Exploring the Therapeutic Potential of Defective Interfering Particles in Reducing the Replication of SARS-CoV-2

Author:

Locke Macauley12ORCID,Grebennikov Dmitry34ORCID,Sazonov Igor5ORCID,López-García Martín1ORCID,Loguinova Marina6ORCID,Meyerhans Andreas78ORCID,Bocharov Gennady39ORCID,Molina-París Carmen12ORCID

Affiliation:

1. Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK

2. Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

3. Marchuk Institute of Numerical Mathematics, Russian Academy of Sciences, 119333 Moscow, Russia

4. World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia

5. Faculty of Science and Engineering, Swansea University, Swansea SA1 8EN, UK

6. The National Medical Research Centre for Endocrinology, 117292 Moscow, Russia

7. Infection Biology Laboratory, Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, 08003 Barcelona, Spain

8. Institució Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain

9. Institute for Computer Science and Mathematical Modelling, Sechenov First Moscow State Medical University, 119991 Moscow, Russia

Abstract

SARS-CoV-2 still presents a global threat to human health due to the continued emergence of new strains and waning immunity among vaccinated populations. Therefore, it is still relevant to investigate potential therapeutics, such as therapeutic interfering particles (TIPs). Mathematical and computational modeling are valuable tools to study viral infection dynamics for predictive analysis. Here, we expand on the previous work on SARS-CoV-2 intra-cellular replication dynamics to include defective interfering particles (DIPs) as potential therapeutic agents. We formulate a deterministic model that describes the replication of wild-type (WT) SARS-CoV-2 virus in the presence of DIPs. Sensitivity analysis of parameters to several model outputs is employed to inform us on those parameters to be carefully calibrated from experimental data. We then study the effects of co-infection on WT replication and how DIP dose perturbs the release of WT viral particles. Furthermore, we provide a stochastic formulation of the model that is compared to the deterministic one. These models could be further developed into population-level models or used to guide the development and dose of TIPs.

Publisher

MDPI AG

Reference70 articles.

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