Is there value in kinetic modeling of thrombin generation? No (unless…)
2012; Elsevier BV; Volume: 10; Issue: 8 Linguagem: Inglês
10.1111/j.1538-7836.2012.04802.x
ISSN1538-7933
AutoresH.C. Hemker, Sébastien Kerdelo, Romy Kremers,
Tópico(s)Vitamin K Research Studies
ResumoSee also Stuijver DJF, Hooper JMW, Orme SM, van Zaane B, Squizzato A, Piantanida E, Hess K, Alzahrani S, Ajjan RA. Fibrin clot structure and fibrinolysis in hypothyroid individuals: the effects of normalising thyroid hormone levels. This issue, pp 1708–10. See also Stuijver DJF, Hooper JMW, Orme SM, van Zaane B, Squizzato A, Piantanida E, Hess K, Alzahrani S, Ajjan RA. Fibrin clot structure and fibrinolysis in hypothyroid individuals: the effects of normalising thyroid hormone levels. This issue, pp 1708–10. Many attempts have been made to model the blood coagulation system, as can be seen from a non‐exhaustive survey of the literature [1Ataullakhanov F.I. Guria G.T. Sarbash V.I. Volkova R.I. Spatiotemporal dynamics of clotting and pattern formation in human blood.Biochim Biophys Acta. 1998; 1425: 453-68Crossref PubMed Scopus (49) Google Scholar, 2Baldwin S.A. Basmadjian D. A mathematical model of thrombin production in blood coagulation. Part I: The sparsely covered membrane case.Ann Biomed Eng. 1994; 22: 357-70Crossref PubMed Scopus (21) Google Scholar, 3Brummel‐Ziedins K.E. Orfeo T. Gissel M. Mann K.G. Rosendaal F.R. Factor Xa generation by computational modeling: an additional discriminator to thrombin generation evaluation.PLoS One. 2012; 7Crossref Scopus (28) Google Scholar, 4Burghaus R. Coboeken K. Gaub T. Kuepfer L. Sensse A. Siegmund H.U. Weiss W. Mueck W. Lippert J. Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation.PLoS One. 2012; 6: e17626Crossref Scopus (32) Google Scholar, 5Butenas S. van't Veer C. Mann K.G. 'Normal' thrombin generation.Blood. 1999; 94: 2169-78Crossref PubMed Google Scholar, 6Chatterjee M.S. Denney W.S. Jing H. Diamond S.L. Systems biology of coagulation initiation: kinetics of thrombin generation in resting and activated human blood.PLoS Comput Biol. 2012; 6: e1000950Crossref Scopus (123) Google Scholar, 7Danforth C.M. Orfeo T. Everse S.J. Mann K.G. Brummel‐Ziedins K.E. Defining the boundaries of normal thrombin generation: investigations into hemostasis.PLoS One. 2012; 7Crossref Scopus (51) Google Scholar, 8Danforth C.M. Orfeo T. Mann K.G. Brummel‐Ziedins K.E. Everse S.J. The impact of uncertainty in a blood coagulation model.Math Med Biol. 2009; 26: 323-36Crossref PubMed Scopus (53) Google Scholar, 9Hockin M.F. Jones K.C. Everse S.J. Mann K.G. A model for the stoichiometric regulation of blood coagulation.J Biol Chem. 2002; 277: 18322-33Abstract Full Text Full Text PDF PubMed Scopus (332) Google Scholar, 10Iliadis A. Cheruy A. Daver J. Desnoyers P. A model of the blood coagulation system.C R Acad Sci Hebd Seances Acad Sci D. 1977; 284: 2411-14PubMed Google Scholar, 11Jones K.C. Mann K.G. A model for the tissue factor pathway to thrombin II. A mathematical simulation.J Biol Chem. 1994; 269: 23367-73Abstract Full Text PDF PubMed Google Scholar, 12Khanin M.A. Semenov V.V. A mathematical model of the kinetics of blood coagulation.J Theor Biol. 1989; 136: 127-34Crossref PubMed Scopus (67) Google Scholar, 13Krasotkina Y.V. Sinauridze E.I. Ataullakhanov F.I. Spatiotemporal dynamics of fibrin formation and spreading of active thrombin entering non‐recalcified plasma by diffusion.Biochim Biophys Acta. 2000; 1474: 337-45Crossref PubMed Scopus (29) Google Scholar, 14Nesheim M.E. Tracy R.P. Mann K.G. 'Clotspeed', a mathematical simulation of the functional properties of prothrombinase.J Biol Chem. 1984; 259: 1447-53Abstract Full Text PDF PubMed Google Scholar, 15Ovanesov M.V. Krasotkina J.V. Ul'yanova L.I. Abushinova K.V. Plyushch O.P. Domogatskii S.P. Vorob'ev A.I. Ataullakhanov F.I. Hemophilia A and B are associated with abnormal spatial dynamics of clot growth.Biochim Biophys Acta. 2002; 1572: 45-57Crossref PubMed Scopus (60) Google Scholar, 16Panteleev M.A. Balandina A.N. Lipets E.N. Ovanesov M.V. Ataullakhanov F.I. 