Modeling quasispecies and drug resistance in hepatitis C patients treated with a protease inhibitor

Modeling the XBB strain of SARS-CoV-2: Competition between variants and impact of reinfection

XBB, an Omicron subvariant of SARS-CoV-2 that began to circulate in late 2022, has been dominant in the US since early 2023. To quantify the impact of XBB on the progression of COVID-19, we propose a new mathematical model which describes the interplay between XBB and other SARS-CoV-2 variants at the population level and which incorporates the effects of reinfection. We apply the model to COVID-19 data in the US that include surveillance data on the cases and variant proportions from the New York City, the State of New York, and the State of Washington. Our fitting and simulation results show that the transmission rate of XBB is significantly higher than that of other variants and the reinfection from XBB may play an important role in shaping the pandemic/epidemic pattern in the US.

A multi-strain model with asymptomatic transmission: Application to COVID-19 in the US

COVID-19, induced by the SARS-CoV-2 infection, has caused an unprecedented pandemic in the world. New variants of the virus have emerged and dominated the virus population. In this paper, we develop a multi-strain model with asymptomatic transmission to study how the asymptomatic or pre-symptomatic infection influences the transmission between different strains and control strategies that aim to mitigate the pandemic. Both analytical and numerical results reveal that the competitive exclusion principle still holds for the model with the asymptomatic transmission. By fitting the model to the COVID-19 case and viral variant data in the US, we show that the omicron variants are more transmissible but less fatal than the previously circulating variants. The basic reproduction number for the omicron variants is estimated to be 11.15, larger than that for the previous variants. Using mask mandate as an example of non-pharmaceutical interventions, we show that implementing it before the prevalence peak can significantly lower and postpone the peak. The time of lifting the mask mandate can affect the emergence and frequency of subsequent waves. Lifting before the peak will result in an earlier and much higher subsequent wave. Caution should also be taken to lift the restriction when a large portion of the population remains susceptible. The methods and results obtained her e may be applied to the study of the dynamics of other infectious diseases with asymptomatic transmission using other control measures.

The Basic Reproduction Number and Delayed Action of T Cells for Patients Infected with SARS-CoV-2

COVID-19 has been prevalent for the last two years. The transmission capacity of SARS-CoV-2 differs under the influence of different epidemic prevention policies, making it difficult to measure the infectivity of the virus itself. In order to evaluate the infectivity of SARS-CoV-2 in patients with different diseases, we constructed a viral kinetic model by adding the effects of T cells and antibodies. To analyze and compare the delay time of T cell action in patients with different symptoms, we constructed a delay differential equation model. Through the first model, we found that the basic reproduction number of severe patients is greater than that of mild patients, and accordingly, we constructed classification criteria for severe and mild patients. Through the second model, we found that the delay time of T cell action in severe patients is much longer than that in mild patients, and accordingly, we present suggestions for the prevention, diagnosis, and treatment of different patients.

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.

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.

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.

Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to drugs

Knowledge of the within-host frequencies of resistance-associated amino acid variants (RAVs) is important to the identification of optimal drug combinations for the treatment of hepatitis C virus (HCV) infection. Multiple RAVs may exist in infected individuals, often below detection limits, at any resistance locus, defining the diversity of accessible resistance pathways. We developed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of mutants at chosen loci. Using a codon-level description of amino acids, we performed stochastic simulations of intracellular dynamics with every possible nucleotide variant as the infecting strain and estimated the relative infectivity of each variant and the resulting distribution of variants produced. We employed these quantities in a deterministic multi-strain model of extracellular dynamics and estimated mutant frequencies. Our predictions captured database frequencies of the RAV R155K, resistant to NS3/4A protease inhibitors, presenting a successful test of our formalism. We found that mutational pathway maps, interconnecting all viable mutants, and strong founder effects determined the mutant spectrum. The spectra were vastly different for HCV genotypes 1a and 1b, underlying their differential responses to drugs. Using a fitness landscape determined recently, we estimated that 13 amino acid variants, encoded by 44 codons, exist at the residue 93 of the NS5A protein, illustrating the massive diversity of accessible resistance pathways at specific loci. Accounting for this diversity, which our model enables, would help optimize drug combinations. Our model may be applied to describe the within-host evolution of other flaviviruses and inform vaccine design strategies.

