Computational modeling of hypercoagulability in COVID-19

Abstract

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected more than 100 million people worldwide and claimed millions of lives. While the leading cause of mortality in COVID-19 patients is the hypoxic respiratory failure from acute respiratory distress syndrome, there is accumulating evidence that shows excessive coagulation also increases the fatalities in COVID-19. Thus, there is a pressing demand to understand the association between COVID-19-induced hypercoagulability and the extent of formation of undesired blood clots. Mathematical modeling of coagulation has been used as an important tool to identify novel reaction mechanisms and to identify targets for new drugs. Here, we employ the coagulation factor data of COVID-19 patients reported from published studies as inputs for two mathematical models of coagulation to identify how the concentrations of coagulation factors change in these patients. Our simulation results show that while the levels of many of the abnormal coagulation factors measured in COVID-19 patients promote the generation of thrombin and fibrin, two key components of blood clots, the increased level of fibrinogen and then the reduced level of antithrombin are the factors most responsible for boosting the level of fibrin and thrombin, respectively. Altogether, our study demonstrates the potential of mathematical modeling to identify coagulation factors responsible for the increased clot formation in COVID-19 patients where clinical data is scarce.

Keywords:

• Coagulation
• COVID-19
• Mathematical Modeling
• Precision Medicine

Acknowledgement(s)

We would like to thank Dr.Anass Bouchnita (University of Texas Austin) and Dr. Vitaly Volpert(University Lyon - Villeurbanne France) for helpful discussion.

Disclosure statement

Not applicable since all data are cited through published journals and this data presented in this paper is generated from the simulations.

Additional information

Funding

G.Z. and H. L. thank 2021 Salomon Award of Brown University for providing funding for this research.

Notes on contributors

Ge Zhu

Methodology, G.Z. S.M. M.A. and H.L.; software, G.Z. and S.M.; formal analysis, G.Z. S.M. M.A. and H.L.; investigation, G.Z. S.M. M.A. and H.L.; resources, G.Z. S.M. M.A. and H.L.; data curation, G.Z. and H.L.; writing—original draft preparation, G.Z. S.M. M.A. and H.L.; visualization, G.Z. and H.L.; supervision, H.L. and M.A.; All authors have read and agreed to the published version of the manuscript.