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It is, indeed, hard to see how such a complicated system could be understood without the help of computer models, especially when not only chemistry but also flow and diffusion have to be taken into account. The question that we want to discuss here is a more restricted one: how far can such modeling, at this moment, be profitably used for diagnostic, pharmacologic and epidemiologic purposes? There is no doubt that kinetic modeling is a wonderful tool with which to test a working hypothesis, i.e. to answer the question of what would happen if things are the way that we think they are. A problem arises, however, when an insidious shift is made from the hypothetical to the confident; from 'what would happen if things are the way that I think they are' to 'things are as I put them in the computer and now I can tell you what happens'. Clinicians and epidemiologists are not interested in hypotheses on the mechanism of thrombin generation; they want to know what happens in a patient. Therefore, they are happy with the confident approach and want more of it. The problem is that however sure the author of the program may feel, models in a computer cannot be guaranteed to truthfully represent reality, and always remain contaminated with hypotheses. In biochemical research, this is not a problem, because, in time, such errors will be recognized and corrected, and no harm will be done. With patients, we cannot afford mistaken information. The use of a not quite correct model will, at a given – and unknown – moment, generate results that do not represent what happens in a patient. Even worse, it can produce wrong results at any moment without the user being aware of it. For medical use, we need precisely that unique model that truthfully represents the patient both qualitatively (what reactions?) and quantitatively (what constants rule these reactions?). It requires exact understanding of how thrombin generation works. The question of how useful computer simulation is for medical application thus reduces to the question of how sure we can be about the information that we put into the computer. It is the gist of our argument that, despite the tremendous increase in our knowledge of the coagulation mechanism, we are not yet able to propose the unique model that corresponds to thrombin generation in a sample of plasma, let alone in a patient. We will develop our argument in two phases. First, we will show that even perfect resemblance between a simulated curve and the outcome of an experiment does not prove that the underlying assumptions can be accepted. We will show that there are infinitely many models that can simulate a given thrombin generation curve, and that computer simulation cannot distinguish between them. As Popper showed us [24Popper K.R. The Logic of Scientific Discovery. Basic Books, 1959Google Scholar], falsification is the name of the scientific game. One black swan demonstrates that not all swans are white, no matter how many white swans I see. Dissimilarity is therefore convincing, whereas similarity is not. In the second part of our argument, we will show that there is no convincing similarity between the curves that are obtained experimentally from plasma and the simulated ones obtained with the presently available methods. How long is a stick that casts a shadow of 1 m? We can perform the experiment and play around with sticks and a light source. We can also make a mathematical model and enter the coordinates of the ends of the stick and of the light source relative to the plane on which the shadow is cast (seven parameters) in our formulas. This model, fed