Abstract
Coagulation is a process crucial to stop bleeding from a damaged vessel. The network is comprised of a complex interplay of various pro-coagulant and anti-coagulant factors. Several systems models for the coagulation pathway have been published, as a way to investigate the pathway complexity. Most published models describe the thrombin generation profile (TGA) or prothrombin time (PT) following extrinsic pathway activation by tissue factor, or activated partial prothrombin time (aPTT) through activation of the intrinsic pathway by contact activation. In the clinical setting, thrombin-anti-thrombin complex (TAT) and prothrombin fragment 1+2 (PF1+2) are often used as biomarkers for in vivo coagulation activity in the non-bleeding state. Even in the absence of any treatment, there are detectable levels of PF1+2 and TAT in healthy volunteers indicating low level coagulation activity exists in the normal baseline, non-bleeding state. In this study, we developed a mathematical model for coagulation to describe the baseline activity of PF1+2 and TAT. We then used the model to understand the impact of the coagulation pathway activity during non-bleeding state on thrombin generation activated by tissue factor.
First, a coagulation model from Hockin et al. (J Biol Chem. 2002;277(21):18322-33) was modified to describe internally generated data for thrombin generation and aPTT modulation following addition of various concentrations of recombinant factor VIIa or plasma derived factor Xa to normal or hemophilic human plasma. Next, protein synthesis and degradation were incorporated into the model and platelet-dependent reactions were tuned down to describe the baseline coagulation activity in non-bleeding healthy subjects. Using a simulated annealing algorithm, the new parameters were optimized to fit published data for PF1+2 and TAT in healthy volunteers and changes of PF1+2 following treatment with eptacog alfa (recombinant factor VIIa). In the model, the baseline coagulation activities can be described by a very low level of tissue factor, which is much lower than the detection limit of a regular ELISA method. Using this baseline model, we tested the effect of this baseline coagulation activity on tissue factor-activated thrombin generation, and found that very small baseline levels of activated enzymes significantly shorten the lag time of thrombin generation, but did not affect the peak thrombin. We also performed a global sensitivity analysis to identify key proteins in the coagulation network whose modulation will have the biggest impact on PF1+2 and TAT levels.
Disclosures
Lee: Pfizer Inc.: Employment. Nayak:Pfizer Inc.: Employment. Pittman:Pfizer Inc.: Employment. Arkin:Pfizer Inc.: Employment. Martin:Pfizer Inc.: Employment. Heatherington:Pfizer Inc.: Employment. Vicini:Pfizer Inc.: Employment. Hua:Pfizer Inc: Employment.
A Systems Biology Model for the Coagulation Network in Non-Bleeding State Describes Baseline Activity of Coagulation
