Modeling of human factor Va inactivation by activated protein C

Abstract

BackgroundBecause understanding of the inventory, connectivity and dynamics of the components characterizing the process of coagulation is relatively mature, it has become an attractive target for physiochemical modeling. Such models can potentially improve the design of therapeutics. The prothrombinase complex (composed of the protease factor (F)Xa and its cofactor FVa) plays a central role in this network as the main producer of thrombin, which catalyses both the activation of platelets and the conversion of fibrinogen to fibrin, the main substances of a clot. A key negative feedback loop that prevents clot propagation beyond the site of injury is the thrombin-dependent generation of activated protein C (APC), an enzyme that inactivates FVa, thus neutralizing the prothrombinase complex. APC inactivation of FVa is complex, involving the production of partially active intermediates and “protection” of FVa from APC by both FXa and prothrombin. An empirically validated mathematical model of this process would be useful in advancing the predictive capacity of comprehensive models of coagulation.ResultsA model of human APC inactivation of prothrombinase was constructed in a stepwise fashion by analyzing time courses of FVa inactivation in empirical reaction systems with increasing number of interacting components and generating corresponding model constructs of each reaction system. Reaction mechanisms, rate constants and equilibrium constants informing these model constructs were initially derived from various research groups reporting on APC inactivation of FVa in isolation, or in the presence of FXa or prothrombin. Model predictions were assessed against empirical data measuring the appearance and disappearance of multiple FVa degradation intermediates as well as prothrombinase activity changes, with plasma proteins derived from multiple preparations. Our work integrates previously published findings and through the cooperative analysis of in vitro experiments and mathematical constructs we are able to produce a final validated model that includes 24 chemical reactions and interactions with 14 unique rate constants which describe the flux in concentrations of 24 species.ConclusionThis study highlights the complexity of the inactivation process and provides a module of equations describing the Protein C pathway that can be integrated into existing comprehensive mathematical models describing tissue factor initiated coagulation.

From Principle to Practice: Bridging the Gap in Patient Profiling

“…In the current study only one method of determining thrombin generation was used for each population but based on the extensive empirical validation of our mathematical model [25], [26], [27] we expect that simulated and empirical thrombin generation data would be similar. Our video plot (Movie S1) shows that the atrial fibrillation group is stably anticoagulated within 5 days of commencing warfarin therapy.…”

“…For each unique plasma sample, the time course of thrombin generation was simulated using two empirically validated mathematical models termed the “Base model” [25], [26] and the “Protein C model” [27]. In principle, the models differ in their ability to represent the anticoagulant properties of the vasculature.…”

Models for thrombin generation and risk of disease

“…Our empirically validated mathematical models of the blood coagulation system have been previously described . These models are built around a series of ODEs, which make use of rate constants derived from experimental measurements made under conditions of saturating concentrations of phospholipids .…”

“…The base model describing the tissue factor pathway makes use of the following inputs: empirically determined functional concentrations of fII, fV, fVII/VIIa, fVIII, fIX, fX, TFPI, and AT. The protein C model uses all inputs from the base model as well as the empirically determined protein C concentration and thrombomodulin concentrations potentially representative of those found in the vasculature . Following data entry, simulations are initiated with a tissue factor stimulus and solved for any of the ~60 species over time.…”

Developing individualized coagulation profiling of disease risk: Thrombin generation dynamic models of the pro and anticoagulant balance

“…protein S, protein Z); insufficient empirical data comparing pro-and anticoagulant mechanisms under flow to those observed in closed model systems; a lack of experimental data that includes the combined effects of shear, vessel wall and blood contributions, so that the predictions of such models can be validated. There are several groups working on developing and validating more comprehensive models [27][28][29][30].…”