into a computer, will make a program that simulates shadow casting by sticks. It will show us that we can vary the position and length of the stick and the light source in infinitely many ways to obtain a shadow of 1 m. This illustrates that, as long as there are more parameters in the input than in the output, a desired outcome can be arrived at in many different ways. From the shadow alone, we cannot even decide whether it is caused by a stick or a hoop or a board; that is, we cannot determine the 'mechanism' behind the observed phenomenon. In simulation of thrombin generation, the situation is much worse. In the literature on simulation of the clotting system, the number of parameters used in the simulation varies between five and 21 for the reactants and between nine and 86 for the kinetic parameters (Table 1). The outcome of the calculation is a curve that can usually be described with five parameters. The case becomes worse when clotting time is the single output parameter (e.g. [6Chatterjee M.S. Denney W.S. Jing H. Diamond S.L. Systems biology of coagulation initiation: kinetics of thrombin generation in resting and activated human blood.PLoS Comput Biol. 2012; 6: e1000950Crossref Scopus (123) Google Scholar]). As with the shadow, there is a large excess of input over output, and consequently an infinite number of combinations of parameters will give the same outcome. There is not a unique solution, and therefore similarity does not prove that the underlying assumptions are correct.Table 1The parameters used for simulation in the literatureModelReactionsParameters K m II (nm) k cat II (min−1)ReferencesConcentration*ConstantsChatterjee, 20107618866503810 50Uzome M. Okorie U.M. Denney W.S. Chatterjee M.S. Neeves K.B. Diamond S.L. Determination of surface tissue factor thresholds that trigger coagulation at venous and arterial shear rates: amplification of 100 fM circulating tissue factor requires flow.Blood. 2008; 111: 3507-3513Crossref PubMed Scopus (110) Google ScholarWajima, 200954195410– 51Wajima T. Isbister G.K. Duffull S.B. A comprehensive model for the humoral coagulation network in humans.Clin Pharmacol Ther. 2009; 86: 290-8Crossref PubMed Scopus (79) Google ScholarZhu, 200755217810001700 52Zhu D. Mathematical modeling of blood coagulation cascade: kinetics of intrinsic and extrinsic pathways in normal and deficient conditions.Blood Coagul Fibrinolysis. 2007; 18: 637-46Crossref PubMed Scopus (24) Google ScholarWillems, 1991148212102000 21Willems G.M. Lindhout T. Hermens W.T. Hemker H.C. Simulation model for thrombin generation in plasma.Haemostasis. 1991; 21: 197-207PubMed Google ScholarJones, 199416720–– 11Jones K.C. Mann K.G. A model for the tissue factor pathway to thrombin II. A mathematical simulation.J Biol Chem. 1994; 269: 23367-73Abstract Full Text PDF PubMed Google ScholarQiao, 2005166192501900 17Qiao Y.H. Liu J.L. Zeng Y.J. A kinetic model for simulation of blood coagulation and inhibition in the intrinsic path.J Med Eng Technol. 2005; 29: 70-4Crossref PubMed Scopus (11) Google ScholarXu, 20058717–– 53Xu C. Hu Xu X. Zeng Y. Wen Chen Y. Simulation of a mathematical model of the role of the TFPI in the extrinsic pathway of coagulation.Comput Biol Med. 2005; 35: 435-45Crossref PubMed Scopus (5) Google ScholarBungay, 200331975–– 26Bungay S.D. Gentry P.A. Gentry R.D. A mathematical model of lipid‐mediated thrombin generation.Math Med Biol. 2003; 20: 105-29Crossref PubMed Scopus (62) Google ScholarNagashima, 200219643–– 54Nagashima H. Studies on the different modes of action of the anticoagulant protease inhibitors DX‐9065a and Argatroban I. Effects on thrombin generation.J Biol Chem. 2002; 277: 50439-44Abstract Full Text Full Text PDF PubMed Scopus (33) Google ScholarXu, 2002141018–– 55Xu C.Q. Zeng Y.J. Gregersen H. Dynamic model of the role of platelets in the blood coagulation system.Med Eng Phys. 