficacy, and mode of action of therapeutics. In this review, we present the standard model of virus dynamics and highlight situations where added model complexity accounting for intracellular processes is needed. We present several examples from acute and chronic viral infections where such inclusion in explicit and implicit manner has led to improvement in parameter estimates, unification of conclusions, guidance for targeted therapeutics, and crossover among model systems. We also discuss trade-offs between model realism and predictive power and highlight the need of increased data collection at finer scale of resolution to better validate complex models.

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

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.

Replication efficiencies of human cytomegalovirus-infected epithelial cells are dependent on source of virus production

Human cytomegalovirus (HCMV) is a prevalent betaherpesvirus, and infection can lead to a range of symptomatology from mononucleosis to sepsis in immunocompromised individuals. HCMV is also the leading viral cause of congenital birth defects. Lytic replication is supported by many cell types with different kinetics and efficiencies leading to a plethora of pathologies. The goal of these studies was to elucidate HCMV replication efficiencies for viruses produced on different cell types upon infection of epithelial cells by combining experimental approaches with data-driven computational modeling. HCMV was generated from a common genetic background of TB40-BAC4, propagated on fibroblasts (TB40Fb) or epithelial cells (TB40Epi), and used to infect epithelial cells. We quantified cell-associated viral genomes (vDNA), protein levels (pUL44, pp28), and cell-free titers over time for each virus at different multiplicities of infection. We combined experimental quantification with data-driven simulations and determined that parameters describing vDNA synthesis were similar between sources. We found that pUL44 accumulation was higher in TB40Fb than TB40Epi. In contrast, pp28 accumulation was higher in TB40Epi which coincided with a significant increase in titer for TB40Epi over TB40Fb. These differences were most evident during live-cell imaging, which revealed syncytia-like formation during infection by TB40Epi. Simulations of the late lytic replication cycle yielded a larger synthesis constant for pp28 in TB40Epi along with increase in virus output despite similar rates of genome synthesis. By combining experimental and computational modeling approaches, our studies demonstrate that the cellular source of propagated virus impacts viral replication efficiency in target cell types.

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation to analyze extracellular viral markers

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

The reproduction number and its probability distribution for stochastic viral dynamics

We consider stochastic models of individual infected cells. The reproduction number, R, is understood as a random variable representing the number of new cells infected by one initial infected cell in an otherwise susceptible (target cell) population. Variability in R results partly from heterogeneity in the viral burst size (the number of viral progeny generated from an infected cell during its lifetime), which depends on the distribution of cellular lifetimes and on the mechanism of virion release. We analyse viral dynamics models with an eclipse phase: the period of time after a cell is infected but before it is capable of releasing virions. The duration of the eclipse, or the subsequent infectious, phase is non-exponential, but composed of stages. We derive the probability distribution of the reproduction number for these viral dynamics models, and show it is a negative binomial distribution in the case of constant viral release from infectious cells, and under the assumption of an excess of target cells. In a deterministic model, the ultimate in-host establishment or extinction of the viral infection depends entirely on whether the mean reproduction number is greater than, or less than, one, respectively. Here, the probability of extinction is determined by the probability distribution of R, not simply its mean value. In particular, we show that in some cases the probability of infection is not an increasing function of the mean reproduction number.

Hepatitis D Virus and HBsAg Dynamics in the era of new Antiviral Treatments

PURPOSE OF REVIEW

Hepatitis D virus (HDV) infection is the most severe form of chronic viral hepatitis, with no FDA-approved therapy. Progress in the development of effective HDV treatments is accelerating. This review highlights how mathematical modeling is improving understanding of HDV-HBsAg-host dynamics during antiviral therapy and generating insights into the efficacy and modes of action (MOA) of new antiviral agents.

RECENT FINDINGS

Clinical trials with pegylated-interferon-λ, bulevertide, nucleic acid polymers, and/or lonafarnib against various steps of the HDV-life cycle have revealed new viral-kinetic patterns that were not observed under standard treatment with pegylated-interferon-α. Modeling indicated that the half-lives of circulating HDV and HBsAg are ~ 1.7 d and ~ 1.3 d, respectively, estimated the relative response of HDV and HBsAg during different antiviral therapies, and provided insights into the efficacy and MOA of drugs in development for treating HDV, which can inform response-guided therapy to individualize treatment duration. Mathematical modeling of HDV and HBsAg kinetics provides a window into the HDV virus lifecycle, HDV-HBsAg-host dynamics during antiviral therapy, and the MOA of new drugs for HDV.

Development and validation of a liquid chromatographic-tandem mass spectrometric assay for the quantification of the direct acting antivirals glecaprevir and pibrentasvir in plasma

BACKGROUND

Direct acting antiviral (DAA) therapies are effective in the treatment and management of chronic HCV infections. Glecaprevir (GLE) and pibrentasvir (PIB) are pangenotypic DAAs that are delivered alone or as a fixed-dose oral formulation to treat chronic HCV infections with or without cirrhosis. Sensitive and dynamic bioanalytical assays are needed to understand the pharmacology of GLE and PIB.

METHODS

Drug free K2EDTA plasma was spiked with GLE, PIB, and their internal standards. Drugs were extracted from plasma via protein precipitation, and subsequently quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The method was validated according to regulatory recommendations, and evaluated in remnant plasma samples from individuals prescribed GLE and PIB.

RESULTS

The analytical measuring ranges for GLE and PIB were 0.25-2000 ng/mL and 0.25-1000 ng/mL, respectively. The method showed acceptable accuracy and precision for both DAAs. GLE and PIB in plasma were stable following six freeze thaw cycles and at room temperature for up to 67 h. All analytes were stable in whole blood incubated at room temperature for 24 h, and at 40 °C and 100% humidity for 2 h. Negligible percent matrix effects were observed for PIB and PIB-IS across the measuring range of the assay. Significant ion suppression was observed for GLE, with an average matrix effects of 27.9%. However, relative matrix effects were < 6.3% between drug and internal standard, and deemed acceptable. Assay validation assessments in alternative matrices also met acceptance criteria. Both DAAs were successfully measured in remnant plasma samples from individuals administered GLE and PIB.

