loading page

Using in silico viral kinetic models to guide therapeutic strategies during a pandemic: An example in SARS-CoV-2
  • +4
  • Kashyap Patel,
  • Michael Dodds,
  • Antonio Goncalves,
  • Mohamed Kamal,
  • Craig Rayner,
  • Carl Kirkpatrick (NO NEW ASSIGNMENTS),
  • Patrick Smith
Kashyap Patel
Certara
Author Profile
Michael Dodds
Certara LP
Author Profile
Antonio Goncalves
INSERM
Author Profile
Mohamed Kamal
Roche Innovation Center
Author Profile
Craig Rayner
Certara LP
Author Profile
Carl Kirkpatrick (NO NEW ASSIGNMENTS)
Monash University
Author Profile
Patrick Smith
Certara LP
Author Profile

Abstract

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

Peer review status:Published

28 Aug 2020Submitted to British Journal of Clinical Pharmacology
31 Aug 2020Submission Checks Completed
31 Aug 2020Assigned to Editor
03 Sep 2020Reviewer(s) Assigned
05 Oct 2020Review(s) Completed, Editorial Evaluation Pending
05 Oct 2020Editorial Decision: Revise Major
30 Oct 20201st Revision Received
30 Oct 2020Assigned to Editor
30 Oct 2020Submission Checks Completed
30 Oct 2020Review(s) Completed, Editorial Evaluation Pending
06 Nov 2020Reviewer(s) Assigned
30 Nov 2020Editorial Decision: Revise Minor
02 Dec 20202nd Revision Received
02 Dec 2020Submission Checks Completed
02 Dec 2020Assigned to Editor
02 Dec 2020Review(s) Completed, Editorial Evaluation Pending
06 Dec 2020Reviewer(s) Assigned
13 Dec 2020Editorial Decision: Accept
29 Dec 2020Published in British Journal of Clinical Pharmacology. 10.1111/bcp.14718