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Direct-acting antiviral agents (DAAs) are known to interfere with various intracellular stages of the hepatitis C virus (HCV) life cycle and have demonstrated efficacy in treating HCV infection. However, DAA monotherapy can lead to drug resistance due to mutations. This paper explores the impact of DAA therapy on HCV dynamics using a multiscale age-structured partial differential equation (PDE) model that incorporates intracellular viral RNA replication within infected cells and two strains of viruses representing a drug-sensitive strain and a drug-resistant mutant variant, respectively. We derived an equivalent ordinary differential equation (ODE) model from the PDE model to simplify mathematical analysis and numerical simulations. We studied the dynamics of the two virus strains before treatment and investigated the impact of mutations on the evolution kinetics of drug-sensitive and drug-resistant viruses, as well as the competition between the two strains during treatment. We also explored the role of DAAs in blocking HCV RNA replication and releasing new virus particles from cells. During treatment, mutations do not significantly influence the dynamics of various virus strains; however, they can generate low-level HCV that may be completely inhibited due to their poor fitness. The fitness of the mutant strain compared to the drug-sensitive strain determines which strain dominates the virus population. We also investigated the prevalence and drug resistance evolution of HCV variants during DAA treatment.
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Success of prophylactic antiviral therapy for SARS-CoV-2: Predicted critical efficacies and impact of different drug-specific mechanisms of action
Repurposed drugs that are safe and immediately available constitute a first line of defense against new viral infections. Despite limited antiviral activity against SARS-CoV-2, several drugs are being tested as medication or as prophylaxis to prevent infection. Using a stochastic model of early phase infection, we evaluate the success of prophylactic treatment with different drug types to prevent viral infection. We find that there exists a critical efficacy that a treatment must reach in order to block viral establishment. Treatment by a combination of drugs reduces the critical efficacy, most effectively by the combination of a drug blocking viral entry into cells and a drug increasing viral clearance. Below the critical efficacy, the risk of infection can nonetheless be reduced. Drugs blocking viral entry into cells or enhancing viral clearance reduce the risk of infection more than drugs that reduce viral production in infected cells. The larger the initial inoculum of infectious virus, the less likely is prevention of an infection. In our model, we find that as long as the viral inoculum is smaller than 10 infectious virus particles, viral infection can be prevented almost certainly with drugs of 90% efficacy (or more). Even when a viral infection cannot be prevented, antivirals delay the time to detectable viral loads. The largest delay of viral infection is achieved by drugs reducing viral production in infected cells. A delay of virus infection flattens the within-host viral dynamic curve, possibly reducing transmission and symptom severity. Thus, antiviral prophylaxis, even with reduced efficacy, could be efficiently used to prevent or alleviate infection in people at high risk.
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- Award ID(s):
- 2031756
- PAR ID:
- 10293716
- Editor(s):
- Faeder, James R.
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 17
- Issue:
- 3
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1008752
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1008752