CONCLUSIONS

An LC-MS/MS method for GLE and PIB quantification in plasma has been developed and validated. The assay met acceptable performance criteria and may be used in downstream applications to characterize DAA pharmacology for HCV treatment.

Choice of spatial discretisation influences the progression of viral infection within multicellular tissues

There has been an increasing recognition of the utility of models of the spatial dynamics of viral spread within tissues. Multicellular models, where cells are represented as discrete regions of space coupled to a virus density surface, are a popular approach to capture these dynamics. Conventionally, such models are simulated by discretising the viral surface and depending on the rate of viral diffusion and other considerations, a finer or coarser discretisation may be used. The impact that this choice may have on the behaviour of the system has not been studied. Here we demonstrate that under realistic parameter regimes - where viral diffusion is small enough to support the formation of familiar ring-shaped infection plaques - the choice of spatial discretisation of the viral surface can qualitatively change key model outcomes including the time scale of infection. Importantly, we show that the choice between implementing viral spread as a cell-scale process, or as a high-resolution converged PDE can generate distinct model outcomes, which raises important conceptual questions about the strength of assumptions underpinning the spatial structure of the model. We investigate the mechanisms driving these discretisation artefacts, the impacts they may have on model predictions, and provide guidance on the design and implementation of spatial and especially multicellular models of viral dynamics. We obtain our results using the simplest TIV construct for the viral dynamics, and therefore anticipate that the important effects we describe will also influence model predictions in more complex models of virus-cell-immune system interactions. This analysis will aid in the construction of models for robust and biologically realistic modelling and inference.

Sub-stoichiometric Modulation of Viral Targets-Potent Antiviral Agents That Exploit Target Vulnerability

The modulation of oligomeric viral targets at sub-stoichiometric ratios of drug to target has been advocated for its efficacy and potency, but there are only a limited number of documented examples. In this Viewpoint, we summarize the invention of the HIV-1 maturation inhibitor fipravirimat and discuss the emerging details around the mode of action of this class of drug that reflects inhibition of a protein composed of 1,300-1,600 monomers that interact in a cooperative fashion. Similarly, the HCV NS5A inhibitor daclatasvir has been shown to act in a highly sub-stoichiometric fashion, inhibiting viral replication at concentrations that are ∼23,500 lower than that of the protein target.

Recent Updates on Viral Oncogenesis: Available Preventive and Therapeutic Entities

Human viral oncogenesis is a complex phenomenon and a major contributor to the global cancer burden. Several recent findings revealed cellular and molecular pathways that promote the development and initiation of malignancy when viruses cause an infection. Even, antiviral treatment has become an approach to eliminate the viral infections and prevent the activation of oncogenesis. Therefore, for a better understanding, the molecular pathogenesis of various oncogenic viruses like, hepatitis virus, human immunodeficiency viral (HIV), human papillomavirus (HPV), herpes simplex virus (HSV), and Epstein-Barr virus (EBV), could be explored, especially, to expand many potent antivirals that may escalate the apoptosis of infected malignant cells while sparing normal and healthy ones. Moreover, contemporary therapies, such as engineered antibodies antiviral agents targeting signaling pathways and cell biomarkers, could inhibit viral oncogenesis. This review elaborates the recent advancements in both natural and synthetic antivirals to control viral oncogenesis. The study also highlights the challenges and future perspectives of using antivirals in viral oncogenesis.

Successes in antiviral drug discovery: a tribute to Nick Meanwell

Drug discovery is a difficult task, and is even more challenging when the target evolves during therapy. Antiviral drug therapy is an excellent example, exemplified by the evolution of therapeutic approaches for treatment of hepatitis C and HIV-1. Nick Meanwell and his colleagues made important contributions leading to molecules for treatment of hepatitis C and HIV-1, each with distinct mechanisms of action. This review summarizes the discovery and impact of these drugs, and will highlight, where applicable, the broader contributions of these discoveries to medicinal chemistry and drug discovery.

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation for analyzing extracellular viral markers

Chronic infection of hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. The quantifying and understanding dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, it requires a liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because of the ethical aspect. We here aimed to develop a non-invasive method for quantifying cccDNA in the liver using surrogate markers present in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations (PDEs), integrates experimental data from in vitro and in vivo investigations. By applying this model, we successfully predicted the amount and dynamics of intrahepatic cccDNA using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The non-invasive quantification of cccDNA using our proposed methodology holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

COVID-19 therapeutics: Clinical application of repurposed drugs and futuristic strategies for target-based drug discovery

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes the complicated disease COVID-19. Clinicians are continuously facing huge problems in the treatment of patients, as COVID-19-specific drugs are not available hence the principle of drug repurposing serves as a one-and-only hope. Globally, the repurposing of many drugs is underway; few of them are already approved by the regulatory bodies for their clinical use and most of them are in different phases of clinical trials. Here in this review, our main aim is to discuss in detail the up-to-date information on the target-based pharmacological classification of repurposed drugs, the potential mechanism of actions, and the current clinical trial status of various drugs which are under repurposing since early 2020. At last, we briefly proposed the probable pharmacological and therapeutic drug targets that may be preferred as a futuristic drug discovery approach in the development of effective medicines.