2002; 24: 587-93Abstract Full Text Full Text PDF PubMed Scopus (27) Google ScholarHockin, 2002271042–– 9Hockin M.F. Jones K.C. Everse S.J. Mann K.G. A model for the stoichiometric regulation of blood coagulation.J Biol Chem. 2002; 277: 18322-33Abstract Full Text Full Text PDF PubMed Scopus (332) Google ScholarKhanin, 1989659–– 12Khanin M.A. Semenov V.V. A mathematical model of the kinetics of blood coagulation.J Theor Biol. 1989; 136: 127-34Crossref PubMed Scopus (67) Google ScholarLo, 2005311039–3810 56Lo K. Denney W.S. Diamond S.L. Stochastic modeling of blood coagulation initiation.Pathophysiol Haemost Thromb. 2005; 34: 80-90Crossref PubMed Scopus (52) Google Scholar*Only unactivated, unbound factors as present in plasma. Open table in a new tab *Only unactivated, unbound factors as present in plasma. We can illustrate this by an experimental 'reductio ad absurdum' [25Wagenvoord R. Hemker P.W. Hemker H.C. The limits of simulation of the clotting system.J Thromb Haemost. 2006; 4: 1331-8Crossref PubMed Scopus (48) Google Scholar]. We determined thrombin generation curves and their confidence limits in samples from a hemophilic patient before and after injection of a factor VIII preparation. Then, we made a mathematical simulation of the minimal mechanism illustrated in Fig. 1. It appeared that the curves from the patient could be simulated by this model, in which FVIII does not even occur (Fig. 2). This shows that simulation lends itself to even the most unlikely reaction mechanisms.Figure 2Simulation of intrinsic thrombin generation with an oversimplified model. Thrombin generation triggered by 0.5 pm tissue factor is measured by calibrated automated thrombin generation [49Hemker H.C. Giesen P. AlDieri R. Regnault V. de Smed E. Wagenvoord R. Lecompte T. Beguin S. The calibrated automated thrombogram (CAT): a universal routine test for hyper‐ and hypocoagulability.Pathophysiol Haemost Thromb. 2002; 32: 249-53Crossref PubMed Scopus (572) Google Scholar] in plasma from a hemophilic patient before infusion of a factor VIII preparation (baseline) and at the indicated times thereafter. Black lines: experimental data. Red lines: simulated data obtained with the set of equations that describes the action of the mechanism shown in Fig. 1. Reproduced from ref. [25].View Large Image Figure ViewerDownload Hi-res image Download (PPT) Note that, in these experiments, we demonstrated similarity to within the limits of experimental error. One would expect those who practice simulation to go to great lengths to demonstrate that their simulations do, indeed, fit experimental data. Usually, however, rigorous comparison is conveniently abandoned. From personal experience, we can imagine why. After developing the appropriate mathematical techniques (which were not yet standard at the time), we were among the very first to practice computer simulation of thrombin generation [21Willems G.M. Lindhout T. Hermens W.T. Hemker H.C. Simulation model for thrombin generation in plasma.Haemostasis. 1991; 21: 197-207PubMed Google Scholar]. We soon found that what we thought to be the most likely mechanisms did not stand comparison with reality. This leaves the choice between abandoning the comparison or not publishing. We chose the latter. In theory, it must be possible to simulate thrombin generation as it occurs in plasma to within the limits of experimental error. Up to now, we have not seen this done, either in the literature or in our own laboratory. However, in spite of the paucity of experimental verification in the literature, we did observe a discrepancy between published simulations and experimental results in several instances. 