HCV Kinetic Models and Their Implications in Drug Development

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.

Qualitative behaviour of a stochastic hepatitis C epidemic model in cellular level

In this paper, a mathematical model describing the dynamical of the spread of hepatitis C virus (HCV) at a cellular level with a stochastic noise in the transmission rate is developed from the deterministic model. The unique time-global solution for any positive initial value is served. The Ito's Formula, the suitable Lyapunov function, and other stochastic analysis techniques are used to analyze the model dynamics. The numerical simulations are carried out to describe the analytical results. These results highlight the impact of the noise intensity accelerating the extinction of the disease.

Life cycle process dependencies of positive-sense RNA viruses suggest strategies for inhibiting productive cellular infection

Life cycle processes of positive-strand (+)RNA viruses are broadly conserved across families, yet they employ different strategies to grow in the cell. Using a generalized dynamical model for intracellular (+)RNA virus growth, we decipher these life cycle determinants and their dependencies for several viruses and parse the effects of viral mutations, drugs and host cell permissivity. We show that poliovirus employs rapid replication and virus assembly, whereas the Japanese encephalitis virus leverages its higher rate of translation and efficient cellular reorganization compared to the hepatitis C virus. Stochastic simulations demonstrate infection extinction if all seeding (inoculating) viral RNA degrade before establishing robust replication critical for infection. The probability of this productive cellular infection, 'cellular infectivity', is affected by virus-host processes and defined by early life cycle events and viral seeding. An increase in cytoplasmic RNA degradation and delay in vesicular compartment formation reduces infectivity, more so when combined. Synergy among these parameters in limiting (+)RNA virus infection as predicted by our model suggests new avenues for inhibiting infections by targeting the early life cycle bottlenecks.

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.

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.

Interferon at the cellular, individual, and population level in hepatitis C virus infection: Its role in the interferon-free treatment era

The advent of powerful direct-acting antiviral agents (DAAs) has revolutionized the treatment of hepatitis C. DAAs cure nearly all patients with short duration, oral treatments. Significant efforts are now underway to optimize DAA-based treatments. We discuss the potential role of interferon in this optimization. Clinical studies present compelling evidence that DAAs perform better in treatment-naive individuals than in individuals who previously failed treatment with interferon, a surprising correlation because interferon and DAAs are thought to act independently. Recent mathematical models explore a mechanistic hypothesis underlying this correlation. The hypothesis invokes the action of interferon at the cellular, individual, and population levels. Strong interferon responses prevent the productive infection of cells, reduce viral replication, and impede the development of resistance to DAAs in infected individuals and improve cure rates elicited by DAAs in treated populations. The models develop descriptions of these processes, integrate them into a comprehensive framework, and capture clinical data quantitatively, providing a successful test of the hypothesis. Individuals with strong endogenous interferon responses thus present a promising subpopulation for reducing DAA treatment durations. This review discusses the conceptual advances made by the models, highlights the new insights they unravel, and examines their applicability to optimize DAA-based treatments.

A time-to-event analysis describing virologic response in patients with chronic hepatitis C infection

The objective of this project was to describe longitudinal change in chronic hepatitis C virologic reponse using time-to-event (TTE) analysis and to identify patient characteristics that determine the dynamics of this change. We compiled demographic, clinical, and genetic data from 715 chronic hepatitis C virus (HCV) patients treated with pegylated interferon (PEG-IFN) alfa-2a and ribavirin. TTE modelling described the time between antiviral treatment initiation and the first observation of undetectable HCV RNA. A lognormal TTE model was selected to describe time to first undetectable HCV RNA. The identified predictors of prolonged time to achieve undetectable HCV RNA include HCV genotype 1, low pre-treatment ALT level, older age, or with elevated baseline haemoglobin level. In conclusion, a cohort of patients with low probability of achieving SVR can be identified. This project identifies patients with a low risk of responding to PEG-IFN alfa-2a and ribavirin combination.

The paradox of highly effective sofosbuvir-based combination therapy despite slow viral decline: can we still rely on viral kinetics?

High sustained virologic response (SVR) rates have been observed after 6 weeks of anti-HCV treatment using sofosbuvir, ledipasvir and a non-nucleoside polymerase-inhibitor (GS-9669) or a protease-inhibitor (GS-9451) and after 12 weeks with sofosbuvir + ledipasvir. Here we analyze the viral kinetics observed during these treatments to decipher the origin of the rapid cure and to evaluate the possibility of further reducing treatment duration. We found that viral kinetics were surprisingly slow in all treatment groups and could not reproduce the high SVR rates observed. Based on experimental results suggesting that NS5A- or protease-inhibitors can generate non-infectious virus, we incorporated this effect into a mathematical model. We found that to predict observed SVR rates it was necessary to assume that ledipasvir, GS-9669 and GS-9451 rapidly reduce virus infectivity. We predicted with this model that 4 weeks of triple therapy could be sufficient to achieve SVR in patients with undetectable viremia at week 1, but would be suboptimal in general. In conclusion, the rapid cure rate achieved with these combinations is largely disconnected from viral loads measured during treatment. A model assuming that rapid cure is due to a drug effect of generating non-infectious virus could be a basis for future response guided therapy.