Simultaneous Generation of Methyl Esters and CO in Lignin Transformation

Lignin is an abundant renewable carbon source. Due to its complex structure, utilization of lignin is very challenging. Herein, we describe an efficient strategy for the simultaneous utilization of lignin, in which the methoxy groups in lignin react with carboxylic acids to generate methyl carboxylates and the other alkyl and phenyl carbons react with oxygen to predominantly form CO that can be used directly in carbonylation reactions. The method was applied to the methylation of various functionalized aryl and alkyl carboxylic acids, including natural compounds, to produce valuable chemicals, including pharmaceuticals. No solid or liquid residues remain after the reaction. Mechanistic studies demonstrate that a well-ordered C-C and C-O bond activation sequence takes place to realize total transformation of lignin. This work opens a way for transformation of the entire lignin polymer into valuable products, exemplified by the synthesis of the pharmaceutical, Ramipril, on a gram scale.

Computational modeling of protracted HCMV replication using genome substrates and protein temporal profiles

Human cytomegalovirus (HCMV) is a major cause of illness in immunocompromised individuals. The HCMV lytic cycle contributes to the clinical manifestations of infection. The lytic cycle occurs over ∼96 h in diverse cell types and consists of viral DNA (vDNA) genome replication and temporally distinct expression of hundreds of viral proteins. Given its complexity, understanding this elaborate system can be facilitated by the introduction of mechanistic computational modeling of temporal relationships. Therefore, we developed a multiplicity of infection (MOI)-dependent mechanistic computational model that simulates vDNA kinetics and late lytic replication based on in-house experimental data. The predictive capabilities were established by comparison to post hoc experimental data. Computational analysis of combinatorial regulatory mechanisms suggests increasing rates of protein degradation in association with increasing vDNA levels. The model framework also allows expansion to account for additional mechanisms regulating the processes. Simulating vDNA kinetics and the late lytic cycle for a wide range of MOIs yielded several unique observations. These include the presence of saturation behavior at high MOIs, inefficient replication at low MOIs, and a precise range of MOIs in which virus is maximized within a cell type, being 0.382 IU to 0.688 IU per fibroblast. The predicted saturation kinetics at high MOIs are likely related to the physical limitations of cellular machinery, while inefficient replication at low MOIs may indicate a minimum input material required to facilitate infection. In summary, we have developed and demonstrated the utility of a data-driven and expandable computational model simulating lytic HCMV infection.

Global dynamics of an age-dependent multiscale hepatitis C virus model

In this paper, we focus on the global dynamics of a multiscale hepatitis C virus model. The model takes into account the evolution of the virus in cells and RNA. For the model, we establish the globally asymptotical stability of both infection-free and infected equilibria. We first give the basic reproduction number [Formula: see text] of the model, and then find that the system holds infected equilibrium when [Formula: see text]. Using eigenvalue analysis, Lyapunov functional, persistence theory and so on, it is proved that infection-free and infected equilibria are globally asymptotically stable when [Formula: see text] and [Formula: see text], respectively. Thus, extinction and persistence of viruses in cells are theoretically judged. Finally, we show our theoretical results by means of numerical simulation.

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

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.

Disrupting autorepression circuitry generates "open-loop lethality" to yield escape-resistant antiviral agents

Across biological scales, gene-regulatory networks employ autorepression (negative feedback) to maintain homeostasis and minimize failure from aberrant expression. Here, we present a proof of concept that disrupting transcriptional negative feedback dysregulates viral gene expression to therapeutically inhibit replication and confers a high evolutionary barrier to resistance. We find that nucleic-acid decoys mimicking cis-regulatory sites act as "feedback disruptors," break homeostasis, and increase viral transcription factors to cytotoxic levels (termed "open-loop lethality"). Feedback disruptors against herpesviruses reduced viral replication >2-logs without activating innate immunity, showed sub-nM IC50, synergized with standard-of-care antivirals, and inhibited virus replication in mice. In contrast to approved antivirals where resistance rapidly emerged, no feedback-disruptor escape mutants evolved in long-term cultures. For SARS-CoV-2, disruption of a putative feedback circuit also generated open-loop lethality, reducing viral titers by >1-log. These results demonstrate that generating open-loop lethality, via negative-feedback disruption, may yield a class of antimicrobials with a high genetic barrier to resistance.

Modeling-Based Response-Guided DAA Therapy for Chronic Hepatitis C to Identify Individuals for Shortening Treatment Duration

Shortening duration of direct-acting antiviral therapy for chronic hepatitis C could provide cost savings, reduce medication exposure, and foster adherence and treatment completion in special populations. The current analysis indicates that measuring hepatitis C virus at baseline and on days 7 and 14 of therapy can identify patients for shortening therapy duration.

An age-structured model of hepatitis B viral infection highlights the potential of different therapeutic strategies

Hepatitis B virus (HBV) is a global health threat, and its elimination by 2030 has been prioritised by the World Health Organisation. Here we present an age-structured model for the immune response to an HBV infection, which takes into account contributions from both cell-mediated and humoral immunity. The model has been validated using published patient data recorded during acute infection. It has been adapted to the scenarios of chronic infection, clearance of infection, and flare-ups via variation of the immune response parameters. The impacts of immune response exhaustion and non-infectious subviral particles on the immune response dynamics are analysed. A comparison of different treatment options in the context of this model reveals that drugs targeting aspects of the viral life cycle are more effective than exhaustion therapy, a form of therapy mitigating immune response exhaustion. Our results suggest that antiviral treatment is best started when viral load is declining rather than in a flare-up. The model suggests that a fast antibody production rate always leads to viral clearance, highlighting the promise of antibody therapies currently in clinical trials.

Electrochemical sensing of dobutamine, paracetamol, amlodipine, and daclatasvir in serum based on thiourea SAMs over nano-gold particles-CNTs composite

We report in this work a one-step approach for the formation of self-assembled monolayers (SAMs) from thiourea (TU) over gold nanoparticles dispersed in carbon nanotubes (CNTs-Aunano). The fabrication of the...