1In Fig. 3, we show an experimental thrombin generation curve obtained with normal plasma, together with the simulated curves according to three different published models [9Hockin M.F. Jones K.C. Everse S.J. Mann K.G. A model for the stoichiometric regulation of blood coagulation.J Biol Chem. 2002; 277: 18322-33Abstract Full Text Full Text PDF PubMed Scopus (332) Google Scholar, 26Bungay S.D. Gentry P.A. Gentry R.D. A mathematical model of lipid‐mediated thrombin generation.Math Med Biol. 2003; 20: 105-29Crossref PubMed Scopus (62) Google Scholar, 27Panteleev M.A. Balandina A.N. Lipets E.N. Ovanesov M.V. Ataullakhanov F.I. Task‐oriented modular decomposition of biological networks: trigger mechanism in blood coagulation.Biophys J. 2010; 98: 1751-61Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar]. The discrepancies are obvious. This is a preliminary result from a larger study in which this discrepancy will be shown to be the rule and not the exception. Admittedly, some of the differences between simulation and reality may result from differences in trigger or clotting factor concentrations. Concentrations of tissue factor, for example, may appear identical on paper, but nevertheless differ between one laboratory and another because rigorous standardization is lacking. This may cause shifts in lag time, in peak height, and in area under the curve. It cannot, however, account for the difference in such aspects of the curve as its asymmetry. The differences between the different computed curves clearly show that, minimally, two of the three must be wrong.2It has long been known that, in clotting blood, there is always some prothrombin that is not converted. Recently, this was shown to amount to 25% in minimally altered whole blood [28Rand M.D. Lock J.B. van't Veer C. Gaffney D.P. Mann K.G. Blood clotting in minimally altered whole blood.Blood. 1996; 88: 3432-45Crossref PubMed Google Scholar]; in our own experience, it is ∼ 10% in platelet‐poor and platelet‐rich plasma. In computer simulations and in reconstituted systems, however, all prothrombin is converted [9Hockin M.F. Jones K.C. Everse S.J. Mann K.G. A model for the stoichiometric regulation of blood coagulation.J Biol Chem. 2002; 277: 18322-33Abstract Full Text Full Text PDF PubMed Scopus (332) Google Scholar, 11Jones K.C. Mann K.G. A model for the tissue factor pathway to thrombin II. A mathematical simulation.J Biol Chem. 1994; 269: 23367-73Abstract Full Text PDF PubMed Google Scholar, 29Butenas S. Branda R.F. van't Veer C. Cawthern K.M. Mann K.G. Platelets and phospholipids in tissue factor‐initiated thrombin generation.Thromb Haemost. 2001; 86: 660-7Crossref PubMed Scopus (81) Google Scholar].3When direct reversible inhibitors of FXa are ingested or added to plasma, and when thrombin generation is measured at sufficiently close time points, one sees, at certain concentrations of the inhibitor, a slow rising plateau instead of a distinct peak (Fig. 4). This unexpected pattern is not seen in simulations of the effect of this type of inhibitor [30Orfeo T. Gissel M. Butenas S. Undas A. Brummel‐Ziedins K.E. Mann K.G. Anticoagulants and the propagation phase of thrombin generation.PLoS One. 2011; 6Crossref PubMed Scopus (24) Google Scholar, 31Nagashima H. Studies on the different modes of action of the anticoagulant protease inhibitors DX‐9065a and Argatroban II. Effects on fibrinolysis.J Biol Chem. 2002; 277: 50445-9Abstract Full Text Full Text PDF PubMed Scopus (19) Google Scholar].Figure 4The pattern of thrombin generation observed with reversible factor Xa inhibitors. Right panel: thrombin generation patterns after ingestion of rivaroxaban. Left panel: in vitro; from top to bottom, 0, 50, 100, 200, 400 and 600 nm rivaroxaban. Right panel: in vivo, after ingestion of 10 mg at t = 0; from top to bottom, t = 0 h, t = 48 h (overlaps), t = 24 h, t = 12 h, t = 2 h, t = 8 h, t = 4 h, t = 6h.View Large Image Figure ViewerDownload Hi-res image Download (PPT)4If one adds a small amount of thrombin to plasma, insufficient to make it clot (e.g. 1 nm), then, in a thrombin generation experiment, it will generate 25–50% less thrombin than an untreated control sample [32Al Dieri R. Bloemen S. Hemker H.C. Activated clotting factor V inhibits tissue factor‐initiated thrombin generation.J Thromb Haemost. 