Efficacy of the Combination of Sofosbuvir, Velpatasvir, and the NS3/4A Protease Inhibitor GS-9857 in Treatment-Naïve or Previously Treated Patients With Hepatitis C Virus Genotype 1 or 3 Infections

BACKGROUND & AIMS

We performed a phase 2 trial of the efficacy and safety of 4, 6, and 8 weeks of sofosbuvir, given in combination with the NS5A inhibitor velpatasvir and the NS3/4A protease inhibitor GS-9857, in patients with hepatitis C virus (HCV) infection.

METHODS

We enrolled 161 treatment-naïve or previously treated patients infected with HCV genotypes 1 or 3 with or without compensated cirrhosis at 2 centers in New Zealand, from September 2014 through March 2015. All patients received sofosbuvir (400 mg) and velpatasvir (100 mg) plus GS-9857 (100 mg) once daily. The primary efficacy end point was sustained virologic response at 12 weeks after therapy (SVR12). The duration of therapy was determined by baseline patient characteristics: 4 or 6 weeks for treatment-naïve patients without cirrhosis, 6 weeks for treatment-naïve patients with cirrhosis, and 6 or 8 weeks for treatment-experienced patients with or without cirrhosis.

RESULTS

Four weeks of sofosbuvir, velpatasvir, and GS-9857 produced an SVR12 in 4 of 15 (27%) treatment-naïve patients with HCV genotype 1 without cirrhosis. Six weeks of this combination produced a SVR12 in 14 of 15 (93%) treatment-naïve patients with HCV genotype 1 without cirrhosis, in 13 of 15 (87%) treatment-naïve genotype 1 patients with cirrhosis, in 15 of 18 (83%) treatment-naïve patients with HCV genotype 3 with cirrhosis, and in 20 of 30 (67%) patients with HCV genotype 1 who had failed an all-oral regimen of 2 or more direct-acting antiviral agents. Eight weeks of the drug combination produced an SVR12 in 17 of 17 (100%) patients with HCV genotype 1, in 19 of 19 (100%) patients with HCV genotype 3 and cirrhosis who had failed pegylated interferon plus ribavirin, in 25 of 28 (89%) patients with HCV genotype 1 who had failed protease inhibitor-based triple therapy, and in 4 of 4 (100%) patients with HCV genotype 3 who had failed an all-oral regimen of ≥2 direct-acting antiviral agents. The most common reported adverse events were headache, nausea, and fatigue.

CONCLUSIONS

Eight weeks of treatment with the combination of sofosbuvir, velpatasvir, and GS-9857 produced an SVR12 in most treatment-naïve or previously treated patients with HCV genotype 1 or 3 infections, including those with compensated cirrhosis. ClinicalTrials.gov, Number: NCT02202980.

Ebola Virus Infection: Review of the Pharmacokinetic and Pharmacodynamic Properties of Drugs Considered for Testing in Human Efficacy Trials

The 2014-2015 outbreak of Ebola virus disease is the largest epidemic to date in terms of the number of cases, deaths, and affected areas. In October 2015, no antiviral agents had proven antiviral efficacy in patients. However, in September 2014, the World Health Organization inventoried and has since regularly updated a list of potential drug candidates with demonstrated antiviral efficacy in in vitro or animal models. This includes agents belonging to various therapeutic classes, namely direct antiviral agents (favipiravir and BCX4430), a combination of antibodies (ZMapp), type I interferons, RNA interference-based drugs (TKM-Ebola and AVI-7537), and anticoagulant drugs (rNAPc2). Here, we review the pharmacokinetic and pharmacodynamic information presently available for these drugs, using data obtained in healthy volunteers for pharmacokinetics and data obtained in human clinical trials or animal models for pharmacodynamics. Future studies evaluating these drugs in clinical trials are critical to confirm their efficacy in humans, propose appropriate doses, and evaluate the possibility of treatment combinations.

Modelling hepatitis C therapy--predicting effects of treatment

Mathematically modelling changes in HCV RNA levels measured in patients who receive antiviral therapy has yielded many insights into the pathogenesis and effects of treatment on the virus. By determining how rapidly HCV is cleared when viral replication is interrupted by a therapy, one can deduce how rapidly the virus is produced in patients before treatment. This knowledge, coupled with estimates of the HCV mutation rate, enables one to estimate the frequency with which drug resistant variants arise. Modelling HCV also permits the deduction of the effectiveness of an antiviral agent at blocking HCV replication from the magnitude of the initial viral decline. One can also estimate the lifespan of an HCV-infected cell from the slope of the subsequent viral decline and determine the duration of therapy needed to cure infection. The original understanding of HCV RNA decline under interferon-based therapies obtained by modelling needed to be revised in order to interpret the HCV RNA decline kinetics seen when using direct-acting antiviral agents (DAAs). There also exist unresolved issues involving understanding therapies with combinations of DAAs, such as the presence of detectable HCV RNA at the end of therapy in patients who nonetheless have a sustained virologic response.