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Using in silico viral kinetic models to guide therapeutic strategies during a pandemic: An example in SARS‐CoV‐2
Author(s) -
Patel Kashyap,
Dodds Michael,
Gonçalves Antonio,
Kamal Mohamed A.,
Rayner Craig R.,
Kirkpatrick Carl M.,
Smith Patrick F.
Publication year - 2021
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.14718
Subject(s) - in silico , viral load , pandemic , covid-19 , basic reproduction number , population , psychological intervention , medicine , intensive care medicine , virology , biology , computational biology , immunology , virus , disease , infectious disease (medical specialty) , biochemistry , environmental health , psychiatry , gene
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.