2012; Google Scholar]. This behavior is not observed in simulation experiments, simply because the mechanism that explains this phenomenon is not included in the simulation. Extremely interesting results are obtained when the thrombin generation system is reconstructed from purified factors, also called synthetic coagulation proteome experiments [5Butenas S. van't Veer C. Mann K.G. 'Normal' thrombin generation.Blood. 1999; 94: 2169-78Crossref PubMed Google Scholar, 29Butenas S. Branda R.F. van't Veer C. Cawthern K.M. Mann K.G. Platelets and phospholipids in tissue factor‐initiated thrombin generation.Thromb Haemost. 2001; 86: 660-7Crossref PubMed Scopus (81) Google Scholar, 33Butenas S. Brummel K.E. Branda R.F. Paradis S.G. Mann K.G. Mechanism of factor VIIa‐dependent coagulation in hemophilia blood.Blood. 2002; 99: 923-30Crossref PubMed Scopus (192) Google Scholar, 34van 't Veer C. Mann K.G. Regulation of tissue factor initiated thrombin generation by the stoichiometric inhibitors tissue factor pathway inhibitor, antithrombin‐III, and heparin cofactor‐II.J Biol Chem. 1997; 272: 4367-77Abstract Full Text Full Text PDF PubMed Scopus (174) Google Scholar, 35Orfeo T. Butenas S. Brummel‐Ziedins K.E. Gissel M. Mann K.G. Anticoagulation by factor Xa inhibitors.J Thromb Haemost. 2010; 8: 1745-53Crossref PubMed Scopus (32) Google Scholar]. Such a 'wet' model has the advantage over plasma that we can be sure about the reactants that play a role. It has the advantage over the computer model that no kinetic parameters have to be allotted. It is gratifying to see that there is often a promising similarity between a wet and a computer model (e.g. [35Orfeo T. Butenas S. Brummel‐Ziedins K.E. Gissel M. Mann K.G. Anticoagulation by factor Xa inhibitors.J Thromb Haemost. 2010; 8: 1745-53Crossref PubMed Scopus (32) Google Scholar]). This shows that there is nothing wrong with simulation as such, and that dissimilarities between plasma and computed curves must be attributed to unsatisfying models at the basis of the computation. From the above, we conclude that it is unlikely that the models that are entered into the computer represent the physiologic mechanism that is operative in plasma. This is not surprising, because essential parts of the required information are still missing. The model requires three types of information: 1The set of reactions, i.e. the reaction mechanism. There is little doubt that the reactions that are fed into the models used are necessary. It is not known, however, whether they are sufficient to represent reality. The above cited experiment shows that unknown interactions between known clotting factors may exist. These might result from recently recognized mechanisms [32Al Dieri R. Bloemen S. Hemker H.C. Activated clotting factor V inhibits tissue factor‐initiated thrombin generation.J Thromb Haemost. 2012; Google Scholar] or mechanisms that are as yet unknown (e.g. the cause of the strange curves found with reversible inhibitors of FXa; Fig. 4). Moreover, there are proteins, such as fibrinogen or β2‐glycoprotein‐1, that have a definite and profound influence on thrombin generation but that are not normally taken into account in simulations. The protein C‐independent inhibitory action of protein S, although important in vivo, is usually not taken into consideration either [36Heeb M.J. Mesters R.M. Tans G. Rosing J. Griffin J.H. Binding of protein S to factor Va associated with inhibition of prothrombinase that is independent of activated protein C.J Biol Chem. 1993; 268: 2872-7Abstract Full Text PDF PubMed Google Scholar, 37ten Cate‐Hoek A.J. Dielis A.W. Spronk H.M. van Oerle R. Hamulyak K. Prins M.H. ten Cate H. Thrombin generation in patients after acute deep vein thrombosis.Thromb Haemost. 