Iterative community-driven development of a SARS-CoV-2 tissue simulator

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

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

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 t_1/2~1 hr. Immediately post-reperfusion, virus declined in a biphasic manner in four patients consisting of a rapid decline (t_1/2 = 5 min) followed by a slower decline (t_1/2 = 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 t_1/2 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.

Lyapunov function and global asymptotic stability for a new multiscale viral dynamics model incorporating the immune system response: Implemented upon HCV

In this paper, a new mathematical model is formulated that describes the interaction between uninfected cells, infected cells, viruses, intracellular viral RNA, Cytotoxic T-lymphocytes (CTLs), and antibodies. Hence, the model contains certain biological relations that are thought to be key factors driving this interaction which allow us to obtain precise logical conclusions. Therefore, it improves our perception, that would otherwise not be possible, to comprehend the pathogenesis, to interpret clinical data, to control treatment, and to suggest new relations. This model can be used to study viral dynamics in patients for a wide range of infectious diseases like HIV, HPV, HBV, HCV, and Covid-19. Though, analysis of a new multiscale HCV model incorporating the immune system response is considered in detail, the analysis and results can be applied for all other viruses. The model utilizes a transformed multiscale model in the form of ordinary differential equations (ODE) and incorporates into it the interaction of the immune system. The role of CTLs and the role of antibody responses are investigated. The positivity of the solutions is proven, the basic reproduction number is obtained, and the equilibrium points are specified. The stability at the equilibrium points is analyzed based on the Lyapunov invariance principle. By using appropriate Lyapunov functions, the uninfected equilibrium point is proven to be globally asymptotically stable when the reproduction number is less than one and unstable otherwise. Global stability of the infected equilibrium points is considered, and it has been found that each equilibrium point has a specific domain of stability. Stability regions could be overlapped and a bistable equilibria could be found, which means the coexistence of two stable equilibrium points. Hence, the solution converges to one of them depending on the initial conditions.

Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response

The COVID-19 pandemic has highlighted a need for improved frameworks for drug discovery, repurposing, clinical trial design and therapy optimization and personalization. Mechanistic computational models can play an important role in developing these frameworks. We discuss how mechanistic models, which consider viral entry, replication in target cells, viral spread in the body, immune response, and the complex factors involved in tissue and organ damage and recovery, can clarify the mechanisms of humoral and cellular immune responses to the virus, viral distribution and replication in tissues, the origins of pathogenesis and patient-to-patient heterogeneity in responses. These models are already improving our understanding of the mechanisms of action of antivirals and immune modulators. We discuss how closer collaboration between the experimentalists, clinicians and modelers could result in more predictive models which may guide therapies for viral infections, improving survival and leading to faster and more complete recovery.

Multiscale model of hepatitis C virus dynamics in plasma and liver following combination therapy

This work explores the application of a physiologically structured population (PSP) framework in modeling hepatitis C virus (HCV) kinetics. To do so, a model was developed for the viral RNA load in plasma and liver as observed in 15 patients treated with a combination therapy of pegylated interferon, ribavirin, and telaprevir. By including both intracellular and extracellular processes of the HCV lifecycle, the model provided a description of the treatment effect on the intracellular HCV lifecycle. Combining PSP models with a nonlinear mixed effects approach in a single model permits a natural inclusion of the direct‐acting antiviral effect on intracellular processes, which can then be integrated with the viral kinetics within the host while accounting for the interindividual variability between patients. This should allow an exploration of the treatment effect within the entire chronic HCV‐infected population.

Comparing antiviral strategies against COVID-19 via multiscale within-host modelling

Within-host models of COVID-19 infection dynamics enable the merits of different forms of antiviral therapy to be assessed in individual patients. A stochastic agent-based model of COVID-19 intracellular dynamics is introduced here, that incorporates essential steps of the viral life cycle targeted by treatment options. Integration of model predictions with an intercellular ODE model of within-host infection dynamics, fitted to patient data, generates a generic profile of disease progression in patients that have recovered in the absence of treatment. This is contrasted with the profiles obtained after variation of model parameters pertinent to the immune response, such as effector cell and antibody proliferation rates, mimicking disease progression in immunocompromised patients. These profiles are then compared with disease progression in the presence of antiviral and convalescent plasma therapy against COVID-19 infections. The model reveals that using both therapies in combination can be very effective in reducing the length of infection, but these synergistic effects decline with a delayed treatment start. Conversely, early treatment with either therapy alone can actually increase the duration of infection, with infectious virions still present after the decline of other markers of infection. This suggests that usage of these treatments should remain carefully controlled in a clinical environment.

HCV Spread Kinetics Reveal Varying Contributions of Transmission Modes to Infection Dynamics

The hepatitis C virus (HCV) is capable of spreading within a host by two different transmission modes: cell-free and cell-to-cell. However, the contribution of each of these transmission mechanisms to HCV spread is unknown. To dissect the contribution of these different transmission modes to HCV spread, we measured HCV lifecycle kinetics and used an in vitro spread assay to monitor HCV spread kinetics after a low multiplicity of infection in the absence and presence of a neutralizing antibody that blocks cell-free spread. By analyzing these data with a spatially explicit mathematical model that describes viral spread on a single-cell level, we quantified the contribution of cell-free, and cell-to-cell spread to the overall infection dynamics and show that both transmission modes act synergistically to enhance the spread of infection. Thus, the simultaneous occurrence of both transmission modes represents an advantage for HCV that may contribute to viral persistence. Notably, the relative contribution of each viral transmission mode appeared to vary dependent on different experimental conditions and suggests that viral spread is optimized according to the environment. Together, our analyses provide insight into the spread dynamics of HCV and reveal how different transmission modes impact each other.

