A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes
Abstract
:1. Introduction
2. Materials and Methods
Linear Stability Analysis
3. Results
3.1. Singular Therapies
3.2. Combination Therapy
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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V | Viral load |
M | Population of antigen-presenting cells |
F | Interferon |
S | Antigenic compatibility |
Respiratory Epithelial Cells | |
H | Population of healthy, susceptible cells |
D | Population of damaged cells |
I | Population of infected cells |
L | Population of latently infected cells |
R | Population of resistant cells |
Innate Immune System | |
E | Population of effector cells |
Adaptive Immune System | |
P | Population of plasma cells |
A | Antibodies |
Parameter | Description |
---|---|
Virus reproduction rate in infected cells | |
Virus elimination rate by antibodies | |
Rate by which virus enters healthy cells | |
Natural virus degradation rate | |
Max. rate of virus removal | |
= 23,000 | Michaelis–Menten constant in virus removal |
Rate by which cells enter latent eclipse phase | |
Epithelial cell regeneration rate | |
Rate of virus resistance loss | |
Cell infection rate by virus | |
Rate by which susceptible cells gain resistance | |
Rate by which infected cells are removed by immune effector cells | |
Infected cell natural death rate | |
Rate of producing antigen-presenting cells by damaged cells | |
Rate of producing antigen-presenting cells stimulation by virus | |
Antigen-presenting cell natural death rate | |
= 125,000 | Interferon production rate by antigen-presenting cell |
Interferon production rate by infected cell | |
Rate by which interferon binds to healthy cells | |
Interferon natural decay rate | |
Rate by which effector cells are produced by antigen-presenting cells | |
Rate of effector cell death by infected cell | |
Effector cell natural death rate | |
Plasma cell production rate | |
Plasma cell natural death rate | |
Antibody production rate by plasma cells | |
Rate by which antibody binds to virus | |
Antibody natural death rate | |
Rate of antibody specificity change |
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Pateras, J.; Ghosh, P. A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes. Mathematics 2022, 10, 3739. https://doi.org/10.3390/math10203739
Pateras J, Ghosh P. A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes. Mathematics. 2022; 10(20):3739. https://doi.org/10.3390/math10203739
Chicago/Turabian StylePateras, Joseph, and Preetam Ghosh. 2022. "A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes" Mathematics 10, no. 20: 3739. https://doi.org/10.3390/math10203739
APA StylePateras, J., & Ghosh, P. (2022). A Computational Framework for Exploring SARS-CoV-2 Pharmacodynamic Dose and Timing Regimes. Mathematics, 10(20), 3739. https://doi.org/10.3390/math10203739