2008; 100: 240-5Crossref PubMed Scopus (62) Google Scholar].2Concentrations of the reactants. Despite the fact that rigorous standardization of clotting factor determination has not been achieved, this is the least problematic part. As soon as we know what reactants participate, the concentrations in the sample to be simulated can, in principle, be obtained by the standard methods of protein chemistry and immunology.3The kinetic constants that govern the interactions. However, which should be chosen? The values available vary enormously (Table 2), and stem from experiments on purified proteins that are obtained under experimental conditions that differ strongly from clotting plasma. Also, it is impossible to purify clotting factors without a certain loss of specific activity. We demonstrated, for example, that the antithrombin ('antithrombin 3′) used in [29Butenas S. Branda R.F. van't Veer C. Cawthern K.M. Mann K.G. Platelets and phospholipids in tissue factor‐initiated thrombin generation.Thromb Haemost. 2001; 86: 660-7Crossref PubMed Scopus (81) Google Scholar] had < 50% of the biological activity of the plasma protein [38Hemker H.C. Thrombin generation in a reconstituted system: a comment.Thromb Haemost. 2002; 87: 551-4Crossref PubMed Scopus (11) Google Scholar]. A loss of specific activity leads automatically to an overestimation of kcat (turnover number) in enzymes and of kdec (decay constant) in inhibitors.Table 2The parameters of prothrombin conversion according to the literatureModel K m II (nm) k cat II (min−1)Medium*ReferencesNesheim, 197910302100No albumin 57Nesheim M.E. Taswell J.B. Mann K.G. The contribution of bovine factor V and factor Va to the activity of prothrombinase.J Biol Chem. 1979; 254: 10952-62Abstract Full Text PDF PubMed Google ScholarRosing, 1980210–– 58Rosing J. Tans G. Govers‐Riemslag J.W. Zwaal R.F. Hemker H.C. The role of phospholipids and factor Va in the prothrombinase complex.J Biol Chem. 1980; 255: 274-83Abstract Full Text PDF PubMed Google ScholarPusey, 1983400660No albumin 59Pusey M.L. Nelsestuen G.L. The physical significance of Km in the prothrombinase reaction.Biochem Biophys Res Commun. 1983; 114: 526-32Crossref PubMed Scopus (28) Google Scholarvan Rijn, 1984140–– 60van Rijn J.L. Govers‐Riemslag J.W. Zwaal R.F. Rosing J. Kinetic studies of prothrombin activation: effect of factor Va and phospholipids on the formation of the enzyme–substrate complex.Biochemistry. 1984; 23: 4557-64Crossref PubMed Scopus (71) Google ScholarTracey, 198510002100– 61Tracy P.B. Eide L.L. Mann K.G. Human prothrombinase complex assembly and function on isolated peripheral blood cell populations.J Biol Chem. 1985; 260: 2119-24Abstract Full Text PDF PubMed Google ScholarKrishnaswamy, 198710601344No albumin 62Krishnaswamy S. Church W.R. Nesheim M.E. Mann K.G. 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Production of thrombin by the prothrombinase complex is regulated by membrane‐mediated transport of prothrombin.J Biol Chem. 1991; 266: 1379-82Abstract Full Text PDF PubMed Google ScholarWillems, 19938.61500– 64Willems G.M. Giesen P.L. Hermens W.T. Adsorption and conversion of prothrombin on a rotating disc.Blood. 1993; 82: 497-504Crossref PubMed Google ScholarCamire, 199825019000.01% Tween‐80, no albumin 65Camire R.M. Kalafatis M. Tracy P.B. Proteolysis of factor V by cathepsin G and elastase indicates that cleavage at Arg1545 optimizes cofactor function by facilitating factor Xa binding.Biochemistry. 1998; 37: 11896-906Crossref PubMed Scopus (41) Google Scholar*Medium: 0.02 or 0.05 m Tris‐HCl, 0.10/0.175 m NaCl, pH 7.4/7.5, 2–3 mm CaCl2, 0.5 mg mL−1 albumin. Open table in a new tab *Medium: 0.02 or 0.05 m Tris‐HCl, 0.10/0.175 m NaC
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