Adams2003 - Thrombin Inhibitors
Ontology highlight
ABSTRACT:
SUBMITTER: 
Matthew Roberts
PROVIDER: MODEL1808150002 | BioModels | 2018-08-15
REPOSITORIES: BioModels
Publications
Predicting the pharmacology of thrombin inhibitors.
Adams T E TE Everse S J SJ Mann K G KG
Journal of thrombosis and haemostasis : JTH 20030501 5
Thrombotic disorders can lead to uncontrolled thrombin generation and clot formation within the circulatory system leading to vascular thrombosis. Direct inhibitors of thrombin have been developed and tested in clinical trials for the treatment of a variety of these thrombotic disorders. The bleeding complications observed during these trials have raised questions about their clinical use. The development of a computer-based model of coagulation using the kinetic rates of individual reactions an ...[more]
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Project description:This model is from the article:
A model for the stoichiometric regulation of blood coagulation.
Hockin MF, Jones KC, Everse SJ, Mann KG.
Journal of Biological ChemistryVolume 277, Issue 21, 24 May 2002, Pages 18322 -18333
11893748,
Abstract:
We have developed a model of the extrinsic blood coagulation system that includes the stoichiometric anticoagulants. The model accounts for the formation, expression, and propagation of the vitamin K-dependent procoagulant complexes and extends our previous model by including: (a) the tissue factor pathway inhibitor (TFPI)-mediated inactivation of tissue factor (TF).VIIa and its product complexes; (b) the antithrombin-III (AT-III)-mediated inactivation of IIa, mIIa, factor VIIa, factor IXa, and factor Xa; (c) the initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; (d) factor VIIIa dissociation/activity loss; (e) the binding competition and kinetic activation steps that exist between TF and factors VII and VIIa; and (f) the activation of factor VII by IIa, factor Xa, and factor IXa. These additions to our earlier model generate a model consisting of 34 differential equations with 42 rate constants that together describe the 27 independent equilibrium expressions, which describe the fates of 34 species. Simulations are initiated by "exposing" picomolar concentrations of TF to an electronic milieu consisting of factors II, IX, X, VII, VIIa, V, and VIIII, and the anticoagulants TFPI and AT-III at concentrations found in normal plasma or associated with coagulation pathology. The reaction followed in terms of thrombin generation, proceeds through phases that can be operationally defined as initiation, propagation, and termination. The generation of thrombin displays a nonlinear dependence upon TF, AT-III, and TFPI and the combination of these latter inhibitors displays kinetic thresholds. At subthreshold TF, thrombin production/expression is suppressed by the combination of TFPI and AT-III; for concentrations above the TF threshold, the bolus of thrombin produced is quantitatively equivalent. A comparison of the model with empirical laboratory data illustrates that most experimentally observable parameters are captured, and the pathology that results in enhanced or deficient thrombin generation is accurately described.
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Mathematical modelling of a part of the blood coagulation mechanism.
This model is described in the article:
Mathematical modelling in blood coagulation : simulation and parameter estimation.
Stortelder W.J.H., Hemker P.W., Hemker, H.C.
CWI. Modelling, Analysis and Simulation, No. R 9720, p.1-11.
Abstract:
This paper describes the mathematical modelling of a part of the blood coagulation mechanism. The model includes the activation of factor X by a purified enzyme from Russel's Viper Venom (RVV), factor V and prothrombin, and also comprises the inactivation of the products formed. In this study we assume that in principle the mechanism of the process is known. However, the exact structure of the mechanism is unknown, and the process still can be described by different mathematical models. These models are put to test by measuring their capacity to explain the course of thrombin generation as observed in plasma after recalcification in presence of RVV. The mechanism studied is mathematically modelled as a system of differential-algebraic equations (DAEs). Each candidate model contains some freedom, which is expressed in the model equations by the presence of unknown parameters. For example, reaction constants or initial concentrations are unknown. The goal of parameter estimation is to determine these unknown parameters in such a way that the theoretical (i.e., computed) results fit the experimental data within measurement accuracy and to judge which modifications of the chemical reaction scheme allow the best fit. We present results on model discrimination and estimation of reaction constants, which are hard to obtain in another way.
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2011-08-26 | BIOMD0000000358 | BioModels