Understanding Hepatitis B Virus Dynamics and the Antiviral Effect of Interferon Alpha Treatment in Humanized Chimeric Mice

Whereas the mode of action of lamivudine (LAM) against hepatitis B virus (HBV) is well established, the inhibition mechanism(s) of interferon alpha (IFN-α) is less completely defined. To advance our understanding, we mathematically modeled HBV kinetics during 14-day pegylated IFN-α-2a (pegIFN), LAM, or pegIFN-plus-LAM (pegIFN+LAM) treatment of 39 chronically HBV-infected humanized uPA/SCID chimeric mice. Serum HBV DNA and intracellular HBV DNA were measured frequently. We developed a multicompartmental mathematical model and simultaneously fit it to the serum and intracellular HBV DNA data. Unexpectedly, even in the absence of an adaptive immune response, a biphasic decline in serum HBV DNA and intracellular HBV DNA was observed in response to all treatments. Kinetic analysis and modeling indicate that the first phase represents inhibition of intracellular HBV DNA synthesis and secretion, which was similar under all treatments with an overall mean efficacy of 98%. In contrast, there were distinct differences in HBV decline during the second phase, which was accounted for in the model by a time-dependent inhibition of intracellular HBV DNA synthesis, with the steepest decline observed during pegIFN+LAM treatment (1.28/day) and the slowest (0.1/day) during pegIFN monotherapy. Reminiscent of observations in patients treated with pegIFN and/or LAM, a biphasic HBV decline was observed in treated humanized mice in the absence of an adaptive immune response. Interestingly, combination treatment did not increase the initial inhibition of HBV production but rather enhanced second-phase decline, providing insight into the dynamics of HBV treatment response and the mode of action of IFN-α against HBV. IMPORTANCE Chronic hepatitis B virus (HBV) infection remains a global health care problem, as we lack sufficient curative treatment options. Elucidating the dynamics of HBV infection and treatment response at the molecular level could facilitate the development of novel, more effective HBV antivirals. Currently, the only well-established small animal HBV infection model available is the chimeric uPA/SCID mice with humanized livers; however, the HBV inhibition kinetics under pegylated IFN-α-2a (pegIFN) in this model system have not been determined in sufficient detail. In this study, viral kinetics in 39 humanized mice treated with pegIFN and/or lamivudine were monitored and analyzed using a mathematical modeling approach. We found that the main mode of action of IFN-α is blocking HBV DNA synthesis and that the majority of synthesized HBV DNA is secreted. Our study provides novel insights into HBV DNA dynamics within infected human hepatocytes.

Fuzzy logic and Lyapunov-based non-linear controllers for HCV infection

Hepatitis C is the liver disease caused by the Hepatitis C virus (HCV) which can lead to serious health problems such as liver cancer. In this research work, the non‐linear model of HCV having three state variables (uninfected hepatocytes, infected hepatocytes and virions) and two control inputs has been taken into account, and four non‐linear controllers namely non‐linear PID controller, Lyapunov Redesign controller, Synergetic controller and Fuzzy Logic‐Based controller have been proposed to control HCV infection inside the human body. The controllers have been designed for the anti‐viral therapy in order to control the amount of uninfected hepatocytes to the desired safe limit and to track the amount of infected hepatocytes and virions to their reference value which is zero. One control input is the Pegylated interferon (peg‐IFN‐α) which acts in reducing the infected hepatocytes and the other input is ribavirin which blocks the production of virions. By doing so, the uninfected hepatocytes increase and achieve the required safe limit. Lyapunov stability analysis has been used to prove the stability of the whole system. The comparative analysis of the proposed nonlinear controllers using MATLAB/Simulink have been done with each other and with linear PID. These results depict that the infected hepatocytes and virions are reduced to the desired level, enhancing the rate of sustained virologic response (SVR) and reducing the treatment period as compared with previous strategies introduced in the literature.

Drug Design Strategies to Avoid Resistance in Direct-Acting Antivirals and Beyond

Drug resistance is prevalent across many diseases, rendering therapies ineffective with severe financial and health consequences. Rather than accepting resistance after the fact, proactive strategies need to be incorporated into the drug design and development process to minimize the impact of drug resistance. These strategies can be derived from our experience with viral disease targets where multiple generations of drugs had to be developed to combat resistance and avoid antiviral failure. Significant efforts including experimental and computational structural biology, medicinal chemistry, and machine learning have focused on understanding the mechanisms and structural basis of resistance against direct-acting antiviral (DAA) drugs. Integrated methods show promise for being predictive of resistance and potency. In this review, we give an overview of this research for human immunodeficiency virus type 1, hepatitis C virus, and influenza virus and the lessons learned from resistance mechanisms of DAAs. These lessons translate into rational strategies to avoid resistance in drug design, which can be generalized and applied beyond viral targets. While resistance may not be completely avoidable, rational drug design can and should incorporate strategies at the outset of drug development to decrease the prevalence of drug resistance.

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Mechanistic Modeling of SARS-CoV-2 and Other Infectious Diseases and the Effects of Therapeutics

Modern viral kinetic modeling and its application to therapeutics is a field that attracted the attention of the medical, pharmaceutical, and modeling communities during the early days of the AIDS epidemic. Its successes led to applications of modeling methods not only to HIV but a plethora of other viruses, such as hepatitis C virus (HCV), hepatitis B virus and cytomegalovirus, which along with HIV cause chronic diseases, and viruses such as influenza, respiratory syncytial virus, West Nile virus, Zika virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which generally cause acute infections. Here we first review the historical development of mathematical models to understand HIV and HCV infections and the effects of treatment by fitting the models to clinical data. We then focus on recent efforts and contributions of applying these models towards understanding SARS-CoV-2 infection and highlight outstanding questions where modeling can provide crucial insights and help to optimize nonpharmaceutical and pharmaceutical interventions of the coronavirus disease 2019 (COVID-19) pandemic. The review is written from our personal perspective emphasizing the power of simple target cell limited models that provided important insights and then their evolution into more complex models that captured more of the virology and immunology. To quote Albert Einstein, "Everything should be made as simple as possible, but not simpler," and this idea underlies the modeling we describe below.