Virological and Immunological Outcomes of Coinfections

Coinfections involving viruses are being recognized to influence the disease pattern that occurs relative to that with single infection. Classically, we usually think of a clinical syndrome as the consequence of infection by a single virus that is isolated from clinical specimens. However, this biased laboratory approach omits detection of additional agents that could be contributing to the clinical outcome, including novel agents not usually considered pathogens. The presence of an additional agent may also interfere with the targeted isolation of a known virus. Viral interference, a phenomenon where one virus competitively suppresses replication of other coinfecting viruses, is the most common outcome of viral coinfections. In addition, coinfections can modulate virus virulence and cell death, thereby altering disease severity and epidemiology. Immunity to primary virus infection can also modulate immune responses to subsequent secondary infections. In this review, various virological mechanisms that determine viral persistence/exclusion during coinfections are discussed, and insights into the isolation/detection of multiple viruses are provided. We also discuss features of heterologous infections that impact the pattern of immune responsiveness that develops.

Modelling how responsiveness to interferon improves interferon-free treatment of hepatitis C virus infection

Direct-acting antiviral agents (DAAs) for hepatitis C treatment tend to fare better in individuals who are also likely to respond well to interferon-alpha (IFN), a surprising correlation given that DAAs target specific viral proteins whereas IFN triggers a generic antiviral immune response. Here, we posit a causal relationship between IFN-responsiveness and DAA treatment outcome. IFN-responsiveness restricts viral replication, which would prevent the growth of viral variants resistant to DAAs and improve treatment outcome. To test this hypothesis, we developed a multiscale mathematical model integrating IFN-responsiveness at the cellular level, viral kinetics and evolution leading to drug resistance at the individual level, and treatment outcome at the population level. Model predictions quantitatively captured data from over 50 clinical trials demonstrating poorer response to DAAs in previous non-responders to IFN than treatment-naïve individuals, presenting strong evidence supporting the hypothesis. Model predictions additionally described several unexplained clinical observations, viz., the percentages of infected individuals who 1) spontaneously clear HCV, 2) get chronically infected but respond to IFN-based therapy, and 3) fail IFN-based therapy but respond to DAA-based therapy, resulting in a comprehensive understanding of HCV infection and treatment. An implication of the causal relationship is that failure of DAA-based treatments may be averted by adding IFN, a strategy of potential use in settings with limited access to DAAs. A second, wider implication is that individuals with greater IFN-responsiveness would require shorter DAA-based treatment durations, presenting a basis and a promising population for response-guided therapy.

HCV kinetic and modeling analyses project shorter durations to cure under combined therapy with daclatasvir and asunaprevir in chronic HCV-infected patients

BACKGROUND & AIMS

High cure rates are achieved in HCV genotype-1b patients treated with daclatasvir and asunaprevir, DCV/ASV. Here we analyzed early HCV kinetics in genotype-1b infected Japanese subjects treated with DCV/ASV and retrospectively projected, using mathematical modeling, whether shorter treatment durations might be effective.

METHODS

HCV RNA levels were measured frequently during DCV/ASV therapy in 95 consecutively treated patients at a single center in Japan. Mathematical modeling was used to predict the time to cure, i.e, <1 virus copy in the extracellular body fluid. Patients with HCV<15 IU/ml at week 1 (n = 27) were excluded from modeling analysis due to insufficient HCV RNA data points.

