Viral kinetic modeling: state of the art
Abstract
Viral kinetic 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 viral kinetic 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, viral kinetic modeling has provided new insights into the understanding of the disease progression and the modes of action of several drugs. In conclusion, we expect that viral kinetic 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.
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Biology and Biophysics
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE; National Institutes of Health (NIH)
- OSTI Identifier:
- 1282052
- Grant/Contract Number:
- AC52-06NA25396; R01-AI028433; P20-GM10345; R01-AI078881; R34-HL109334; R01-OD011095
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Pharmacokinetics and Pharmacodynamics
- Additional Journal Information:
- Journal Volume: 41; Journal Issue: 5; Journal ID: ISSN 1567-567X
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Viral kinetics; Hepatitis C; Influenza; Mathematical modeling; Antiviral drug; Resistance emergence
Citation Formats
Canini, Laetitia, and Perelson, Alan S. Viral kinetic modeling: state of the art. United States: N. p., 2014.
Web. doi:10.1007/s10928-014-9363-3.
Canini, Laetitia, & Perelson, Alan S. Viral kinetic modeling: state of the art. United States. https://doi.org/10.1007/s10928-014-9363-3
Canini, Laetitia, and Perelson, Alan S. Wed .
"Viral kinetic modeling: state of the art". United States. https://doi.org/10.1007/s10928-014-9363-3. https://www.osti.gov/servlets/purl/1282052.
@article{osti_1282052,
title = {Viral kinetic modeling: state of the art},
author = {Canini, Laetitia and Perelson, Alan S.},
abstractNote = {Viral kinetic 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 viral kinetic 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, viral kinetic modeling has provided new insights into the understanding of the disease progression and the modes of action of several drugs. In conclusion, we expect that viral kinetic 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.},
doi = {10.1007/s10928-014-9363-3},
journal = {Journal of Pharmacokinetics and Pharmacodynamics},
number = 5,
volume = 41,
place = {United States},
year = {Wed Jun 25 00:00:00 EDT 2014},
month = {Wed Jun 25 00:00:00 EDT 2014}
}
Web of Science
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