Global stability of age-of-infection multiscale HCV model with therapy

In order to treat the diseases caused by hepatitis C virus (HCV) more efficiently, the concentration of HCV in blood, cells, tissues and the body has attracted widespread attention from related scholars. This paper studies a dynamic dependent HCV model (more specifically, including age structure and treatment methods model) that concludes states of infection-free and infected equilibrium. Through eigenvalue analysis and Volterra integral formula, it proves that $E_0$ is globally asymptotically stable when $\mathcal{R}<1$. After explaining the existence, uniqueness and positive properties of the solution of the system, we have proved the global asymptotic stability of $E^*$ when $\mathcal{R}>1$ by constructing a suitable Lyapunov function. Through the above proofs, it can be concluded that effective treatment measures can significantly reduce the number of HCVs, so many related researchers are aware of the importance of highly efficient nursing methods and are committed to applying relevant methods to practice.

What drives the dynamics of HBV RNA during treatment?

Hepatitis B virus RNA (HBV RNA)-containing particles are encapsidated pre-genomic RNA (pgRNA) detectable in chronically infected patients in addition to virions (HBV DNA) that have been suggested as a marker of the treatment efficacy. This makes promising the use of core protein allosteric modulators, such as RG7907, which disrupt the nucleocapsid assembly and profoundly reduce HBV RNA. Here, we developed a multiscale model of HBV extending the standard viral dynamic models to analyse the kinetics of HBV DNA and HBV RNA in 35 patients treated with RG7907 for 28 days. We compare the predictions with those obtained in patients treated with the nucleotide analog tenofovir. RG7907 blocked 99.3% of pgRNA encapsidation (range: 92.1%-99.9%) which led to a decline of both HBV DNA and HBV RNA. As a consequence of its mode of action, the first phase of decline of HBV RNA was rapid, uncovering the clearance of viral particles with half-life of 45 min. In contrast, HBV DNA decline was predicted to be less rapid, due to the continuous secretion of already formed viral capsids (t_1/2  = 17 ± 6 h). After few days, both markers declined at the same rate, which was attributed to the loss of infected cells (t_1/2  ≅ 6 ± 0.8 days). By blocking efficiently RNA reverse transcription but not its encapsidation, nucleotide analog in contrast was predicted to lead to a transient accumulation of HBV RNA both intracellularly and extracellularly. The model brings a conceptual framework for understanding the differences between HBV DNA and HBV RNA dynamics. Integration of HBV RNA in viral dynamic models may be helpful to better quantify the treatment effect, especially in viral-suppressed patients where HBV DNA is no longer detectable.

COVID-19 Antiviral and Treatment Candidates: Current Status

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 has severely impacted global health and economy. There is currently no effective approved treatment for COVID-19; although vaccines have been granted emergency use authorization in several countries, they are currently only administered to high-risk individuals, thereby leaving a gap in virus control measures. The scientific and clinical communities and drug manufacturers have collaborated to speed up the discovery of potential therapies for COVID-19 by taking advantage of currently approved drugs as well as investigatory agents in clinical trials. In this review, we stratified some of these candidates based on their potential targets in the progression of COVID-19 and discuss some of the results of ongoing clinical evaluations.

HIV influences clustering and intracellular replication of hepatitis C virus

HCV and HIV coinfection is common and HIV leads to increased HCV viraemia and accelerated disease progression. However, the biological basis of this interaction remains poorly understood and little is known about the impact of HIV on HCV replication at the cellular level. We analysed HCV RNA, based on single-cell laser-capture microdissection, in liver biopsies from monoinfected (n = 4) and HCV/HIV-coinfected (n = 5) participants. HCV RNA was assayed in 3200 hepatocytes with information of spatial position. We compared HCV RNA levels and clustering properties of infection between mono- and coinfected participants, and developed a mathematical model of infection. Although the median plasma HCV RNA level and the fraction of infected cells were comparable in monoinfected (7.0 log₁₀ IU/mL and ~ 30%) and coinfected (7.3 log₁₀ IU/mL and ~ 40%) participants, the median HCV RNA per infected hepatocyte in monoinfected (2.8IU) was significantly lower than in coinfected (8.2IU) participants (p = .03). Clustering of infected cells was more prominent in monoinfected participants (91% of samples) than in coinfected participants (~48%), p = .0045, suggesting that spatial spread may be influenced by HIV coinfection. Interestingly, when clustering does occur, the size of clusters is similar in both types of infection. A mathematical model of infection suggested that HIV allows higher intracellular accumulation of HCV RNA by impeding the export of HCV RNA. Our observations show that HIV coinfection impacts intracellular accumulation of HCV RNA and the clustering of HCV-infected cells, but to a less extent the fraction of HCV-infected cells.

Global evolutionary analysis of chronic hepatitis C patients revealed significant effect of baseline viral resistance, including novel non-target sites, for DAA-based treatment and retreatment outcome

Direct-acting antivirals (DAAs) have proven highly effective against chronic hepatitis C virus (HCV) infection. However, some patients experience treatment failure, associated with resistance-associated substitutions (RASs). Our aim was to investigate the complete viral coding sequence in hepatitis C patients treated with DAAs to identify RASs and the effects of treatment on the viral population. We selected 22 HCV patients with sustained virologic response (SVR) to match 21 treatment-failure patients in relation to HCV genotype, DAA regimen, liver cirrhosis and previous treatment experience. Viral-titre data were compared between the two patient groups, and HCV full-length open reading frame deep-sequencing was performed. The proportion of HCV NS5A-RASs at baseline was higher in treatment-failure (82%) than matched SVR patients (25%) (p = .0063). Also, treatment failure was associated with slower declines in viraemia titres. Viral population diversity did not differ at baseline between SVR and treatment-failure patients, but failure was associated with decreased diversity probably caused by selection for RAS. The NS5B-substitution 150V was associated with sofosbuvir treatment failure in genotype 3a. Further, mutations identified in NS2, NS3-helicase and NS5A-domain-III were associated with DAA treatment failure in genotype 1a patients. Six retreated HCV patients (35%) experienced 2nd treatment failure; RASs were present in 67% compared to 11% with SVR. In conclusion, baseline RASs to NS5A inhibitors, but not virus population diversity, and lower viral titre decline predicted HCV treatment failure. Mutations outside of the DAA targets can be associated with DAA treatment failure. Successful DAA retreatment in patients with treatment failure was hampered by previously selected RASs.

