title: The balance of pro- and anticoagulant processes underlying thrombin generation.
abstract: BACKGROUND
  The generation of thrombin in time is the combined effect of the processes of prothrombin conversion and thrombin inactivation. Measurement of prothrombin consumption used to provide valuable information on hemostatic disorders, but is no longer used, due to its elaborate nature.
  
  
  OBJECTIVE
  Because thrombin generation (TG) curves are easily obtained with modern techniques, we developed a method to extract the prothrombin conversion curve from the TG curve, using a computational model for thrombin inactivation.
  
  
  METHODS
  Thrombin inactivation was modelled computationally by a reaction scheme with antithrombin, α(2) Macroglobulin and fibrinogen, taking into account the presence of the thrombin substrate ZGGR-AMC used to obtain the experimental data. The model was validated by comparison with data obtained from plasma as well as from a reaction mixture containing the same reactants as plasma.
  
  
  RESULTS
  The computational model fitted experimental data within the limits of experimental error. Thrombin inactivation curves were predicted within 2 SD in 96% of healthy subjects. Prothrombin conversion was calculated in 24 healthy subjects and validated by comparison with the experimental consumption of prothrombin during TG. The endogenous thrombin potential (ETP) mainly depends on the total amount of prothrombin converted and the thrombin decay capacity, and the peak height is determined by the maximum prothrombin conversion rate and the thrombin decay capacity.
  
  
  CONCLUSIONS
  Thrombin inactivation can be accurately predicted by the proposed computational model and prothrombin conversion can be extracted from a TG curve using this computational prediction. This additional computational analysis of TG facilitates the analysis of the process of disturbed TG.
authors.affiliation: Synapse bv, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands.
authors.forename: R M W
authors.initials: RM
authors.lastname: Kremers
authors.forename: T C
authors.initials: TC
authors.lastname: Peters
authors.forename: R J
authors.initials: RJ
authors.lastname: Wagenvoord
authors.forename: H C
authors.initials: HC
authors.lastname: Hemker
mesh_terms: D000991:Antithrombins / Q000378:metabolism; D001777:Blood Coagulation / Q000187:drug effects; D001780:Blood Coagulation Tests; D019295:Computational Biology; D003198:Computer Simulation; D017984:Enoxaparin / Q000494:pharmacology; D065427:Factor Xa Inhibitors / Q000494:pharmacology; D005340:Fibrinogen / Q000378:metabolism; D006801:Humans; D008954:Models, Biological; D011516:Prothrombin / Q000378:metabolism*; D015203:Reproducibility of Results; D000069552:Rivaroxaban / Q000494:pharmacology; D013917:Thrombin / Q000378:metabolism*; D013927:Thrombosis / Q000097:blood / Q000188:drug therapy; D013997:Time Factors; D000511:alpha-Macroglobulins / Q000378:metabolism
chemical_list: C578813:A2M protein, human; D000991:Antithrombins; D017984:Enoxaparin; D065427:Factor Xa Inhibitors; D000511:alpha-Macroglobulins; D011516:Prothrombin; D005340:Fibrinogen; D000069552:Rivaroxaban; D013917:Thrombin
keywords: antithrombins; computational biology; prothrombin; prothrombinase; thrombin