RESULTS

Eighty nine of the 95 included patients (94%) achieved cure, 3 (3%) relapsed due to treatment-emergent resistance, and 3 (3%) completed therapy but were lost during follow up. Model fits from 68 patients with sufficient data points indicate that after a short pharmacological delay (15.4 min [relative standard error, rse = 26%]), DCV/ASV effectiveness in blocking HCV production was 0.999 [rse~0%], HCV half-life in blood was t1/2 = 1.7 hr [rse = 21%], and HCV-infected cell loss rate was 0.391/d [rse = 5%]. Modeling predicted that 100% and 98.5% of patients who had HCV<15 IU/ml at days 14 and 28 might have been cured with 6 and 8 weeks of therapy, respectively. There was a trend (p = 0.058) between younger age and shorter time to cure.

CONCLUSION

Modeling early HCV kinetics under DCV/ASV predicts that most patients would achieve cure with short treatment durations, suggesting that 24 weeks of DCV/ASV treatment can be significantly shortened.

The antigenic variability of HCV in viral HLA-Ag binding is related to the activation of the host immune response

Our previous data show that hepatitis C virus (HCV) genotype 1 patients expressing the HLA-DQB1 * 0301 allele have a combined response probability of 69%, while the remaining 31% do not respond, probably because the HCV immunodominant epitope (IE) against the DQB1 * 0301 allele is mutated. HCV IE (region sequenced in NS3 is a region encoding aa 1253–1272) from 37 patients (21 Sustained Virological Response, SVR; 16 non-SVR) HLA-DQB1 * 0301+, were analysed by pyrosequencing. In vitro cultures were also determined by CD4+ proliferation, using non-mutated IE (wild-type synthetic peptide) and synthetic mutated peptide. The pyrosequencing study revealed 34 different haplotypes. The SVR patients had fewer haplotypes (P = 0.07), mutations/haplotypes (P = 0.01) and polymorphic sites (P = 0.02) than non-SVR. Three polymorphic sites were associated with the non-SVR patients: haplotype 7 (L5P); haplotype 11 (L7P); and haplotype 15, (L15S) (P = 0.02). The in vitro study (n = 7) showed that in 4/7 patients (Group 1) the CD4+ proliferation obtained with wild-type synthetic peptide was higher than that obtained with the negative control and with the synthetic mutated peptide (P = 0.039). However, in the remaining 3/7 patients (Group 2) this pattern was not observed (P = 0.7). Our findings suggest that HLA-DQB1 * 0301+ patients with high antigenic variability in HCV IE (NS31253-1272) have a lower SVR rate, due to reduced CD4+ proliferation as a result of incorrect viral HLA-Ag binding.

Models of Viral Population Dynamics

Models of viral population dynamics have contributed enormously to our understanding of the pathogenesis and transmission of several infectious diseases, the coevolutionary dynamics of viruses and their hosts, the mechanisms of action of drugs, and the effectiveness of interventions. In this chapter, we review major advances in the modeling of the population dynamics of the human immunodeficiency virus (HIV) and briefly discuss adaptations to other viruses.

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.

HCV Kinetic Models and Their Implications in Drug Development

Chronic infection with hepatitis C virus (HCV) affects about 170 million people worldwide and is a major cause of liver complications. Mathematical modeling of viral kinetics under treatment has provided insight into the viral life cycle, treatment effectiveness, and drugs' mechanisms of action. Here we review the implications of viral kinetic models at the different stages of development of anti-HCV agents.

Combination versus sequential monotherapy in chronic HBV infection: a mathematical approach

Sequential monotherapy is the most widely used therapeutic approach in the treatment of hepatitis B virus (HBV) chronic infection. Unfortunately, under therapy, in some patients the hepatitis virus mutates and gives rise to variants which are drug resistant. We wonder whether those patients would have benefited from the choice of combination therapy instead of sequential monotherapy. To study the action of these two therapeutic approaches and to explain the emergence of drug resistance, we propose a stochastic model for the infection within a patient who is treated with two drugs, either sequentially or contemporaneously, and who, under the first kind of therapy develops a strain of the virus which is resistant to both drugs. Our stochastic model has a deterministic approximation which is a slight modification of a classic three-strain model. We discuss why stochastic simulations are more suitable than the study of the deterministic approximation, when modelling the rise of mutations (this is mainly due to the amplitude of the stochastic fluctuations). We run stochastic simulations with suitable parameters and compare the time when, under the two therapeutic approaches, the resistant strain first reaches detectability in the serum viral load. Our results show that the best choice is to start an early combination therapy, which allows one to stay drug resistance free for a longer time and in many cases leads to viral eradication.

