Literature DB >> 19516255

A comprehensive model for the humoral coagulation network in humans.

T Wajima1, G K Isbister, S B Duffull.   

Abstract

Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

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Year:  2009        PMID: 19516255     DOI: 10.1038/clpt.2009.87

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  40 in total

1.  Is there value in kinetic modeling of thrombin generation? Yes.

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Journal:  J Thromb Haemost       Date:  2012-08       Impact factor: 5.824

2.  A Joint Model for Vitamin K-Dependent Clotting Factors and Anticoagulation Proteins.

Authors:  Qing Xi Ooi; Daniel F B Wright; R Campbell Tait; Geoffrey K Isbister; Stephen B Duffull
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3.  Development and evaluation of a prototype of a novel clotting time test to monitor enoxaparin.

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Journal:  Pharm Res       Date:  2011-08-06       Impact factor: 4.200

4.  Development of a new pre- and post-processing tool (SADAPT-TRAN) for nonlinear mixed-effects modeling in S-ADAPT.

Authors:  Jurgen Bernd Bulitta; Ayhan Bingölbali; Beom Soo Shin; Cornelia Barbara Landersdorfer
Journal:  AAPS J       Date:  2011-03-03       Impact factor: 4.009

5.  Performance and robustness of the Monte Carlo importance sampling algorithm using parallelized S-ADAPT for basic and complex mechanistic models.

Authors:  Jurgen B Bulitta; Cornelia B Landersdorfer
Journal:  AAPS J       Date:  2011-03-04       Impact factor: 4.009

Review 6.  Model-based clinical drug development in the past, present and future: a commentary.

Authors:  Holly Kimko; José Pinheiro
Journal:  Br J Clin Pharmacol       Date:  2015-01       Impact factor: 4.335

7.  The pharmacometrician's dilemma: the tension between mechanistic and empirical approaches in mathematical modelling and simulation - a continuation of the age-old dispute between rationalism and empiricism?

Authors:  Robert Bies; Sarah Cook; Stephen Duffull
Journal:  Br J Clin Pharmacol       Date:  2016-07-24       Impact factor: 4.335

8.  Selection and Qualification of Simplified QSP Models When Using Model Order Reduction Techniques.

Authors:  Chihiro Hasegawa; Stephen B Duffull
Journal:  AAPS J       Date:  2017-11-27       Impact factor: 4.009

9.  Understanding and reducing complex systems pharmacology models based on a novel input-response index.

Authors:  Jane Knöchel; Charlotte Kloft; Wilhelm Huisinga
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-12-14       Impact factor: 2.745

10.  Review of quantitative systems pharmacological modeling in thrombosis.

Authors:  Limei Cheng; Guo-Wei Wei; Tarek Leil
Journal:  Commun Inf Syst       Date:  2019-12-06
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