Computational Modeling of Pro-inflammatory Cytokine-Enhanced Blood Coagulation

Area of Science:

• Computational Biology
• Biophysics
• Systems Biology

Background:

• The interaction between inflammation and coagulation is crucial for thrombotic risk in various diseases.
• Existing mathematical models of blood coagulation lack frameworks for inflammation-induced hypercoagulability.

Purpose of the Study:

• To develop a quantitative mathematical model simulating the interplay between pro-inflammatory cytokines and the coagulation cascade.
• To capture inflammation-driven hypercoagulability and quantify its impact on thrombin generation (TG) dynamics.

Main Methods:

• Developed a model incorporating cytokine up-regulation of tissue factor and suppression of anticoagulants.
• Included feedback amplification of cytokines by thrombin.
• Integrated the model with TG assays and applied it to virtual patient cohorts (COVID-19, sickle cell disease, type 2 diabetes mellitus, hemophilia A).

Main Results:

• Model simulations show distinct shifts in TG dynamics based on disease-specific inflammatory environments.
• COVID-19 and type 2 diabetes mellitus exhibit shortened TG lag times and increased thrombin peaks.
• Sickle cell disease shows shortened lag times with a reduced thrombin peak, modulated by cytokine concentration and exposure duration.

Conclusions:

• The computational model links inflammatory signaling to disease-specific coagulation responses, augmenting conventional TG assays.
• This framework provides a platform for exploring thromboinflammatory regulation and generating hypotheses.
• The model lays the foundation for future clinical prediction and individualized therapeutic strategies.