Zika virus dynamics: Effects of inoculum dose, the innate immune response and viral interference

Experimental Zika virus infection in non-human primates results in acute viral load dynamics that can be well-described by mathematical models. The inoculum dose that would be received in a natural infection setting is likely lower than the experimental infections and how this difference affects the viral dynamics and immune response is unclear. Here we study a dataset of experimental infection of non-human primates with a range of doses of Zika virus. We develop new models of infection incorporating both an innate immune response and viral interference with that response. We find that such a model explains the data better than models with no interaction between virus and the immune response. We also find that larger inoculum doses lead to faster dynamics of infection, but approximately the same total amount of viral production.

The relationship between the infecting dose of a pathogen and the subsequent viral dynamics is unclear in many disease settings, and this relationship has implications for both the timing and the required efficacy of antiviral therapy. Since experimental challenge studies often employ higher doses of virus than would generally be present in natural infection assessment of this relationship is particularly important for translation of findings. In this study we used mathematical modelling of viral load data from a multi-dose study of Zika virus infection in a macaque model to describe the impact of varying the dose of Zika virus on model parameters, and developed a novel mathematical model incorporating viral interference with the innate immune response.

HCVMultiscaleFit: A Simulator For Parameter Estimation in Multiscale Models Of Hepatitis C Virus Dynamics

Callibration 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 or fitting (callibration) that solves all cases of data points available presents a formidable challenge, as efficiency considerations need to be employed in order for the method to become practical. In the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), 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 derviative approximations. These steps that were successful in significantly speeding up a highly non-efficient approach, rendering it practical, can also be adapted to multiscale models of other viruses and other sophisticated differential equation models. The newly efficient methods that were developed as a result of the above approach are described. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for multiscale models. We provide a simulator called HCVMultiscaleFit with a Graphical User Interface that applies these methods and is useful to perform parameter estimation for simulating viral dynamics during antiviral treatment.

Mathematical modeling of hepatitis C RNA replication, exosome secretion and virus release

Hepatitis C virus (HCV) causes acute hepatitis C and can lead to life-threatening complications if it becomes chronic. The HCV genome is a single plus strand of RNA. Its intracellular replication is a spatiotemporally coordinated process of RNA translation upon cell infection, RNA synthesis within a replication compartment, and virus particle production. While HCV is mainly transmitted via mature infectious virus particles, it has also been suggested that HCV-infected cells can secrete HCV RNA carrying exosomes that can infect cells in a receptor independent manner. In order to gain insight into these two routes of transmission, we developed a series of intracellular HCV replication models that include HCV RNA secretion and/or virus assembly and release. Fitting our models to in vitro data, in which cells were infected with HCV, suggests that initially most secreted HCV RNA derives from intracellular cytosolic plus-strand RNA, but subsequently secreted HCV RNA derives equally from the cytoplasm and the replication compartments. Furthermore, our model fits to the data suggest that the rate of virus assembly and release is limited by host cell resources. Including the effects of direct acting antivirals in our models, we found that in spite of decreasing intracellular HCV RNA and extracellular virus concentration, low level HCV RNA secretion may continue as long as intracellular RNA is available. This may possibly explain the presence of detectable levels of plasma HCV RNA at the end of treatment even in patients that ultimately attain a sustained virologic response.

Approximately 70 million people are chronically infected with hepatitis C virus (HCV), which if left untreated may lead to cirrhosis and liver cancer. However, modern drug therapy is highly effective and hepatitis C is the first chronic virus infection that can be cured with short-term therapy in almost all infected individuals. The within-host transmission of HCV occurs mainly via infectious virus particles, but experimental studies suggest that there may be additional receptor-independent cell-to-cell transmission by exosomes that carry the HCV genome. In order to understand the intracellular HCV lifecycle and HCV RNA spread, we developed a series of mathematical models that take both exosomal secretion and viral secretion into account. By fitting these models to in vitro data, we found that secretion of both HCV RNA as well as virus probably occurs and that the rate of virus assembly is likely limited by cellular co-factors on which the virus strongly depends for its own replication. Furthermore, our modeling predicted that the parameters governing the processes in the viral lifecycle that are targeted by direct acting antivirals are the most sensitive to perturbations, which may help explain their ability to cure this infection.

Quantification of Ebola virus replication kinetics in vitro

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.

Dynamical Analysis of a Multiscale Model of Hepatitis C Virus Infection Using a Transformed ODEs Model

A mathematically identical ordinary differential equations (ODEs) model was derived from a multiscale partial differential equations (PDEs) model of hepatitis c virus infection, which helps to overcome the limitations of the PDE model in clinical data analysis. We have discussed about basic properties of the system and found the basic reproduction number of the system. A condition for the local stability of the uninfected and the infected steady states is presented. The local stability analysis of the model shows that the system is asymptotically stable at the disease-free equilibrium point when the basic reproduction number is less than one. When the basic reproduction number is greater than one endemic equilibrium point exists, and the local stability analysis proves that this point is asymptotically stable. Numerical sensitivity analysis based on model parameters is performed and therefore the result describes the influence of each parameter on the basic reproduction number.