Naturally occurring dominant drug resistance mutations occur infrequently in the setting of recently acquired hepatitis C

BACKGROUND

Direct-acting antivirals (DAAs) are predicted to transform hepatitis C therapy, yet little is known about the prevalence of naturally occurring resistance mutations in recently acquired HCV. This study aimed to determine the prevalence and frequency of drug resistance mutations in the viral quasispecies among HIV-positive and -negative individuals with recent HCV.

METHODS

The NS3 protease, NS5A and NS5B polymerase genes were amplified from 50 genotype 1a participants of the Australian Trial in Acute Hepatitis C. Amino acid variations at sites known to be associated with possible drug resistance were analysed by ultra-deep pyrosequencing.

RESULTS

A total of 12% of individuals harboured dominant resistance mutations, while 36% demonstrated non-dominant resistant variants below that detectable by bulk sequencing (that is, <20%) but above a threshold of 1%. Resistance variants (<1%) were observed at most sites associated with DAA resistance from all classes, with the exception of sofosbuvir.

CONCLUSIONS

Dominant resistant mutations were uncommonly observed in the setting of recent HCV. However, low-level mutations to all DAA classes were observed by deep sequencing at the majority of sites and in most individuals. The significance of these variants and impact on future treatment options remains to be determined. Clinicaltrials.gov NCT00192569.

Viral kinetic modeling: state of the art

Viral kinetic (VK) modeling has led to increased understanding of the within host dynamics of viral infections and the effects of therapy. Here we review recent developments in the modeling of viral infection kinetics with emphasis on two infectious diseases: hepatitis C and influenza. We review how VK modeling has evolved from simple models of viral infections treated with a drug or drug cocktail with an assumed constant effectiveness to models that incorporate drug pharmacokinetics and pharmacodynamics, as well as phenomenological models that simply assume drugs have time varying-effectiveness. We also discuss multiscale models that include intracellular events in viral replication, models of drug-resistance, models that include innate and adaptive immune responses and models that incorporate cell-to-cell spread of infection. Overall, VK modeling has provided new insights into the understanding of the disease progression and the modes of action of several drugs. We expect that VK modeling will be increasingly used in the coming years to optimize drug regimens in order to improve therapeutic outcomes and treatment tolerability for infectious diseases.

A new stochastic model for subgenomic hepatitis C virus replication considers drug resistant mutants

As an RNA virus, hepatitis C virus (HCV) is able to rapidly acquire drug resistance, and for this reason the design of effective anti-HCV drugs is a real challenge. The HCV subgenomic replicon-containing cells are widely used for experimental studies of the HCV genome replication mechanisms, for drug testing in vitro and in studies of HCV drug resistance. The NS3/4A protease is essential for virus replication and, therefore, it is one of the most attractive targets for developing specific antiviral agents against HCV. We have developed a stochastic model of subgenomic HCV replicon replication, in which the emergence and selection of drug resistant mutant viral RNAs in replicon cells is taken into account. Incorporation into the model of key NS3 protease mutations leading to resistance to BILN-2061 (A156T, D168V, R155Q), VX-950 (A156S, A156T, T54A) and SCH 503034 (A156T, A156S, T54A) inhibitors allows us to describe the long term dynamics of the viral RNA suppression for various inhibitor concentrations. We theoretically showed that the observable difference between the viral RNA kinetics for different inhibitor concentrations can be explained by differences in the replication rate and inhibitor sensitivity of the mutant RNAs. The pre-existing mutants of the NS3 protease contribute more significantly to appearance of new resistant mutants during treatment with inhibitors than wild-type replicon. The model can be used to interpret the results of anti-HCV drug testing on replicon systems, as well as to estimate the efficacy of potential drugs and predict optimal schemes of their usage.

