Model‐informed drug repurposing : Viral kinetic modelling to prioritize rational drug combinations for COVID‐19
Aim We hypothesized that viral kinetic modelling could be helpful to prioritize rational drug combinations for COVID‐19. The aim of this research was to use a viral cell cycle model of SARS‐CoV‐2 to explore the potential impact drugs, or combinations of drugs, that act at different stages in the viral life cycle might have on various metrics of infection outcome relevant in the early stages of COVID‐19 disease. Methods Using a target‐cell limited model structure that has been used to characterize viral load dynamics from COVID‐19 patients, we performed simulations to inform on the combinations of therapeutics targeting specific rate constants. The endpoints and metrics included viral load area under the curve (AUC), duration of viral shedding and epithelial cells infected. Based on the known kinetics of the SARS‐CoV‐2 life cycle, we rank ordered potential targeted approaches involving repurposed, low‐potency agents. Results Our simulations suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time‐window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype. Conclusions Our work highlights the use of model‐informed drug repurposing approaches to better rationalize effective treatments for COVID‐19..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:87 |
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| Enthalten in: |
British Journal of Clinical Pharmacology - 87(2021), 9, Seite 3439-3450 |
| Beteiligte Personen: |
Dodds, Michael G. [VerfasserIn] |
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| BKL: |
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| Anmerkungen: |
© 2021 The British Pharmacological Society |
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| Umfang: |
12 |
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| doi: |
10.1111/bcp.14486 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 100 | 1 | |a Dodds, Michael G. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Model‐informed drug repurposing |b Viral kinetic modelling to prioritize rational drug combinations for COVID‐19 |
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| 520 | |a Aim We hypothesized that viral kinetic modelling could be helpful to prioritize rational drug combinations for COVID‐19. The aim of this research was to use a viral cell cycle model of SARS‐CoV‐2 to explore the potential impact drugs, or combinations of drugs, that act at different stages in the viral life cycle might have on various metrics of infection outcome relevant in the early stages of COVID‐19 disease. Methods Using a target‐cell limited model structure that has been used to characterize viral load dynamics from COVID‐19 patients, we performed simulations to inform on the combinations of therapeutics targeting specific rate constants. The endpoints and metrics included viral load area under the curve (AUC), duration of viral shedding and epithelial cells infected. Based on the known kinetics of the SARS‐CoV‐2 life cycle, we rank ordered potential targeted approaches involving repurposed, low‐potency agents. Results Our simulations suggest that targeting multiple points central to viral replication within infected host cells or release from those cells is a viable strategy for reducing both viral load and host cell infection. In addition, we observed that the time‐window opportunity for a therapeutic intervention to effect duration of viral shedding exceeds the effect on sparing epithelial cells from infection or impact on viral load AUC. Furthermore, the impact on reduction on duration of shedding may extend further in patients who exhibit a prolonged shedder phenotype. Conclusions Our work highlights the use of model‐informed drug repurposing approaches to better rationalize effective treatments for COVID‐19. | ||
| 700 | 1 | |a Krishna, Rajesh |4 aut | |
| 700 | 1 | |a Goncalves, Antonio |4 aut | |
| 700 | 1 | |a Rayner, Craig R. |4 aut | |
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