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.

Should a viral genome stay in the host cell or leave? A quantitative dynamics study of how hepatitis C virus deals with this dilemma

Virus proliferation involves gene replication inside infected cells and transmission to new target cells. Once positive-strand RNA virus has infected a cell, the viral genome serves as a template for copying (“stay-strategy”) or is packaged into a progeny virion that will be released extracellularly (“leave-strategy”). The balance between genome replication and virion release determines virus production and transmission efficacy. The ensuing trade-off has not yet been well characterized. In this study, we use hepatitis C virus (HCV) as a model system to study the balance of the two strategies. Combining viral infection cell culture assays with mathematical modeling, we characterize the dynamics of two different HCV strains (JFH-1, a clinical isolate, and Jc1-n, a laboratory strain), which have different viral release characteristics. We found that 0.63% and 1.70% of JFH-1 and Jc1-n intracellular viral RNAs, respectively, are used for producing and releasing progeny virions. Analysis of the Malthusian parameter of the HCV genome (i.e., initial proliferation rate) and the number of de novo infections (i.e., initial transmissibility) suggests that the leave-strategy provides a higher level of initial transmission for Jc1-n, whereas, in contrast, the stay-strategy provides a higher initial proliferation rate for JFH-1. Thus, theoretical-experimental analysis of viral dynamics enables us to better understand the proliferation strategies of viruses, which contributes to the efficient control of virus transmission. Ours is the first study to analyze the stay-leave trade-off during the viral life cycle and the significance of the replication-release switching mechanism for viral proliferation.

A theoretical-experimental analysis of viral dynamics reveals a stay/leave trade-off during the viral life cycle and demonstrates the significance for viral proliferation of a replication-release switching mechanism.

Discovery and characterization of a novel HCV inhibitor targeting the late stage of HCV life cycle

BACKGROUND

Currently approved anti-HCV drugs, the direct-acting antivirals (DAAs), are highly effective and target the viral RNA replication stage of the HCV life cycle. Due to high mutation rate of HCV, drug resistant variants can arise during DAA monotherapy. Thus, a combination of DAAs is necessary to achieve a high response rate. Novel HCV inhibitors targeting the HCV late stage such as assembly and release may further improve combination therapy with the DAAs. Here we characterize one late stage-targeting candidate compound, 6-(4-chloro-3-methylphenoxy)-pyridin-3-amine (MLS000833705).

METHODS

We treated HCV-infected cells with MLS000833705 and other HCV inhibitors and examined HCV RNA and infectious titres. We evaluated the colocalization of HCV core and lipid droplets by confocal microscopy. We performed HCV core-proteinase K digestion assay and several lipid assays to study the mechanism of MLS000833705.

RESULTS

We showed that MLS000833705 decreased extracellular HCV RNA levels more than intracellular HCV RNA levels in HCV infectious cell culture. Similarly, MLS000833705 reduced infectious HCV titres substantially more in the culture supernatant than intracellularly. Confocal microscopy showed that MLS000833705 did not affect the colocalization of HCV core protein with cellular lipid droplets where HCV assembles. HCV core-proteinase K digestion assay showed that MLS000833705 inhibited the envelopment of HCV capsid.

CONCLUSIONS

Our study demonstrates that MLS000833705 is a late-stage HCV inhibitor targeting HCV morphogenesis and maturation. Therefore, MLS000833705 can be used as a molecular probe to study HCV maturation and secretion and possibly guide development of a new class of HCV antivirals.

Short-Duration Pan-Genotypic Therapy With Glecaprevir/Pibrentasvir for 6 Weeks Among People With Recent Hepatitis C Viral Infection

BACKGROUND AND AIMS

Among treatment-naive individuals with chronic hepatitis C viral (HCV) infection and without cirrhosis, glecaprevir/pibrentasvir for 8 weeks is recommended. The aim of this analysis was to evaluate the efficacy of glecaprevir/pibrentasvir for 6 weeks in people with acute and recent HCV infection.

APPROACH AND RESULTS

In this open-label, single-arm, multicenter, international pilot study, adults with recent HCV (duration of infection < 12 months) received glecaprevir/pibrentasvir 300/120 mg daily for 6 weeks. Primary infection was defined by first positive anti-HCV antibody and/or HCV RNA within 6 months of enrollment and either acute clinical hepatitis within the past 12 months (symptomatic seroconversion illness or alanine aminotransferase > 10 × upper limit of normal) or anti-HCV antibody seroconversion within 18 months. Reinfection was defined as new positive HCV RNA within 6 months of enrollment and evidence of prior spontaneous or treatment-induced clearance. The primary endpoint was sustained virologic response at 12 weeks posttreatment (SVR12). Thirty men (median age 43 years, 90% men who have sex with men) received treatment, of whom 77% (n = 23) were human immunodeficiency virus-positive, 47% (n = 14) had ever injected drugs, and 13% (n = 4) had HCV reinfection. The majority had HCV genotype 1 (83%, n = 25), followed by genotype 4 (10%, n = 3) and genotype 3 (7%, n = 2). At baseline, median estimated duration of infection was 29 weeks (range 13, 52) and median HCV RNA was 6.2 log₁₀ IU/mL (range 0.9, 7.7). SVR12 in the intention-to-treat and per-protocol populations was achieved in 90% (27/30) and 96% (27/28), respectively. There was one case of relapse, and there were two cases of nonvirological failure (death, n = 1; loss to follow-up, n = 1). No treatment-related serious adverse events were seen.

CONCLUSIONS

Glecaprevir/pibrentasvir for 6 weeks was highly effective among people with acute and recent HCV infection, supporting further evaluation of shortened-duration pan-genotypic therapy in this setting.