Treatment of hepatitis C with an interferon-based lead-in phase: a perspective from mathematical modelling

BACKGROUND

The standard of care for HCV genotype 1 is a protease inhibitor (telaprevir or boceprevir) combined with pegylated interferon (PEG-IFN) and ribavirin (RBV). A lead-in phase of PEG-IFN/RBV therapy before addition of the protease inhibitor has been used, with the aim of improving response rates by reducing the development of protease inhibitor resistance. However, whether such a strategy can bring benefit to patients is unclear.

METHODS

A viral dynamic model was used to compare in silico HCV dynamics in patients treated with a period of PEG-IFN/RBV lead-in therapy followed by the addition of a protease inhibitor versus immediate triple therapy without lead-in.

RESULTS

The model predicts that both regimens result in a similar end-of-treatment viral load change (viral decline or breakthrough). Thus, the current lead-in strategy may not decrease the rate of viral breakthrough/relapse or increase the rate of sustained virological response. This agrees with available data from clinical trials of several HCV protease inhibitors, such as telaprevir, boceprevir and faldaprevir.

CONCLUSIONS

These results suggest that current PEG-IFN/RBV lead-in strategies may not improve treatment outcomes. However, viral kinetics during a period of PEG-IFN/RBV therapy, combined with other factors, such as the IL28B polymorphism and baseline viral load, can identify IFN-sensitive patients and help develop response-guided therapies.

Mathematical analysis of multiscale models for hepatitis C virus dynamics under therapy with direct-acting antiviral agents

Chronic hepatitis C virus (HCV) infection remains a world-wide public health problem. Therapy with interferon and ribavirin leads to viral elimination in less than 50% of treated patients. New treatment options aiming at a higher cure rate are focused on direct-acting antiviral agents (DAAs), which directly interfere with different steps in the HCV life cycle. In this paper, we describe and analyze a recently developed multiscale model that predicts HCV dynamics under therapy with DAAs. The model includes both intracellular viral RNA replication and extracellular viral infection. We calculate the steady states of the model and perform a detailed stability analysis. With certain assumptions we obtain analytical approximations of the viral load decline after treatment initiation. One approximation agrees well with the prediction of the model, and can conveniently be used to fit patient data and estimate parameter values. We also discuss other possible ways to incorporate intracellular viral dynamics into the multiscale model.

Analysis of hepatitis C virus decline during treatment with the protease inhibitor danoprevir using a multiscale model

The current paradigm for studying hepatitis C virus (HCV) dynamics in patients utilizes a standard viral dynamic model that keeps track of uninfected (target) cells, infected cells, and virus. The model does not account for the dynamics of intracellular viral replication, which is the major target of direct-acting antiviral agents (DAAs). Here we describe and study a recently developed multiscale age-structured model that explicitly considers the potential effects of DAAs on intracellular viral RNA production, degradation, and secretion as virus into the circulation. We show that when therapy significantly blocks both intracellular viral RNA production and virus secretion, the serum viral load decline has three phases, with slopes reflecting the rate of serum viral clearance, the rate of loss of intracellular viral RNA, and the rate of loss of intracellular replication templates and infected cells, respectively. We also derive analytical approximations of the multiscale model and use one of them to analyze data from patients treated for 14 days with the HCV protease inhibitor danoprevir. Analysis suggests that danoprevir significantly blocks intracellular viral production (with mean effectiveness 99.2%), enhances intracellular viral RNA degradation about 5-fold, and moderately inhibits viral secretion (with mean effectiveness 56%). The multiscale model can be used to study viral dynamics in patients treated with other DAAs and explore their mechanisms of action in treatment of hepatitis C.

