Using in silico viral kinetic models to guide therapeutic strategies during a pandemic : An example in SARS-CoV-2

© 2020 The British Pharmacological Society..

AIMS: We propose the use of in silico mathematical models to provide insights that optimize therapeutic interventions designed to effectively treat respiratory infection during a pandemic. A modelling and simulation framework is provided using SARS-CoV-2 as an example, considering applications for both treatment and prophylaxis.

METHODS: A target cell-limited model was used to quantify the viral infection dynamics of SARS-CoV-2 in a pooled population of 105 infected patients. Parameter estimates from the resulting model were used to simulate and compare the impact of various interventions against meaningful viral load endpoints.

RESULTS: Robust parameter estimates were obtained for the basic reproduction number, viral release rate and infected-cell mortality from the infection model. These estimates were informed by the largest dataset currently available for SARS-CoV-2 viral time course. The utility of this model was demonstrated using simulations, which hypothetically introduced inhibitory or stimulatory drug mechanisms at various target sites within the viral life-cycle. We show that early intervention is crucial to achieving therapeutic benefit when monotherapy is administered. In contrast, combination regimens of two or three drugs may provide improved outcomes if treatment is initiated late. The latter is relevant to SARS-CoV-2, where the period between infection and symptom onset is relatively long.

CONCLUSIONS: The use of in silico models can provide viral load predictions that can rationalize therapeutic strategies against an emerging viral pathogen.

Media Type:

Electronic Article

Year of Publication:

2021

Contained In:

British journal of clinical pharmacology - Vol. 87, No. 9 (2021), p. 3425-3438

Language:

English

Contributors:

Patel, Kashyap
Dodds, Michael
Gonçalves, Antonio
Kamal, Mohamed A
Rayner, Craig R
Kirkpatrick, Carl M
Smith, Patrick F

Links:

Volltext

Keywords:

*COVID-19
*SARS-CoV-2
COVID-19
Computer Simulation
Humans
In silico
Journal Article
Pandemic
Pandemics
Research Support, Non-U.S. Gov't
Respiratory infection
SARS-Cov-2
Treatment strategies
Viral Load
Viral kinetics

Notes:

Date Completed 06.09.2021

Date Revised 07.09.2021

published: Print-Electronic

Citation Status MEDLINE

Copyright: From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

Physical Description:

Online-Ressource

doi:

10.1111/bcp.14718

PMID:

33373059

PPN (Catalogue-ID):

NLM320388018