Chronic infection with hepatitis C virus (HCV) remains an important health-care problem worldwide despite significant progress in the development of HCV therapy since the discovery of the virus in 1989. Current treatment options are focused on direct-acting antiviral agents (DAAs) that target specific steps of the HCV life cycle. Danoprevir, one of the DAAs that inhibit the HCV NS3-4A protease, has induced substantial viral load reductions in patients receiving therapy. We study the viral decline during therapy using a multiscale age-structured model that accounts for the dynamics of intracellular viral replication, and which includes the major steps in the HCV life cycle that are targeted by DAAs. We examine the biological parameters contributing to different phases of the viral decline after treatment initiation. We also explore the mechanisms of action of danoprevir and estimate its treatment effectiveness. The multiscale model provides a theoretical framework for studying virus dynamics in hepatitis C patients treated with other DAAs currently in clinical development, and may help one to optimally combine drugs with complementary modes of action to maximize the HCV cure rate.

Combinations of lambda interferon with direct-acting antiviral agents are highly efficient in suppressing hepatitis C virus replication

The clinical efficacy of a pegylated form of human lambda 1 interferon (IFN-λ1; also referred to herein as lambda) has been demonstrated in patients chronically infected with hepatitis C virus (HCV) representing genotypes 1 through 4. In these proof-of-concept studies, lambda showed an improved safety profile compared to the pegylated form of alpha interferon (referred to herein as alfa). In the study described in this report, an assessment of the in vitro antiviral activity of type III IFNs toward different HCV replicons revealed that the unpegylated recombinant form of IFN-λ1 (rIFN-λ1) exerted the most robust effect, while rIFN-λ3 exhibited greater activity than rIFN-λ2. More importantly, cross-resistance to rIFN-λ1 was not observed in replicon cell lines known to have reduced susceptibility to investigational direct-acting antiviral (DAA) agents targeting the essential HCV nonstructural protein NS3, NS5A, or NS5B. When combined with either rIFN-α, the NS3 protease inhibitor (NS3 PI) asunaprevir (ASV), the NS5A replication complex inhibitor (NS5A RCI) daclatasvir (DCV), or the NS5B polymerase site I inhibitor (NS5B I) BMS-791325, rIFN-λ1 displayed a mixture of additive and synergistic effects. In three-drug combination studies, inclusion of lambda with ASV and DCV also yielded additive to synergistic effects. In line with these observations, it was demonstrated that a regimen that used a combination of rIFN-λ1 with one or two DAAs was superior to an IFN-free regimen in clearing HCV RNA in genotype 1a cell lines representing wild-type and NS3 protease inhibitor-resistant sequences. Overall, these data support further clinical development of lambda as part of alternative combination treatments with DAAs for patients chronically infected with HCV.

Hepatitis C virus viral assays in the direct-acting antiviral era

In the direct-acting antiviral (DAA) era of hepatitis C virus (HCV) therapy, health care providers must be knowledgeable about genotype and subtype of HCV infection and interpretation of quantitative HCV viral assays to monitor treatment responses. They may also choose to assess interleukin 28B genotypes or resistance-associated variants after ineffective DAA therapy. DAA therapies require understanding of performance characteristics of quantitative HCV RNA assays and the definitions of terms used to report results. Only quantitative HCV RNA assays with a limit of detection of 10 to 15 IU/mL are appropriate for managing patients on DAA therapy.

Hepatitis C viral kinetics: the past, present, and future

Mathematical modeling of hepatitis C viral kinetics has been an important tool in understanding hepatitis C virus (HCV) infection dynamics and in estimating crucial in vivo parameters characterizing the effectiveness of HCV therapy. Because of the introduction of direct-acting antiviral agents, there is a need to extend previous models so as to understand, characterize, and compare various new HCV treatment regimens. Here we review recent modeling efforts in this direction.